Author response:

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

Public Reviews:

Reviewer #1 (Public Review): 

(1) In Figure 1, the authors show that TF3C binds to the amino terminus of MYCN (Myc box I region), as shown previously. The data in Figure 1 B-D support, but do not rigorously confirm a 'direct' interaction because it has not been ruled out that accessory proteins mediating the association may be present in the mixture.

In Figure 1B-D we have purified MYCN and the TFIIIC/TauA complex separately and then mixed the purified preparations, demonstrating that the purified proteins interact. We have additionally performed mass spectrometry, which shows that the TauA/MYCN complex is formed without further accessory proteins, as the molecular weight would be higher. Based on the Coomassie stained SDS-PAGE gels, there is no plausible contaminating band in the purified complex that could be mediating the interaction between MYCN and TauA, either in the purified complex (Figure 1C), or in the purified protein used to reconstitute the complex (Figure S1A & S1B).

(2) The authors indicate in Figure 2 that TF3C has essentially no effect on MYCNdependent gene expression and/or transcription elongation. Yet a previous study (PMID: 29262328) associated with several of the same authors concluded that TF3C positively affects transcription elongation. The authors make no attempt to reconcile these disparate results and need to clarify this point.

We agree that the data in this manuscript do not support the role on transcription elongation. This point was also raised by Reviewer 3. Comparing our new results to the data published previously we can summarize that the data sets in the two studies show three key results: First, the traveling ratio of RNAPII changes upon induction of MYCN. Second, RNAPII decreases at the transcription start side and third, it increases towards the end side.

We agree that in the previous study we linked the traveling ratio directly to elongation. However performing ChIP-seq with different RNAPII antibodies showed us that for example RNAPII (N20), which is unfortunately discontinued, gives different results compared to RNAPII (A10). Combining our new results using the RNAPII (8WG16) antibody shows that the traveling ratio is not only reflecting transcription elongation but also includes that the RNAPII is kicked-off chromatin at the start side.

(3) Figures 2B and C show that unphosphorylated pol2 is TSS-centered, and Ser2-P pol2 occupation is centered beyond the TES. From this data, however, the reader can't tell how much of the phospho-Ser2- pol2 is centered on the TSS. The authors should include overall plots over TSS and TES, and also perhaps the gene-body to allow a better comparison for TSS and TES plotted for both antibodies over the collected gene sets.

We focused on the TSS for unphosphorylated RNAPII and the TES for pSer2-RNAPII, as these are the regions with specific enrichment of the respective antibodies. As requested for comparison, we now include metagenes showing TSS, gene-body, and TES for both antibodies as new Figure S2A and B. Additionally, we included density plots for unphosphorylated RNAPII at the TES as well as for pSer2-RNAPII at the TSS as a Figure for the Reviewers (Figure 1).

(4) The authors see more TF3C at promoters in cells with MYCN (Figure 2F). What are the levels of TF3C in the absence and presence of MYCN?

As shown in the immunoblot in Figure S1E, TF3C5 levels do not change upon induction of MYCN. We therefore think that MYCN helps to recruit TFIIIC5 to RNAPII promoter sites. This is also in accordance to what we previously reported 1.

(5) The finding that TF3C is increased at TSS (Figure 2F) doesn't necessarily indicate that 1) MYCN is recruiting TF3C there, and 2) that this is due to the phosphorylation status of pol2. It could mean many other things. The logic of conflating these 3 points based on the data shown is questionable.

We showed previously that knock-down of MYCN affects TFIIIC5 binding, showing that MYCN is required for binding of TFIIIC5 at promoter sites 1.

Additionally, we included data with DRB treated cells (Figure 2F), which prevents RNAPII loading by preventing downstream de novo elongation. Those data show that TFIIIC5 binding at the TSS is massively increased upon induction of MYCN and additionally upon treatment with DRB. Conversely, we observed that the major effect of TFIIIC knock-down was at the nonphosphorylated RNAPII at the TSS on MYCN induction (Figure 2B). Therefore, we would argue that our assumption fits well to the data presented in the manuscript.

(6) Figure 3A doesn't add much to the paper, as it is overplotted and no relationship is clear, except that Pol2 and MYCN occupy many of the same sites. Perhaps a less complex or different type of plot would allow the interactions to be better visible.

We agree with the comment and since in another comment we were asked to show the same window for all shown Hi-ChIP data plots, we changed Figure 3A.

(7) That depletion of TF3C leads to increased promoter hubs may or may not have anything to do with its association with MYCN (Figure 4E). This could be a direct consequence of its known structural function in cohesin complexes, and the MYCN changes as a secondary consequence of this (also see point 4, above).

As shown in Büchel et al. (2017) 1 MYCN is needed to recruit RAD21 and depletion of RAD21 has no impact on the recruitment of MYCN. Since RAD21 is part of the cohesin complex we would exclude that the MYCN changes are a secondary consequence.

(8) Depletion of TF3C5 results in a loss of EXOSC5 (exosome) at TSS in the presence and absence of MYCN (Figure 5B). As TF3C5 is a cohesin, could this simply be a consequence of genomic structure changes?

We agree that the discovered changes in EXOSC5 can be due to depletion of TFIIIC5. TFIIIC has been shown to recruit cohesin 1 and condensin complexes 2, as well as inducing chromatin architectural changes 3. However, MYCN is needed to recruit TFIIIC and depletion of TFIIIC had no impact on MYCN recruitment 1. Furthermore, MYCN has been shown to recruit exosome 4. Therefore, we would argue that either MYCN can directly play a role or thru chromatin architectural changes.

(9) The authors suggest that RNA dynamics are affected by changes in exosome function (RNA degradation, etc). What effect, if any does TF3C depletion have on the overall gene expression profile?

We show in the manuscript that TFIIIC depletion in unperturbed cells has no effect on the global gene expression profile in the time frame analyzed (Figure 2E and S2B).

Reviewer #2 (Public Review):

(1) Dynamic inferences are made without kinetic experiments.

While we agree that we did not collect kinetic data to study the dynamics of RNA polymerase we would argue that the integration of our different data sets make it possible to draw conclusions about dynamic interferences. The transcription cycle and its sequential steps have been well described. In this sense, we use the non-phosphorylated RNAPII data that is situated between RNAPII recruitment and initiation and RNAPII-pSer2 that shows pause-release to elongation to draw conclusions on the dynamic. Likewise, we also made use of our previous published datasets.

Reviewer #2 (Recommendations For The Authors):  

(1) A number of changes are reported in hub size, expression, etc. upon treatment with tamoxifen to activate MCN-ER. But MYC is already present in the SHEP cells, so why doesn't MYC support these same phenomena? It would seem that either the ability to cooperate with TFIIIC to clear non-productive polymerase complexes from promoters is particular to MYCN, or else it reflects a quantitative increase in total MYC proteins due to the entry of MYCN-ER into the nucleus with tamoxifen. The authors should address or discuss this issue.

It could be that protein levels are the limiting factor between MYC and MYCN observed effects in this system. This interpretation would be in accordance with the results of Lorenzin et al. 5, which reported that different levels of MYC had different targets based on the affinity to Eboxes and protein level. A similar profile of MYC levels compared to function was also reported regarding SPT5 6. Those high protein levels mimic what is found in certain tumors in contrast to physiological levels. In this sense, the observed differences can also be between physiological and oncological levels of MYC proteins.

On the other hand, it has been described both a core MYC- and an isoform specific-signature of target genes. MYCN is described to be involved in gene expression during the S-phase of the cell cycle 7. This suggests that there are differences between MYC and MYCN other than gene sets. The interaction with TFIIIC appears to be one of these differences. We have found multiple TFIIIC subunits as part of the MYCN interactome, but the interaction of TFIIIC with MYC is weaker and we are uncertain how relevant it is 7,8. We show here that depletion of different subunits of the TFIIIC complex show a MYCN-dependent growth defect (Figure 1 E). Similarly, nuclear exosome is a MYCN-specific dependence 4, and we show here that MYCNdependent recruitment of the exosome requires TFIIIC5. We take this as an indication that there is an intrinsic difference between MYC and MYCN and that MYCN engages TFIIIC for this pathway.

(2) Reciprocal to TFIIIC recruitment to MYCN- rRNA, and other RNAPIII genes. Does this happen targets would be MYCN association with tRNA genes, 5S, and if so, is this association TFIIIC dependent? What happens to the expression of these genes?

We did observe MYCN in interactions involving tRNA and other RNAPIII sites, such as SINE elements and tRNAs (Figure 4B, 4D, S3F, and S4B). There was no relevant number of 5S rRNA involved in interactions – either because the difficulty to properly map these repetitive regions or due to biology. In any case, none of those regions appeared to be specifically dependent on TFIIIC as the overall number of interactions increased in TFIIIC depletion regardless of the genomic annotation (Figure S4B). Regarding the expression of RNAPIII genes, we are constrained by technical limitations of poly(A) enrichment RNA-seq to globally analyze it in an unbiased way. However, we addressed this point for tRNAs expression in an earlier work 1 and found that tRNA levels do not change upon TFIIIC depletion. We think this is because tRNAs are stable transcripts and RNAPIII recycling can occur in a TFIIICindependent manner 9. Conversely, we reported no significant expression changes in RNAPII genes upon TFIIIC depletion in this work.

(3) The authors show that TFIIIC depletion does not alter the RNA-expression profile; how do they account for this? Can they comment on "background" transcription that it would seem should be suppressed by TFIIIC-dependent removal of various hypofunctional polymerases?

Since TFIIIC is important for the removal of non-functional RNAPII we would not expect changes to the gene expression profile upon depletion of TFIIIC in the time frame analyzed. Monitoring the elongating form of RNAPII by measuring pSer2 indeed shows us that transcription elongation is not affected.

(4) Global changes in expression are difficult to assess with DESEQ2. This hypernormalizing algorithm is not really suited to distinguish differential, but universal upregulation from some targets being truly upregulated while others are downregulated. The authors should comment.

The authors acknowledge that DESEQ2 relies on the conjecture that genewise estimates of dispersion are generally unchanged among samples. We address this comment in two different ways. We include those in the Figure for the Reviewers (Figure 2). The first was to sequence samples deeper to avoid any bias created by random effect of lower coverage, the range of total reads increased from 6.8-9.3 to 16.5-20.7 million reads. The second was to compare the fold average bin dot plot for RNA-seq of SH-EP-MYCN-ER showing mRNA expression normalized by control per bin using the DESEQ2 (Figure 2A) normalization to TMM in edgeR (Figure 2B) and to quantile normalization (Figure 2C). No major differences were found from the original data or using the different methods, but we updated the Figure 2E in the manuscript to include the deeper sequencing dataset, we also adjusted it to show -/+ MYCN and transformed to log2 to make it more intuitive. Overall, it enhances our original understanding that gene expression remains largely unaffected by TFIIIC5 knockdown.

(5) On page 7, the authors claim that MYCN-ER increased Ser-2 can reflect MYCN-stimulated transcription elongation. In fact, without kinetic studies, this is not fully supported. Accumulation of Ser-2 RNAPII along a gene can reflect increased initiation of full-speed RNAPs or a pile-up of RNAPs slowing down. This should be resolved or qualified.

While we agree that we did not collect kinetic data to study the dynamics of RNA polymerase we would argue that the integration of our different data sets make it possible to draw conclusions about dynamic interferences. We showed on the one side that pSer-2 accumulates on the TES and on the other side the induction of MYCN-ER up-regulates gene expression which proves productive transcription elongation.

(6) pLHiChIP needs to be better described, the Mumbach reference is not sufficient.

We have reformulated the pLHiChIP in the method section and hope that this will provide now a better description of the method.

(7) Can the authors recheck all the labels in Figure 2D-I believe there is an error involving + or - MYCN.

We carefully rechecked all the labels in Figure 2 and it was correct as it was. We understand the confusion that may have created comparing Figure 2D and Figure 2E. To avoid confusion, we updated Figure 2E to show the same direction of Figure 2D. We also log2 transformed the y-axis of Figure 2E to foster a more intuitive reading.

(8) Why are there different scales for the regions of chromosome 17 shown in Figures 3 and 4? It would be easier to compare if the examples were all shown at the same scale (about 2 MB is shown in another Figure).

We now show the same region of chromosome 17 in Figure 3 and 4.

Reviewer #3 (Public Review):

(1) The connection between the three major findings presented in this study regarding the role of TFIIIC in the regulation of MYCN function remains unclear. Specifically, how the TFIIICdependent restriction of MYCN localization to promoter hubs enhances the association of factors involved in nascent RNA degradation to prevent the accumulation of inactive RNA polymerase II at promoters is not apparent. As they are currently presented, these findings appear as independent observations. Cross-comparison of the different datasets obtained may provide some insight into addressing this question.

We previously observed that TFIIIC does not affect MYCN recruitment, while MYCN affects TFIIIC binding 1. Moreover, our group reported that MYCN recruits exosome 4 and BRCA1 to promoter-proximal regions 10 to clear out non-functional RNAPII. We are currently reporting that MYCN-TFIIIC complexes exclude non-functional RNAPII. However, MYCN-active promoter hubs have more RNAPII and more transcription than MYCN-active promoter outside hubs. Furthermore, TFIIIC binding occurs upstream of BRCA1 and exosome recruitments as depletion of TFIIIC leads to recruitment decrease of both factors. Therefore, we argue that TFIIIC is required for the proper function of those MYCN-active promoter hubs.

(2) Another concern involves the disparities in RNA polymerase II ChIP-seq results between this study and earlier ones conducted by the same group. In Figure 2, the authors demonstrate that activation of MYCN results in a reduction of non-phosphorylated RNA polymerase II across all expressed genes. This discovery contradicts prior findings obtained using the same methodology, where it was concluded that the expression of MYCN had no significant effect on the chromatin association of hypo-phosphorylated RNA polymerase II (Buchel et al, 2017). In this regard, the choice of the 8WG16 antibody raises concern, as fluctuations in the signal may be attributed to changes in the phosphorylation levels of the Cterminal domain. It remains unclear why the authors decided against using antibodies targeting the N-terminal domain of RNA polymerase II, which are unaffected by phosphorylation and consistently demonstrated a significant signal reduction upon MYCN activation in their previous studies (Buchel et al, 2017) (Herold et al, 2019). Similarly, the authors previously proposed that depletion of TFIIIC5 abrogates the MYCN-dependent increase of Ser2phosphorylated RNA polymerase II (Buchel et al, 2017), whereas they now show that it has no obvious impact. These aspects need clarification.

We politely disagree that our discoveries are contradicting each other. Comparing our new results to the data published previously we can summarize that the data sets in the two studies show three key results: First, the traveling ratio of RNAPII changes upon induction of MYCN. Second, RNAPII decreases at the transcription start side and third, it increases towards the end side.

We agree that in the previous study we linked the traveling ratio directly to elongation. However performing ChIP-seq with different RNAPII antibodies showed us that for example RNAPII (N20), which is unfortunately discontinued, gives different results compared to RNAPII (A10). Combining our new results using the RNAPII (8WG16) antibody shows that the traveling ratio is not only reflecting transcription elongation but also includes that the RNAPII is kicked-off chromatin at the start side.

In the previous study we only performed manual ChIP experiments for RNAPII (8WG16) and pSer2. Now we did a global analysis which is more meaningful and is also reflected in the RNA sequencing data.

(3) Finally, the varied techniques employed to explore the role of TFIIIC in MYCNdependent recruitment of nascent RNA degradation factors make it challenging to draw definitive conclusions about which factor is affected and which one is not. While conducting ChIPseq experiments for all factors may be beyond the scope of this manuscript, incorporating proximity ligation assays (PLA) or ChIP-qPCR assays with each factor would have enabled a more direct and comprehensive comparison.

We understand the criticism that we are comparing different assays. We have performed PLAs with different antibodies. Since the controls of the PLAs were not sufficient for us, we refrain from using them. ChIP-qPCR experiments are much more challenging to do side by side compared to PLAs, which is why we decided against looking at all factors with this method.

Recommendations For The Authors:

Reviewer #3 (Recommendations For The Authors):

(1) Figure 2: Why did the authors choose the 8WG16 antibody? Does TFIIIC5 depletion suppress the MYCN-dependent reduction of total RNA polymerase II binding to promoters that they consistently showed in previous studies? Given that phosphorylation of the CTD impacts 8WG16 recognition, including Ser5-phosphorylated RNA polymerase II ChIPseq experiments might clarify this issue.

We used the RNAPII (8WG16) antibody to exactly map non-phosphorylated RNAPII which shows us the binding of non-functional RNAPII.

(2) Figures 3 and 4: As it stands, the manuscript does not convincingly establish a functional connection between the results in Figures 2, 3, and 4 or elucidate potential mechanisms. Are changes in RNA polymerase II levels upon MYCN activation more pronounced at promoters located at MYCN hubs? Do changes in MYCN-enriched chromatin contacts upon TFIIIC5 depletion somehow correlate with alterations in RNA polymerase II levels? Performing similar cross-comparisons as in Figure 3C may help address this issue. Furthermore, it not clear how the authors concluded that MYCN/TFIIIC5-bound genes are not part of these so-called promoter hubs.

In Figure 3C we show that RNAPII levels are more pronounced upon MYCN activation at promoters located at MYCN hubs. Additionally, we show non-phosphorylated ChIP-seq on TSS and RNAPII-pSer2 ChIP-seq on TES density plots for promoters with MYCN interactions in the Figure for the Reviewers (Figure 3). We found no other difference than binding compared to the overall global analysis for all expressed genes showed in Figure 2B and Figure 2C. This goes on the same direction of the high expression observed of those genes in MYCN interactions observed in Figure 3C.

The changes observed in Figures 2B and 2C are global and do include the promoters with MYCN interactions. At the same time, it is required a higher number of replicates to statistically distinguish the MYCN interaction differences between TFIIIC5 presence and depletion. We acknowledge this limitation, and we therefore restrain any attempt towards this end. We base our conclusions on the other parts of the manuscript and on our previous studies that show that MYCN recruits TFIIIC, BRCA1, and the exosome to promoter proximal regions 1,4,10.

(3) Figure 5: According to the PLA results, activation of MYCN could enhance RNA polymerase II-NELFE interaction in a TFIIC5-dependent manner. Considering the raised issues regarding the use of the 8WG16 antibody, this result might be of relevance.

Nevertheless, PLA does not seem to be the optimal technique to address these questions, and I would rather suggest performing ChIP-qPCR experiments for all the factors to be compared. Finally, do the authors conclude that the TFIIIC5 effect on MYCN-dependent changes in RNA polymerase II depends upon the recruitment of EXOSC5 and BRCA1? If so, it would be interesting to determine whether depletion of these factors phenocopies the effects observed with TFIIC5.

We understand the criticism that we are comparing different assays. We have performed PLAs with different antibodies. Since the controls of the PLAs were not sufficient for us, we refrain from using them.

(4) In Figure S2 the labels should be EtOH, 4-OHT, and Input.

We changed this accordingly.

(5) On page 7, the sentence "We have shown previously that TFIIIC5 depletion does not cause significant changes in expression of multiple tRNA genes that are transcribed by RNAPIII (Buchel et al., 2017)" appears to lack a connection.

We agree with the reviewer and we deleted this sentence from the manuscript.

Author response image 1.

(A) Density plot of ChIP-Rx signal for non-phosphorylated RNAPII. Data show mean (line) ± standard error of the mean (SEM indicated by the shade) of different gene sets based on an RNA-seq of SH-EP-MYCN-ER cells ± 4-OHT. The y-axis shows the number of spike-in normalized reads and it is centered to the TES ± 2 kb. N = number of genes in the gene set defined in the methods. (B) Density plot of ChIP-Rx signal for RNAPII pSer2 as described for panel A. The signal is centered to the TSS ± 2 kb.

Author response image 2.

Bin dot plot for RNA-seq of SH-EP-MYCN-ER showing mRNA expression normalized by control per bin comparing the fold average using DESEQ2 (A), normalization to TMM in edgeR (B) and to quantile normalization (C).

Author response image 3.

Average density plot of ChIP-Rx signal for non-phosphorylated RNAPII (A) or RNAPII pSer2 (B) at promoters with MYCN interactions.

References

(1) Büchel, G., Carstensen, A., Mak, K.-Y., Roeschert, I., Leen, E., Sumara, O., Hofstetter, J., Herold, S., Kalb, J., and Baluapuri, A. (2017). Association with Aurora-A controls NMYC-dependent promoter escape and pause release of RNA polymerase II during the cell cycle. Cell reports 21, 3483-3497.

(2) Yuen, K.C., Slaughter, B.D., and Gerton, J.L. (2017). Condensin II is anchored by TFIIIC and H3K4me3 in the mammalian genome and supports the expression of active dense gene clusters. Sci Adv 3, e1700191. 10.1126/sciadv.1700191.

(3) Ferrari, R., de Llobet Cucalon, L.I., Di Vona, C., Le Dilly, F., Vidal, E., Lioutas, A., Oliete, J.Q., Jochem, L., Cutts, E., Dieci, G., et al. (2020). TFIIIC Binding to Alu Elements Controls Gene Expression via Chromatin Looping and Histone Acetylation. Mol Cell 77, 475-487 e411. 10.1016/j.molcel.2019.10.020.

(4) Papadopoulos, D., Solvie, D., Baluapuri, A., Endres, T., Ha, S.A., Herold, S., Kalb, J., Giansanti, C., Schulein-Volk, C., Ade, C.P., et al. (2021). MYCN recruits the nuclear exosome complex to RNA polymerase II to prevent transcription-replication conflicts. Mol Cell. 10.1016/j.molcel.2021.11.002.

(5) Lorenzin, F., Benary, U., Baluapuri, A., Walz, S., Jung, L.A., von Eyss, B., Kisker, C., Wolf, J., Eilers, M., and Wolf, E. (2016). Different promoter affinities account for specificity in MYC-dependent gene regulation. Elife 5. 10.7554/eLife.15161.

(6) Baluapuri, A., Hofstetter, J., Dudvarski Stankovic, N., Endres, T., Bhandare, P., Vos, S.M., Adhikari, B., Schwarz, J.D., Narain, A., Vogt, M., et al. (2019). MYC Recruits SPT5 to RNA Polymerase II to Promote Processive Transcription Elongation. Mol Cell 74, 674-687 e611. 10.1016/j.molcel.2019.02.031.

(7) Baluapuri, A., Wolf, E., and Eilers, M. (2020). Target gene-independent functions of MYC oncoproteins. Nat Rev Mol Cell Biol. 10.1038/s41580-020-0215-2.

(8) Koch, H.B., Zhang, R., Verdoodt, B., Bailey, A., Zhang, C.D., Yates, J.R., 3rd, Menssen, A., and Hermeking, H. (2007). Large-scale identification of c-MYCassociated proteins using a combined TAP/MudPIT approach. Cell Cycle 6, 205-217. 10.4161/cc.6.2.3742.

(9) Ferrari, R., Rivetti, C., Acker, J., and Dieci, G. (2004). Distinct roles of transcription factors TFIIIB and TFIIIC in RNA polymerase III transcription reinitiation. Proc Natl Acad Sci U S A 101, 13442-13447. 10.1073/pnas.0403851101.

(10) Herold, S., Kalb, J., Büchel, G., Ade, C.P., Baluapuri, A., Xu, J., Koster, J., Solvie, D., Carstensen, A., and Klotz, C. (2019). Recruitment of BRCA1 limits MYCN-driven accumulation of stalled RNA polymerase. Nature 567, 545-549.

Author response:

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

Reviewer #1 (Public Review):

Summary:

Ewing sarcoma is an aggressive pediatric cancer driven by the EWS-FLI oncogene. Ewing sarcoma cells are addicted to this chimeric transcription factor, which represents a strong therapeutic vulnerability. Unfortunately, targeting EWS-FLI has proven to be very difficult, and a better understanding of how this chimeric transcription factor works is critical to achieving this goal. Towards this perspective, the group had previously identified a DBD-𝛼𝛼4 helix (DBD) in FLI that appears to be necessary to mediate EWS-FLI transcriptomic activity. Here, the authors used multi-omic approaches, including CUT&tag, RNAseq, and MicroC to investigate the impact of this DBD domain. Importantly, these experiments were performed in the A673 Ewing sarcoma model where endogenous EWS-FLI was silenced, and EWS-FLI-DBD proficient or deficient isoforms were re-expressed (isogenic context). They found that the DBD domain is key to mediating EWS-FLI cis activity (at msat) and to generating the formation of specific TADs. Furthermore, cells expressing DBD-deficient EWS-FLI display very poor colony-forming capacity, highlighting that targeting this domain may lead to therapeutic perspectives.

We thank Reviewer 1 for their strong summary of Ewing sarcoma background and accurate description of our experimental approaches and findings.

Strengths:

The group has strong expertise in Ewing sarcoma genetics and epigenetics and also in using and analyzing this model (Theisen et al., 2019; Boone et al., 2021; Showpnil et al., 2022).

We thank the reviewer.  

They aim at better understanding how EWS-FLI mediated its oncogenic activity, which is critical to eventually identifying novel therapies against this aggressive cancer.

We are happy to see that our overall aim was also appreciated by Reviewer 1.

They use the most recent state-of-the-art omics methods to investigate transcriptome, epigenetics, and genome conformation methods. In particular, Micro-C enables achieving up to 1kb resolved 3D chromatin structures, making it possible to investigate a large number of TADs and sub-TADs structures where EWS-FLI1 mediates its oncogenic activity.

We thank Reviewer 1 for their acknowledgement of our approaches and the resolution achieved with our Micro-C experiments.  

They performed all their experiments in an Ewing sarcoma genetic background (A673 cells) which circumvents bias from previously reported approaches when working in non-orthologous cell models using similar approaches.

We agree with the reviewer about the importance of using model systems that accurately capture features of the disease being studied. As we have added an additional cell line in the revision we should note that this second model also represents a Ewing sarcoma genetic background while representing tumors expressing another oncogenic fusion found in this disease. 

Weaknesses:

The main weakness comes from the poor reproducibility of Micro-C data . Indeed, it appears that the distances/clustering observed between replicates are typically similar or even larger than between biological conditions. For instance, in Figure 1B, I do not see any clustering when considering DBD1, DBD2, DBD+1, DBD+2.

Lanes 80-83: "KD replicates clustered together with DBD replicate 1 on both axes and with DBD replicate 2 on the y-axis. DBD+ replicates, on the other hand, clustered away from both KD and DBD replicates. These observations suggest that the global chromatin structure of DBD replicates is more similar to KD than DBD+ replicates."

When replacing DBD replicate 1 with DBD replicate 2, their statement would not be true anymore.

Additional replicates to clarify this aspect seem absolutely necessary since those data are paving the way for the entire manuscript.

These are valid concerns and we thank the reviewers for highlighting this limitation of poor clustering of Micro-C replicates on MDS plot. We account for this variability between different replicates when identifying differentially interacting regions. By using an adjusted p-value < 0.05, we aim to ensure that repeating the experiments we will discover the same differentially interacting regions with a false discovery rate of 5%.

We also would like to note that the replicates cluster much closely on PCA plot of RNA-seq data (Supplementary Figure 1C) and as well as on PCA plot of H3K27ac CUT&Tag data (Figure 4A). Notably, the RNA-seq result has now reproduced when performed with different sets of hands across multiple studies (Boone, et. al., 2021 and this report), as well as in a second cell line (as reported in this manuscript revision). These observations suggest that the cells of these replicates are functionally similar to each other at a population level. Chromatin organization detected by Micro-C is a highly heterogenous within cells of a population (Misteli, et. al., 2020). Moreover, despite increased resolution with Micro-C over Hi-C, the conventional sequencing depth that Micro-C is performed at makes resolving finer scale 3D interactions, particularly between enhancers and promoters, challenging (Goel, et. al., 2023). Thus biologically relevant interactions driving EWSR1::ETS transcriptional regulation through de novo enhancers may have relatively weak signal in Micro-C. Both the strength of the signal and the heterogeneous chromatin state present in bulk samples could affect the average signal leading to poor clustering replicates (Hafner and Boettiger, 2022). 

Importantly, rather than add an additional replicate of a single cell line, we repeated our study in an additional cell line, TTC466, and largely reproduced our high-level findings for transcription, enhancer formation, and 3D chromatin. Specific limitations of the TTC466 study are addressed in the Discussion section (392-420). The reproduction of weak/moderate clustering in the MDS plot in both A673 and TTC466 cell lines suggests the α4 helix of EWSR1::ETS fusions are important for reshaping 3D chromatin. However, higher resolution analyses focused on specific EWSR1::ETS-bound loci are likely an important area of future study required to fully understand the role of the α4 helix in chromatin regulation in Ewing sarcoma.

Similarly:

- In Figure 1C, how would the result look when comparing DBD2/KD2/DBD+2? Same when comparing DBD 1 with KD1 and DBD+1. Would the difference go in the same direction?

This is a great point. We added distance decay plots of individual replicates in Supplementary Figure 2 and added discussion of these results in lines 88-89 of the text.

- Figure 1D-E. How would these plots look like when comparing each replicate to each other's? How much difference would be observed when comparing, for instance, DBD1/DBD2 ? or DBD1/DBD+1?

Unfortunately, separate replicates are required to conduct Differentially Interacting Region analysis as it determines statistically significant interactions. Therefore, we are unable to plot these analyses with individual replicates. 

- Figure 2: again, how would these analyses look like when performing the analysis with only DBD1/DBD+1/KD1 or DBD2/DBD+2/KD?

This is a good suggestion. It is possible to do such analysis. However, we will lose resolution as such that we may not accurately detect TADs, especially smaller TADs. Therefore, we decided to combine the biological replicates.   

Another major question is the stability of EWS-FLI DBD vs EWS-FLI DBD+ proteins. In the WB, FLAG intensities seem also higher (2/3 replicates) in DBD+ condition compared to the DBD condition (Figure S1B).

This is a valid concern with shRNA knock-down/rescue system and we regularly validate new constructs to ensure that they have similar expression levels as rescue with the wildtype fusion before proceeding to more exhaustive experimental workups. We would note that while we have not tested for differences in protein stability, for these constructs we largely see similar expression levels across multiple experiments, multiple cell lines, and multiple sets of hands. There may be some variations in expression level from experiment to experiment, but western blotting is a semiquantitative assay and it is also not possible to rule out that slight differences in band intensity may be a result of error in gel loading. For this reason, alongside western blotting for construct expression, we also validate construct function using RNA-seq and colony formation assays (as reported in this manuscript) and these show good agreement across biological replicates.  

Indeed, it seems that they have more FLAG (i.e., EWS-FLI) peaks in the DBD+ condition compared to the DBD condition (Figure 2B). 

We appreciate the comment since the legend of Figure 2B led to a misunderstanding. Figure 2B depicts the number of TADs detected in DBD and DBD+ conditions (height of the bar graphs) and the proportion of those TADs overlapped with FLAG, CTCF, both or neither peaks on y-axis. The number of FLAG peaks is actually lower in DBD+ as compared to DBD as shown in Figure 5A-B.  We clarified our Figure 2 legend to accurately describe the various proportions (color coded section) of TADs bound by DBD/DBD+ FLAG and CTCF.

Would it be possible that DBD+ is just more expressed or more stable than DBD? The higher stability of the re-expressed DBD+ could also partially explain their results independently of the 3D conformational change. In other words, can they exclude that DBD+ and DBD binding are not related to their respective protein stability or their global re-expression levels?

It is possible that DBD+ protein is overexpressed or more stable than DBD. With our current set of data, we cannot conclusively exclude if binding by DBD and DBD+ are not related to their expression level or stability. We would note, as above, that western blots, RNA-seq, and agar assays have largely reproduced across experiments, hands, and cell lines and that western blot is an imperfect assay for assessing protein stability.

Surprisingly, WB FLI bands in DBD+ conditions are systematically (3/3 replicates) fainter than in DBD conditions (Figure S1B). How do the authors explain these opposite results between FLI and FALG in the WB?

This is an excellent observation that highlights one of the intricacies of studying EWSR1::FLI1 in our KD/rescue system. Often the limiting factor for an experiment is whether or not the KD condition maintains KD through a second viral transduction for rescue and selection. We have observed over many years of working with this system that rescue conditions which are fully functional (i.e. wildtype EWSR1::FLI1, DBD+, etc.) tend to maintain better KD of endogenous EWSR1::FLI1. Constructs that don’t rescue EWSR1::FLI1 function sometimes maintain KD to a lesser degree, though frequently to a functional degree (i.e. cells are not transformed and EWSR1::FLI1 transcriptional regulation is not rescued). We suspect this observation, also raised by Reviewer 1 is resulted from a potential selection of cells with more endogenous EWSR1::FLI1 escaping KD in in DBD conditions due to selective pressures during expansion in tissue culture.

We should note that the antibody used for detecting FLI recognizes residues that are deleted in

DBD and DBD+ constructs, such that the FLI1 blot in Supplementary Figure 1B does not detect either construct. It only detects endogenous EWSR1::FLI1 and the 3X-FLAG-EWSR1::FLI1 construct in the middle lane that runs at a slightly higher molecular weight. The FLAG antibody is the only antibody that detects all three rescue constructs.    

Reviewer #2 (Public Review):

Summary:

The manuscript by Bayanjargal et al. entitled "The DBD-alpha4 helix of EWS::FLI is required for GGAA microsatellite binding that underlies genome regulation in Ewing sarcoma" reports on the critical role of a small alpha helix in the DNA binding domain (DBD) of the FLI1 portion of EWS::FLI1 that is critical for binding to repetitive stretches of GGAA-motifs, i.e. GGAA microsatellites, which serve as potent neoenhancers in Ewing sarcoma.

We thank Reviewer 2 for their succinct and accurate summary of our manuscript. 

Strengths:

The paper is generally well-written, and easy to follow and the data presented are of high quality, welldescribed and underpin the conclusions of the authors. The report sheds new light on how EWS::FLI1 mechanistically binds to and activates GGAA microsatellite enhancers, which is of importance to the field.

We appreciate the reviewer’s assessment of our work. 

Weaknesses:

While there are no major weaknesses in this paper, there are a few minor issues that the authors may wish to address before publication:

(1) While the official protein symbol for the gene EWSR1 is indeed EWS, the protein symbol for the gene FLI1 is identical, i.e. FLI1. The authors nominate the fusion oncoprotein EWS::FLI1 (even in the title) but it appears more adequate to use EWS::FLI1.

We appreciate the reviewer for bringing this to our attention. Indeed, the most recent guideline for fusion proteins nomenclature is to use the full gene symbols separated by double colons. Therefore, the accurate nomenclature is EWSR1::FLI1. We replaced instances of EWS::FLI with EWSR1::FLI1 and have used the EWSR1::ERG nomenclature in our revised manuscript.  

(2) The used cell lines should be spelled according to their official nomenclature (e.g. A-673 instead of A673).

Corrected, thanks!

(3) It appears as if the vast majority of results were generated in a single Ewing sarcoma cell line (A-673) which is an atypical Ewing sarcoma cell line harboring an activating BRAF mutation and may be genomically quite unstable as compared to other Ewing sarcoma cell lines (Kasan et al. 2023 preprint at bioRxiv https://www.biorxiv.org/content/10.1101/2023.11.20.567802v1). Hence, it may be supportive for the paper to recapitulate/cross-validate a few key results in other Ewing sarcoma cell lines, e.g. by using EWS::ERG-positive cell lines. Perhaps the authors could make use of available published data.

We thank Reviewer 2 for this helpful comment. We replicated the experiments in TTC-466 cells containing EWSR1::ERG fusion and found that as for A-673 cells the DBD-α4 helix is important for transcriptional, enhancer, and 3D chromatin regulation (Supplementary Figures 9-18).  

(4) Figure 6 and Supplementary Figure 5 are very interesting but focus on two selected target genes of the fusion (FCGRT and CCND1). It would be interesting to see whether these findings also extend to common EWS::ETS transcriptional signatures that have been reported. The authors could explore their data and map established consensus EWS::ETS signatures to investigate which other hubs might be affected at relevant target genes.

We expanded our analysis to other genes demonstrated to be regulated by EWSR1::FLI1 nucleated transcriptional hubs (Chong, et. al., 2018) and included NKX2-2 and GSTM4 gene regions in

Supplementary Figure 7-8 in A-673 cells. We also investigated the same gene regions of FCGRT, CCND1, NKX2-2, GSTM4 in TTC466 cells and report them in Supplementary Figures 14-17. For the purpose brevity, we decided to include the above examples. We may need to develop different tools to conduct further analysis to understand the gene regulatory networks driven by DBD and DBD+ in relation to hub formation. Although it is a great suggestion to map such network, this may be outside the scope of this manuscript. We thank the reviewer for bringing such a good point to our attention.  

(5) Table 1 is a bit hard to read. In my opinion, it is not necessary to display P-values with up to 8 decimal positions. The gene symbols should be displayed in italic font.

Suggestions are adapted, thanks!

Reviewing Editor (Recommendations For The Authors):

We would draw the authors' attention to the following issues that would best benefit from additional revision.

As indicated by Referee 1, an important issue concerns the apparent poor reproducibility of Micro-C data. In Figure 1B, the clustering of the DBD1, DBD2, DBD+1, and DBD+2 is poor.

It appears that the distances/clustering observed between replicates are typically similar or even larger than between biological conditions. Lines 80-83: "KD replicates clustered together with DBD replicate 1 on both axes and with DBD replicate 2 on the y-axis. DBD+ replicates, on the other hand, clustered away from both KD and DBD replicates. If one replaced DBD replicate 1 with DBD replicate 2, this statement would no longer be true. The referees believe that it is important to fully account for these potential discrepancies. Most of the study is based on analyses of these data sets, so if there are issues with them it has repercussions on the entire study. We note however that in Figure 4A the clustering of the H3K27ac data is much more convincing. The referees also feel that it is important to show immunoblots of the expression of DBD and DBD+ levels in the experiments performed here. While this was previously shown in the Boone et al publication in 2021, it could be illustrated again here.

We thank the editors for concisely summarizing the main weaknesses of the paper and underscoring the importance of the Micro-C data in the rest of the paper. While the Editors note tighter clustering of the H3K27ac (Figure 4A), we would like to note that the replicates cluster much closely on PCA plot of RNA-seq data (Supplementary Figure 1C). Notably, the RNA-seq result has now reproduced when performed with different sets of hands across multiple studies (Boone, et. al., 2021 and this report), as well as in a second cell line (as reported in this manuscript revision). Though not as tight, the H3K27ac CUT&Tag also reproduces in TTC466 cells. Thus, we interpret these findings to indicate that our replicates are functionally similar to each other. As discussed above in the response to Reviewer 1 in more detail, there are several factors that could affect how these functional similarities are represented in Micro-C data. Micro-C is ultimately a readout of the chromatin organization in a heterogeneous population of cells (Misteli et al., 2020). Additionally, sequencing depth limitations in conventional Micro-C experiments limit the ability to faithfully assess the enhancer-promoter interactions that may be relevant for our model system (Goel, et. al., 2023). Thus, both the strength of the biologically relevant signal and the heterogeneous chromatin state present in bulk samples could affect the average signal and lead to poorly clustering replicates (Hafner and Boettiger, 2022). 

To address these important concerns about rigor and reproducibility of the analyses, we repeated our study in an additional cell line, TTC466, and largely reproduced our high-level findings for transcription, enhancer formation, and 3D chromatin. These additional studies were not without their own limitations and these are addressed in the Discussion section (392-420). The reproduction of weak/moderate clustering in the MDS plot in both A673 and TTC466 cell lines suggests the α4 helix of EWSR1::ETS fusions are important for reshaping 3D chromatin. However, additional genomic analyses geared toward higher resolution at specific EWSR1::ETS-bound loci are likely an important area of future study required to fully understand the role of the α4 helix in chromatin regulation in Ewing sarcoma. Live cell imaging, as performed by Chong, et. al., 2018 and additional biochemical techniques may also be informative and are beyond the scope of this report.

With regards to concerns about construct expression, we have included immunoblots of the rescue constructs in both cell lines (Supplementary Figure 1B and 9A) and discussed Reviewer 1’s specific concerns in detail above.  

The referees also raise the issue of using an additional cell line to make a more general message. Although it would perhaps be asking too much to repeat the MicroC experiments, consolidation of the observations could be performed by focusing on specific loci such as FCGRT and CCND1 that were analyzed in this study. Could the authors use 4C-type experiments to reproduce the conclusions in an additional cell line? It would also be pertinent to consolidate the findings at these loci by 4C-type approaches even in the cell line used here. For the moment, all conclusions are based on the same set of data and a single technical approach.

We repeated the experiments in TTC466 cells and analyzed the data using same cut-offs used in A-673 cells. This allows us to compare between the two cell lines. We hope this new set of experiments and analyses address the reviewers’ concerns.  

Reviewer #1 (Recommendations For The Authors):

All the data are performed in A673 cells. Knowing the transcriptomic and epigenetic heterogeneity of Ewing sarcoma cells, some of the experiments supporting their findings should be replicated in at least another Ewing sarcoma model.

Per our discussion above, we have replicated our experiments in an additional cell line model of Ewing sarcoma. Importantly, the TTC466 cell line used expresses the EWSR1::ERG fusion found in 10-15% of Ewing sarcoma cases.  

Supplementary Figure 2B. Proportion of TAD boundaries bound by FLAG (i.e., EWS-FLI1) and CTCF. The number/proportion of FLAG (i.e., EWS-FLI) peaks observed at CTCF peak/TAD boundaries seems unexpectedly high. How do they explain this result since EWS-FLI peaks are rather intra-TAD to mediate their enhancer function?

In our previous study, we showed that EWSR1::FLI1 binding can be detected at boundaries of TADs (Showpnil, et. al., 2022). We think therefore it is likely that EWSR1::FLI1 binding is able to mediate enhancer function both inside TADs as well as at the borders of TADs and may, in some cases, function as an insulator between TADs.  

For the >50kb loop analysis, what was the low-range threshold? Up to 15-20 kp, contact frequency interactions may be caused by PFA crosslink (did they use a 5kb threshold ?). Were those excluded from that analysis?

We acknowledge that we did not use a lower threshold to exclude those short-range loop interactions. In our previous study, we observed that EWSR1::FLI1 binding reduces long-range interactions in favor of short-range interactions (Showpnil, et. al., 2022) and wanted to be able to capture short-range loops in our analysis.  

In Figure 2D, they observed that within TADs containing FLAG peaks at GGAA microsatellites, the intensity of the DBD+ FLAG peaks was higher compared to DBD FLAG peaks. How would this analysis look when considering the ETS FLAG peaks (i.e., EWS-FLI rather repressive peaks)? Could they compare TAD with GGAA msat vs TAD with ETS peaks?

We agree that this is an interesting observation. In our prior analyses we found no discernible relationship between EWSR1::FLI1 binding and changes in 3D chromatin associated with repression (Showpnil, et. al., Nucleic Acids Research, 2022). In contrast, EWSR1::FLI1-bound superenhancers had greater H3K27ac deposition when overlapping both a bound GGAA repeat and a non-microsatellite site. While there have been several additional reports about the relevance of EWSR1::FLI1 binding at nonmicrosatellite peaks, motifs at these loci have not yet been rigorously defined as GGAA repeats were by Johnson, et. al. in PLoS One, 2017. Each ETS factor binds different motifs containing the core 5’-GGAA-3’ with varying affinities depending on the flanking residues. There may be >100-fold difference in sequence-specific binding affinity for “high” vs. “low” affinity motifs. Better defining the types of ETS motifs bound by EWSR1::FLI1 and the functional changes associated with them thus represents an interesting area of future study.

Figure 1F: What is the biological meaning of these results (29.7, 39.5, and 54Mbp)? These distances are typically the size of a chromosome arm and clearly beyond classical chromatin loop/TAD structures in which EWS-FLI mediates its cis-activity.

We agree with referee here. This panel is now removed in our revised manuscript.  

How do DBD, KD, and DBD+ conditions compare with WT parental cells in the omics data? (Figures 1B, 4A). Do DBD+ conditions overlap with WT conditions? It would be nice to have these analyses also for Micro-C and Cut&Tag data. To be acknowledged here, the transcriptome data showing this aspect in Figure S1C are very convincing.

This is a fair point. We were not able to obtain similar sequencing depth of wtEF Micro-C libraries to that of KD, DBD and DBD+ due to disproportional use of wtEF libraries in troubleshooting. Therefore, we decided to exclude wtEF condition from these analysis. 

EWS-FLI cis-regulation at CCND1 also occurs through a much closer EWS-FLI peak (~-20kb msat upstream of CCND1 TSS) which was not taken into consideration. EWS-FLI peak intensity in both DBD and DBD+ at this msta seems similar. How would this fit into their model?

The referee is correct. The closest peak upstream of CCND1 TSS is about ~19kb away. We highlighted this peak with the dashed boxes near the CCND1 TSS (Supplementary Figure 6). Peak intensity of DBD+ FLAG is slightly higher compared to DBD. Nonetheless, we acknowledge that the difference is small. We suspect that the DBD-α4 helix is affecting binding dynamics at GGAA repeats, but these genomics approaches are not well suited to detect small, but significant, changes in binding affinity or dynamics. In this case a more biochemical approach may be needed. Even though, both protein can still bind the same microsatellites, it is possible that they might differ in their stability of binding or in the recruitment of additional proteins. These possibilities are discussed in the Discussion section (444-463).  

For the Micro-C, they sequenced only 7 to 8 million reads per condition. This coverage seems particularly low, especially for their analyses using 1-5kb bins. How does this compare with other published Micro-C data? Can this explain the variability observed between replicates?

We apologize for the inconsistent verbiage of sequencing coverage that may have caused confusion. 7 to 8 million reads were used for shallow sequencing and QC analysis. Once a sample passed QC, we then sequenced 300 million reads per sample. 300M is now changed to 300 million to prevent a misunderstanding at line 598.  

They mention:

"In our recent studies of EWS::FLI, we found a small alpha helix in the DNA binding domain DBD-𝛼𝛼4, to

be required for transcription and regulation by the fusion protein (Boone et al., 2021). Interestingly, this study did not find any change in chromatin accessibility (ATAC-Seq) and genome localization of EWS::FLI constructs (CUT&RUN) when DBD-𝛼𝛼4 helix was deleted leaving the mechanistic basis for the requirement of DBD-𝛼𝛼4 in transcription regulation unclear. "

And

"To assay the enhancer landscape, we collected H3K27ac CUT&Tag data from KD, DBD, and DBD+ cells. Principal component analysis of H3K27ac localization shows that the DBD replicates were clustered closer to the KD replicates while being in between the KD and the DBD+ replicates (Figure 4A), suggesting that DBD-𝛼𝛼4 helix is required to reshape the enhancer landscape."

But now H3K27ac CUT&Tag show strong differences which were not observed in ATAC seq. How to explain this discrepancy?

Though both H3K27ac and ATAC signal are associated with enhancers and promoters in euchromatin, they are not exactly measurements of the same thing. H3K4me2 is a mark more closely associated with ATAC signal than H3K27ac (Henikoff, et. al., 2020). Nonetheless, there are clear differences between the prior publication (Boone, et. al., 2021) and this work with regards to similar ATAC signal for each replicate and differences in H3K27ac. We suspect this may be related to a tighter association between H3K27ac and EWSR1::FLI1-mediated genome regulation and ATAC. Notably, there were very few differentially accessible regions between EWSR1::FLI1-depleted cells and conditions with EWSR1::FLI1 expression (either endogenous or wildtype rescue) using the A673 KD/Rescue system in Boone, et. al., 2021. In contrast, other A673 KD-rescue studies have reported differences in H3K27ac in EWSR1::FLI1 expressing conditions relative to EWSR1::FLI1-depleted conditions (Theisen, et. al., 2021). .  

The authors mention:

"Our study thus uncovered a surprising role for FLI DBD in the process of hub formation which is usually attributed to the EWS low complexity domain."

Not sure this can be claimed, hubs are composed of many other factors that are not investigated here. Furthermore, promoter enhancer hubs/loops often include combined ETS and mSat chains to generate transcriptional hubs which have not been considered here. None of these points were discussed here.

We replaced “uncovered” with “suggest” in our revised manuscript at line 476.  

What are the barcode patterns in Supp 5, are those frequently observed in their Micro-C data, likely mapping artifacts, do they have any impact on their analyses?

The barcode patterns in now Supplementary Figure 6 are blind spots in the hg19 genome assembly. Since they are few in numbers, we don’t expect these blind spots to impact our analysis.

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

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Reply to the reviewers

Manuscript number: RC-2024-02516

Corresponding author(s): Christopher Shoemaker

__1. __General Statements [optional]

Thank you to all the reviewers for their helpful efforts on behalf of our manuscript. We appreciate the time and effort they have invested in providing valuable feedback.

Overall, the positive reception from our reviewers highlighted their appreciation for our approach and findings. Moreover, their comments underscored the relevance and potential impact of our findings, particularly within the fields of autophagy and protein interaction networks. Their detailed and constructive critiques will also help refine both the content and presentation of our work.

In response to the reviews, we have proposed targeted revisions to the manuscript, all of which are well within our lab's capabilities and can be executed efficiently. We have detailed our responses to each specific point raised by the reviewers below. * *

• *

__2. __Description of the planned revisions

• *

Reviewer #1

Evidence, reproducibility and clarity

1. EVIDENCE, REPRODUCIBILITY AND CLARITY Summary:

Selective autophagy receptors (SARs) of the Sequestosome-1 like receptor group (SLRs) including SQSTM1(Sequestosome-1)/p62, NBR1, TAX1BP1, NDP52, CALCOCO1 and Optineurin are soluble SARs that engage cargo and ATG8 family proteins as well as components of the core autophagy machinery like FIP200/RBCC1 to bring about the autophagic degradation of the cargo and themselves. In the autophagic degradation of protein aggregates (aggrephagy) the most studied SAR p62 collaborates with the archetypal autophagy receptor NBR1 and also TAX1BP1 to bring about effective turnover of ubiquitinated cargos sequestered into p62 bodies or droplets by liquid-liquid phase separation. How this intricate co-operation of these SARs is orchestrated is incompletely understood. In the paper by North et al entitled "The LC3-interacting region of NBR1 is a protein interaction hub enabling optimal flux" the authors use peptide arrays to map the binding sites for ATG8-family proteins LC3A and GABARAPL1, FIP200 and TAX1BP1 to the autophagy receptor NBR1. The authors find that three short linear interaction motifs (SLiMs), the LIR, FIR and TIR interacting with ATG8 family proteins, FIP200 and TAX1BP1, respectively, partly overlap in a short region of NBR1 that can adopt different conformations to accommodate the different binding partners. In short, the different interactions are mediated by distinct overlapping determinants, rather than a single, convergent, SLiM. While the important binding determinants for ATG8 proteins and FIP200 show more overlap and it was not possible here to find mutations that distinguish LIR and FIR binding, TAX1BP1 bound more to a region downstream of the LIR and a specific mutation in NBR1 and in TAX1BP1 could abolish binding. Checking the role of phosphorylations in augmenting binding using phosphomimetic mutations it was seen that while FIP200 and Atg8-family binding were generally augmented by phosphorylation, TAX1BP1 binding did not respond to these mutations. Very interestingly, the authors found that co-expression of TAX1BP1 with tandem-tagged NBR1 in pentaKO cells (not expressing the SLRs p62, NBR1, NDP52, TAX1BP1 and OPTN) increased significantly the autophagic turnover of NBR1. None of the other SLRs could do this. Instead, this over-expression assay revealed a competition.

Major points:

1) In Fig 4 the peptide array binding assay is not sufficient as it is only semiquantitative. The data shown should be accompanied by a more direct binding assay allowing the determination of kDs for the binding where the WT peptides are directly compared to the phosphor mimicking mutant peptides. Here the fluorescence anisotropy assay the authors use in Suppl Fig. 1E or ITC, OctetRed96 or another assay suitable for kD determinations should be used.

Response: Thank you for the constructive comments regarding our peptide array binding assay. We agree that the semi-quantitative nature of this method limits its ability to provide detailed binding affinity measurements. To address this, we will purify multiple peptides and assess the binding affinities between phosphomimetic+/- LIR peptides and Atg8s, FIP200, and TAX1BP1. While testing all peptides may be cost and time prohibitive, we will prioritize a representative range for this validation effort.

2) As this paper is already dominated by the use of peptides it would significantly enhance the quality of the data if the authors had included studied with peptides phosphorylated at the specific positions to allow comparison with the phosphomimetic substitutions to aspartate.

Response: Thank you for your insightful comment. We agree that incorporating studies with peptides phosphorylated at specific positions could provide a more nuanced comparison with the phosphomimetic substitutions to aspartate. Previous studies, including Popelka and Klionsky (2022) and Kliche et al. (2022), have indeed suggested that phosphomimetic substitutions do not perfectly replicate phosphorylation events.

In response, we plan to order a peptide array containing phosphorylated peptides, not merely phosphomimetics, and will conduct additional experiments with TAX1BP1, FIP200, and LC3A. This approach will allow us to directly assess the effects of actual phosphorylation compared to phosphomimetic substitutions.

While we acknowledge the possibility of subtle differences in binding affinity or regulatory interactions, we anticipate that the primary conclusions of our study—namely, that TAX1BP1 is largely insensitive to phosphorylation, whereas FIP200 and LC3A binding activities are affected—will remain unchanged. These experiments will provide valuable data to confirm the robustness of our conclusions under the conditions of true phosphorylation.

3) The quality of the 2D peptide array probing of GST-LC3A binding in Fig 3A is poor. Is this a stripped and re-probed membrane? I do not think these data are publication quality and the experiment should be redone unless the authors have very good arguments against my suggestion. It would also be nice to see a 2D peptide array of GABARAPL1 binding too to make the comparative study complete.

Response: Thank you for your constructive feedback regarding the quality of the 2D peptide array probing of GST-LC3A in Figure 3A. As you rightly pointed out, the membrane was indeed stripped and reprobed, with LC3A being the final probe. This method sometimes introduces artifacts, such as the 'ring' effect observed, which are common with this technique. However, the results consistently aligned with established consensus sequences for LC3, reinforcing the reliability of our findings despite the suboptimal image quality.

Recognizing the concerns about the quality of the blot, we are prepared to repeat this experiment using a new commercial vendor, as our previous collaborator is no longer available. We anticipate some differences in the appearance of the blots due to changes in dot size and spacing from the new supplier. Given these variations, we propose adding the revised blot to the supplementary materials rather than the main figures to avoid disrupting the visual continuity of the data presentation.

Additionally, in response to the reviewer’s suggestion, we will include a 2D peptide array probing for GABARAPL1. This will enhance the comparative analysis within our study.

One alternative (related to Reviewer 3, comment 3) that we can deliver is using our LIR arrays to derive consensus sequences for LC3 binders and GABARAPL1 binders. In doing this, we find the same differences in LC3 and GABARAP binding preferences that were reported previously in Rogov et al 2017. Recovering these known, and somewhat subtle, differences in binding preference further bolster the validity of our approach.

4) For the data shown in Fig 6 it should be noted that although these are very interesting results a clear limitation of the study is that the results on the autophagic turnover is based on overexpressing the SLRs in the pentaKO cells. In a physiological setting with all relevant actors in place and with a different stoichiometry the effects could likely be different.

Response: We appreciate the observation regarding the limitations of our study due to the use of overexpressed SLRs in pentaKO cells. As the reviewer rightly points out, the stoichiometry and interaction dynamics in a physiological setting might differ significantly. Critically, after submission of this manuscript, a recent preprint by Sascha Martens’ group (Bauer et al. BioRxiv) has shown similar results using endogenously tagged p62, TAX1BP1, and NBR1. This study corroborates our results, suggesting that the interactions we observed are not merely artifacts of overexpression but reflect genuine biological phenomena. We will incorporate a detailed discussion of this study in the Discussion section of our manuscript to contextualize our findings within a more physiologically relevant framework.

Therefore, we believe that our reductionist approach, while not fully reflective of physiological conditions, offers valuable and generalizable insights into the intricate cooperation of SARs in autophagy.

Minor points:

1) It would be beneficial for the reader to show a cartoon of the domain organization of both TAX1BP1 and NBR1 in Figure 1. NBR1 is shown in supplemental figure 1, but there is no depiction of the domain organization of TAX1BP1.

Response: As suggested, a domain schematic for NBR1 and TAX1BP1 will be included.

2) The authors say at the bottom of page 4 "Complementary in vivo studies reveal that while SLRs typically compete". But do they actually typically compete? Is this not a result of the experimental strategies employed? There is more a shortage of SLRs based on cargo competition as shown recently by Peter Kim's group that excessive pexophagy may reduce mitophagy etc. (Germain et al. 2023).

Response: Thank you for pointing out this overstatement. We will soften this statement.

3) In Fig. 3D it should be shown that D, E, A and V are preferred residues at position +1 for LC3A binding.

Response: As suggested, we will amend the figure to include these residues at the +1 position.

4) In such a 2D mutational analysis it is often just as important to determine which residues are not allowed for binding. It would therefore be nice if the authors could summarize/visualize their results in a better way in Fig 3D to also show the residues that lead to loss of binding. These could be shown below the sequence and the use of color to distinguish basic, acidic, hydrophobic and aromatic residues could be attempted.

Response: As suggested, we will add to this figure to make it more comprehensive by including residues that are both preferred and lead to loss of binding. Furthermore, we have incorporated the use of color to distinguish the traits of different residues (basic, acidic, hydrophobic and aromatic) that are dis(favored) at each position.

5) Line 327: To be clear about the fact that this is an overexpression assay "simultaneous expression" should be corrected to simultaneous overexpression".

Response: We will make the suggested change.

6) There are LIRs and FIRs that overlap and those that do not. To check the degree of overlaps that may occur among known LIRs the authors made a peptide array with 100 established LIR sequences taken from the LIR-Central database (Chatzichristofi et al., 2023). The peptide array was probed with LC3A (29 bound), GABARAPL1 (49 bound), the FIP200 Claw domain (57 bound) and the TAX1BP1 CC2 domain (49 bound). As much as one third (32) of the LIR peptides were not bound by any of the four probes. Do the authors have a good explanation for the fact that so many peptides did not bind?

Response: Thank you for highlighting the significant number of LIR peptides that did not bind to any of the probes in our study. At first, we were similarly surprised by this. In our manuscript, we will expand on several factors that might explain this observation:

• Specificity of Atg8 Family Proteins: The LIR-Central database indicates that these sequences bind at least one Atg8-family protein, but not necessarily all. Our assay might not have included the specific Atg8 proteins that some LIRs preferentially bind.
• Peptide Solubility and Conformation: The solubility and conformational stability of peptides printed on an array can vary, affecting binding efficiency. Certain sequences may not adopt the optimal conformation for binding under these assay conditions.
• Sequence Context and Accessibility: The native context in which the LIR motif is contained, including neighboring amino acids, can influence binding. Peptide arrays strip these peptides of their physiological context. As short linear interaction motifs, the assumption is that context will not strongly affect binding, but it’s known that many LIRs adopt partially structured motifs that influence binding (e.g. a C-terminal helix). Our peptide array approach is likely to impede such secondary structures from forming and may limit binding.
• Misannotated sequences. The LIRs included from the database have varying levels of validation. Some sequences might be misannotated and, therefore, do not bind any of the probes. These discussion points will be included in the manuscript to provide a comprehensive explanation for the observed data.

7) Strangely enough, the NBR1 peptide used in Figure 2A did not bind any of the probes while the NBR1 peptides used in Fig. 1C bound very well. Do the authors have any explanation for this?

Response: Thank you for noting the discrepancy in NBR1 peptide binding observed in Figure 2A compared to Figure 1C. This observation was noted by all reviewers. The difference likely arises from the solubility issues associated with the NBR1 peptide in the format used for Figure 2A, where the peptide sequence included the LIR motif plus 10 amino acids on each side. The core LIR sequence of NBR1 (YIII) is highly hydrophobic, which can affect its solubility and, consequently, its observed binding in our peptide array.

To overcome this, we optimized the LIR sequence of NBR1 for peptide arrays (amino acids 725-749), which includes seven residues before the LIR and 14 residues after. This shift enhanced solubility and facilitated more reliable probing in our experiments (notably Fig 3). In Fig2A and other assays, both the standard and the optimized formats of the NBR1 LIR were included: the standard format to maintain consistency with other LIRs extracted from the LIR-Central database and the optimized version as a control to validate our results.

We will detail this explanation in the manuscript, clarifying the rationale behind the observed binding differences.

Significance

SIGNIFICANCE

I found this paper very interesting to read with a lot of interesting new detailed and useful information on binding specificity for the proteins and motifs involved. It is a generally well performed study with interesting results. I also very much enjoyed the Discussion section which opens up for several interesting possible scenarios. The study also produced important point mutants that can be used in future studies to selectively abolish TAX1BP1 binding to NBR1. I think this is a "must read" paper for researchers interested in selective autophagy and co-operation between SARs, and more generally for getting some insight into how SLiMs may work. As such, this paper will be of interest for all interested in autophagy research and for a wider audience too as it is in essence about how overlapping SLiMs may be employed to orchestrate multiple protein-protein interactions using distinct overlapping determinants, rather than a single, convergent, SLiM. It is also one of the very few papers I have come across exploiting the power of the peptide array method so extensively with success for mapping protein binding sites.

It could perhaps be interesting if the authors discussed their results in relation to another study from the group of Sascha Martens on the role of TAX1BP1 in p62 bodies or condensates (doi: https://doi.org/10.1101/2024.05.17.594671). These two papers should be read together as they are both very interesting and important contributions.

Response: Thank you for pointing out this important reference that was posted shortly after our manuscript was submitted. As mentioned above, we will include an expanded discussion section to discuss these corroborating findings. We will also include a citation to Ferrari et al (PMID: ) on Tau evasion of autophagy through exclusion of TAX1BP1.

Reviewer #2

Evidence, reproducibility and clarity

Summary In this manuscript, North et al. examined how short linear interaction motifs (SLiMs) help to orchester selective autophagy receptors (SARs) function during cargo engulfment in autophagosomes. In particular, the authors focused on NBR1 as a model SAR to address the role of its role in the clearance of protein aggregates (aggrephagy). Using binding assays, the authors showed that a SLiM harboring NBR1's LIR motif also mediates binding to FIP200 and TAX1BP1. Intrigued by these overlapping binding sites, the authors probed 100 LIRs for their binding to TAX1BP1's coiled-coil 2 region (CC2), FIP200's claw domain and two different ATG8 family members and found heterogenous binding pattern and distinct correlation between these four binding partners. Using mutational peptide arrays of NBR1's SLiM, the authors revealed unique binding determinants of these NBR1 partners and their potential differential regulation by phosphorylation. Taking advantage of their new NBR1 binding insights, the authors structurally modeled the binding of TAX1BP1's CC2 to NBR1's SLiM and identified crucial residues in both proteins for this interaction. Lastly, the authors turned to autophagy flux assays in cells and showed that TAX1BP1 acts synergistically with NBR1 to increase its lysosomal delivery. Overall, the claims and the conclusions are largely supported by the data. However, a few critical issues should be addressed.

Are the data and the methods presented in such a way that they can be reproduced?

Are the experiments adequately replicated and statistical analysis adequate?

Major comments

1) What are the expression levels of the different tf-SAR fusions compared to the endogenous levels of the respective SAR? And are tf-NBR1 protein levels changed upon co-expression of the other SARs?

__Response: __We appreciate the questions concerning the expression levels of tf-SAR fusions relative to the endogenous levels of the respective SARs, similar to inquiries from Reviewer 1 (major comment 4). In our study, the levels of tf-NBR1 are notably higher than the endogenous levels. Interestingly, we observed that the co-expression of autophagy-competent NBR1 and TAX1BP1 generally leads to a decrease in the levels of both proteins, likely due to enhanced autophagic turnover. This pattern is not seen with autophagy-deficient mutants, suggesting a functional interaction affecting protein stability.

Furthermore, a recent preprint by Sascha Martens’ group (Bauer et al., BioRxiv) has presented findings that echo our results using endogenously tagged versions of p62, TAX1BP1, and NBR1. This study supports our observations, indicating that the interactions and effects we report are not artifacts of overexpression but are reflective of genuine biological processes. These findings will be thoroughly discussed in the Discussion section of our manuscript to provide context for our results within a physiologically relevant framework.

Therefore, we believe that our reductionist approach, while not fully reflective of physiological conditions, offers valuable and generalizable insights into the intricate cooperation of SARs in autophagy.

2) Which of the 100 LIRs have been shown to specifically bind LC3A or GABARAPL1? The authors should include this information from the literature in Figure 2 (e.g., highlighted by color or else).

__Response: __Thank you for your suggestion to detail the specific interactions between the 100 LIRs and Atg8 homologs like LC3A and GABARAPL1 in Figure 2. While each LIR in the LIR-Central database has been validated, detailed information on which LIRs bind specific Atg8 homologs—and with what relative affinity—is often lacking in the literature. This gap makes it challenging to present comprehensive binding preferences in a visually coherent way within Figure 2.

Nevertheless, we recognize the value of such information. We plan to conduct a thorough literature review on all 100 LIRs included in our study. Should we find sufficient and reliable data regarding binding specificities, we will incorporate this into Figure 2, potentially using color coding or another method to highlight these relationships clearly.

We can also perform the reciprocal experiment by using our LIR arrays to derive consensus sequences for LC3 binders and GABARAPL1 binders. In doing this, we find the same differences in LC3 and GABARAP preferences that were reported previously in Rogov et al 2017. Recovering these known, and somewhat subtle, differences in binding preference further bolster the validity of our approach. These new data will be added to the manuscript.

3) How effective is the stripping of the peptide array? The authors should provide evidence that there is no carry over binding from sequential probing the array. As a control, the authors should at least repeat probing for the last binder in their sequential binding assay with a new peptide array that has not yet been incubated with a different binder and then stripped.

__Response: __This is an important question, related to Reviewer 1 (comment 3), as the stripping of the peptide array can be variably affective. Prior to performing any of the arrays included in this manuscript, we did several validation arrays to identify the proper ordering of probes (e.g. what proteins can be stripped, which cannot). FIP200 and TAX1BP1 probing was performed on fresh or successfully stripped blots. LC3A probing was done last, as there is substantial previous literature defining the LC3 motif. However, the results of the LC3A binding consistently aligned with established consensus sequences for LC3, reinforcing the reliability of our findings despite the stripping process. Therefore, while stripping sometimes introduces artifacts, such as the 'ring effect’ observed in Figure 3A, the results did not appear to be influenced by prior probes.

As suggested, we are prepared to repeat the LC3A probing on a new array to fully cement this interpretation. We note, however, that this will be done using a new commercial vendor, as our previous collaborator is no longer available (The original blots were ordered over 3 years ago). We anticipate some differences in the appearance of the blots due to changes in dot size and spacing from the new supplier. Given these variations, we propose adding the revised blot to the supplementary materials rather than the main figures to avoid disrupting the visual continuity of the data presentation.

4) What is the number of replicates for the peptide array assays?

__Response: __Due to cost considerations, peptide array assays in our study were conducted as one or two replicates. We understand the limitations this presents in terms of statistical robustness and variability assessment. However, where possible, we supplemented these assays with additional validation experiments and controls to ensure reliability of our findings. For critical experiments, including key interaction validations, we used independent biochemical assays to confirm the results obtained from the peptide arrays.

5) The authors should test whether the enhancement of NBR1 flux by TAX1BP1 is only due to the contribution of an additional LIR or potential other functions of TAX1BP1 (e.g. ubiquitin binding or FIP200 binding). The authors should expand the panel shown in Figure 6E with TAX1BP1 mutant which are deficient in ubiquitin or FIP200 binding.

__Response: __We thank the reviewer for their suggestion. We will include data with TAX1BP1 mutants that are deficient in ubiquitin or FIP200 binding

Minor comments

6) Molecular weight markers are missing on immunoblots.

__Response: __We apologize for this oversight. We will amend figure to include molecular weight markers.

7) It would be more informative (since some proteins have more than one LIR) if the actual LIR motif would be displayed next to the peptide array (as e.g. done for NBR1) and not only in the supplements.

__Response: __We appreciate this thoughtful input and will consider its implementation carefully. We will explore the feasibility of integrating this detail in a manner that maintains figure clarity.

8) Along this line in Figure 2A, NBR1's LIR (marked with a red star) is among the LIRs for which no binding was observed. The authors should explain this.

Response: Thank you for noting the discrepancy in NBR1 peptide binding observed in Figure 2A compared to Figure 1C. This observation was noted by all reviewers. The difference likely arises from the solubility issues associated with the NBR1 peptide in the format used for Figure 2A, where the peptide sequence included the LIR motif plus 10 amino acids on each side. The core LIR sequence of NBR1 (YIII) is highly hydrophobic, which can affect its solubility and, consequently, its observed binding in our peptide array.

To overcome this, we optimized the LIR sequence of NBR1 for peptide arrays (amino acids 725-749), which includes seven residues before the LIR and 14 residues after. This shift enhanced solubility and facilitated more reliable probing in our experiments (notably Fig 3). In Fig2A and other assays, both the standard and the optimized formats of the NBR1 LIR were included: the standard format to maintain consistency with other LIRs extracted from the LIR-Central database and the optimized version as a control to validate our results.

We will detail this explanation in the manuscript, clarifying the rationale behind the observed binding differences.

Significance

Collectively, the work of North and colleagues provide valuable new mechanistic insights into the network of interaction that governs the function of SARs. Importantly, this works extends the knowledge in the field that SARs are acting in an orchestrated manner which reinforces their delivery to lysosomes. However, given the involvement of several SARs in the same process, it is crucial to dissect the binding modalities among these factors. In this regard, the current study on fine mapping binding sites provides an important contribution. In particular, in probing the in vitro findings in reconstituted KO cells. This part is really strong. In addition, the identification of critical residues for these bindings events represents important tools for the autophagy community which will be among the basic research audience most interested in this technical study.

__ __

Author response:

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

Reviewer #1 (Public Review):

Summary:

The study entitled "Rifampicin tolerance and growth fitness among isoniazid-resistant clinical Mycobacterium tuberculosis isolates: an in-vitro longitudinal study" by Vijay et al. provides valuable insights into the association of rifampicin tolerance and growth fitness with isoniazid resistance among clinical isolates of M. tuberculosis. Antibiotic tolerance in M. tuberculosis is an important topic since it contributes to the lengthy and complicated treatment required to cure tuberculosis disease and may portend the emergence of antibiotic resistance. The authors found that rifampicin tolerance was correlated with bacterial growth, rifampicin minimum inhibitory concentrations, and isoniazid-resistance mutations.

Strengths:

The large number of clinical isolates evaluated and their longitudinal nature during treatment for TB (including exposure to rifampin) are strengths of the study.

Weaknesses:

Some of the methodologies are not well explained or justified and the association of antibiotic tolerance with growth rate is not a novel finding. In addition, the molecular mechanisms underlying rifampicin tolerance only in rapidly growing isoniazid-resistant isolates have not been elucidated and the potential implications of these findings for clinical management are not immediately apparent.

We thank the reviewer for the comments, we have modified the method section and figure 1 to clarify the method as suggested by the reviewer.

Although we agree that previous studies have shown the association of slow growth rate with antibiotic tolerance, ours is the most comprehensive assessment of rifampicin tolerance among clinical isolates, to our knowledge. In particular, we show that the degree of tolerance in clinical isolates can vary over several orders of magnitude: which had not been previously documented or appreciated. Furthermore, the association of high tolerance among IR isolates is a new finding, and given the potential for tolerance to increase risk of de novo drug resistance, our study suggests that IR isolates with high rifampicin tolerance may present a risk for development of MDR-TB.

In addition, we have also analysed the longitudinal isolates and the genetic variants emerging in them associated with increase in rifampicin tolerance. This analysis reveals possible multiple pathways to increase in rifampicin tolerance among clinical M. tuberculosis isolates. Possible clinical implication includes associating high rifampicin tolerance and isoniazid resistance as a risk factor for tuberculosis treatment failure. This study helps to develop further clinical studies to evaluate the role of rifampicin tolerance in IR isolates and treatment outcome. We have focused on these aspects in the discussion of the revised manuscript.

Reviewer #2 (Public Review):

Summary:

This study by Vijay and colleagues addresses a clinically important, and often overlooked aspect of Tb treatment. Detecting for variations in the level of antibiotic tolerance amongst otherwise antibiotic-susceptible isolates is difficult to routinely screen for, and consequently not performed. The authors, present a convincing argument that indeed, there is significant variation in the susceptibility of isoniazid-resistant strains to killing by rifampicin, in some cases at the same tolerance levels as bona fide resistant strains. On the whole, the study is easy to follow and the results are justified. This work should be of interest to the wider TB community at both a clinical and basic level.

Weaknesses:

The manuscript is long, repetitive in places, and the figures could use some amending to improve clarity (this could be a me-specific issue as they look ok on my screen, yet the colour is poor when printed).

We thank the reviewer for the comments, we have modified the revised manuscript as per the reviewer suggestions.

It would have been great to have seen some correlation between increased rifampicin tolerance and treatment outcome, although I'm not sure if this data is available to the researchers. I agree with the researchers the use of a single media condition is a limitation. However, this is true of a lot of studies. Rifampicin tolerance and treatment outcome analysis.

We agree with the reviewer that correlation between rifampicin tolerance and treatment outcome is important. This needs to be performed in future studies with better design to correlate rifampicin tolerance with treatment progression or outcome data.  

Reviewer #3 (Public Review):

Summary:

The authors have initiated studies to understand the molecular mechanisms underlying the devolvement of multi-drug resistance in clinical Mtb strains. They demonstrate the association of isoniazid-resistant isolates by rifampicin treatment supporting the idea that selection of MDR is a microenvironment phenomenon and involves a group of isolates.

Strengths:

The methods used in this study are robust and the results support the authors' claims to a major extent.

Weaknesses:

The manuscript needs a thorough vetting of the language. At present, the language makes it very difficult to comprehend the methodology and results.

We thank the reviewer for the comments, we have revised the manuscript as per the reviewer’s suggestions.

Reviewer #1 (Recommendations For The Authors):

Major comments:

(1) Methods: The authors attempt to differentiate between "fast"- and "slow"-growing bacteria in order to determine if the growth rate is associated with rifampicin tolerance. This is accomplished by assessing growth on solid agar at 15 and 60 days post-incubation, respectively. However, mycobacterial growth rate is not a binary phenomenon but rather a continuous variable. Moreover, it is not clear why 15 and 60 days were selected. Also, instead of a "slow growth" phenotype, the 60-day time point might simply reflect a longer lag phase. Were the plates examined at any interval time points? It would be interesting to know whether colony growth was delayed overall in the populations observed only at 60 days, or simply if the appearance of microcolonies visible to the naked eye was delayed (with normal growth afterwards).

We thank the reviewer for the comments, we want to clarify that we have not used agar plates but most-probable number method to determine the survival fraction post antibiotic treatment. We have clarified this in the revised manuscript and revised figure 1. The MPN method is a binary measure (growth/ no growth) and therefore cannot differentiate between long lag time and other mechanisms. In our original analysis, we included an intermediate time point of 30 days, but these data (included as supp fig. 1) cannot address the issue of lag phase directly. Since the 30-day time point did not add to the overall analysis and interpretation, we had not included them in the original submission.

(2) Methods/Results/Discussion: Some important clinical information is missing-how were the patients treated who had IR isolates? Did they receive the standard regimen for DS TB or was another drug substituted for isoniazid? Exposure to different drugs could affect the rifampicin-tolerant populations during the intensive phase (Figure 5).

Thank you for this comment, we have included the information regarding the treatment regimen in the revised manuscript.

Were there differences in microbiological (sputum culture conversion rate at 8 weeks or time to culture negativity) or clinical outcomes based on isoniazid susceptibility? Perhaps more importantly, were there differences in microbiological/clinical outcomes based on the proportion of bacterial subpopulations with rifampicin tolerance for a particular isolate? There should be more discussion on the potential clinical implications of the study's findings.

We agree with the reviewer that correlation between rifampicin tolerance and treatment progression or outcome is important. This needs to be performed in future studies with better design to correlate rifampicin tolerance with treatment progression or outcome data.  

(3) Results (Figure 3A): Although an interesting finding, the increased rifampicin tolerance observed only in the "rapidly" growing populations of isoniazid-resistant isolates (IR) vs. isoniazid-susceptible (IS) isolates is not explained. In contrast, equally, increased rifampicin tolerance is seen in the "slowly" growing populations of both IR and IS isolates. It would be interesting to know if these slowly growing populations show specific tolerance to rifampicin or if, as expected, slow growth confers tolerance to a range of different bactericidal antibiotics.

We thank the reviewer for the suggestions. we agree these will be interesting to investigate in a future study but are outside the scope of the current study.

(4) Results (Figure 3B): The basis for the classification into tertiles is not clear and appears somewhat arbitrary-does this represent the survival of a particular isolate following rifampicin exposure relative to the other isolates based on isoniazid susceptibility (IS or IR) or the % growth relative to other populations for the same isolate? Figure 3B is missing a y-axis label. Is it a log10 MPN ratio?

We thank the reviewer for pointing this, we want to clarify that for the classification into tertiles, first we pooled both group of isolates isoniazid susceptible (IS) and isoniazid resistant (IR) into a single population. Subsequently, we categorized this unified population into three distinct groups: low, medium, and high, based on their survival fraction following rifampicin treatment. Consequently, the 'low,' 'medium,' and 'high' tertiles represent the survival of each isolate following rifampicin exposure relative to the total number of isolates  combing both IS and IR isolates.

For clarity, we provide a breakdown of the criteria for each tertile:

+Low tertile: Consists of isolates with the lowest survival fraction (bottom 25%).

+Medium tertile: Encompasses isolates with survival fractions that fall between the bottom 25% and the top 25%.

+High tertile: Comprises isolates with the highest survival fractions (top 25%). This we have modified in the revised manuscript to clarify.

We have also modified the Figure 3B to correct the y-axis label.

(5) Results (lines 185-186): For correlating relative growth in the absence of antibiotics, 19 clinical isolates "outliers" were removed without explanation.

We have added explanation for the “outliers” which were removed earlier due to deviation from normal distribution, we have also provided the supplementary figure 3 which includes these outliers.

(6) Results (lines 203-211): The authors attempted to investigate a potential association between the mechanism of M. tuberculosis isoniazid resistance and the degree of rifampicin tolerance. However, the vast majority of IR clinical isolates (n=71) had a katG_S315X mutation and only 8 isolates had alternative mutations (inhA_I21T and fabG1_C-15X). Given the wide range of rifampicin tolerance observed within these isoniazid-resistant isolates, they concluded that other genetic or epigenetic determinants must be playing a role. WGS of longitudinally collected isolates from the same patients during TB treatment yielded non-synonymous SNPs in a list of genes previously reported to be associated with persistence, tolerance, and mycobacterial survival. However, precise mechanisms (including, e.g., expression of efflux pumps) are not investigated.

We thank the reviewer for summarising the findings. Yes, we agree that investigating the precise mechanism of rifampicin tolerance is beyond the scope of the current work.

Minor comments:

(1) Abstract (line 41): The nonstandard abbreviations "IR" and "IS" have not been introduced prior to this usage.

We have modified this in the abstract.

(2) Introduction (line 60): Insert "phenomena" or "mechanisms" after "two".

We have modified this in the introduction.

(3) Introduction (lines 66-69): This sentence is confusing, especially the second part ("supporting this studies...").

We have modified the lines to clarify.

(4) Introduction (line 84): In the current text, it appears as if "IR" is the abbreviation for "isoniazid". Therefore, I recommend changing "resistance to isoniazid" to "isoniazid resistance".

We have modified this in the revised manuscript.

(5) Results (line 141): Insert "the" before "rest".

We have modified this in the revised manuscript.

(6) Results (line 187): Replace "did not had" with "did not have".

We have modified this in the revised manuscript.

Reviewer #2 (Recommendations For The Authors):

Abstract:

The abstract is long and repetitive. It needs reworking and shortening to improve clarity and highlight the main takeaway message.

We thanks the reviewer for the suggestions and have modified this in the revised manuscript.

The introduction is interesting and contains relevant information. However, it is long and takes a while to get to the point of the study. It needs re-writing to emphasise key prior results and the purpose of this study.

We thanks the reviewer for the suggestions and we have modified this in the revised manuscript.

Results:

As the study relies predominately on the use of MPN, I think a simple schematic of how the experiment is performed would be informative. Could this be added to Figure 1?

We have revised the figure 1 in the manuscript to include the schematic representation.

Some of the differences in MKD90, whilst they may be significant, are small so it would at least provide context as to the relevance of these differences. This may also alleviate my confusion as to how the authors can measure the time required to achieve MDK90 as 1.23-1.31 days when the first time point that is taken is day 2 (the data in Figure 2). They have FigS6 but this is small and hard to follow.

We thank the reviewer for this suggestion, we have modified this in the revised manuscript and figureS6.

Figure 2:

Would be helpful to have -1 on the Y axis.

The grey dots don't print very well (Might be my printer)

We have modified this in the revised manuscript, figure 2.

Line 142: The authors note a difference in RIF tolerance at day 15 that disappeared by day 60. I assume they are referring to the day 5 timepoint although this isn't clear as written.

Yes, it is referring to the day 5 time point and we have clarified this in the revised manuscript.

The section starting at line 148 (fig 3) is interesting, but it is difficult to read and follow what the difference is between this data and the prior data in Figure 2. It also wasn't until about line 165 that the purpose became clear. Overall the conclusions are sound and interesting.

We have modified this in the revised manuscript.

Line 154: What are the early and late time recovery time points?

Is Figure 3A the same data as Figure 2?

We have clarified this in the revised manuscript, the figure 3A is the same data as Figure 2.

I found Figure 6 hard to follow. I'm not sure how better to present this data, but it should be improved. Some further clarification in the text would be helpful.

We thank the reviewer for the suggestions. We have added more explanation in the text to clarify figure 6.

Conclusions:

The conclusions are sound, based on the data presented. The clinical relevance is highlighted, yet appropriately phrased to not be too far-reaching.

Again, I think the conclusions could be condensed considerably. It is repetitive in places, which distills the main outcomes of this otherwise interesting and important study. The authors appropriately highlight some of the limitations of their study.

We thank the reviewer for these comments and have modified this in the revised manuscript.

Reviewer #3 (Recommendations For The Authors):

The manuscript "Rifampicin tolerance and growth fitness among isoniazid-resistant clinical Mycobacterium tuberculosis isolates: an in-vitro longitudinal study" by Srinivasan et.al., details the identification/ development of isoniazid-resistant strains in clinical isolates following testament with rifampicin. This is an important aspect of understanding MDR development in TB strains. the results are promising and gel well with the hypothesis. However, the manuscript requires a thorough language modification. While the overall idea is clear the methodology does not come out clearly.

Specific comments:

(1) It is not clear whether rifampicin treatments were given for 2 and 5 days before kill curves or for 15 and 60 days? The methodology needs to be phased clearly. Why was this time interval of 15 days and 60 days taken? is there a rationale for this?

We thank the reviewer for the suggestions, we have modified the method and figure 1 to clarify this in the revised manuscript.

(2) A concentration of 2ug/ml was used for in vitro culture in this study. While the authors themselves indicate that this is well above the MIC, this might represent a non- natural dose and hence may force the evolution of strains. What will be the scenario in the natural course of antibiotic treatment (dose at MIC or less than MIC)?

We have observed that till 5 days there is no significant resistant emergence but after 5 days only resistance emerges, therefore we avoided determining the survival fraction after resistance emergence, the kill curve represents mostly tolerant sub population. ADD: Pharmacokinetic studies of rifampicin dosing suggest that peak concentrations of >2-32 µg/mL are typical for standard doses of the drug, therefore we believe the chosen concentration of 2 µg/mL to be physiologically relevant.

(3) As described in line 155, the survival spanned a broad distribution, across a million times in difference. This is rather surprising that 5 days of rifampicin treatment would lead to such a spread in resistance patterns. Did the authors study the different populations to understand this phenomenon? This is important given the scale of resistance developed in this short time.

We want to clarify that the broad range of survival fraction reflect the difference in tolerant sub-populations but not resistant sub-population to rifampicin as they are determined post rifampicin treatment in rifampicin free media, this has been clarified in the revised figure 1.

Overall, the manuscript is a detailed study with new insights into the development of multi-drug resistance by Mtb. A thorough vetting for language is essential for a greater impact of the study.

We thank the reviewer and have attempted to improve the clarity of the language to increase the potential impact of our findings.

Author response:

The following is the authors' response to the current reviews.

Reviewer #1 (Public Review):

I'll begin by summarizing what I understand from the results presented, and where relevant how my understanding seems to differ from the authors' claims. I'll then make specific comments with respect to points raised in my previous review (below), using the same numbering. Because this is a revision I'll try to restrict comments here to the changes made, which provide some clarification, but leave many issues incompletely addressed.

As I understand it the main new result here is that certain recurrent network architectures promote emergence of coordinated grid firing patterns in a model previously introduced by Kropff and Treves (Hippocampus, 2008). The previous work very nicely showed that single neurons that receive stable spatial input could 'learn' to generate grid representations by combining a plasticity rule with firing rate adaptation. The previous study also showed that when multiple neurons were synaptically connected their grid representations could develop a shared orientation, although with the recurrent connectivity previously used this substantially reduced the grid scores of many of the neurons. The advance here is to show that if the initial recurrent connectivity is consistent with that of a line attractor then the network does a much better job of establishing grid firing patterns with shared orientation.

Beyond this point, things become potentially confusing. As I understand it now, the important influence of the recurrent dynamics is in establishing the shared orientation and not in its online generation. This is clear from Figure S3, but not from an initial read of the abstract or main text. This result is consistent with Kropff and Treves' initial suggestion that 'a strong collateral connection... from neuron A to neuron B... favors the two neurons to have close-by fields... Summing all possible contributions would result in a field for neuron B that is a ring around the field of neuron A.' This should be the case for the recurrent connections now considered, but the evidence provided doesn't convincingly show that attractor dynamics of the circuit are a necessary condition for this to arise. My general suggestion for the authors is to remove these kind of claims and to keep their interpretations more closely aligned with what the results show.

We would like to clarify that the simple (flexible) attractor is a weaker condition than the ones previously used to align grid cells. However, by no means we claim that it is a necessary condition for grid maps to align. Other architectures, certainly more complex ones but perhaps even simpler ones, can align grid maps in our model.

Major (numbered according to previous review)

(1) Does the network maintain attractor dynamics after training? Results now show that 'in a trained network without feedforward Hebbian learning the removal of recurrent collaterals results in a slight increase in gridness and spacing'. This clearly implies that the recurrent collaterals are not required for online generation of the grid patterns. This point needs to be abundantly clear in the abstract and main text so the reader can appreciate that the recurrent dynamics are important specifically during learning.

We respectfully disagree with the interpretation of this result. In this model cells self-organize to produce aligned grid maps. In such systems it makes sense to characterize the equilibrium states of the system. We turned learning off in Figure S3 to show that the recurrent connections have a contractive effect on grid spacing. But artificially turning off learning means that one can no longer make claims about the equilibrium states of the system, since it can no longer evolve freely. In a functional network, if the recurrent attractor is removed, the system will evolve towards poor gridness and no alignment no matter what the starting point is, as also shown in Figure S3. Several experimental results invite us to think of grid cells as the equilibrium solution of a series of constraints that is ready to change at any time: Barry et al, 2012; Yoon et al, 2013; Carpenter et al, 2015; Krupic et al, 2015; Krupic et al, 2018; Jayakumar et al, 2019.

One point in which we perhaps agree with the reviewer is that information about the hexagonal maps is kept in the feedforward weights, while behavior and the recurrent collaterals act as constraints of which these feedforward weights are the equilibrium solution.

(2) Additional controls for Figure 2 to test that it is connectivity rather than attractor dynamics (e.g. drawing weights from Gaussian or exponential distributions). The authors provide one additional control based on shuffling weights. However, this is far from exhaustive and it seems difficult on this basis to conclude that it is specifically the attractor dynamics that drive the emergence of coordinated grid firing.

Again, we do not claim that this is the only way in which grid maps can be aligned, but it is the simplest one proposed so far. We were asked if it was the specific combination of input weights to a cell rather than the organization provided by the attractor which resulted in aligned maps. By shuffling the inputs to a cell we keep the combination of inputs invariant but lose the attractor architecture. Since grid maps in this new situation are not aligned, we can safely conclude that it is not the combination of inputs per se, but the specific organization of these inputs that allows grid alignment. It is not fully clear to us what ‘exhaustive’ means in this context.

(3) What happens if recurrent connections are turned off? The new data clearly show that the recurrent connections are not required for online grid firing, but this is not clear from the abstract and is hard to appreciate from the main text.

This point is related to (1). Absent this constraint, Figure S3 shows that the system evolves toward larger spacing, with poorer gridness and no alignment.

(4) This is addressed, although the legend to Fig. S2D could provide an explanation / definition for the y-axis values.

We have now added: Mean input fields are the sum of all inputs of a given kind entering a neuron at a given moment in time, averaged across cells and time.

(5) Given the 2D structure of the network input it perhaps isn't surprising that the network generates 2D representations and this may have little to do with its 1D connectivity. The finding that the networks maintain coordinated grids when recurrent connections are switched off supports my initial concern and the authors explanation, to me at least, remain confusing. I think it would be helpful to consider that the connectivity is specifically important for establishing the coordinated grid firing, but that the online network does not require attractor dynamics to generate coordinated grid firing.

This point is related to (1) and (3). We agree with the reviewer that the input lies within a 2D manifold, but this is not something that the network has to find out because it receives one datapoint of information at a time. This alone is not enough to form aligned grid cells, since each grid cell can find a roughly equivalent equilibrium in a different direction. It is only the constraint imposed by the recurrent collaterals that aligns grid maps, and, as we show, this constraint does not need to be constructed ad hoc to work on 2D, as previously thought. When recurrent connections are switched off, the system evolves toward unaligned grid maps, with larger spacing and lower gridness. Regarding the results obtained after modifying the network and turning off learning, we think they have a very limited scope (in this case showing the contractive effect of recurrent collaterals on grid spacing), given that the system is artificially being kept out of its natural equilibrium.

(6) Clarity of the introduction. This is somewhat clearer, but I wonder if it would be hard for someone not familiar with the literature to accurately appreciate the key points.

We have made our best effort to improve the clarity of the introduction.

(7) Remapping. I'm not sure why this is ill posed. It seems the proposed model can not account for remapping results (e.g. Fyhn et al. 2007). Perhaps the authors could just clearly state this as a limitation of the model (or show that it can do this).

We view our model as perfectly consistent with Fyhn et al, 2007. Remapping is not triggered by the network itself, though, but rather by a re-arrangement of the inputs requiring the network to learn new associations. Different simulations of the same model with identical parameters can be interpreted as remapping experiments.

Reviewer #3 (Public Review):

Summary:

The paper proposes an alternative to the attractor hypothesis, as an explanation for the fact that grid cell population activity patterns (within a module) span a toroidal manifold. The proposal is based on a class of models that were extensively studied in the past, in which grid cells are driven by synaptic inputs from place cells in the hippocampus. The synapses are updated according to a Hebbian plasticity rule. Combined with an adaptation mechanism, this leads to patterning of the inputs from place cells to grid cells such that the spatial activity patterns are organized as an array of localized firing fields with hexagonal order. I refer to these models below as feedforward models.

It has already been shown by Si, Kropff, and Treves in 2012 that recurrent connections between grid cells can lead to alignment of their spatial response patterns. This idea was revisited by Urdapilleta, Si, and Treves in 2017. Thus, it should already be clear that in such models, the population activity pattern spans a manifold with toroidal topology. The main new contributions in the present paper are (i) in considering a form of recurrent connectivity that was not directly addressed before. (ii) in applying topological analysis to simulations of the model. (iii) in interpreting the results as a potential explanation for the observations of Gardner et al.

We wanted to note that we do not see this paper as proposing an alternative to the attractor hypothesis, given that we use attractor networks, but rather as an exploration of possibilities not yet visited by this hypothesis.

Strengths:

The exploration of learning in a feedforward model, when recurrent connectivity in the grid cell layer is structured in a ring topology, is interesting. The insight that this not only align the grid cells in a common direction but also creates a correspondence between their intrinsic coordinate (in terms of the ring-like recurrent connectivity) and their tuning on the torus is interesting as well, and the paper as a whole may influence future theoretical thinking on the mechanisms giving rise to the properties of grid cells.

Weaknesses:

(1) In Si, Kropff and Treves (2012) recurrent connectivity was dependent on the head direction tuning, in addition to the location on a 2d plane, and therefore involved a ring structure. Urdapilleta, Si, and Treves considered connectivity that depends on the distance on a 2d plane. The novelty here is that the initial connectivity is structured uniquely according to latent coordinates residing on a ring.

The recurrent architectures in the cited works are complex and require arranging cells in a 2D manifold to calculate connectivity based on their relative 2D position. In other words, the 2D structure is imprinted in the architecture, as in our 2D condition. In this work the network is much simpler and only requires neighboring relations in 1D. Such relationships have been shown to spontaneously emerge in the hippocampal formation (Pastalkova et al, 2008; Gonzalo Cogno et al, 2024).

(2) The paper refers to the initial connectivity within the grid cell layer as one that produces an attractor. However, it is not shown that this connectivity, on its own, indeed sustains persistent attractor states. Furthermore, it is not clear whether this is even necessary to obtain the results of the model. It seems possible that (possibly weaker) connections with ring topology, that do not produce attractor dynamics but induce correlations between neurons with similar locations on the ring would be sufficient to align the spatial response patterns during the learning of feedforward weights.

Regarding the first part of the comment, the recurrent collaterals create one or at times multiple bumps of activity in the network so that neighboring (interconnected) cells activate together. An initial random state of activity rapidly falls into this dynamic, constrained by the attractor. To us this is not surprising given that this connectivity is the classical means of creating a continuous attractor. Perhaps there is some deeper meaning in this comment that we are not fully grasping.

Regarding the second part of the comment, we fully agree with the reviewer. We are presenting what so far is the simplest connectivity that can align grid maps, but by no means we claim that it is the simplest possible one. Regarding weaker connections with ring topology, we show in Figure S2 that a ring attractor with too weak or too strong connections is incapable of aligning grids, since a balance between feedforward and feedback inputs is required.

(3) Given that all the grid cells are driven by an input from place cells that span a 2d manifold, and that the activity in the grid cell network settles on a steady state which is uniquely determined by the inputs, it is expected that the manifold of activity states in the grid cell layer, corresponding to inputs that locally span a 2d surface, would also locally span a 2d plane. The result is not surprising. My understanding is that this result is derived as a prerequisite for the topological analysis, and it is therefore quite technical.

We understand that the reviewer is referring to the motivation behind studying local dimensionality. We agree that the topological analysis approach is quite technical, but it provides unique insights. The theorem of closed surfaces, which allows us to deduce a toroidal topology from Betti numbers (1,2,1), only applies to closed surfaces. One thus needs to show that the point cloud is a surface (local dimensionality of 2) and is closed (no borders or singularities). If borders or singularities were present, a toroidal topology could not be claimed from these Betti numbers. Thus, it is a crucial step of the analysis.

(4) The modeling is all done in planar 2d environments, where the feedforward learning mechanism promotes the emergence of a hexagonal pattern in the single neuron tuning curve. Under the scenario in which grid cell responses are aligned (i.e. all neurons develop spatial patterns with the same spacing and orientation) it is already quite clear, even without any topological analysis that the emerging topology of the population activity is a torus.

However, the toroidal topology of grid cells in reality has been observed by Gardner et al also in the wagon wheel environment, in sleep, and close to boundaries (whereas here the analysis is restricted to the a sub-region of the environment, far away from the walls). There is substantial evidence based on pairwise correlations that it persists also in various other situations, in which the spatial response pattern is not a hexagonal firing pattern. It is not clear that the mechanism proposed in the present paper would generate toroidal topology of the population activity in more complex environments. In fact, it seems likely that it will not do so, and this is not explored in the manuscript.

We agree that our work was constrained to exploration in 2D and that the situations posed by the reviewer are challenging, but we do not see them as unsurmountable. The wagon wheel shows a preservation of toroidal topology locally, where the behavior of the animal is rather 2-dimensional. Globally, hexagonal maps are lost, which is compatible with some flexibility in the way grid maps are formed. If sleep meant that all inputs are turned off, our model would predict a dynamic dictated by the architecture (1D for the ring attractor, for example), but we do not really know that this is the case. In the future, we intend to explore predictive activity along the linear attractor, which could both result in path integration and in some level of preservation of the activity when inputs are completely turned off.

Regarding boundaries, as we have argued before, the cited work chooses to filter away what looks like more than half of the overall explained variance through PCA, and this is only before applying a non-linear dimensionality reduction algorithm. It is specifically shown that the analyzed components are the ones with global periodicity throughout the environment. Thus, it is conceivable that through this approach, local irregularities found only at the borders are disregarded in favor of a clearer global picture. While using a different methodology, our approach follows a similar spirit, albeit with far less noisy data.

(5) Moreover, the recent work of Gardner et al. demonstrated much more than the preservation of the topology in the different environments and in sleep: the toroidal tuning curves of individual neurons remained the same in different environments. Previous works, that analyzed pairwise correlations under hippocampal inactivation and various other manipulations, also pointed towards the same conclusion. Thus, the same population activity patterns are expressed in many different conditions. In the present model, this preservation across environments is not expected. Moreover, the results of Figure 6 suggest that even across distinct rectangular environments, toroidal tuning curves will not be preserved, because there are multiple possible arrangements of the phases on the torus which emerge in different simulations.

We agree with this observation. A symmetry in our implementation results in the fact that only ~50% of times the system falls in the preferred solution, and the rest of the times it falls into other local minima. Whether this result is at odds with current observations can be debated on the basis of probabilities. However, we believe that the symmetry we found is purely circumstantial, and that it can be broken by elements such as head direction modulation or other ingredients used to achieve path integration. In other words, we acknowledge that symmetry is an issue of the implementation we show here (which has been kept as simple as possible to serve as a proof-of-principle) but we do not think that it is a defining feature of flexible attractors in general. We expect that future implementations that incorporate path integration capabilities will not present this kind of symmetry in the space of solutions.

Regarding the rigid phase translation across modalities, while this effect is very clear in Gardner et al, it is less so in other datasets. The analyses shown in Hermansen et al (2024) can rather be interpreted as somewhere in the way between perfect rigid translation and fully randomized phases across navigation modalities.

(6) In real grid cells, there is a dense and fairly uniform representation of all phases (see the toroidal tuning of grid cells measured by Gardner et al). Thus, the highly clustered phases obtained in the model (Fig. S1) seem incompatible with the experimental reality. I suspect that this may be related to the difficulty in identifying the topology of a torus in persistent homology analysis based on the transpose of the matrix M.

We partly agree with this observation and note that a pattern of ordered phases is an issue not only for the 1D attractor but also for the 2D one, which appears much more uniform than in experimental data. The low number of neurons we used for computational economy and the full connectivity could be key ingredients to generate these phase patterns. To show that this is not a defining feature of flexible attractors, apart from the fact that these patterns appear also with non-flexible 2D architectures, we included in Figure S1 simulations with ‘fragmented 1D’ architectures. In this case the architecture is a superposition of 20 random 1D stripe-like attractors. While the alignment of maps achieved with this architecture is almost at the same level as the one obtained with 1D and 2D attractors, the phases are much more similar to what has been observed experimentally, and less uniform than what is obtained with 2D attractors.

(7) The motivations stated in the introduction came across to me as weak. As now acknolwledged in the manuscript, attractor models can be fully compatible with distortions of the hexagonal spatial response patterns - they become incompatible with this spatial distortions only if one adopts a highly naive and implausible hypothesis that the attractor state is updated only by path integration. While attractor models are compatible with distortions of the spatial response pattern, it is very difficult to explain why the population activity patterns are tightly preserved across multiple conditions without a rigid two-dimentional attractor structure. This strong prediction of attractor models withstood many experimental tests - in fact, I am not aware of any data set where substantial distortions of the toroidal activity manifold were observed, despite many attempts to challenge the model. This is the main motivation for attractor models. The present model does not explain these features, yet it also does not directly offer an explanation for distortions in the spatial response pattern.

Some interesting examples are experiments in 3D, where grid cells presumably communicate with each other through the same recurrent collaterals, but global periodicity is lost and only some local order is preserved even away from boundaries (Ginosar et al, 2021; Grieves et al, 2021). While these datasets have not been explored using topological analysis, they serve as strong motivators to understanding 2D grid cells as one equilibrium solution that arises under some set of constraints, but belongs to a wider space of possible solutions that may arise as well under more flexible constraints. Even (and especially) if one adheres to the hypothesis that grid cells are pre-wired into a 2D torus, a concept like flexible attractors might become useful to understand how their activity is rendered in 3D. Another strong motivation is our lack of understanding of how a perfectly balanced 2D structure is formed and maintained. Simpler architectures could be thought of as alternatives, but also as an intermediate step towards it.

Regarding the rigid phase translation across modalities, while this effect is very clear in Gardner et al, it is less so in other datasets. The analyses shown in Hermansen et al (2024) can rather be interpreted as somewhere in the way between perfect rigid translation and fully randomized phases.

In a separate point, although it might not be strictly related to the comment, we do not fully share the idea that persistent activity patterns during sleep are necessary or sufficient conditions for attractor dynamics, although we do agree that attractors could be the mechanism behind them and any alternative is at least as complex as attractors. On the necessity side, attractors in the hippocampus are not constantly engaged (Wills et al, 2005). For sufficiency, one should prove that no other network is capable of reproducing the phenomenon, and to our best knowledge we are still far from that point.

(8) There is also some weakness in the mathematical description of the dynamics. Mathematical equations are formulated in discrete time steps, without a clear interpretation in terms of biophysically relevant time scales. It appears that there are no terms in the dynamics associated with an intrinsic time scale of the neurons or the synapses (a leak time constant and/or synaptic time constants). I generally favor simple models without lots of complexity, yet within this style of modelling, the formulation adopted in this manuscript is unconventional, introducing a difficulty in interpreting synaptic weights as being weak or strong, and a difficulty in interpreting the model in the context of other studies.

We chose to keep the model as simple as possible and in the line of previous publications developing it. However, we see the usefulness of putting it in what in the meantime has become a canonical framework. Fortunately this has been done by D’Albis and Kempter (2017). In our simplified version of the model there is no leak term and adaptation on its own brings down activity in the absence of input, but we agree that such a term could be added, albeit not without modifying all other network parameters.

In my view, the weaknesses discussed above limit the ability of the model, as it stands, to offer a compelling explanation for the toroidal topology of grid cell population activity patterns, and especially the rigidity of the manifold across environments and behavioral states. Still, the work offers an interesting way of thinking on how the toroidal topology might emerge.

Reviewer 1:

Reviewer #1 (Recommendations For The Authors):

See comments above. In addition:

(1) Abstract: '...interconnected by a two-dimensional attractor guided by path integration'. This is unclear. I think the intended meaning might be along the lines of '...their being computed by a 2D continous attractor that performs path integration'?

'path integration allowing for no deviations from the hexagonal pattern' This is incorrect. Local modulation of the gain of the speed input to a standard CAN would distort the grid pattern.

'Using topological data analysis, we show that the resulting population activity is a sample of a torus' Activity in the model?

'More generally, our results represent a proof of principle against the intuition that the architecture and the representation manifold of an attractor are topological objects of the same dimensionality, with implications to the study of attractor networks across the brain' I guess one might hold this intuition, but it strikes me as obvious that if you impose an sufficiently strong n-dimensional input on a network then it it's activity could have the same dimensionality. I don't really see this as being a point worth highlighting. Perhaps the more interesting point, it that during learning the recurrent connectivity aligns the grid fields of neurons in the network, and this may be a specific function of the 1D attractor dynamcis, although I don't think the authors have made this point convincing.

'The flexibility of this low dimensional attractor allows it to negotiate the geometry of the representation manifold with the feedforward inputs'. See above for comments on the use of 'negotiate'.

'while the ensemble of maps preserves features of the network architecture'. I don't understand this. What is the 'ensemble of maps' and what are the features referred to.

We have reviewed the abstract considering these points. Regarding the ‘strong n-dimensional input’, we want to point out that it is not the input itself that generates a torus (the no attractor condition does not lead to a torus) but rather the interplay between the input and the attractor.

‘Perhaps the more interesting point …’, we do not fully understand how this sentence deviates from our own conclusions. We here show that a strong n-dimensional input is not enough to align grid cells (produce a n-torus), it is the interplay between inputs and attractor dynamics that does so, even if the attractor is not n-dimensional in terms of architecture.

The ensemble of maps refers to the transpose of the population activity matrix, where each point in the cloud is a map, and the features refer to the persistent homology.

(2) The manuscript still fails to clarify the difference between a model that path integrates in two dimensions and a model that simply represents information with a given dimensionality. The argument that it's surprising that a network with 1D architecture represents a higher dimensional input strikes me as incorrect and an unnecessary attempt to argue for conceptual importance. At least to me this isn't surprising. It would be surprising if the 1D network could path integrate but this doesn't seem to be the case.

In response to the reviewer’s concerns, we have made clear in the introduction and discussion that this model has no path integration capabilities, although we aim to develop a model capable of path integration using the kind of simple architecture presented here. We want to highlight here that equating attractor dynamics with path integration would be a conceptual mistake.

(3) Other wording also seems to make unnecessary conceptual claims. E.g. The repeated use of 'negotiate' implies some degree of intelligence, or at least an exchange of information, that isn't shown to exist. I wonder if more precise language could be used? As I understand it the dimensionality is bounded by the inputs on the one hand, and the network connectivity on the other, with the actual dimensionality being a function of the recurrent and feedforward synaptic weights. There's clearly some role for the relative weights and the properties of plasticity rules, but I don't see any evidence for a negotiation.

An interesting observation in Figure S2 is that grid maps are aligned only if the relative strength of feedforward and recurrent inputs is similar. If one of them can impose over the other, grid maps do not align. This equilibrium can metaphorically be thought of as a negotiation instance, where the negotiation is an emergent property of the system rather than something happening at an individual synapse.

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

Reviewer #1:

Reviewer #1 (Recommendations For The Authors):

Major

(1) What is the evidence that, after training, the 1D network maintains its attractor dynamics when feedforward inputs are active? If the claim is that it does then it's important to provide evidence, e.g. responses to perturbations, or other tests. The alternative is that after training the recurrent inputs are drowned out by the feed forward spatial inputs.

We agree with the reviewer on the importance of this point. In our model, networks are always learning, and the population activity represented by aligned grid maps in a trained network is a dynamic equilibrium that emerges from the interplay between feedforward and collateral constraints. If Hebbian learning is turned off, one gets a snapshot of the network at that moment. We now show in Fig. S3 that in a trained network without feedforward Hebbian learning the removal of recurrent collaterals results in a slight increase in gridness and spacing. The expansion is due to the fact that, as we argue in the Results section, the attractor has a contractive effect on grid maps, which could relate to observations in novel environments (Barry et al, 2007). If Hebbian learning is turned on in the same situation, the maps, no longer constrained by the attractor, drift toward the equilibrium solution of the ‘No attractor’ condition, with significantly larger spacing, no alignment and lower individual gridness. Thus, the attractor is the force preventing them to do so when feedforward Hebbian learning is on.

These observations point to the key role played by the attractor not only in forming but also in sustaining grid activity. The dynamic equilibrium framework fits well known properties of the system, such as its capacity to recalibrate very fast (Jayakumar et al, 2019), although this particular feature cannot be modeled with the current version of our model, that lacks path integration capabilities.

(2) It would be useful to include additional control conditions for Figure 2 to test the hypothesis that it is simply connectivity, rather than attractor dynamics, that drives alignment.

This could be achieved by randomly assigning strengths to the recurrent connections, e.g. drawing from exponential or Gaussian distributions.

We agree and have included Fig. S2b-d, showing that the same distribution of collateral input weights entering each neuron, but lacking the 1D structure provided by the attractor, does not align grid maps. This is achieved by shuffling rows in the connectivity matrix, while avoiding self connections to make the comparison fair (self connections substantially alter the dynamic of the network, making it much more rigid). We observed that individual grid maps have very low gridness levels, even lower than in the no-attractor condition. In contrast, they have levels of population gridness slightly higher than in the no-attractor condition, but closer to 0 than to levels achieved with attractors. Our interpretation of these results is that irregular connectivity achieves some alignment in a few arbitrary directions and/or locations, which improves the coordination between maps at the expense of impairing rather than improving hexagonal responses of individual cells. Such observations stand in clear context to what is observed with continuous attractors with an orderly architecture.

These results suggest that it is the structure of the attractor that allows grid cells to be aligned rather than the mere presence of recurrent collateral connections.

(3) It seems conceivable that once trained the recurrent connections would no longer be required for alignment. Can this be evaluated by considering what happens if the recurrent connections are turned off after training (or slowly turned off during training)? Does the network continue to generate aligned grid fields?

This point has elements in common with point 1. As we argued in that response, the attractor has two main effects on grid maps: it aligns them and it contracts them. If the attractor is turned off, feedforward Hebbian learning progressively drives maps toward the solution obtained for the ‘no attractor’ condition, characterized by maps with larger spacing, poorer gridness and lack of alignment.

(4) After training what is the relative strength of the recurrent and feedforward inputs to each neuron?

Both recurrent and feedforward synaptic-strength matrices are normalized throughout training, so that the overall incoming synaptic strength to each neuron is invariant. Because of this, although individual feed-forward and recurrent input fields vary dynamically, their average is constant, with the exception of the very first instances of the simulation, before a stable regime is reached in grid-cell activity levels. We have included Fig. S2d, showing the dynamics of feedforward and recurrent mean fields throughout learning as well as their ratio. In addition, Fig. S2a shows that the strength of recurrent relative to feedforward inputs is an important parameter, since alignment is only obtained in an intermediate range of ratios.

(5) It would be helpful to also evaluate the low dimensional structure of the input to the network. Assuming it has a 2D structure, as it represents 2D space, can an explanation be provided for why it is surprising that the trained network also encodes activity with a 2D manifold? It strikes me that the more interesting finding might relate to alignment of the grids rather than claims about a 1D attractor encoding a 2D representation. Either way, stronger evidence and clearer discussion would be helpful.

The reviewer is correct in assuming that the input has a 2D structure, that can be represented by a sheet embedded in a high dimensional space and thus has the Betti numbers [1,0,0]. The surprising element in our results is that we are showing for the first time that the population activity of an attractor network is constrained to a manifold that results from the negotiation between the architecture of the attractor and the inputs, and does not merely reflect the former as previously assumed. In this sense, the alignment of grid cells by a 1D attractor is an instance of the more general case that 1D attractors can encode 2D representations.

It is certainly the case that the 2D input is a strong constraint pushing population activity toward a 2D manifold. However, the final form of the 2D manifold is strongly constrained by the attractor, as shown by the contrast with the no-attractor condition (a 2D sheet, as in the input, vs a torus when the attractor is present). The 1D attractor is able to flexibly adapt to the constraint posed by the inputs while doing its job (as demonstrated in previous points), which results in 2D grid maps aligned by a 1D attractor. Generally speaking, this work provides a proof of principle demonstrating that the topology of the attractor architecture and the manifold of the population activity space need not be identical, as previously widely assumed by the attractor community, and need not even have the same dimensionality. Instead, a single architecture can potentially be applied to many purposes. Hence, our work provides a valuable new perspective that applies to the study of attractors throughout the brain.

(6) The introduction should be clearer about the different types of grid model and the computations they implement. E.g. The authors' previous model generates grid fields from spatial inputs, but if my understanding is correct it isn't able to path integrate. By contrast, while the many 2D models with continuous attractor dynamics also generate grid representations, they do so by path integration mechanisms that are computationally distinct from the spatial transformation implemented by feedforward models (see also general comments above).

We agree with the reviewer and have made this point explicit in the introduction.

(7) A prediction from continuous attractor models is that when place cells remap the low dimensional manifold of the grid activity is unaffected, except that the location of the activity bump is moved. It strikes me as important to test whether this is the case for the model presented here (my intuition is that it won't be, but it would be important to establish either way).

We want to emphasize that our model is a continuous attractor model, so the question regarding the difference between what our model and continuous attractor network models predict is an ill-posed one. One of our main conclusions is precisely that attractors can work in a wider spectrum of ways than previously thought.

In lack of a better definition, our multiple simulations could be thought of as training in different arenas. It is true that in our model maps take time to form, but this is also the case in novel environments (Barry et al, 2007 ), and continuous attractor models exclusively or strongly guided by self motion cues struggle to replicate this phenomenon. We show that the current version of our model accepts multiple solutions (in practice four but conceptually infinite countable), all of them resulting in a torus for the population activity (i.e. the same topology or low dimensional manifold). It is not clear to us how easy it would be to differentiate between most of these solutions in experimental data, with only incomplete information. This said, incorporating a symmetry-breaking ingredient to the model, for example related to head direction modulation, could perhaps lead to the prevalence of a single type of solution. We intend to explore this possibility in the future in order to add path-integration capabilities to the system, as described in the discussion.

(8) The Discussion implies that 1D networks could perform path integration in a manner similar to 2D networks. This is a strong claim but isn't supported by evidence in the study. I suggest either providing evidence that this is the case for models of this kind or replacing it with a more careful discussion of the issue.

The current version of our model has no path integration capabilities, as is now made explicit in the Introduction and Discussion. In addition, we have now made clear that the idea that path integration could perhaps be implemented using 1D networks is, although reasonable, purely speculative.

Minor

(1) Introduction. 'direct excitatory communication between them'. Suggest rewording to 'local synaptic interactions', as communication can also be purely inhibitory (e.g. Burak and Fiete, 2009) or indirect by excitation of local interneurons (e.g. Pastoll et al., Neuron, 2013).

We agree and have adopted this phrasing.

(2) The decision to focus the topology analysis on the 60 cm wide central square appears somewhat arbitrary. Are the irregularities referred to a property of the trained networks or would they also emerge with analysis of simulated ideal data? Can more justification be expanded and supplementary analyses be shown when the whole arena is used?

In practical terms, a subsampling of the data to around half was needed because the persistent homology packages struggle to handle large amounts of data, especially in the calculation of H2. We decided to cut a portion of contiguous pixels in the open field at least larger than the hexagonal tile representing the whole grid population period (as represented in Figure 6). Leaving the borders aside was a logical choice since it is known that the solution at the borders is particularly influenced by the speed anisotropy of the virtual rat (see Si, Kropff & Treves, 2012), in a way that mimics how borders locally influence grid maps in actual rats (Krupic et al, 2015). The specific way in which our virtual rat handles borders is arbitrary and might not generalize. A second issue around borders is that maps are differently affected by incomplete smoothing, although this issue does not apply to our data because we did not smooth across neighboring pixels. In sum, considering the central 60 cm wide square was sufficient to contain the whole torus and a reasonable compromise that would allow us to perform all analyses in the part of the environment less influenced by boundaries.

(3) It could help the general reader to briefly explain what a persistence diagram is.

This is developed in the Appendix, but we have now added a reference to it and a brief description in the main text.

(4) For the analyses in Figure 3-4, and separately for Figure 5, it might help the reader to provide visualizations of the low dimensional point cloud.

All these calculations take place in the original high-dimensional point cloud. Doing them in a reduced space would be incorrect because there is no dimensionality reduction technique that guarantees the preservation of topology. In Figure 7 we reduce the dimensionality of data but emphasize that it is only done for visualization purposes, not to characterize topology. We also point out in this Figure that the same non-linear dimensionality reduction technique applied to objects with identical topology yields a wide variety of visualizations, some of them clear and some less clear. This observation further exemplifies why one cannot assume that a dimensionality-reduction technique preserves topology, even for a low-dimensional object embedded in a high-dimensional space.

(5) The detailed comparison of the dynamics of each model is limited by the number of data points. Why not address this by new simulations with more neurons?

We are not sure we understand this comment. In Figure 2, the dynamics for each model are markedly different. These are averages over 100 simulations. We are not sure what benefit would be obtained from adding more neurons. Before starting this work we searched for the minimal number of neurons that would result in convergence to an aligned solution in 2D networks, which we found to be around 100. Optimizing this parameter in advance was important to reduce computational costs throughout our work.

(6) Could the variability in Figure 7 also be addressed by increasing the number of data points?

As we argued in a previous point, there is no reason to expect preservation of topology after applying Isomap. We believe this lack of topology preservation to be the main driver of variability.

(7) Page/line numbers would be useful.

We agree. However, the text is curated by biorxiv which, to our best knowledge, does not include them.

Reviewer 2:

Reviewer #2 (Recommendations For The Authors):

(1) I highly suggest that the author rewrite some parts of the Results. There are lots of details which should be put into the Methods part, for example, the implementation details of the network, the analysis details of the toroidal topology, etc. It will be better to focus on the results part first in each section, and then introduce some of the key details of achieving these results, to improve the readability of the work.

This suggestion contrasts with that of Reviewer #1. As a compromise, we decided to include in the Results section only methodological details that are key to understanding the conclusions, and describe everything else in the Methods section.

(2) 'Progressive increase in gridness and decrease in spacing across days have been observed in animals familiarizing with a novel environment...' From Fig.2c I didn't see much decrease. The authors may need to carry out some statistical test to prove this. Moreover, even the changes are significant, this might be not the consequence of the excitatory collateral constraint. To prove this, the authors may need to offer some direct evidence.

We agree that the decrease is not evident in this figure due to the scale, so we are adding the correlation in the figure caption as proof. In addition, several arguments, some related to new analyses, demonstrate that the attractor contracts grid maps. First, the ‘no attractor’ condition has a markedly larger spacing compared to all other conditions (Fig. 2a). We also now show that spacing monotonically decreases with the strength of recurrent relative to feedforward weights, in a way that is rather independent of gridness (Fig. S2a). Second, as we now show in Fig. S2b-d, simulations with a shuffled 1D attractor, such that the sum of input synapses to each neuron are the same as in the 1D condition but no structure is present, lead to a spacing that is mid-way between the ‘no attractor’ condition and the conditions with attractors. Third, as we now show in Fig. S3a, turning off both recurrent connections and feedforward learning in a trained network results in a small increase in spacing. Fourth, as we now show in Fig. S3b, turning off recurrent connections while feedforward learning is kept on increases grid spacing to levels comparable to those of the ‘no attractor’ condition. All these elements support a role of the attractor in contracting grid spacing.

(3) Some of the items need to be introduced first before going into details in the paper, for instance, the stipe-like attractor network, the Betti number, etc.

We have added in the Results section a brief description and references to full developments in the Appendix.

Reviewer 3 (Public Review):

(1) It is not clear to me that the proposal here is fundamentally new. In Si, Kropff and Treves (2012) recurrent connectivity was dependent on the head direction tuning and thus had a ring structure. Urdapilleta, Si, and Treves considered connectivity that depends on the distance on a 2d plane.

In the work of Si et al connectivity is constructed ad-hoc for conjunctive cells to represent a torus, it depends on head-directionality but also on the distance in a 2D plane. The topology of this architecture has not been assessed, but it is close to the typical 2D ‘rigid’ constraint. In the work of Urdapilleta et al, the network is a simple 2D one. The difference with our work is that we focus on the topology of the recurrent network and do not use head-direction modulation. In this context, we prove that a 1D network is enough to align grid cells and, more generally, we provide a proof of principle that the topology of the architecture and the representation space of an attractor network do not need to be identical, as previously assumed by the attractor community. These two important points were neither argued, speculated nor self-evident from the cited works.

(2) The paper refers to the connectivity within the grid cell layer as an attractor. However, would this connectivity, on its own, indeed sustain persistent attractor states? This is not examined in the paper. Furthermore, is this even necessary to obtain the results in the model? Perhaps weak connections that do not produce an attractor would be sufficient to align the spatial response patterns during the learning of feedforward weights, and reproduce the results? In general, there is no exploration of how the strength of collateral interactions affects the outcome.

The reviewer makes several important points. Local excitation combined with global inhibition is the archetypical architecture for continuous attractors (see for example Knierim and Zhang, Annual review of neuroscience, 2012). Thus, in the absence of feedforward input, we observe a bump of activity. As in all continuous attractors, this bump is not necessarily ‘persistent’ and instead is free to move along the attractor.

We cannot prove that there is not a simpler architecture that has the same effect as our 1D or 1DL conditions, and we think that there are some interesting candidates to investigate in the future. What we now prove in new Fig. S2b-d is that it is not the strength of recurrent connections themselves, but instead the continuous attractor structure that aligns grid cells in our model. To demonstrate this, we shuffle incoming recurrent connections to each neuron in the 1D condition (while avoiding self-connections for fairness), and show that training does not lead to grid alignment. We also show in Fig. S1 that an architecture represented by 20 overlapping 1DL attractors, each formed by concatenating 10 random cells, aligns grid cells to levels slightly lower but similar to the 1D or 1DL attractors. This architecture can perhaps be considered as simpler to build in biological terms than all the others, but it is still constituted by continuous attractors.

The strength of recurrent collaterals, or more precisely the recurrent to feedforward ratio, is crucial in our model to achieve a negotiated outcome from constraints imposed by the attractor and the inputs. We now show explicit measures of this ratio in Fig. S2, as well as examples showing that an imbalance in this ratio impairs grid alignment. When the ratio is too high or too low, both individual and population gridness are low. Interestingly, grid spacing behaves differently, decreasing monotonically with the relative strength of recurrent connections.

(3) I did not understand what is learned from the local topology analysis. Given that all the grid cells are driven by an input from place cells that spans a 2d manifold, and that the activity in the grid cell network settles on a steady state that depends only on the inputs, isn't it quite obvious that the manifold of activity in the grid cell layer would have, locally, a 2d structure?

The dimensionality of the input is important, although not the only determinant of the topology of the activity. The recurrent collaterals are the other determinant, and their architecture is a crucial feature. For example, as we now show in Figure S2b-d, shuffled recurrent synaptic weights fail to align grid cells. In the 1D condition, if feedforward inputs were absent, the dynamics of the activity would be confined to a ring. The opposite condition is our ‘no attractor’ condition, in which activity in the grid cell layer mimics the topology of inputs, a 2D sheet (and not a torus). It is in the intermediate range, when both feedforward and recurrent inputs are important, that a negotiated solution (a torus) is achieved.

The analyses of local dimensionality and local homology of Figure 3 are crucial steps to demonstrate toroidal topology. According to the theorem of classification of closed surfaces, global homology is not enough to univocally define the topology of a point cloud, and thus this step cannot be skipped. The step is aimed to prove that the point cloud is indeed a closed surface.

(4) The modeling is all done in planar 2d environments, where the feedforward learning mechanism promotes the emergence of a hexagonal pattern in the single neuron tuning curve. This, combined with the fact that all neurons develop spatial patterns with the same spacing and orientation, implies even without any topological analysis that the emerging topology of the population activity is a torus.

We cannot agree with this intuition. In the ‘no attractor’ condition, individual maps have hexagonal symmetry with standardized spacing, but given the lack of alignment the population activity is not a closed surface and thus not a torus. It can rather be described as a 2D sheet embedded in a high dimensional space, a description that also applies to the input space.

While it is rather evident that an ad hoc toroidal architecture folds this 2D population activity into a torus, it is less evident and rather surprising that 1D architectures have the same capability. This is the main novelty in our work.

(5) Moreover, the recent work of Gardner et al. demonstrated much more than the preservation of the topology in the different environments and in sleep: the toroidal tuning curves of individual neurons remained the same in different environments. Previous works, that analyzed pairwise correlations under hippocampal inactivation and various other manipulations, also pointed towards the same conclusion. Thus, the same population activity patterns are expressed in many different conditions. In the present model, the results of Figure 6 suggest that even across distinct rectangular environments, toroidal tuning curves will not be preserved, because there are multiple possible arrangements of the phases on the torus which emerge in different simulations.

We agree with the reviewer in the main point, although the recently found ring activity in the absence of sensory feedback (Gonzalo Cogno et al, 2023) suggests that what is happening in the EC is more nuanced than a pre-wired torus. Solutions in Figure 6 are different ways of folding a 1D strip into a torus, with or without the condition of periodicity in the 1D strip. Whether or not these different solutions would be discernible from one another in a practical setup is not clear to us. For example, global homology, as addressed in the Gardner paper, is the same for all these solutions. Furthermore, while our solutions of up to order 3 are highly discernable, higher order solutions, potentially achievable with other network parameters, would be impossible to discern by eye in representations similar to the ones in Figure 6. In addition, while we chose to keep our model in the simplest possible form as a clear proof of principle, new elements introduced to the model such as head directionality could break the symmetry and lead to the prevalence of one preferred solution for all simulation replicates. We plan to investigate this possibility in the future when attempting to incorporate path-integration capabilities to the model.

(6) In real grid cells, there is a dense and fairly uniform representation of all phases (see the toroidal tuning of grid cells measured by Gardner et al). Here the distribution of phases is not shown, but Figure 7 suggests that phases are non uniformly represented, with significant clustering around a few discrete phases. This, I believe, is also the origin for the difficulty in identifying the toroidal topology based on the transpose of the matrix M: vectors representing the spatial response patterns of individual neurons are localized near the clusters, and there are only a few of them that represent other phases. Therefore, there is no dense coverage of the toroidal manifold that would exist if all phases were represented equally. This is not just a technical issue, however: there appears to be a mismatch between the results of the model and the experimental reality, in terms of the phase coverage.

As mentioned in the results section, Figure 7 is meant for visualization purposes only, and serves more as cautionary tale regarding the imprevisible risks of non-linear dimensionality reduction than as a proof of the organization of activity in the network. Isomap is a non-linear transformation that deforms each of our solutions in a unique way so that, while all have the topology of a torus embedded in a high dimensional space, only a few of them exhibited one of two possible toroidal visualizations in a 3D Isomap reduction. Isomap, as well as all other popular dimensionality reduction techniques, provide no guarantee of topology invariance. A better argument to judge the homogenous distribution of phases is persistent homology, which identifies relatively large holes (compared to the sampling spacing) in the original manifold embedded in a high dimensional space. In our case, persistent homology identified only two holes significantly larger than noise (the two cycles of a torus) and one cavity in all conditions that included attractors. Regarding the specific distribution of phases in different conditions, however, see our reply below.

(7) The manuscript makes several strong claims that incorrectly represent the relation between experimental data and attractor models, on one hand, and the present model on the other hand. For the latter, see the comments above. For the former, I provide a detailed list in the recommendations to the authors, but in short: the paper claims that attractor models induce rigidness in the neural activity which is incompatible with distortions seen in the spatial response patterns of grid cells. However, this claim seems to confuse distortions in the spatial response pattern, which are fully compatible with the attractor model, with distortions in the population activity patterns, which would be incompatible with the attractor model. The attractor model has withstood numerous tests showing that the population activity manifold is rigidly preserved across conditions - a strong prediction (which is not made, as far as I can see, by feedforward models). I am not aware of any data set where distortions of the population activity manifold have been identified, and the preservation has been demonstrated in many examples where the spatial response pattern is disrupted. This is the main point of two papers cited in the present manuscript: by Yoon et al, and Gardner et al.

First of all, we would like to note that our model is a continuous attractor model. Different attractor models have different outcomes, and one of the main conclusions of our manuscript is that attractors can do a wider range of operations than previously thought.

We agree with the reviewer that distortions in spatial activity (which speak against a purely path-integration guided attractor) should not be confused with distortions in the topology of the population activity (which would instead speak against the attractor dynamics itself). We have rephrased these observations in the manuscript. In fact, we believe that the capacity of grid cells to present distorted maps without a distortion of the population activity topology, as shown for example by Gardner and colleagues, could result from a tension between feedforward and recurrent inputs, the potential equilibriums of which our manuscript aims to characterize.

(8) There is also some weakness in the mathematical description of the dynamics. Mathematical equations are formulated in discrete time steps, without a clear interpretation in terms of biophysically relevant time scales. It appears that there are no terms in the dynamics associated with an intrinsic time scale of the neurons or the synapses, and this introduces a difficulty in interpreting synaptic weights as being weak or strong. As mentioned above, the nature of the recurrent dynamics within the grid cell network (whether it exhibits continuous attractor behavior) is not sufficiently clear.

We agree with the reviewer that our model is rather simple, and we value the extent to which this simplicity allows for a deep characterization. All models are simplifications and the best model in any given setup is the one with the minimum amount of complexity necessary to describe the phenomenon under study. We believe that to understand whether or not a 1D continuous attractor architecture can result in a toroidal population activity, a biophysically detailed model, with prohibitive computational costs, would have been unnecessarily complex. This argument does not intend to demerit biophysically detailed models, which are capable of addressing a wider range of questions regarding, for example, the spiking dynamics of grid cells, which cannot be addressed by our simple model.

Reviewer #3 (Recommendations For The Authors):

The work points to an interesting scenario for the emergence of toroidal topology, but the interpretation of this idea should be more nuanced. I recommend reconsidering the claims about limitations of the attractor theory, and acknowledging the limitations of the present theory.

I don't see the limitations mentioned above as a reason to reject the ideas proposed in this manuscript, for two main reasons: first, additional research might reveal a regime of parameters where some issues can be resolved (e.g. the clustering of phases). In addition, the mechanism described here might act at an early stage in development to set up initial dynamics along a toroidal manifold, while other mechanisms might be responsible for the rigidity of the toroidal manifold in an adult animal. But all this implies that the novelty in the present manuscript is weaker than implied, the ability to explain experimental observations is more limited than implied, and these limitations should be acknowledged and discussed.

I recommend reporting on the distribution of grid cell phases and, if indeed clustered, this should be discussed. It will be helpful to explore whether this is the reason for the difficulty in identifying the toroidal topology based on the collection of spatial response patterns (using the transpose of the matrix M).

Ideally, a more complete work would also explore in a more systematic and parametric way the influence of the recurrent connectivity's strength on the learning, and whether a toroidal manifold emerges also in non-planar, such as the wagon-wheel environment studied in Gardner et al.

Part of these recommendations have been addressed in the previous points (public review). Regarding the reason why the transpose of M does not fully recapitulate architecture with our conservative classification criteria, we believe that there is no reason why it should in the first place. We view the fact that the transpose of M recapitulates some features of the architecture as a purely phenomenological observation, and we think it is important as a proof that M is not exactly the same for the different conditions. We imagined that if M matrices were exactly the same this could be due to poor spatial sampling by our bins. Knowing that they are intrinsically different is important even if the reason why they have these specific features is not fully clear to us.

Although we do not think that the distribution of phases is related to the absence of a cavity in the transpose of M or to the four clusters found in Isomap projections, it remains an interesting question that we did not explore initially. We are now showing examples of the distribution of phases in Figure S1. We observed that in both 2D and 1D conditions phases are distributed following rather regular patterns. Whether or not these patterns are compatible with experimental observations of phase distribution is to our view debatable, given that so far state-of-the-art techniques have only allowed to simultaneously record a small fraction of the neurons belonging to a given module. This said, we think that it is important to note that ordered phase patterns are an anecdotal outcome of our simulations rather than a necessary outcome of flexible attractors or attractors in general. To prove this point, we simulated a condition with a new architecture represented by the overlap of 20 short 1DL attractors, each recruiting 10 random neurons from the pool of 100 available ones.

The rest of the parameters of the simulations were identical to those in the other conditions.

By definition, the topology of this architecture has Betti numbers [20,0,0]. We show in Figure S1 that this architecture aligns grid cells, with individual and population gridness reaching slightly lower levels compared to the 1D condition. However, the distribution of phases of these grid cells has no discernible pattern. This result is an arbitrary example that serves as a proof-of-principle to show that flexible attractors can align grid cells without exhibiting ordered phases, not a full characterization of the outcome of this type of architecture, which we leave for future work. For the rest of our work, we stick to the simplest versions of 1D architectures, which allow for a more in-depth characterization.

The wagon-wheel is an interesting case in which maps loose hexagonal symmetry although the population activity lies in a torus, perhaps evidencing the tension between feedforward and recurrent inputs and suggesting that grid cell response does not obey the single master of path integration. If we modeled it with a 1D attractor, we believe the outcome would strongly depend on virtual rat trajectory. If the trajectory was strictly linear, the population activity would be locally one-dimensional and potentially represented by a ring. Instead, if the trajectory allowed for turns, i.e. a 2D trajectory within a corridor-like maze, the population activity would be toroidal as in our open field simulations, while maps would not have perfect hexagonal symmetry, mimicking experimental results.

More minor comments:

Recurrent dynamics are modeled as if there is no intrinsic synaptic or membrane time constant. This may be acceptable for addressing the goals of this paper, but it is a bit unusual and it will be helpful to explain and justify this choice.

As mentioned above, we believe that the best model in a given setup is the one with the lowest number of complexities that can still address the phenomenon under study. One does not use general relativity to build a bridge, although it provides a ‘more accurate’ description of the physics involved. All models are simplifications, and the more complex a model, the more it has to be taken as a black box.

The Introduction mentions that in most models interaction between co-modular neurons occurs through direct excitatory communication, but in quite a few models the interaction is inhibitory. The crucial feature is that the interaction is strongly inhibitory between neurons that differ in their tuning, and either less inhibitory or excitatory between neurons with similar phases.

We agree that directed inhibition has been shown to be as efficient as directed excitation, and we have modified the introduction to reflect this.

The Discussion claims that the present work is the first one in which the topology of the recurrent architecture differs from the topology of the emergent state space. However, early works on attractor models of grid cells showed how neural connectivity which is arranged on a 2d plane, without any periodic boundary conditions, leads to a state space that exhibits the toroidal topology. Therefore, this claim should be revised.

We agree, although the 2D sheet in this case acts as a piece of the torus, and locally the input space and architecture are identical objects. It could be argued that architectures that represent a 2D local slice of the torus, the whole torus, or several cycles around the torus form a continuous family parametrized by the extension of recurrent connections, and as a consequence it is not surprising that these works have not made claims about the incongruence between architecture and representation topologies. The 2D sheet connectivity is still constructed ad hoc to organize activity in a 2D bump, and there is no negotiation between disparate constraints because locally the constraints imposed by input and architecture are the same. We believe this situation is conceptually different from our flexible 1D attractors. We have adapted our claim to include this technical nuance.

Why are neural responses in the perimeter of the environment excluded from the topological analysis? The whole point of the toroidal manifold analysis on real experimental data is that the toroidal manifold is preserved regardless of the animal's location and behavioral condition.

We agree, although experimental data needs to go through extensive pre-processing such as dimensionality reduction before showing a toroidal topology. Such manipulations might smooth away the specific effects of boundaries on maps, together with other sources of noise. In our case, the original reason to downsample the dataset is related to the explosion in computational time that we experience with the ripser package when using more than ~1000 data points. For a proof-of-principle characterization we were much more interested in what happened in the center of the arena, where a 1D attractor could fold itself to confine population activity into a torus. The area we chose was sufficiently large to contain the whole torus. Borders do affect the way the attractor folds (they also affect grid maps in real rats). We feel that these imperfections could be interesting to study in relation to the parameters controlling how our virtual rat behaves at the borders, but not at this proof-of-principle stage.

The periodic activity observed in Ref. 29 could in principle provide the basis for the ring arrangement of neurons. However, it is not yet clear whether grid cells participate in this periodic activity.

We agree. So far it seems that entorhinal cells in general participate in the ring, which would imply that all kinds of cells are involved. However, it could well be that only some functional types participate in the ring and grid cells specifically do not, as future experiments will tell.

Author response:

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

eLife assessment

This valuable work explores death coding data to understand the impact of COVID-19 on cancer mortality. The work provides solid evidence that deaths with cancer as a contributing cause were not above what would be expected during pandemic waves, suggesting that cancer did not strongly increase the risk of dying of COVID-19. These results are an interesting exploration into the coding of causes of death that can be used to make sense of how deaths are coded during a pandemic in the presence of other underlying diseases, such as cancer.

We thank the editor and reviewers for the time they took to review our manuscript and for the thoughtful suggestions they provided. We have completed several revisions based on their feedback and we feel our paper is stronger as a result. However, none of these revisions change the overall conclusions of our study.

Reviewer #1 (Public Review):

Summary:

In the paper "Disentangling the relationship between cancer mortality and COVID-19", the authors study whether the number of deaths in cancer patients in the USA went up or down during the first year (2020) of the COVID-19 pandemic. They found that the number of deaths with cancer mentioned on the death certificate went up, but only moderately. In fact, the excess with-cancer mortality was smaller than expected if cancer had no influence on the COVID mortality rate and all cancer patients got COVID with the same frequency as in the general population. The authors conclude that the data show no evidence of cancer being a risk factor for COVID and that the cancer patients were likely actively shielding themselves from COVID infections.

Strengths:

The paper studies an important topic and uses sound statistical and modeling methodology. It analyzes both, deaths with cancer listed as the primary cause of death, as well as deaths with cancer listed as one of the contributing causes. The authors argue, correctly, that the latter is a more important and reliable indicator to study relationships between cancer and COVID. The authors supplement their US-wide analysis by analysing three states separately.

Weaknesses:

The main findings of the paper can be summarized as six numbers. Nationally, in 2022, multiple-cause cancer deaths went up by 2%, Alzheimer's deaths by 31%, and diabetes deaths by 39%. At the same time, assuming no relationship between these diseases and either Covid infection risk or Covid mortality risk, the deaths should have gone up by 7%, 46%, and 28%. The authors focus on cancer deaths and as 2% < 7%, conclude that cancer is not a risk factor for COVID and that cancer patients must have "shielded" themselves against Covid infections.

However, I did not find any discussion of the other two diseases. For diabetes, the observed excess was 39% instead of "predicted by the null model" 28%. I assume this should be interpreted as diabetes being a risk factor for Covid deaths. I think this should be spelled out, and also compared to existing estimates of increased Covid IFR associated with diabetes.

And what about Alzheimer's? Why was the observed excess 31% vs the predicted 46%? Is this also a shielding effect? Does the spring wave in NY provide some evidence here? Why/how would Alzheimer's patients be shielded? In any case, this needs to be discussed and currently, it is not.

We thank the reviewer for their positive feedback on the paper and for these suggestions. It is true that we have emphasized the impact on cancer deaths, as this was the primary aim of the paper. In the revised version, we have expanded the results and discussion sections to more fully describe the other chronic conditions we used as comparators (lines 267-284;346 – 386).

Note that we are somewhat reluctant to designate any of these conditions as risk factors based solely on comparing the time series model with the demographic model of our expectations. As we mention in the discussion, there is considerable uncertainty around estimates from the demographic model in terms of the size of the population-at-risk, the mean age of the population-at-risk, and the COVID-19 infection rates and infection fatality ratios. Our demographic model is primarily used to demonstrate the effects of competing risks across types of cancers and chronic conditions, since these findings are robust to model assumptions. In contrast, the demographic model should be used with caution if the goal is to titrate the level of these risk factors (as the level of imputed risk is dependent on model assumptions). In the updated version of the manuscript, we have included uncertainty intervals in Table 3, using the upper and lower bounds of the estimated infection rates and IFRs, to better represent this uncertainty. We have also discussed this uncertainty more explicitly in the text and ran sensitivity analyses with different infection rate assumptions in the discussion (lines 354-362; 367 -370).

We would like to note that rather than interpreting the absolute results, we used this demographic model as a tool to understand the relative differences between these conditions. From the demographic model we determined that we would expect to see much higher mortality in diabetes and Alzheimer’s deaths compared to cancer deaths due to three factors (1. Size of population-at-risk, 2. Mean age of the population-at-risk, 3. Baseline risk of mortality from the condition), that are separate from the COVID-19 associated IFR. And in general, this is what we observed.

In comparing the results from the demographic model to the observed excess, diabetes does standout as an outlier from cancer and Alzheimer’s disease in that the observed excess is consistently above the null hypothesis which does lend support to the conclusion that diabetes is in fact a risk factor for COVID-19. A conclusion which is also supported by many other studies. Our findings for hematological cancers are also similar, in that we find consistent support for this condition being a risk factor. We have commented on this in the discussion and added a few references (lines 346-354; 395-403).

Our hypothesis regarding non-hematological cancer deaths (lower than anticipated mortality due to shielding) could also apply to Alzheimer’s deaths. Furthermore, we used the COVID-19 attack rate for individuals >65 years (based on the data that is available), but we estimate that the mean age of Alzheimer’s patients is actually 80-81 years, so this attack rate may in fact be a bit too high, which would increase our expected excess. We have commented on this in the discussion (lines 363-377).

Reviewer #2 (Public Review):

The article is very well written, and the approach is quite novel. I have two major methodological comments, that if addressed will add to the robustness of the results.

(1) Model for estimating expected mortality. There is a large literature using a different model to predict expected mortality during the pandemic. Different models come with different caveats, see the example of the WHO estimates in Germany and the performance of splines (Msemburi et al Nature 2023 and Ferenci BMC Medical Research Methodology 2023). In addition, it is a common practice to include covariates to help the predictions (e.g., temperature and national holidays, see Kontis et al Nature Medicine 2020). Last, fitting the model-independent for each region, neglects potential correlation patterns in the neighbouring regions, see Blangiardo et al 2020 PlosONE.

Thank you for these comments and suggestions. We agree there are a range of methods that can be used for this type of analysis, and they all come with their strengths, weaknesses, and caveats. Broadly, the approach we chose was to fit the data before the pandemic (2014-2019), and project forward into 2020. To our knowledge it is not a best practice to use an interpolating spline function to extrapolate to future years. This is demonstrated by the WHO estimates in Germany in the paper you mention. This was our motivation for using polynomial and harmonic terms.

Based on the above:

a. I believe that the authors need to run a cross-validation to justify model performance. I would suggest training the data leaving out the last year for which they have mortality and assessing how the model predicts forward. Important metrics for the prediction performance include mean square error and coverage probability, see Konstantinoudis et al Nature Communications 2023. The authors need to provide metrics for all regions and health outcomes.

Thank you for this suggestion. We agree that our paper could be strengthened by including cross validation metrics to justify model performance. Based on this suggestion, and your observations regarding Alzheimer’s disease, we have done two things. First, for the full pre-pandemic period (2014-2019) for each chronic condition and location we tested three different models with different degree polynomials (1. linear only, 2. linear + second degree polynomial, 3. linear + second degree polynomial + third degree polynomial) and used AIC to select the best model for each condition and location. Next, also in response to your suggestion, we estimated coverage statistics. Using the best fit model from the previous step, we then fit the model to data from 2014-2018 only and used the model to predict the 2019 data. We calculated the coverage probability as the proportion of weekly observed data points that fell within the 95% prediction interval. For all causes of death and locations the coverage probability was 100% (with the exception of multiple cause kidney disease in California, which is only shown in the appendix). The methods and results have been updated to reflect this change and we have added a figure to the appendix showing the selected model and coverage probability for each cause of death and location (lines 504 – 519; 847-859; Appendix 1- Figure 11).

b. In the context of validating the estimates, I think the authors need to carefully address the Alzheimer case, see Figure 2. It seems that the long-term trends pick an inverse U-shape relationship which could be an overfit. In general, polynomials tend to overfit (in this case the authors use a polynomial of second degree).It would be interesting to see how the results change if they also include a cubic term in a sensitivity analysis.

Thank you for this observation. Based on the changes described above, the model for Alzheimer’s disease now includes a cubic term in the national data and in Texas and California. The model with the second-degree polynomial remained the best fit for New York (Appendix 1 – Figure 11).

c. The authors can help with the predictions using temperature and national holidays, but if they show in the cross-validation that the model performs adequately, this would be fine.

At the scale of the US, adding temperature or environmental covariates is difficult and few US-wide models do so (see Goldstein 2012 and Quandelacy 2014 for examples from influenza). Furthermore, because we are looking at chronic disease outcomes, it is unclear that viral covariates or national holidays would drive these outcomes in the same way as they would if we were looking at mortality outcomes more directly related to transmissible diseases (such as respiratory mortality). Our cross validation also indicates that our models fit well without these additional covariates.

d. It would be nice to see a model across the US, accounting for geography and spatial correlation. If the authors don't want to fit conditional autoregressive models in the Bayesian framework, they could just use a random intercept per region.

We think the reviewer is mistaken here about the scale of our national analysis. Our national analysis did not fit independent models for each state or region. Rather, we fit a single model to the weekly-level national mortality data where counts for the whole of the US have been aggregated. We have clarified in the text (lines 156, 464). As such, we do not feel a model accounting for spatial correlation would be appropriate nor would we be able to include a random intercept for each region. We did fit three states independently (NY, TX, CA), but these states are very geographically distant from each other and unlikely to be correlated. These states were chosen in part because of their large population sizes, yet even in these states, confidence intervals were very wide for certain causes of death. Fitting models to each of the 50 US states, most of which are smaller than those chosen here, would exacerbate this issue.

(2) I think the demographic model needs further elaboration. It would be nice to show more details, the mathematical formula of this model in the supplement, and explain the assumptions

Thank you for this comment. We have added additional details on the demographic model to the methods. We have also extended this analysis to each state to further strengthen our conclusions (lines 548-590).

Reviewing Editor Recommendations:

I think that perhaps something that is missing is that the authors never make their underlying assumption explicit: they are assuming that if cancer increases the risk of dying of COVID-19, this would be reflected in the data on multiple causes of death where cancer would be listed as one of the multiple causes rather than as the underlying cause, and that their conclusions are predicated on this assumption. I would suggest explicitly stating this assumption, as opposed to other reasons why cancer mortality would increase (ex. if cancer care worsened during pandemic waves leading to poorer cancer survival).

Response: Thank you for this suggestion. We have added a few sentences to the introduction to make this assumption clear (lines 106-112).

Reviewer #1 (Recommendations For The Authors):

- It could make sense to add "in the United States" into the title, as the paper only analyses US data.

- It may make sense to reformulate the title from "disentangling the relationship..." into something that conveys the actual findings, e.g. "Lack of excess cancer mortality during Covid-19 pandemic" or something similar. Currently, the title tells nothing about the findings.

Thank you for these suggestions. We have added “in the US” to the title. However, we feel that our findings are a bit more subtle than the suggested reformulation would imply, and we prefer to leave it in its current form.

- Abstract, lines 42--45: This is the main finding of the paper, but I feel it is simplified too strongly in the abstract. Your simulations do *not* "largely explain" excess mortality with cancer; they give higher numbers! Which you interpret as "shielding" etc., but this is completely absent from the abstract. This sentence makes the impression that you got a good fit between simulated excess and real excess, which I would say is not the case.

Thank you for this comment. We have rephrased the sentence in the abstract to better reflect our intentions for using the demographic model (lines 46-49). As stated above, the purpose of the demographic model was not to give a good fit with the observed excess mortality. Rather, we used the demographic model as a tool to understand the relative differences between these conditions in terms of expected excess mortality given the size, age-distribution, and underlying risk of death from the condition itself, assuming similar IFR and attack rates. And based on this, we conclude that it is not necessarily surprising that we see higher excess mortality for diabetes and Alzheimer’s compared to cancer.

- Results line 237: you write that it's "more consistent with the null hypothesis", however clearly it is *not* consistent with the null hypothesis either (because 2% < 7%). You discuss in the Discussion that it may be due to shielding, but it would be good to have at least one sentence about it already here in the Results, and refer to the Discussion.

We have mentioned this in the results and refer to the discussion (lines 277-278).

- Results line 239: why was it closer to the assumption of relative risk 2? If I understand correctly, your model prediction for risk=1 was 7% and for risk=2 it was 13%. In NY you observed 8% (line 187). How is this closer to risk=2?

Thank you for this observation. We have updated the demographic model with new data, extended the model to state-level data, and included confidence intervals on these estimates. We have also added additional discussion around the differences between our observations and expectations (lines 249-284).

- Discussion line 275: "we did not expect to see large increases" -- why exactly? Please spell it out here. Was it due to the age distribution of the cancer patients? Was it due to the high cancer death risk?

We demonstrate that it is the higher baseline risk of death for cancer that seems to be driving our low expectations for cancer excess mortality (lines 304-320). We have added this to the sentence to clarify our conclusions on this point and have added a figure to better illustrate this concept of competing risks (Figure 6).

- Methods, line 405: perhaps it makes sense to cite some other notable papers on Covid excess mortality such as Msemburi et al Nature 2023, Karlinsky & Kobak eLife 2021, Islam et al BMJ 2021, etc.

Thank you for mentioning this oversight. We certainly should have cited these papers and have included them in the updated version.

- Methods line 410: why did you use a 5-week moving average? Why not fit raw weekly death counts? NB regression should be able to deal with it.

Smoothing time series data with a moving average prior to running regression models is a very common practice. We did a sensitivity analysis using the raw data. This produced excess estimates with slightly larger confidence intervals, but does not change the overall conclusions of the paper.

- Methods line 416: please indicate the software/library/package you used for fitting NB regression.

We fit the NB regression using the MASS package in R version 4.3. We have added this to the methods (line 519).

- Line 489: ORCHID -> ORCID

Author response:

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

Reviewer #1 (Public Review):

Summary:

Codol et al. present a toolbox that allows simulating biomechanically realistic effectors and training Artificial Neural Networks (ANNs) to control them. The paper provides a detailed explanation of how the toolbox is structured and several examples that demonstrate its usefulness.

Main comments:

(1) The paper is well written and easy to follow. The schematics help in understanding how the toolbox works and the examples provide an idea of the results that the user can obtain.

We thank the reviewer for this comment.

(2) As I understand it, the main purpose of the paper should be to facilitate the usage of the toolbox. For this reason, I have missed a more explicit link to the actual code. As I see it, researchers will read this paper to figure out whether they can use MotorNet to simulate their experiments, and how they should proceed if they decide to use it. I'd say the paper provides an answer to the first question and assures that the toolbox is very easy to install and use. Maybe the authors could support this claim by adding "snippets" of code that show the key steps in building an actual example.

This is an important point, which we also considered when writing this paper. We instead decided to focus on the first approach, because it is easier to illustrate the scientific use of the toolbox using code or interactive (Jupyter) notebooks than a publication format. We find the “how to proceed” aspect of the toolbox can more easily and comprehensively be covered using online, interactive tutorials. Additionally, this allows us to update these tutorials as the toolbox evolves over different versions, while it is more difficult to update a scientific article. Consequently, we explicitly avoided code snippets on the article itself. However, we appreciate that the paper would gain in clarity if this was more explicitly stated early. We have modified the paper to include a pointer to where to find tutorials online. We added this at the last paragraph of the introduction section:

The interested reader may consult the full API documentation, including interactive tutorials on the toolbox website at https://motornet.org.

(3) The results provided in Figures 1, 4, 5 and 6 are useful, because they provide examples of the type of things one can do with the toolbox. I have a few comments that might help improving them:

a. The examples in Figures 1 and 5 seem a bit redundant (same effector, similar task). Maybe the authors could show an example with a different effector or task? (see point 4).

The effectors from figures 1 and 5 are indeed very similar. However, the tasks in figure 1 and 5 present some important differences. The training procedure in figure 1 never includes any perturbations, while the one from figure 5 includes a wide range of perturbations of different magnitudes, timing and directions. The evaluation procedure of figure 1 includes center-out reaches with permanent viscous (proportional to velocity) external dynamics, while that of figure 5 are fixed, transient, square-shaped perturbation orthogonal to the reach direction. Finally, the networks in figure 1 undergo a second training procedure after evaluation while the network of figure 5 do not.

While we agree that some variation of effectors would be beneficial, we do show examples of a point-mass effector in figure 6. Overall, figure 5 shows a task that is quite different from that of figure 1 with a similar effector, while the opposite is true for figure 6. We have modified the text to clarify this for the reader, by adding the following.

End of 1st paragraph, section 2.4.

Therefore, the training protocol used for this task largely differed from section 2.1 in that the networks are exposed to a wide range of mechanical perturbations with varying characteristics.

1st paragraph of section 2.5

[…] this asymmetrical representation of PMDs during reaching movements did not occur when RNNs were trained to control an effector that lacked the geometrical properties of an arm such as illustrated in Figure 4c-e and section 2.1.

b. I missed a discussion on the relevance of the results shown in Figure 4. The moment arms are barely mentioned outside section 2.3. Are these results new? How can they help with motor control research?

We thank the reviewer for this comment. This relates to a point from reviewer 2 indicating that the purpose of each section was sometimes difficult to grasp as one reads. Section 2.3 explains the biomechanical properties that the toolbox implements to improve realism of the effector. They are not new results in the sense that other toolboxes implement these features (though not in differentiable formats) and these properties of biological muscles are empirically well-established. However, they are important to understand what the toolbox provides, and consequently what constraints networks must accommodate to learn efficient control policies. An example of this is the results in figure 6, where a simple effector versus a more biomechanically complex effector will yield different neural representations.

Regarding the manuscript itself, we agree that more clarity on the goal of every paragraph may improve the reader’s experience. Consequently, we ensured to specify such goals at the start of each section. Particularly, we clarify the purpose of section 2.3 by adding several sentences on this at the end of the first paragraph in that section. We also now clearly state the purpose of section 2.3 with the results of figure 6 and reference figure 4 in that section.

c. The results in Figure 6 are important, since one key asset of ANNs is that they provide access to the activity of the whole population of units that produces a given behavior. For this reason, I think it would be interesting to show the actual "empirical observations" that the results shown in Fig. 6 are replicating, hence allowing a direct comparison between the results obtained for biological and simulated neurons.

These empirical observations are available from previous electrophysiological and modelling work. Particularly, polar histograms across reaching directions like panel C are displayed in figures 2 and 3 of Scott, Gribble, Graham, Cabel (2001, Nature). Colormaps of modelled unit activity across time and reaching directions like panel F are also displayed in figure 2 of Lillicrap, Scott (2013, Neuron). Electrophysiological recordings of M1 neurons during a similar task in non-human primates can also be seen on “Preserved neural population dynamics across animals performing similar behaviour” figure 2 B (https://doi.org/10.1101/2022.09.26.509498) and “Nonlinear manifolds underlie neural population activity during behaviour” figure 2 B as well (https://doi.org/10.1101/2023.07.18.549575). Note that these two pre-prints use the same dataset.

We have added these citations to the text and made it explicit that they contain visualizations of similar modelling and empirical data for comparison:

This heterogeneous set of responses matches empirical observations in non-human primate primary motor cortex recordings (Churchland & Shenoy, 2007; Michaels et al., 2016) and replicate similar visualizations from previously published work (Fortunato et al., 2023; Lillicrap & Scott, 2013; Safaie et al., 2023).

(4) All examples in the paper use the arm26 plant as effector. Although the authors say that "users can easily declare their own custom-made effector and task objects if desired by subclassing the base Plant and Task class, respectively", this does not sound straightforward. Table 1 does not really clarify how to do it. Maybe an example that shows the actual code (see point 2) that creates a new plant (e.g. the 3-joint arm in Figure 7) would be useful.

Subclassing is a Python process more than a MotorNet process, as python is an object-oriented language. Therefore, there are many Python tutorials on subclassing in the general sense that would be beneficial for that purpose. We have amended the main text to ensure that this is clearer to the reader.

Subclassing a MotorNet object, in a more specific sense, requires overwriting some methods from the base MotorNet classes (e.g., Effector or Environment classes, which correspond to the original Plant and Task object, respectively). Since we made the decision (mentioned above) to not include code in the main text, we added tutorials to the online documentation, which include dedicated tutorials for MotorNet class subclassing. For instance, this tutorial showcases how to subclass Environment classes:

https://colab.research.google.com/github/OlivierCodol/MotorNet/blob/master/examples/3-environments.ipynb

(5) One potential limitation of the toolbox is that it is based on Tensorflow, when the field of Computational Neuroscience seems to be, or at least that's my impression, transitioning to pyTorch. How easy would it be to translate MotorNet to pyTorch? Maybe the authors could comment on this in the discussion.

We have received a significant amount of feedback asking for a PyTorch implementation of the toolbox. Consequently, we decided to enact this, and the next version of the toolbox will be exclusively in PyTorch. We will maintain the Application Programming Interface (API) and tutorial documentation for the TensorFlow version of the toolbox on the online website. However, going forward we will focus exclusively on bug-fixing and expanding from the latest version of MotorNet, which will be in PyTorch. We now believe that the greater popularity of PyTorch in the academic community makes that choice more sustainable while helping a greater proportion of research projects.

These changes led to a significant alteration of the MotorNet structure, which are reflected by changes made throughout the manuscript, notably in Figure 3 and Table 1.

(6) Supervised learning (SL) is widely used in Systems Neuroscience, especially because it is faster than reinforcement learning (RL). Thus providing the possibility of training the ANNs with SL is an important asset of the toolbox. However, SL is not always ideal, especially when the optimal strategy is not known or when there are different alternative strategies and we want to know which is the one preferred by the subject. For instance, would it be possible to implement a setup in which the ANN has to choose between 2 different paths to reach a target? (e.g. Kaufman et al. 2015 eLife). In such a scenario, RL seems to be a more natural option Would it be easy to extend MotorNet so it allows training with RL? Maybe the authors could comment on this in the discussion.

The new implementation of MotorNet that relies on PyTorch is already standardized to use an API that is compatible with Gymnasium. Gymnasium is a standard and popular interfacing toolbox used to link RL agents to environments. It is very well-documented and widely used, which will ensure that users who wish to employ RL to control MotorNet environments will be able to do so relatively effortlessly. We have added this point to accurately reflect the updated implementation, so users are aware that it is now a feature of the toolbox (new section 3.2.4.).

Impact:

MotorNet aims at simplifying the process of simulating complex experimental setups to rapidly test hypotheses about how the brain produces a specific movement. By providing an end-to-end pipeline to train ANNs on the simulated setup, it can greatly help guide experimenters to decide where to focus their experimental efforts.

Additional context:

Being the main result a toolbox, the paper is complemented by a GitHub repository and a documentation webpage. Both the repository and the webpage are well organized and easy to navigate. The webpage walks the user through the installation of the toolbox and the building of the effectors and the ANNs.

Reviewer #2 (Public Review):

MotorNet aims to provide a unified interface where the trained RNN controller exists within the same TensorFlow environment as the end effectors being controlled. This architecture provides a much simpler interface for the researcher to develop and iterate through computational hypotheses. In addition, the authors have built a set of biomechanically realistic end effectors (e.g., an 2 joint arm model with realistic muscles) within TensorFlow that are fully differentiable.

MotorNet will prove a highly useful starting point for researchers interested in exploring the challenges of controlling movement with realistic muscle and joint dynamics. The architecture features a conveniently modular design and the inclusion of simpler arm models provides an approachable learning curve. Other state-of-the-art simulation engines offer realistic models of muscles and multi-joint arms and afford more complex object manipulation and contact dynamics than MotorNet. However, MotorNet's approach allows for direct optimization of the controller network via gradient descent rather than reinforcement learning, which is a compromise currently required when other simulation engines (as these engines' code cannot be differentiated through).

The paper could be reorganized to provide clearer signposts as to what role each section plays (e.g., that the explanation of the moment arms of different joint models serves to illustrate the complexity of realistic biomechanics, rather than a novel discovery/exposition of this manuscript). Also, if possible, it would be valuable if the authors could provide more insight into whether gradient descent finds qualitatively different solutions to RL or other non gradient-based methods. This would strengthen the argument that a fully differentiable plant is useful beyond improving training time / computational power required (although this is a sufficiently important rationale per se).

We thank the reviewer for these comments. We agree that more clarity on the section goals may improve the reader’s experience and ensured this is the case throughout the manuscript. Particularly, we added the following on the first paragraph of section 2.3, for which an explicit goal was most missing:

In this section we illustrate some of these biomechanical properties displayed by MotorNet effectors using specific examples. These properties are well-characterised in the biology and are often implemented in realistic biomechanical simulation software.

Regarding the potential difference in solutions obtained from reinforcement or supervised learning, this would represent a non-trivial amount of work to do so conclusively and so may not be within the scope of the current article. We do appreciate however that in some situations RL may be a more fitting approach to a given task design. In relation to this point we now specify in the discussion that the new API can accommodate interfacing with reinforcement learning toolboxes for those who may want to pursue this type of policy training approach when appropriate (new section 3.2.4.).

Reviewer #3 (Public Review):

Artificial neural networks have developed into a new research tool across various disciplines of neuroscience. However, specifically for studying neural control of movement it was extremely difficult to train those models, as they require not only simulating the neural network, but also the body parts one is interested in studying. The authors provide a solution to this problem which is built upon one of the main software packages used for deep learning (Tensorflow). This allows them to make use of state-of-the-art tools for training neural networks.

They show that their toolbox is able to (re-)produce several commonly studied experiments e.g., planar reaching with and without loads. The toolbox is described in sufficient detail to get an overview of the functionality and the current state of what can be done with it. Although the authors state that only a few lines of code can reproduce such an experiment, they unfortunately don't provide any source code to reproduce their results (nor is it given in the respective repository).

The possibility of adding code snippets to the article is something we originally considered, and which aligns with comment two from reviewer one (see above). Hopefully this provides a good overview of the motivation behind our choice not to add code to the article.

The modularity of the presented toolbox makes it easy to exchange or modify single parts of an experiment e.g., the task or the neural network used as a controller. Together with the open-source nature of the toolbox, this will facilitate sharing and reproducibility across research labs.

I can see how this paper can enable a whole set of new studies on neural control of movement and accelerate the turnover time for new ideas or hypotheses, as stated in the first paragraph of the Discussion section. Having such a low effort to run computational experiments will be definitely beneficial for the field of neural control of movement.

We thank the reviewer for these comments.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The main goal of the authors was to study the testis-specific role of the protein FBXO24 in the formation and function of the ribonucleoprotein granules (membraneless electron-dense structures rich in RNAs and proteins).

We appreciate the summary comment of reviewer #1.

Strengths:

The wide variety of methods used to support their conclusions (including transgenic models)

We appreciate the positive comment of reviewer #1.

Weaknesses:

The lack of specific antibodies against FBXO24. Some of the experiments showing a specific phenotype are descriptive and lack of logical explanation about the possible mechanism (i.e. AR or the tail structure).

Because we could not obtain specific antibodies against FBXO24, we generated Fbxo24-FLAG transgenic mice, which can be used to show the interaction between FBXO24 and IPO5. For the mechanism of impaired acrosome reaction, we added some results and discussion as written in the response to the question (1) of reviewer #1 (public review). For the mechanism of abnormal flagellar structure, we added new results and fixed the manuscript as written in the response to the major comments of reviewer #3 (recommendations for the authors).

Questions:

The paper is excellent and employs a wide variety of methods to substantiate the conclusions. I have very few questions to ask:

(1) KO mice cannot undergo acrosome reaction (AR) even spontaneously. How do you account for this, given that no visible defects were observed in the acrosome?

One possibility is that Fbxo24 KO spermatozoa cannot undergo capacitation; however, it is difficult to analyze the capacitation status such as tyrosine phosphorylation because most Fbxo24 KO spermatozoa are not alive (Figure S3A). Other possibility is that AR-related proteins are affected in Fbxo24 KO spermatozoa. Therefore, we analyzed the amounts of AR-related proteins with mass spectrometry (Figure S3C). Although previous studies indicate that the assembly of the SNARE complex is a key event prior to AR [Hutt et al., 2005 (PMID: 15774481); Katafuchi et al., 2000 (PMID: 11066067); Schulz et al., 1997 (PMID: 9356173); Tomes et al., 2002 (PMID: 11884041)], no clear differences were detected for SNARE proteins (Figure S3C and D). PLCD4 that is important for AR [Fukami et al., 2001 (PMID: 11340203)) was also detected in Fbxo24 KO spermatozoa (Figure S3C). Although we could not find differences in the amounts of AR-related proteins, it is still possible that FER1L5, another AR-related protein [Morohoshi et al., 2023 (PMID: 36696506)] not detected in the mass spectrometry analyses, or AR-related proteins not yet identified are affected in Fbxo24 KO spermatozoa. We added these results and discussion (line 160-166 and 305-312).

(2) KO sperm are unable to migrate in the female tract, and, more intriguingly, they do not pass through the utero-tubal junction (UTJ). The levels of ADAM3 are normal, suggesting that the phenotype is influenced by other factors. The authors should investigate the levels of Ly6K since mice also exhibit the same phenotype but with normal levels of ADAM3.

We detected LY6K in Fbxo24 KO spermatozoa with immunoblotting, but no difference was found.

We added the results (Figure S3E and line 172–175).

(3) In Figure 4A, the authors assert that "RBGS Tg mice revealed that mitochondria were abnormally segmented in Fbxo24 KO spermatozoa." I am unable to discern this from the picture shown in that panel. Could you please provide a more detailed explanation or display the information more explicitly?

We are sorry for the ambiguous explanation on the morphology of sperm mitochondria sheath. Fbxo24 KO cauda epidydimal spermatozoa shows disorganized mitochondria sheath rather than “segmented”. We fixed the sentence (line 190-192) and added white arrowheads that indicate the disorganized regions (Figure 4A).

Reviewer #2 (Public Review):

Summary:

The manuscript by Kaneda et al "FBXO24 ensures male fertility by preventing abnormal accumulation of membraneless granules in sperm flagella" is a significant paper on the role of FBXO24 in murine male germ cell development and sperm ultrastructure and function. The body of experimental evidence that the authors present is extraordinarily strong in both breadth and depth. The authors investigate the protein's functions in male germ cells and sperm using a wide variety of approaches but focusing predominantly on their novel mouse model featuring deletion of the Fbxo24 gene and its product. Using this mouse, and a cross of it with another model that expresses reporters in the head and midpiece, they logically build from one experiment to the next. Together, their data show that this protein is involved in the regulation of membraneless electron-dense structures; loss of FBXO24 led to an accumulation of these materials and defects in the sperm flagellum and fertilizing ability. Interestingly, the authors found that several of the best-known components of electron-dense ribonucleoprotein granules that are found in the intermitochondrial cement and chromatoid body were not disrupted in the Fbxo24 knockout, suggesting that the electron-dense material and these structures are not all the same, and the biology is more complicated than some might have thought. They found evidence for the most changes in IPO5 and KPNB1, and biochemical evidence that FBXO24 and IPO5 could interact.

We appreciate the summary comment of reviewer #2.

Strengths:

The authors are to be commended for the thoroughness of their experimental approaches and the extent to which they investigated impacts on sperm function and potential biochemical mechanisms. Very briefly, they start by showing that the Fbxo24 message is present in spermatids and that the protein can interact with SKP1, in a way that is dependent on its F-box domain. This points toward a potential function in protein degradation. To test this, they next made the knockout mouse, validated it, and found the males to be sterile, although capable of plugging a female. Looking at the sperm, they identified a number of ultrastructural and morphological abnormalities, which they looked at in high resolution using TEM. They also cross their model with RBGS mice so that they have reporters in both the acrosome and mitochondria. The authors test a variety of sperm functions, including motility parameters, ability to fertilize by IVF, cumulus-free IVF, zona-free-IVF, and ICSI. They found that ICSI could rescue the knockout but not other assisted reproductive technologies. Defects in male fertility likely resulted from motility disruption and failure to get through the utero-tubal junction but defects in acrosome exocytosis also were noted. The authors performed thorough investigations including both targeted and unbiased approaches such as mass spectrometry. These enabled them to show that although the loss of the FBXO24 protein led to more RNA and elevated levels of some proteins, it did not change others that were previously identified in the electron-dense RNP material.

The manuscript will be highly significant in the field because the exact functions of the electron-dense RNP materials have remained somewhat elusive for decades. Much progress has been made in the past 15 years but this work shows that the situation is more complex than previously recognized. The results show critical impacts of protein degradation in the differentiation process that enables sperm to change from non-descript round cells into highly polarized and compartmentalized mature sperm, with an equally highly compartmentalized flagellum. This manuscript also sets a high bar for the field in terms of how thorough it is, which reveals wide-ranging impacts on processes such as mitochondrial compaction and arrangement in the midpiece, the correct building of the major cytoskeletal elements in the flagellum, etc.

We appreciate the positive comment of reviewer #2.

Weaknesses:

There are no real weaknesses in the manuscript that result from anything in the control of the authors. They attempted to rescue the knockout by expressing a FLAG-tagged Fbxo24 transgene, but that did not rescue the phenotype, either because of inappropriate levels/timing/location of expression, or because of interference by the tag. They also could not make anti-FBXO24 that worked for coimmunoprecipitation experiments, so relied on the FLAG epitope, an approach that successfully showed co-IP with IPO5 and SKP1.

We could not rescue the phenotype with Fbxo24-FLAG transgene, but different Fbxo24 mutant mice show the same phenotypes (Figure S6G). Further, another group showed that Fbxo24 KO mice exhibited abnormal mitochondrial coiling [Li et al., 2024 (PMID: 38470475)], confirming that

FBXO24 is involved in the mitochondrial sheath formation.

Reviewer #3 (Public Review):

Summary:

In this manuscript, the authors found that FBXO24, a testis-enriched F-box protein, is indispensable for male fertility. Fbxo24 KO mice exhibited malformed sperm flagellar and compromised sperm motility.

We appreciate the summary comment of reviewer #3.

Strengths:

The phenotype of Fbxo24 KO spermatozoa was well analyzed.

We appreciate the positive comment of reviewer #3.

Weaknesses:

The authors observed numerous membraneless electron-dense granules in the Fbxo24 KO spermatozoa. They also showed abnormal accumulation of two importins, IPO5 and KPNB1, in the Fbxo24 KO spermatozoa. However, the data presented in the manuscript do not support the conclusion that FBXO24 ensures male fertility by preventing the abnormal accumulation of membraneless granules in sperm flagella, as indicated in the manuscript title.

Fbxo24 KO mice showed abnormal accumulation of membraneless granules in sperm flagella and male infertility, suggesting that FBXO24 is involved in these processes, but there are no results that show the direct relationship as reviewer #3 mentioned. Therefore, we fixed the title.

Recommendations For The Authors:

Reviewer #2 (Recommendations For The Authors):

On page 4, lines 152-154, the authors introduce the RBGS mouse model and use it in their experiments.

However, they left out an obvious but helpful sentence that tells the reader that they crossed the Fbxo24-null mouse with the RBGS. As one continues reading it is clear, but best to avoid even slight confusion.

We revised the explanation in the result section (line 150-153).

Reviewer #3 (Recommendations For The Authors):

In this manuscript, the authors found that FBXO24, a testis-enriched F-box protein, is indispensable for male fertility. Fbxo24 KO mice exhibited malformed sperm flagellar and compromised sperm motility. The phenotype of Fbxo24 KO spermatozoa was well analyzed.

The authors observed numerous membraneless electron-dense granules in the Fbxo24 KO spermatozoa. They also showed abnormal accumulation of two importins, IPO5 and KPNB1, in the Fbxo24 KO spermatozoa. However, the data presented in the manuscript do not support the conclusion that FBXO24 ensures male fertility by preventing the abnormal accumulation of membraneless granules in sperm flagella, as indicated in the manuscript title.

Fbxo24 KO mice showed abnormal accumulation of membraneless granules in sperm flagella and male infertility, suggesting that FBXO24 is involved in these processes, but there are no results that show the direct relationship as reviewer #3 mentioned. Therefore, we fixed the title.

Major comments:

In the title, abstract, introduction, and some sections such as lines 275-276, the authors conclude that FBXO24 prevents the accumulation of importins and RNP granules during spermiogenesis. However, the provided data do not substantiate this claim. To provide conclusive evidence to support the current title, the authors need to present evidence supporting: 1) direct degradation of IPO5 and KPNB1 by FBXO24; 2) the direct requirement of IPO5 for the formation of the membraneless granules, and 3) infertility resulting from the presence of membraneless granules, rather than other issues such as abnormal ODF and AX.

(1) direct degradation of IPO5 and KPNB1 by FBXO24.

To examine if IPO5 can be degraded by FBXO24, we performed a ubiquitination assay using HEK293T cells. Ubiquitination of IPO5 was upregulated in the presence of WT FBXO24 but not with the mutant ΔF-box FBXO24, suggesting that IPO5 can be ubiquitinated by FBXO24. We did not examine the ubiquitination of KPNB1 because we failed to construct a plasmid vector expressing mouse KPNB1. We think that KPNB1 is not the substrate because we did not detect the interaction between FBXO24 and KPNB1 (Figure 5E). We added the results of the ubiquitination assay (Figure

5F and line 261-265) and mentioned it in the abstract (line 35).

(2) the direct requirement of IPO5 for the formation of the membraneless granules.

(3) infertility resulting from the presence of membraneless granules, rather than other issues such as abnormal ODF and AX.

We revealed that IPO5 aggregate under stress condition in COS7 cells (Figure 6C and D); however, we did not examine whether IPO5 is required for the formation of the membraneless granules. We consider that protein degradation systems such as PROTAC or Trim-Away to knockdown IPO5 at the protein level in Fbxo24 KO mice could be a good way to see if the membraneless granules are diminished and male fertility is rescued. However, it takes time to apply the degradation systems in vivo. Therefore, we would like to leave this rescue experiment for future studies. We fixed the title and  abstract (line 37-38), and removed the last sentence of the introduction.

Also, the other group reported the analyses of Fbxo24 KO mice [Li et al., 2024 (PMID: 38470475)] right after we submitted our manuscript to the eLife. They reported not only disorganized flagellar structures but also abnormal head morphology, which may lead to male infertility. The differences from our study may be due to different mouse genetic backgrounds. We mentioned it in the discussion section (line 348-353).

Minor comments:

(1) The authors claimed a significant increase in the total amount of RNAs in Fbxo24 KO spermatozoa (lines 259-261), suggesting that the ...contain RNAs. More direct evidence supporting this claim should be provided.

We show that the amounts of IPO5 and KBNB1 increased in Fbxo24 KO spermatozoa (Figure 5A and B), both of which could be incorporated into RNP granules in COS7 cells (Figure 6C and D), supporting the idea that membraneless electron-dense structures may be RNP granules. However, because we did not show direct evidence that electron-dense structures contain RNAs, we removed the sentences (line 259-261 of the 1st submission manuscript). 

(2) The author should provide an explanation for the absence of a FLAG band in the input Tg in Figure 5D and the larger size of the IPO5 band in the FLAG-IP group compared to the input. Similar observations are also noted in Figure 5E.

The FLAG band is weak because the protein amount is low. When we increase the contrast, we can see the FLAG band. We added an image with high contrast (Figure 5D). Sometimes, proteins run differently with SDS-PAGE after immunoprecipitation, likely due to varying protein composition in the sample. We explained it in the figure legend (line 868-869).

(3) In Line 526, clarify the procedure for sperm purification, and determine the potential for contamination from somatic cells.

We did not perform sperm purification, but when we observed spermatozoa obtained from cauda epididymis, we rarely observed either somatic cells or immature spermatogenic cells. We added  pictures in Figure S7. Further, we added detailed explanation about how to collect spermatozoa from the epididymis (line 549-550).

(4) Define the Y-axis in Figure 2E, F, and G.

We have revised the figures.

Author response:

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

Reviewer #1 (Public Review):

Summary:

In this paper, the authors investigate the impact of fecal microbiota transfer (FMT) on intestinal recovery from enterotoxigenic E. coli infection following antibiotic treatment. Using a piglet model of intestinal infection, the authors demonstrate that FMT reduces weight loss and diarrhea and enhances the expression of tight junction proteins. Sequencing analysis of the intestinal microbiota following FMT showed significant increases in Akkermansia muciniphila and Bacteroides fragilis. Using additional mouse and organoid models, the authors examine the impact of these microbes on intestinal recovery and modulation of the Wnt signaling pathway. Overall, the data support the notion that FMT following ETEC infection is beneficial, however, additional investigation is required to fully elucidate the mechanisms involved.

Strengths:

Initial experiments used a piglet model of infection to test the value of FMT on recovery from E. coli. The FMT treatment was beneficial and the authors provide solid evidence that the treatment increased the diversity of the microbiota and enhanced the recovery of the intestinal epithelium. Sequencing data highlighted an increase in Akkermansia muciniphila and Bacteroides fragilis after FMT.

The mouse data are consistent with the observations in pigs, and reveal that daily gavage with A. muciniphila or B. fragilis enhances intestinal recovery based on histological analysis, expression of tight junction proteins, and analysis of intestinal barrier function.

The authors demonstrate the benefit of probiotic treatment following infection using a range of model systems.

Weaknesses:

Without sequencing the pre-infection pig microbiota or the FMT input material itself, it's challenging to firmly say that the observed bloom in Akkermansia muciniphila and Bacteroides fragilis stemmed from the FMT.

Response: We have determined the relative abundance of each bacterium in fecal bacterial suspension, referring to Hu et al. (2018). The absolute abundances of Akkermansia muciniphila and Bacteroides fragilis in the FMT were 1.3 × 103 ± 2.6 × 103 and 4.5 × 103 ± 6.1 × 103 respectively.

Reference:

Hu LS, Geng SJ, Li Y, et al. Exogenous Fecal Microbiota Transplantation from Local Adult Pigs to Crossbred Newborn Piglets. Front. Microbiol. 2018, 8.

The lack of details for the murine infection model, such as weight loss and quantification of bacterial loads over time, make it challenging for a reader to fully appreciate how treatment with Akkermansia muciniphila and Bacteroides fragilis is altering the course of infection. Bacterial loads of E. coli were only quantified at one time point, and the mice that received A. muciniphila and B. fragilis had very low levels of E. coli. Therefore, it is not clear if all mice were subjected to the same level of infection in the first place. The reduced translocation of E. coli to the organs and enhanced barrier function may just reflect the low level of infection in these mice. Further, the authors' conclusion that the effect is specific to A. muciniphila or B. fragilis would be more convincing if the experiments included an inert control bacterium, to demonstrate that gavage with any commensal microbe would not elicit a similar effect.

The weight loss was added in Figure S2A. All mice were subjected to the same level of infection in the first place.

Many of the conclusions in the study are drawn from the microscopy results. However, the methods describing both light microscopy and electron microscopy lack sufficient detail. For example, it is not clear how many sections and fields of view were imaged or how the SEM samples were prepared and dehydrated. The mucus layer does not appear to be well preserved, which would make it challenging to accurately measure the thickness of the mucus layer.

For light microscopy, 3-4 fields were selected from each mouse to count about 30 crypts. The method of electron microscopy was complemented on line 263-270. We have removed data of the mucus layer.

Gene expression data appears to vary across the different models, for example, Wnt3 expression in mice versus organoids. Additional experiments may be required to clarify the mechanisms involved. Considering that both of the bacteria tested elicited similar changes in Wnt signaling, this pathway might be broadly modulated by the microbiota.

The reason why the Wnt3 expression pattern is different in mice and in porcine intestinal organoids may be caused by the different infection periods of ETEC in vivo and in vitro. Furthermore, in vivo, the stem cell niche of intestinal stem cells is not only regulated by intestinal epithelial cells, but also affected by mesenchymal cells in connective tissues (Luo et al., 2022). However, in vitro models, stem cell niche is only regulated by epithelial secretory factors, which may also account for the differences in in vitro and in vivo results.

It has been reported that B. fragilis pretreatment significantly increased the relative abundance of A. muciniphila in the intestine of CDI mice, and the growth and maintenance of A. muciniphila were involved in the restoration of intestinal barrier integrity after CDI infection, indicating that there might exist a bacterial metabolic symbiosis between A. muciniphila and B. fragilis (Deng et al., 2018).

References:

Luo HM, Li MX, Wang F, et al. The role of intestinal stem cell within gut homeostasis: Focusing on its interplay with gut microbiota and the regulating pathways. Int. J. Biol. Sci. 2022, 18(13): 5185-5206.

Deng HM, Yang SQ, Zhang YC, et al. Bacteroides fragilis Prevents Clostridium difficile Infection in a Mouse Model by Restoring Gut Barrier and Microbiome Regulation. Front. Microbiol. 2018, 9.

The unconventional choice to not include references in the results section makes it challenging for the reader to put the results in context with what is known in the field. Similarly, there is a lack of discussion acknowledging that B. fragilis is a potential pathogen, associated with intestinal inflammation and cancer (Haghi et al. BMC Cancer 19, 879 (2019) ), and how this would impact its utility as a potential probiotic.

Bacteroides fragilis is one of the symbiotic anaerobes within the mammalian gut and is also an opportunistic pathogen which often isolated from clinical specimens. Bacteroides fragilis was first isolated from the pathogenic site and considered to be pathogenic bacteria. However, with the deepening of research, it is gradually realized that in the long-term evolution process, Bacteroides fragilis colonized in the gut has established a friendly relationship with the host, which is an essential component for maintaining the health of the host, especially for obesity, diabetes and immune deficiency diseases. We have supplemented the discussion on line 598-603.

Reviewer #2 (Public Review):

Ma X. et al proposed that A. muciniphila was a key strain that promotes the proliferation and differentiation of intestinal stem cells by acting on the Wnt/β-catenin signaling pathway. They used various models, such as the piglet model, mouse model, and intestinal organoids to address how A. muciniphila and B. fragilis offer protection against ETEC infection. They showed that FMT with fecal samples, A. muciniphila or B. fragilis protected piglets and/or mice from ETEC infection, and this protection is manifested as reduced intestinal inflammation/bacterial colonization, increased tight junction/Muc2 proteins, as well as proper Treg/Th17 cells. Additionally, they demonstrated that A. muciniphila protected basal-out and/or apical-out intestinal organoids against ETEC infection via Wnt signaling. While a large body of work has been performed in this study, there are quite a few questions to be addressed.

Major comments:

- The similar protective effect of FMT with fecal samples, A. muciniphila or B. fragilis is perhaps not that surprising, considering that FMT likely restores microbiota-mediated colonization resistance against ETEC infection. While FMT with fecal samples increases SCFAs, it is unclear whether/how FMT with A. muciniphila or B. fragilis alter the microbiota composition/abundance as well as metabolites in the current models in a way that offers protection.

We examined changes in the gut microbiota of mice treated with A. muciniphila and B. fragilis through 16s rRNA, and results showed that both A. muciniphila and B. fragilis improved the alpha and beta diversities of the microbiota, while these results were not included in this manuscript.

- Does ETEC infection in piglets/mice cause histological damage in the intestines? These data should be shown.

The results of scanning electron microscopy (Figure 3A) showed the intestinal damage of piglets after ETEC infection. H&E staining and transmission electron microscopy (Figure 5A and 5B) showed the intestinal damage of mice after ETEC infection.

- Line 447, "ETEC adheres to intestinal epithelial cells". However, there is no data showing the adherence (or invasion) of ETEC to intestinal epithelial cells, irrespective of piglets/mouse/organoids.

The scanning electron microscope (Figure 3A bottom) showed that ETEC K88 infected piglets existed obvious rod-shaped bacterial adhesion on the surface of microvilli. Figure 2C showed the colonization of ETEC K88 in the jejunum and colon of piglets. Figure S2A showed the E. coli colonization in intestines and other tissues of mice.

- In both basal-out and apical-out intestinal organoid models, A. muciniphila protects organoids against ETEC infection. Did ETEC enter into intestinal epithelial cells at all after only one hour of infection? Is the protection through certain A. muciniphila metabolites?

It has been reported that the duration of the co-culture for studying the host-microbiota cross-talk by apical-out organoids model is 1 hour (Poletti et al., 2021). In addition, Co et al. (2019) used apical-out organoids model to study host-pathogen interactions, with Salmonella enterica serovar Typhimurium or Listeria monocytogenes invading organoids for an hour.

References:

Poletti M, Arnauts K, Ferrante M, et al. Organoid-based Models to Study the Role of Host-microbiota Interactions in IBD. J. Crohns Colitis. 2021, 15(7): 1222-1235.

Co JY, Margalef-Catala M, Li XN, et al. Controlling Epithelial Polarity: A Human Enteroid Model for Host-Pathogen Interactions. Cell Reports. 2019, 26(9): 2509-2520.

Reviewer #3 (Public Review):

Summary:

The manuscript by Ma et al. describes a multi-model (pig, mouse, organoid) investigation into how fecal transplants protect against E. coli infection. The authors identify A. muciniphila and B. fragilis as two important strains and characterize how these organisms impact the epithelium by modulating host signaling pathways, namely the Wnt pathway in lgr5 intestinal stem cells.

Strengths:

The strengths of this manuscript include the use of multiple model systems and follow-up mechanistic investigations to understand how A. muciniphila and B. fragilis interacted with the host to impact epithelial physiology.

Weaknesses:

The major weakness is that, as presented, the manuscript is quite difficult to follow, even for someone familiar with the field. The lack of detail in figure legends, organization of the text, and frequent use of non-intuitive abbreviated group names without a clear key (ex. EP/EF, or C E A B) make comprehension challenging. The results section is perhaps too succinct and does not provide sufficient information to understand experimental design and interpretation without reading the methods section first or skipping to the discussion (as an example: WNT-c59 treatment). Extensive revisions could be encouraged to aid in communicating the potentially exciting findings.

The abbreviations of experimental groups are firstly defined in the Methods and Materials, and we have supplemented the experimental design in the results section on line 397-399, 439-442 and 516-520.

The bioinformatics section of the methods requires revision and may indicate issues in the pipeline. Merging the forward and reverse reads may represent a problem for denoising. Also since these were sequenced on a NovaSeq, the error learning would have to be modified or the diversity estimates would be inappropriately multiplied. "Alpha diversity and beta diversity were calculated by normalized to the same sequence randomly." Not sure what this means, does this mean subsampled? "Blast was used for sequence alignment", does this mean the taxonomic alignment? This would need to be elaborated on and database versions should be included. The methods, including if any form of multiple testing was included, for LEFSE was also not included.

Denoising was conducted using UNOISE3 to correct for sequencing errors. Subsequent analysis of alpha diversity and beta diversity were all performed based on the output normalized data. Multiple sequence alignment was performed using MUSCLE (v3.8.31) software to obtain the phylogenetic relationships of all OTUs sequences. We have supplemented the method of multiple testing on line 323-328.

Reviewer #1 (Recommendations For The Authors):

At some points, the rationale for using both porcine and murine models was unclear, and it would be helpful for the reader to elaborate on the benefits of these models and why they were used in the introduction. Similarly, it would be helpful to describe the benefits of basal-in organoids versus injecting standard organoids with bacteria.

The main subject of this study was piglets, supplemented by a mouse model for validation. Interpretation of measurements from organoid microinjection experiments must account for multiple confounding variables such as heterogeneous exposure concentrations and durations, as well as impacts of disrupting the organoid wall. We have added the description in the introduction on line 88-90.

Line 165 -- The number of piglets used seems high, is it correct approximately 100 pigs were used?

Nine litters were selected for processing, while only 18 piglets were finally slaughtered.

There is very little discussion of the preliminary experiment that the authors used to determine how much bacteria to use. I recommend either discussing the data and how the doses were chosen or omitting it. It was not clear if the authors used pasteurized or live bacteria in the experiments. It would also be interesting to include a discussion of the observation that relatively low levels of Akkermansia (10^6 CFU) appeared more beneficial than the higher doses, typically used in these types of experiments.

We removed these results. The experiments used live bacteria.

Microscopy methods for both light microscopy and EM would be stronger with added details including how many sections and fields of view were imaged and how the numbers of goblet cells normalized across samples. Without having a clear cross-section of a crypt, it is not clear to me how the images can be used to accurately quantify the number of cells per crypt. Additional details in the methods on how many total crypts were counted should also be included.

For light microscopy, 3-4 fields were selected from each mouse to count about 30 crypts. We have removed the data of the mucus layer and goblet cells.

Line 236 -- missing which gene was used.

The Genbank Accession was added on line 232-233.

Line 310 -- OTU nomenclature.

We have supplemented the OTU nomenclature on line 314.

Line 413 -- This line seems inconsistent with the data analysis described in the methods section. The authors may need to expand their description of the 16S data analysis to be clear and reproducible.

We have redescribed the 16S data analysis on line 312-328.

Line 413 -- it is not surprising that 16s analysis did not capture species, it will have limited resolution beyond the genus level.

We deleted this sentence.

Methods are missing some details on the data analysis, eg. methods/programs and statistical analysis of PCoA and NMDS, LefSe.

The methods and statistical analysis of PCoA, NMDS and LEfSe were supplemented on line 323-328.

Fig 4C -- The images do not clearly capture the mucus layer or how it was analyzed. The sections appear to be cut at a slight angle, with multiple partial sections of crypts. I think this might make it challenging to count goblet cells, especially if the counts are normalized over the number of crypts or villi. The mucus layer does not appear well preserved. For example, I would expect to see an intact mucus layer lining the colon in the PBS control group. Re-cutting sections with a clean cross-section through the tissue will make data analysis easier.

We have removed data of the mucus layer.

Fig 4D -- The images appear to be of the mouse proximal colon, whereas the mucus layer and most muc2 will be in the distal colon. If the authors have tissue sections of the distal colon, this may give a clearer image of the mucus layer and might be more consistent with the TEM images in Fig. 4B.

We apologize for the absence of the distal colon sections.

To fully preserve the mucus layer, in addition to fixing in Carnoy's solution, the embedding process must be run without the standard washes in 70% ethanol (see: Johansson and Hansson. Methods Mol Biol. (2012) 229; doi: 10.1007/978-1-61779-513-8_13). The mucus will wash away during standard paraffin embedding if the tissue is washed with 70% ethanol, and I wonder if that has occurred in these samples.

The tissue wasn’t washed with 70% ethanol.

Fig 6A and 6B -- Although the legend indicates that the data is representative of two independent experiments, it is not clear how many fields of view or cells were imaged. In the bar graphs, it is not clear how many crypts were analyzed and from how many fields of view.

3-4 fields were selected from each mouse to count about 30 crypts.

**For all of the bar graphs, this could be addressed by displaying all of the data points, rather than just the mean, to give the reader a sense of how many cells were counted. (as was done in Fig 7B).

We have changed the bar graphs with data points.

498-501 -- The text says that the gene expression patterns in the organoids are consistent with the in vivo data, but the data patterns of gene expression appear to be different. For example, patterns for Wnt3 and B-catenin expression in mice, appear to be the opposite of what was observed in the organoid?

Lines 509-512 mean that the expression patterns of mice in organoids and in vivo is consistent. Figure 7C was incorrectly written as Figure 8C, we have changed it.

Since Akkermansia does not grow under aerobic conditions, it should be made clear that the organoid co-culture treatment does not involve actively growing bacterial cultures.

Reunanen et al. found that Akkermansia can tolerate oxygen, more than 90% Akkermansia can keep for 1 h under oxic, 5% CO2 conditions.

Reference:

Reunanen J, Kainulainen V, Huuskonen L, et al. Akkermansia muciniphila Adheres to Enterocytes and Strengthens the Integrity of the Epithelial Cell Layer. Appl. Environ. Microbiol. 2015, 81(11): 3655-3662.

Minor points

Line 50 -"evidence".

We have changed to “evidence” on line 49.

Line 64, 422 - italicize, check italics throughout.

We have checked italics throughout the manuscript.

Line 64 - may need to be reworded.

We have changed to “Clostridioides difficile” on line 66.

Line 77 - pathogen.

We have changed to “pathogen” on line 77.

Line 161 - the.

We have removed “the” on line 161.

Line 178 - mouse.

We have changed to “mouse” on line 179.

Line 313 -- wording is confusing.

We have changed the description on line 319-320.

Line 318 -- Silva version #.

The version is Silva 132. We have added it on line 316.

Line 334 - Manufacturer for Live/Dead cell stain?

The Live/Dead cell stain was used BD Biosciences FVS510. We have added it on line 345.

Line 433 -- FD4 not defined until here.

We have refined the FD4 on line 218-219.

Line 512 -- but did not promote.

We have changed to “but did not promote” on line 526.

Line 517 -- Looks like this should be "basal-in organoids" instead of basal-out?

We have changed the "basal-out" to "apical-to" on line 531.

Line 546 -- induced neonatal should be protected?

They are in separate pens.

Jumps from Fig 7B to Fig 8C in the text.

We apologize for the wrong writing, and we have change it.

Reviewer #2 (Recommendations for The Authors):

The title itself is a bit misleading. Please consider changing it. The authors meant that A. muciniphila prevents pathogen invasion, but does not function in pathogen invasion.

We have changed the title.

Major comments:

- Figures 4A, 4D, and 6B should include presentation of cross-section pictures.

We provided cross-section pictures to the journal.

- Figures 7, 8, and 9 should indicate clearly whether mouse or piglet organoids are used. For instance, in the main text, line 490, it indicates piglet organoids, but in Figure 7A legend, it indicates mouse tissue.

We apologize for the misspelling, and have changed to “mice” on line 501-502.

- In Figure 7A, the 3rd row, 2nd panel, crypts formed into spherical organoids; whereas in Figure 8, ETEC infection of basal-out organoids formed budding organoids. This needs to be better explained.

Mouse intestinal organoids were cultured ex vivo from crypts isolated from mice infected with ETEC, while porcine intestinal organoids were co-cultured with ETEC in vitro.

Minor comments:

- In the result section, the numbering of Figures or supplementary Figures is problematic, i.e it should start with Figure 1..., Figure S1, but not directly go to Figure S2A etc.

The Figure 1 was in Materials and Methods.

- Line 458, please add the gating strategy used in the flow cytometry study.

The gating strategy was added on line 351-356.

- The effect of A. muciniphila on the proliferation of intestinal epithelium through the Wnt/β-catenin signaling pathway is well known (such as PMID: 32138776). The authors should discuss this in detail.

We have supplemented the discussion on line 637-639.

Reviewer #3 (Recommendations For The Authors):

It is somewhat unusual that the results from the piglets are in the supplement as this is a major strength of the manuscript (Fig S2).

We have put these results into Figure 2 of the manuscript.

"Collectively, our results may provide theoretical basis that FMT is a promising mitigation method for pathogenic bacteria infection and a new strategy for precise application of FMT in clinical and livestock production"- This is somewhat of an odd statement as the introduction of the manuscript completely skips over most of what is known about FMTs in the context of C. difficile. Also if anything, does the authors' own data not point mostly at using A. muciniphila on its own? Clinical trials are well underway in humans.

We have changed the sentences to “Collectively, our results may provide theoretical basis that A. muciniphila is a promising method to repair intestinal barrier damage and a new strategy for the precise application of A. muciniphila in livestock production.” on line 98-100.

Line 26: I am not sure probiotic is the right word here given its strict scientific definition. Perhaps beneficial or protective would be more appropriate.

We have changed “probiotic” to “beneficial” on line 25.

Line 27: I believe AIMD is antibiotic-induced microbiome-depletion in most usages which may be more accurate and informative than dysregulated.

The type, dosing, and time of antibiotic we used were applied to induce microbiota disorder.

It would appear that there are issues in the reference formatting where a number of journal names are missing.

We have re-edited the reference formatting.

Line 64- I believe eLife requires the standard practice of italicizing genus and species names. Also Clostridium difficile should now be referred to as Clostridioides difficile.

We have changed to “Clostridioides difficile” and italicized it on line 66 and 569. The italicizing genus and species names were checked throughout the manuscript.

Figure S2C: is it not clear why the melt curve was included here, but the legend should make it more clear what is being shown. I assume this is to provide evidence of specificity?

The melting curve was used to demonstrate that only the ETEC K88 could be amplified by the primers we used. We have added an illustration in the figure legend.

Figure 2D: there should be a quantitative analysis done on the staining of Muc2.

We have quantified the staining of MUC2 in Figure 3D.

Figure 3: The legends are not sufficient. For example: it is not clear what Figure 3A actually shows as the y-axis is not labelled and it is not clear what the relationship is between this and the anosim which is a function for permanova.

Anosim analysis was performed using the R software with anosim package function based on the rank order of Bray-Curtis distance values to test the significance of differences between groups. The y-axis is the rank of the distance between samples.

Line 416- OTU not OUT.

We have changed to “OTU” on line 428.

Figure 4- the naming key needs to be included in the figure legend. C, E, A, and B are immediately obvious.

The naming key was included in the figure legend.

Methods: additional information on the flow cytometry gating strategy/controls should be included.

The gating strategy was added on line 351-356.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

The manuscript addresses a fundamental question about how different types of communication signals differentially affect brain state and neurochemistry. In addition, their manuscript  highlights the various processes that modulate brain responses to communication signals, including prior experience, sex, and hormonal status. Overall, the manuscript is well-written and the research is appropriately contextualized.

That being said, it remains important for the authors to think more about their analytical approaches. In particular, the effect of normalization and the explicit outlining and interpretations of statistical models. As mentioned in the original review, the normalization of neurochemical data seems unnecessary given the repeated-measures design of their analysis and by normalizing all data to the baseline data and including this baseline data in the repeated measures analysis,   one artificially creates a baseline period with minimal variation that dramatically differs in variance from other periods (akin to heteroscedasticity). If the authors want to analyze how a stimulus changes neurochemical concentrations, they could analyze the raw data but depict normalized data in their figures (similar to other papers). Or they could analyze group differences in the normalized data of the two stimulus periods (i.e., excluding the baseline period used for normalization).

We appreciate the reviewer’s point on the difference in variance caused by including the 100% baseline values in the analysis. After consulting with our statistician, we chose the latter of the two approaches suggested by the reviewer. Specifically, we reran the analysis to exclude the baseline and focus only on the playback windows and the group differences. The text in the results, the significance signs in the figures, and the discussion are corrected accordingly. Despite these changes, our major conclusions remains as before.

We also followed this reviewer’s suggestions to clarify the statistical model in studying the experience effect. After further consultation with our statistician, we reran the analysis on experience effect, including all the groups of EXP and INEXP animals together. We have corrected text in the figure captions, results, discussion, and data analysis sections of the manuscript related to the effect of experience and its interactions. This has not changed the conclusion made related to the experience effect in the dataset.

It would also be useful for the authors to provide further discussion of the potential contributions of different types of experiences (mating vs. restraint) to the change in behavior and neurochemical responses to the vocalization playbacks and to try to disentangle sensory and  motor contributions to neurochemical changes.

We have acknowledged in the Discussion that previous studies suggest that the effect of experience involving stress could be generalized. We believe that this is an important area of future research. Our Discussion acknowledges that the relationship between sensory and motor contributions to neurochemical changes remains an area of interest. We further point out that the time resolution of microdialysis data renders the suggested discussion highly speculative. We plan to use other methods to assess this in future experiments.

Reviewer #3 (Public Review):

The work by Ghasemahmad et al. has the potential to significantly advance our understanding of how neuromodulators provide internal-state signals to the basolateral amygdala (BLA) while an animal listens to social vocalizations.

Ghasemahmad et al. made changes to the manuscript that have significantly improved the work. In particular, the transparency in showing the underlying levels of Ach, DA, and 5HIAA is excellent. My previous concerns have been adequately addressed.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

I appreciate the authors responses to my previous queries (and to the comments by other reviewers). The introduction does a better job contextualizing the data, and the additional details in the results and Methods sections help readers digest the material. I continue to think the topic  is interesting and the manuscript is potentially impactful. However, I continue to be concerned about their analytical approaches and other aspects of the revised manuscript.

(a) Normalization

In my original review I wrote: "The normalization of neurochemical data seems unnecessary   given the repeated-measures design of their analysis and could be problematic; by normalizing     all data to the baseline data (p. 24), one artificially creates a baseline period with minimal   variation (all are "0"; Figures 2, 3 & 5) that could inflate statistical power." I continue to feel that an analysis of normalized data that includes the baseline data is inappropriate because of the minimal variation in the normalized data for the baseline period. When the normalized data for   the baseline period is included in the analysis, there is clearly variation in the extent of variability within each of the time periods (no variability at baseline, variability during periods 1 & 2; analogous to heteroscedasticity). For example, when analyzing the RAW DATA about the change in ACh release in experienced males listening to restraint vocalizations (thank you for releasing the raw data), there was a non-significant effect of time (baseline, period 1, and period 2; linear mixed effects model; F(2,12)=3.2, p=0.0793). However, when the normalized data for  this dataset was analyzed (with baseline values being set at 100% for each mouse), there was a statistically significant effect (F(2,12)=4.5, p=0.0352). This example is just to illustrate how normalization can affect (e.g., inflate) statistical power.

That being said, I do think that it is reasonable to analyzed normalized data if the period used for normalization is NOT included in the analysis (see Figure 3 of one of the paper the authors listed in their response to reviewers: Galvez-Marquez et al., 2022). However, from the reading of this manuscript, it does seem like normalized baseline data are analyzed to assess how stimuli affect neurochemical concentrations.

We appreciate the reviewer’s point on the difference in variance caused by including the 100% baseline values in the analysis. After consulting with our statistician, we chose one of the two approaches suggested by the reviewer. Specifically, we reran the analysis to exclude the baseline and focus only on the playback windows and the group differences. The text in the results, the significance signs in the figures, and the discussion are corrected accordingly. Despite these changes, our major conclusions remains as before. We have included some descriptive statistics in the text because we think these are informative.

We decided to take this approach because the inter-individual variability in the raw data levels, caused by non-experimental factors, is too great to be useful. As we have stated before, these values are affected by probe placement, collection process, or differences in the HPLC or LC/MS runs. These effects are widely recognized in the field.

It is worth pointing out a few things about the papers listed by the authors. Li et al. (2023) does depict normalized microanalysis data but it isn't clear that any analysis of the normalized data is conducted. The same can be said about Holly et al. (2016). Further, in Bagley et al (2011), the authors depict normalized data in the figures but conduct analyses on the raw data ("After  chronic morphine treatment, systemic naloxone injection increased GABA outflow in PAG by 41% (from 24.6 {plus minus} 2.9 nM to a peak of 34.8 {plus minus} 3.8 nM, n = 6, P = 0.016), but did not alter GABA levels after vehicle treatment (39.8 {plus minus} 8.3 to 38.6 {plus  minus} 7.4 nM with naloxone at matched peak time, n = 4; Fig. 3a)". This latter approach (analyzing raw data in a repeated-measures manner and depicted normalized data) seems reasonable for the authors of the current study.

(b) Clarification and modification of statistical models

When analyzing the effect of experience on neuromodulator release, the authors analyze the experienced and inexperienced mice independently (e.g., figure 3 vs. 6). The ideal way to assess the effects of experience is to create a factorial model. For example, one could analyze a full factorial model with experience (exp vs. inexp), stimulus time (mating vs. restraint) and time  (baseline, period 1 vs period 2, assuming raw data are used). If one wanted to exclude the  baseline period because group differences in baseline are not informative, conducting a factorial analysis of normalized data with just the data from period 1 and 2 seems fine. I believe an analysis like this will help increase the legitimacy of the analysis. For example, when analyzing the normalized data (periods 1 and 2) of experienced and inexperienced males in response to mating or restraint vocalizations, you find a significant interaction between experience and stimulus type. Finding an effect of experience in an analysis that includes both experienced and inexperienced mice is ideal from an analytical framework.

In Figure 6, it is not clear what the statistical model is and what the interactions mean. For example, in the figure legend for figure 6, the authors report time*context and time*sex interactions. However, in this analysis there are two groups of inexperienced males (males that   are listening to restraint vocalizations, males that are listening to mating vocalizations) and one group of females (females that are listening to mating vocalizations); in other words, this is an unbalanced analysis. So, when the authors indicate a time*context interaction, does that mean  they are comparing the male-restraint group to the combination of males and females listening to mating vocalizations? And when they talk about a time*sex interaction, are they analyzing how males listening to either mating or restraint vocalizations differ from females listening to a   mating vocalization? This all seems peculiar to me.

- A similar set of questions could be raised about interaction effects depicted in Figure 4.

Overall, I would like this manuscript to be reviewed by a statistician to provide additional input on how best to analyze the data.

We followed the reviewer’s suggestions to clarify the statistical model in studying the experience effect. After further consultation with the statistician, we reran the analysis on experience effect, including all the groups of EXP and INEXP animals together.

Design: Intercept + Sex +Context + Experience+ Sex* Experience + Context* Experience.

The model is not full factorial as recommended by the statistician, because we don’t have females in the restraint group and that would make an unbalanced design. Therefore, running GLM based on the above model and included factors, as advised by the statistician, is the best way of approaching the analysis for the current dataset.

We have corrected text in the figure captions, results, discussion, and data analysis sections of the manuscript related to the effect of experience and its interactions. The GLM models are clarified for all the figures in the “data analysis” section of the manuscript. We have clarified that the major effect of experience on neuromodulators was seen in the ACh data.

(c) Analysis of post-stimulus period

I agree with Reviewer 3 that analyzing the post-stimulus period would be useful. As mentioned     in the original review, these data could serve as an opportunity to show that the neurochemical levels returned to baseline and add further support for the model described in Figure 6. In   addition, these data could help reveal the link  between  neurochemical  release,  auditory responses, and behavior. If neurochemical changes reflect auditory responses, then these should back to baseline during the post-stimulus period. In addition, if behavioral variation (e.g.,    between mice hearing mating vs. restraint stimuli) persists following the termination of playback, then one could similarly assess whether neurochemical variation persists following playback. If   the latter is the case, then the neurochemical release could be more related to the behavior than to the playback stimulus itself.

We did not change this analysis. Our response to Reviewer 3’s comment is shown below.

“We decided not to include analyses of the post-stimulus period because this period is subject to wider individual and neuromodulator-specific effects and because it weakens statistical power in addressing the core question—the change in neuromodulator release DURING vocal playback. We agree that the general question is of interest to the field, but we don’t think our study is best designed to answer that question.”

This was accepted by Reviewer 3. We also note that release patterns have multiple time courses (e.g., Aitta-aho et al., 2018 for ACh), and thus may not support an assumption that levels should return to baseline shortly after playback offset.

Minor comments:

Page 7, line 15: I suggest changing "vocalization-dependent" to "stimulus-dependent" because the former could connote patterns of release related to the animal itself vocalizing.

Changed to: “There were also distinct patterns of ACh and DA release into the BLA depending on the type of vocalization playback (Fig 3C,D).”

Discussion section: The authors should point out a few caveats with their experiments in the Discussion section. First, experienced animals received both mating (social) and restraint experiences, and it is not clear to what degree each type of experience affected neural and behavioral responses (i.e., specificity of experience effects). For example, mating experience can lead to a wide range of physiological changes, including a resilience to stress (e.g., Leuner et al., PLoS One, 2010; Arnold et al., Hormones and Behavior, 2019), so it is possible that mating experiences by themselves could have induced these changes. Or it could be that experiencing restraint stress affects responses to mating stimuli. This could be added to the first paragraph in page 16. (The authors could also discuss which aspects of the sexual encounters might be most important for the behavioral and neural plasticity.)

We have added text to raise this issue, stating that it is unknown wither the experience effects are specific and citing the above references concerning the generalized effects of certain experiences.

Discussion section: It would also be useful for the authors to discuss the extent to which behavior might be driving the neurochemical changes. Some of the analyses suggest that the release is independent of the behavior (e.g., reflects a sensory responses) but this could be emphasized    more in the Discussion.

We believe that we have addressed this issue sufficiently in our previous response to related issues raised by this reviewer. As we note, there are limitations in the time resolution of microdialysis data that render the suggested discussion highly speculative. We plan to use other methods to assess this in future experiments.

Figure 2, legend: Please note that the text above the images describes the stimulus played back to these animals and their hormonal state, and not the type of experienced they underwent (i.e.,  clarify the titles)

Changed as requested.

I also agree with Reviewer 3 that "mating experience" is a misnomer for this manuscript. "Social experience with a female" is a more accurate descriptor. If they wanted to specifically provide mating experience, males should have only been tested with estrus (receptive females). I don't think this wording change detracts from their findings.

We have not changed this term. As noted in our previous response to Reviewer #3, we stated: “In the mating experience, mounting or attempted mounting was required for the animal to be included in subsequent testing.” Due to this requirement, the term “mating behavior” is informative and appropriate. In our view, “Social experience with a female” does not adequately describe our inclusion criterion or the experience.

Reviewer #3 (Recommendations For The Authors):

The work by Ghasemahmad et al. has the potential to significantly advance our understanding of how neuromodulators provide internal-state signals to the basolateral amygdala (BLA) while an animal listens to social vocalizations.

Ghasemahmad et al. made changes to the manuscript that have significantly improved the work. In particular, the transparency in showing the underlying levels of Ach, DA, and 5HIAA is excellent. My previous concerns have been adequately addressed. I only have a few minor suggestions for the text and one figure.

Minor suggestions:

Page 2, Ln 9: add adult before male and female mice

Changed as requested

Page 4, Ln 10: add a period after Tsukano et al., 2019)

Changed as requested

Page 6, Ln 9: what did you mean by "their interaction"? Being more specific, but concise, would help the readers.

We revised the wording to clarify that the neuromodulatory systems interact in the emission of positive and negative vocalizations.

Page 6, Ln 17: You mention Stim 1 and Stim 2, but the stimuli are not defined at this point. The clear explanation is provided in the following paragraph. Maybe consider switching the order  and define the stimuli before you describe the liquid chromatography/mass spectrometry technique.

We have revised and merged these paragraphs so that Stim 1 and Stim 2 are defined on first use. We also revised our description of the depiction and analysis of neurochemical data.

Page 11, Ln 12: replace well-proven with well-documented

Changed as requested

Figure 2: There are two arrows pointing towards a single track. I assume one of the arrows is a duplicate. If so, delete one of the arrows. If not, please explain what the second arrow represents.

Arrow removed

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The authors show that upon treatment with Doxorubicin (Doxo), there is an increase in senescence and inflammatory markers in the muscles. They also show these genes get upregulated in C2C12 myoblasts when treated with conditioned media or 15d-PGJ2. 15dPGJ2 induces cell death in the myoblasts, decreases proliferation (measured by cell numbers), and decreases differentiation and fusion. 15d-PGJ2 modified Cys184 of HRas, which is required for its activation as indicated by the FRET analysis with RAF RBD. They also showed that 15d-PGJ2 activates ERK signaling, but not Akt signaling, through the electrophilic center. 15d-PGJ2 inhibits Golgi localization of HRAS (only WT, not C181 or C184 mutant). They also showed that expressing the WT HRas followed by 15d-PGJ2 treatment led to a decrease in the levels of MHC mRNA and protein, and this defect is dependent on C184. This is a well-written manuscript with interesting insights into the mechanism of action of 15d-PGJ2. However, some clarification and experiments will help the paper advance the field significantly.

Strengths:

The data clearly shows that 15d-PGJ2 has a negative role in the myoblast cells and that it leads to modification of HRas protein. Moreover, the induction of biosynthetic enzymes in the PGD2 pathway also supports the induction of 15d-PGJ2 in Doxorubicin-treated cells. Both conditioned media experiments and the 15d-PGJ2 experiments show that 15d-PGJ2 could be the active component secreted by the senescent myoblasts.

Weaknesses:

The genes that are upregulated in the muscles upon injection with Doxo are also markers for inflammation. Since Doxo is also known to induce systemic inflammation, it is important to delineate these two effects (Inflammatory cells vs senescent cells). The expression of beta Gal and other markers of senescence in the tissue sections will help to delineate these.

As pointed out Doxo induces systemic inflammation along with inducing DNA damage-mediated senescence. Therefore, along with the inflammatory markers of the SASP (CXCL1/2, TNF1α, IL6, PTGS1/2, PTGDS) we also observed an increase in the mRNA levels of canonical markers of DNA damage-mediated senescence. We observed an increase in the mRNA levels of cell cycle and senescence associated proteins p16 and p21 (Fig. 1C). We also observed an increased nuclear accumulation of p21 (Fig. 1A) and increased levels of phosphorylated H2A.X in the nucleus (Fig. 1B).

In Figure 2, where the defect in the differentiation of myoblasts upon treatment with 15d-PGJ2 is shown, most of the cells die within 48 hours at higher concentrations, making it difficult to perform the experiments. This also shows that 15d-PGJ2 was toxic to these cells. Lower concentrations show a decrease in the differentiation based on the lower number of nuclei in fibers and low expression of MyoD, MyoG, and MHC. However, it is unclear if this is due to increased cell death or defective differentiation. It would be a lot more informative if the cell count, cell division, and cell death could be plotted for these concentrations of the drug during the experiment.

We measured the viability of C2C12 cells after 24 hours of treatment with 15d-PGJ2 using the MTT assay and observed that the viability of cells was decreased after treatment with 15d-PGJ2 (10 µM) but not with 15d-PGJ2 (1 µM, 2 µM, 4 µM, or 5 µM) (see Fig. S2A of the updated manuscript). The results and figures of the manuscript have been updated accordingly.

Also, in the myoblast experiments, are the effects of treatment with Dox reversible?

The treatment with Doxorubicin is irreversible as the senescent phenotype was not reversed after withdrawal of Doxorubicin, even after 20 days.

In Figure 3, most of the experiments are done at a high concentration, which induces almost complete cell death within 48 hours.

Figure 3 is an acute experiment for only 1 hour, at which time no cell death was observed. Specifically, we measured the phosphorylation of Erk and Akt proteins after 1 hour of treatment with 15d-PGJ2 (10 µM) during which we did not observe any cell death.

Even at such a high concentration of 15dPGJ2, the increase in ERK phosphorylation is minimal.

We observe a ~30% increase in the phosphorylation of Erk proteins after treatment with 15d-PGJ­2 in 0.2% serum medium compared to treatment with vehicle (DMSO). This is reproducible and significant.

The experiment Figure 4C shows that C181 and C84 mutants of the HRas show higher levels in Golgi compared with WT. However, this could very well be due to the defect in palmitoylation rather than the modification with 15d-PGJ2.

Our data does not suggest higher levels of C184S mutant in the Golgi compared with WT (Fig. S4A). We observed that the ratio of HRas levels in the Golgi to the HRas levels in the plasma membrane were similar in C2C12 cells expressing HRas C184S and HRas WT (Fig. S4A graph columns 1 and 5).

Though the authors allude to the possibility that intracellular redistribution of HRas by 15d-PGJ2 requires C181 palmitoylation, the direct influence of C184 modification on C181 palmitoylation is not shown. To have a meaningful conclusion, the authors need to compare the palmitoylation and modification with 15d-PGJ2.

Palmitoylation of HRas C181S is required for the localization of HRas at the plasma membrane. The inhibition of palmitoylation of C181, either by mutation (C181S) or treatment with protein palmitoyl transferase inhibitor (2-Bromopalmitate), results in the accumulation of HRas at Golgi(Rocks et al., 2005) (Fig. S4A). Modification of HRas at C184 by 15d-PGJ2 (Fig. 3A) could inhibit the palmitoylation of HRas at C181. However, our data does not support this hypothesis as modification of HRas WT by 15d-PGJ2 does not increase the level of HRas at the Golgi, like in the case of inhibition of cysteine palmitoylation due to C181S mutation.

To test if the inhibition of myoblast differentiation depends on HRas, they overexpressed the HRas and mutants in the C2C12 lines. However, this experiment does not take the endogenous HRAs into consideration, especially when interpreting the C184 mutant. An appropriate experiment to test this would be to knock down or knock out HRas (or make knock-in mutations of C184) and show that the effect of 15d-PGJ2 disappears.

Endogenous HRas (wild type) is present in the C2C12 cells overexpressing the EGFP-tagged HRas constructs. Therefore, we only observe a partial rescue in the differentiation after 15d-PGJ2 treatment in C2C12 cells expressing the C184S mutant (Fig. 4D and E). However, since HRas is expressed under high expression CMV promoter and in the absence of other regulatory elements, the overexpressed constructs do show a dominant effect over the endogenous HRas, showing cysteine mutant dependent inhibition of differentiation of myoblasts after treatment with 15d-PGJ2 (Fig. 4D and E).

Moreover, in this specific experiment, it is difficult to interpret without a control with no HRas construct and another without the 15d-PGJ2 treatment.

The mRNA levels of MyoD, MyoG, and MHC in C2C12 cells expressing HRas constructs after treatment with 15d-PGJ2 were normalized to the mRNA levels in C2C12 cells expressing corresponding constructs and were treated with vehicle (DMSO). mRNA levels in C2C12 cells treated with vehicle were not shown as they were normalized to 1. MHC protein levels in C2C12 cells expressing HRas constructs after 15d-PGJ2 treatment were normalized to that in C2C12 cells treated with vehicle (DMSO). Since the hypothesis to study the effect of HRas cysteine mutations on the differentiation of myoblasts after treatment with 15d-PGJ2, C2C12 cells expressing HRas WT serve as adequate control. Fig. 2 shows the effect of 15d-PGJ2 on muscle differentiation when HRas was not overexpressed.

Moreover, the overall study does not delineate the toxic effects of 15d-PGJ2 from its effect on the differentiation.

The inhibition of differentiation in C212 cells after treatment with 15d-PGJ2 cannot be attributed to the general toxicity of 15d-PGJ2 in cells. We show that the inhibition of differentiation of myoblasts after 15d-PGJ2 depends on modification of HRas at C184 i.e. failure to modify HRas at C184 (Fig. 3A) and resultant activation (Fig. 3B) by 15d-PGJ2 rescues this inhibition of differentiation of C2C12 cells (Fig. 4D and E), dissecting the inhibition of differentiation of myoblasts by 15d-PGJ2 from general toxic effects of 15d-PGJ2 on cell physiology.

Please note that the effect of 15d-PGJ2 on cell physiology is context-specific. On one hand, 15d-PGJ2 has been shown to exert tumor-suppressor effects by inhibiting the proliferation of ovarian cancer cells and lung adenocarcinoma cells (de Jong et al., 2011; Slanovc et al., 2024), 15d-PGJ2 also exerts pro-carcinogenic effects by induction of epithelial to mesenchymal transition in breast cancer cells MCF7 and inhibition of tumor-suppressor protein p53 in MCF7 and PC-3 cells (Choi et al., 2020; Kim et al., 2010).

Reviewer #2 (Public Review):

Summary:

In this study, Swarang and colleagues identified the lipid metabolite 15d-PGJ2 as a potential component of senescent myoblasts. They proposed that 15d-PGJ2 inhibits myoblast proliferation and differentiation by binding and regulating HRas, suggesting its potential as a target for restoring muscle homeostasis post-chemotherapy.

Strengths:

The regulation of HRas by 15d-PGJ2 is well controlled.

Weaknesses:

(1) I still think the novelty is limited by previous published findings. The authors themselves noted that the accumulation of 15d-PGJ2 in senescent cells has been reported in various cell types, including human fibroblasts, HEPG2 hepatocellular carcinoma cells, and HUVEC endothelial cells (PMCID: PMC8501892). Although the current study observed similar activation of 15d-PGJ2 in myoblasts, it appears to be additive rather than fundamentally novel. The covalent adduct of 15d-PGJ2 with Cys-184 of H-Ras was reported over 20 years ago (PMID: 12684535), and the biochemical principles of this interaction are likely universal across different cell types. The regulation of myogenesis by both HRas and 15d-PGJ2 has also been previously extensively reported (PMID: 2654809, 1714463, 17412879, 20109525, 11477074). The main conceptual novelty may lie in the connection between these points in myoblasts. But as discussed in another comment, the use of C2C12 cells as a model for senescence study is questionable due to the lack of the key regulator p16. The findings in C2C12 cells may not accurately represent physiological-relevant myoblasts. It is recommended that these findings be validated in primary myoblasts to strengthen the study's conclusions.

This is the first study to show a molecular mechanism where activation of HRas signaling in skeletal myoblasts due to covalent modification by 15d-PGJ2 at C184 of HRas inhibits the differentiation of skeletal myoblasts.

(2) The C2C12 cell line is not an ideal model for senescence study.

C2C12 cells are a well-established model for studying myogenesis. However, their suitability as a model for senescence studies is questionable. C2C12 cells are immortalized and do not undergo normal senescence like primary cells as C2C12 cells are known to have a deleted p16/p19 locus, a crucial regulator of senescence (PMID: 20682446). The use of C2C12 cells in published studies does not inherently validate them as a suitable senescence model. These studies may have limitations, and the appropriateness of the C2C12 model depends on the specific research goals.

Several reports have shown that cells undergo senescence independent of p16 expression. MCF7 human breast adenocarcinoma cells have been shown to undergo DNA damage mediated and Oncogene induced senescence as seen after treatment with Doxorubicin (PMID: PMC7025418) and expression of constitutively active HRas (PMID: 17135242), despite the homozygous deletion of p16 locus (ISBN 9780124375512 Chapter 17 Table 2) by upregulation of cell cycle inhibitor protein p21. In this study, we observe an increase in the senescence markers in C2C12 cells after treatment with Doxo (Fig. 1). We also observed an increase in the markers of DNA damage-mediated senescence in MCF7 after treatment with Doxo (Data will be included in the revised manuscript). Based on these observations, we have concluded that C2C12 cells undergo senescence despite lacking the p16/p19 locus.

In the study by Moustogiannis et al. (PMID: 33918414), they claimed to have aged C2C12 cells through multiple population doublings. However, the SA-β-gal staining in their data, which is often used to confirm senescence, showed almost fully confluent "aged" C2C12 cells. This confluent state could artificially increase SA-β-gal positivity, suggesting that these cells may not truly represent senescence. Moreover, the "aged" C2C12 cells exhibited normal proliferation, which contradicts the definition of senescence. Similar findings were reported in another study of C2C12 cells subjected to 58 population doublings (PMID: 21826704), where even at this late stage, the cells were still dividing every 2 or 3 days, similar to younger cells at early passages. More importantly, I do know how the p16 was detected in that paper since the locus was already mutated. In terms of p21, there was no difference in the proliferative C2C12 cells at day 0.

In the study by Moiseeva et al. in 2023 (PMID: 36544018), C2C12 cells were used for senescence modeling for siRNA transfection. However, the most significant findings were obtained using primary satellite cells or confirmed with complementary data.

In conclusion, while molecular changes observed in studies using C2C12 cells may be valid, the use of primary myoblasts is highly recommended for senescence studies due to the limitations and questionable senescence characteristics of the C2C12 cell line.

(3) Regarding source of increased PGD in the conditioned medium, I want to emphasize that it's unclear whether the PGD or its metabolites increase in response to DNA damage or the senescence state. Thus, using a different senescent model to exclude the possibility of DNA damage-induced increase will be crucial.

Though Senescence can be induced by several stress stimuli like DNA damage, Oncogene expression, ROS, Mitochondrial Dysfunction, etc., DNA damage remains critical for the induction of the SASP (reviewed in PMID: 20078217). Also, other models of senescence, like Oncogene Induced Senescence (reviewed in PMID: 17671427), ROS Induced Senescence (PMID: 24934860), Mitochondrial Dysfunction Associated Senescence (MiDAS) (PMID: 26686024) have shown upregulation of DNA damage-associated signaling pathways. In this study, we have explored the SASP of cells undergoing senescence upon chemotherapy drug Doxorubicin-mediated DNA damage.

(4) Similarly for the in vivo Doxorubicin (Doxo) injection, both reviewers have raised concerns about the potential side effects of Doxo, including inflammation, DNA damage, and ROS generation. These effects could potentially confound the results of the study. The physiological significance of this study will heavily rely on the in vivo data. However, the in vivo senescence component is confounded by the side effects of Doxo.

We concur that this is a limitation of this study and the subsequent work will demonstrate the origin of prostaglandin biosynthesis after treatment with Doxo in vivo.

(5) Figure 2A lacks an important control from non-senescent cells during the measurement of C2C12 differentiation in the presence of conditioned medium. The author took it for granted that the conditioned medium from senescent cells would inhibit myogenesis, relying on previous publications (PMID: 37468473). However, that study was conducted in the context of myotonic dystrophy type 1. To support the inhibitory effect in the current experimental settings, direct evidence is required. It would be necessary to include another control with conditioned medium from normal, proliferative C2C12 cells.

Conditioned medium of senescent cells of several types, like senescent myoblasts in case of DM1 (PMID: 37468473), adipocytes undergoing senescence due to H2O2 treatment, Insulin Resistance, and Replicative senescence (PMID: 37321332), has been shown to inhibit the differentiation of myoblasts. Therefore, in this study, we measured the effect of prostaglandin PGD2 and its metabolites on the differentiation of myoblasts by inhibiting the biosynthesis of PGD2 in senescent myoblasts by treatment with AT-56. We inhibited the synthesis of PGD2 in senescent cells by treatment with AT-56, and then collected the conditioned medium. Conditioned medium collected from senescent C2C12 cells treated with vehicle (DMSO) served as a control for the experiment.

(6) Statistical analyses problems.

Only t-test was used throughout the study even when there are more than two groups. Please have a statistician to evaluate the replicates and statistical analyses used.

In experiments with more than two groups, the t-test was used for column-wise comparison of the experiment samples to the control sample. Multiple sample comparisons using one-way or two-way ANOVA were avoided as experimental samples were individually compared to the control sample.

For the 15d-PGJ2/cell concentration measurements in Figure 1F, there were only two replicates, which was provided in the supplementary table after required. Was that experiment repeated with more biological replicates?

Additional replicates of the experiment will be included in the revised manuscript.

For figure 1C, Fig 1F, 1G, 1J, 2C, 2E, 3A, 3E, 3F, 4D, 4E, please include each data points in bar graphs as used in Fig 1D, or at least provide how many biological replicates were used for each experiment?

Appropriate revisions will be made in the figure legends of the revised manuscript.

There is no error bar in a lot of control groups (Fig 2C, 2E, 3EF, 4E, S4B).

There are no error bars for the control groups in the figures 2C, 2E, 3E, 3F, 4E, and S4B as the experimental samples of each replicate were normalized to the corresponding control sample, rendering the values for the control sample of each replicate to 1.

For qPCR data in Figure 1C, the author responded in that the data in was plotted using 2-ΔCT instead of 2-ΔΔCT to show the variability in the expression of mRNAs isolated from animals treated with Saline. This statement does not align with the method section. Please revise.

Appropriate revisions will be made to the method sections of the revised manuscript.

(7) For Figure 1, the title may not be appropriate as there is insufficient data to support the inhibition of myoblast differentiation.

Appropriate revisions will be made to the revised manuscript.

Recommendations for the authors:

After careful review, the editors advise you to carefully address the following concerns.

(1) There were concerns that in the revised manuscript, the DMSO and Doxo experiments depicted in Figure 1H appeared quite homogenous despite the author's description to the contrary. This leads to concerns about the type of statistics employed and the possible low number of replicates of experiments shown in Fig. 1.

(2) Experiments in Figure 1F, 1I, and 1J had as few as n=2 experiments. Figures 1C, 1D, 1F, 1G, and 1J, the statistics used a two-tailed student's t-test; for all other experiments, they marked N/A for statistics. Using a t-test for multi-group comparisons (as indicated in the figure legend) and relying on only 2 replicates for many experiments are not appropriate.

Additional replicates for the experiments shown in figures 1F, 1I, and 1J have been done and the data will be revised along with updated statistical tests during the revision of the manuscript.

(3) In several experiments, the difference between technical replicates is too high.

Reviewer #1 (Recommendations For The Authors):

Most of my concerns were addressed in the revised manuscript.

We thank the reviewer for their time in reviewing the manuscript and consideration of the author’s response to their comments in during the previous round of review.

Reviewer #2 (Recommendations For The Authors):

Validating the findings in a primary myoblast is highly recommended for senescence studies due to the limitations and questionable senescence characteristics of the C2C12 cell line.

We have explained the statistical tests used in the manuscript in the general comment section of the reviewer’s comments.

Validate the finding in a different senescent model to exclude the possibility of DNA damage-response.

We have explained the statistical tests used in the manuscript in the general comment section of the reviewer’s comments.

For Fig 2A, add another control with a conditioned medium from normal, proliferative C2C12 cells.

We have explained the statistical tests used in the manuscript in the general comment section of the reviewer’s comments.

Please have a statistician to evaluate the replicates and statistical analyses used.

We have explained the statistical tests used in the manuscript in the general comment section of the reviewer’s comments.

For the barplots (figure 1C, Fig 1F, 1G, 1J, 2C, 2E, 3A, 3E, 3F, 4D, 4E), please include each data points, or at least provide how many biological replicates were used for each experiment.

Appropriate revisions will be made in the figure legends of the revised manuscript.

For Figure 1, the title may not be appropriate as there is insufficient data to support the inhibition of myoblast differentiation.

Appropriate revisions will be made to the revised manuscript.

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

eLife assessment

This manuscript provides useful information about the lipid metabolite 15d-PGJ2 as a potential regulator of myoblast senescence. The authors provide experimental evidence that 15d-PGJ2 inhibits myoblast proliferation and differentiation by binding and regulating HRas. However, the manuscript is incomplete in its current form, as it lacks robust support from the data regarding the main conclusions related to senescence and technical concerns related to the senescence models used in this study.

We are grateful to the editors and the reviewers for their time and comments in sharpening the science and the writing of the manuscript. We have attached a detailed response to emphasize that the manuscript does include robust evidence regarding the claims, which could have been missed during the review process. We have provided a better context for these points now.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The authors show that upon treatment with Doxorubicin (Doxo), there is an increase in senescence and inflammatory markers in the muscles. They also show these genes get upregulated in C2C12 myoblasts when treated with conditioned media or 15d-PGJ2. 15dPGJ2 induces cell death in the myoblasts, decreases proliferation (measured by cell numbers), and decreases differentiation and fusion. 15d-PGJ2 modified Cys184 of HRas, which is required for its activation as indicated by the FRET analysis with RAF RBD. They also showed that 15d-PGJ2 activates ERK signaling, but not Akt signaling, through the electrophilic center. 15d-PGJ2 inhibits Golgi localization of HRAS (only WT, not C181 or C184 mutant). They also showed that expressing the WT HRas followed by 15d-PGJ2 treatment led to a decrease in the levels of MHC mRNA and protein, and this defect is dependent on C184. This is a well-written manuscript with interesting insights into the mechanism of action of 15d-PGJ2. However, some clarification and experiments will help the paper advance the field significantly.

Strengths:

The data clearly shows that 15d-PGJ2 has a negative role in the myoblast cells and that it leads to modification of HRas protein. Moreover, the induction of biosynthetic enzymes in the PGD2 pathway also supports the induction of 15d-PGJ2 in Doxorubicin-treated cells. Both conditioned media experiments and the 15d-PGJ2 experiments show that 15d-PGJ2 could be the active component secreted by the senescent myoblasts.

Weaknesses:

The genes that are upregulated in the muscles upon injection with Doxo are also markers for inflammation. Since Doxo is also known to induce systemic inflammation, it is important to delineate these two effects (inflammatory cells vs senescent cells). The expression of beta Gal and other markers of senescence in the tissue sections will help to delineate these.

As pointed out Doxo induces systemic inflammation along with inducing DNA damage-mediated senescence. Therefore, along with the inflammatory markers of the SASP (CXCL1/2, TNF1α, IL6, PTGS1/2, PTGDS) we also observed an increase in the mRNA levels of canonical markers of DNA damage-mediated senescence. We observed an increase in the mRNA levels of cell cycle and senescence associated proteins p16 and p21 (Fig. 1C). We also observed an increased nuclear accumulation of p21 (Fig. 1A) and increased levels of phosphorylated H2A.X in the nucleus (Fig. 1B).

In Figure 2, where the defect in the differentiation of myoblasts upon treatment with 15d-PGJ2 is shown, most of the cells die within 48 hours at higher concentrations, making it difficult to perform the experiments. This also shows that 15d-PGJ2 was toxic to these cells. Lower concentrations show a decrease in the differentiation based on the lower number of nuclei in fibers and low expression of MyoD, MyoG, and MHC. However, it is unclear if this is due to increased cell death or defective differentiation. It would be a lot more informative if the cell count, cell division, and cell death could be plotted for these concentrations of the drug during the experiment.

We measured the viability of C2C12 cells after 24 hours of treatment with 15d-PGJ2 using the MTT assay and observed that the viability of cells was decreased after treatment with 15d-PGJ2 (10 µM) but not with 15d-PGJ2 (1 µM, 2 µM, 4 µM, or 5 µM) (see Fig. S2A of the updated manuscript). The results and figures of the manuscript have been updated accordingly.

Also, in the myoblast experiments, are the effects of treatment with Dox reversible?

The treatment with Doxorubicin is irreversible as the senescent phenotype was not reversed after withdrawal of Doxorubicin, even after 20 days.

In Figure 3, most of the experiments are done at a high concentration, which induces almost complete cell death within 48 hours.

Figure 3 is an acute experiment for only 1 hour, at which time no cell death was observed. Specifically, we measured the phosphorylation of Erk and Akt proteins after 1 hour of treatment with 15d-PGJ2 (10 µM) during which we did not observe any cell death. 

Even at such a high concentration of 15dPGJ2, the increase in ERK phosphorylation is minimal.

We observe a ~30% increase in the phosphorylation of Erk proteins after treatment with 15d-PGJ2 in 0.2% serum medium compared to treatment with vehicle (DMSO). This is reproducible and significant.

The experiment Figure 4C shows that C181 and C84 mutants of the HRas show higher levels in Golgi compared with WT. However, this could very well be due to the defect in palmitoylation rather than the modification with 15d-PGJ2.

Our data does not suggest higher levels of C184S mutant in the Golgi compared with WT (Fig. S4A). We observed that the ratio of HRas levels in the Golgi to the HRas levels in the plasma membrane were similar in C2C12 cells expressing HRas C184S and HRas WT (Fig. S4A graph columns 1 and 5).

Though the authors allude to the possibility that intracellular redistribution of HRas by 15d-PGJ2 requires C181 palmitoylation, the direct influence of C184 modification on C181 palmitoylation is not shown. To have a meaningful conclusion, the authors need to compare the palmitoylation and modification with 15d-PGJ2.

Palmitoylation of HRas C181S is required for the localization of HRas at the plasma membrane. The inhibition of palmitoylation of C181, either by mutation (C181S) or treatment with protein palmitoyl transferase inhibitor (2-Bromopalmitate), results in the accumulation of HRas at Golgi(Rocks et al., 2005) (Fig. S4A). Modification of HRas at C184 by 15d-PGJ2 (Fig. 3A) could inhibit the palmitoylation of HRas at C181. However, our data does not support this hypothesis as modification of HRas WT by 15d-PGJ2 does not increase the level of HRas at the Golgi, like in the case of inhibition of cysteine palmitoylation due to C181S mutation.

To test if the inhibition of myoblast differentiation depends on HRas, they overexpressed the HRas and mutants in the C2C12 lines. However, this experiment does not take the endogenous HRAs into consideration, especially when interpreting the C184 mutant. An appropriate experiment to test this would be to knock down or knock out HRas (or make knock-in mutations of C184) and show that the effect of 15d-PGJ2 disappears. 

Endogenous HRas (wild type) is present in the C2C12 cells overexpressing the EGFP-tagged HRas constructs. Therefore, we only observe a partial rescue in the differentiation after 15d-PGJ2 treatment in C2C12 cells expressing the C184S mutant (Fig. 4D and E). However, since HRas is expressed under high expression CMV promoter and in the absence of other regulatory elements, the overexpressed constructs do show a dominant effect over the endogenous HRas, showing cysteine mutant dependent inhibition of differentiation of myoblasts after treatment with 15dPGJ2 (Fig. 4D and E).

Moreover, in this specific experiment, it is difficult to interpret without a control with no HRas construct and another without the 15d-PGJ2 treatment.

The mRNA levels of MyoD, MyoG, and MHC in C2C12 cells expressing HRas constructs after treatment with 15d-PGJ2 were normalized to the mRNA levels in C2C12 cells expressing corresponding constructs and were treated with vehicle (DMSO). mRNA levels in C2C12 cells treated with vehicle were not shown as they were normalized to 1. MHC protein levels in C2C12 cells expressing HRas constructs after 15d-PGJ2 treatment were normalized to that in C2C12 cells treated with vehicle (DMSO). Since the hypothesis to study the effect of HRas cysteine mutations on the differentiation of myoblasts after treatment with 15d-PGJ2, C2C12 cells expressing HRas WT serve as adequate control. Fig. 2 shows the effect of 15dPGJ2 on muscle differentiation when HRas was not overexpressed.

Moreover, the overall study does not delineate the toxic effects of 15d-PGJ2 from its effect on the differentiation.

The inhibition of differentiation in C212 cells after treatment with 15d-PGJ2 cannot be attributed to the general toxicity of 15d-PGJ2 in cells. We show that the inhibition of differentiation of myoblasts after 15d-PGJ2 depends on modification of HRas at C184 i.e. failure to modify HRas at C184 (Fig. 3A) and resultant activation (Fig. 3B) by 15d-PGJ2 rescues this inhibition of differentiation of C2C12 cells (Fig. 4D and E), dissecting the inhibition of differentiation of myoblasts by 15d-PGJ2 from general toxic effects of 15d-PGJ2 on cell physiology.

Please note that the effect of 15d-PGJ2 on cell physiology is context-specific. On one hand, 15d-PGJ2 has been shown to exert tumor-suppressor effects by inhibiting the proliferation of ovarian cancer cells and lung adenocarcinoma cells (de Jong et al., 2011; Slanovc et al., 2024), 15d-PGJ2 also exerts pro-carcinogenic effects by induction of epithelial to mesenchymal transition in breast cancer cells MCF7 and inhibition of tumor-suppressor protein p53 in MCF7 and PC-3 cells (Choi et al., 2020; Kim et al., 2010).

Reviewer #2 (Public Review):

Summary:

In this study, Swarang and colleagues identified the lipid metabolite 15d-PGJ2 as a potential component of senescent myoblasts. They proposed that 15d-PGJ2 inhibits myoblast proliferation and differentiation by binding and regulating HRas, suggesting its potential as a target for restoring muscle homeostasis post-chemotherapy.

Strengths:

The regulation of HRas by 15d-PGJ2 is well controlled.

Weaknesses:

The novelty of the study is compromised as the activation of PGD and 15d-PGJ2, as well as the regulation of HRas and cell proliferation, have been previously reported. 

Literature does not support this statement, and it is important to clarify this misimpression for the field as a whole. 

Let us clarify- 

Covalent modification of HRas by 15d-PGJ2 has been reported only twice in the literature(Luis Oliva et al., 2003; Yamamoto et al., 2011) in fibroblasts and neurons respectively. 

Interaction between Hras and 15d-PGJ2 in skeletal muscles has not been shown before, even though both Hras and 15d-PGJ2 are shown to be key regulators of muscle homeostasis. 

Activation of Hras by 15d-PGJ2 was reported first by Luis Oliva et al (Luis Oliva et al., 2003). However, this study does not comment on the functional implications of activation of Hras signaling. 

Recently, our lab contributed to a study where the functional implication of activation of Hras signaling due to covalent modification by 15d-PGJ2 was shown in the maintenance of senescence phenotype (Wiley et al., 2021). 

15d-PGJ2 was shown to inhibit the differentiation of myoblasts by Hunter et al (Hunter et al., 2001). This study hypothesized that the inhibition of myoblast differentiation is via 15d-PGJ2 mediated activation of the PPARγ signaling, the study also showed inhibition of myoblast differentiation independent of PPARγ activity, suggesting the presence of other mechanisms.

This is the first study to show a molecular mechanism where activation of Hras signaling in skeletal myoblasts due to covalent modification by 15d-PGJ2 at C184 of Hras inhibits the differentiation of skeletal myoblasts.

Additionally, there are major technical concerns related to the senescence models, limiting data interpretation regarding the relevance to senescent cells.

Major concerns:

(1) The C2C12 cell line is not an ideal model for senescence study due to its immortalized nature and lack of normal p16 expression. A more suitable myoblasts model is recommended, with a more comprehensive characterization of senescence features.

C2C12 is a good model for DNA damage-based senescence that is used in this manuscript. Several reports in the literature have shown the induction of senescence in C2C12 cells. Moiseeva et al 2023 show induction of senescence in C2C12 cells after etoposide-mediated DNA damage. Moustogiannis et al 2021 show the induction of replicative senescence in C2C12 cells. In this study, we show that C2C12 cells undergo DNA damage-mediated senescence after treatment with Doxo. We measured the induction of senescence in C2C12 cells upon DNA damage using several physiological (Nuclear Size, Cell Size, and SA β-gal) and molecular markers (mRNA levels of p21 and SASP factors (IL6 and TGFβ), protein levels of p21) of senescence (see Fig. 1 of the updated manuscript). The results and the figures in the manuscript have been updated accordingly.

(2) The source of increased PGD or its metabolites in the conditioned medium is unclear. Including other senescence models, such as replicative or oncogeneinduced senescence, would strengthen the study.

Fig. 1E shows time-dependent increase in the expression of PGD2 biosynthetic enzymes in senescent C2C12 cells. Fig. 1F shows an increase in the levels of 15dPGJ2 secreted by senescent C2C12 cells in the conditioned medium. This data shows that senescent C2C12 cells are the source of PGD and its metabolites in the conditioned medium.

Again, C2C12 is not suitable for replicative senescence due to its immortalized status.

We and others have shown that C2C12 cells undergo senescence, and this manuscript only used DNA damage induced senescence.

(3) In the in vivo part, it is unclear whether the increased expression of PTGS1, PTGS2, and PTGDS is due to senescence or other side effects of DOXO.

We concur that this is a limitation of this study and the subsequent work will demonstrate the origin of prostaglandin biosynthesis after treatment with Doxo in vivo.

(4) Figure 2A lacks an important control from non-senescent cells during the measurement of C2C12 differentiation in the presence of a conditioned medium.

Figure 2A tests the effect of prostaglandin PGD2 and its metabolites secreted by the senescent cells on the differentiation of myoblasts. Therefore, we inhibited the synthesis of PGD2 in senescent cells by treatment with AT-56, and then collected the conditioned medium. Conditioned medium collected from senescent C2C12 cells treated with vehicle (DMSO) served as a control for the experiment, whereas differentiation of C2C12 cells without any treatment serves as a positive control.

There is no explanation of how differentiation was quantified or how the fusion index was calculated.

The fusion index was calculated using a published myotube analyzer software (Noë et al., 2022). Appropriate information has been added to the materials and methods section of the manuscript.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Line 3: Expand SA in "SA β-gal".

The manuscript has been updated accordingly (See line 3).

Line 68: HRas is highly regulated by lipid modifications.

The manuscript has been updated accordingly (See line 67).

Figures

Figure S1A seemed incomplete (maybe some processing issue).

The Figure has been updated in the revised manuscript (See Fig. S1A).

Figure S1B-H are mislabeled.

The figure has been updated in the revised manuscript (See Fig. S1C, D, E, and F).

Figures S1E-H are not mentioned in the manuscript.

The manuscript has been updated accordingly (See line 120).

Many supplementary figures are not cited in the article.

The manuscript has been updated accordingly. (See lines 85, 120, 123, 166, 225, 356, 364, 412, and 413)

Reviewer #2 (Recommendations For The Authors):

(1) Clarify the injection method for Doxorubicin in B6J mice on line 83 (IP or IM).

Mice were injected intraperitoneally with Doxorubicin (as mentioned in the materials and methods, see lines 83 and 794)

(2) Address missing information in figures or figure legends.

There is missing piece in Sup Fig 1A.

The figure has been updated in the revised manuscript (See Fig. S1A).

Correct labels in Sup Fig 1C and 1D.

The figure has been updated in the revised manuscript (See Fig. S1C, D, E, and F).

How would the authors explain the dramatic differences in the morphology of C2C12 cells treated with DOXO between bright field and SA-beta-gal staining images in Sup Fig 1B and 1C.

The SA β-gal image after treatment with Doxo does show a flattened cell morphology. Another field of view from the same experiment has been added in the figure to show the difference in the cell morphology more prominently in the revised manuscript (See Fig. 1H).

Provide explanations for Sup Fig 1E-1G, including the meaning of the y-axis and the blue dots and red lines.

We have provided an explanation for the multiple reaction monitoring mass spectrometry used to measure the concentration of 15d-PGJ2 in the conditioned medium in the revised manuscript (see lines 119-130 and the legends of Fig. S1C, D, and E)

(3) Please review the calculation of qPCR data in Figure 1C for correctness, ensuring reference samples with an average expression level of 1.

The data in Fig. 1C was plotted using 2-ΔCT instead of 2-ΔΔCT to show the variability in the expression of mRNAs isolated from animals treated with Saline.

(4) Please explain the calculation of 15d-PGJ2/cell concentration in Figure 1F and provide raw data for review, considering the substantial changes and small error bars. The method or result section lacks an explanation of how this calculation was performed. Additionally, there is no mention of the cell number count.

All the raw values (concentration of 15d-PGJ2 measured using mass spec and cell numbers counted at the time of collection of conditioned medium) are provided in the supplementary table 1. The standard curve to calculate the concentration of 15dPGJ2 in the conditioned medium is shown in Fig. S1F. The cell number was counted after trypsinization using a hemocytometer on the day of collection of the conditioned medium.

(5) Please clarify how cell number normalization and doubling time calculation were done in Fig 2B. Consider replacing the figure with a growth curve showing confluence on the y-axis for easier interpretation.

Cells were counted every 24 hours and the normalization was done to the number of cells counted on day 0 of the treatment (to consider attaching efficiency and other cell culture parameters). Doubling time was calculated as the reciprocal of the slope of the graph of log2(normalized cell number) vs time.

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Reply to the reviewers

Reviewer 1

The paper is overall convincing. However, a little more attention to data presentation and possibly the addition of at least another technique (see below) would greatly strengthen the findings.

As we hope to demonstrate below, we have taken steps to improve our manuscript on both fronts (data presentation and experimental evidence).

The absence of statistics catches immediately the eye. I am sure that the shown differences are statistically significant (thanks to the number of analyzed cells), but reporting the result of some statistical test would help the reader in identify the relevant data in a plot. This is somehow necessary considering that sometimes in the text something is deemed to be "significant" or "not significant", and I felt that I really needed that when looking at the plot in Fig. 3D.

To facilitate the interpretation of figures that contain data from multiple strains (such as the one mentioned by the reviewer), we have carried out a nonparametric single-step multiple comparison test (Games-Howell) to identify mutants whose means differ significantly from each other. To avoid overcrowding the figures, we have graphically summarized the p-values of all pairwise comparisons in a small matrix within the corresponding panel, and provided 99% confidence intervals and p-values of all differences in the Supplement.

Related to the previous point: for every N/C distribution analysis, a number of analyzed cells is reported. By the way it is written, it seems that the replication relies solely by the cells in that specific population, i.e.: each cell is treated as a replicate. At least I could not find if that is not the case in the legends or in the methods. I wonder what the results would be (and their significance) if each replicate would be a new assay on another population.

Cell populations exhibit significant variability in their phenotypic characteristics. Consequently, the quantification of a specific feature (e.g., the Sfp1 nuclear/cytoplasmic ratio) across a sample of cells from a given population results in a distribution rather than a single fixed value. For each quantification, we report the number of cells that were used to construct the corresponding distribution, i.e. the sample size. To compare samples from different populations (e.g., different Sfp1 mutant strains), we run them in parallel during microscopy experiments and compare their means, as described above. Throughout our study, we have tried to ensure that we quantify a sufficiently large number of cells to overcome cell-to-cell variability and enhance the reliability of our results.

In this context, the question of the reviewer is not entirely clear to us, as individual measurements of a sample are not replicates. However, one can replicate the entire experiment on a different day by re-growing the different strains, running microscopy, quantifying the new movies etc. In this sense, the experiments shown in the manuscript consist of single replicates, i.e. experiments that were carried out on the same day, with all the relevant mutants and controls quantified together. However, we have monitored many of our mutants multiple times over the course of our work. For example, Fig. 1 below shows replicates of the Sfp1 N/C ratio distributions at steady-state in the analog-sensitive (A) and wild-type (B) background, which were quantified several times across various experiments. While day-to-day variability in the empirical distributions of the same mutant exists to a small extent, it is quite small.

The scale of x axes in N/C ratio plots. Besides not being consistent throughout the figures, it originates from 1, visually enhancing the differences.

We believe the reviewer was referring to the y-axes, as the x-axes represent time. Summarizing the N/C ratio dynamics of different Sfp1 mutants has been challenging. First, the average N/C ratios at steady-state vary considerably across different mutants, as shown in the panels that summarize steady-state N/C ratios. To compare the magnitude and features of their responses, normalization is necessary. We chose to normalize the time series of each mutant to have a mean of 1 prior to the onset of a perturbation. This allows the normalized time series to represent the percentage-wise changes in the Sfp1 N/C ratio upon perturbation.

Using a common y-axis scale for all plots of N/C ratio dynamics not ideal, as some responses are subtler than others. Additionally, we do not believe that N/C dynamics across different figures need to (or should) be compared to each other. However, within a figure, panels that require comparison are placed in the same row and share the same y-axis scale. We believe that this approach optimizes data visualization and facilitates important visual comparisons.

Related to the previous point: it is evident from the plots that the N/C ratio is always positive, even in the most deficient of the analyzed mutants. This implies that a relevant fraction of Sfp1 is still nuclear. I thus wonder what the impact of these mutations would be on the actual function of Sfp1. For this reason, I feel that qPCR evaluation of transcripts of Sfp1 target genes is particularly needed. Since lack of Sfp1 is known to yield some of the smallest cells possible, it would also be cool to have an estimate of the size of mutants where Sfp1 is less nuclear. These analyses could confer phenotypical relevance to the data, but would also help in assessing a currently unexplored possibility, that phosphorylation events by PKA influence Sfp1 function besides its localization, i.e.: the still somehow nuclear fraction is not as functional as wt Sfp1 in promoting transcription.

It is indeed the case that the recorded N/C ratios are larger than 1 in all strains that we have monitored. We have never observed an N/C ratio smaller than 1 using widefield microscopy for two main reasons: first, out-of-focus light from the cytosol above and below the nucleus is added to the nuclear signal, causing the nuclear signal to always be non-zero, even for predominantly cytosolic proteins. Second, both in- and out of focus vacuoles are devoid of the fluorescent protein fusions that we quantify, which reduces the average brightness of the cytosol. For these reasons, even when a protein is largely cytosolic, the average N/C ratio over a cell population is no lower than around 1.5. Keeping these points in mind, one can observe that our most delocalized Sfp1 mutants have an N/C ratio that is around 1.6-1.7, which is very close to the lower limit. This means that these Sfp1 mutants are largely cytosolic, and the nuclear fraction (if non-zero) is quite small.

We agree that assessing the phenotypic relevance of Sfp1 mutations is of interest. However, this was impossible with our original strains, as we introduced each Sfp1 mutant as an extra copy in the HO locus while leaving the endogenous Sfp1 locus intact. This was done in order to avoid any phenotypic changes that might result from changes in Sfp1 activity.

To address the suggestion of the reviewer, we therefore deleted the endogenous Sfp1 copy in strains carrying sfp1PKA2A, sfp1PKA2D and sfp113A, leaving only the mutated Sfp1 copy at the HO locus. Surprisingly, the growth rate and drug sensitivity (determined by halo assays) of these single-copy mutants did not differ much in comparison to the mutants carrying the functional Sfp1 copy and from the wild-type (Supp. Figs. 4J and 7). This observation aligns with findings for the single-copy sfp1-1 mutant in [Lempiäinen et al. 2009], which corresponds to sfp1TOR7A in our work. [Lempiäinen et al. 2009] had suggested that Sch9 compensates for the loss of Sfp1 activity via a feedback mechanism, which could explain our results as well. If this is the case, acute depletion of wild-type Sfp1 could unveil transient changes in cell growth, before the compensatory effect of Sch9 was established. Unfortunately, we were unable to efficiently degrade wild-type Sfp1 carrying a C-terminal auxin-inducible degron. Instead, we followed the same approach with [Lempiäinen et al. 2009] and deleted SCH9.

As we describe in the last section of Results, the difference was dramatic for sfp113A __mutants, which were extremely slow-growing in the absence of Sch9 (doubling time was around 4 hours, but it was hard to estimate because we could not grow the cells consistently). Interestingly, SCH9 deletion had a negative impact on sfp1__PKA2D __but not sfp1__PKA2A __cells (__Supp. Fig. 7). Overall, these results demonstrate that Sch9 can compensate for loss of Sfp1 activity, which makes it challenging to study the impact of Sfp1 mutations on cellular phenotypes.

To further understand to what extent Sch9 compensates for loss of Sfp1 phosphorylation, we carried out RNA-seq on WT and cells carrying a single copy of sfp113A (with the endogenous SFP1 copy removed). Despite the fact that sfp113A __grow as well as WT, RNA-seq picked up several differentially expressed genes related to amino acid biosynthesis. This surprising finding is presented in the last section of Results, and in __Supplementary Figures 8, 9 and 10. We explore the relevance of these results and their connection with past literature on Sfp1 and Sch9 in the Discussion section.

I found some typos here and there, and it would greatly help to report them if in the manuscript line numbers were included.

We apologize for the typos. We have tried to eliminate them, and we have also added line numbers to the manuscript.

Reviewer 2

There is no biochemical evidence presented that the putative PKA sites (S105 and S136) are genuinely phosphorylated by PKA. The fact that they match the PKA consensus motif, alone, does not guarantee this. In order to claim that they are looking at the effect of PKA by mutagenizing these residues, the authors have to demonstrate the PKA-dependency of S105 and S136 phosphorylation by, for example, mass spec experiments or western blotting with phospho-specific antibodies (Cell Signaling Technology #9624 for example). Also, does the band-shift caused by PKA inhibition (Fig 3C) is canceled by the S105A/S136A mutation?

We took several actions to demonstrate that the putative PKA sites are indeed phosphorylated by PKA. We first tried to detect Sfp1 phosphorylation using the antibody mentioned by the reviewer, but failed as the sensitivity of this antibody appears to be quite low. On the other hand, mass spectrometry did not produce the right fragments to detect the sites of interest. We therefore resorted to an in vitro kinase assay using [γ-32P]ATP together with purified PKA and Sfp1. Unfortunately, bacterial overexpression of MBP-tagged Tpk1, Tpk2 and Tpk3 (the catalytic subunits of PKA) was quite challenging and we were unable to produce soluble protein. We therefore resorted to commercially available bovine PKA (bPKA, PKA catalytic subunit, Sigma-Aldrich 539576), which shows high homology to the yeast Tpk kinases [Toda et al. 1987]. Moreover 87% of bPKA substrates have been shown to also be Tpk1 substrates [Ptacek et al. 2005], and bPKA has been used to identify new Tpk substrates in budding yeast [Budovskaya et al. 2005__]. As we show in the revised manuscript, bovine PKA does phosphorylate Sfp1. Moreover, phosphorylation is reduced by 50% in the double S105A, S136A mutant (Fig.1F), and becomes undetectable in the 13A mutant__ (Supp Fig. 6). Together with the rapid response of Sfp1 localization to acute PKA inhibition which we had already reported, we believe that these results provide strong evidence that Sfp1 is a direct PKA substrate, and that the two phosphosites that we identified are functional.

As the above in vivo experiments do not exclude S105/S136 phosphorylation by other kinases downstream of PKA, in order to claim the direct phosphorylation, the authors need in vitro PKA kinase assay. These biochemical experiments are not trivial, but I think absolutely necessary for this story.

One cannot exclude that S105/S136 are also phosphorylated by other kinases of the AGC family (note that [Lempiäinen et al. 2009] has already excluded Sch9). However, as we hope to have shown, PKA indeed phosphorylates Sfp1. Examining if other kinases besides PKA and TORC1 target Sfp1 is a very interesting question that should be addressed in future work.

The authors only look at the localization of Sfp1. To assess its functionality and so physiological impact, it would be informative to measure the mRNA level of target ribosomal genes in various Sfp1 mutants they created.

As we described in our response to Reviewer 1 above, we did perform RNA-seq on WT and cells carrying a single copy of sfp113A. We observed a notable absence of differentially expressed ribosomal genes and ribosome-related categories in the GO analysis (Supp. Figs. 8, 9 and 10). Together with our observations on SCH9 deletion (Supp. Fig. 7), these results suggest that Sch9 can largely compensate for the loss of Sfp1 activity. On the other hand, the emergence of differentially expressed amino acid biosynthesis genes is a finding that merits further investigation, as it connects with previous observations made with Sch9 deletion mutants and the [ISP+] prion form of Sfp1 (cf. Discussion).

In the experiments using analog-sensitive PKA (Fig 1D and E for example), they directly compare wildtype-PKA versus analog sensitive-PKA, or with 1-NM-PP1 versus without 1-NM-PP1. This makes interpretation difficult, particularly because 1-NM-PP1 itself has a significant impact even in the wild PKA strain. The real question is the difference between wild-type Sfp1 versus mutant Sfp1. In the current form, they compare Fig 1D versus 1E, these two do not look like a single, side-by-side experiment. They should compare wild-type Sfp1 versus mutant Sfp1 side-by-side.

Figure 1D shows that 1-NM-PP1 has a transient off-target effect on Sfp1 localization in WT cells, which could also affect Sfp1 mutants. This observation prompted us to use wild-type PKA as a control when testing the effect of 1-NM-PP1 on sfp1PKA2D in cells carrying PKAas (Figure 1E). As Fig. 1E shows, the effect of 1-NM-PP1 on sfp1PKA2D localization in PKAas cells is quite similar to the off-target effect in cells carrying sfp1__PKA2D __and wild-type PKA. This behavior of sfp1__PKA2D __is clearly different from the response of wild-type Sfp1 to PKAas inhibition, which results in sustained delocalization. We have made the latter observation repeatedly, both in this study and our previously published work [Guerra et al. 2021].

In Figure 3, the argument around the additive effects of PKA and TORC1 is confusing. The authors say they are additive referring Figure 3E, but say they are not additive referring Figure 3B. Which is true? In fact, Figure 3B appears to show an additive effect as well.

We did not use the word "additive" in the text, because we find it difficult to interpret. Instead, we state that PKA and TORC1 appear to control Sfp1 phosphorylation independently of each other. PKA and TORC1 phosphorylation converges to the same response, affecting Sfp1 localization. It appears that loss of either kinase delocalizes Sfp1, while loss of both kinases may only have a small additional effect.

Author response:

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

Reviewer #1 (Public Review):

Summary:

This study identifies new types of interactions between Drosophila gustatory receptor neurons (GRNs) and shows that these interactions influence sensory responses and behavior. The authors find that HCN, a hyperpolarization-activated cation channel, suppresses the activity of GRNs in which it is expressed, preventing those GRNs from depleting the sensillum potential, and thereby promoting the activity of neighboring GRNs in the same sensilla. HCN is expressed in sugar GRNs, so HCN dampens the excitation of sugar GRNs and promotes the excitation of bitter GRNs. Impairing HCN expression in sugar GRNs depletes the sensillum potential and decreases bitter responses, especially when flies are fed on a sugar-rich diet, and this leads to decreased bitter aversion in a feeding assay. The authors' conclusions are supported by genetic manipulations, electrophysiological recordings, and behavioral assays.

Strengths:

(1) Non-synaptic interactions between neurons that share an extracellular environment (sometimes called "ephaptic" interactions) have not been well-studied, and certainly not in the insect taste system. A major strength of this study is the new insight it provides into how these interactions can impact sensory coding and behavior.

We appreciate the reviewer’ view that our findings may allow researchers to better understand sensory coding and behavior. However, we respectfully disagree that the SP homeostasis in Drosophila gustation we describe here pertains to ephaptic interaction. Although SP reduction was proposed as the basis of post-ephaptic hyperpolarization in Drosophila olfaction, we find that SP changes are found to be too slow to mediate the fast action of ephaptic inhibition in gustation, reported in the ref#17. We observed a slow, sweet-dependent SP depletion (Fig. 5B, revised), which takes more than one hour. The real-time change of SP was also slow even upon contact with 200-mM sucrose; this result was set aside for another manuscript in preparation. Therefore, we believe the main findings in this paper concern the homeostatic preservation of SP for the maintenance of gustatory function, not ephaptic interaction.

(2) The authors use many different types of genetic manipulations to dissect the role of HCN in GRN function, including mutants, RNAi, overexpression, ectopic expression, and neuronal silencing. Their results convincingly show that HCN impacts the sensillum potential and has both cell-autonomous and nonautonomous effects that go in opposite directions. There are a couple of conflicting or counterintuitive results, but the authors discuss potential explanations.

(3) Experiments comparing flies raised on different food sources suggest an explanation for why the system may have evolved the way that it did: when flies live in a sugar-rich environment, their bitter sensitivity decreases, and HCN expression in sugar GRNs helps to counteract this decrease.

Weaknesses/Limitations:

(1) The genetic manipulations were constitutive (e.g. Ih mutations, RNAi, or misexpression), and depleting Ih from birth could lead to compensatory effects that change the function of the neurons or sensillum. Using tools to temporally control Ih expression could help to confirm the results of this study.

We attempted to address this point by using the tub-Gal80ts system. The result is now included as Fig. 1-figure supplement 2. At 29C, a non-permissive temperature for GAL80ts which allows GAL4-dependent expression Ih-RNAi, we observed that bGRN responses were decreased and sGRN responses were increased compared to the control maintained at 18°C, and this is in parallel with the result in Fig. 1C,D. For this experiment, we inserted “To exclude the possibility that Ih is required for normal gustatory development, we temporally controlled Ih RNAi knockdown to occur only in adulthood, which produced similar results (Fig. 1-figure supplement 2).” (~line 113).

(2) The behavioral experiment shows a striking loss of bitter sensitivity, but it was only conducted for one bitter compound at one concentration. It is not clear how general this effect is. The same is true for some of the bitter GRN electrophysiological experiments that only tested one compound and concentration.

We conducted additional behavioral experiments with other bitters such as lobeline and theophylline (Fig. 5-figure supplement 1), which showed sensitivity losses in Ih mutants similar to caffeine. For these results, the following is inserted at ~line 274: “These results were recapitulated with other bitters, lobeline and theophylline (Fig. 5-figure supplement 1).”

We also added single sensillum recording data with bitters, berberine, lobeline, theophylline and umbelliferone, which yielded results similar to those obtained with caffeine (Fig. 1-figure supplement 1). This is described with the sentence at ~line 105 “Other bitter chemical compounds, berberine, lobeline, theophylline, and umbelliferone, also required Ih for normal bGRN responses (Fig. 1-figure supplement 1).”

(3) Several experiments using the Gal4/UAS system only show the Gal4/+ control and not the UAS/+ control (or occasionally neither control). Since some of the measurements in control flies seem to vary (e.g., spiking rate), it is important to compare the experimental flies to both controls to ensure that any observed effects are in fact due to the transgene expression.

We appreciate the reviewers for raising this point. Indeed, there was a small logical flaw with the controls. We have now included all the necessary controls for Fig. 1C-F, Fig. 2I,J, Fig. 4E, and Fig. 5D, as reviewers suggested. These experiments remained statistically significant after including the new control groups.

(4) I was surprised that manipulations of sugar GRNs (e.g. Ih knockdown, Gr64a-f deletion, or Kir silencing) can impact the sensillum potential and bitter GRN responses even in experiments where no sugar was presented.

We are afraid there is a misunderstanding on the early part of the paper. We suspected that the manipulations impacted bGRNs and SP due to the sweetness in the regular cornmeal food, as stated in lines 214-220 “Typically, we performed extracellular recordings on flies 4-5 days after eclosion, during which they were kept in a vial with fresh regular cornmeal food containing ~400 mM D-glucose. The presence of sweetness in the food would impose long-term stimulation of sGRNs, potentially requiring the delimitation of sGRN excitability for the homeostatic maintenance of gustatory functions. To investigate this possibility, we fed WT and Ihf03355 flies overnight with either non-sweet sorbitol alone (200 mM) or a sweet mixture of sorbitol (200 mM) + sucrose (100 mM).”

I believe the authors are suggesting that the effects of sugar GRN activity (e.g., from consuming sugar in the fly food prior to the experiment) can have long-lasting effects, but it wasn't entirely clear if this is their primary explanation or on what timescale those long-lasting effects would occur. How much / how long of a sugar exposure do the flies need for these effects to be triggered, and how long do those effects last once sugar is removed?

We attempted to address this point with additional experiments (Fig. 5A,B). The reduction of SP could be observed in WT and HCN-deficient mutants with similar degrees 1 hr after the flies were transferred from nonsweet sorbitol-containing vials to sweet sucrose-containing ones. Moreover, the mutants, but not WT, showed further depression of SP when the sweetness persisted in the media for 4 hrs and overnight. This long-term exposure to sweetness longer than 1 hr may simulates the feeding on the regular sweet cornmeal food. The recovery of SP was also tested by removing flies from the sweet media after overnight-long sweet exposure and placing them in sorbitol food. SPs of WT and the mutants were recovered to the similar levels 1 hr after separating the animals from sweetness, although the HCN-lacking mutants showed much lower SP right after overnight sweetness exposure. The unimpaired recovery of the mutants suggests that HCN is independent of generating transepithelial potential itself. Therefore, regardless of HCN, SP changes are not fast even in the presence of strong sweetness, and SP is much better guarded when sGRNs express HCN in a sweet environment.

We inserted the following at ~line 260 to describe the newly added recovery experiment: “Following overnight sweet exposure, SPs of WT and Ihf03355 were recovered to similar levels after 1-hr incubation with sorbitol only food. However, it was after 4 hrs on the sorbitol food that the two lines exhibited SP levels similar to those achieved by overnight incubation with sorbitol only food (Fig. 5B). These results indicate that SP depletion by sweetness is a slow process, and that the dysregulated reduction and recovery of SPs in Ihf03355 manifest only after long-term conditioning with and without sweetness, respectively.”.

(5) The authors mention that HCN may impact the resting potential in addition to changing the excitability of the cell through various mechanisms. It would be informative to record the resting potential and other neuronal properties, but this is very difficult for GRNs, so the current study is not able to determine exactly how HCN affects GRN activity.

On this point, we cannot but rely on previous studies of biophysical and electrophysiological characterization on mammalian HCN channels and a heterologous expression study that revealed a robust hyperpolarization-activated cation current from Drosophila HCN channels (PMID: 15804582).

Reviewer #2 (Public Review):

Summary:

In this manuscript, the authors start by showing that HCN loss-of-function mutation causes a decrease in spiking in bitter GRNs (bGRN) while leaving sweet GRN (sGRN) response in the same sensillum intact. They show that a perturbation of HCN channels in sweet-sensing neurons causes a similar decrease while increasing the response of sugar neurons. They were also able to rescue the response by exogenous expression. Ectopic expression of HCN in bitter neurons had no effect. Next, they measure the sensillum potential and find that sensillum potential is also affected by HCN channel perturbation. These findings lead them to speculate that HCN in sGRN increases sGRN spiking which in turn affects bGRNs. To test this idea that carried out multiple perturbations aimed at decreasing sGRN activity. They found that decreasing sGRN activity by either using receptor mutant or by expressing Kir (a K+ channel) in sGRN increased bGRN responses. These responses also increase the sensillum potential. Finally, they show that these changes are behaviorally relevant as conditions that increase sGRN activity decrease avoidance of bitter substances.

Strengths:

There is solid evidence that perturbation of sweet GRNs affects bitter GRN in the same sensillum. The measurement of transsynaptic potential and how it changes is also interesting and supports the authors' conclusion.

Weaknesses:

The ionic basis of how perturbation in GRN affects the transepithelial potential which in turn affects the second neuron is not clear.

We speculate that HCN-dependent membrane potential regulation, rather than ionic composition change, is responsible for the observed SP preservation, as further discussed as an author response in the section of “Recommendations for the authors”. The transepithelial potential can be dissipated by increased conductance through receptor-linked ion channels following gustatory receptor activation in GRNs. The volume of the sensillum lymph is very small according to electron micrographs of horizontally sliced bristles (PMID: 11456419). Therefore, robust excitation of a gustatory neuron may easily deplete the extracellular potential built as a form of polarized ion concentrations across the tight junction. When the consumption is too strong and extended, the neighboring neuron, which share TEP with the activated GRN, can be negatively affected. We propose that HCN suppresses overexcitation of sGRNs by means of membrane potential stabilization. This stabilization prevents sGRNs from excessively reducing the TEP, thereby protecting the activity of neighboring bGRNs.

Reviewer #3 (Public Review):

Ephaptic inhibition between neurons housed in the same sensilla has been long discovered in flies, but the molecular basis underlying this inhibition is underexplored. Specifically, it remains poorly understood which receptors or channels are important for maintaining the transepithelial potential between the sensillum lymph and the hemolymph (known as the sensillum potential), and how this affects the excitability of neurons housed in the same sensilla.

Although a reduction of sensillum potential was proposed to underlie membrane hyperpolarization of post-ephaptic olfactory neurons in Drosophila, our preliminary data (not shown due to a manuscript in preparation) and the results included in the paper (Fig. 5B) strongly suggest that SP reduction is not a requisite for ephaptic inhibition at least in GRNs. Ephaptic inhibition is expected to be instantaneous, whereas we find that SP reduction in gustation is very slow. Therefore, we would like to indicate that the findings we report in this manuscript are not directly related to ephaptic inhibition.

Lee et al. used single-sensillum recordings (SSR) of the labellar taste sensilla to demonstrate that the HCN channel, Ih, is critical for maintaining sensillum potential in flies. Ih is expressed in sugar-sensing GRNs (sGRNs) but affects the excitability of both the sGRNs and the bitter-sensing GRNs (bGRNs) in the same sensilla. Ih mutant flies have decreased sensillum potential, and bGRNs of Ih mutant flies have a decreased response to the bitter compound caffeine. Interestingly, ectopic expression of Ih in bGRNs also increases sGRN response to sucrose, suggesting that Ih-dependent increase in sensillum potential is not specific to Ih expressed in sGRNs. The authors further demonstrated, using both SSR and behavior assays, that exposure to sugars in the food substrate is important for the Ih-dependent sensitization of bGRNs. The experiments conducted in this paper are of interest to the chemosensory field. The observation that Ih is important for the activity in bGRNs albeit expressed in sGRNs is especially fascinating and highlights the importance of non-synaptic interactions in the taste system.

Despite the interesting results, this paper is not written in a clear and easily understandable manner. It uses poorly defined terms without much elaboration, contains sentences that are borderline unreadable even for those in the narrower chemosensory field, and many figures can clearly benefit from more labeling and explanation. It certainly needs a bit of work.

We would like to revise the language aspect of the manuscript after finalizing the scientific revision.

Below are the major points:

(1) Throughout the paper, it is assumed that Ih channels are expressed in sugar-sensing GRNs but not bitter-sensing GRNs. However, both this paper and citation #17, another paper from the same lab, contain only circumstantial evidence for the expression of Ih channels in sGRNs. A simple co-expression analysis, using the Ih-T2A-GAL4 line and Gr5a-LexA/Gr66a-LexA line, all of which are available, could easily demonstrate the co-expression. Including such a figure would significantly strengthen the conclusion of this paper.

We did conduct confocal imaging with Ih-T2A-Gal4 in combination with GRN Gal4s (ref#17 version2). The expression is very broad, including both neurons and non-neuronal cells. We observed much stronger sGRN expression than bGRN expression. But the promiscuous expression of the reporter in many cells hindered us from clearly demonstrating the void of the reporter in bGRNs. However, the functional and physiological examination of Ih-T2A-Gal4 with the neuronal modifiers such as TRPA1 and Kir2.1 in ref#17 indicates the strong and little expression of Ih in sGRNs and bGRNs, respectively. Furthermore, the RNAi kd results present another line of evidence that HCN expressed in sGRNs regulates SP and bGRN activity (Fig. 1C,D, Fig. 1-figure supplement 2). Ih-RNAi expression in bGRNs did not result in any statistically significant changes in the activities of sGRNs and bGRNs compared to controls (Fig. 1C,D, revised), advocating that Ih acts in sGRNs for the functional homeostasis of SP and GRNs, as we claim.

(2) Throughout this paper, it is often unclear which class of labellar taste sensilla is being recorded. S-a, S-b, I-a, and I-b sensilla all have different sensitivities to bitters and sugars. Each figure should clearly indicate which sensilla is being recorded. Justification should be provided if recordings from different classes of sensilla are being pooled together for statistics.

We mainly performed SSR (single sensillum recording) on i-type bristles as they have the simplest composition of GRNs compared to s- and L-type bristles. As single s-types also contain each of s- and bGRN, we measured SP also for s-types (Figs. 2, 3F and 4D). In case of Fig.3-figure supplement 1, L-types were tested for the relationship between water cell activity and SP. Now all the panels are labelled with the tested bristle types.

(3) In many figures, there is a lack of critical control experiments. Examples include Figures 1C-F (lacking UAS control), Figure 2I-J (lacking UAS control), Figure 4E (lacking the UAS and GAL4 control, and it is also strange to compare Gr64f > RNAi with Gr66a > RNAi, instead of with parental GAL4 and UAS controls.), and Figure 5D (lacking UAS control). Without these critical control experiments, it is difficult to evaluate the quality of the work.

Thank you for pointing this out. We appreciate the feedback and have addressed these concerns by including all the requested controls in the figures. Specifically, we have added the UAS controls for Figs 1C-F and 2I-J, as well as the UAS and GAL4 controls for Fig. 4E. We have also included the UAS control for Fig. 5D.

(4) Figure 2A could benefit from more clarification about what exactly is being recorded here. The text is confusing: a considerable amount of text is spent on explaining the technical details of how SP is recorded, but very little text about what SP represents, which is critical for the readers. The authors should clarify in the text that SP is measuring the potential between the sensillar lymph, where the dendrites of GRNs are immersed, and the hemolymph. Adding a schematic figure to show that SP represents the potential between the sensillar lymph and hemolymph would be beneficial.

SP was defined at lines 55-56 in the first paragraph of introduction, which also contains the background information for SP as a transepithelial potential. As reviewer suggested, we now also included a sentence describing SP (“SP is known as a transepithelial potential between the sensillum lymph and the hemolymph, generated by active ion transport through support cells”, line 126) and a drawing to illustrate the concept of SP (Fig. 2A), and revised the legend.

(5) The sGRN spiking rate in Figure 4B deviates significantly from previous literature (Wang, Carlson, eLife 2022; Jiao, Montell PNAS 2007, as examples), and the response to sucrose in the control flies is not dosage-dependent, which raises questions about the quality of the data. Why are the responses to sucrose not dosage-dependent? The responses are clearly not saturated at these (10 mM to 100 mM) concentrations.

Our recordings show different spiking frequencies from others’ work, because the frequencies are from 5-sec bins not only first 0.5 sec. This lowers the frequencies, as spikes are relatively more frequent in the beginning of the recording (Fig. 4-figure supplement 1).

Why are the responses to sucrose not dosage-dependent? The responses are clearly not saturated at these (10 mM to 100 mM) concentrations.

We were also puzzled with the flat dose dependence to sucrose. This result may suggest the existence of another mechanism moderating sucrose responses of sGRNs. This flat curve reappeared with other genotypes with the same concentration range (5-50 mM) in Fig. 4E. However, 1-mM sucrose produced much lower spiking frequencies (Fig. 4E), suggesting that sGRN responses are saturated at 5 mM sucrose with our recording/analysis condition.

(6) In Figure 4C, instead of showing the average spike rate of the first five seconds and the next 5 seconds, why not show a peristimulus time histogram? It would help the readers tremendously, and it would also show how quickly the spike rate adapts to overexpression and control flies. Also, since taste responses adapt rather quickly, a 500 ms or 1 s bin would be more appropriate than a 5-second bin.

Taste single sensillum recording starts by contacting stimulants, which bars us from recording pre-stimulus responses of GRNs. Therefore, we showed post-stimulus graphs with 1-sec bins (Fig. 4-figure supplement 1) as we reviewer suggested.

(7) Lines 215 - 220. The authors state that the presence of sugars in the culture media would expose the GRNs to sugar constantly, without providing much evidence. What is the evidence that the GRNs are being activated constantly in flies raised with culture media containing sugars? The sensilla are not always in contact with the food.

We agree with reviewer. We replaced “long-term stimulation of sGRNs” with “strong and frequent stimulation of sGRNs for extended period”. The word long-term may be interpreted to be constant.

(8) Line 223. To show that bGRN spike rates in Ih mutant flies "decreased even more than WT", you need to compare the difference in spike rates between the sorbitol group and the sorbitol + sucrose group, which is not what is currently shown.

The data were examined by ANOVA and a multiple comparison test (Dunn’s) between all the groups regardless of genotypes and conditions in the panel (all the groups sharing the y axis). Therefore, the differences were statistically examined. However, the cited expression we used read like it was about the slope or extent of the decrease. We intended to indicate the difference in the absolute values of spiking frequencies after overnight sweet exposure between the genotypes, while bGRN activities were statistically indifferent between WT and Ih mutants when they were kept only on sorbitol food. We revised it to “decreased to the level significantly lower than WT”. We also changed the graph style to effectively present the trend of changes in bGRN sensitivity with comparison between genotypes. Again, the groups were statistically examined together regardless of the genotypes and conditions.

(9) To help readers better understand the proposed mechanisms here, including a schematic figure would be helpful. This should show where Ih is expressed, how Ih in sGRNs impacts the sensillum potential, how elevated sensillum potential increases the electrical driving force for the receptor current, and affects the excitability of the bGRNs in the same sensilla, and how exposure to sugar is proposed to affect ion homeostasis in the sensillum lymph.

As reviewer suggested, we included two panels to show working model for gustatory homeostasis via SP maintenance by HCN (Fig. 5E,F).

Reviewer #1 (Recommendations For The Authors):

(1) The relationship between this paper and the authors' bioRxiv preprint posted last year is not clear. In the introduction they made it seem like this paper is a follow-up that builds on the preprint, but most or all of the experiments in this paper were already performed in the preprint. I guess the authors are planning to divide the original paper into two papers. I would suggest updating the preprint to avoid confusion.

Thank you for the comment. We updated the preprint to be without a part of Fig.6 and entire Fig.7 along with associated texts. As reviewer pointed out, our eLife paper was spun off from the part of the preprint paper, because we feel that the two stories could confuse readers when presented together.

(2) Have the authors considered testing responses of water GRNs? They reside in the same sensilla as sugar neurons, so are they also increased affected by Ih mutation or RNAi in sugar neurons? This would strengthen the evidence that the indirect (non-cell autonomous) effects of Ih are due to the sensillum potential and not some specific interaction between sweet and bitter cells.

As reviewer proposed, we appraised water GRN activity in the L-type bristles of WT, Ihf03355 and a genomic rescue line for Ihf03355. Spiking responses in water GRNs were evoked by hypo-osmolarity of electrolyte (0.1 mM tricholine citrate-TCC). Interestingly, the Ih mutant showed reduced 0.1 mM TCC-provoked spiking frequencies compared to WT. This impairment was rescued by the genomic fragment containing an intact Ih locus (Figure 3-figure supplement 1A).

Additionally, SPs in L-type bristles were reduced by Ih deficiencies but increased in Gr64af, suggesting that HCN regulates sGRNs in L-type bristles as well (Figure 3-figure supplement 1B). Again, the bristles of animals with both mutations together exhibited SPs similar to those of WT.

Furthermore, when we conducted cDNA rescue experiments in L bristles, introduction of Ih-RF cDNA in sGRNs restored SPs, while expressing it in bGRNs did not unlike the results from the i- and s-bristles (Fig. 2K,L), likely because L-bristles lack bGRNs. These cDNA rescue and genetic interaction experiments were conducted using flies fed on fresh cornmeal food with strong sweetness, suggesting that the sweetness in the media is the likely key factor producing the genetic interaction and necessitating HCN, consistent with other results in the manuscript. Therefore, SP regulation by HCN is observed in the L-type bristles.

Minor comments:

Line 52: typo, "Many of"

Thank you. Corrected

Line 95: typo, "sensilla do an sGRN"

Corrected

Line 98: typo, "we observed reduced the spiking responses"

Corrected

Line 206: typo, "a relatively low sucrose concentrations"

Corrected

Line 260: "inverse relationship between the two GRNs in excitability" - I am not exactly sure what data you are referring to.

Although alleles did not show increased sGRN activities, knockdown of Ih decreased bGRN activity but increased sGRN activity (Fig. 1C,D, Fig.1-figure supplement 2B), while suppression of sGRNs increased bGRN activity (Fig. 3). To clarify this point, we revised the phrase to “the inverse relationship between the two GRNs in excitability observed in Fig. 1C,D, Fig. 1-figure supplement 2B, and Fig. 3”.

Methods: typo, "twenty of 3-5 days with 10 males and 10 females"

Corrected to “Twenty flies, aged 3-5 days and consisting of 10 males and 10 females,”

Methods: typo, "Kim's wipes" should be "Kimwipes"

Corrected

Reviewer #2 (Recommendations For The Authors):

(1) More clarification is necessary on Transepithelial potential (TEP). TEP is typically created by having pumps and tight junctions between the sensillar lymph and the hemolymph.

We have an introduction to TEP or SP in the context of sensory functions (lines 40-57) with relevant references. The involvement of pumps and tight junction was mentioned in the same paragraph; “Glia-like support cells exhibit close physical association with sensory receptor neurons, and conduct active transcellular ion transport, which is important for the operation of sensory systems” (line 40) and “Tight junctions between support cells separate the externally facing sensillar lymph from the internal body fluid known as hemolymph” (line 53).

It is not clear how HCN channels in one of the neurons might change the composition of the sensillum lymph. An explanation of their model of how TEP depends on HCN is necessary.

Although the ionic composition of the sensillum lymph is a contributing factor to the sensillum potential, it is more conceptually relevant to describe our findings with the perspective of membrane potential regulation given the role of HCN in membrane potential stabilization as discussed in our manuscript.

We speculate that HCN controls the membrane potential at rest and/or in motion to modulate sGRN activity towards saving SP despite the sweetness in the niche. We positioned our results in relation to SP in discussion; “Our results provide multiple lines of evidence that HCN suppresses HCN-expressing GRNs, thereby sustaining the activity of neighboring GRNs within the same sensilla. We propose that this modulation occurs by restricting SP consumption through HCN-dependent neuronal suppression rather than via chemical and electrical synaptic transmission.” (lines 252-255). Moreover, it is unclear whether HCN is localized to the dendrite bathed in the sensillum lymph to influence the ionic composition of the lymph. It would be very interesting to study in future whether the ionic flow through HCN channels itself is critical for the function of HCN in this context, and whether HCN is exclusively present in the dendrite to support the postulation. However, we would like to remind reviewer that Kir2.1 and HCN channels in sGRNs showed similar effects on SP and bGRNs, while they differ in Na+ conductance.

In the initially submitted manuscript (lines 325-343), we discussed the potential mechanism by which Kir2.1 and HCN channels commonly increase SP in terms of how the membrane potential regulation in the soma can control the SP consumption in the dendrite of sGRNs.

Another point about the TEP that needs some explanation is that these sensilla are open to the environment as tastants must flow in and are different from mechanical sensilla in that sense.

This is a very important question regarding the general physiology of the taste sensilla, as the sensillum lymph is in contact with the external environment through the pore of the sensillum. It is indeed interesting to consider how the composition and potential of the lymph are maintained despite the relatively vast volume of food the sensilla encounter during gustation and the continuous evaporation to air between episodes of gustation. However, we believe that this question, while important, is distinct from the primary focus of our manuscript.

Are the TEP measurements in Figure 2 under control conditions where there are no tastants?

There is no tastant in the SP-measuring glass electrode other than the electrolyte. We apologize that we did not specify the recording electrode condition. We inserted a clause in the method; “For SP recordings, the recording electrode contained 2 mM TCC as the electrolyte, and…”

Does the TEP change dynamically as sGRN is activated?

SP does shift in response to sweets. Please see Fig. 5B. Also, we showed SP changes by mechanical stimuli, which depended on the mechanoreceptor, NompC (Fig. 2D-F). Mechanoreceptor neurons share the sensillum lymph with GRNs.

(2) More clarification on the potential transduction mechanism and how TEP affects one neuron differentially. Essentially, sGRN perturbation affects sGRN activity and it affects the TEP. More explanation is needed for the potential ionic mechanism of each.

Our results strongly suggest that HCN lowers the activity of HCN-expressing GRNs, mitigating SP consumption. This modulation is crucial because the SP serves as a driving force for neuronal activation within the sensillum. HCN is particularly necessary in sGRNs because of the flies’ sweet feeding niche, which is expected to result in frequent and strong activation of sGRNs. The SP saved by HCN-dependent delimitation of sGRNs can be used to raise the responsibility of bGRNs.

(3) The authors refer to their own unreviewed paper (Reference 17). This paper is on a similar topic and there seems to be some overlap. Clarification on this point would be important.

We revised the biorxiv preprint, so that the preprint version 2 does not contain the parts overlapping with this eLife paper. This eLife paper was originally part of the preprint paper, but it was separated to clarify the messages of the two stories. As we explained in Discussion (lines 276-297), HCN provides resistance to both hyperpolarization and depolarization of the membrane potential. Simply put, one paper focuses on the role of HCN in resisting hyperpolarization, while the other (this paper in eLife) focuses on resisting depolarization.

(4) Methods are sparse. Many details on the method are necessary. For example, Sensilla recordings are being done by the tip-dip method (I assume). What does "number of experiments" mean in Figure 1? Is it the number of animals or the number of sensilla? How many trials/sensilla?

We indicated the extracellular recording was performed by the tip-dip method; “In vivo extracellular recordings were performed by the tip-dip method as detailed previously”. We also added a statement on the number of experiments; “The number of experiments indicated in figures are the number of naïve bristles tested. The naïve bristles were from at least three different animals.”

(5) Figure 1: I understand the author's interpretation. But if one compares WT in Figure 1A to Gr64a-IhRNAi in 1C, we can come to the conclusion that there is no change. In other words, the control in Figure 1C (grey) has a much higher response than WT. Similar conclusions can be made for other experiments. Is the WT response stable enough to make the conclusions made here?

The genetic background of each genotype may influence GRN activity to some extent. RNAi knockdown experiments are well-known for their hypomorphic nature, and their effects should be evaluated by comparison with their parental controls such as Gal4 and UAS lines. As all reviewers pointed out, we added the results from UAS control. This effort confirms that Gr89a>Ih RNAi is statistically indifferent to UAS control as well as Gr64f-Gal4 control in bGRN spiking evoked by 2-mM caffeine, while Gr64f>Ih RNAi showed reduced bGRN responses to 2 mM caffeine compared to all the controls.

(6) Figure 3: Why is bGRN spiking not plotted against sensillum potential to observe the dependence more directly?

This is a very interesting suggestion. We are not, however, equipped to measure spiking and sensillum potential simultaneously. Therefore, they are independent experiments, and we treated them accordingly.

(7) Figure 4: Why bGRN response is only affected at high caffeine concentrations is not clear.

We were also surprised by the differences in the dose dependence results of b- and sGRNs, genetically manipulated to mis-express and over-express HCN in Fig. 4A and 4E, respectively. Each gustatory neuron likely has distinct sets of players and parameters that set its own membrane potential and excitability.

We can think of a possibility that there might be a range of membrane potentials within which HCN does not engage. In bGRNs, the resting membrane potential may lie low within this range, so that some degrees of membrane depolarization by low concentrations of caffeine do not significantly close HCN channels, thus preventing their hyperpolarizing effects. On the other hand, the membrane potential of sGRNs may be high within this range, showing suppressive effects at all tested sucrose concentrations. However, we find this explanation is too speculative to include in the main text, while we stated in the original manuscript, “implying a complex cell-specific regulation of GRN excitability.” (line 210).

(8) Minor:

L98 - there is a small typo

Corrected

L274: "funny" !?

“Funny” currents, denoted If, were initially observed by electrophysiologists and later attributed to HCN channels, now indicated by Ih (thus the gene name Ih in Drosophila). These currents were termed "funny" due to their unusual properties compared to other currents. For more detailed information, please refer to the cited references.

L257: Neuropeptide seemed to be abrupt

We attempted to discuss possible mechanisms that mediate excitability changes across GRNs beyond the mechanism by SP shifts. Neuropeptides, which are chemical neurotransmitters along with small neurotransmitters, were mentioned following the discussion on synaptic transmission to suggest alternative pathways for excitability regulation. This inclusion is meant to provide a comprehensive overview of potential mechanisms influencing GRN activity.

Reviewer #3 (Recommendations For The Authors):

Congratulations on your fascinating research! The results are certainly of interest to the chemosensory field. However, I suggest using academic editing services to enhance the clarity of your text and ensure that the terminology and jargon align with standard usage in the field. The current choice of words may not be consistent with commonly used terms. As it is now, the writing might not fully showcase the compelling story and the effort behind your study, and is underselling your interesting results. Proper refinement could make sure your valuable findings are appropriately recognized.

We appreciate your comments and apologize for any difficulties reviewers faced during the review process. We are currently prioritizing the review of scientific content and plan to address language issues in a subsequent revision. It would be very helpful for future revisions if the problematic sentences or expressions could be indicated in detail after this revision. This will allow us to ensure that our terminology and expression align with standard usage in the field, and that our findings are clearly and effectively communicated.

Minor points:

(1) Line 110: what is Ih-RF?

We apologize that we relied on a reference in describing the cDNA. The following clause was inserted with additional reference and the Flybase id: “(Flybase id: FBtr0290109), which previously rescued Ih deficiency in other contexts17,26 ,”  

(2) Line 158: Gr64af mutant flies still have Gr5a and a residual response to fructose and sucrose (Slone, Amrein 2007).

We revised the line to “is severely impaired in sucrose and glucose sensing”, since there is a substantial loss of sucrose and glucose sensing in both Gr64af from Kim et al 2018 and DGr64 from Slone et al 2007, when they were examined by the proboscis extension reflex assay. This was also confirmed in the study by Jiao et al 2009. We also deleted “sugar-ageusic” and instead describe the mutant “impaired in sucrose and glucose sensing” in Fig. 3 legend.

(3) Lines 264-273 seem unnecessary. This paper is not about the function of HCN in mammals, and these discussions seem largely irrelevant.

We feel that it is important to position our results within a broader context by discussing the potential implications of our findings for sensory systems of other animals. As we stated, HCN channels have been localized in mammalian sensory systems, but their roles are often not well understood. By including this discussion, we aim to highlight the relevance of our findings beyond the model organism used in our study and suggest possible areas for future research in mammalian systems.

Author response:

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

Reviewer #1 (Public Review):

Overall, the manuscript is very well written, the approaches used are clever, and the data were thoroughly analyzed. The study conveyed important information for understanding the circuit mechanism that shapes grid cell activity. It is important not only for the field of MEC and grid cells, but also for broader fields of continuous attractor networks and neural circuits.

We appreciate the positive comments.

(1) The study largely relies on the fact that ramp-like wide-field optogenetic stimulation and focal optogenetic activation both drove asynchronous action potentials in SCs, and therefore, if a pair of PV+ INs exhibited correlated activity, they should receive common inputs. However, it is unclear what criteria/thresholds were used to determine the level of activity asynchronization, and under these criteria, what percentage of cells actually showed synchronized or less asynchronized activity. A notable percentage of synchronized or less asynchronized SCs could complicate the results, i.e., PV+ INs with correlated activity could receive inputs from different SCs (different inputs), which had synchronized activity. More detailed information/statistics about the asynchronization of SC activity is necessary for interpreting the results.

The short answer here is that spiking responses from the pairs of SCs that we sampled appear asynchronous. We now show this in the form of cross-correlograms for all recorded pairs of SCs (Figure 2, Figure Supplement 1). The correlograms lack peaks that would indicate synchronous activation. Thus, while our dataset is not large enough to rule out occasional direct synchronisation of SCs, this appears unlikely to account for synchronised input to PV+INs.

This conclusion is consistent with consideration of mechanisms that could in principle synchronise SCs:

First, if responses to ramping light inputs was fully deterministic, then this could lead to fixed relative timing of spikes fired by different SCs. This is unlikely given the influence of stochastic channel gating on SC spiking (Dudman and Nolan 2009) and is inconsistent with trial to trial variability in spike timing (Figure 2, Figure Supplement 2).

Second, as SCs are glutamatergic they could excite one another. However, excitatory connections between stellate cells are rare (Pastoll et al. 2013; Couey et al. 2013; Fuchs et al. 2016) and when detected they have low amplitude (mean < 0.25 mV; (Winterer et al. 2017)). Our finding that spiking by pairs of SCs is not correlated is consistent with this.

Third, strong interaction between stellate cells mediated by local inhibitory pathways (Pastoll et al. 2013; Couey et al. 2013) could coordinate their activity. The lack of correlation between spiking of pairs of SCs suggests that such coordination is rarely recruited by our ramping protocols. Nevertheless, recruitment of inhibition may happen to some extent as experiments in Figure 4 show that correlated input from SCs to more distant, but not nearby PV+INs, is reduced by blocking inhibitory synapses. Given that we don't find evidence for synchronised spiking of SCs, this additional common input to widely separated PV+INs is instead best explained by recruitment of interneurons that act directly on the target SCs. We have modified Figure 8 to make this clear.

Thus, for experiments with ramping light stimuli, synchronous activation of SCs is unlikely to explain common input to PV+INs. Input from the same SC best explains correlated responses of nearby PV+IN inhibitory populations, while recruitment of an additional inhibitory pathway may contribute to correlated responses of more distant PV+INs.

For experiment using focal stimulation, substantial trial-to-trial variation in SC spike timing argues strongly against deterministic coordination. Indirect coordination of presynaptic neurons is also extremely unlikely given that focal activation is sparse and brief, while inputs from many presynaptic SCs are required to drive a postsynaptic interneuron to spike (e.g. (Pastoll et al. 2013; Couey et al. 2013)). Results from these experiments thus corroborate results from experiments using ramping light stimulation.

In revising the manuscript we have tried to ensure these arguments are clear (e.g. p 5, para 3; p 6, para 2; p 10, para 1).

(2) The hypothesis about the "direct excitatory-inhibitory" synaptic interactions is made based on the GABAzine experiments in Figure 4. In the Figure 8 diagram, the direct interaction is illustrated between PV+ INs and SCs. However, the evidence supporting this "direct interaction" between these two cell types is missing. Is it possible that pyramidal cells are also involved in this interaction? Some pieces of evidence or discussions are necessary to further support the "direction interaction".

Indirect connections between stellate cells mediated via fast spiking inhibitory interneurons are well established by previous studies (e.g. (Pastoll et al. 2013; Couey et al. 2013; Fuchs et al. 2016), and so were not addressed here. Previous work also establishes that connections from stellate cells to pyramidal cells are extremely rare (Winterer et al. 2017). Because the Sim1:Cre mouse line is specific to stellate cells and does not drive transgene expression in pyramidal cells (Sürmeli et al. 2015), it's therefore unlikely that pyramidal cells play a role.

To make these points clearer we have modified the text in the discussion (p 5, para 3; p 10, paras 1 & 2). We have also modified Figure 8 to highlight that the indirect interaction may be best accounted for by inhibitory pathways onto PV+INs rather than via SCs (which our new cross-correlation analyses indicate is unlikely).

Reviewer #2 (Public Review):

In this study, Huang et al. employed optogenetic stimulation alongside paired whole-cell recordings in genetically defined neuron populations of the medial entorhinal cortex to examine the spatial distribution of synaptic inputs and the functional-anatomical structure of the MEC. They specifically studied the spatial distribution of synaptic inputs from parvalbumin-expressing interneurons to pairs of excitatory stellate cells. Additionally, they explored the spatial distribution of synaptic inputs to pairs of PV INs. Their results indicate that both pairs of SCs and PV INs generally receive common input when their relative somata are within 200-300 ums of each other. The research is intriguing, with controlled and systematic methodologies. There are interesting takeaways based on the implications of this work to grid cell network organization in MEC.

We appreciate the positive comments.

(1) Results indicate that in brain slices, nearby cells typically share a higher degree of common input. However, some proximate cells lack this shared input. The authors interpret these findings as: "Many cells in close proximity don't seem to share common input, as illustrated in Figures 3, 5, and 7. This implies that these cells might belong to separate networks or exist in distinct regions of the connectivity space within the same network.". Every slice orientation could have potentially shared inputs from an orthogonal direction that are unavoidably eliminated. For instance, in a horizontal section, shared inputs to two SCs might be situated either dorsally or ventrally from the horizontal cut, and thus removed during slicing. Given the synaptic connection distributions observed within each intact orientation, and considering these distributions appear symmetrically in both horizontal and sagittal sections, the authors should be equipped to estimate the potential number of inputs absent due to sectioning in the orthogonal direction. How might this estimate influence the findings, especially those indicating that many close neurons don't have shared inputs?

Given we find high probabilities of correlated inputs to nearby cells in both planes, our conclusion that nearby cells are likely to receive common inputs appears to be independent of the slice plane. For cells further apart, where the degree of correlated input becomes more variable, it is possible that cell pairs that have low input correlations measured in one slice plane would have high input correlations if measured in a different plane. An argument against this is that as the cell pairs are further apart, it is less likely that an orthogonal axon would intersect dendritic trees of both cells. Nevertheless, we can't rule this out given the data here. We have amended the discussion to highlight this possibility (p 10, para 1). We agree it would be interesting to address this point further with quantitative analyses but this will be difficult without detailed reconstructions of the circuit.

(2) The study examines correlations during various light-intensity phases of the ramp stimuli. One wonders if the spatial distribution of shared (or correlated) versus independent inputs differs when juxtaposing the initial light stimulation phase, which begins to trigger spiking, against subsequent phases. This differentiation might be particularly pertinent to the PV to SC measurements. Here, the initial phase of stimulation, as depicted in Figure 7, reveals a relatively sparse temporal frequency of IPSCs. This might not represent the physiological conditions under which high-firing INs function. While the authors seem to have addressed parts of this concern in their focal stim experiments by examining correlations during both high and low light intensities, they could potentially extract this metric from data acquired in their ramp conditions. This would be especially valuable for PV to SC measurements, given the absence of corresponding focal stimulation experiments.

We understand the gist of the question here as being can differences in correlation scores between initial vs later phases of responses to ramping light inputs be used to infer spatial organisation? These differences are likely to reflect heterogeneity in the spiking of the input neurons, for example through differences in spike threshold, spike frequency adaptation and saturation of spiking (e.g. Figure 2, Figure Supplement 1A, and also see (Pastoll et al. 2020)). We don't expect these differences to have any spatial organisation along the mediolateral axis, and while spike threshold follows a dorsoventral organisation there is nevertheless substantial local variation between neurons (Pastoll et al. 2020). It's therefore unlikely we can use differences in early versus late correlations to make the inferences proposed by the reviewer.

With respect to PV to SC measurements, similar heterogeneity is likely. We note that we were unable to carry out focal stimulation experiments for PV to SC connections as PV neurons did not spike in response to focal optogenetic stimulation.

With respect to physiological conditions, our aim here is simply to assess connectivity in well controlled conditions, e.g. voltage-clamp, minimal spontaneous activity, known neuronal locations, etc. It's not clear that physiological activation patterns would improve on these tests and quite likely data would be noisier and harder to interpret.

(3) Re results from Figure 2: Please fully describe the model in the methods section. Generally, I like using a modeling approach to explore the impact of convergent synaptic input to PVs from SCs that could effectively validate the experimental approach and enhance the interpretability of the experimental stim/recording outcomes. However, as currently detailed in the manuscript, the model description is inadequate for assessing the robustness of the simulation outcomes. If the IN model is simply integrate-and-fire with minimal biophysical attributes, then the findings in Fig 2F results shown in Fig 2F might be trivial. Conversely, if the model offers a more biophysically accurate representation (e.g., with conductance-based synaptic inputs, synapses appropriately dispersed across the model IN dendritic tree, and standard PV IN voltage-gated membrane conductances), then the model's results could serve as a meaningful method to both validate and interpret the experiments.

We appreciate the simulation descriptions were insufficient and have modified the manuscript to include additional details and clarification (p 14, paras 1-3).

We're not sure we follow the logic here with respect to model types. The experiments were carried out in the voltage-clamp recording configuration with the goal of identifying correlated inputs independently from how they are integrated by the postsynaptic neuron. Given that membrane potential doesn't change (and so the CdVm/dt term of the membrane equation = 0), integrate and fire and point conductance-based models both simplify down to summing of input currents. We achieve this by convolving spike times with experimentally measured synaptic current waveforms. An assumption of our approach is that we achieve a reasonable space clamp. We believe this is justified given that stellate cells and PV interneurons are reasonably electrotonically compact, and that our analysis relies on consistent correlations rather than absolute amplitudes or time constants of the postsynaptic response and so should tolerate moderate space clamp errors.

Reviewer #3 (Public Review):

This paper presents convincing data from technically demanding dual whole-cell patch recordings of stellate cells in medial entorhinal cortex slice preparations during optogenetic stimulation of PV+ interneurons. The authors show that the patterns of postsynaptic activation are consistent with dual recorded cells close to each other receiving shared inhibitory input and sending excitatory connections back to the same PV neurons, supporting a circuitry in which clusters of stellate cells and PV+IN interact with each other with much weaker interactions between clusters. These data are important to our understanding of the dynamics of functional cell responses in the entorhinal cortex. The experiments and analysis are quite complex and would benefit from some revisions to enhance clarity.

These are technically demanding experiments, but the authors show quite convincing differences in the correlated response of cell pairs that are close to each other in contrast to an absence of correlation in other cell pairs at a range of relative distances. This supports their main point of demonstrating anatomical clusters of cells receiving shared inhibitory input.

We appreciate the positive comments.

The overall technique is complex and the presentation could be more clear about the techniques and analysis. In addition, due to this being a slice preparation they cannot directly relate the inhibitory interactions to the functional properties of grid cells which was possible in the 2-photon in vivo imaging experiment by Heys and Dombeck, 2014.

We have modified the manuscript to try to improve the presentation (specific changes are detailed below). We agree that an important future challenge is to relate our findings to in vivo observations (p 11, para 2).

Reviewer #1 (Recommendations For The Authors):

Major points

(1) The study largely relies on the fact that ramp-like wide-field optogenetic stimulation and focal optogenetic activation both drove asynchronous action potentials in SCs, and therefore, if a pair of PV+ INs exhibited correlated activity, they should receive common inputs. In Figure 2 and its supplementary figures, the authors also showed examples of asynchronized activity. However, it is unclear to me what criteria/thresholds were used to determine the level of activity asynchronization, and under these criteria, what percentage of cells actually showed synchronized or less asynchronized activity. A notable percentage of synchronized or less asynchronized SCs could complicate the results, i.e., PV+ INs with correlated activity could receive inputs from different SCs (different inputs), which had synchronized activity. Related to this concern, it would also be important to simulate what level of activity asynchronization in SCs could still lead to correlated PV+ IN activity above shuffle, and among the recorded SCs, what percentage of cells belong to this synchronized/less asynchronized category.

We address this point in our response to the public review. In brief, we have added additional cross-correllograms showing that ramp activation of SC pairs does not cause detectable synchronous activation. We also clarify that sensitivity of correlations of some widely separated pairs to GABA-blockers is suggestive of SCs activating common inhibitory inputs to cell pairs.

(2) The above concern is more relevant to the focal stimulation experiments, in which the authors tried to claim that a pair of PV+ INs with correlated activity could receive inputs from the same SCs neurons. The authors also showed that the stimulation patterns leading to the activation of PV+ INs were more similar if PV+ INs had correlated activity (Figure 5D). However, if nearby SCs were more synchronized than distal SCs within this stimulation scale, even though a pair of PV+ INs showed correlated activity, they could still receive inputs from different but nearby SCs. In this case, it would be helpful to quantify the relationship between the level of activity synchronization of SCs and their distances. In Figure 5 Supplementary Figure 1, the data were only provided for 8 cells. If feasible, collecting data from more cells would be needed for the proposed analysis.

We explain in our responses to point 1 above and in the public review that direct synchronisation of SCs is unlikely. This is particularly unlikely for focal stimulation experiments as the timing of responses of individual SCs is extremely variable between trials. Thus, even if there were strong synaptic connections between SCs, which the evidence suggests there is not (Pastoll et al. 2013; Couey et al. 2013; Fuchs et al. 2016), then this would be unlikely to result in reliably timed coordinated firing.

(3) It is unclear what the definition of "common inputs" is. Do they refer to inputs from the same group of cells? If different groups of cells provide synchronized inputs, will the inputs be considered "common inputs" or "different inputs"?

We used "common" in an attempt to be consistent with classic work by Yoshimura et al. and in an attempt to be succinct. Thus, by common input we are referring to cell pairs for which a proportion of their input is from the same presynaptic neuron(s), as opposed to cell pairs for which their input is from different neurons and therefore have no common input. We have attempted to make sure this is clear in the revised manuscript (e.g description of simulations on p 4, para 2).

(4) In the introduction and abstract, it was mentioned that "dense, but specific, direct excitatory-inhibitory synaptic interactions may operate at the scale of grid cell clusters". It is unclear to me how "dense" was demonstrated in the data. Can the authors clarify?

Thanks for flagging this, we were insufficiently clear. We have revised the text to refer to cell pairs for which a proportion of their input is from the same presynaptic neurons (e.g. p 3, para 1), and separately about indirect coordination, by which we mean inputs to cell pairs that appear correlated because of coordination between upstream neurons.

(5) The hypothesis about the "direct excitatory-inhibitory" synaptic interactions is made based on the GABAzine experiments in Figure 4. In the Figure 8 diagram, the direct interaction is illustrated between PV+ INs and SCs. Is there any evidence supporting this "direct interaction"?

The direct interaction from SCs to PV+INs and from PV+INs to SCs were previously demonstrated by experiments with recordings from pairs of neurons (e.g. (Pastoll et al. 2013; Couey et al. 2013; Fuchs et al. 2016; Winterer et al. 2017). Our results in Figures 3-5, which show that exciting SCs by light activation of ChR2 leads to excitation of PV+INs, and in Figure 7, which show that light activation of PV+INs expressing ChR2 leads to inhibition of SCs, are consistent with these previous conclusions. We have modified the manuscript to make sure this is clear (p 2, para 3).

Is it possible that pyramidal cells are also involved in this interaction? If this is unlikely, the author may provide some pieces of evidence (e.g., timing of responses after optogenetic stimulation) or some discussions.

This is unlikely given that previous studies indicate that connections from stellate to pyramidal cells are weak or absent (Winterer et al. 2017). We now clarify this in the Discussion (p 10, para 1).

Minor points (1) Page 4: the last paragraph: the author claimed that CCpeakmean was reduced and CClagvar increased with cell separation. Although the trends are visible in the figures, the author may provide appropriate statistics to support this statement, such as a correlation between cell separation and CCpeakmean CClagvar./

We have inserted summaries of linear model fits into the legends for Figure 3E-F, Figure 5F-H and Figure 7D.

(2)  If I understood correctly, in the second last paragraph on page 6, "pairs of SCs" should be changed to "pairs of PV+ INs".

Thanks. Corrected.

(3)  Page 9: the 7th line to the end: where is Figure S4?

Corrected to 'Figure 3, Figure Supplement 2'.

(4)  Page 27: at the end of figure caption B: two ".

Corrected.

(5)  Figures 3A and B: what are the red vertical rectangles?

These are the regions shown on an expanded time base in C and D. This is now clarified in the legend.

(6)  Page 28 Figure caption of D and E: (C) and (D) should be (D) and (E).

Corrected.

(7)  The first sentence of the third paragraph in INTRODUCTION: 'later' should be 'layer'.

Corrected.

Reviewer #2 (Recommendations For The Authors):

- Some related work has been done by Beed et al. 2013 to map the spatial distribution of inputs to neurons in MEC. Certainly, there are differences in the approaches and the key questions, but the contribution of this study would benefit from a more detailed comparison of the results from Beed vs the current study and should be included in the discussion.

It's hard to include a detailed comparison of results, at least without losing focus, as the two studies address different questions with different approaches. We already noted that 'Local optical activation of unidentified neurons has also been used to infer connectivity principles but with a focus on responses of single postsynaptic neurons (Beed et al., 2013, 2010)'. In addition, we now note that 'Our focal optogenetic stimulation approach also offers insight into the spatial organization of presynaptic neuronal populations, with the advantage, compared to focal glutamate uncaging previously used to investigate connectivity in the MEC (Beed et al., 2013, 2010), that the identity of the presynaptic cell population is genetically defined'.

- There are a few places where the language is ambiguous or needs a more detailed description for clarity. • 3rd paragraph under "Focal activation of SCs generates common input to nearby PV+Ins". The correlation probability description in this paragraph and a similar sentence in the methods are very hard to understand. I had to look up the analysis in Yoshimura et al. 2005 to understand what was done here. It's a nice analysis, but the manuscript could benefit from a more detailed description of this measure in the methods.

We agree, it is a somewhat complex metric and is challenging to explain. In the interests of keeping the main text succinct, we have left the bare bones explanation as it was in the Results, but have expanded the explanation in the Methods. We hope this is now clear.

- " Alternatively, if there is no clear spatial organization of SC to PV+INs connections, then the similarity between stimulus locations for pairs of SCs should have a random distribution." This sentence is hard to understand. I think the use of the phrase "similarity of stimulus location" is a strange phrasing and is driving the confusion in this sentence.

We have replaced this with 'correspondence between active stimulus locations'.

- In the discussion under "Spatial extent and functional organization of L2 circuits" there is a grammatical mistake (seems to be 2x phrasing of "leads to common synaptic input").

Corrected.

- Citation in the introduction/discussion. Introduction: in addition to Gu et al. 2018, Heys et al 2014 also showed there are non-random correlations among putative grid cells as a function of their somatic distance. In the discussion section, in addition to Gu et al. 2018, Heys et al. 2014 showed there is anatomical clustering of grid cells in MEC. This earlier work investigating functional correlations among neurons in the superficial aspect of MEC in vivo should be cited and is particularly relevant in these two sections of the manuscript.

Thanks, we apologise for the oversight. We're well aware of this important study and have now cited it.

-Typo - Paragraph 3 of the intro; "later" should be layer.

Corrected.

-Figure 5 (D-E) there is a typo high correlation probability is D and low correlation is E (text says C/D).

Corrected.

Reviewer #3 (Recommendations For The Authors):

The paper is missing the bibliography section. This makes the review somewhat difficult as some cited papers are not immediately familiar based on the citation.

Thanks and our apologises for making extra work by omitting this. It is now included.

Page 2 - "cell clusters" - they should also cite the paper by Heys and Dombeck, 2014 that shows a spatial scale of inhibitory interactions computed based on correlations of grid cells recorded using 2-photon calcium imaging.

Added (see above).

Page 2 - "later 2 of the MEC" - layer.

Corrected.

Page 2 - "synaptic interactions" - again they should mention the work by Heys and Dombeck, 2014 that indirectly measured the spatial scale of inhibition.

Now cited in this paragraph.

Page 4 "we simulated responses" and Figure 2E - in each simulation - did they fit the magnitude and time constant of the simulated EPSCs to individual EPSCs in the data? Or did they randomly vary these to find the best fit?

The parameters for the simulations are given in the Methods and were chosen to correspond to the experimental values. We have rewritten this section to make the simulation methods clearer. Simulations using different time constants within a physiological range support similar conclusions.

Page 4 - "we identified 35/71" - Are these the cells that appear in yellow as correlated in Figures 3E-F? If so, the text should indicate that these cells are shown in yellow.

We have added this and have also updated the legends for additional clarification.

Figure 2, Figure Supplement 1 - B,C - the following phrase is not clear: "when the 4 / 8 of each neurons inputs from SCs also project to the other neuron (B)," Should the "the" be removed? Also, by 4/8 do they mean 50%, or do they mean 4 to 8?

Thanks, we've reworded to improve the clarity.

E - "receiving presynaptic inputs consisted of 4 overlapping SCs" - should it say "consisting"?

Corrected.

Figure 3, Figure Supplement 1 part E - "the same data as (C )" - should this be the same data as (D)?? I do not see how doing clustering on the shuffled data in (C ) would give two groups, but it makes sense if it is from (D).

That's right, now corrected.

Page 5 - "used action potentials" - this is confusing. Is the word "used" supposed to be there?

Corrected.

Page 5 - "widefield activation experiments" - they should cite the experiments that they are referring to here.

Added.

Page 5 - "effect of blocking" - "Figure 4" - I find it very odd that the agent GABAzine in Figure 4 is not explicitly mentioned in the main text (though it is mentioned in the methods). The main text should indicate that blocking was performed using GABAzine.

Added.

Page and page 14 and Figure 5 - "shifted" - do they mean shuffled?

We do. The classic papers by Yoshimura et al. used shifted so we keep this here so it's clear we've used their approach. We've added additional explanation to try to make sure the meaning is clear.

Figure 5 A, B, D, and E would benefit from a more detailed description. They should state whether the labels "1a" and "1b" and "2a" and "2b" refer to different recorded neurons in each pair. They should indicate that 2a and 2b are a different pair? Are the x, y axes of the images corresponding to anatomical position? Does "B" indicate the location of recordings shown in Figure 5B? The authors probably think this is all obvious, but it is not immediately obvious to the reader.

We have added additional clarification.

Page 8 - "Beed et al." - These papers by Beed ought to be cited in the introduction as well as they are highly relevant.

We now cite Beed et al. 2013 in the Introduction when we discuss local inhibitory input to SCs. While the Beed et al. 2010 paper is an important contribution to understanding about pathways from deep to superficial layers, the introduction focuses on communication between identified pre- and postsynaptic populations within layer 2 and therefore we haven't found a way to cite it without losing focus. We do cite this paper multiple times elsewhere.

Page 10 - "Excitatory-inhibitory interactions" - this summary of attractor models ought to cite the paper by Burak and Fiete as well.

The discussion focuses on models with excitatory-inhibitory connectivity and cites an important paper from the Fiete group. The model by Burak and Fiete, while also important, is purely inhibitory and so is not well constrained by the known circuitry, and therefore could not be correctly cited here.

Page 10 - "be consistent with models…or that focus on pyramidal neurons have also been proposed" - this seems ungrammatical as if two different sentences were merged.

Corrected.

References

Couey, Jonathan J, Aree Witoelar, Sheng-Jia Zhang, Kang Zheng, Jing Ye, Benjamin Dunn, Rafal Czajkowski, et al. 2013. “Recurrent Inhibitory Circuitry as a Mechanism for Grid Formation.” Nat. Neurosci. 16 (3): 318–24. https://doi.org/10.1038/nn.3310.

Dudman, Joshua T, and Matthew F Nolan. 2009. “Stochastically Gating Ion Channels Enable Patterned Spike Firing through Activity-Dependent Modulation of Spike Probability.” Plos Comput. Biol. 5 (2): e1000290. https://doi.org/10.1371/journal.pcbi.1000290.

Fuchs, Elke C, Angela Neitz, Roberta Pinna, Sarah Melzer, Antonio Caputi, and Hannah Monyer. 2016. “Local and Distant Input Controlling Excitation in Layer II of the Medial Entorhinal Cortex.” Neuron 89 (1): 194–208. https://doi.org/10.1016/j.neuron.2015.11.029.

Pastoll, Hugh, Derek L Garden, Ioannis Papastathopoulos, Gülşen Sürmeli, and Matthew F Nolan. 2020. “Inter- and Intra-Animal Variation in the Integrative Properties of Stellate Cells in the Medial Entorhinal Cortex.” Elife 9 (February). https://doi.org/10.7554/eLife.52258.

Pastoll, Hugh, Lukas Solanka, Mark C W van Rossum, and Matthew F Nolan. 2013. “Feedback Inhibition Enables Theta-Nested Gamma Oscillations and Grid Firing Fields.” Neuron 77 (1): 141–54. https://doi.org/10.1016/j.neuron.2012.11.032.

Sürmeli, Gülşen, Daniel Cosmin Marcu, Christina McClure, Derek L F Garden, Hugh Pastoll, and Matthew F Nolan. 2015. “Molecularly Defined Circuitry Reveals Input-Output Segregation in Deep Layers of the Medial Entorhinal Cortex.” Neuron 88 (5): 1040–53. https://doi.org/10.1016/j.neuron.2015.10.041.

Winterer, Jochen, Nikolaus Maier, Christian Wozny, Prateep Beed, Jörg Breustedt, Roberta Evangelista, Yangfan Peng, Tiziano D’Albis, Richard Kempter, and Dietmar Schmitz. 2017. “Excitatory Microcircuits within Superficial Layers of the Medial Entorhinal Cortex.” Cell Rep. 19 (6): 1110–16. https://doi.org/10.1016/j.celrep.2017.04.041.

Author response:

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

Reviewer #1 (Public Review):

While there are many models for sequence retrieval, it has been difficult to find models that vary the speed of sequence retrieval dynamically via simple external inputs. While recent works [1,2] have proposed some mechanisms, the authors here propose a different one based on heterogeneous plasticity rules. Temporally symmetric plasticity kernels (that do not distinguish between the order of pre and post spikes, but only their time difference) are expected to give rise to attractor states, asymmetric ones to sequence transitions. The authors incorporate a rate-based, discrete-time analog of these spike-based plasticity rules to learn the connections between neurons (leading to connections similar to Hopfield networks for attractors and sequences). They use either a parametric combination of symmetric and asymmetric learning rules for connections into each neuron, or separate subpopulations having only symmetric or asymmetric learning rules on incoming connections. They find that the latter is conducive to enabling external inputs to control the speed of sequence retrieval.

Strengths:

The authors have expertly characterised the system dynamics using both simulations and theory. How the speed and quality of retrieval varies across phases space has been well-studied. The authors are also able to vary the external inputs to reproduce a preparatory followed by an execution phase of sequence retrieval as seen experimentally in motor control. They also propose a simple reinforcement learning scheme for learning to map the two external inputs to the desired retrieval speed.

Weaknesses:

(1) The authors translate spike-based synaptic plasticity rules to a way to learn/set connections for rate units operating in discrete time, similar to their earlier work in [5]. The bio-plausibility issues of learning in [5] carry over here, for e.g. the authors ignore any input due to the recurrent connectivity during learning and effectively fix the pre and post rates to the desired ones. While the learning itself is not fully bio-plausible, it does lend itself to writing the final connectivity matrix in a manner that is easier to analyze theoretically.

We agree with the reviewer that learning is not `fully bio-plausible’. However, we believe that extending the results to a model in which synaptic plasticity depends on recurrent inputs is beyond the scope of this work. We have added a mention of this issue in the Discussion in the revised manuscript.

(2) While the authors learn to map the set of two external input strengths to speed of retrieval, they still hand-wire one external input to the subpopulation of neurons with temporally symmetric plasticity and the other external input to the other subpopulation with temporally asymmetric plasticity. The authors suggest that these subpopulations might arise due to differences in the parameters of Ca dynamics as in their earlier work [29]. How these two external inputs would connect to neurons differentially based on the plasticity kernel / Ca dynamics parameters of the recurrent connections is still an open question which the authors have not touched upon.

The issue of how external inputs could self-organize to drive the network to retrieve sequences at appropriate speeds is addressed in the Results section, paragraph `Reward-driven learning’. These inputs are not `hand-wired’ - they are initially random and then acquire the necessary strengths to allow the network to retrieve the sequences at different speeds thanks to a simple reinforcement learning scheme. We have rewritten this section to clarify this issue.

(3) The authors require that temporally symmetric and asymmetric learning rules be present in the recurrent connections between subpopulations of neurons in the same brain region, i.e. some neurons in the same brain region should have temporally symmetric kernels, while others should have temporally asymmetric ones. The evidence for this seems thin. Though, in the discussion, the authors clarify 'While this heterogeneity has been found so far across structures or across different regions in the same structure, this heterogeneity could also be present within local networks, as current experimental methods for probing plasticity only have access to a single delay between pre and post-synaptic spikes in each recorded neuron, and would therefore miss this heterogeneity'.

We agree with the reviewer that this is currently an open question. We describe this issue in more detail in the Discussion of the revised manuscript.

(4) An aspect which the authors have not connected to is one of the author's earlier work:

Brunel, N. (2016). Is cortical connectivity optimized for storing information? Nature Neuroscience, 19(5), 749-755. https://doi.org/10.1038/nn.4286 which suggests that the experimentally observed over-representation of symmetric synapses suggests that cortical networks are optimized for attractors rather than sequences.

We thank the reviewer for this suggestion. We have added a paragraph in the discussion that discusses work on statistics of synaptic connectivity in optimal networks. We expect that in networks that contain two subpopulations of neurons, the degree of symmetry should be intermediate between a network storing fixed point attractors exclusively, and a network storing sequences exclusively.

Despite the above weaknesses, the work is a solid advance in proposing an alternate model for modulating speed of sequence retrieval and extends the use of well-established theoretical tools. This work is expected to spawn further works like extending to a spiking neural network with Dale's law, more realistic learning taking into account recurrent connections during learning, and experimental follow-ups. Thus, I expect this to be an important contribution to the field.

We thank the reviewer for the insightful comments.

Reviewer #2 (Public Review):

Sequences of neural activity underlie most of our behavior. And as experience suggests we are (in most cases) able to flexibly change the speed for our learned behavior which essentially means that brains are able to change the speed at which the sequence is retrieved from the memory. The authors here propose a mechanism by which networks in the brain can learn a sequence of spike patterns and retrieve them at variable speed. At a conceptual level I think the authors have a very nice idea: use of symmetric and asymmetric learning rules to learn the sequences and then use different inputs to neurons with symmetric or asymmetric plasticity to control the retrieval speed. The authors have demonstrated the feasibility of the idea in a rather idealized network model. I think it is important that the idea is demonstrated in more biologically plausible settings (e.g. spiking neurons, a network with exc. and inh. neurons with ongoing activity).

Summary

In this manuscript authors have addressed the problem of learning and retrieval sequential activity in neuronal networks. In particular, they have focussed on the problem of how sequence retrieval speed can be controlled?

They have considered a model with excitatory rate-based neurons. Authors show that when sequences are learned with both temporally symmetric and asymmetric Hebbian plasticity, by modulating the external inputs to the network the sequence retrieval speed can be modulated. With the two types of Hebbian plasticity in the network, sequence learning essentially means that the network has both feedforward and recurrent connections related to the sequence. By giving different amounts of input to the feed-forward and recurrent components of the sequence, authors are able to adjust the speed.

Strengths

- Authors solve the problem of sequence retrieval speed control by learning the sequence in both feedforward and recurrent connectivity within a network. It is a very interesting idea for two main reasons: 1. It does not rely on delays or short-term dynamics in neurons/synapses 2. It does not require that the animal is presented with the same sequences multiple times at different speeds. Different inputs to the feedforward and recurrent populations are sufficient to alter the speed. However, the work leaves several issues unaddressed as explained below.

Weaknesses

- The main weakness of the paper is that it is mostly driven by a motivation to find a computational solution to the problem of sequence retrieval speed. In most cases they have not provided any arguments about the biological plausibility of the solution they have proposed e.g.:

- Is there any experimental evidence that some neurons in the network have symmetric Hebbian plasticity and some temporally asymmetric? In the references authors have cited some references to support this. But usually the switch between temporally symmetric and asymmetric rules is dependent on spike patterns used for pairing (e.g. bursts vs single spikes). In the context of this manuscript, it would mean that in the same pattern, some neurons burst and some don't and this is the same for all the patterns in the sequence. As far as I see here authors have assumed a binary pattern of activity which is the same for all neurons that participate in the pattern.

There is currently only weak evidence for heterogeneity of synaptic plasticity rules within a single network, though there is plenty of evidence for such a heterogeneity across networks or across locations within a particular structure (see references in our Discussion). The reviewer suggests another interesting possibility, that the temporal asymmetry could depend on the firing pattern on the post-synaptic neuron. An example of such a behavior can be found in a paper by Wittenberg and Wang in 2006, where they show that pairing single spikes of pre and post-synaptic neurons lead to LTD at all time differences in a symmetric fashion, while pairing a pre-synaptic spike with a burst of post-synaptic spikes lead to temporally asymmetric plasticity, with a LTP window at short positive time differences. We now mention this possibility in the Discussion, but we believe exploring fully this scenario is beyond the scope of the paper.

- How would external inputs know that they are impinging on a symmetric or asymmetric neuron? Authors have proposed a mechanism to learn these inputs. But that makes the sequence learning problem a two stage problem -- first an animal has to learn the sequence and then it has to learn to modulate the speed of retrieval. It should be possible to find experimental evidence to support this?

Our model does not assume that the two processes necessarily occur one after the other. Importantly, once the correct external inputs that can modulate sequence retrieval are learned, sequence retrieval modulation will automatically generalize to arbitrary new sequences that are learned by the network.

- Authors have only considered homogeneous DC input for sequence retrieval. This kind of input is highly unnatural. It would be more plausible if the authors considered fluctuating input which is different from each neuron.

We have modified Figure 1e and Figure 2c to show the effects of fluctuating inputs on pattern correlations and single unit activity. We find that these inputs do not qualitatively affect our results.

- All the work is demonstrated using a firing rate based model of only excitatory neurons. I think it is important that some of the key results are demonstrated in a network of both excitatory and inhibitory spiking neurons. As the authors very well know it is not always trivial to extend rate-based models to spiking neurons.

I think at a conceptual level authors have a very nice idea but it needs to be demonstrated in a more biologically plausible setting (and by that I do not mean biophysical neurons etc.).

We have included a new section in the discussion with an associated figure (Figure 7) demonstrating that flexible speed control can be achieved in an excitatory-inhibitory (E-I) spiking network containing two excitatory populations with distinct plasticity mechanisms.

Reviewer #1 (Recommendations For The Authors):

In the introduction, the authors state: 'symmetric kernels, in which coincident activity leads to strengthening regardless of the order of pre and post-synaptic spikes, have also been observed in multiple contexts with high frequency plasticity induction protocols in cortex [21]'. To my understanding, [21]'s final model 3, ignores LTD if the post-spike also participates in LTP, and only considers nearest-neighbour interactions. Thus, the kernel would not be symmetric. Can the authors clarify what they mean and how their conclusion follows, as [21] does not show any kernels either.

In this statement, we were not referring to the model in [21], but rather the experimentally observed plasticity kernels at different frequencies. In particular, we were referring to the symmetric kernel that appears in the bottom panel of Figure 7c in that paper.

The authors should also address the weaknesses mentioned above. They don't need to solve the issues but expand (and maybe indicate resolutions) on these issues in the Discussion.

For ease of reproducibility, the authors should make their code available as well.

We intend to publish the code required to reproduce all figures on Github.

Reviewer #2 (Recommendations For The Authors):

-  Show the ground state of the network before and after learning.

We have decided not to include such a figure, as we have not analyzed the learning process, but instead a network with a fixed connectivity matrix which is assumed to be the end result of a learning process.

-  Authors have only considered a network of excitatory neurons. This does not make sense. I think they should demonstrate a network of both exc. and inch. neurons (spiking neurons) exhibiting ongoing activity.

See our comment to Reviewer #2 in the previous section.

-  Show how the sequence dynamics unfolds when we assume a non-zero ongoing activity.

We are not sure what the reviewer means by `non-zero ongoing activity. We show now the dynamics of the network in the presence of noisy inputs, which can represent ongoing activity from other structures (see Fig 1e and 2c).

-  From the correlation (==quality) alone it is difficult to judge how well the sequence has been recovered. Authors should consider showing some examples so that the reader can get a visual estimate of what 0.6 quality may mean. High speed is not really associated with high quality (Fig 2b). So it is important to show how the sequence retrieval quality is for non-linear and heterogeneous learning rules.

We believe that some insight into the relationship between speed and quality for the case of non-linear and heterogeneous learning rules is addressed by the correlation plots for chosen input configurations (see Fig. 3a and and 5b). We leave a full characterization for future work.

-  Authors should show how the retrieval and quality of sequences change when they are recovered with positive input, or positive input to one population and negative to another. In the current version sequence retrieval is shown only with negative inputs. This is a somewhat non-biological setting. The inhibitory gating argument (L367-389) is really weak.

We would like to clarify that with the parameters chosen in this paper, the transfer function has half its maximal rate at zero input. This is due to the fact we chose the threshold to be zero, using the fact that any threshold can be absorbed in the external inputs. Thus, negative inputs really mean sub-threshold inputs, and they are consistent with sub-threshold external excitatory inputs. We have clarified this issue in the revised manuscript.

-  Authors should demonstrate how the sequence retrieval dynamics is altered when they assume a fluctuating input current for sequence retrieval instead of a homogeneous DC input.

See our comment to Reviewer #2 in the previous section.

-  Authors should show what are the differences in synaptic weight distribution for the two types of learning (bi-linear and non-linear). I am curious to know if the difference in the speed in the two cases is related to the weight distribution. In general I think it is a good idea to show the synaptic weight distribution before and after learning.

As mentioned above, we do not study any learning process, but rather a network with a fixed connectivity matrix, assumed to represent the end result of learning. In this network, the distribution of synaptic weights converges to a Gaussian in the large p and cN limits, independently of the functions f and g, because of the central limit theorem, if there are no sign constraints on weights. In the presence of sign constraints, the distribution is a truncated Gaussian.

-  I suggest the use of a monochromatic color scale for figure 2b and 3b.

Figure 3: The sentence describing panel 2 seems incomplete.

Also explain why there is non-monotonic relationship between I_s and speed for some values of

I_a in 3b

There is a non-monotonic relationship for retrieval quality, not speed. We have clarified this in the manuscript text, but don’t currently have an explanation for why this phenomenon occurs for these specific values of I_a.

Author response:

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

Additional Discussion Points

(1) There is not much exploration of potential mechanisms, i.e., the impact of PV neuron activity on the broader circuit. Additionally, the study exclusively focuses on PV cells and does not explore the role of other prefrontal populations, particularly those known to respond to cueevoked fear states. The discussion should consider how PV activity might impact the broader circuit and whether the present findings are specific to PV cells or applicable to other interneuron subtypes.

We have added an extensive discussion of potential mechanisms and the potential contributions of other interneuron subtypes:

“For example, PV neurons aid in improving visual discrimination through sharpening response selectivity in visual cortex (Lee et al., 2012). In prefrontal cortex, PV neurons are critical for task performance, particularly during performance of tasks that require flexible behavior such as rule shift learning (Cho et al., 2020) and reward extinction (Sparta et al., 2014). Further, PV neurons play an essential role in the generation of cortical gamma rhythms, which contribute to synchronization of selective populations of pyramidal neurons (Sohal et al., 2009; Cardin et al., 2009). Courtin et al (2014) showed that brief suppression of dorsomedial prefrontal (dmPFC) PV neural activity enhanced fear expression, one of the main functions of the dmPFC, by synchronizing the spiking activity of dmPFC pyramidal neurons (Courtin et al., 2014). This result is potentially relevant to our findings, but likely involves different circuit mechanisms because of the difference in timescale, targeted area, and downstream projection targets (Vertes, 2004). These and other studies support the idea that PV neural activity supports the execution of a behavior by shaping rather than suppressing cortical activity, potentially by selecting among conflicting behaviors by the synchronization of different pyramidal populations (Warden et al., 2012; Lee et al., 2014).

The roles of other inhibitory neural subtypes (such as somatostatin (SOM)-expressing and vasoactive intestinal peptide (VIP)-expressing IL GABA neurons) in avoidance behavior are currently unknown, but are likely important given the role of SOM neurons in gamma-band synchronization (Veit et al., 2017), and the role of VIP neurons in regulating PV and SOM neural activity (Cardin, 2018).” 

(2) There is some discordance between changes in neural activity and behavior. For example, in Figure 4C, the relationship between PV neuron activity and movement emerges almost immediately during learning, but successful active avoidance emerges much more gradually. Why is this?

We have added extensive text to the discussion that addresses this issue:

“Interestingly, the rise in IL PV neural activity during movement does not require avoidance learning. IL PV neurons begin to respond during movement immediately after the animal has received a single shock in an environment, but learning to cross the chamber to avoid the signaled shock takes tens of trials. Why is there a discordance between the emergence of the IL PV signal during movement and avoidance learning?

The components underlying active avoidance have been debated over the years, but are thought to involve at least two essential behaviors – suppressing freezing, and moving to safety (LeDoux et al., 2017). Freezing is the default response of mice upon hearing a shock-predicting tone, and can be learned in a single trial (Ledoux, 1996; Fanselow, 2010; Zambetti et al., 2022). When a predator is in the distance, freezing can increase the chance of survival by reducing the chances of detection. However, a strategic avoidance behavior may prevent a future encounter with the predator altogether. The importance of IL PV neural activity in defensive behavior may be to suppress reactive defensive behaviors such as freezing in order to permit a flexible goaldirected response to threat.

The freezing suppression and avoidance movement components of the avoidance response are dissociable, both because freezing precedes avoidance learning, and because animals intermittently move prior to avoidance learning. Our finding that the rise in PV activity during movement emerges immediately after receiving a single shock, tens of trials before animals have learned the avoidance behavior, suggests that the IL PV signal is associated with the suppression of freezing. Further, IL PV neurons do not respond during movement toward cued rewards because in reward-based tasks there is no freezing response in conflict with reward approach behavior.” 

(3) vmPFC was defined here as including the infralimbic (IL) and dorsal peduncular (DP) regions. While the role of IL has been frequently characterized for motivated behavior, relatively few studies have examined DP. Perhaps the authors are just being cautious, given the challenges involved in the viral targeting of the IL region without leakage to nearby regions such as DP. But since the optical fibers were positioned above the IL region, it is possible that DP did not contribute much to either the fiber photometry signals or the effects of the optogenetic manipulations. Perhaps DP should be completely omitted, which is more consistent with the definitions of vmPFC in the field.

Yes, we included DP to be cautious as our viral expression sometimes leaks into DP, though the optic fiber targets IL. We have replaced vmPFC with IL throughout the manuscript. 

(4) In the Discussion, the authors should consider why PV cells exhibit increased activity during both movement initiation and successful chamber crossing during avoidance. While the functional contribution of the PV signal during movement initiation was tested with optogenetic inhibition, some discussion on the possible role of the additional PV signal during chamber crossing is of interest readers who are intrigued by the signaling of two events. Is the chamber crossing signal related to successful avoidance or learned safety (e.g., see Sangha, Diehl, Bergstrom, Drew 2020)?

IL PV neural activity starts to increase at movement initiation, peaks at chamber crossing (when movement speed is highest), and decreases after chamber crossing (Figure 1E). Thus, the increase in PV neural activity at movement initiation and at chamber crossing are different phases of the same event. 

We think this signal is unlikely to be a safety signal, and have added text to the discussion to clarify this issue:

“We think the IL PV signal is unlikely to be a safety signal (Sangha et al., 2020). First, the PV signal rises during movement not only in the avoidance context, but during any movement in a “threatening” context (i.e. a context where the animal has been shocked). For example, PV neural activity rises during movement during the intertrial interval in the avoidance task. Further, the emergence of the PV signal during movement happens quickly – after the first shock – and significantly before the animal has learned to move to the safe zone. This suggests a close association with enabling movement in a threatening environment, when animals must suppress a freezing response in order to move. Additionally, the rise in PV activity was specifically associated with movement and not with tone offset, the indicator of safety in this task. Finally, if IL PV neural activity reflects safety signals one would expect the response to be enhanced by learning, but the amplitude of the IL PV response was unaffected by learning after the first shock.”

(5) The primary conclusion here that PV cells control the fear response should be considered within the context of prior findings by the Herry laboratory. Courtin et al (2014) demonstrated a select role of prefrontal PV cells in the regulation of fear states, accomplished through their control over prefrontal output to the basolateral amygdala. The observations in this paper, which used both ChR2 and Arch-T to address the impact of vmPFC PV activity on reactive behavior, are highly relevant to issues raised both in the Introduction and Discussion.

Courtin et al (2014)’s finding is very important. We did not discuss this paper originally because Courtin et al. is about dmPFC, which has a different role in fear processing than IL/vmPFC. We have added text about this finding to the discussion:

“Courtin et al (2014) showed that brief suppression of dorsomedial prefrontal (dmPFC) PV neural activity enhanced fear expression, one of the main functions of the dmPFC, by synchronizing the spiking activity of dmPFC pyramidal neurons (Courtin et al., 2014). This result is potentially relevant to our findings, but likely involves different circuit mechanisms because of the difference in timescale, targeted area, and downstream projection targets (Vertes, 2004).

Additional analyses

(1) As avoidance trials progress (particularly on days 2 and 3), do PFC PV responses attenuate? That is, does continued unreinforced tone presentations lead to reduced reliance of PV cellmediated suppression in order for successful avoidance to occur?

We added Figure 1—Figure supplement 1M and 1N and a sentence on page 5: “IL PV neural activity during the avoidance movement was not attenuated by learning or repeated reinforcement (Figure 1—Figure supplement 1M and N, N = 8 mice, p = 0.8886, 1-way ANOVA).” We only included data from days 1 and 2, since we started to introduce short and long tone trials on day 3 which might interfere. 

(2) In Figure 3D, it would be very informative and further support the claim of "no role for movement during reward" if the response of these cells during the "initiation of movement during reward-approach" was shown (similar to Figure 1F for threat avoidance).

Thank you for the question. We added Figure 3—Figure supplement 1B and C to show IL PV neural activity aligned to initiation of movement during reward-approach. IL PV activity decreased after movement initiation for reward approach (N = 6 mice, p=0.0382, paired t-test). This further solidifies our claim that IL PV neuron activity only increases for threat avoidance.   

Reviewer 1 (Recommendations For The Authors):

(1) Fig1G shows the average response of PV cells during chamber crossing on an animal-toanimal basis. It would be informative to also see a similar plot for movement initiation.

We have added the suggested figure in Figure 1—Figure supplement 1B.  

(2) In the Results section (Page 5), there is a small issue with the logic. It says: "As vmPFC inactivation impairs avoidance behavior, the activity of inhibitory vmPFC PV neurons might be predicted to be low during successful avoidance trials." As opposed to "low", it should say "high", right? If inhibition impairs avoidance, then high responding by these cells would be presumed to drive the avoidance response, as supported by your findings.

We have re-worded the text in this section. Based on prior findings that IL inactivation impairs avoidance (Moscarello et al., 2013), we predicted that inhibitory PV neurons would be less active during avoidance, because activating these neurons could suppress IL. However, we found that they were selectively active during avoidance.

(3) In the caption/legend for Fig1E, it says that the "black ticks" indicate "tone onset". But it should say "movement initiation".

We thank the reviewer for pointing out this error. The ticks do indicate tone onset, and we have corrected the figure to reflect this. 

Reviewer 2 (Recommendations For The Authors):

(4) Perhaps replace the term 'good outcomes' with 'reinforcing outcomes' or simply 'reinforcement'.

Thank you for the suggestion. We have replaced ‘good outcomes’ with ‘reinforcing outcomes’.

Reviewer 3 (Recommendations For The Authors):

(5) It would be useful to provide some (perhaps speculative) explanation for the discordance between the PV activity-movement relationship and success of active avoidance in Fig. 4C

We have added text to the discussion that addresses this issue:

“Interestingly, the rise in IL PV neural activity during movement does not require avoidance learning. IL PV neurons begin to respond during movement immediately after the animal has received a single shock in an environment, but learning to cross the chamber to avoid the signaled shock takes tens of trials. Why is there a discordance between the emergence of the IL PV signal during movement and avoidance learning?

The components underlying active avoidance have been debated over the years, but are thought to involve at least two essential behaviors – suppressing freezing, and moving to safety (LeDoux et al., 2017). Freezing is the default response of mice upon hearing a shock-predicting tone, and can be learned in a single trial (Ledoux, 1996; Fanselow, 2010; Zambetti et al., 2022). When a predator is in the distance, freezing can increase the chance of survival by reducing the chances of detection. However, a strategic avoidance behavior may prevent a future encounter with the predator altogether. The importance of IL PV neural activity in defensive behavior may be to suppress reactive defensive behaviors such as freezing in order to permit a flexible goaldirected response to threat.

The freezing suppression and avoidance movement components of the avoidance response are dissociable, both because freezing precedes avoidance learning, and because animals intermittently move prior to avoidance learning. Our finding that the rise in PV activity during movement emerges immediately after receiving a single shock, tens of trials before animals have learned the avoidance behavior, suggests that the IL PV signal is associated with the suppression of freezing. Further, IL PV neurons do not respond during movement toward cued rewards because in reward-based tasks there is no freezing response in conflict with reward approach behavior.” 

(6) I don't really understand what is shown in Figure 4D -- exactly what time points does this represent? Was habituation performed everyday?

Figure 4D shows data from the approach task, not the avoidance task. This data is from welltrained mice, not the first day of training on this task. There was a pre-task recording period every day.

(7) Why was optogenetic inhibition only delivered from 0.5-2.5 sec after the tone cue?

We wanted to avoid any possibility that perception of the tone would be disrupted, so we delayed the onset of optogenetic inhibition. We chose 0.5 sec onset because animals typically begin to move ~1 second after tone onset.

(8) The regression analysis with shuffled time points is not well explained -- some additional methodological details are needed (Fig. 2H).

We added the following to the methods section to provide a clearer explanation: 

“DF/F (t) was modeled as the linear combination of all event kernels. Given the event occurrence time points of all event types, we can use linear regression to decompose characteristic kernels for each event type. Kernel coefficients of the model were solved by minimizing the mean square errors between the model and the actual recorded signals. To prove that kernel ki is an essential component for the raw calcium dynamics, we compared the explanation power of the full model to the reduced model where the time points of the occurrence of event ki were randomly assigned. Thus, the kernel coefficients should not reflect the response to the event in the reduced model. 

Editor's notes:

-  Should you choose to revise your manuscript, please include full statistical reporting including exact p-values wherever possible alongside the summary statistics (test statistic and df) and 95% confidence intervals. These should be reported for all key questions and not only when the pvalue is less than 0.05.

Thank you for pointing this out. We have included all the test statistics and exact p values as suggested.

-  Please note the sex of the mice and distribution of sexes in each group for each experiment.

We have added the sex of mice for all experiments in the methods section.

Author response:

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

Reviewer #1 (Public Review):

Summary:

This work successfully identified and validated TRLs in hepatic metastatic uveal melanoma, providing new horizons for enhanced immunotherapy. Uveal melanoma is a highly metastatic cancer that, unlike cutaneous melanoma, has a limited effect on immune checkpoint responses, and thus there is a lack of formal clinical treatment for metastatic UM. In this manuscript, the authors described the immune microenvironmental profile of hepatic metastatic uveal melanoma by sc-RNAseq, TCR-seq, and PDX models. Firstly, they identified and defined the phenotypes of tumor-reactive T lymphocytes (TRLs). Moreover, they validated the activity of TILs by in vivo PDX modelling as well as in vitro coculture of 3D tumorsphere cultures and autologous TILs. Additionally, the authors found that TRLs are mainly derived from depleted and late activated T cells, which recognize melanoma antigens and tumor-specific antigens. Most importantly, they identified TRLs associated phenotypes, which provide new avenues for targeting expanded T cells to improve cellular and immune checkpoint immunotherapy.

Strengths:

Jonas A. Nilsson, et al. has been working on new therapies for melanoma.  The team has also previously performed the most comprehensive genome-wide analysis of uveal melanoma available, presenting the latest insights into metastatic disease. In this work, the authors performed paired sc-RNAseq and TCR-seq on 14 patients with metastatic UM, which is the largest single-cell map of metastatic UM available. This provides huge data support for other  studies of metastatic UM.

We thank the reviewer for these kind words about our work.

Weaknesses:

Although the paper does have strengths in principle, the weaknesses of the paper are that these strengths are not  directly demonstrated. That is,  insufficient analyses are performed to fully support the key claims in the manuscript by the data presented. In particular:

The author's description of the overall results of the article should be logical, not just a description of the observed phenomena. For example, the presentation related to the results of TRLs lacked logic. In addition, the title of the article emphasizes the three subtypes of hepatic metastatic UM  TRLs, but these three subtypes are not specifically discussed in the results as well as the discussion section. The title of the article is not a very comprehensive generalization and should be carefully considered by the authors.

We thank the reviewer for the critical reading of our work. We have added more data and more discussion.

The authors' claim that they are the first to use autologous TILs and sc-RNAseq to study immunotherapy needs to be supported by the corresponding literature to be more convincing. This can help the reader to understand the innovation and importance of the methodology.

We have gone through the manuscript and found that we only refer to being first in using PDX models and autologous TILs to study immunotherapy responses by single-cell sequencing. While there are data to be deduced from other studies, we still believe this to be an accurate statement.

In addition, the authors argue that TILs from metastatic UM can kill tumor cells. This is the key and bridging point to the main conclusion of the article. Therefore, the credibility of this conclusion should be considered.  Metastatic UM1 and UM9 remain responsive to autologous tumors under in vitro conditions with their autologous TILs.

UM1 responds also in vivo in the subcutaneous model in the paper. We have also finished an experiment where we show that this model also responds in a liver metastasis model. These data have been added in this revised version of the paper. We add two main figures and one supplementary figure where we characterize the response in vivo and also by single-cell sequencing of TILs.

In contrast, UM22, also as a metastatic UM, did not respond to TIL treatment. In particular, the presence of MART1-responsive TILs. The reliability of the results obtained by the authors in the model of only one case of UM22 liver metastasis should be considered. The authors should likewise consider whether such a specific cellular taxon might also exist in other patients with metastatic UM, producing an immune response to tumor cells. The results would be more comprehensive if supported by relevant data.

The reviewer has interpreted the results absolutely right, the allogenic and autologous MART1-specific TILs cells while reactive in vitro against UM22, cannot kill this tumor either in a subcutaneous or liver metastases model. We hypothesize this has to do with an immune exclusion phenotype and show weak immunohistochemistry that suggest this. We hope the addition of more UM1 data can be viewed as supportive of tumor-reactivity also in vivo.

In addition, the authors in that study used previously frozen biopsy samples for TCR-seq, which may be associated with low-quality sequencing data, high risk of outcome indicators, and unfriendly access to immune cell information. The existence of these problems and the reliability of the results should be considered. If special processing of TCR-seq data from frozen samples was performed, this should also be accounted for.  

We agree with the reviewers and acknowledge we never anticipated the development of single-cell sequencing techniques when we started biobank 2013. We performed dead cell removal before the 10x Genomics experiment. We have also done extensive quality controls and believe that the data from the biopsies should be viewed as a whole and that quantitative intra-patient comparisons cannot be done.

Reviewer #2 (Public Review):  

Summary:  

The study's goal is to characterize and validate tumor-reactive T cells in liver metastases of uveal melanoma (UM), which could contribute to enhancing immunotherapy for these patients. The authors used single-cell RNA and TCR sequencing to find potential tumor-reactive T cells and then used patientderived xenograft (PDX) models and tumor sphere cultures for functional analysis. They discovered that tumor-reactive T cells exist in activated/exhausted T cell subsets and in cytotoxic effector cells. Functional experiments with isolated TILs show that they are capable of killing UM cells in vivo and ex vivo.

Strengths:  

The study highlights the potential of using single-cell sequencing and functional analysis to identify T cells that can be useful for cell therapy and marker selection in UM treatment. This is important and novel as conventional immune checkpoint therapies are not highly effective in treating UM. Additionally, the study's strength lies in its validation of findings through functional assays, which underscores the clinical relevance of the research. 

We thank the reviewer for these kind words about our work.

Weaknesses:  

The manuscript may pose challenges for individuals with limited knowledge of single-cell analysis and immunology markers, making it less accessible to a broader audience.

The first draft of the manuscript (excluding methods) was written by a person (J.A.N) who is not a bioinformatician. It has been corrected to include the correct nomenclature where applicable but overall it is written with the aim to be understandable. We have made an additional effort in this version. 

Reviewer #1 (Recommendations For The Authors):  

(1) Firstly, the authors should provide high-resolution pictures to ensure readability for readers. 

We have converted to pdf ourselves and that improved resolution. We are happy to provide high-resolution to the office if needed for the printing.

(2) Furthermore, some parts of the article are more colloquial, and the authors should consider the logic and academic nature of the overall writing of the article. For example, authors should double-check whether the relevant expressions in the results are correct. For example, 'TCR' in the fourth part of the results should be 'TRLs'.

We thank the reviewer for the recommendations and have gone through the manuscript.

(3) Moreover, UM22 is described several times in the results as a metastatic UM and should be clearly defined in the methodology.

The UM22 and UM1 samples are described in-depth in Karlsson et al., Nature Communications, 2020, a paper that is cited in the beginning of Results as part of the narrative. The current work can be viewed as an extension of that work.

(4) Finally, it is recommended that authors describe a part of the results in full before citing the corresponding picture, otherwise, it will lead to confusion among readers.

We have made an effort in the revised version to describe the new data in more detail.

Reviewer #2 (Recommendations For The Authors):  

The manuscript is very interesting and important to understanding key aspects of uveal melanoma immune profile and functionality. However, in my opinion, there are a few aspects that could be addressed.  

- The manuscript lacks comprehensive details about the samples used, such as their disease progression, response to treatment, or any relevant information that could shed light on potential differences between samples. It would be valuable to know whether these samples were collected before any systemic treatment or if any of the patients underwent immunotherapy post-sample collection, along with the outcomes of such treatments. Providing this information would enrich the manuscript and provide a more holistic view of the research.

We thank the reviewer for the recommendation and have included a new Supplementary table 7 with information about the samples. We have also pasted in individual samples’ contribution to the UMAP to add further holistic view.  

- The results presented and discussed in the manuscript seem to indicate that there were no significant differences across the various samples, including comparisons between lymph-node and liver metastases. However, this lack of variation or the reasons for not discussing any observed differences should be clarified. If there are distinctions between the samples, it would be beneficial to discuss these findings in the manuscript.

We thank the reviewer for the recommendation. Whereas 14 samples are many for a uveal melanoma study it is not really powered to do intra-patient comparisons.

- The manuscript may pose difficulties for individuals with limited knowledge of single-cell analysis and immunology markers, potentially limiting its accessibility. To make the research more inclusive, the authors might consider presenting the technical aspects of their work in a less descriptive manner and providing explanations for those less familiar with the technology. This would help a broader audience grasp the significance of the study's findings. 

The manuscript is from a multidisciplinary team where all have read and commented. The draft was written by a tumor biologist and edited by a bioinformatician for accuracy. We honestly think it is more understandable than most studies in this bioinformatics era. But we have tried to describe the new data in an easier way.

for - progress trap - dark futures scenario - like Easter Island but on a global scale

comment - The potential global breakdown of global industrialized society, rupturing supply chains so that our highly interdependent world becomes the very Achilles Heel that hastens its demise is chilling - It could mean a huge disruption to the most important aspect of civilization - the continuing accruing and inter-generational transmission of knowledge - It would be catastrophic to lose that, but it is entirely possible - As Wright himself famously said, to use a computer metaphor, we humans are like 50,000 year old hardware, running modern software - By that, he meant that our cognitive physiology (brain and sensory processing system) has not changed for tens of thousands of years, yet cultural evolution happens at exponentially faster rates, so much so that our biological systems are not adapted to keep up with the pace, and that spells disaster - When we no longer have the sensory or cognitive apparatus to sense danger, and we are offloading that to AI, we are in an extremely vulnerable situation

progress trap - Gedanken - Think of our ancestors from 50,000 years ago. - What Wright is saying with his metaphor is that if that child from 50,000 years ago were transported by a time machine to modernity, (s)he would have little problem integrating into modern society - LIKEWISE, if we lose all the knowledge fruits of accumulated over so many thousands of years, it would be like being born into a human tribe 50,000 years ago. - We would likely still have language, but all our technology may have to start from scratch!

Author response:

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

eLife assessment 

The authors provide solid data on a functional investigation of potential nucleoid-associated proteins and the modulation of chromosomal conformation in a model cyanobacterium. While the experiments presented are convincing, the manuscript could benefit from restructuring towards the precise findings; alternatively, additional data buttressing the claims made would significantly enhance the study. These valuable findings will be of interest to the chromosome and microbiology fields.

We appreciate editors for taking time for assessment and reviewers for giving critical suggestions. Both reviewers were concerned about our interpretation of 3C data, and Reviewer #2 suggested the biochemistry of cyAbrB2 to reinforce our claim. We agree with the concern and suggest editors add a sentence “How cyAbrB2 affects chromosome structure is still elusive from this study, and the biochemical assays are needed in the future experiment.” to the eLife assessment.

The major revision points are the following;

Reconstruction of Figures

Previous Figure 5E has been omitted

Additional 3C data on the nifJ region

Rephrasing the conclusion of 3C data

Additional discussion on cyAbrB2 and NAPs

Reviewer #1 (Public Review): 

Strength: 

At first glance, I had a very positive impression of the overall manuscript. The experiments were well done, the data presentation looks very structured, and the text reads well in principle.

Weakness: 

Having a closer look, the red line of the manuscript is somewhat blurry. Reading the abstract, the introduction, and parts of the discussion, it is not really clear what the authors exactly aim to target. Is it the regulation of fermentation in cyanobacteria because it is under-investigated? Is it to bring light to the transcriptional regulation of hydrogenase genes? The regulation by SigE? Or is it to get insight into the real function of cyAbrB2 in cyanobacteria? All of this would be good of course. But it appears that the authors try to integrate all these aspects, which in the end is a little bit counterintuitive and in some places even confusing. From my point of view, the major story is a functional investigation of the presumable transcriptional regulator cyAbrB2, which turned out to be a potential NAP. To demonstrate/prove this, the hox genes have been chosen as an example due to the fact that a regulatory role of cyAbrB2 has already been described. In my eyes, it would be good to restructure or streamline the introduction according to this major outcome. 

As you pointed out, the major focus of this study is cyAbrB2 as a potential NAPs. To focus on NAPs, we simplified the first paragraph of the discussion (ll.246-263) and added the section comparing cyAbrB2 with other known NAPs (11.269-299). To emphasize the description of cyAbrB2, we also rearranged the figures and divided the analysis on cyAbrB2 ChIP into two figures. We reduced the first paragraph of the introduction but mostly preserved the composition of the introduction to keep the general to specific pattern, even though the manuscript is blurry.

Points to consider: 

The authors suggest that the microoxic condition is the reason for the downregulation of e.g. photosynthesis (l.112-114). But of course, they also switched off the light to achieve a microoxic environment, which presumably is the trigger signal for photosynthesis-related genes. I suggest avoiding making causal conclusions exclusively related to oxygen and recommend rephrasing (for example, "were downregulated under the conditions applied").

We agree with this point. We rephrased l.114 to “by the transition to dark microoxic conditions from light aerobic conditions” (ll.108-109).

The authors hypothesized that cyAbrB2 modulates chromosomal conformation and conducted a 3C analysis. But if I read the data in Figure 5B & C correctly, there is a lot of interaction in a range of 1650 and 1700 kb, not only at marked positions c and j. Positions c and j have been picked because it appears that cyAbrB2 deletion impacts this particular interaction. But is it really significant? In the case of position j the variation between the replicates seems quite high, in the case of position c the mean difference is not that high. Moreover, does all this correlate with cyAbrB2 binding, i.e. with positions of gray bars in panel A? If this was the case, the data obtained for the cyabrB2 mutant should look totally different but they are quite similar to WT. That's why the sentence "By contrast, the interaction frequency in Δcyabrb2 mutant were low and unchanged in the aerobic and microoxic conditions" does not fit to the data shown. But I have to mention that I am not an expert in these kinds of assays. Nevertheless, if there is a biological function that shall be revealed by an experiment, the data must be crystal clear on that. At least the descriptions of the 3C data and the corresponding conclusions need to be improved. For me, it is hard to follow the authors' thoughts in this context. 

According to your suggestion, we again have carefully observed the 3C data. Furthermore, we conducted an additional 3C experiment on nifJ region (Figures 7F-J). Then we admit we had overinterpreted the 3C data. Therefore, we rewrote the result and discussion of the 3C assay in line with the data (ll.220-245) and removed the previous Figure 5E. Following are individual responses.

Positions c and j have been picked because it appears that cyAbrB2 deletion impacts this particular interaction. But is it really significant?

We could not find statistically significant differences at locus c and j. Therefore, we added this in the result section “Note that the interaction scores exhibit considerable variability and we could not detect statistical significance at those loci.” (ll.231-232)

does all this correlate with cyAbrB2 binding, i.e. with positions of gray bars in panel A?

As you are concerned, interaction frequency and cyAbrB2 binding do not correlate. Therefore, we withdraw the previous claim and stated as follows; “Moreover, our 3C data did not support bridging at least in hox region and nifJ region, as the high interaction locus and cyAbrB2 binding region did not seem to correlate (Figure 7).” (ll.280-282)

If this was the case, the data obtained for the cyabrB2 mutant should look totally different but they are quite similar to WT.

We rewrote it as follows; “Then we compared the chromatin conformation of wildtype and cyabrb2∆. Although overall shapes of graphs did not differ, some differences were observed in wildtype and cyabrb2∆ (Figures 7B and 7G); interaction of locus (c) with hox region were slightly lower in cyabrb2∆ and interaction of loci (f’) and (g’) with nifJ region were different in wildtype and cyabrb2∆. Note that the interaction scores exhibit considerable variability and we could not detect statistical significance at those loci.” (ll.228-232)

That's why the sentence "By contrast, the interaction frequency in Δcyabrb2 mutant were low and unchanged in the aerobic and microoxic conditions" does not fit to the data shown.

We rewrote the sentence as follow; “While the interaction scores exhibit considerable variability, the individual data over time demonstrate declining trends of the wildtype at locus (c) and (j) (Figure S8). In ∆cyabrb2, by contrast, the interaction frequency of loci (c) and (j) was unchanged in the aerobic and microoxic conditions (Figure 7E). The interaction frequency of locus (c) in ∆cyabrb2 was as low as that in the microoxic condition of wildtype, while that of locus (j) in ∆cyabrb2 was as high as that in the aerobic condition of wildtype (Figures 7B and 7C).” (ll.238-243)

The figures are nicely prepared, albeit quite complex and in some cases not really supportive of the understanding of the results description. Moreover, they show a rather loose organization that sometimes does not fit the red line of the results section. For example, Figure 1D is not mentioned in the paragraph that refers to several other panels of the same figure (see lines110-128). Panel 1D is mentioned later in the discussion. Does 1D really fit into Figure 1 then? Are all the panels indeed required to be shown in the main document? As some elements are only briefly mentioned, the authors might also consider moving some into the supplement (e.g. left part of Figure 1C, Figure 2A, Figure 3B ...) or at least try to distribute some panels into more figures. This would reduce complexity and increase comprehensibility for future readers. Also, Figure 3 is a way too complex. Panel G could be an alone-standing figure. The latter would also allow for an increase in font sizes or to show ChIP data of both conditions (L+O2 and D-O2) separately. Moreover, a figure legend typically introduces the content as a whole by one phrase but here only the different panels are described, which fits to the impression that all the different panels are not well connected. Of course, it is the decision of the authors what to present and how but may they consider restructuring and simplifying.

According to the advice, we have rearranged the Figure composition.

The left side of Figure 1C has been moved to supplement. Instead, representative expression fold changes of “Transient”, “Plateau”, “Continuous”, and “Late” genes are shown for comprehensibility. We left Figure 1D in Figure 1, as this diagram shows our motive to focus on hox and nifJ. We moved Figure 2A to supplement. We did not move Fig3B, as this figure shows the distribution of cyAbrB2 (“long tract of AT-rich DNA”) comprehensively and simply. We agree that Figure 3 was too complex. Therefore, we moved Figures 3F and 3G to a new independent figure (Figure 4). In Figure 4C (former 3G), we show the ChIP data of the L+O2 condition only, and the change of ChIP data under the D-O2 condition is shown in Figure 5. The schematic image showing cyanobacterial chromosome and NAPs (previous Figure 5E) was omitted because it was overinterpreting.

The authors assume a physiological significance of transient upregulation of e.g. hox genes under microoxic conditions. But does the hydrogenase indeed produce hydrogen under the conditions investigated and is this even required? Moreover, the authors use the term "fermentative gene". But is hydrogen indeed a fermentation product, i.e. are protons the terminal electron acceptor to achieve catabolic electron balance? Then huge amounts of hydrogen should be released. Comment should be made on this.

This is a very important point; Yes, hydrogenase indeed produces hydrogen under the conditions we investigated, and proton accepts a majority of reducing power under the dark microoxic condition. We wrote in the introduction section as follows; “Hydrogen is generated in quantities comparable to lactate and dicarboxylic acids as the result of electron acceptance in the dark microoxic condition (Akiyama and Osanai 2023; Iijima et al. 2016)” (ll.54-55). The detailed explanation is below, although omitted from the manuscript.

A recent study (Akiyama and Oasanai 2023) quantified the consumed glycogen and secreted fermentative products (hydrogen, lactate, dicarboxylic acid, and acetate) in the Synechocystis under the dark microoxic condition, the same conditions as we investigated. The system of the study consists of a 10 mL liquid layer and a 10 mL gas layer, cultivated for 3 days under dark microoxic conditions. Then the amounts of lactic acid, dicarboxylic acid, and hydrogen were approximately 2 µmol, 3.5 µmol, and 11µmol (assuming the gas layer was at 1 atm and ignoring aqueous population), respectively. On the other hand, glycogen equivalent to 15µmol of glucose was consumed in the system. This estimate supports hydrogen accounts for a substantial portion of fermentative products during dark microoxic conditions.

The necessity of hydrogen production under dark microoxic conditions was demonstrated in (Gutekunst et al. 2014). They show hydrogenase activity is required for the mixotrophic growth in the light-dark and microoxic cycle with arginine. The necessity remains unclear in our conditions because we only performed continuous dark microoxic conditions without glucose.

The authors also mention a reverse TCA cycle. But is its existence an assumption or indeed active in cyanobacteria, i.e. is it experimentally proven? The authors are a little bit vague in this regard (see lines 241-246).

We misused the Terminology. We mean to mention the “reductive branch of TCA”. Cyanobacteria conduct the branched TCA cycle under microoxic conditions. One of the branches is the reductive branch, which reduces oxaloacetate to produce malate. We corrected “reverse TCA cycle” to “reductive branch of TCA”. (Figure 1D and ll.260-262)

Reviewer #2 (Public Review): 

This work probes the control of the hox operon in the cyanobacterium Synechocystis, where this operon directs the synthesis of a bidirectional hydrogenase that functions to produce hydrogen. In assessing the control of the hox system, the authors focused on the relative contributions of cyAbrB2, alongside SigE (and to a lesser extent, SigA and cyAbrB1) under both aerobic and microoxic conditions. In mapping the binding sites of these different proteins, they discovered that cyAbrB2 bound many sites throughout the chromosome repressed many of its target genes, and preferentially bound regions that were (relatively) rich in AT-residues. These characteristics led the authors to consider that cyAbrB2 may function as a nucleoid-associated protein (NAP) in Synechocystis, given its functional similarities with other NAPs like H-NS. They assessed the local chromosome conformation in both wild-type and cyabrB2 mutant strains at multiple sites within a 40 kb window on either side of the hox locus, using a region within the hox operon as bait. They concluded that cyAbrB2 functions as a nucleoid-associated protein that influences the activity of SigE through its modulation of chromosome architecture.

The authors approached their experiments carefully, and the data were generally very clearly presented and described.

Based on the data presented, the authors make a strong case for cyAbrB2 as a nucleoid-associated protein, given the multiple ways in which it seems to function similarly to the well-studied Escherichia coli H-NS protein. It would be helpful to provide some additional commentary within the discussion around the similarities and differences of cyAbrB2 to other nucleoid-associated proteins, and possible mechanisms of cyAbrB2 control (post-translational modification; protein-protein interactions; etc.). The manuscript would also be strengthened with the inclusion of biochemical experiments probing the binding of cyAbrB2, particularly focusing on its oligomerization and DNA polymerization/bridging potential.

We agree with the comment that the biochemical experiments will deepen our insights into the cyAbrB2 and chromatin conformation. As the reviewer pointed out, the biochemical assay will provide valuable information on mechanisms of cyAbrB2 control, such as post-transcriptional modification, cooperation with cyAbrB1, oligomerization, and the structure of cyAbrB2-bound DNA. However, we think those potential findings are worth of new independent research paper, rather than a part of this paper. Therefore, we added a discussion mentioning biochemistry as the future work (ll.275-290; the section of “The biochemistry of cyAbrB2 will shed light on the regulation of chromatin conformation in the future”).

Previous work had revealed a role for SigE in the control of hox cluster expression, which nicely justified its inclusion (and focus) in this study. However, the results of the SigA studies here suggested that SigA both strongly associated with the hox promoter, and its binding sites were shared more frequently than SigE with cyAbrB2. The focus on cyAbrB2 is also well-justified, given previous reports of its control of hox expression; however, it shares binding sites with an essential homologue cyAbrB1. Interestingly, while the B1 protein appears to bind similar sites, instead of repressing hox expression, it is known as an activator of this operon. It seems important to consider how cyAbrB1 activity might influence the results described here.

We infer that the minor side of the bimodal SigE peak is the genuine population that contributes to hox transcription, as hox genes are expressed in a SigE-dependent manner (Figure S2). We considered the strong SigA peak upstream of the hox operon binds the promoter of TU1715, the opposite direction of the hox operon. We added a description of the single SigA peak and bimodal SigE peak near the TSS of the hox operon as follows;

“A bimodal peak of SigE was observed at the TSS of the hox operon in a microoxic-specific manner (Figure 6C bottom panel). The downstream side of the bimodal SigE peak coincides with SigA peak and the TSS of TU1715. Another side of the bimodal peak lacked SigA binding and was located at the TSS of the hox operon (marked with an arrow in Figure 6C), although the peak caller failed to recognize it as a peak.” (ll.206-209)

The point that cyAbrB1 binds similar sites as cyAbrB2, despite regulating hox expression in the opposite direction, is very interesting. Therefore, we referred to the transcriptome data of the cyAbrB1 knockdown strain and compared the impact of cyAbrB1 knockdown and cyAbrB2 deletion. We described in result and discussion as follows;

“we referred to the recent study performing transcriptome of cyAbrB1 knockdown strain, whose cyAbrB1 protein amount drops by half (Hishida et al. 2024). Among 24 genes induced by cyAbrB1 knockdown, 12 genes are differentially downregulated genes in cyabrb2∆ in our study (Figure S5D).” (ll.162-165)

“CyAbrB1, the homolog of cyAbrB2, may cooperatively work, as cyAbrB1 directly interacts with cyAbrB2 (Yamauchi et al. 2011), their distribution is similar, and they partially share their target genes for suppression (Figures 3A S5C and S5D). The possibility of cooperation would be examined by the electrophoretic mobility shift assay of cyAbrB1 and cyAbrB2 as a complex. Despite their similar repressive function, cyAbrB1 and cyAbrB2 regulate hox expression in the opposite directions, and their mechanism remains elusive.” (ll.292-296)

Hox operon differs from this general tendency. To see if cyAbrB1 behaves differently from cyAbrB2 in the hox operon, we did an additional ChIP-qPCR experiment on cyAbrB1 in the aerobic condition and the dark microoxic condition (Figure 5C). However, we could not find the difference.

Reviewer #1 (Recommendations For The Authors): 

Figure 1B: I recommend changing the header in the grey bar to terms like "upregulated" and "downregulated", which are also used in the legend description. Upregulation of genes can also be a result of de-repression, which is why the term "activated" is somewhat misleading.

Corrected.

Lines 114-116: It is unclear what the authors exactly mean here. Please clarify. 

We rephrase the sentence “The enrichment in the butanoate metabolism pathway indicates the upregulation of genes involved in carbohydrate metabolism. We further classified genes according to their expression dynamics.” (ll.110-111)

Reviewer #3 (Recommendations For The Authors): 

Major/experimental comments: 

(1) For the chromosome conformation capture experiments, it is indicated that these were conducted at aerobic (1hr) and microoxic (4 hr) conditions. But the data presented in Figure 1 suggest that 1 hr corresponds to the beginning of microoxic growth, and that time 0 is aerobic. The composite 3C data in Figure 5 show some interesting but specific differences. It is appreciated that the authors presented the profiles for individual samples in Figure S7, and the differences here do not seem to be as compelling. Are the major differences being highlighted significantly (statistically) different (e.g. at the (c) and (j) loci)? Might the differences be starker if an earlier aerobic condition (e.g. time 0) had been used instead of the 1 hr - microoxic - timepoint?

Previous Figure 5 consisted of three time points (solid line: aerobic condition, dashed line:1hr of microoxic condition, and dotty line:4hr of microoxic condition). We omitted data of 4hr in the main figure (Figure 7) as 4hr in microoxic conditions makes data complicated. Three time points are shown in the profiles of individual loci (Figure S8).

There is no statistical significance found in (c) and (j) loci by t-test. Therefore, we have toned down the interpretation of 3C data as follows; “Our 3C result demonstrated that cyAbrB2 influences the chromosomal conformation of hox and nifJ region to some extent (Figure 7).” (ll.325-326)

(2) This is a complicated system that involves multiple regulatory proteins, each of which is differentially affected by the growth conditions (aerobic/microoxic). It is obviously beyond the scope of this work to probe deeply into all of these proteins. The focus here was on cyAbrB2, and to a slightly lesser extent SigE; however, based on the data presented, it seems that SigA and cyAbrB1 may be equally important contributors to hox control/expression, and in the case of cyAbrB1, possibly also to chromosome conformation. cyAbrB1 appears to have the same binding sites as cyAbrB2, and has been reported to interact with cyAbrB2. Given this association, it is possible that the two proteins may affect the binding of each other, and that loss of one might lead to enhanced binding by the other (or binding may require heterooligomerization?). Probing the regulatory interplay between these two proteins (or at least discussing it) feels important. Conducting e.g. mobility shift assays with each protein, both individually and together, could possibly allow for some understanding of how they function together. 

We agree that the biochemistry of cyAbrB2 and cyAbrB1 may explain why cyAbrB1 and cyAbrB2 bind long tracts of AT-rich genome regions in vitro. We would like to put the biochemistry future plan as we think biochemistry data is beyond the present study.

The idea that cyAbrB1 and cyAbrB2 cooperate to form heterooligomers and broad binding to the genome is a very rational and interesting prediction. We add this idea to the discussion “Overall, the biochemistry integrating assay conditions (PTM, buffer condition, and cooperation with cyAbrB1) and output (DNA binding, oligomerization, and DNA structure) will deepen the understanding of cyAbrB2 as cyanobacterial NAPs.”(ll.287-290). We also compared our transcriptome of ∆_cyabrb2 with the recent study of cyabrb1 knockdown (ll. 162-165), and concluded “they partially share their target genes for suppression (Figures 3A S5C and S5D)” (l. 293).

(3) Throughout the manuscript, there is reference made to cyAbrB2 binding becoming 'blurry' or non-specific under microoxic conditions. It is not clear what this means. It appears that when cyAbrB2 binds, any given protected region can be quite extensive, which can be suggestive of polymerization along the chromosome. Are the boundaries for binding sites typically clearly delineated, and this changes when the cultures are growing under microoxic conditions? There is also no mention made anywhere about oligomerization potential for cyAbrB2, which would be important for the polymerization, and bridging suggested for cyAbrB2 in the model presented in Figure 5. Previous publications (Song et al., 2022; Ishi et al., 2008) have suggested that it can exist as a dimer in vivo, but that in vitro it is largely monomeric. The manuscript would benefit from some additional biochemical analyses of cyAbrB2 binding activity, with a particular focus on DNA binding and oligomerization/bridging potential, and some additional discussion about these characteristics as well. 

Throughout the manuscript, there is reference made to cyAbrB2 binding becoming 'blurry' or non-specific under microoxic conditions. It is not clear what this means.

In order to clearly describe “cyAbrB2 binding becomes blurry”, we rearranged the figure composition and made an exclusive figure (Figure 5). We also rephrased the description by adopting the reviewer’s word “boundaries for binding sites”, as this phrase well describes the change. “When cells entered microoxic conditions, the boundaries of the cyAbrB2 binding region and cyAbrB2-free region became obscure (Figure 5), “(ll.319-320)

There is also no mention made anywhere about oligomerization potential for cyAbrB2,

We added the discussion about oligomerization “DNA-bound cyAbrB2 is expected to oligomerize, based on the long tract of cyAbrB2 binding region in our ChIP-seq data. However, no biochemical data mentioned the DNA deforming function or oligomerization of cyAbrB2 in the previous studies and preference for AT-rich DNA is not fully demonstrated in vitro (Dutheil et al. 2012; Ishii and Hihara 2008; Song et al. 2022)”(ll. 277-280) and “Overall, the biochemistry integrating assay conditions (PTM, buffer condition, and cooperation with cyAbrB1) and output (DNA binding, oligomerization, and DNA structure) will deepen the understanding of cyAbrB2 as cyanobacterial NAPs.” (ll.287-290)

The manuscript would benefit from some additional biochemical analyses of cyAbrB2 binding activity, with a particular focus on DNA binding and oligomerization/bridging potential, and some additional discussion about these characteristics as well. 

We added the discussion integrally considering known features of cyAbrB2, novel findings on cyAbrB2, and the comparison with known NAPs (ll.269-290).

(4) Given that the major take-away for the authors (based on the title) seems to be the nucleoid-associated protein potential for cyAbrB2, the Discussion would benefit from some additional focus in this area. How similar is cyAbrB2 to other nucleoid-associated proteins? (e.g. H-NS, Lsr2) How does counter-silencing work for other nucleoid-associated proteins? Can the authors definitively exclude the possibility of binding site competition/occlusion, given that cyAbrB2 covers the promoter region of hox? What is other nucleoid-associated proteins have been characterized in the cyanobacteria? 

We agree with the point, so we additionally discussed cyAbrB2 comparing with H-NS and Lsr2, the canonical NAPs (ll. 269-290).

We did not deny the possibility of the exclusion of RNAP by cyAbrB2, but the previous manuscript insufficiently discussed that. To emphasize that cyAbrB2 excludes RNA polymerase, we simplified Figure 6 and employed mosaic plots showing anti-co-occurrence of cyAbrB2 binding regions and SigE peaks. Furthermore, we added discussion about SigE exclusion by cyAbrB2 (ll. 355-359)

We mention the possibility of other nucleoid-associated proteins in cyanobacteria in the discussion. “Furthermore, the conformational changes by deletion of cyAbrB2 were limited, suggesting there are potential NAPs in cyanobacteria yet to be characterized.” (ll.336-339)

(5) Previous work (Song et al., 2022) showed that changing the AT content of cyAbrB2 binding sites did not affect its ability to bind DNA. There are also previous papers suggesting that cyAbrB2 may be subject to diverse post-translational modifications (e.g. phosphorylation - Spat et al., 2023; glutationylation - Sakr et al., 2013), as well as association with cyAbrB1. These collectively suggest there may be other factors that contribute to cyAbrB2 binding specificity/activity. These seem like relevant points to discuss, particularly given the transient nature of the cyAbrB2 effects on some genes.

We have included the discussion about AT content, post-translational modifications and transient regulations, and association with cyAbrB1 (ll. 284-295)

(6) Given the major binding site for SigA upstream of the hox operon, it seems that it likely also contributes to hox cluster expression, together with SigE. Is there a sense for the relative contribution of each sigma factor to hox cluster expression? And whether both are subject to the same inhibitory effect of cyAbrB2? 

As described above response to the public review, the SigA binding site upstream of the hox operon should be assigned to the TSS of TU1715 (Figure 6C). Transcription of hox operon is highly dependent on SigE as shown in Figure S2, and residual transcription in sigE∆ strain is derived from other sigma factors (SigABCD). Estimating the relative contribution of sigma factors other than SigE is difficult at present because SigABCDE can partially compensate for each other.

As the different impact of NAPs on the primary and alternative sigma factor is observed in H-NS (Shin et al. 2005), whether both the primary sigma factor (SigA) and the alternative sigma factor (SigE) are inhibited by cyAbrB2 to the same extent is a very interesting question.

We calculated the odds ratio of SigE and SigA being in the cyAbrB2-free region and wrote in the result; “SigE preferred the cyAbrB2-free region in the aerobic condition more than SigA did (Odds ratios of SigE and SigA being in the cyAbrB2-free region were 4.88 and 2.74, respectively).” (ll.193-195) and discussed “The higher exclusion pressure of cyAbrB2 on SigE may contribute to sharpening the transcriptional response of hox and nifJ on entry to microoxic conditions.” (ll.357-359)

(7) The 3C experiments suggest there are indeed changes in chromosome architecture in the hox region as growth conditions change and when different regulators are present. Across the chromosome, analogous changes are expected; however, it may be premature to draw this conclusion based on changes at one locus. Is there a reason that the authors did not take full advantage of their 3C samples and sequence them, to capture the full chromosome interactome at the two time-points? This would allow broader conclusions to be drawn regarding changes in chromosome structure and the impact of cyAbrB2.

In response to the suggestion, we performed an additional 3C assay on the nifJ region by utilizing residual 3C samples. Expanding to genome-wide sequence (Hi-C) needs concentration of ligated fragments by the biotinylation, which were omitted in our 3C sample.

We rewrote the result as obtained from the 3C data of hox and nifJ (ll.220-245) and omitted the schematic image of an entire chromosome of cyanobacteria (previous Figure 5E).

Editorial comments: 

(1) The data presentation in Figure 1 is very effective. 

(2) Line 87: please rephrase - you can have 'high similarity' or 'high levels of identity', but not high levels of homology - genes/proteins are either homologous or not.

(3) Line 118: classified into four 'groups'? 

(4) Line 590: remove 'the'. 

(5) Figure 2S, panel B: please define acronyms in the legend (GT, IP) and write out 'FLAG' in full for AbrB1.

(2) to (5) have been corrected.

(6) Please provide information on or a reference for the tagging of SigA for use in the ChIP-seq experiments within the Materials and Methods.

Added (l.365)

(7) Line 648: space between 'binding' and 'regions'. 

corrected.

(8) Fig 4E: please make the solid lines thicker - they are currently difficult to see.

We have made Figure 6C (former 4E) larger and the line thicker.

(9) Line 666: location. 

(10) Line 673: Individual. 

(11) Figure S5, panel C graph title: should this be 'Relative'? 

(12) Figure S7: What is 'GT'? Should this be 'WT'? 

(9) to (12) have been corrected.

(13) In addition to the data presented in Figure 3G, it would be nice to have a small table or Venn diagram summarizing the number of cyAbrB2 binding sites that fall into the different categories (full gene/operon; downstream of a gene; within a gene; promoter region). 

In response to the comment, we noticed the categories we had applied (full gene/operon; downstream of a gene; within a gene; promoter region) were arbitrary. Therefore, we categorized transcriptional units (TUs) according to the extent of occupancy by cyAbrB2. (Figures 4B and 4C)

(14) Line 280-281: suggest replacing 'mediates' with 'influences'. 'Mediates' sounds like a direct interaction (for which the evidence is not currently strong without some additional biochemical data), but 'influences' could better accommodate both direct and indirect possibilities. 

(15) Line 410: it is not clear what this means. 

We have omitted “As a result, DNA ~600-fold condensed DNA than 3C samples were ligated.”, as it does not give any information about the experimental procedure.

Author response:

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

Reviewer #1 (Public Review): 

Summary: 

This manuscript builds upon the authors' previous work on the cross-talk between transcription initiation and post-transcriptional events in yeast gene expression. These prior studies identified an mRNA 'imprinting' phenomenon linked to genes activated by the Rap1 transcription factor (TF), a surprising role for the Sfp1 TF in promoting RNA polymerase II (RNAPII) backtracking, and a role for the non-essential RNAPII subunits Rpb4/7 in the regulation of mRNA decay and translation. Here the authors aimed to extend these observations to provide a more coherent picture of the role of Sfp1 in transcription initiation and subsequent steps in gene expression. They provide evidence for (1) a physical interaction between Sfp1 and Rpb4, (2) Sfp1 binding and stabilization of mRNAs derived from genes whose promoters are bound by both Rap1 and Sfp1 and (3) an effect of Sfp1 on Rpb4 binding or conformation during transcription elongation. 

Strengths: 

This study provides evidence that a TF (yeast Sfp1), in addition to stimulating transcription initiation, can at some target genes interact with their mRNA transcripts and promote their stability. Sfp1 thus has a positive effect on two distinct regulatory steps. Furthermore, evidence is presented indicating that strong Sfp1 mRNA association requires both Rap1 and Sfp1 promoter binding and is increased at a sequence motif near the polyA track of many target mRNAs. Finally, they provide compelling evidence that Sfp1-bound mRNAs have higher levels of RNAPII backtracking and altered Rpb4 association or conformation compared to those not bound by Sfp1. 

Weaknesses: 

The Sfp1-Rpb4 association is supported only by a two-hybrid assay that is poorly described and lacks an important control. Furthermore, there is no evidence that this interaction is direct, nor are the interaction domains on either protein identified (or mutated to address function). 

Indeed, our two hybrid, immunoprecipitation and imaging results do not allow us to conclusively discern whether the interaction between Rpb4 and Sfp1 is direct or indirect. While the interaction holds significance, we consider the direct versus indirect distinction to be of secondary importance in the context of this paper. In the current text we indicated that 'our two hybrid, immunoprecipitation and imaging results do not differentiate between a direct or indirect interactions' (see page 6, sentences highlighted in blue)

The contention that Sfp1 nuclear export to the cytoplasm is transcription-dependent is not well supported by the experiments shown, which are not properly described in the text and are not accompanied by any primary data. 

This section has been re-written for better clarity (see page 7). We note that this assay was originally developed and published by Lee, M. S., M. Henry, and P. A. Silver in their 1996 paper in G&D and has since been reported in numerous subsequent studies. Reassuringly, our conclusion is bolstered by the observation that Sfp1 binds to Pol II transcripts co-transcriptionally, suggesting that Sfp1 is exported in the context of the mRNA.

The presence of Sfp1 in P-bodies is of unclear relevance and the authors do not ask whether Sfp1-bound mRNAs are also present in these condensates. 

P-bodies consist of both RNA and proteins (reviewed in doi: 10.1021/acs.biochem.7b01162). The significance of this experiment lies in its contribution to further confirming the co-localization of Sfp1 with mRNAs and Rpb4. This observation could also yield valuable insights for future investigations into the role of Sfp1.

Further analysis of Sfp1-bound mRNAs would be of interest, particularly to address the question of whether those from ribosomal protein genes and other growth-related genes that are known to display Sfp1 binding in their promoters are regulated (either stabilized or destabilized) by Sfp1. 

Fig. 4A, C and D show that RP mRNAs become destabilized in sfp1Δ cells.

The authors need to discuss, and ideally address, the apparent paradox that their previous findings showed that Rap1 acts to destabilize its downstream transcripts, i.e. that it has the opposite effect of Sfp1 shown here. 

We would like to thank Reviewer 1 for this valuable comment. In the revised paper, we delved into our hypothesis suggesting that Rap1 is likely responsible for regulating the imprinting of other proteins, that, in turn, lead to the destabilization of mRNAs, such as Rpb4. See blue paragraph in page 20.

Finally, recent studies indicate that the drugs used here to measure mRNA stability induce a strong stress response accompanied by rapid and complex effects on transcription. Their relevance to mRNA stability in unstressed cells is questionable. 

Half-lives were determined mainly by the GRO analysis of optimally proliferating cells. This  method does not requires any drug or stressful treatment.  The results obtained by this method were consistent with those obtained after thiolutin addition. Using both methods, we discovered that disruption of Sfp1 results in substantial mRNA destabilization. Nevertheless, in our revised manuscript, we show results obtained by subjecting cells to a temperature shift to 42°C, a natural method to inhibit transcription. This approach to determine half-lives has been previously reported in our publications, such as Lotan et al. (2005, 2007) and Goler Baron et al. (2008). This may rule out effects of the drug on half-lives. Indeed, this assay clearly determine HL under heat stress. Thus it can clearly demonstrate that, at least during heat shock, Sfp1 stabilizes mRNAs. Since the results are similar to those obtained by the GRO method at 30oC, we concluded that Sfp1 stabilizes mRNA under optimal and hot conditions.

Reviewer #2 (Public Review): 

Summary: 

The manuscript by Kelbert et al. presents results on the involvement of the yeast transcription factor Sfp1 in the stabilisation of transcripts whose synthesis it stimulates. Sfp1 is known to affect the synthesis of a number of important cellular transcripts, such as many of those that code for ribosomal proteins. The hypothesis that a transcription factor can remain bound to the nascent transcript and affect its cytoplasmic half-life is attractive, but the methods used to demonstrate the half-life effects and the association of Sfp1 with cytoplasmic transcripts remain to be fully validated, as explained in my comments on the results below: 

Comments on methodology and results: 

(1) A two-hybrid-based assay for protein-protein interactions identified Sfp1, a transcription factor known for its effects on ribosomal protein gene expression, as interacting with Rpb4, a subunit of RNA polymerase II. Classical two-hybrid experiments depend on the presence of the tested proteins in the nucleus of yeast cells, suggesting that the observed interaction occurs in the nucleus. Unfortunately, the two-hybrid method cannot determine whether the interaction is direct or mediated by nucleic acids. 

Indeed, our two hybrid, immunoprecipitation and imaging results do not allow us to conclusively discern whether the interaction between Rpb4 and Sfp1 is direct or indirect. While the interaction holds significance, we consider the direct versus indirect distinction to be of secondary importance in the context of this paper. In the current text we indicated that 'our two hybrid, immunoprecipitation and imaging results do not differentiate between a direct or indirect interactions' (see page 6)

(2) Inactivation of nup49, a component of the nuclear pore complex, resulted in the redistribution of GFP-Sfp1 into the cytoplasm at the temperature non-permissive for the nup49-313 strain, suggesting that GFP-Sfp1 is a nucleo-cytoplasmic shuttling protein. This observation confirmed the dynamic nature of the nucleo-cytoplasmic distribution of Sfp1. For example, a similar redistribution to the cytoplasm was previously reported following rapamycin treatment and under starvation (Marion et al., PNAS 2004). In conjunction with the observation of an interaction with Rpb4, the authors observed slower nuclear import kinetics for GFP-Sfp1 in the absence of Rpb4 when cells were transferred to a glucose-containing medium after a period of starvation. Since the redistribution of GFP-Sfp1 was abolished in an rpb1-1/nup49-313 double mutant, the authors concluded that Sfp1 localisation to the cytoplasm depends on transcription. The double mutant yeast cells may show a variety of non-specific effects at the restrictive temperature, and whether transcription is required for Sfp1 cytoplasmic localisation remains incompletely demonstrated. 

We agree with Reviewer 2 that any heat inactivation of a temperature-sensitive (ts) protein can lead to non-specific effects. It is evident that nup49-313 does not prevent Sfp1 export to the cytoplasm. In the case of rpb1-1, these non-specific effects are expected due to transcriptional arrest, which can eventually result in a reduction in protein content. However, this process takes some time, while the impact on export is more rapid. It is worth noting that this assay was developed and previously published by Pam Silver (Henry and Silver G&D 1996) and has been reported in many subsequent papers. Importantly, our conclusion is supported by the observation that Sfp1 binds both nascent RNA (co-transcriptionally) and mature mRNA (cytoplasmic). These observations, along with the reduced mRNA export upon transcription blocking, are consistent with our proposal that Sfp1 is exported in association with mRNA.

(3) Under starvation conditions, which led to the presence of Sfp1 in the cytoplasm and have previously been correlated with a decrease in the transcription of Sfp1 target genes, the authors observed that a plasmid-based expressed GFP-Sfp1 accumulated in cytoplasmic foci. These foci were also labelled by P-body markers such as Dcp2 and Lsm1. The quality of the microscopic images provided does not allow to determine whether Rpb4-RFP colocalises with GFP-Sfp1. 

The submitted PDF figure is of low quality. We believe that high quality figure of the final submission is convincing. 

(4) To understand to which RNA Sfp1 might bind, the authors used an N-terminally tagged fusion protein in a cross-linking and purification experiment. This method identified 264 transcripts for which the CRAC signal was considered positive and which mostly correspond to abundant mRNAs, including 74 ribosomal protein mRNAs or metabolic enzyme-abundant mRNAs such as PGK1. The authors did not provide evidence for the specificity of the observed CRAC signal, in particular, what would be the background of a similar experiment performed without UV cross-linking. In a validation experiment, the presence of several mRNAs in a purified SFP1 fraction was measured at levels that reflect the relative levels of RNA in a total RNA extract. Negative controls showing that abundant mRNAs not found in the CRAC experiment were clearly depleted from the purified fraction with Sfp1 would be crucial to assessing the specificity of the observed protein-RNA interactions. The NON-CRAC+ selected mRNAs were enriched for genes whose expression was previously shown to be upregulated upon Sfp1 overexpression (Albert et al., 2019). The presence of unspliced RPL30 pre-mRNA in the Sfp1 purification was interpreted as a sign of co-transcriptional assembly of Sfp1 into mRNA, but in the absence of valid negative controls, this hypothesis would require further experimental validation.

We would like to thank Reviewer 2 for bringing this issue up, as it helped us to clarify it in the revised paper.

First, we emphasized in the Discussion that many CRAC+ genes do not fall into the category of highly transcribed genes. Please see more detailed discussion below.

Secondly, we examined various features of the 264 genes - classified as CRAC+ - to estimate their specificity and biological significance. Our various experiments revealed that the CRAC+ genes represent a distinct group with many unique features.

The biological significance of the 264 CRAC+ mRNAs was demonstrated by various experiments; all are inconsistent with technical flaws. In fact, all the experiments and analyses that we have pursued indicate the unique nature of the CRAC+ genes. Some examples are:

(1) Fig. 2a and B show that most reads of CRAC+ mRNA were mapped to specific location – close the pA sites.

(2) Fig. 2C shows that most reads of CRAC+ mRNA were mapped to specific RNA motif located near the 3’ ends of the mRNAs.

(3) Most RiBi CRAC+ promoter contain Rap1 binding sites (p= 1.9x10-22), whiles the vast majority of RiBi non-CRAC+  promoters do not. (Fig. 3C).

(4) Fig. 4A shows that RiBi CRAC+ mRNAs become destabilized due to Sfp1 deletion, whereas RiBi non-CRAC+ mRNAs do not. Fig. 4B shows similar results due to Sfp1 depletion.

(5) Fig. 6B shows that the impact of Sfp1 on backtracking is substantially higher for CRAC+ than for non-CRAC+ genes. This is most clearly visible in RiBi genes.

(6) Fig. 7A shows that the Sfp1-dependent changes along the transcription units is substantially more rigorous for CRAC+ than for non-CRAC+.

(7) In Fig. S4B, the chromatin binding profile of Sfp1 is shown to be different for CRAC+ and non-CRAC+ genes.

Taken together, the many unique features, in fact, any feature that we examined, indicate the specificity and significance of this group, demonstrating that our CRAC results are biologically significant.

Most importantly, these genes do not all fall into the category of highly transcribed genes.  On the contrary, as depicted in Figure 6A (green dots), it is evident that CRAC+ genes exhibit a diverse range of Rpb3 ChIP and GRO signals. Furthermore, as illustrated in Figure 7A, when comparing CRAC+ to Q1 (the most highly transcribed genes), it becomes evident that the Rpb4/Rpb3 profile of CRAC+ genes behaves differently from the Q1 group. Evidently, despite the heterogeneous transcription of CRAC+ genes (as mentioned above), the Rpb4/Rpb3 profile decreases more substantially than that of the highly transcribed genes (Q1).  Moreover, despite similar expression levels among all RiBi mRNAs, only a portion of them binds Sfp1.

Thus, all our results indicate that CRAC+ genes represent biologically significant group, irrespective of the expression of it members. In response to this comment, we included a new paragraph discussing the validity of our conclusions. See page 18, blue paragraph.

(5) To address the important question of whether co-transcriptional assembly of Spf1 with transcripts could alter their stability, the authors first used a reporter system in which the RPL30 transcription unit is transferred to vectors under different transcriptional contexts, as previously described by the Choder laboratory (Bregman et al. 2011). While RPL30 expressed under an ACT1 promoter was barely detectable, the highest levels of RNA were observed in the context of the native upstream RPL30 sequence when Rap1 binding sites were also present. Sfp1 showed better association with reporter mRNAs containing Rap1 binding sites in the promoter region. However, removal of the Rap1 binding sites from the reporter vector also led to a drastic decrease in reporter mRNA levels. Whether the fraction of co-purified RNA is nuclear and co-transcriptional or not cannot be inferred from these results. 

The proposed co-transcriptional binding of Sfp1 is based on the findings presented in Figure 5C and Figure S2D, as well as the observed binding of Sfp1 to transcripts containing introns, as shown in Figures 2D and 3B.  The results of Fig. 3 led us to the assertion that the "RNA-binding capacity of Sfp1 is regulated by Rap1-binding sites located at the promoter." We maintain our stance on this conclusion. Indeed, the Rap1 binding site does impact mRNA levels, as highlighted by Reviewer 2. However, "construct E," which possesses a promoter with a Rap1 binding site, exhibits lower transcript levels compared to "construct F," which lacks such a binding site in its promoter. Despite this difference in transcript levels, Sfp1 was able to pull down the former transcript but not the latter, even though expression of the former gene is relatively low. Thus, the results appear to be more reliant on the specific capacity of Sfp1 to interact with the transcript rather than on the transcript's expression level.

(6) To complement the biochemical data presented in the first part of the manuscript, the authors turned to the deletion or rapid depletion of SFP1 and used labelling experiments to assess changes in the rate of synthesis, abundance, and decay of mRNAs under these conditions. An important observation was that in the absence of Sfp1, mRNAs encoding ribosomal protein genes not only had a reduced synthesis rate but also an increased degradation rate. This important observation needs careful validation, as genomic run-on experiments were used to measure half-lives, and this particular method was found to give results that correlated poorly with other measures of half-life in yeast (e.g. Chappelboim et al., 2022 for a comparison). Similarly, the use of thiolutin to block transcription as a method of assessing mRNA half-life has been reported to be problematic, as thiolutin can specifically inhibit the degradation of ribosomal protein mRNA (Pelechano & Perez-Ortin, 2008). Specific repressible reporters, such as those used by Baudrimont et al. (2017), would need to be tested to validate the effect of Sfp1 on the half-life of specific mRNAs. Also, it would be very difficult to infer from the images presented whether the rate of deadenylation is altered by Sfp1.

Various methods exist for assessing mRNA half-lives (HLs), and each of them carries its own set of challenges and biases. Consequently, it becomes problematic to directly compare HL values of a specific mRNA when different methods are employed. The superiority of one particular method over others remains unclear (in my opinion). However, they exhibit a high degree of reliability when it comes to comparing different strains under the identical conditions using a single method.

Estimating HLs through the GRO approach is a non-invasive method, applied on optimally proliferating cells, which has been employed in numerous publications. While no method is without its limitations, our experience along the years reassured approach to be among the most dependable. Our HL determination using thiolutin to block transcription provided results that were consistent with the values obtained by the GRO approach.

Nevertheless, in our revised manuscript, we supplemented the HL data, obtain by thiolutin, with results obtained by subjecting cells to a temperature shift to 42°C, a natural method to block transcription in wild-type (WT) cells. This approach to determine HLs has been previously reported in our publications, such as Lotan et al. (2005, 2007) and Goler Baron et al. (2008). The new results are shown in Fig. S3B. They are consistent with our conclusion that Sfp1 stabilizes mRNAs.

Using a repressible promoter to determine mRNA HL is, unfortunately, not suitable in this paper because the promoter itself is involved in HL regulation. This observation is supported by Bregman et al. (2011) and depicted in Fig. 3, which illustrates that the promoter is critical for mRNA imprinting, consequently regulating HL.

(7) The effects of SFP1 on transcription were investigated by chromatin purification with Rpb3, a subunit of RNA polymerase, and the results were compared with synthesis rates determined by genomic run-on experiments. The decrease in polII presence on transcripts in the absence of SFP1 was not accompanied by a marked decrease in transcript output, suggesting an effect of Sfp1 in ensuring robust transcription and avoiding RNA polymerase backtracking. To further investigate the phenotypes associated with the depletion or absence of Sfp1, the authors examined the presence of Rpb4 along transcription units compared to Rpb3. One effect of spf1 deficiency was that this ratio, which decreased from the start of transcription towards the end of transcripts, increased slightly. The results presented are largely correlative and could arise from the focus on very specific types of mRNAs, such as those of ribosomal protein genes, which are sensitive to stress and are targeted by very active RNA degradation mechanisms activated, for example, under heat stress (Bresson et al., 2020). 

Figure 7A illustrates a significant reduction in Rpb4/Rpb3 ratios along the transcription unit in WT cells. This reduction is notably more pronounced in CRAC+ genes compared to the highly transcribed quartile (Q1), which includes all ribosomal protein (RP) genes, and it is completely absent in sfp1∆ cells. Furthermore, it's important to highlight that the CRAC+ gene group displays a wide range of transcription rates, as measured by either Rpb3 ChIP or GRO (Figure 6A). Given these observations, we do not think that heightened sensitivity of RP mRNA degradation in response to stress is responsible for the pronounced difference in the configuration of the Pol II elongation complex that is detected in CRAC+ genes, mainly because this experiment was performed under standard (non-stress) culture conditions.

Correlative studies are particularly informative when a gene mutation eliminates a correlation, and this is precisely the type of study depicted in Figure 7B-C. The correlations shown in these panels are dependent on Sfp1. Indeed, RP genes are sensitive to stress. However, we used non-stressed conditions. Furthermore, CRAC+ genes did not display any apparent unusual destabilization but rather exhibited higher (not lower) mRNA stability compared to non-CRAC+ genes (Figure 7C).

Author response:

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

Reviewer #1 (Public Review):

Summary:

The paper combines phenotypic and genomic analyses of the "sheltered load" (i.e. the accumulation of deleterious mutations linked to S-loci that are hidden from selection in the homozygous state) in Arabidopsis. The authors compare results to previous theoretical predictions concerning the extent of the load in dominant vs recessive S-alleles, and further develop exciting theory to reconcile differences between previous theory and observed results.

Strengths:

This is a very nice combination of theory and data to address a classical question in the field.

We thank the reviewer for this positive feedback.

Weaknesses:

The "genetic load" is a poorly defined concept in general, and its quantification via the number of putatively deleterious mutations is quite difficult. Furthermore counting up the number of derived mutations at fully constrained nucleotides may not be a great estimate of the load, and certainly does not allow for evaluation of recessivity -- a concept critical to ideas concerning the sheltered load. Alternative approaches - including estimating the severity of mutations - could be helpful as well. This imperfection in available approaches to test theory must be acknowledged more strongly by the authors.

As suggested by the reviewer, we implemented alternative approaches to estimate the severity of deleterious mutations and now report the results of SNPeff and

SIFT4G analyses in Table S6. The results we obtained with these other metrics were overall very similar to those based on our previous counting of mutations at 0-fold and 4-fold degenerate sites. More generally, we tried to improve the presentation of our strategy to estimate the genetic load (clarified in lines 262-268, 271, 292-295, 297. In particular, we made it clear that our population genetic analysis cannot assess the recessivity of the observed mutations (lines 428-434).

Reviewer #2 (Public Review):

Summary:

This study looks into the complex dominance patterns of S-allele incompatibilities in Brassicaceae, through which it attempts to learn more about the sheltering of deleterious load. I found several weak points in the analyses that diminished my excitement about the results. In particular, the way in which deleterious mutations were classified lacked the ability to distinguish the severity of the mutations and thus their expected associated dominance.

First, we would like to clarify that our goal with this study is NOT to learn something about dominance of the linked deleterious mutations (we can not). Instead, we compare the accumulation of deleterious mutations linked to dominant vs recessive S-ALLELES, but are agnostic regarding the dominance level of the LINKED mutations themselves. The rationale is that the different intensities of natural selection between dominant vs recessive S-alleles provide a powerful way to examine the process by which deleterious mutations are sheltered in general. We further clarified this aspect on lines 70-73 and 399-401.

Second, as mentioned above in response to Reviewer 1, we complemented the analysis by predicting the severity of the deleterious mutations by SIFT4G and SNPeff. The results were largely consistent, with the exception that the number of sites included in SIFT4G was low, such that the statistical power was reduced (lines 296-300).

Furthermore, the simulation approach could have provided this exact sort of insight but was not designed to do so, making this comparison to the empirical data also less than exciting for me.

As explained above, studying dominance of the linked mutations we observed is an interesting research question (albeit a difficult one), but it was not our goal here. Instead, our study was designed as an empirical test of the predictions presented in Llaurens et al (2009), and we re-analysed some aspects of the model outcome to illustrate our points.

We now better explain that we based our choice of parameters on the fact that in the theoretical study by Llaurens et al (2009), recessive deleterious mutations are predicted to accumulate in a much more straightforward manner (line 316-318).

We now dedicate a paragraph of the discussion to explain how our stochastic simulations could be improved, and acknowledge that a full exploration of the interaction between dominance of the S-alleles and dominance of the linked deleterious mutations would be an interesting follow-up - albeit beyond the scope of our study (line 437-441).

Major and minor comments:

I think the introduction (or somewhere before we dive into it in the results) of the dominance hierarchy for the S-alleles needs a more in-depth explanation. Not being familiar with this beforehand really made this paper inaccessible to me until I then went to find out more before continuing. I would expect this paper to be broad enough that self-contained information makes it accessible to all readers. For example, lines 110-115 could be in the Introduction.

We thank the reviewer for this useful remark. We now give a more comprehensive description of the dominance hierarchy and introduce the classes of dominance in A. lyrata already in the introduction, on lines 64-70.

Along with my above comment, perhaps it is not my place to comment, but I find the paper not of a broad enough scope to be of interest to a broad readership. This S-allele dominance system is more than simple balancing selection, it is a very complex and specific form of dominance between several haplotypes, and the mechanism of dominance does not seem to be genetic. I am not sure that it thus extrapolates to broad comments on general dominance and balancing selection, e.g. it would not be the same as considering inversions and this form of balancing selection where we also expect recessive deleterious mutations to accumulate.

We disagree with these interpretations by the reviewer, for two reasons:

First, the mechanism of dominance is actually entirely genetic. In fact, we uncovered some years ago that it is based on the molecular interaction between small non-coding RNAs from dominant alleles and their target sites on recessive alleles (Durand et al. Science 2014, see lines 68-70). If there is something specific with this system, it is that the dominance phenomenon is better understood at the mechanistic level than in most other cases, but the resulting phenomenon in itself (a dominance hierarchy) is rather common.

Second, the kind of variation in the intensity of linked selection created by this mechanism is actually a general phenomenon, so our results have broad relevance beyond our particular study system. We modified the introduction to explain this point

more clearly, highlighting in particular the fact that the situation we study closely resembles the case of sex chromosomes, where X (or Z) chromosomes are genetically recessive and Y (or W) chromosomes are genetically dominant. We cite this example in lines 83-87 of the introduction and also several well-studied other examples on lines 480-489 of the discussion.

It would have been particularly interesting, or a nice addition, to see deleterious mutations classed by something like SNPeff or GERP where you can have different classes of moderate to severe deleterious variants, which we would expect also to be more recessive the more deleterious they are. In line with my next comment on the simulations, I think relative differences between mutations expected to be more or less dominant may be even more insightful into the process of sheltering which may or may not be going on here.

We agree with the reviewer, and as detailed above we have now integrated such analyses with SNPeff and SIFT4G (Table S6). These new results reinforce our conclusion that while S-allele dominance influences the fixation of deleterious mutations, it has no effect on their total number. See lines 270-272 and 296-300.

In the simulations, h=0 and s=0.01 (as in Figure 5) for all deleterious mutations seems overly simplistic, and at the convenient end for realistic dominance. I think besides recessive lethals which we expect to be close to h=0 would have a much larger selection coefficient, and other deleterious mutations would only be partially recessive at such an s value. I expect this would change some of the simulation results seen, though to what degree I am not certain. It would be nice to at least check the same exact results for h=0.3 or 0.2 (or additionally also for recessive lethals, e.g. h=0 and s=-0.9). I would also disagree with the statement in line 677, many studies have shown, particularly those on balancing selection, that partially recessive deleterious mutations are not eliminated by natural selection and do play a role in population genetic dynamics. I am also not surprised that extinction was found for higher s values when the mutation rate for such mutations was very high and the distribution of s values was constant. An influx of such highly deleterious mutations is unlikely to ever let a population survive, yet that does NOT mean that in nature, the rare influx of such mutations does lead to them being sheltered. I find overall that the simulation results contribute very little, to none, to this paper, as without something more realistic, like a simultaneous distribution of s and h values, you cannot say which, if any class of these mutations are the ones expected to accumulate because of S-allele dominance.

We understand that the previous version of our manuscript was confusing between dominance of the S-alleles and dominance of the linked deleterious mutations. We clarified that our study focuses on the effect of the former only (lines 99, 263-264 and 581-583).

We agree that a complete exploration of the interaction between dominance of the S-alleles and dominance of the linked mutations being sheltered would have been an asset, but as explained above this is not the focus of our study. The previous work by Llaurens et al (2009) has already established that deleterious mutations can fix within S-allele lineages, especially when linked to dominant S-alleles, and when the number of S-alleles is large. Under the conditions they examined, deleterious mutations were much more strongly eliminated if not fully recessive (h=0 vs h=0.2), so for the present study we decided to simulate fully recessive mutations only. We now formally acknowledge the possibility that some complex interaction may take place between dominance of the S-alleles and dominance of the linked deleterious mutations (lines 440-442). However, as explained above we feel that fully exploring this complex interaction would require a detailed investigation, which is clearly beyond the scope of the present study.

Rather they only show the disappointing or less exciting result that fully recessive, weakly deleterious mutations (which I again think do not even exist in nature as I said above) have minor, to no effect across the classes of S-allele dominance. They provide no insight into whether any type of recessive deleterious mutation can accumulate under the S-allele dominance hierarchy, and that is the interesting question at hand. I would either remove these simulations or redo them in another approach. The authors never mention what simulation approach was used, so I can only assume this is custom, in-house code. Yet I do not find that code provided on the github page. I do not know if the lack of a distribution for h and s values is then a choice or a programming limitation, but I see it as one that should be overcome if these simulations are meant to be meaningful to the results of the study.

The code we used (in C) was adapted from the previous study by Llaurens et al. (2009), which at the time was not deposited in a data repertory, unfortunately. With the agreement of the authors of that study, this code is now available on Github:

(https://github.com/leveveaudrey/model_ssi_Llaurens; line 723).

It is correct that our simulations were not aimed at determining whether “any type of recessive deleterious mutation can accumulate”, but we strongly believe that they help interpreting the observations made in the genomic data.

Recommendations for the authors:

Notes from the editor:

I found Table 1 confusing, with column headings of observed proportion but perhaps numbers reflecting counts.

Thank you for pointing out this confusion. There was indeed an error in the last column, which we have now corrected.

I found Figure 2 a bit hard to parse, with the vertical lines being unclear and the x-axis ticks of insufficient resolution to evaluate the physical extent of the signals.

We increased the size of the label on the x-axis and detailed it on the Figure 2, which is now hopefully more clear. Moreover, we increase the size of the vertical lines.

Finally, I wonder, given the rapid decay of signal in lyrata, whether 25kb is the right choice for evaluating load and whether the pattern may look different on a smaller scale.

It is true that the signal decays rapidly in A. lyrata, as can be seen in the haplotype structure analysis and in line with our previous analysis of the same populations Le Veve et al (MBE 2023; in this study we explored the effect of the choice of the size of the chromosomal region analyzed; lines 266-269). However, for the sake of comparison, we prefer to stick to the same window size. The fact that we still see an effect of dominance in spite of the lower statistical power associated with the more rapid decay (because a smaller number of genes is expected to be impacted) actually reinforces our conclusions.

Reviewer #1 (Recommendations For The Authors):

I have a few additional suggestions to improve the manuscript.

(1) How does the load linked to the S-locus compare to that observed in other genomic regions? It would be useful to provide a comparison of the results quantified in Figures three and four to comparable genomic regions unlinked to the S-locus. How severe is the linked load?

This comparison to the genomic background was actually the core of our previous study (Le Veve et al MBE 2023), which was based on the same populations. This analysis revealed that polymorphism of the 0-fold degenerate sites was more than twice higher in the 25kb immediately flanking the S-locus than in a series of 100 unlinked control regions. Here, the main focus of the present study is on the effect of linkage to particular S-alleles (which was not possible previously because haplotypes had to be phased).

(2) Details of the GLM for data underlying Figures 3 and 4 are somewhat unclear. Is the key explanatory variable (Dominance) treated as continuous? Categorical? Ordinal etc…

Dominance is considered as a continuous variable. We specify this in line 162 of the results, in the legends of Figures 3 and 4, in the Material and Method (lines 627 and 660) and in the legend of Table S4.

(3) I had some trouble understanding the two different p-values in columns five and six of table one. Please provide more detail.

We understand that the two p-values in Table 1 were confusing. The first was related to the binomial test and the second to the permutation test. To be consistent with the rest of the manuscript, we conserved only the p-value of the permutation test.

(4) As mentioned in the "weaknesses" above, the authors should be more clear about what they are quantifying. They are explicitly counting the number of variants at 0-fold degenerate sites as a proxy for the genetic load. How good this proxy is is unclear. The most egregious misstatement here was on line 314 in which they make reference to the "total load." However, this limitation should be acknowledged throughout the manuscript and deserves more attention in the methods and discussion.

As mentioned above, we now integrate additional methods to define and quantify the load (SIFT4G and SNPeff), which reinforced our previous conclusions (lines 271-272, 297-302).

We clarified our wording and replaced the mention of “total load” by “mean number of linked deleterious mutations per copy of S-allele” (line 324-325). In the discussion we tried to better explain the limitations of approaches to estimate the genetic load (line 431-437).

Reviewer #2 (Recommendations For The Authors):

Line 60, it should be specified that this is only for recessive deleterious mutations.

Non-recessive deleterious mutations would certainly not be expected to accumulate.

As explained in details above, the question of whether and how non-recessive deleterious mutations can accumulate when linked to the S-locus is difficult and would in itself deserve a full treatment, which is clearly beyond the scope of the present study. We clarified this point on line 56.

Author Response

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

Major comments (Public Reviews)

Generality of grid cells

We appreciate the reviewers’ concern regarding the generality of our approach, and in particular for analogies in nonlinear spaces. In that regard, there are at least two potential directions that could be pursued. One is to directly encode nonlinear structures (such as trees, rings, etc.) with grid cells, to which DPP-A could be applied as described in our model. The TEM model [1] suggests that grid cells in the medial entorhinal may form a basis set that captures structural knowledge for such nonlinear spaces, such as social hierarchies and transitive inference when formalized as a connected graph. Another would be to use eigen-decomposition of the successor representation [2], a learnable predictive representation of possible future states that has been shown by Stachenfield et al. [3] to provide an abstract structured representation of a space that is analogous to the grid cell code. This general-purpose mechanism could be applied to represent analogies in nonlinear spaces [4], for which there may not be a clear factorization in terms of grid cells (i.e., distinct frequencies and multiple phases within each frequency). Since the DPP-A mechanism, as we have described it, requires representations to be factored in this way it would need to be modified for such purpose. Either of these approaches, if successful, would allow our model to be extended to domains containing nonlinear forms of structure. To the extent that different coding schemes (i.e., basis sets) are needed for different forms of structure, the question of how these are identified and engaged for use in a given setting is clearly an important one, that is not addressed by the current work. We imagine that this is likely subserved by monitoring and selection mechanisms proposed to underlie the capacity for selective attention and cognitive control [5], though the specific computational mechanisms that underlie this function remain an important direction for future research. We have added a discussion of these issues in Section 6 of the updated manuscript.

(1) Whittington, J.C., Muller, T.H., Mark, S., Chen, G., Barry, C., Burgess, N. and Behrens, T.E., 2020. The Tolman-Eichenbaum machine: unifying space and relational memory through generalization in the hippocampal formation. Cell, 183(5), pp.1249-1263.

(2) Dayan, P., 1993. Improving generalization for temporal difference learning: The successor representation. Neural computation, 5(4), pp.613-624.

(3) Stachenfeld, K.L., Botvinick, M.M. and Gershman, S.J., 2017. The hippocampus as a predictive map. Nature neuroscience, 20(11), pp.1643-1653.

(4) Frankland, S., Webb, T.W., Petrov, A.A., O'Reilly, R.C. and Cohen, J., 2019. Extracting and Utilizing Abstract, Structured Representations for Analogy. In CogSci (pp. 1766-1772).

(5) Shenhav, A., Botvinick, M.M. and Cohen, J.D., 2013. The expected value of control: an integrative theory of anterior cingulate cortex function. Neuron, 79(2), pp.217-240. Biological plausibility of DPP-A

We appreciate the reviewers’ interest in the biological plausibility of our model, and in particular the question of whether and how DPP-A might be implemented in a neural network. In that regard, Bozkurt et al. [1] recently proposed a biologically plausible neural network algorithm using a weighted similarity matrix approach to implement a determinant maximization criterion, which is the core idea underlying the objective function we use for DPP-A, suggesting that the DPP-A mechanism we describe may also be biologically plausible. This could be tested experimentally by exposing individuals (e.g., rodents or humans) to a task that requires consistent exposure to a subregion, and evaluating the distribution of activity over the grid cells. Our model predicts that high frequency grid cells should increase their firing rate more than low frequency cells, since the high frequency grid cells maximize the determinant of the covariance matrix of the grid cell embeddings. It is also worth noting that Frankland et al. [2] have suggested that the use of DPPs may also help explain a mutual exclusivity bias observed in human word learning and reasoning. While this is not direct evidence of biological plausibility, it is consistent with the idea that the human brain selects representations for processing that maximize the volume of the representational space, which can be achieved by maximizing the DPP-A objective function defined in Equation 6. We have added a comment to this effect in Section 6 of the updated manuscript.

(1) Bozkurt, B., Pehlevan, C. and Erdogan, A., 2022. Biologically-plausible determinant maximization neural networks for blind separation of correlated sources. Advances in Neural Information Processing Systems, 35, pp.13704-13717.

(2) Frankland, S. and Cohen, J., 2020. Determinantal Point Processes for Memory and Structured Inference. In CogSci.

Simplicity of analogical problem and comparison to other models using this task

First, we would like to point out that analogical reasoning is a signatory feature of human cognition, which supports flexible and efficient adaptation to novel inputs that remains a challenge for most current neural network architectures. While humans can exhibit complex and sophisticated forms of analogical reasoning [1, 2, 3], here we focused on a relatively simple form, that was inspired by Rumelhart’s parallelogram model of analogy [4,5] that has been used to explain traditional human verbal analogies (e.g., “king is to what as man is to woman?”). Our model, like that one, seeks to explain analogical reasoning in terms of the computation of simple Euclidean distances (i.e., A - B = C - D, where A, B, C, D are vectors in 2D space). We have now noted this in Section 2.1.1 of the updated manuscript. It is worth noting that, despite the seeming simplicity of this construction, we show that standard neural network architectures (e.g., LSTMs and transformers) struggle to generalize on such tasks without the use of the DPP-A mechanism.

Second, we are not aware of any previous work other than Frankland et al. [6] cited in the first paragraph of Section 2.2.1, that has examined the capacity of neural network architectures to perform even this simple form of analogy. The models in that study were hardcoded to perform analogical reasoning, whereas we trained models to learn to perform analogies. That said, clearly a useful line of future work would be to scale our model further to deal with more complex forms of representation and analogical reasoning tasks [1,2,3]. We have noted this in Section 6 of the updated manuscript.

(1) Holyoak, K.J., 2012. Analogy and relational reasoning. The Oxford handbook of thinking and reasoning, pp.234-259.

(2) Webb, T., Fu, S., Bihl, T., Holyoak, K.J. and Lu, H., 2023. Zero-shot visual reasoning through probabilistic analogical mapping. Nature Communications, 14(1), p.5144.

(3) Lu, H., Ichien, N. and Holyoak, K.J., 2022. Probabilistic analogical mapping with semantic relation networks. Psychological review.

(4) Rumelhart, D.E. and Abrahamson, A.A., 1973. A model for analogical reasoning. Cognitive Psychology, 5(1), pp.1-28.

(5) Mikolov, T., Chen, K., Corrado, G. and Dean, J., 2013. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781.

(6) Frankland, S., Webb, T.W., Petrov, A.A., O'Reilly, R.C. and Cohen, J., 2019. Extracting and Utilizing Abstract, Structured Representations for Analogy. In CogSci (pp. 1766-1772).

Clarification of DPP-A attentional modulation

We would like to clarify several concerns regarding the DPP-A attentional modulation. First, we would like to make it clear that ω is not meant to correspond to synaptic weights, and thank the reviewer for noting the possibility for confusion on this point. It is also distinct from a biasing input, which is often added to the product of the input features and weights. Rather, in our model ω is a vector, and diag (ω) converts it into a matrix with ω as the diagonal of the matrix, and the rest entries are zero. In Equation 6, diag(ω) is matrix multiplied with the covariance matrix V, which results in elementwise multiplication of ω with column vectors of V, and hence acts more like gates. We have noted this in Section 2.2.2 and have changed all instances of “weights (ω)” to “gates (ɡ)” in the updated manuscript. We have also rewritten the definition of Equation 6 and uses of it (as in Algorithm 1) to depict the use of sigmoid nonlinearity (σ) to , so that the resulting values are always between 0 and 1.

Second, we would like to clarify that we don’t compute the inner product between the gates ɡ and the grid cell embeddings x anywhere in our model. The gates within each frequency were optimized (independent of the task inputs), according to Equation 6, to compute the approximate maximum log determinant of the covariance matrix over the grid cell embeddings individually for each frequency. We then used the grid cell embeddings belonging to the frequency that had the maximum within-frequency log determinant for training the inference module, which always happened to be grid cells within the top three frequencies. Author response image 1 (also added to the Appendix, Section 7.10 of the updated manuscript) shows the approximate maximum log determinant (on the y-axis) for the different frequencies (on the x-axis).

Author response image 1.

Approximate maximum log determinant of the covariance matrix over the grid cell embeddings (y-axis) for each frequency (x-axis), obtained after maximizing Equation 6.

Third, we would like to clarify our interpretation of why DPP-A identified grid cell embeddings corresponding to the highest spatial frequencies, and why this produced the best OOD generalization (i.e., extrapolation on our analogy tasks). It is because those grid cell embeddings exhibited greater variance over the training data than the lower frequency embeddings, while at the same time the correlations among those grid cell embeddings were lower than the correlations among the lower frequency grid cell embeddings. The determinant of the covariance matrix of the grid cell embeddings is maximized when the variances of the grid cell embeddings are high (they are “expressive”) and the correlation among the grid cell embeddings is low (they “cover the representational space”). As a result, the higher frequency grid cell embeddings more efficiently covered the representational space of the training data, allowing them to efficiently capture the same relational structure across training and test distributions which is required for OOD generalization. We have added some clarification to the second paragraph of Section 2.2.2 in the updated manuscript. Furthermore, to illustrate this graphically, Author response image 2 (added to the Appendix, Section 7.10 of the updated manuscript) shows the results after the summation of the multiplication of the grid cell embeddings over the 2d space of 1000x1000 locations, with their corresponding gates for 3 representative frequencies (left, middle and right panels showing results for the lowest, middle and highest grid cell frequencies, respectively, of the 9 used in the model), obtained after maximizing Equation 6 for each grid cell frequency. The color code indicates the responsiveness of the grid cells to different X and Y locations in the input space (lighter color corresponding to greater responsiveness). Note that the dark blue area (denoting regions of least responsiveness to any grid cell) is greatest for the lowest frequency and nearly zero for the highest frequency, illustrating that grid cell embeddings belonging to the highest frequency more efficiently cover the representational space which allows them to capture the same relational structure across training and test distributions as required for OOD generalization.

Author response image 2.

Each panel shows the results after summation of the multiplication of the grid cell embeddings over the 2d space of 1000x1000 locations, with their corresponding gates for a particular frequency, obtained after maximizing Equation 6 for each grid cell frequency. The left, middle, and right panels show results for the lowest, middle, and highest grid cell frequencies, respectively, of the 9 used in the model. Lighter color in each panel corresponds to greater responsiveness of grid cells at that particular location in the 2d space.

Finally, we would like to clarify how the DPP-A attentional mechanism is different from the attentional mechanism in the transformer module, and why both are needed for strong OOD generalization. Use of the standard self-attention mechanism in transformers over the inputs (i.e., A, B, C, and D for the analogy task) in place of DPP-A would lead to weightings of grid cell embeddings over all frequencies and phases. The objective function for the DPP-A represents an inductive bias, that selectively assigns the greatest weight to all grid cell embeddings (i.e., for all phases) of the frequency for which the determinant of the covariance matrix is greatest computed over the training space. The transformer inference module then attends over the inputs with the selected grid cell embeddings based on the DPP-A objective. We have added a discussion of this point in Section 6 of the updated manuscript.

We would like to thank the reviewers for their recommendations. We have tried our best to incorporate them into our updated manuscript. Below we provide a detailed response to each of the recommendations grouped for each reviewer.

Reviewer #1 (Recommendations for the authors)

(1) It would be helpful to see some equations for R in the main text.

We thank the reviewer for this suggestion. We have now added some equations explaining the working of R in Section 2.2.3 of the updated manuscript.

(2) Typo: p 11 'alongwith' -> 'along with'

We have changed all instances of ‘alongwith’ to ‘along with’ in the updated manuscript.

(3) Presumably, this is related to equivariant ML - it would be helpful to comment on this.

Yes, this is related to equivariant ML, since the properties of equivariance hold for our model. Specifically, the probability distribution after applying softmax remains the same when the transformation (translation or scaling) is applied to the scores for each of the answer choices obtained from the output of the inference module, and when the same transformation is applied to the stimuli for the task and all the answer choices before presenting as input to the inference module to obtain the scores. We have commented on this in Section 2.2.3 of the updated manuscript.

Reviewer #2 (Recommendations for the authors)

(1) Page 2 - "Webb et al." temporal context - they should also cite and compare this to work by Marc Howard on generalization based on multi-scale temporal context.

While we appreciate the important contributions that have been made by Marc Howard and his colleagues to temporal coding and its role in episodic memory and hippocampal function, we would like to clarify that his temporal context model is unrelated to the temporal context normalization developed by Webb et al. (2020) and mentioned on Page 2. The former (Temporal Context Model) is a computational model that proposes a role for temporal coding in the functions of the medial temporal lobe in support of episodic recall, and spatial navigation. The latter (temporal context normalization) is a normalization procedure proposed for use in training a neural network, similar to batch normalization [1], in which tensor normalization is applied over the temporal instead of the batch dimension, which is shown to help with OOD generalization. We apologize for any confusion engendered by the similarity of these terms, and failure to clarify the difference between these, that we have now attempted to do in a footnote on Page 2.

Ioffe, S. and Szegedy, C., 2015, June. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In International conference on machine learning (pp. 448-456). pmlr.

(2) page 3 - "known to be implemented in entorhinal" - It's odd that they seem to avoid citing the actual biology papers on grid cells. They should cite more of the grid cell recording papers when they mention the entorhinal cortex (i.e. Hafting et al., 2005; Barry et al., 2007; Stensola et al., 2012; Giocomo et al., 2011; Brandon et al., 2011).

We have now cited the references mentioned below, on page 3 after the phrase “known to be implemented in entohinal cortex”.

(1) Barry, C., Hayman, R., Burgess, N. and Jeffery, K.J., 2007. Experience-dependent rescaling of entorhinal grids. Nature neuroscience, 10(6), pp.682-684.

(2) Stensola, H., Stensola, T., Solstad, T., Frøland, K., Moser, M.B. and Moser, E.I., 2012. The entorhinal grid map is discretized. Nature, 492(7427), pp.72-78.

(3) Giocomo, L.M., Hussaini, S.A., Zheng, F., Kandel, E.R., Moser, M.B. and Moser, E.I., 2011. Grid cells use HCN1 channels for spatial scaling. Cell, 147(5), pp.1159-1170.

(4) Brandon, M.P., Bogaard, A.R., Libby, C.P., Connerney, M.A., Gupta, K. and Hasselmo, M.E., 2011. Reduction of theta rhythm dissociates grid cell spatial periodicity from directional tuning. Science, 332(6029), pp.595-599.

(3) To enhance the connection to biological systems, they should cite more of the experimental and modeling work on grid cell coding (for example on page 2 where they mention relational coding by grid cells). Currently, they tend to cite studies of grid cell relational representations that are very indirect in their relationship to grid cell recordings (i.e. indirect fMRI measures by Constaninescu et al., 2016 or the very abstract models by Whittington et al., 2020). They should cite more papers on actual neurophysiological recordings of grid cells that suggest relational/metric representations, and they should cite more of the previous modeling papers that have addressed relational representations. This could include work on using grid cell relational coding to guide spatial behavior (e.g. Erdem and Hasselmo, 2014; Bush, Barry, Manson, Burges, 2015). This could also include other papers on the grid cell code beyond the paper by Wei et al., 2015 - they could also cite work on the efficiency of coding by Sreenivasan and Fiete and by Mathis, Herz, and Stemmler.

We thank the reviewer for bringing the additional references to our attention. We have cited the references mentioned below on page 2 of the updated manuscript.

(1) Erdem, U.M. and Hasselmo, M.E., 2014. A biologically inspired hierarchical goal directed navigation model. Journal of Physiology-Paris, 108(1), pp.28-37.

(2) Sreenivasan, S. and Fiete, I., 2011. Grid cells generate an analog error-correcting code for singularly precise neural computation. Nature neuroscience, 14(10), pp.1330-1337.

(3) Mathis, A., Herz, A.V. and Stemmler, M., 2012. Optimal population codes for space: grid cells outperform place cells. Neural computation, 24(9), pp.2280-2317.

(4) Bush, D., Barry, C., Manson, D. and Burgess, N., 2015. Using grid cells for navigation. Neuron, 87(3), pp.507-520

(4) Page 3 - "Determinantal Point Processes (DPPs)" - it is rather annoying that DPP is defined after DPP-A is defined. There ought to be a spot where the definition of DPP-A is clearly stated in a single location.

We agree it makes more sense to define Determinantal Point Process (DPP) before DPP-A. We have now rephrased the sentences accordingly. In the “Abstract”, the sentence now reads “Second, we propose an attentional mechanism that operates over the grid cell code using Determinantal Point Process (DPP), which we call DPP attention (DPP-A) - a transformation that ensures maximum sparseness in the coverage of that space.” We have also modified the second paragraph of the “Introduction”. The modified portion now reads “b) an attentional objective inspired from Determinantal Point Processes (DPPs), which are probabilistic models of repulsion arising in quantum physics [1], to attend to abstract representations that have maximum variance and minimum correlation among them, over the training data. We refer to this as DPP attention or DPP-A.” Due to this change, we removed the last sentence of the fifth paragraph of the “Introduction”.

(1) Macchi, O., 1975. The coincidence approach to stochastic point processes. Advances in Applied Probability, 7(1), pp.83-122.

(5) Page 3 - "the inference module R" - there should be some discussion about how this component using LSTM or transformers could relate to the function of actual brain regions interacting with entorhinal cortex. Or if there is no biological connection, they should state that this is not seen as a biological model and that only the grid cell code is considered biological.

While we agree that the model is not construed to be as specific about the implementation of the R module, we assume that — as a standard deep learning component — it is likely to map onto neocortical structures that interact with the entorhinal cortex and, in particular, regions of the prefrontal-posterior parietal network widely believed to be involved in abstract relational processes [1,2,3,4]. In particular, the role of the prefrontal cortex in the encoding and active maintenance of abstract information needed for task performance (such as rules and relations) has often been modeled using gated recurrent networks, such as LSTMs [5,6], and the posterior parietal cortex has long been known to support “maps” that may provide an important substrate for computing complex relations [4]. We have added some discussion about this in Section 2.2.3 of the updated manuscript.

(1) Waltz, J.A., Knowlton, B.J., Holyoak, K.J., Boone, K.B., Mishkin, F.S., de Menezes Santos, M., Thomas, C.R. and Miller, B.L., 1999. A system for relational reasoning in human prefrontal cortex. Psychological science, 10(2), pp.119-125.

(2) Christoff, K., Prabhakaran, V., Dorfman, J., Zhao, Z., Kroger, J.K., Holyoak, K.J. and Gabrieli, J.D., 2001. Rostrolateral prefrontal cortex involvement in relational integration during reasoning. Neuroimage, 14(5), pp.1136-1149.

(3) Knowlton, B.J., Morrison, R.G., Hummel, J.E. and Holyoak, K.J., 2012. A neurocomputational system for relational reasoning. Trends in cognitive sciences, 16(7), pp.373-381.

(4) Summerfield, C., Luyckx, F. and Sheahan, H., 2020. Structure learning and the posterior parietal cortex. Progress in neurobiology, 184, p.101717.

(5) Frank, M.J., Loughry, B. and O’Reilly, R.C., 2001. Interactions between frontal cortex and basal ganglia in working memory: a computational model. Cognitive, Affective, & Behavioral Neuroscience, 1, pp.137-160.

(6) Braver, T.S. and Cohen, J.D., 2000. On the control of control: The role of dopamine in regulating prefrontal function and working memory. Control of cognitive processes: Attention and performance XVIII, (2000).

(6) Page 4 - "Learned weighting w" - it is somewhat confusing to use "w" as that is commonly used for synaptic weights, whereas I understand this to be an attentional modulation vector with the same dimensionality as the grid cell code. It seems more similar to a neural network bias input than a weight matrix.

We refer to the first paragraph of our response above to the topic “Clarification of DPP-A attentional modulation” under “Major comments (Public Reviews)”, which contains our response to this issue.

(7) Page 4 - "parameterization of w... by two loss functions over the training set." - I realize that this has been stated here, but to emphasize the significance to a naïve reader, I think they should emphasize that the learning is entirely focused on the initial training space, and there is NO training done in the test spaces. It's very impressive that the parameterization is allowing generalization to translated or scaled spaces without requiring ANY training on the translated or scaled spaces.

We have added the sentence “Note that learning of parameter occurs only over the training space and is not further modified during testing (i.e. over the test spaces)” to the updated manuscript.

(8) Page 4 - "The first," - This should be specific - "The first loss function"

We have changed it to “The first loss function” in the updated manuscript.

(9) Page 4 - The analogy task seems rather simplistic when first presented (i.e. just a spatial translation to different parts of a space, which has already been shown to work in simulations of spatial behavior such as Erdem and Hasselmo, 2014 or Bush, Barry, Manson, Burgess, 2015). To make the connection to analogy, they might provide a brief mention of how this relates to the analogy space created by word2vec applied to traditional human verbal analogies (i.e. king-man+woman=queen).

We agree that the analogy task is simple, and recognize that grid cells can be used to navigate to different parts of space over which the test analogies are defined when those are explicitly specified, as shown by Erdem and Hasselmo (2014) and Bush, Barry, Manson, and Burgess (2015). However, for the analogy task, the appropriate set of grid cell embeddings must be identified that capture the same relational structure between training and test analogies to demonstrate strong OOD generalization, and that is achieved by the attentional mechanism DPP-A. As suggested by the reviewer’s comment, our analogy task is inspired by Rumelhart’s parallelogram model of analogy [1,2] (and therefore similar to traditional human verbal analogies) in as much as it involves differences (i.e A - B = C - D, where A, B, C, D are vectors in 2D space). We have now noted this in Section 2.1.1 of the updated manuscript.

(1) Rumelhart, D.E. and Abrahamson, A.A., 1973. A model for analogical reasoning. Cognitive Psychology, 5(1), pp.1-28.

(2) Mikolov, T., Chen, K., Corrado, G. and Dean, J., 2013. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781.

(10) Page 5 - The variable "KM" is a bit confusing when it first appears. It would be good to re-iterate that K and M are separate points and KM is the vector between these points.

We apologize for the confusion on this point. KM is meant to refer to an integer value, obtained by multiplying K and M, which is added to both dimensions of A, B, C and D, which are points in ℤ2, to translate them to a different region of the space. K is an integer value ranging from 1 to 9 and M is also an integer value denoting the size of the training region, which in our implementation is 100. We have clarified this in Section 2.1.1 of the updated manuscript.

(11) Page 5 - "two continuous dimensions (Constantinescu et al._)" - this ought to give credit to the original study showing the abstract six-fold rotational symmetry for spatial coding (Doeller, Barry and Burgess).

We have now cited the original work by Doeller et al. [1] along with Constantinescu et al. (2016) in the updated manuscript after the phrase “two continuous dimensions” on page 5.

(1) Doeller, C.F., Barry, C. and Burgess, N., 2010. Evidence for grid cells in a human memory network. Nature, 463(7281), pp.657-661.

(12) Page 6 - Np=100. This is done later, but it would be clearer if they right away stated that Np*Nf=900 in this first presentation.

We have now added this sentence after Np=100. “Hence Np*Nf=900, which denotes the number of grid cells.”

(13) Page 6 - They provide theorem 2.1 on the determinant of the covariance matrix of the grid code, but they ought to cite this the first time this is mentioned.

We have cited Gilenwater et al. (2012) before mentioning theorem 2.1. The sentence just before that reads “We use the following theorem from Gillenwater et al. (2012) to construct :”

(14) Page 6 - It would greatly enhance the impact of the paper if they could give neuroscientists some sense of how the maximization of the determinant of the covariance matrix of the grid cell code could be implemented by a biological circuit. OR at least to show an example of the output of this algorithm when it is used as an inner product with the grid cell code. This would require plotting the grid cell code in the spatial domain rather than the 900 element vector.

We refer to our response above to the topic “Biological plausibility of DPP-A” and second, third, and fourth paragraphs of our response above to the topic “Clarification of DPP-A attentional modulation” under “Major comments (Public Reviews)”, which contain our responses to this issue.

(15) Page 6 - "That encode higher spatial frequencies..." This seems intuitive, but it would be nice to give a more intuitive description of how this is related to the determinant of the covariance matrix.

We refer to the third paragraph of our response above to the topic “Clarification of DPP-A attentional modulation” under “Major comments (Public Reviews)”, which contains our response to this issue.

(16) Page 7 - log of both sides... Nf is number of frequencies... Would be good to mention here that they are referring to equation 6 which is only mentioned later in the paragraph.

As suggested, we now refer to Equation 6 in the updated manuscript. The sentence now reads “This is achieved by maximizing the determinant of the covariance matrix over the within frequency grid cell embeddings of the training data, and Equation 6 is obtained by applying the log on both sides of Theorem 2.1, and in our case where refers to grid cells of a particular frequency.”

(17) Page 7 - Equation 6 - They should discuss how this is proposed to be implemented in brain circuits.

We refer to our response above to the topic “Biological plausibility of DPP-A” under “Major comments (Public Reviews)”, which contains our response to this issue.

18) Page 9 - "egeneralize" - presumably this is a typo?

Yes. We have corrected it to “generalize” in the updated manuscript.

(19) Page 9 - "biologically plausible encoding scheme" - This is valid for the grid cell code, but they should be clear that this is not valid for other parts of the model, or specify how other parts of the model such as DPP-A could be biologically plausible.

We refer to our response above to the topic “Biological plausibility of DPP-A” under “Major comments (Public Reviews)”, which contains our response to this issue.

(20) Page 12 - Figure 7 - comparsion to one-hots or smoothed one-hots. The text should indicate whether the smoothed one-hots are similar to place cell coding. This is the most relevant comparison of coding for those knowledgeable about biological coding schemes.

Yes, smoothed one-hots are similar to place cell coding. We now mention this in Section 5.3 of the updated manuscript.

(21) Page 12 - They could compare to a broader range of potential biological coding schemes for the overall space. This could include using coding based on the boundary vector cell coding of the space, band cell coding (one dimensional input to grid cells), or egocentric boundary cell coding.

We appreciate these useful suggestions, which we now mention as potentially valuable directions for future work in the second paragraph of Section 6 of the updated manuscript.

(22) Page 13 - "transformers are particularly instructive" - They mention this as a useful comparison, but they might discuss further why a much better function is obtained when attention is applied to the system twice (once by DPP-A and then by a transformer in the inference module).

We refer to the last paragraph of our response above to the topic “Clarification of DPP-A attentional modulation” under “Major comments (Public Reviews)”, which contains our response to this issue.

(23) Page 13 - "Section 5.1 for analogy and Section 5.2 for arithmetic" - it would be clearer if they perhaps also mentioned the specific figures (Figure 4 and Figure 6) presenting the results for the transformer rather than the LSTM.

We have now rephrased to also refer to the figures in the updated manuscript. The phrase now reads “a transformer (Figure 4 in Section 5.1 for analogy and Figure 6 in Section 5.2 for arithmetic tasks) failed to achieve the same level of OOD generalization as the network that used DPP-A.”

(24) Page 14 - "statistics of the training data" - The most exciting feature of this paper is that learning during the training space analogies can so effectively generalize to other spaces based on the right attention DPP-A, but this is not really made intuitive. Again, they should illustrate the result of the xT w inner product to demonstrate why this work so effectively!

We refer to the second, third, and fourth paragraphs of our response above to the topic “Clarification of DPP-A attentional modulation” under “Major comments (Public Reviews)”, which contains our response to this issue.

(25) Bibliography - Silver et al., go paper - journal name "nature" should be capitalized. There are other journal titles that should be capitalized. Also, I believe eLife lists family names first.

We have made the changes to the bibliography of the updated manuscript suggested by the reviewer.

Author response:

The following is the authors’ response to the previous reviews

Public Reviews: 

Reviewer #1 (Public Review): 

The goal of the current study was to evaluate the effect of neuronal activity on blood-brain barrier permeability in the healthy brain, and to determine whether changes in BBB dynamics play a role in cortical plasticity. The authors used a variety of well-validated approaches to first demonstrate that limb stimulation increases BBB permeability. Using in vivo-electrophysiology and pharmacological approaches, the authors demonstrate that albumin is sufficient to induce cortical potentiation and that BBB transporters are necessary for stimulus-induced potentiation. The authors include a transcriptional analysis and differential expression of genes associated with plasticity, TGF-beta signaling, and extracellular matrix were observed following stimulation. Overall, the results obtained in rodents are compelling and support the authors' conclusions that neuronal activity modulates the BBB in the healthy brain and that mechanisms downstream of BBB permeability changes play a role in stimulus-evoked plasticity. These findings were further supported with fMRI and BBB permeability measurements performed in healthy human subjects performing a simple sensorimotor task. There is literature to suggest that there are sex differences in BBB dysfunction in pathophysiological conditions and the authors have acknowledged the use of only males as a minor limitation of the study that should be addressed in the future. Future studies should also test whether the upregulation of OAT3 plays a role in cortical plasticity observed following stimulation. Overall, this study provides novel insights into how neurovascular coupling, BBB permeability, and plasticity interact in the healthy brain. 

Reviewer #2 (Public Review): 

Summary: 

This study builds upon previous work that demonstrated that brain injury results in leakage of albumin across the blood brain barrier, resulting in activation of TGF-beta in astrocytes. Consequently, this leads to decreased glutamate uptake, reduced buffering of extracellular potassium and hyperexcitability. This study asks whether such a process can play a physiological role in cortical plasticity. They first show that stimulation of a forelimb for 30 minutes in a rat results in leakage of the blood brain barrier and extravasation of albumin on the contralateral but not ipsilateral cortex. The authors propose that the leakage is dependent upon neuronal excitability and is associated with an enhancement of excitatory transmission. Inhibiting the transport of albumin or the activation of TGF-beta prevents the enhancement of excitatory transmission. In addition, gene expression associated with TGF-beta activation, synaptic plasticity and extracellular matrix are enhanced on the "stimulated" hemisphere. That this may translate to humans is demonstrated by a break down in the blood brain barrier following activation of brain areas through a motor task. 

Strengths: 

This study is novel and the results are potentially important as they demonstrate an unexpected break down of the blood brain barrier with physiological activity and this may serve a physiological purpose, affecting synaptic plasticity. 

The strengths of the study are: 

(1) The use of an in vivo model with multiple methods to investigate the blood brain barrier response to a forelimb stimulation. 

(2) The determination of a potential functional role for the observed leakage of the blood brain barrier from both a genetic and electrophysiological view point 

(3) The demonstration that inhibiting different points in the putative pathway from activation of the cortex to transport of albumin and activation of the TGF-beta pathway, the effect on synaptic enhancement could be prevented.  (4) Preliminary experiments demonstrating a similar observation of activity dependent break down of the blood brain barrier in humans. 

Weaknesses: 

The authors adequately addressed most of my points. A few remain: 

(1) Although the reviewers have addressed the possible effects of anaesthesia on neuro-vascular coupling. They have not mentioned or addressed the possible effects of ketamine (an NMDA receptor antagonist) on synaptic plasticity. Indeed, the low percentage of SEP increase following potentiation (10-20%) could perhaps be explained by partial block of NMDA receptors by ketamine.

We agree and apologize for this oversight. This important issue is now addressed in the Discussion.

“Notably, the antagonistic effect of ketamine on NMDA receptors might attenuate the magnitude of SEP potentiation recorded in our experiments (Anis et al., 1983; Salt et al., 1988).”

(2) The experimental paradigms remain unclear to me. Now, it appears that drugs are applied for 50 minutes and that the stimulation occurs during the "washout period". The more conventional approach would be to have the drug application during the stimulation period to determine if the drugs occlude or enhance the effects of stimulation and then washout the drugs. The problem is that drugs variably washout at different rates depending upon their lipid solubility.

We agree that the more conventional approach would have been to continue applying the drug throughout the experiment and that differential rates of washout may add variability to our experiments. However, despite this limitation, within each treatment group we found that the SEP response at 50 minutes (immediately after the drug application window) does not differ from SEP response at 80 minutes (after 30 minutes of stimulation and washout) [Figure 3H&G]. This suggests that the drug effects were still present despite terminating drug application and performing potentiation-inducing stimulation. Moreover, our analysis showed that animals within each treatment group (except AP5) had similar SEP responses with little intra-group variability.

(3) It is still not clear to what extent the experimenters and those doing the analysis were blinded to group. If one or both were blind to group, then please put this in the methods.

Thank you for this comment. We revised the Methods section to clearly confirm that data was collected and analyzed blindly.  

Reviewer #3 (Public Review): 

Summary: 

This study used prolonged stimulation of a limb to examine possible plasticity in somatosensory evoked potentials induced by the stimulation. They also studied the extent that the blood brain barrier (BBB) was opened by the prolonged stimulation and whether that played a role in the plasticity. They found that there was potentiation of the amplitude and area under the curve of the evoked potential after prolonged stimulation and this was long-lasting (>5 hrs). They also implicated extravasation of serum albumin, caveolae-mediated transcytosis, and TGFb signalling, as well as neuronal activity and upregulation of PSD95. Transcriptomics was done and implicated plasticity related genes in the changes after prolonged stimulation, but not proteins associated with the BBB or inflammation. Next, they address the application to humans using a squeeze ball task. They imaged the brain and suggest that the hand activity led to an increased permeability of the vessels, suggesting modulation of the BBB. 

Strengths: 

The strengths of the paper are the novelty of the idea that stimulation of the limb can induce cortical plasticity in a normal condition, and it involves opening of the BBB with albumin entry. In addition, there are many datasets and both rat and human data. 

Weaknesses: 

The conclusions are not compelling however because of a lack of explanation of methods.

In the revised paper, we added a section titled ‘study design’ that presents an overview of the experimental approach.

The explanation of why prolonged stimulation in the rat was considered relevant to normal conditions should be as clear in the paper as it is in the rebuttal.

We added a new paragraph to the Discussion section explaining this point as we did in the rebuttal:  

“Our animal experiments show that a 30 min limb stimulation (at 6Hz and 2mA) increases cross-BBB influx, while a 1 min stimulation (of similar frequency and magnitude) does not. We believe that both types of stimulations fall within the physiological range because our continuous electrophysiological recordings showed no signs of epileptiform or otherwise pathological activity. Moreover, the recorded SEP levels were similar to those reported in previous physiological LTP studies in rats (Eckert & Abraham, 2010; Han et al., 2015; Mégevand et al., 2009) and humans (McGregor et al., 2016). In humans, skill acquisition often involves motor training sessions that last ≥30 minutes (Bengtsson et al., 2005; Classen et al., 1998) and result in physiological plasticity of sensory and motor systems (Classen et al., 1998; Draganski et al., 2004; Sagi et al., 2012). Hence, the experimental task in our human study (30 minutes of repetitive squeezing of an elastic stress-ball) is likely to represent physiological activity, with neuronal activation in primarily motor and sensory areas (Halder et al., 2005). Future human and animal studies are needed to explore the BBB modulating effects of additional stimulation protocols – with varying durations, frequencies, and magnitudes. Such studies may also elucidate the temporal and ultrastructural characteristics that differentiate between physiological and pathological BBB modulation. “

The authors need to ensure other aspects of the rebuttal are as clear in the paper as in the rebuttal too. 

Thank you for this comment. This was addressed in the revised paper.

The only remaining concern that is significant is that it is hard to understand the figures. 

Thank you for this comment. We revised the figures according to the reviewer’s recommendations. We hope that these changes increase the legibility of the figures. 

Reviewer #3 (Recommendations For The Authors): 

The manuscript is improved but there are still suggestions that do not appear to have been addressed. More experiments are not involved in addressing these concerns but one wants the paper to be clarified in terms of what was done. 

Figures. Please use arrows to point to the effect that the reader should see. Please note what the main point is. 

Major concerns: 

Please add explanations, exact p values, and other revisions in the rebuttal to the paper. 

Rebuttal explanations were added to the paper and p values appear in figure legends.

Fig 1d shows a seizure-like event which the authors don't think is a seizure because it lacks a depolarization ship. This explanation is not convincing because a LFP would not necessarily show a depolarization ship. Another argument of a discussion of the event as a seizure is warranted. Note that expanding the trace might also show it is unlike a seizure. Regarding the idea that 6Hz 2 mA stimuli for 30 min are physiological, the authors make three arguments which are not clear. First, no epileptiform activity was found, but in Fig. 1 it looks like a seizure occurred. Second, memory and skill acquisition in humans open involve a similar training duration - but what about 6Hz 2 mA?

Rats are known to rhythmically move their whiskers at frequencies ranging between 5 and 15 Hz (Mégevand et al., 2009). We agree that there is no clear way to justify the similarity between the experimental design in humans and rats. However, we believe that both paradigms (paw stimulation in rats and ball squeeze in humans) represent non-pathological input that we found to modulate barrier permeability. This argument was added to the discussion of the paper:

“We believe that both types of stimulations fall within the physiological range because in rats, activity between 515 Hz represents physiological rhythmic whisker movement during environment exploration (Mégevand et al., 2009).” 

Seizures are typically induced in rats via direct tetanic stimulation of the brain (at 50 Hz and 0.3-2.5mA) or maximal electroshock test to the cornea (at 50 Hz and 150 mA) (Swinyard et al., 1952). We, therefore, assert that the activity we observe represents physiological responses and not seizures. This argument is beyond the scope of the current paper. 

Please note a limitation is that the high level of serum albumin is unlikely to be physiological but may not have been as high in the animal because of the low diffusion rate and degradation (please add the refs in the rebuttal). 

Thank you, we added the following to the Results section: 

“The relatively high concentration of albumin was chosen to account for factors that lower its effective tissue concentration such as its low diffusion rate and its likelihood to encounter a degradation site or a cross-BBB efflux transporter (Tao & Nicholson, 1996; Zhang & Pardridge, 2001).”

Fig. 1. 

Please consider a box in b to show where the expanded traces in the lower row came from. 

Thank you for the suggestion. We added lines indicating where the trace excerpts were taken from.

c. Please use arrows to point to the parts that the authors want the reader to note. In the legend, explain what t is, and delta HbT.

Thank you. We implemented this suggestion.

d. It is not clear what the double-sided arrows are meant to show compared to the arrow without two sides. 

We replaced the two-headed arrow with two single ones.

e. Please explain what the upward lines at the top signify. What does the red asterisk mean? 

Thank you. We implemented this suggestion.

f. Is the reader supposed to note the yellow area? Please make it with an arrow or circle if so. 

Thank you, we added a white circle to mark the area of tracer accumulation.

g. Please explain what the permeability index is or reference the part of the paper that does. 

Further to this suggestion, we added a refence to the appropriate methods section to the legend.

h. Please use arrows to point to the area of interest. 

Thank you. We implemented this suggestion.

m-n. Please mark areas of interest with arrows.  m. the top right two images are unclear. I suggest making them say ipsi inset and contra inset instead of using asterisks. 

Thank you. We added the ipsi and contra labels to panels in m. The images in panel n represent a phenomenon with no particular region of interest, but rather peri-vascular tracer accumulation along the entire depicted blood vessel. We clarified that panel n represents a separate experiment than panel m: “n. In an animal injected with both EB and NaFlu post stimulation, fluorescence imaging shows extravascular accumulation of both tracers along a cortical small vessel in the stimulated hemisphere.”

Figure 2. 

(2) a. Middle. What are the vertical lines at the top? The rebuttal states that was explained in the revised legends but I don't see it. 

Our apologies. We now included an explanation that “an excerpt of the stimulation trace is shown above the middle LFP trace”.

c and d are very different field potentials in shape and therefore hard to compare. The rebuttal addresses this but the explanation is not in the revised text. 

We agree that there is variability in SEP responses between animals. We now added a statement acknowledging this in the methods section: “To overcome potential variability in SEP morphology between animals (Mégevand et al., 2009), each animal’s plasticity measures (max amplitude and AUC of post stimulation SEP) were compared to the same measures at baseline.” 

In d, it is not clear there is potentiation because the traces are not aligned. 

All panels depicting SEP traces represent raw data with no alignment. The shift observed in panel d exemplifies why we compare post-stimulation parameters of max amplitude and area under curve to baseline in each animal. 

Exact P values are said to have been added in the rebuttal but they were not. 

Exact P values appear in Figure legends.

(3) b. Use arrows to mark the area of interest. 

Thank you. We added a white circle to mark the area of tracer accumulation similar to Figure 1f.

d. Why is there an oscillation superimposed on all traces except CNQX? 

We agree that this is an interesting question. Future studies should determine the source of this SEP pattern.   

(4) What does the line and the number 2 mean? How were data normalized? What was counted? What area of cortex?

The number 2 refers to the scale bar line, meaning a log fold change of 2 reflects the size of the scale bar line. 

The plot shows the log fold change against the mean count of each gene in the contralateral somatosensory cortex between 1 and 24 hours after stimulation.

The x axis title was changed to “mean expression” and the legend was modified to:

“Scatter plot of gene expression from RNA-seq in the contralateral somatosensory cortex 24 vs. 1 h after 30 min stimulation. The y axis represents the log fold change, and the x axis represents the mean expression levels (see methods, RNA Sequencing & Bioinformatics). Blue dots indicate statistically significant differentially expressed genes (DEGs) by Wald Test (n=8 rats per group).”

How were the pericytes, smooth muscle cells, ,etc. distinguished? 

This was explained under Methods->RNA Sequencing & Bioinformatics: “Analysis of cell-specific and vascular zonation genes was performed as described (Vanlandewijck et al., 2018), using the database provided in (http://betsholtzlab.org/VascularSingleCells/database.html).”

What were the chi square statistics? If there were cells used instead of rats, please justify. 

Thank you. The legend was expanded to include the following:

“The contralateral somatosensory cortex was found to have a significantly higher number of DEGs related to synaptic plasticity, than the ipsilateral side (***p<0.001, Chi-square).”     

(5) b. what do the icons mean? 

We agree that the icons were confusing. We simplified this panel to just show when participants were asked to squeeze the ball (black icon). This explanation was added to the Figure legend.

Abbreviations? 

Abbreviations of MRI protocols were added to the figure legend for clarity.

In c-e what are the units of measure? Fold-change? 

The units represent t-statistics values for each voxel. The label ‘t-statistic’ was added to the figure.  

What are the white Iines, + and - signs? 

The white lines point to voxels of highest activation (t-statistic). This was added to the legend.

And these are not +/- signs these are voxels with significant activation which only appear similar.

f. Please explain f and g for clarity. 

Thank you. The explanation was modified for added clarity.

Supplemental Fig. 4. 

Original question: If ipsilateral and contralateral showed many changes why do the authors think the effects were only contralateral? 

The authors replied: Our gene analysis was designed to complement our in vivo and histological findings, by assessing the magnitude of change in differentially expressed genes (DEGs). This analysis showed that: (1) the hemisphere contralateral to the stimulus has significantly more DEGs than the ipsilateral hemisphere; and (2) the DEGs were related to synaptic plasticity and TGF-b signaling. These findings strengthen the hypothesis raised by our in vivo and histological experiments. 

Could the authors clarify the answer to the question in the text? 

Thank you. This section was added to the Discussion. 

Papers referenced in this letter:

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Author response:

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

Reviewer #1 (Public Review):

Summary:

The manuscript by Dubicka and co-workers on calcification in miliolid foraminifera presents an interesting piece of work. The study uses confocal and electron microscopy to show that the traditional picture of calcification in porcelaneous foraminifera is incorrect.

Strengths:

The authors present high-quality images and an original approach to a relatively solid (so I thought) model of calcification.

Weaknesses:

There are several major shortcomings. Despite the interesting subject and the wonderful images, the conclusions of this manuscript are simply not supported at all by the results. The fluorescent images may not have any relation to the process of calcification and should therefore not be part of this manuscript. The SEM images, however, do point to an outdated idea of miliolid calcification. I think the manuscript would be much stronger with the focus on the SEM images and with the speculation of the physiological processes greatly reduced.

We agree that fluorescence studies presented in the paper are not an unequivocal proof by itself for calcification model utilised by studied Miliolida species. However, fluorescence data combined with SEM studies, especially overlap of the elements that show autofluorescence upon excitation at 405 nm (emission 420–480 nm) and acidic vesicles marked by p_H-_sensitive LysoGlow84, may be a hint indicating ACC-bearing vesicles.

We will tone down the the physiological interpretation based on fluorescence studies in the revised version of the manuscript.

Nevertheless, we think that our fluorescent life-imaging experiments provides important observations in miliolida, which is scarce in the existing literature, and therefore are worth being presented as they might be very helpful in better understanding of full calcification model in the future.

Reviewer #2 (Public Review):

Summary:

Dubicka et al. in their paper entitled " Biocalcification in porcelaneous foraminifera" suggest that in contrast to the traditionally claimed two different modes of test calcification by rotallid and porcelaneous miliolid formaminifera, both groups produce calcareous tests via the intravesicular mineral precursors (Mg-rich amorphous calcium carbonate). These precursors are proposed to be supplied by endocytosed seawater and deposited in situ as mesocrystals formed at the site of new wall formation within the organic matrix. The authors did not observe the calcification of the needles within the transported vesicles, which challenges the previous model of miliolid mineralization. Although the authors argue that these two groups of foraminifera utilize the same calcification mechanism, they also suggest that these calcification pathways evolved independently in the Paleozoic.

We do not argue that Miliolida and Rotallida utilize exactly the same calcification mechanism but the both groups use less divergent crystallization pathways, where mesocrystalline chamber walls are created by accumulating and assembling particles of pre-formed liquid amorphous mineral phase.

Strengths:<br /> The authors document various unknown aspects of calcification of Pseudolachlanella eburnea and elucidate some poorly explained phenomena (e.g., translucent properties of the freshly formed test) however there are several problematic observations/interpretations which in my opinion should be carefully addressed.

Weaknesses:

(1) The authors (line 122) suggest that "characteristic autofluorescence indicates the carbonate content of the vesicles (Fig. S2), which are considered to be Mg-ACCs (amorphous MgCaCO3) (Fig. 2, Movies S4 and S5)". Figure S2 which the authors refer to shows only broken sections of organic sheath at different stages of mineralization. Movie S4 shows that only in a few regions some vesicles exhibit red autofluorescence interpreted as Mg-ACC (S5 is missing but probably the authors were referring to S3). In their previous paper (Dubicka et al 2023: Heliyon), the authors used exactly the same methodology to suggest that these are intracellularly formed Mg-rich amorphous calcium carbonate particles that transform into a stable mineral phase in rotaliid Aphistegina lessonii. However, in Figure 1D (Dubicka et al 2023) the apparently carbonate-loaded vesicles show the same red autofluorescence as the test, whereas in their current paper, no evidence of autofluorescence of Mg-ACC grains accumulated within the "gel-like" organic matrix is given. The S3 and S4 movies show circulation of various fluorescing components, but no initial phase of test formation is observable (numerous mineral grains embedded within the o rganic matrix - Figures 3A and B - should be clearly observed also as autofluorescence of the whole layer). Thus the crucial argument supporting the calcification model (Figure 5) is missing.

This is correct that we did not observe the initial phase of test formation in vivo. Therefore, it is not our crucial argument supporting novel components of the new calcification model. We suspect that vesicles preparing and transporting Mg-ACC are produced way before their docking and deposition into the new wall, because such seawater vesicles were observed between the chamber formation stages (Goleń and Tyszka, 2024, personal communication based on independent experiments on a closely related miliolid taxon). It means that our in vivo experiments most likely represent a long, dynamic stage of vesicles formation via seawater endocytosis, their modification (incl. Mg-ACC formation) before the stage of exocytosis during the new chamber formation. Our crucial arguments supporting the calcification model come from the SEM imaging of the specimens fixed during chamber formation, as well as from the transparency of the new chamber wall during its progressive calcification.

There is no support for the following interpretation (lines 199-203) "The existence of intracellular, vesicular intermediate amorphous phase (Mg-ACC pools), which supply successive doses of carbonate material to shell production, was supported by autofluorescence (excitation at 405 nm; Fig. 2; Movies S3 and S4; see Dubicka et al., 2023) and a high content of Ca and Mg quantified from the area of cytoplasm by SEM-EDS analysis (Fig. S6)."

We used laser line 405nm and multiphoton excitaton to detect ACCs. These wavelengths (partly) permeate the shell to excite ACCs autofluorescence. The autofluorescence of the shells is present as well but not clearly visible in movieS4 as the fluorescence of ACCs is stronger. This may be related to the plane/section of the cell which is shown. The laser permeates the shell above the ACCs (short distance) but to excite the shell CaCO3 around foraminifera in the same three-dimensional section where ACCs are shown, the light must pass a thick CaCO3 area due to the three-dimensional structure of the foraminiferan shell. Therefore, the laser light intensity is reduced. In a revised version a movie/image with reduced threshold is shown.

Author response image 1.

Autofluorescence image of studied Miliolida species (exc. 405 nm) showing algal chlorophyll (blue) and CaCO3 (red), both ACC and calcite shell.

It would be very convenient if it was possible to visualize ACC by illumination with a blacklight, but there are very many organic molecules that have an autofluorescence excited by ~405 nm. One of the examples is NADH (Lee et al., 2015. Kor J Physiol Pharmac 19(4): 373-382), an omnipresent molecule in any cell (couldn't copy the appropriate picture here, but the reference has a figure with the em/exc spectra).

The paper of Lee et al. 2015 shows that the excitation spectrum of NADH is ending close to 400 nm. This means that NADH is not or only very weakly excitable at 405nm, what we used as the excitation laser line. 

(2) The authors suggest that "no organic matter was detected between the needles of the porcelain structures (Figures 3E; 3E; S4C, and S5A)". Such a suggestion, which is highly unusual considering that biogenic minerals almost by definition contain various organic components, was made based only on FE-SEM observation. The authors should either provide clearcut evidence of the lack of organic matter (unlikely) or may suggest that intense calcium carbonate precipitation within organic matrix gel ultimately results in a decrease of the amount of the organic phase (but not its complete elimination), alike the pure calcium carbonate crystals are separated from the remaining liquid with impurities ("mother liquor"). On the other hand, if (249-250) "organic matrix involved in the biomineralization of foraminiferal shells may contain collagen-like networks", such "laminar" organization of the organic matrix may partly explain the arrangement of carbonate fibers parallel to the surface as observed in Fig. 3E1.

We agree with the reviewer that biogenic minerals should by definition contain some organic components. We just wrote that "no organic matter was detected between the needles of the porcelain structures” that means that we did not detect any organic structures based only on our FE-SEM observations. We will rephrase this part of the text to avoid further confusion.

(3) The author's observations indeed do not show the formation of individual skeletal crystallites within intracellular vesicles, however, do not explain either what is the structure of individual skeletal crystallites and how they are formed. Especially, what are the structures observed in polarized light (and interpreted as calcite crystallites) by De Nooijer et al. 2009? The author's explanation of the process (lines 213-216) is not particularly convincing "we suspect that the OM was removed from the test wall and recycled by the cell itself".

Thank you for this comment. We will do our best to supplement our explanations. We are aware about the structures observed in polarized light by De Nooijer et al. (2009). However, Goleń et al. (2022, Prostist; + 2 other citations) showed that organic polymers may also exhibit light polarization. Additional experimental studies are needed to separate these types of polarization. We will try to investigate this issue in our future research.

(4) The following passage (lines 296-304) which deals with the concept of mesocrystals is not supported by the authors' methodology or observations. The authors state that miliolid needles "assembled with calcite nanoparticles, are unique examples of biogenic mesocrystals (see Cölfen and Antonietti, 2005), forming distinct geometric shapes limited by planar crystalline faces" (later in the same passage the authors say that "mesocrystals are common biogenic components in the skeletons of marine organisms" (are they thus unique or are they common)? It is my suggestion to completely eliminate this concept here until various crystallographic details of the miliolid test formation are well documented.

Our intension was to express that mesocrystals are common biogenic components in the skeletons of marine organisms however such a miliolid needles forming distinct geometric shapes limited by planar crystalline faces are unique.

Reviewer #1 (Recommendations For The Authors):

Below, I have summarized my main criticisms.

(1) The movies S1-S4 do not indicate what is described. The videos show indeed seawater (S1), cell membranes (S2), and autofluorescence and acidic vesicles (S3 and S4). The presence of all these intracellular structures is not surprising: any eukaryotic cell will have those. The authors, however, claim that they participate in the process of calcification, which is simply not shown. One of the main arguments seems the presence of 'carbonate pools', in the caption these are even claimed to be 'Mg-ACC pools', but this is by no means revealed by an excitation of 405nm/ emission between 420 and 490 nm. It would be very convenient if it was possible to visualize ACC by illumination with a blacklight, but there are very many organic molecules that have an autofluorescence excited by ~405 nm. One of the examples is NADH (Lee et al., 2015. Kor J Physiol Pharmac 19(4): 373-382), an omnipresent molecule in any cell (couldn't copy the appropriate picture here, but the reference has a figure with the em/exc spectra).

The paper of Lee et al. 2015 shows that the excitation spectrum of NADH is ending close to 400 nm. This means that NADH is not or only very weakly excitable at 405nm, what we used as the excitation laser line. 

The fluorescence by this excitation/ emission couple unlikely indicates the vesicles in which these foraminifera calcify. Therefore, most of the interpretation of the authors on what happens with the calcitic needles is not based on results but remains pure speculation.

The fluorescence autofluorescence upon excitation at 405 nm (emission 420–480 nm is typical for CaCO3 both for biocalcite and amorphous calcium carbonate, what was proven by laboratory synthesis of amorphous calcium carbonate (Dubicka et al., in preparation).

(2) The results mention 'granules', which are the supposed Mg-ACC-containing vesicles, but the movies simply don't show any granules. Only fluorescence. Again, the results show a lot of vesicles with autofluorescence, but these are not necessarily related to calcification. Proof could be supplied by showing that the same fluorescent vesicles are 'used up' when the specimens under observation are making a new chamber, but until that is done, the fate of all these vesicles remains uncertain and once more, may not be involved in calcification at all.

We suspect that vesicles preparing and transporting Mg-ACC are produced way before their docking and deposition into the new wall, because such seawater vesicles were observed between the chamber formation stages (Goleń and Tyszka, 2024, personal communication based on independent experiments on a closely related miliolid taxon). It means that our in vivo experiments most likely represent a long, dynamic stage of vesicles formation via seawater endocytosis, their modification (incl. Mg-ACC formation) before the stage of exocytosis during the new chamber formation. Our crucial arguments supporting the calcification model come from the SEM imaging of the specimens fixed during chamber formation, as well as from the transparency of the new chamber wall during its progressive calcification.

(3) The Methods are unclear. How long were the foraminifers kept before being placed under the microscope? Were they fed with anything? This is important since the chlorophyll should not be from any food source. I didn't know that this foraminiferal species has photosynthetic symbionts: genera like Quinqueloculina don't. Is there any reference for this? Normally, I wouldn't care that much, but the authors find the presence of (facultative) symbionts important (lines 305-336). I am a bit suspicious about this since the only evidence for the presence of photosynthetic symbionts is because of the autofluorescence. As the authors said, commonly these miliolid species are regarded as symbiont-barren, so additional proof for these symbionts is necessary.

We agree that additional proof is needed for the presence of photosynthetic symbionts. We rephrased the manuscript accordingly.

(4) It is also unclear (Methods) at what stage the miliolids were photographed (Figure 3). How did chamber formation proceed, what was the timing of the photographs, etc. These pictures are to me the most interesting finding of this study, but need to be described much better.

All individuals of living foraminifera were fixed at the overall stage of chamber formation. However, every individual presents a complete set of successive steps (substages) of chamber wall calcification fixed at once. Fig. 3A and B present nearly the most proximal (youngest) part of the new chamber with a thick wall of calcite nanograins within a gel-like organic matrix. Fig. 3C and D present a bit more distal (intermediate) part of the calcified chamber. Fig. 3E shows the most distal part of the new chamber. This part is anchored to the older, underlying solid calcified chamber (not shown in this figure). All these steps are synchronous, however, represent gradual successive stages of calcification. The main text and Figs 4 and 5 explain this phenomenon in details.

There are many small issues with the text too. These include:

Line 28/29: in many other groups, calcification is thought to be polyphyletic (e.g. sponges: Chombard et al., 1997. Biol Bull 193: 359-367).

Corrected

Line 29/30: there may be even more 'types of shells'. The first author has shown in earlier papers that nodosarids have a unique shell architecture. Spirillinids also seem to have their own way of calcification. It is unclear what is meant here by 'two contrasting models'.

By now there are known only two models of foraminiferal calcification. Lagenida biocalcification has not been studied.

Line 33: 'Both groups'? This paper only shows calcification in miliolids.

However, we refer to previous study.

Line 42: Perhaps, but there is no data on the pseudopodial network in this manuscript.

We refer to Angell, 1980 studies

Line 43: Likely, but that is not what this manuscript is showing.

Line 42-44: The authors should make a choice and be clear. The point of this paper is that miliolids and rotalids calcify in ways that are actually not as different as they seemed previously. Still, they are said to have different 'chamber formation modes'. If they are calcifying in a similar way (which I think is not necessarily supported by the results), isn't calcification in these groups like variations on the same theme? How does this relate to the independent origins of calcification within these two groups?

Our intension is to show that Miliolida and Rotaliida utilize less divergent calcification pathways, following the recently discovered biomineralization principles.

Line 49-51: is this a well-established distinction? If so, please add a reference. If not: what is fundamentally different between B and C? Does only the size of the intracellular vesicle matter?

Rephrased

Line 60: please include a reference for the intracellular calcification by coccolithophores.

Added

Line 67: this is wrong. It is the alignment of the needles at the surface that makes them all reflect light in the same way and gives the shells a porcelaneous appearance. A close-up of the miliolid's shell surface shows this arrangement. Underneath this layer, the orientation of the needles is more random.

We referred to Johan Hohenegger papers.

Line 114: how else?

Line 114-116: I don't see the relevance here. If seawater is taken up, the vesicle containing this seawater has to have a membrane around it. By definition. The text here ('These vesicles') suggests that Calcein and FM1-43 were combined (which they easily could have), but the methods describe that they are used successively.

Yes, we used two dyes separately.

Lines 122-130: I think the interpretation of this autofluorescence signal is wrong. Even if it was true, these lines belong to the Discussion.

This paragraph has been placed within discussion

Line 138: What are 'mobile clusters'? I don't see a relation between the location of the symbionts and the other vesicles (Figure 2).

Line 147-148: How can an SEM image show the absence of organic matter?

We meant the absence of the gel-like OM visible in the previous stages of the chamber formation

Line 148: Should be 'Figs. 3E; 3E1; S4C'.

Corrected

Lines 143-150: this can be merged with the following paragraph.

Done

Lines 151-169: why is there no indication of the time? Figures 3 and 4 link the pictures in time to show the development of the growing chamber wall. However, neither here nor in the methods, is there any recording of the time after the beginning of chamber formation. Now, the images are linked (Figure 4) as if they were taken at regular intervals, but this is not documented.

Lines 170-184: this should go to the Discussion.

Done

Line 193-195: this is likely, but not visible in Figure 1.

It was visible by optical microscopy and described by Angell, 1980

Line 199-201: I don't understand this: the fluorescent vesicles were not observed during chamber formation so any link between the SEM and CLSM scans remains pure speculation.

Line 203-204: needed for what?

For better documentation of Miliolid ACC-bearing granules

Line 220: is this shown in any of the images? 

Angell, 1980

Line 230: It sounds nice, but I don't think a 'paradigm shift' is appropriate here. However interesting and important foraminiferal biomineralization is, the authors show that the crystals of miliolids are likely formed differently than previously thought. If this is a 'paradigm shift', then most scientific findings are.

In our opinion this is definitely a shift of paradigm

Line 231: I don't think anyone suggested miliolids and coccolithophores share 'the same' pathway. They are shown (cocco's) and thought (miliolids) to secrete their calcite intracellularly.

Changed to similar, intracellular

Line 258: References should only be to peer-reviewed studies.

Line 430: Burgers'

Corrected

Reviewer #2 (Recommendations For The Authors):

Please separate clearly the results (observations) from the discussion (interpretations): various interpretational/commentary phrases should be removed from the Results section to Discussion e.g., lines 124-130, 131-135.

Interpretation have been separated from results as suggested by Reviewer.

[line 49] " living cells have evolved three major skeleton crystallization pathways". I would rather say "organisms" not "cells" as the coordination of the calcification process in multicellular organisms clearly involves processes that are beyond the individual cell activity.

Corrected

Author response:

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

Reviewer #1 (Public Review):

Original comment: There is no explanation for how this work could be a breakthrough in simulation gregarious feeding as is stated in the manuscript.

Reviewer response: I think I understand where the authors are trying to take this next step. If the authors were to follow up on this study with the proposed implementation of inhalant/exhalent velocities profiles (or more preferably velocity/pressure fields), then that study would be a breakthrough in simulating such gregarious feeding. Based on what has been done within the present study, I think the term "breakthrough" is instead overly emphatic. An additional note on this. The authors are correct that incorporating additional models could be used to simulation a population (as has been successfully done for several Ediacaran taxa despite computational limitations), but it's not the only way. The authors 1 might explore using periodic boundary conditions on the external faces of the flow domain. This could require only a single Olivooid model to assess gregarious impacts - see the abundant literature of modeling flow through solar array fields.

We appreciate the reviewer 1 for the suggestion. Modeling gregarious feeding via periodic boundary conditions is surely a practical way with limited computational resources. Modeling flow through solar array fields can also be an inspiring case. However, to realism the simulation of gregarious feeding behavior on an uneven seabed and with irregular organism spatial distribution, just using periodic boundary conditions may not be sufficient (see Author response image 1 for a simple example). We will go on exploring the way of realizing the simulations of large-scale gregarious feeding.

Author response image 1.

An example of modeling gregarious feeding behavior on an uneven seabed.

Original comment: The claim that olivooid-type feeding was most likely a prerequisite transitional form to jet-propelled swimming needs much more support or needs to be tailored to olivooids. This suggests that such behavior is absent (or must be convergent) before olivooids, which is at odds with the increasing quantities of pelagic life (whose modes of swimming are admittedly unconstrained) documented from Cambrian and Neoproterozoic deposits. Even among just medusozoans, ancestral 1 state reconstruction suggests that they would have been swimming during the Neoproterozoic (Kayal et al., 2018; BMC Evolutionary Biology) with no knowledge of the mechanics due to absent preservation. Author response: Thanks for your suggestions. Yes, we agree with you that the ancestral swimming medusae may appear before the early Cambrian, even at the Neoproterozoic deposits. However, discussions on the affinities of Ediacaran cnidarians are severely limited because of the lack of information concerning their soft anatomy. So, it is hard to detect the mechanics due to absent preservation. Olivooids found from the basal Cambrian Kuanchuanpu Formation can be reasonably considered as cnidarians based on their radial symmetry, external features, and especially the internal anatomies (Bengtson and Yue 1997; Dong et al. 2013; 2016; Han et al. 2013; 2016; Liu et al. 2014; Wang et al. 2017; 2020; 2022). The valid simulation experiment here was based on the soft tissue preserved in olivooids.

Reviewer response: This response does not sufficiently address my earlier comment. While the authors are correct that individual Ediacaran affinities are an area of active research and that Olivooids can reasonably be considered cnidarians, this doesn't address the actual critique in my comment. Most (not all) Ediacaran soft-bodied fossils are considered to have been benthic, but pelagic cnidarian life is widely acknowledged to at least be present during later White Sea and Nama assemblages (and earlier depending on molecular clock interpretations). The authors have certainly provided support for the mechanics of this type of feeding being co-opted for eventual jet propulsion swimming in Olivooids. They have not provided sufficient justifications within the manuscript for this to be broadened beyond this group.

Thanks for your sincere commentary. We of course agree with the possibility of the emergence of swimming cnidarians before the lowermost Cambrian Fortunian Stage. See lines 16-129: “Ediacaran fossil assemblages with complex ecosystems consist of exceptionally preserved soft-bodied eukaryotes of enigmatic morphology, which their affinities are mostly unresolved (Tarhan et al., 2018, Integrative and Comparative Biology, 58 (4), 688–702; Evans et al., 2022, PNAS, 11(46), e220747511).” Undoubtedly Olivooids belong to cnidarians charactered by their external and internal biological structures. Limited by the fossil records, we could only speculate on the transition from the benthic to the swimming of ancestral cnidarians via the valid fossil preservation, e.g. olivooids. The transition may require processes such as increasing body size, thickening the mesoglea, and degenerating the periderm, etc. And these processes may also evolve independently or comprehensively. Moreover, the ecological behaviors of the ancestral cnidarians may evolve independently at different stages from Ediacaran to Cambrian. We therefore could not provide more sufficient justifications beyond olivooids.

Original comment: L446: two layers of hexahedral elements is a very low number for meshing boundary layer flow

Reviewer response: As the authors point out in the main text, these organisms are small (millimeters in scale) and certainly lived within the boundary layer range of the ocean. While the boundary layer is not the main point, it still needs to be accurately resolved as it should certainly affect the flow further towards the far field at this scale. I'm not suggesting the authors need to perfectly resolve the boundary layer or focus on using turbulence models more tailored to boundary layer flows (such as k-w), but the flow field still needs sufficient realism for a boundary bounded flow. The authors really should consider quantitatively assessing the number of hexahedral elements within their mesh refinement study.

To address this concern, we run another four simulations based on mesh4 within our mesh refinement study to assess the number of hexahedral elements (five layers and eight layers of hexahedral elements with different thickness of boundary layer mesh (controlled by thickness adjustment factor), respectively). the results had been supplemented to Table supplement 2. As shown in the results, the number of layers of hexahedral elements seems does not significant influence the result, but the thickness of boundary layer mesh can influence the maximum flow velocity of the contraction phase. However, the results of all the simulations were generally consistent, as shown in Author response image 2. The description of the results above were added to section “Mesh sensitivity analysis”.

Author response image 2.

Results of mesh refinement study of different boundary layer mesh parameters.

Author response:

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

Gating of Kv10 channels is unique because it involves coupling between non-domain swapped voltage sensing domains, a domain-swapped cytoplasmic ring assembly formed by the N- and C-termini, and the pore domain. Recent structural data suggests that activation of the voltage sensing domain relieves a steric hindrance to pore opening, but the contribution of the cytoplasmic domain to gating is still not well understood. This aspect is of particular importance because proteins like calmodulin interact with the cytoplasmic domain to regulate channel activity. The effects of calmodulin (CaM) in WT and mutant channels with disrupted cytoplasmic gating ring assemblies are contradictory, resulting in inhibition or activation, respectively. The underlying mechanism for these discrepancies is not understood. In the present manuscript, Reham Abdelaziz and collaborators use electrophysiology, biochemistry and mathematical modeling to describe how mutations and deletions that disrupt inter-subunit interactions at the cytoplasmic gating ring assembly affect Kv10.1 channel gating and modulation by CaM. In the revised manuscript, additional information is provided to allow readers to identify within the Kv10.1 channel structure the location of E600R, one of the key channel mutants analyzed in this study. However, the mechanistic role of the cytoplasmic domains that this study focuses on, as well as the location of the ΔPASCap deletion and other perturbations investigated in the study remain difficult to visualize without additional graphical information. This can make it challenging for readers to connect the findings presented in the study with a structural mechanism of channel function.

The authors focused mainly on two structural perturbations that disrupt interactions within the cytoplasmic domain, the E600R mutant and the ΔPASCap deletion. By expressing mutants in oocytes and recording currents using Two Electrode Voltage-Clamp (TEV), it is found that both ΔPASCap and E600R mutants have biphasic conductance-voltage (G-V) relations and exhibit activation and deactivation kinetics with multiple voltage-dependent components. Importantly, the mutant-specific component in the G-V relations is observed at negative voltages where WT channels remain closed. The authors argue that the biphasic behavior in the G-V relations is unlikely to result from two different populations of channels in the oocytes, because they found that the relative amplitude between the two components in the G-V relations was highly reproducible across individual oocytes that otherwise tend to show high variability in expression levels. Instead, the G-V relations for all mutant channels could be well described by an equation that considers two open states O1 and O2, and a transition between them; O1 appeared to be unaffected by any of the structural manipulations tested (i.e. E600R, ΔPASCap, and other deletions) whereas the parameters for O2 and the transition between the two open states were different between constructs. The O1 state is not observed in WT channels and is hypothesized to be associated with voltage sensor activation. O2 represents the open state that is normally observed in WT channels and is speculated to be associated with conformational changes within the cytoplasmic gating ring that follow voltage sensor activation, which could explain why the mutations and deletions disrupting cytoplasmic interactions affect primarily O2. 

Severing the covalent link between the voltage sensor and pore reduced O1 occupancy in one of the deletion constructs. Although this observation is consistent with the hypothesis that voltage-sensor activation drives entry into O1, this result is not conclusive. Structural as well as functional data has established that the coupling of the voltage sensor and pore does not entirely rely on the S4-S5 covalent linker between the sensor and the pore, and thus the severed construct could still retain coupling through other mechanisms, which is consistent with the prominent voltage dependence that is observed. If both states O1 and O2 require voltage sensor activation, it is unclear why the severed construct would affect state O1 primarily, as suggested in the manuscript, as opposed to decreasing occupancy of both open states. In line with this argument, the presence of Mg2+ in the extracellular solution affected both O1 and O2. This finding suggests that entry into both O1 and O2 requires voltage-sensor activation because Mg2+ ions are known to stabilize the voltage sensor in its most deactivated conformations. 

We agree with the reviewer that access to both states requires a conformational change in the voltage sensor. This was stated in our revised article: “In contrast, to enter O2, all subunits must complete both voltage sensor transitions and the collective gating ring transition.” We interpret the two gating steps as sequential; the effective rotation of the intracellular ring would happen only once the sensor is in its fully activated position.

We also agree that the S4-S5 segment cannot be the only interaction mechanism, as we demonstrated in our earlier work (Lörinczi et al., 2015; Tomczak et al., 2017).  

Activation towards and closure from O1 is slow, whereas channels close rapidly from O2. A rapid alternating pulse protocol was used to take advantage of the difference in activation and deactivation kinetics between the two open components in the mutants and thus drive an increasing number of channels towards state O1. Currents activated by the alternating protocol reached larger amplitudes than those elicited by a long depolarization to the same voltage. This finding is interpreted as an indication that O1 has a larger macroscopic conductance than O2. In the revised manuscript, the authors performed single-channel recordings to determine why O1 and O2 have different macroscopic conductance. The results show that at voltages where the state O1 predominates, channels exhibited longer open times and overall higher open probability, whereas at more depolarized voltages where occupancy of O2 increases, channels exhibited more flickery gating behavior and decreased open probability. These results are informative but not conclusive because additional details about how experiments were conducted, and group data analysis are missing. Importantly, results showing inhibition of single ΔPASCap channels by a Kv10-specific inhibitor are mentioned but not shown or quantitated - these data are essential to establish that the new O1 conductance indeed represents Kv10 channel activity.

We observed the activity of a channel compatible with Kv10.1 ΔPAS-Cap (long openings at low-moderate potentials, very short flickery activity at strong depolarizations) in 12 patches from oocytes obtained from different frog operations over a period of two and a half months once the experimental conditions could be established. As stated in the text, we did not proceed to generate amplitude histograms because we could not resolve clear single-channel events at strong depolarizations. Astemizole abolished the activity and (remarkably) strongly reduced the noise in traces at strong depolarizations, which we interpret as partially caused by flicker openings.

Author response image 1.

We include two example recordings of Astemizole application (100µM) on two different patches. Both recordings are performed at -60 mV (to decrease the likelihood that the channel visits O2) with 100 mM internal and 60 mM external K+. In both cases, the traces in Astemizole are presented in red.

It is shown that conditioning pulses to very negative voltages result in mutant channel currents that are larger and activate more slowly than those elicited at the same voltage but starting from less negative conditioning pulses. In voltage-activated curves, O1 occupancy is shown to be favored by increasingly negative conditioning voltages. This is interpreted as indicating that O1 is primarily accessed from deeply closed states in which voltage sensors are in their most deactivated position. Consistently, a mutation that destabilizes these deactivated states is shown to largely suppress the first component in voltage-activation curves for both ΔPASCap and E600R channels.

The authors then address the role of the hidden O1 state in channel regulation by calmodulation. Stimulating calcium entry into oocytes with ionomycin and thapsigarging, assumed to enhance CaM-dependent modulation, resulted in preferential potentiation of the first component in ΔPASCap and E600R channels. This potentiation was attenuated by including an additional mutation that disfavors deeply closed states. Together, these results are interpreted as an indication that calcium-CaM preferentially stabilizes deeply closed states from which O1 can be readily accessed in mutant channels, thus favoring current activation. In WT channels lacking a conducting O1 state, CaM stabilizes deeply closed states and is therefore inhibitory. It is found that the potentiation of ΔPASCap and E600R by CaM is more strongly attenuated by mutations in the channel that are assumed to disrupt interaction with the C-terminal lobe of CaM than mutations assumed to affect interaction with the N-terminal lobe. These results are intriguing but difficult to interpret in mechanistic terms. The strong effect that calcium-CaM had on the occupancy of the O1 state in the mutants raises the possibility that O1 can be only observed in channels that are constitutively associated with CaM. To address this, a biochemical pull-down assay was carried out to establish that only a small fraction of channels are associated with CaM under baseline conditions. These CaM experiments are potentially very interesting and could have wide physiological relevance. However, the approach utilized to activate CaM is indirect and could result in additional nonspecific effects on the oocytes that could affect the results.

Finally, a mathematical model is proposed consisting of two layers involving two activation steps for the voltage sensor, and one conformational change in the cytoplasmic gating ring - completion of both sets of conformational changes is required to access state O2, but accessing state O1 only requires completion of the first voltage-sensor activation step in the four subunits. The model qualitatively reproduces most major findings on the mutants. Although the model used is highly symmetric and appears simple, the mathematical form used for the rate constants in the model adds a layer of complexity to the model that makes mechanistic interpretations difficult. In addition, many transitions that from a mechanistic standpoint should not depend on voltage were assigned a voltage dependence in the model. These limitations diminish the overall usefulness of the model which is prominently presented in the manuscript. The most important mechanistic assumptions in the model are not addressed experimentally, such as the proposition that entry into O1 depends on the opening of the transmembrane pore gate, whereas entry into O2 involves gating ring transitions - it is unclear why O2 would require further gating ring transitions to conduct ions given that the gating ring can already support permeation by O1 without any additional conformational changes.

In essence, we agree with the reviewer; we already have addressed these points in our revised article:

Regarding the voltage dependence we write “the κ/λ transition could reasonably be expected to be voltage independent because we related it to ring reconfiguration, a process that should occur as a consequence of a prior VSD transition. We have made some attempts to treat this transition as voltage independent but state-specific with upper-layer bias for states on the right and lower-layer bias for states on the left. This is in principle possible, as can already be gleaned from the similar voltage ranges of the left-right transition (α/β) and the κL/λ transition. However, this approach leads to a much larger number of free, less well constrained kinetic parameters and drastically complicated the parameter search. ” As you can see, we also formulated a strategy to free the model of the potentially spurious voltage dependence and (in bold here) explained why we did not follow this route in this study. 

Regarding the need for gating ring transitions after O1, we wrote, “Thus, the underlying gating events can be separated into two steps: The first gating step involves only the voltage sensor without engaging the ring and leads to a pre-open state, which is non-conducting in the WT but conducting in our mutants. The second gating event operates at higher depolarizations, involves a change in the ring, and leads to an open state both in WT and in the mutants. ” 

We interpret your statements such that you expect the conducting state to remain available once O1 is reached. However, the experimental evidence speaks against that the pore availability remains regardless of the further gating steps beyond O1. The description of model construction is informative here: “... we could exclude many possible [sites at which O1 connects to closed states] because the attachment site must be sufficiently far away from the conventional open state [O2]. Otherwise, the transition from "O1 preferred" to "O2 preferred" via a few closed intermediate states is very gradual and never produces the biphasic GV curves [that we observed]. ” 

In other words, voltage-dependent gating steps beyond the state that offers access to O1 appear to close the pore, after it was open. That might occur because only then (for states in which at least one voltage sensor exceeded the intermediate position) the ring is fixed in a particular state until all sensors completed activation. In the WT, closing the pore in deactivated states might rely on an interaction that is absent in the mutant because, at least in HERG: “the interaction between the PAS domain and the C-terminus is more stable in closed than in open KV11.1 (HERG) channels, and a single chain antibody binding to the interface between PAS domain and CNBHD can access its epitope in open but not in closed channels, strongly supporting a change in conformation of the ring during gating ”

Reviewer #3 (Public Review):

In the present manuscript, Abdelaziz and colleagues interrogate the gating mechanisms of Kv10.1, an important voltage-gated K+ channel in cell cycle and cancer physiology. At the molecular level, Kv10.1 is regulated by voltage and Ca-CaM. Structures solved using CryoEM for Kv10.1 as well as other members of the KCNH family (Kv11 and Kv12) show channels that do not contain a structured S4-S5 linker imposing therefore a non-domain swapped architecture in the transmembrane region. However, the cytoplasmatic N- and C- terminal domains interact in a domain swapped manner forming a gating ring. The N-terminal domain (PAS domain) of one subunit is located close to the intracellular side of the voltage sensor domain and interacts with the C-terminal domain (CNBHD domain) of the neighbor subunit. Mutations in the intracellular domains has a profound effect in the channel gating. The complex network of interactions between the voltage-sensor and the intracellular domains makes the PAS domain a particularly interesting domain of the channel to study as responsible for the coupling between the voltage sensor domains and the intracellular gating ring.

The coupling between the voltage-sensor domain and the gating ring is not fully understood and the authors aim to shed light into the details of this mechanism. In order to do that, they use well established techniques such as site-directed mutagenesis, electrophysiology, biochemistry and mathematical modeling. In the present work, the authors propose a two open state model that arises from functional experiments after introducing a deletion on the PAS domain (ΔPAS Cap) or a point mutation (E600R) in the CNBHD domain. The authors measure a bi-phasic G-V curve with these mutations and assign each phase as two different open states, one of them not visible on the WT and only unveiled after introducing the mutations.

The hypothesis proposed by the authors could change the current paradigm in the current understanding for Kv10.1 and it is quite extraordinary; therefore, it requires extraordinary evidence to support it.

STRENGTHS: The authors use adequate techniques such as electrophysiology and sitedirected mutagenesis to address the gating changes introduced by the molecular manipulations. They also use appropriate mathematical modeling to build a Markov model and identify the mechanism behind the gating changes.

WEAKNESSES: The results presented by the authors do not fully support their conclusions since they could have alternative explanations. The authors base their primary hypothesis on the bi-phasic behavior of a calculated G-V curve that do not match the tail behavior, the experimental conditions used in the present manuscript introduce uncertainties, weakening their conclusions and complicating the interpretation of the results. Therefore, their experimental conditions need to be revisited. 

We respectfully disagree. We think that your suggestions for alternative explanations are addressed in the current version of the article. We will rebut them once more below, but we feel the need to point out that our arguments are already laid out in the revised article.

I have some concerns related to the following points:

(1) Biphasic gating behavior

The authors use the TEVC technique in oocytes extracted surgically from Xenopus Leavis frogs. The method is well established and is adequate to address ion channel behavior. The experiments are performed in chloride-based solutions which present a handicap when measuring outward rectifying currents at very depolarizing potentials due to the presence of calcium activated chloride channel expressed endogenously in the oocytes; these channels will open and rectify chloride intracellularly adding to the outward rectifying traces during the test pulse. The authors calculate their G-V curves from the test pulse steady-state current instead of using the tail currents. The conductance measurements are normally taken from the 'tail current' because tails are measured at a fix voltage hence maintaining the driving force constant. 

We respectfully disagree. In contrast to other channels, like HERG, a common practice for Kv10 is not to use tail currents. It is long known that in this channel, tail currents and test-pulse steady-state currents can appear to be at odds because the channels deactivate extremely rapidly, at the border of temporal resolution of the measurements and with intricate waveforms. This complicates the estimation of the instantaneous tail current. Therefore, the outward current is commonly used to estimate conductance (Terlau et al., 1996; Schönherr et al., 1999; Schönherr et al., 2002; Whicher and MacKinnon, 2019), while the latter authors also use the extreme of the tail for some mutants.

Due to their activation at very negative voltage, the reversal potential in our mutants can be measured directly; we are, therefore, more confident with this approach. Nevertheless, we have determined the initial tail current in some experiments. The behavior of these is very similar to the average that we present in Figure 1. The biphasic behavior is unequivocally present.

Author response image 2.

Calculating the conductance from the traces should not be a problem, however, in the present manuscript, the traces and the tail currents do not agree. 

The referee’s observation is perfectly in line with the long-standing experience of several labs working with KV10: tail current amplitudes in KV10 appear to be out of proportion for the WT open state (O2). Importantly, this is due to the rapid closure, which is not present in O1. As a consequence, the initial amplitude of tail currents from O1 are easier to estimate correctly, and they are much more obvious in the graphs. Taken together, these differences between O1 and O2 explain the misconception the reviewer describes next.

The tail traces shown in Fig1E do not show an increasing current amplitude in the voltage range from +50mV to +120mV, they seem to have reached a 'saturation state', suggesting that the traces from the test pulse contain an inward chloride current contamination. 

As stated in the text and indicated in Author response image 3, the tail currents In Figure 1E increase in amplitude between +50 and +120 mV, as can be seen in the examples below from different experiments (+50 is presented in black, +120 in red). As stated above, the increase is not as evident as in traces from other mutants because the predominance of O2 also implies a much faster deactivation.

Author response image 3. 

We are aware that Ca2+-activated Cl- currents can represent a problem when interpreting electrophysiological data in oocytes. In fact, we show in Supplement 1 to Figure 8 that this can be the case during the Ca2+-CaM experiments, where the increase in Ca2+ would certainly augment Cl- contribution to the outward current. This is why we performed these experiments in Cl--free solutions. As we show in Figure 8, the biphasic behavior was also present in those experiments. 

Importantly, Cl- free bath solutions would not correct contamination during the tail, since this would correspond to Cl- exiting the oocyte. Yet, if there would be contamination of the outward currents by Cl-, one would expect it to increase with larger depolarizations as the typical Ca2+activated Cl- current in oocytes does. As the reviewer states, this does not seem to be the case.

In addition, this second component identified by the authors as a second open state appears after +50mV and seems to never saturate. The normalization to the maximum current level during the test pulse, exaggerates this second component on the calculated G-V curve. 

We agree that this second component continues to increase; the reviewer brought this up in the first review, and we have already addressed this in our reply and in the discussion of the revised version: “This flicker block might also offer an explanation for a feature of the mutant channels, that is not explained in the current model version: the continued increase in current amplitude, hundreds of milliseconds into a strong depolarization (Supp. 4 to Fig. 9). If the relative stability of O2 and C2 continued to change throughout depolarization, such a current creep-up could be reproduced. However, this would require either the introduction of further layers of On ↔Cn states, or a non-Markovian modification of the model’s time evolution.” With non-Markovian, we mean a Langevin-type diffusive process. 

It's worth noticing that the ΔPASCap mutant experiments on Fig 5 in Mes based solutions do not show that second component on the G-V.

For the readers of this conversation, we would like to clarify that the reviewer likely refers to experiments shown in Fig. 5 of the initial submission but shown in Fig. 6 of the revised version (“Hyperpolarization promotes access to a large conductance, slowly activating open state.” Fig. 5 deals with single channels). We agree that these data look different, but this is because the voltage protocols are completely different (compare Fig. 6A (fixed test pulse, varied prepulse) and Fig. 2A (varied test pulse, fixed pre-pulse). Therefore, no biphasic behavior is expected. 

Because these results are the foundation for their two open state hypotheses, I will strongly suggest the authors to repeat all their Chloride-based experiments in Mes-based solutions to eliminate the undesired chloride contribution to the mutants current and clarify the contribution of the mutations to the Kv10.1 gating.

In summary, we respectfully disagree with all concerns raised in point (1). Our detailed arguments rebutting them are given above, but there is a more high-level concern about this entire exchange: the referee casts doubt on observations that are not new. Several labs have reported for a group of mutant KCNH channels: non-monotonic voltage dependence of activation (see, e.g., Fig. 6D in Zhao et al., 2017), multi-phasic tail currents (see e.g. Fig. 4A in Whicher and MacKinnon, 2019, in CHO cells where Cl- contamination is not a concern), and activation by high [Ca2+]i (Lörinczi et al., 2016). Our study replicates those observations and hypothesizes that the existence of an additional conducting state can alone explain all previously unexplained observations. We highlight the potency of this hypothesis with a Markov model that qualitatively reproduces all phenomena. We not only factually disagree with the individual points raised, but we also think that they don't touch on the core of our contribution

(2) Two step gating mechanism.

The authors interpret the results obtained with the ΔPASCap and the E600R as two step gating mechanisms containing two open states (O1 and O2) and assign them to the voltage sensor movement and gating ring rotation respectively. It is not clear, however how the authors assign the two open states.

The results show how the first component is conserved amongst mutations; however, the second one is not. The authors attribute the second component, hence the second open state to the movement of the gating ring. This scenario seems unlikely since there is a clear voltagedependence of the second component that will suggest an implication of a voltage-sensing current.

We do not suggest that the gating ring motion is not voltage dependent. We would like to point out that voltage dependence can be conveyed by voltage sensor coupling to the ring; this is the widely accepted theory of how the ring can be involved. Should the reviewer mean it in a narrow sense, that the model should be constructed such that all voltage-dependent steps occur before and independently of ring reconfiguration and that only then an additional step that reflects the (voltage-independent) reconfiguration solely, we would like to point the reviewer to the article, where we write: “the κ/λ transition could reasonably be expected to be voltage independent because we related it to ring reconfiguration, a process that should occur as a consequence of a prior VSD transition. We have made some attempts to treat this transition as voltage independent but state-specific with upper-layer bias for states on the right and lower-layer bias for states on the left. This is in principle possible, as can already be gleaned from the similar voltage ranges of the left-right transition (α/β) and the κL/λ transition. However, this approach leads to a much larger number of free, less well constrained kinetic parameters and drastically complicated the parameter search. ” As you can see, we also formulated a strategy to free the model from the potentially spurious voltage dependence and (in bold here) explained why we did not follow this route in this study. 

The split channel experiment is interesting but needs more explanation. I assume the authors expressed the 2 parts of the split channel (1-341 and 342-end), however Tomczak et al showed in 2017 how the split presents a constitutively activated function with inward currents that are not visible here, this point needs clarification.

As stated in the panel heading, the figure legend, and the main text, we did not use 1-341 and 342-end as done in Tomczak et al. Instead, “we compared the behavior of ∆2-10 and ∆210.L341Split,”. Evidently, the additional deletion (2-10) causes a shift in activation that explains the difference you point out. However, as we do not compare L341Split and ∆210.L341Split but ∆2-10 and ∆2-10.L341Split, our conclusion remains that “As predicted, compared to ∆2-10, ∆2-10.L341Split showed a significant reduction in the first component of the biphasic GV (Fig. 2C, D).” Remarkably, the behavior of the ∆3-9 L341Split described in Whicher and MacKinnon, 2019 (Figure 5) matches that of our ∆2-10 L341Split, which we think reinforces our case.

Moreover, the authors assume that the mutations introduced uncover a new open state, however the traces presented for the mutations suggest that other explanations are possible. Other gating mechanisms like inactivation from the closed state, can be introduced by the mutations. The traces presented for ΔPASCap but specially E600R present clear 'hooked tails', a direct indicator of a populations of inactive channels during the test pulse that recover from inactivation upon repolarization (Tristani-Firouzi M, Sanguinetti MC. J Physiol. 1998). 

There is a possibility that we are debating nomenclature here. In response to the suggestion that all our observations could be explained by inactivation, we attempted a disambiguation of terms in the reply and the article. As the argument is brought up again without reference to our clarification attempts, we will try to be more explicit here:

If, starting from deeply deactivated states, an open state is reached first, and then, following further activation steps, closed states are reached, this might be termed “inactivation”. In such a reading, our model features many inactivated states. The shortest version of such a model is C-O-I. It is for instance used by Raman and Bean (2001; DOI: 10.1016/S00063495(01)76052-3) to explain NaV gating in Purkinje neurons. If “inactivation” is meant in the sense that a gating transition exists, which is orthogonal to an activation/deactivation axis, and that after this orthogonal transition, an open state cannot be reached anymore, then all of the upper floor in our model is inactivated with respect to the open state O1. Finally, the state C2 is an inactivated state to O2. In this view, “inactivation” explains the observed phenomena. 

However, we must disagree if the referee means that a parsimonious explanation exists in which a single conducting state is the only source for all observed currents.   

There is a high-level reason: we found a single assumption that explains three different phenomena, while the inactivation hypothesis with one conducting state cannot explain one of them (the increase of the first component under raised CaM). But there is also a low-level reason: the tails in Tristani-Firouzi and Sanguinetti 1998 are fundamentally different from what we report herein in that they lack a third component. Thus, those tails are consistent with recovery from inactivation through a single open state, while a three-component tail is not. In the framework of a Markov model, the time constants of transitions from and to a given state (say O2), cannot change unless the voltage changes. During the tail current, the voltage does not change, yet we observe: 

i) a rapid decrease with a time constant of at most a few milliseconds (Fig 9 S2, 1-> 2),  ii) a slow increase in current, peaking after approximately 25 milliseconds and iii) a relaxation to zero current with a time constant of >50 ms. 

According to the reviewer’s suggestion, these processes on three timescales should all be explained by depopulating and repopulating the same open state while all rates are constant. There might well be a complicated multi-level state diagram with a single open state with different variants, like (open and open inactivated) that could produce triphasic tails with these properties if the system had not reached a steady state distribution at the end of the test pulse. It cannot, however, achieve it from an equilibrated system, and certainly, it cannot at the same time produce “biphasic activation” and “activation by CaM”. 

The results presented by the authors can be alternatively explained with a change in the equilibrium between the close to inactivated/recovery from inactivation to the open state. 

Again, we disagree. The model construction explains in detail that the transition from the first to the second phase is not gradual. Shifting equilibria cannot reproduce this. We have extensively tested that idea and can exclude this possibility.

Finally, the authors state that they do not detect "cumulative inactivation after repeated depolarization" but that is considering inactivation only from the open state and ignoring the possibility of the existence of close state inactivation or, that like in hERG, that the channel inactivates faster that what it activates (Smith PL, Yellen G. J Gen Physiol. 2002). 

We respectfully disagree. We explicitly model an open state that inactivates faster (O2->C2) than it activates. Once more, this is stated in the revised article, which we point to for details. Again, this alternative mechanism does not have the potential to explain all three effects. As discussed above about the chloride contamination concerns, this inactivation hypothesis was mentioned in the first review round and, therefore, addressed in our reply and the revised article. We also explained that “inactivation” has no specific meaning in Markov models. In the absence of O1, all transitions towards the lower layer are effectively “inactivation from closed states”, because they make access to the only remaining open state less likely”. But this is semantics. What is relevant is that no network of states around a single open state can reproduce the three effets in a more parsimonious way than the assumption of the second open state does.

(3) Single channel conductance.

The single channels experiments are a great way to assess the different conductance of single channel openings, unfortunately the authors cannot measure accurately different conductances for the two proposed open states. The Markov Model built by the authors, disagrees with their interpretation of the experimental results assigning the exact same conductance to the two modeled open states. To interpret the mutant data, it is needed to add data with the WT for comparison and in presence of specific blockers. 

We respectfully disagree. As previously shown, the conductance of the flickering wild-type open state is very difficult to resolve. Our recordings do not show that the two states have different single-channel conductances, and therefore the model assumes identical singlechannel conductance. 

The important point is that the single-channel recordings clearly show two different gating modes associated with the voltage ranges in which we predict the two open states. One has a smaller macroscopic current due to rapid flickering (aka “inactivation”). These recordings are another proof of the existence of two open states because the two gating modes occur.  Wild-type data can be found in Bauer and Schwarz, (2001, doi:10.1007/s00232-001-0031-3) or Pardo et al., (1998, doi:10.1083/jcb.143.3.767) for comparison.

We appreciate the effort editors and reviewers invested in assessing the revised manuscript. Yet, we think that the demanded revision of experimental conditions and quantification methods contradicts the commonly accepted practice for KV10 channels. Some of the reviewer comments are skeptical about the biphasic behavior, which is an established and replicated finding for many mutants and by many researchers. The alternative explanations for these disbelieved findings are either “semantics” or cannot quantitatively explain the measurements. Therefore, only the demand for more explanations and unprecedented resolution in singlechannel recordings remains. We share these sentiments.

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

(1) The authors must show that the second open state is not just an artifact of endogenous activity but represents the activity of the same EAG channels. I suggest that the authors repeat these experiments in Mes-based solutions. 

(2) Along the same lines, it is necessary to show that these currents can be blocked using known EAG channel blockers such as astemizole. Ultimately, it will be important to demonstrate using single-channel analysis that these do represent two distinct open states separated by a closed state. 

We have addressed these concerns using several approaches. The most substantial change is the addition of single-channel recordings on ΔPASCap. In those experiments, we could provide evidence of the two types of events in the same patch, and the presence of an outward current at -60 mV, 50 mV below the equilibrium potential for chloride. The channels were never detected in uninjected oocytes, and Astemizole silenced the activity in patches containing multiple channels. These observations, together with the maintenance of the biphasic behavior that we interpret as evidence of the presence of O1 in methanesulfonate-based solutions, strongly suggest that both O1 and O2 obey the expression of KV10.1 mutants.

(3) Currents should be measured by increasing the pulse lengths as needed in order to obtain the true steady-state G-V curves. 

We agree that the endpoint of activation is ill-defined in the cases where a steady-state is not reached. This does indeed hamper quantitative statements about the relative amplitude of the two components. However, while the overall shape does change, its position (voltage dependence) would not be affected by this shortcoming. The data, therefore, supports the claim of the “existence of mutant-specific O1 and its equal voltage dependence across mutants.”

(4) A more clear and thorough description should be provided for how the observations with the mutant channels apply to the behavior of WT channels. How exactly does state O1 relate to WT behavior, and how exactly do the parameters of the mathematical model differ between WT and mutants? How can this be interpreted at a structural level? What could be the structural mechanism through which ΔPASCap and E600R enable conduction through O1? It seems contradictory that O1 would be associated exclusively with voltage-sensor activation and not gating ring transitions, and yet the mutations that enable cation access through O1 localize at the gating ring - this needs to be better clarified. 

We have undertaken a thorough rewriting of all sections to clarify the structural correlates that may explain the behavior of the mutants. In brief, we propose that when all four voltage sensors move towards the extracellular side, the intracellular ring maintains the permeation path closed until it rotates. If the ring is altered, this “lock” is incompetent, and permeation can be detected (page 34). By fixing the position of the ring, calmodulin would preclude permeation in the WT and promote the population of O1 in the mutants.

(5) Rather than the t80% risetime, exponential fits should be performed to assess the kinetics of activation. 

We agree that the assessment of kinetics by a t80% is not ideal. We originally refrained from exponential fits because they introduce other issues when used for processes that are not truly exponential (as is the case here). We had planned to perform exponential fits in this revised version, but because the activation process is not exponential, the time constants we could provide would not be accurate, and the result would remain qualitative as it is now. In the experiments where we did perform the fits (Fig. 3), the values obtained support the statement made. 

(6) It is argued based on the G-V relations in Figure 2A that none of the mutations or deletions introduced have a major effect on state O1 properties, but rather affect state O2. However, the occupancy of state O2 is undetermined because activation curves do not reach saturation. It would be interesting to explore the fitting parameters on Fig.2B further to test whether the data on Fig 2A can indeed only be described by fits in which the parameters for O1 remain unchanged between constructs. 

We agree that the absolute occupancy of O2 cannot be properly determined if a steady state is not reached. This is, however, a feature of the channel. During very long depolarizations in WT, the current visually appears to reach a plateau, but a closer look reveals that the current keeps increasing after very long depolarizations (up to 10 seconds; see, e.g., Fig. 1B in Garg et al., 2013, Mol Pharmacol 83, 805-813. DOI: 10.1124/mol.112.084384). Interestingly, although the model presented here does not account for this behavior, we propose changes in the model that could. “If the relative stability of O2 and C2 continued to change throughout the depolarization such a current creep-up could be reproduced. However, this would require either the introduction of further layers of On↔Cn states or a non-Markovian modification of the model’s evolution.” Page 34.

(7) The authors interpret the results obtained with the mutants DPASCAP and E600R -tested before by Lorinczi et al. 2016, to disrupt the interactions between the PASCap and cNBHD domains- as a two-step gating mechanism with two open states. All the results obtained with the E600R mutant and DPASCap could also be explained by inactivation/recovery from inactivation behavior and a change in the equilibrium between the closed states closed/inactivated states and open states. Moreover, the small tails between +90 to +120 mV suggest channels accumulate in an inactive state (Fig 1E). It is not convincing that the two open-state model is the mechanism underlying the mutant's behavior.  

We respectfully disagree with the notion that a single open state can provide a plausible explanation for "All the results obtained with the E600R mutant and DPASCap". We think that our new single channel results settle the question, but even without this direct evidence, a quantitative assessment of the triphasic tail currents all but excludes the possibility of a single open state. We agree that it is, in principle, possible to obtain some form of a multiphasic tail with a single open state using the scheme suggested in this comment: at the end of the test pulse, a large fraction of the channels must be accumulated in inactive states, and a few are in the open state. The hyperpolarization to -100mV then induces a rapid depopulation of the open state, followed by slower replenishments from the inactive state. Exactly this process occurs in our model, when C2 empties through O2 (Supp. 5 to Fig 9, E600R model variant). However, this alone is highly unlikely to quantitatively explain the measured tail currents, because of the drastically different time scales of the initial current decay (submillisecond to at most a few milliseconds lifetime) and the much slower transient increase in current (several tens of milliseconds) and the final decay with time constants of >100 ms (see for instance data in Fig. 1 E for E600R +50 to +120mV test pulse). To sustain the substantial magnitude of slowly decaying current by slow replenishment of an open state with a lifetime of 1 ms requires vast amounts of inactivated channels. A rough estimation based on the current integral of the initial decay and the current integral of the slowly decaying current suggests that at the end of the test pulse, the ratio inactivated/open channels would have to be 500 to 1500 for this mechanism to quantitatively explain the observed tail currents. To put this in perspective: This would suggest that without inactivation all the expressed channels in an oocyte would provide 6 mA current during the +100 mV test pulse. While theoretically possible, we consider this a less likely explanation than a second open state.

(8) Different models should be evaluated to establish whether the results in Figure 4 can also be explained by a model in which states O1 and O2 have the same conductance. It would be desirable if the conductance of both states were experimentally determined - noise analysis could be applied to estimate the conductance of both states. 

In the modified model, O1 and O2 have the same single-channel conductance. The small conductance combined with the fast flickering did not allow an accurate determination, but we can state that there is no evidence that the single-channel conductance of the states is different.

(9) Although not included, it looks like the model predicts some "conventional inactivation" This can be appreciated in Fig 8, and in the traces at -60mV. Interestingly, the traces obtained in the absence of Cl- also undergo slow inactivation, or 'conventional inactivation' as referred to by the authors. Please revise the following statement "Conventional inactivation was never detected in any mutants after repeated or prolonged depolarization. In the absence of inactivation, the pre-pulse dependent current increase at +40 mV could be related to changes in the relative occupancy of the open states". 

We have carefully edited the manuscript to address this concern. The use of the term inactivation admittedly represents a challenge. We agree that the state that results from the flickering block (C2) could be defined as “inactivated” because it is preceded by an open state. Yet, in that case, the intermediate states that the channel travels between O1 and O2 would also be sensu stricto “inactivated”, but only in the mutants. We have made this clear in page 17.

Recommendations for improving the writing and presentation.

(1) Methods section: Please state the reversal potential calculated for the solution used. It looks like the authors used an Instantaneous I-V curve method to calculate the reversal potential; if that's correct, please show the I-V and the traces together with the protocol used. 

We have provided the calculated reversal potentials for excised patches. We cannot predict the reversal potential in whole oocytes because we have no control over the intracellular solution. The reversal potential was determined in the mutants through the current at the end of the stimulus because the mutants produced measurable inward currents. The differences in reversal potential were not significant among mutants.

Pulse protocols have been added to the figures.

(2) Figure 1 suggestion: Combine the two panels in panel D and move the F panel up so the figure gets aligned in the lower end.

Thank you, this has been done.

(3) Please clarify the rationale for using the E600R-specific mutant. I assume it is based on the Lorinzci et al. 2016 effect and how this is similar to the DPASCap phenotype, or is it due to the impact of this mutation in the interactions between the N-term and the cNBHD? 

We have explained the rationale for the use of E600R explicitly on page 6.

(4) Fig S1A is not present in the current version of the manuscript. Include a cartoon as well as a structural figure clearly depicting the perturbations introduced by E600R, ΔPASCap, and the other deletions that are tested. Additional structural information supporting the discussion would also be helpful to establish clearer mechanistic links between the experimental observations described here and the observed conformational changes between states in Kv10 channel structures. 

We have corrected this omission, thank you for pointing it out.

(5) It would be informative to see the traces corresponding to the I-V shown in Fig 7 A and B at the same indicated time points (0, 60, 150, and 300s). Did the authors monitor the Ca2+ signal rise after the I&T treatment to see if it coincides with the peak in the 60s? 

In Figure 7 (now Figure 8) we used voltage ramps instead of discrete I-V protocols because of the long time required for recording the latter. This is stated on page 19. Ca2+ was monitored through Cl- current after ionomycin/thapsigargin. The duration of the Ca2+ increase was reproducible among oocytes and in good agreement with the changes observed in the biphasic behavior of the mutants (Supplement 1 to Figure 8).

(6) Fig 4. Please state in the legend what the different color traces correspond to in E600R and DPASCap. Is there a reason to change the interpulse on DPASCap to -20mV and not allow this mutant to close? Please state. How do the authors decide the 10 ms interval for the experiments in Fig 2? 

Thank you for pointing this out, we have added the description. We have explained why we use a different protocol for ΔPASCap and the reason for using 10 ms interval (we believe the referee means Figure 4) on page 12.  

(7) Fig. 5. Since the pre-pulse is supposed to be 5s, but the time scale doesn't correspond with a pre-pulse of 5 s before the test pulse to +40mV. Has the pre-pulse been trimmed for representation purposes? If so, please state. 

The pre-pulse was 5s, but as the reviewer correctly supposed, the trace is trimmed to keep the +40 mV stimulus visible. This has now been clearly stated in the legend.

(8) The mutant L322H is located within the S4 helix according to the Kv10.1 structure (PDB 5K7L), not in the 'S3-S4 linker'; please correct. 

This has been done, thank you.

The introduction of this mutant should also shift the voltage dependence toward more hyperpolarizing potentials (around 30mV, according to Schoenherr et al. 1999). It looks like that shift is present within the first component of the G-V. Still, since the max amplitude from the second component could be contaminated by endogenous Cl- currents, this effect is minimized. Repeating these experiments in the no Cl- solutions will help clarify this point and see the effect of the DPASCap and E600R in the background of a mutation that accelerates the transitions between the closed states (see Major comment 1). Did the authors record L322H alone for control purposes? 

We have decided not to measure L322H alone or repeat the measurements in Cl--free solutions because we do not see a way to use the quantitative assessment of the voltage dependence of L322H and the L322H-variants of the eag domain mutants. Like in our answer to main point 3, we base our arguments not on the precise voltage dependence of the second component but on the shape of the G-V curves instead, specifically the consistent appearance of the first component and the local conductance minimum between the first and second components. After the introduction of L322H the first component is essentially absent.

We think that the measurements of the L322H mutants cannot be interpreted as a hyperpolarizing shift in the first component. The peak of the first conductance component occurs around -20 mV in ΔPASCap and E600R (Fig. 7 C, D). After a -30mV shift, in L322H+DPASCap and L322H+E600R, this first peak would still be detected within the voltage range in our experiments, but it is not. A contamination of the second component would have little impact on this observation, which is why we refrain from the suggested measurements.  

(9) The authors differentiate between an O1 vs. O2 state with different conductances, and maybe I missed it, but there's no quantitative distinction between the components; how are they different?

Please see the response to the main comments 1 and 2. This has been addressed in singlechannel recordings.

(10) Please state the voltage protocols, holding voltages, and the solutions (K+ concentration and Cl-presence/absence) used for the experiments presented in the legends on the figures. Hence, it's easier to interpret the experiments presented. 

Thank you, this has been done.

(11) The authors state on page 7 that "with further depolarizations, the conductance initially declined to rise again in response to strong depolarizations. This finding matches the changes in amplitude of the tail currents, which, therefore, probably reflect a true change in conductance" However, the tails in the strong voltage range (+50 to +120 mV) for the E600R mutant argue against this result. Please review.

The increase in the amplitude of the tail current is also present in E600R, but the relative increase is smaller. We have decided against rescaling these traces because the Figure is already rather complex. We indicated this fact with a smaller arrow and clarified it in the text (page 8).

(12) The authors mention that the threshold of activation for the WT is around -20mV; however, the foot of the G-V is more around -30 or -40mV. Please revise. 

Thank you. We have done this. 

(13) The authors state on page 9 that the 'second component occurs at progressively more depolarized potentials for increasingly larger N-terminal deletions" However E600R mutant that conserves the N-terminal intact has a shift as pronounced as the DPASCap and larger than the D2-10. How do the authors interpret this result? 

We have corrected this statement in page 10 : “…the second component occurs at progressively more depolarized potentials for increasingly larger N-terminal deletions and when the structure of the ring is altered through disruption of the interaction between N- and C-termini (E600R)”.

(14) The equation defined to fit the G-Vs, can also be used to describe the WT currents. If the O1 is conserved and present in the WT, this equation should also fit the WT data properly. The 1-W component shown could also be interpreted as an inactivating component that, in the WT, shifts the voltage-dependence of activation towards depolarizing potentials and is not visible. Still, the mutants do show it as if the transition from closed-inactivated states is controlled by interactions in the gating ring, and disturbing them does affect the transitions to the open state. 

Out of the two open states in the mutant, O2 is the one that shares properties with the WT (e.g. it is inaccessible during Ca2+-CaM binding) while O1 is the open state with the voltage dependence that is conserved across the mutants. We, therefore, believe that this question is based on a mix-up of the two open states. We appreciate the core of the question: does the pattern in the mutants’ G-V curves find a continuation in the WT channel? 

Firstly, the component that is conserved among mutants does not lead to current in the WT because the corresponding open state (O1) is not observed in WT. However, the gating event represented by this component should also occur in WT and –given its apparent insensitivity to eag domain mutations–  this gating step should occur in WT with the same voltage dependence as in all the mutants. This means that this first component sets a hard boundary for the most hyperpolarized G-V curve we can expect in the WT, based on our mutant measurements. Secondly, the second component shows a regular progression across mutants: The more intact the eag domain is, the more hyperpolarized the Vhalf values of transition term (1-W) and O2 activation. In Δ2-10, the transition term already almost coincides with O1 activation (estimated Vhalf values of -33.57 and -33.47 mV). A further shift of (1-W) in the WT is implausible because, if O1 activation is coupled to the earliest VSD displacement, the transition should not occur before O1 activation. Still, the second component might shift to more hyperpolarized values in the WT, depending on the impact of amino acids 2 to 10 on the second VSD transition.

In summary, in WT the G-V should not be more hyperpolarized than the first component of the mutants, and the (1-W)-component probably corresponds to the Δ2-10 (1-W)-component. In WT the second component should be no more depolarized than the second component of Δ2-10. The WT G-V (Fig.1B) meets all these predictions derived from the pattern in the mutant GVs: When we use Eq. 4 to fit the WT G-V with A1=0 (O1 is not present in WT) and the parameters of the transition term (1-W)  fixed to the values attained in Δ2-10, we obtain a fit for the O2 component with Vhalf\=+21mV. This value nicely falls into the succession of Vhalf values for Δeag, ΔPASCap, and Δ2-10 (+103mV,+80mV,+52mV) and, at the same time, it is not more hyperpolarized than the conserved first component (Vhalf -34mV). Our measurements therefore support that the O2 component in the mutants corresponds to the single open state in the WT. 

(15) Page 15, the authors state that 'The changes in amplitude and kinetics in response to rising intracellular Ca2+ support our hypothesis that Ca-CaM stabilized O1, possibly by driving the channels to deep closed states (Fig 5 and 6)' (pg 15). This statement seems contradictory; I can't quite follow the rationale since Ca2+ potentiates the current (Fig 7), and the addition of the L322H mutant in Fig 7 makes the shift of the first component to negative potentials visible.

Please check the rationale for this section. 

We have explained this more explicitly in the discussion (page 32). “Because access to O1 occurs from deep closed states, this could be explained by an increased occupancy of such deactivated states in response to CaM binding. This appears to be the case since CaM induces a biphasic behavior in the mutant channels that show reduced access to deep closed states; thus, L322H mutants behave like the parental variants in the presence of Ca2+-CaM. This implies a mechanistic explanation for the effect of Ca2+-CaM on WT since favoring entry into deep closed states would result in a decrease in current amplitude in the absence of (a permeable) O1”.

Also, Figs 5 and 6 seem miscited here. 

Thank you, we have corrected this.

(16) For Figure 5, it would be helpful if each of the current traces corresponding to a particular voltage had a different color. That way, it will be easier to see how the initial holding voltage modulates current. 

We have considered this suggestion, and we agree that it would make it easier to follow. Yet, since we have identified the mutants with different colors, it would be inconsistent if we used another color palette for this Figure. Supplement 3 to Figure 9 shows the differences in a clearer way.

(17) Add zero-current levels to all current traces.

We have done this.

(18) The mathematical model should be described better. Particularly, the states from which O1 can be accessed should be described more clearly, as well as whether the model considers any direct connectivity between states O1 and O2. The origin of the voltage-dependence for transitions that do not involve voltage-sensor movements should be discussed. Also, it separation of kappa into kappa-l and kappa-r should be described. 

We have extensively rewritten the description of the mathematical model to address these concerns.

(19) Page 4, "reveals a pre-open state in which the transmembrane regions of the channel are compatible with ion permeation, but is still a nonconducting state". Also, page 27, "renders a hydrophobic constriction wider than 8 Å, enough to allow K+ flow, but still corresponds to a non-conducting state". These sentences are confusing - how can the regions be compatible with ion permeation, and still not be conducting? Is cation conductance precluded by a change in the filter, or elsewhere? How is it established that it represents a non-conducting state? 

We have rephrased to clarify this apparent inconsistence. Page 4: “(…) in which the transmembrane regions of the channel are compatible with ion permeation (the permeation path is dilated, like in open states) but the intracellular gate is still in the same conformation as in closed states (Zhang et al., 2023).” Page 31: “The presence of an intact intracellular ring would preclude ionic flow in the WT, and its alteration would explain the permeability of this state in the mutants.”

I think this is too much modesty, or a kind of self-undercutting to try to convey the importance of the point. But functionally it undercuts the significance of the earlier parts of the chapter. At the end here I'm understanding the argument as being

1. The antislavery campaign shows what state-of-the-art data visualization meant c. 1800, and these two different visualizations from Clarkson make the case that he should be considered one of the canonical figures.
2. That's important because it heightens a set of ethical and political questions about whether and when to visualize. Clarkson's work can be considered a countervisualization or something -- possibly a concept to introduce ? -- because it's taking advantage of the trade etc. Also highlights dataviz as a political-rhetorical form, not just a scientific practice about astronomy etc.
3. Just because we admire things about Clarkson's career doesn't mean should literally canonize him as a saint. Equiano's reaction shows that even at that time there are a different set of requirements.

And then there is the metaphor of water and streams. This does a few things: 1. provides a counterpoint to the God's-eye, object view by adding a contingency of flow and direction, fluidity, and contingency. 2. was useful for ~1800 readers who ALSO weren't always looking for this objective god's-eye view, which is OK. (I think the infographic/dataviz distinction from the introduction here is useful, because it underlines that the more 'subjective' or whatever flow timelines are an ADVANCE on Priestley's straight lines and can be seen as such. 3. Motivates your own data visualization of the streams of with the also-canonical Mississippi visualization. I may have missed this but I think the connection here is almost fully implicit. This could be one key to motivating the water thing as your own choice.

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Reply to the reviewers

Reviewer #1:

We thank the reviewer for his/her time and for the constructive comments. Below please find our detailed responses to your points.

STING is a key signalling hub in the innate immune system, receiving multiple inputs from upstream activators (such as cGAS) and in turn triggering multiple downstream events (such as IFN induction, NF-kB signalling, autophagy, cell death). Mutations in the STING gene cause a rare inflammatory disease called SAVI. Using a previously established STING ki mouse that recapitulates some of the clinical observations in SAVI patients, this manuscript tests the hypothesis that TNF signalling drives pathology. Using anti-TNF antibody and TNF receptor knockout, the authors show that TNF indeed plays important roles in causing disease in this mouse model. For example, the loss of T cells and neurons is prevented when TNF signalling is blocked, and lung pathology is rescued in STING ki mice lacking TNF receptors. Overall, the manuscript is well written and laid out, and the experimental work is of a high technical standard.

Major comments

1. • Most figures show pooled data from two independent experiments including a total of 5-8 mice. Given the variability in some of the readouts, this raises the question of whether there is sufficient statistical power to draw conclusions. For example, in Figure 2, the conclusion that "Infliximab did not alter the expression of inflammatory mediators" seems questionable given the results in Figure 2F and G. Did the authors perform a power calculation? What effect size can the authors detect given the variability and number of replicates? Similarly, in Figure 3, the authors conclude that "Disruption of TNFR signaling did not significantly prevent T cell lymphopenia"; however, with some more replicates, the data in Figure 3D would likely reach significance. Similar concerns apply to several panels in Figures 4 and 6 and to Figure S5M. Ideally, the authors should perform additional repeat experiments to increase the number of replicates. If that is not possible, power calculations need to be provided and conclusions should explicitly mention the minimum effect size that the author can detect given the small sample size (for example "Infliximab did not alter the expression of inflammatory mediators more than x-fold").* Thank you for this suggestion. However, it is not possible to repeat the treatment of mice with Infliximab for generation of more replicates. The blockade of TNF signalling by treatment with drugs did not cure the murine SAVI disease. According to animal welfare restrictions, we cannot perform additional treatment experiments with Infliximab or Etanercept.

We analysed the effect size d, f and power of all these presented results and collected them in table S4. Additional explanations about effect sizes were added in the corresponding text to Figures 2 and 3. The demonstrated results in Figure 4 and 6 already contain significant data. We did not include the calculation of effects sizes here. All effect size and power calculations are summarized in table S4.

• The authors should not make unjustified overstatements. For example, STING KI; TNFR1/2 KO mice should not be referred to as a "new mouse model". The manuscript simply tests the role of TNFR1/2 in the already published STING N153S model. In line 687, avoid using "impressively" and in line 734 avoid using "massively".*

• *

Thank you for this suggestion. We changed this sentence into:…”these newly generated mouse lines of TNFR”…., see line 796. Additionally, in line 687 (actual line 705) we omitted “impressively” and in line 734 “massively produced” into “elevated” (actual line 752).

Minor comments

• Line 767-769: The statement that spike activates cGAS is misleading, because this effect is an indirect consequence of cell-to-cell fusion (Liu et al 2022).*

• *

Thank you for this suggestion. We changed this sentence into: Cell fusion caused by the SARS-CoV-2 spike protein is a potent… (actual line 785).

Reviewer #1 (Significance (Required)):

• *

The main strengths of this study are (1) the use of complementary antibody-based and genetic methods to test the role of TNF signalling; (2) the use of multiple different readouts; and (3) the analysis of many different cell types / organ systems. The main weaknesses are (1) small sample sizes limiting statistical power (see above) and (2) the exclusive use of mouse models.

• *

Overall, my opinion is that the advance is important, both fundamentally and clinically. Studies of this and the related V154M mouse model previously showed an important role of non-IFN pathways in driving disease. This study indicates that TNF signalling may cause pathology. This not only extends our understanding of STING's role in autoinflammation but also opens a direct therapeutic avenue using approved TNF targeting drugs.

• *

This study will be primarily of interest to specialised audiences working on STING and SAVI, and secondarily to the wider innate immunity field.

• *

This reviewer has expertise in the field of nucleic acid sensing, including cGAS-STING.

• *

• *

Reviewer #2:

We thank the reviewer for his/her time and for the constructive comments. Below please find our detailed responses to your points.

*In this paper, Luksch et al (2024) examines the role of TNF signaling in STING-associated vasculopathy with onset in infancy (SAVI). By using pharmacological inhibition and genetic inactivation of TNF receptors in a murine SAVI model (STING ki), the research found that pharmacologically inhibiting TNF signaling improved T cell lymphopenia but had limited effects on lung disease. Genetic inactivation of TNFR signaling, particularly TNFR1, enhanced thymocyte survival and expanded the peripheral T cell pool, reducing inflammation and neurodegeneration. The development and progression of severe lung disease in STING ki mice are also reliant on TNFR1 signaling, while TNFR2 deletion did not alleviate lung inflammation. The authors also explored the severe inflammatory lung disease manifestation, showing that primary lung endothelial cells in STING ki mice allowed more neutrophil attachment compared to those in STING WT mice, indicating chronic STING activity in endothelial cells disrupts the endothelial barrier and promotes severe lung disease. The study highlights TNFR signaling as crucial in SAVI and COVID-19 progression and suggests blocking TNFR1 signaling as a potential therapeutic approach for both diseases. *

• *

Major comments:

The paper establishes a strong connection between TNFR1 depletion and the reduction of SAVI disease severity in lung and neuroinflammation, suggesting TNFR1 blockade as a viable therapeutic strategy for SAVI. To strengthen the arguments and improve the therapeutic potential, the authors should address the following major comments:

1. • The authors conclude that TNFR1 signaling drives murine SAVI disease, as evidenced by the reduced severity of lung disease in TNFR1 -/- mice. While the genetic model is convincing, the discrepancy between pharmacological inhibition and genetic models needs clarification. Before attributing the pharmacological failure to late administration, have the authors considered that Infliximab might not sufficiently deplete TNF to achieve therapeutic benefits? In figure 2H, serum TNF levels were not significantly altered in STING ki mice treated with Infliximab. Have the authors considered using other TNF inhibitors or alternative methods to measure TNF depletion efficacy in STING ki murine models, such as qPCR, flow cytometry, or immunohistochemistry in lymph nodes or lung tissues?* Thank you for this suggestion. In a preliminary experiment, we already treated STING WT and STING ki mice with Etanercept which is not included in the paper. 3-week-old mice were treated with subcutaneously injection of 25 mg/kg Etanercept or saline, twice per week, for 7 weeks. After treatment, all mice were euthanized and single cell suspensions of blood and spleen were used for flow cytometry analysis. Lung tissue was harvested for histological analysis. Quantification of gene expression was performed by snap frozen lung and kidney tissue and quantification of secreted proteins was analysed by snap frozen serum.

The transcription of ISGs and proinflammatory mediators in lung tissue was not significantly improved by the Etanercept treatment of mice, see additional figure below (A – D). Interestingly, the amount of secreted CXCL9 in the serum was reduced in Etanercept treated mice compared to vehicle treated mice (E). We concluded that our treatment strategy had no impact in the manifestation and progression of murine SAVI disease, in highly inflamed tissues / organs. However, we found a reduction (partially significant) of proinflammatory mediator transcriptions in the kidney of Etanercept treated mice compared to vehicle control mice. Murine SAVI disease is a systemic autoinflammatory disease without histological alteration in kidney tissue of 10 weeks old mice. Remarkably, transcription of ISGs and proinflammatory mediators is highly upregulated in SAVI mice. Treatment with Etanercept improved this aberrant gene expression in murine SAVI influenced tissue / organ (I – K). These results encouraged us to perform the treatment with infliximab because we expected a more pronounced effect since infliximab can bind the monomeric and trimeric form while etanercept can only bind to the active trimeric from of TNF.

Etanercept treatment of STING WT (in black) and ____STING ki (in red)____ mice.

(A) Relative expression level of Cxcl10, (B) Mx1, (C) Tnf and (D) Il1b in lung tissue of Etanercept or saline treated STING WT and STING ki mice. (E) Quantification of CXCL9, (F) CXCL10, (G) IL-6 and (H) TNF in serum samples from STING WT and STING ki mice after treatment. (I) Relative expression level of Cxcl10, (J) Mx1, (K) Tnf and (L) Il1b in kidney tissue of treated mice.

• The TNF pathway exhibits redundancy, as multiple signaling molecules or pathways can compensate for the loss of TNF function to maintain cellular processes and immune responses. The authors showed that thymocytes of STING ki mice lacking TNFR1/2 expressed significantly lower levels of IFN-related genes (Cxcl10, Sting1), and mice lacking TNFR1 and TNFR1/2 expressed reduced levels of NF-κB-related genes. Does this imply that IFN and NF-κB pathways are downstream of TNF signaling driving SAVI progression? It would be valuable to hear the authors' comments or postulations on the potential mechanisms of TNF driving SAVI progression in the discussion, and the methods to dissect the mechanisms further using genetic or pharmacological methods.*

Thank you for this suggestion. STING is a key player in various proinflammatory mechanism and is directly involved in IFN and NF-κB signalling. We assume that these signalling pathways are adaptable to various proinflammatory situations. Knock out of TNFR1 and TNFR1/2 results in a strong inhibition of all inflammatory reactions in the whole organisms. We think, it is not possible to conclude mechanisms of murine SAVI manifestation and progression from the results of these mouse lines only. These observations provide new hypothesis, but cannot completely explain the mechanism.

• The authors mentioned that the pharmacological inhibition of TNF by Infliximab is ineffective due to late administration compared to the onset of SAVI. How would this affect the therapeutic treatment of TNF if the treatment is going to be later than the disease onset? Can the authors elaborate on the potential ways to circumvent the timing of treatment? Would TNFR1 antagonists experience the same issue? To understand disease progression and optimal targeting times, the creation of an inducible TNFR1/2 -/- mouse model could be beneficial. This is optional, but the authors are encouraged to comment on improving TNFR1/2 -/- mouse SAVI models to further study the therapeutic potential of TNF signaling blockage in treating SAVI.*

We agree with the suggestion. In the next project, we want to generate STING ki mice with inducible knock out.

Minor comments:

• The authors separate STING WT and STING ki into different graphs, which can sometimes make it hard to compare STING WT and STING ki baseline levels. It would be beneficial to merge the two genotypes into single graphs for easier comparison.*

Thank you for this suggestion. In the first version of this manuscript, we collected results from STING WT and STING ki mice in one graph with 8 bars in different colours and textures in the case of TNFR knock out lines. These graphs were overloaded and very confusing. It is was not possible to mark statistical calculations inside these graphs without losing the focus. Hence, we created the demonstrated design of graphs. We think this is the most convincing version.

• Figure S5 lacks statistical annotations, although the legends mention them. Are the statistics usually shown when a comparison is mentioned in the text, or are they only displayed when the differences are significant? It would be helpful if the authors could clarify this and ensure that all relevant statistical comparisons are clearly reflected in the graphs, regardless of the significance level. This consistency would improve the clarity and interpretation of the data presented.*

• *

Thank you for this suggestion. We removed the significance level from the legend of Figure S5 (actually line 1199).

• *

The authors did an excellent job discussing the study's implications, but some of this content could be moved to the introduction. The hypothesis that "tumor necrosis factor (TNF) signaling is involved in the manifestation and progression of murine SAVI disease" can be introduced more naturally once the authors present previous findings on TNF's association with various autoimmune disorders. This would set a clear context for the study's objectives and rationale.

We agree with this suggestion and inserted the sentence: “In our previous investigations, we observed an elevated transcription of Tnf in spleen and thymus of STING ki mice (Siedel et al., 2020).” (actual line 89/90).

General Assessment: The study identifies enhanced TNF signaling as a driver of SAVI and specifies TNFR1 blockage as a promising treatment to reduce disease severity. It thoroughly characterizes pharmacological inhibition and genetic perturbations of TNF signaling in murine SAVI models and creates a novel mouse model for studying TNF-targeted therapies in SAVI treatment.

*However, the study is limited in characterizing the discrepancy between pharmacological inhibition and genetic depletion of TNF and understanding the underlying mechanisms of TNF driving chronic STING activation and tissue inflammation. *

Advances: The study extends knowledge in the field by demonstrating that enhanced TNF signaling drives SAVI, establishing causation rather than mere correlation. The authors provide strong rationale for treating SAVI with TNF inhibitors/blockage, previously used in other autoimmune disorders like IBD or Crohn's disease, but not in SAVI. They also present a valuable genetic model for studying TNFR signaling blockage in SAVI progression, which is important for both the field of SAVI and future therapy development.

Audience: The research provides translational and clinical insights by suggesting that targeting TNFR1 signaling could inspire novel treatments for SAVI. The study also advances basic research on SAVI disease progression. Immunologists and clinicians studying and treating autoimmune disorders are the intended audience, but the findings have broader implications. The study highlights the potential role of TNF signaling in COVID-19 disease progression and treatment, thus attracting interest beyond the field of autoimmune disorders.

• *

Field of expertise:

cGAS-STING regulation in chromosomally unstable cancers, genomic instability, nuclear envelope rupture and repair

Do not have sufficient expertise in:

Immunological underpinning of autoimmune disorders, clinical models or manifestations of SAVI

• *

• *

Reviewer #3:

We thank the reviewer for his/her time and for the constructive comments. Below please find our detailed responses to your points.

• *

Uncontrolled activation of STING is linked to autoinflammatory disease "STING-associated vasculopathy with onset in infancy (SAVI)". The authors had previously published a mouse model of SAVI, which was generated by knocking in the disease causing variant N153S into the endogenous murine Sting1 gene (STING ki) (Luksch et.al., 2019). In the current study, the author further investigated the role of tumor necrosis factor (TNF) signaling in manifestation and progression of murine SAVI disease by using the approach of pharmacologic and genetic inhibition of TNF receptors TNFR1 and TNFR2. Overall, the authors were able to demonstrate the following novel findings:

• *

1) Infliximab treatment of STING ki mice significantly increased the number of blood CD8+ T cells and thymic cells count. The authors claimed that the pharmacological inhibition of TNF signalling has a partial rescue effect of T cell lymphopenia. However, pharmacologic inhibition of TNF signalling however has no effect on lung disease.

2) On the other hand, STING ki;Tnfr1-/- (lacking TNFR1) showed the similar modest rescue of the CD8+ T and CD4+ T cells in blood compared to the WT C57BL/6 (BL6) but not with STING ki;Tnfr2-/- (lacking TNFR2). STING ki;Tnfr1-/-, STING ki;Tnfr2-/- and STING ki;Tnfr1/2-/- had modest rescue of thymic cell count and reduced spleen cell count (reduced splenomegaly). Along with the rescued thymic content and reduced splenomegaly, genetic ablation of TNF signalling (STING ki;Tnfr1-/-) also prevented manifestation of severe inflammatory lung disease.

3) To investigate the role of lung endothelial cells in the development of interstitial lung disease, primary murine lung endothelial cells from STING WT, STING ki and STING WT;Tnfr1/2-/- and STING ki;Tnfr1/2-/- mice were isolated and bulk RNAseq was performed. This showed decreased level of several proinflammatory cytokines (e.g. Tnf, Il1b) and chemokines (e.g. Cxcl1, Cxcl2, Cxcl9, Cxcl10, Ccl2, Ccl3 and Ccl4) in STING ki mice lacking TNFR1/2 compared to STING ki mice.

4) Neutrophils were isolated from bone marrow and were added to cultured primary lung endothelial cell monolayers. The experiments demonstrated that the attachment and transmigration of neutrophil cells were dependent on expression of STING gain-of-function mutation in endothelial cells.

• *

A few points require clarification before publication of this study.

• Tnfr1-/-, Tnfr2-/- and Tnfr1/2-/- did not show any statistical significant improvement of thymic cell count in STING ki mice. As such, the statement in the conclusion/summary section of discussion regarding Tnfr1 can restore thymocyte numbers should be toned-down.
• Thank you for this suggestion. In Figure 4 E, we demonstrated that knock out of TNFR1 leads to increasing of SP CD8 thymocyte count and partially of SP CD4 thymocyte count (Fig. 4 D). In agreement with this suggestion, we marked this subpopulation of thymocytes in the discussion and summary section, see actual line 684 and see actual line 794.

2) The section on Neuroinflammation and neurodegeneration and dependency of TNFR1/2 signaling is very currently difficult to follow (based on how the data are presented in figures and text). This section requires to be re-written for clarity.

• *

Thank you for this suggestion. We re-wrote this section, see line 472 - 499.

Neuroinflammation and neurodegeneration in dependency of TNFR1/2 signaling

The extent of inflammation in mouse brain resulting from constitutive activation of STING N153S was reported by quantifying the density of Iba1-positive microglia (Fig.5 A). Consistent with our previous findings (Szego et al., 2022), the density of Iba1-positive microglia in the substantia nigra was higher in STING ki;BL6 mice than in STING WT mice (Fig.5 B). TNFR deficiency did not affect neuroinflammation because there was no significant difference between the density of Iba1-positive microglia between STING ki;BL6 mice and STING ki;Tnfr1/2-/- mice (Fig.5 B). This suggests that the TNF pathway is not required for STING-induced microglia activation in the substantia nigra.

In addition, we measured the extent of STING-induced astrogliosis by quantifying the density of GFAP-positive cells (Fig. 5 A). Consistent with our previous findings, the density of GFAP-positive astroglia was higher in STING ki than in STING WT mice (Fig. 5C). Yet, as for microglia, there was no significant difference between the density of GFAP-positive astroglia between STING ki;BL6 mice and STING ki;Tnfr1/2-/- mice (Fig.5 C), suggesting that the TNF pathway is not required for STING-induced astrogliosis in the substantia nigra.

Finally, we measured the extent of STING-induced neurodegeneration by quantifying the density of TH-positive dopaminergic neurons in the substantia nigra (Fig. 5A). As in our previous findings, the density of TH-positive neurons was lower in STING ki;BL6 mice than in STING WT mice (Fig.5 D). The density of TH-positive neurons in the substantia nigra of STING ki;Tnfr1/2-/- mice was higher than the density of TH-positive neurons in the substantia nigra of STING ki;BL6 mice (Fig. 5 D), suggesting that the STING-induced degeneration of TH-positive neurons was blunted in Tnfr1/2-/- mice and that TNFR1/2 are involved in the STING-induced degeneration of dopaminergic neurons.

Hence, there is a discrepancy between STING-induced effects on glial cells as opposed to STING-induced effects on neurons. The dependence of STING-induced neurodegeneration but not glial response on TNFR1/2 suggests that the STING-induced degeneration of dopaminergic neurons is not a direct consequence of microglia or astroglia activation. This is consistent with the emerging concept of a neuron-specific inflammatory response (Welikovitch et al., 2020).

*The powerful use of in vivo genetic KO models and TNF inhibitor makes this study a valuable contribution to the field - helping further decipher the importance of the NF-KB/TNF branch of STING in SAVI (knowledge gap). The audience for this work would be specialised to STING biology and potential clinical treatments of SAVI. *

• *

Our expertise is in nucleic acids sensing (such as STING) and auto-immunity.

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Response to Reviewers

We thank the reviewers for their comments and suggestions, which we think are helpful and will improve the manuscript, and intend to address with the changes and planned revisions below.

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

Bello et al look at the SNP rs28834970 associated with Alzheimer's disease (AD), with C being the risk allele, on chromatin accessibility and expression of a nearby gene, PTK2B, in microglia. Their contention is that the single SNP affects chromatin accessibility and binding of the transcription factor CEBP[beta] in an intronic region of PTK2B and thereby affects PTKB expression. I had a few questions that I think are critical to be addressed. Please note that my numbering of panels is based on the figures, not the legends, which do not seem to quite agree with each other. There are also some figure legends that say "IFNg" while the figures say "LPS", which should be fixed.

We apologise for the mistake in the figure legend that made this confusing, which we have now revised.

The abstract says that editing a line that is homozygous for protective alleles to homozygous for risk results in "subtle downregulation of PTK2B expression". It isn't clear to me that the presented data fully supports this contention, which is central to the argument of the paper. In figure 2e, the authors show in both RNAseq and ddPCR that there is numerically lower PTK2B expression but this is not indicated to be statistically significant by one-way paired ANOVA. If there is no nominally significant difference in the edited lines, compared to the proposed significant differences in lines carrying the full risk haplotype (figure 1), then it would not seem sensible to ascribe the effects to the single edited base pair.

We agree with the reviewer that given the effect of the SNP on PTK2B expression in the edited lines is small and only significant in macrophages, we should not interpret the effects to be mediated solely through PTK2B expression, and have substantially reworded the manuscript accordingly.

Whilst the effects in the eQTL analysis are significant, it is worth noting that this is likely due to the much larger number of donors (133-217) giving greater power to detect the subtle changes in expression (~1.1 to 2 fold in eQTL). This change is of a similar magnitude in our SNP edited lines (~1.2 fold in SNP edited lines) as would be expected of most common regulatory variants so we believe that it could be the primary causal variant. However, we cannot exclude that other variants in the haplotype could contribute to the effect, so have also reworded accordingly to make this clear.

Given this uncertainty about the overall strength of effect of the single base pair change it would seem important to evaluate the proposed mechanism of CEBPb binding. It wasn't clear whether the ATAC-seq data summarized in the volcano plot in 2C is proposed to be a cause or a consequence of the CEBPb binding change. I am assuming that the 'fold change' estimate here is CC compared to TT, which would be consistent with direction of effect in figure 1, but please clarify.

We apologise for the mistake in the figure legend that made this confusing, which we have now revised along with clarification in the revised text. It is difficult to be sure whether changes in chromatin accessibility are a cause or consequence of CEBPb binding, but the fact that the binding of CEBPb is increased in the CC allele (Fig 2a, Fig 2c), that the C allele better matches the consensus sequence (Fig 2b) and there is increased chromatin accessibility (Fig 2a, Supp Fig 3b) suggests that CEBPb binding is causing the formation of the region of chromatin accessibility.

In contrast to the subtle effects at PTK2B, the global transcriptional effects in figure 3 look quite strong. Are any of these changes dependent on PTK2B, that is to say, are they mimicked by partial suppression of PTK2B expression or activity?

We agree that the downstream effects of the SNP are much stronger than the effects on PTK2B expression, and we have substantially reworded the manuscript to make it clear that we are unsure that the effects of the SNP are all mediated via PTK2B.

However, we note that there is evidence in the literature of a loss in CCL4 and CCL5 expression upon PTK2B knockout in macrophages (https://www.nature.com/articles/s41467-021-27038-5) and inhibition of PTK2B in monocytes results in a reduction in CCL5 and CXCL1 (https://www.nature.com/articles/s41598-019-44098-2) consistent with our observations.

Experiments to manipulate PTK2B expression in microglia and readout changes at the RNA level would take a few months to complete, but we would be willing to do this if the reviewer felt this was necessary.

Finally, in figure 4, it should be clarified as to why lower expression of PTK2B would be expected to have a detrimental effect on Alzheimer's risk. If understood correctly, and again fixing the figure legends would be helpful, the CC edited lines (risk) have lower chemokine induction than the unedited TT lines.

We apologise for the error in this figure which we have corrected in the revised version. You are correct that the CC lines have a lower chemokine level in both unstimulated and stimulated cells, and we have now discussed further how this may be linked to increased disease risk.

"Even though overexpression of these chemokines is characteristic of neuroinflammation, correlated with disease progression and found in late stages of AD, knockout of chemokines, such as CCL2, and chemokine receptors, such as CCR2 and CCR5, in mice is associated with increased Aβ deposition and accumulation [47,50-52,107]. It has also been found that patients carrying CCR5Δ32 mutation, which prevents CCR5 surface expression, develop AD at a younger age[108]. Therefore, we hypothesize that in individuals carrying the C/C allele of rs28834970 downregulation of these chemokines in macrophages and microglia harbouring the C/C allele of rs28834970 affects Aβ-induced microglia chemotaxis, leukocytes recruitment and clearance of Aβ, and may increase the risk of developing symptomatic AD"

Reviewer #1 (Significance (Required)):

Going from GWAS hits, which represent blocks of high LD inherited variants, to single functional variants is a difficult problem in human genetics. The current paper attempts to isolate the effect of a single variant within an LD block on IPSC derived macrophages and microglia. This idea might be useful in nominating PTK2B as a therapeutic target for AD, although there is some question in my mind as to direction of effect.

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

SUMMARY: In this manuscript the authors explore the biological effects of an intronic SNP in the PTK2B gene, previously shown to be associated with late onset Alzheimer's disease (AD) risk. Based on the likely effect of the SNP locus on PTK2B expression in the macrophage lineage, the authors explore the consequences of introducing with the Crispr/Cas9 technique the biallelic SNP base change (C/C vs T/T) in a human IPSC line that is then differentiated into macrophages or microglia. They observe that C/C increases chromatin accessibility and CEBPb binding in comparison to T/T, with a slight decrease in PTK2B expression, significant in macrophages but not in microglia. The authors then investigate the transcriptome changes induced by the C/C mutation and find alteration in many genes, including a decreased expression of a number of cytokine or receptor proteins involved in inflammatory responses. The authors also mention a decreased effect on IFNg-induced reduced mobility but the data are missing (see Figure errors below). Overall the authors propose that the risk SNP is associated with a decreased PTK2B expression and hypothesize a link between this change and a decreased function of macrophages/microglia that may contribute to AD pathology.

MAJOR COMMENTS

1- The authors claim that their results show that the investigated SNP has a causal effects in "microglial function" (Title) and in Alzheimer's disease (AD) (Abstract 2nd sentence "Here we validate a causal single nucleotide polymorphism (SNP) associated with an increased risk of Alzheimer's disease". The word "causal" is repeated many times. However the authors should qualify their claim with respect to AD. Their results do show that the SNP has an effect on chromatin accessibility, CEBP binding, PTK2B expression and transcriptome, but the link between these changes is not formally demonstrated and their potential role in AD-like phenotype is not explored. The "causal" role is not formally and logically demonstrated. It remains an interesting, plausible hypothesis and the results provide strong arguments in support of that hypothesis but do not prove it, yet.

Concerning the title, "causal effects on microglial function" is awkward, anything that has effects is logically "causal" in these effects. The title should be "... has effects on microglial functions" or "... alters microglial function".

We agree with the reviewer that given the effect of the SNP on PTK2B expression in the edited lines is small and only significant in macrophages, we should not interpret the effects to be mediated solely through PTK2B expression, or that they cause AD. We have substantially reworded the manuscript throughout to account for this.

2- One major difficulty in the results is to link the slight decrease in PTK2B transcript, which is only significant in macrophages, with the rest of the phenotype. Because what matters to make this link is not the mRNA but the protein, and because mRNA levels are often not strictly correlated with the protein levels, the authors should measure the PTK2B/PYK2 protein levels in their differentiated cell lines in basal conditions and following activation (as they do for other readouts) using immunoblotting. A robust and significant diminution in PYK2 protein would strongly support its role in linking PTK2B expression and transcriptome change.

We have performed preliminary analyses of PTK2B expression by Western blot in these cell lines after differentiation, but were unable to observe a significant change in abundance in the edited cell lines. This is not unexpected given the results at the RNA level, since the effect size of this common regulatory variant is likely very small (estimated to be ~1.2 fold from the eQTL analysis), and likely within the variability of this assay.

As mentioned above, we have reworded the manuscript to avoid interpreting that the effects of rs28834970 are mediated solely through effects on PTK2B expression. We think that an experiment to manipulate PTK2B levels (see next point) may be a better way to demonstrate whether these effects are mediated through PTK2B expression.

An optional additional key experiment would be to reverse the transcriptome phenotype by increasing the expression of PTK2B (e.g. by cDNA transfection). Note that these points are important because an alternative hypothesis to explain the effects of C/C mutation on macrophage function would be that the C/C mutation has a long distance effect on other chromatin regions with key role in regulating these cells.

We agree that this would be a valuable experiment, and are planning additional experiments to investigate the effect of manipulating PTK2B levels (through knockout) on microglia.

3- The manuscript contains several errors in the figures and figure legends. In Fig. 2 the legends for the figure items are shuffled. Figure 4 and Supplementary Figure 5 are duplicates of the same one. Consequently important data are not presented.

We apologise for the errors in these figures that were due to a mistake during uploading where the incorrect versions were used. The legends for figure 2 and panels in figure 4 have now been corrected, and show the effects of rs28834970 on microglial migration and chemokine release in the presence or absence of IFNg.

4- When the number of replicates is small (e.g. n = 3) it is preferable to use non parametric tests (rank analysis, e.g. Mann Whitney's test) rather than t test. This applies to Figures 2D (current legend 2A), 2E (current legend 2B), Figure 4A-C, Supplementary Figures 2A, 2B. In Supplementary Fig 4E (MARCO) the number of replicates (presumably 3 because based on RNAseq) and the used test are not indicated. Is it the RNAseq statistical analysis?

We thank the reviewer for this comment. We acknowledge that the t-test may lead to inflated false discovery rates. However, it has been shown that for small sample sizes parametric tests have a power advantage compared to non-parametric ones that may outweigh the possibly exaggerated false positives. See https://genomebiology.biomedcentral.com/articles/10.1186/s13059-022-02648-4#Sec3 which states:

"In conclusion, when the per-condition sample size is less than 8, parametric methods may be used because their power advantage may outweigh their possibly exaggerated false positives."

We have also modified the legend of supplementary figure 4E to clarify the number of replicates used.

5- In addition to the above comment on tests, when the number of replicates is small it is not appropriate (and misleading) to show box plots or bars with SEM. In the indicated figures the individual data points should be shown.

We now show individual replicates on box plots (Figure 2D, 2E and supp figure 4E).

MINOR COMMENTS:

a- Macrophages and microglia are very similar cell types. Could the authors comment more on the differences they observe and how they are related to those previously described?

We have now referenced the original papers and commented on the markers that we see differentially expressed, notably P2RY12 which is a key homeostatic microglia marker that distinguishes these cells from macrophages.

b- In Fig. 2A CEBPb cut and run plot, the differences are not limited to the SNP immediate vicinity, there are also visible differences between T/T and C/C plots in at least a 40-kb range. Is it due to multiple interactions of CEBPb? How can the point difference have broad consequences? Please explain this potentially interesting and relevant finding.

Whilst there may be small changes in CEBPb binding at the second intronic PTK2B chromatin peak, this is not statistically significant given the variability between repeats. In fact, the only significant change we see in CEBPb binding genome-wide is at the locus overlapping the SNP (Fig 2c).

c- Potentially cis-altered genes near the SNP include CHRNA2 and EPHX2 (see Sup. Fig. 3a). Their expression may not be detected in macrophage lineage. If this is the case please indicate in the text, otherwise please include the corresponding data in Sup. Fig. 3b to show the presence or absence of SNP-induced change.

You are correct that CHRNA2 and EPHX2 are not expressed in our macrophages or microglia, and we have now explicitly stated this in the revised text.

d- In general the Figures are not of very high quality and are difficult to read or understand without constantly going back and forth to the legends (which are mislabeled in some instances). To improve:

. Please increase font size whenever possible.

. Please improve Fig. 1d by indicating the position of the SNP, numbering the exons (an intermediate scale plot may be necessary and lines on bottom trace are hardly visible).

. Please indicate the correct color code for T/T and C/C in Fig 3a and b, left panels, which currently doesn't match.

. Please label the Venn's diagrams comparisons in Sup. Fig. 4b.

. In the text and legends the Figure items are identified with letters in upper case, in the figures they are in lower case. Please be consistent.

We have improved the resolution of the images in the pdf and Fig 1d has been revised to include the position of the SNP. The colour code for T/T and C/C is correct in fig 3a and 3b, but since the PCA plots are independently created, we would not always expect the position of the T/T and C/C alleles to be the same. The Venn diagrams in Sup Fig 4b have been updated, and the letters for the figure panels made consistently upper case throughout.

e- In Fig. 2D and 2E, the Y axes should start at zero to avoid artificially increasing the visual differences. If there is a strong reason not to do so (I don't see any here), the Y axis should be clearly interrupted to avoid confusion.

We have altered this accordingly.

f- In the introduction the authors provide some background about previous work about the potential role of PTK2B/PYK2 in AD pathophysiology. The cited preclinical results suggest that PTK2B activity could have a deleterious effect (references in the manuscript). In contrast, some other reports (PMID: 29803828, 33718872) suggest a protective effect of PTK2B/PYK2. Because the evidence in the current manuscript suggests that the risk-associated SNP results in a decreased function of PTK2B/PYK2 (through decreased levels), at least in cells of the macrophage lineage, the authors could broaden their discussion to include these results.

We have now discussed the conflicting evidence in the revised manuscript.

Reviewer #2 (Significance (Required)):

ADVANCE: Late onset Alzheimer's disease is a major medical issue. It has a complex genetic risk component with many associated loci identified in GWAS. Most of these have only a small individual impact on the risk. One of the SNPs associated with increased risk (rs28834970) is located in an intron of the PTK2B gene. Although various reports have investigated the role of the PTK2B gene product, the tyrosine kinase PYK2, in several AD models, the possible link with rs28834970, is unclear.

An important point is to determine whether TàC SNP corresponding to rs28834970 alters PTK2B expression and how it does so. An alternative hypothesis could be that the SNP has a strong linkage disequilibrium with an unidentified allele in human populations that could be responsible for AD risk. The current manuscript is a significant step forward in addressing that question. By generating a biallelic C/C SNP mutation in a human IPSC line the current study allows to eliminate such linked contribution.

The strength of the manuscript is to show an effect on chromatin accessibility, CEBP binding and possibly PTK2B transcripts. It also provides interesting evidence of a broad effect of the C/C mutation on the transcriptome of macrophage lineage cells. In its current form the manuscript presents weaknesses that could be improved. These flaws include issues with the presentation discussed above and the uncomplete demonstration that it is the decrease in PTK2B expression that causes the macrophage/microglia phenotype. If these flaws were overcome the paper would represent a significant advance.

AUDIENCE: The expected audience is specialized in AD with a possible broader range if all weaknesses are addressed.

REVIEWER EXPERTISE: Basic science close to the field.

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Response to Reviewer #1:

We agree with Reviewer 1 that a function of ROPGEFs in this process was expected to some degree. However, we want to point out that this manuscript focuses on the requirement of ROPGEFs and especially the spatio-temporal description of ROP signalling polarisation and activation during pollen germination. Moreover, different to the downstream ROPs, we show ROPGEFs do not act strictly redundant, confirming results from root hair initiation and providing additional evidence that multiple signalling pathways are required for pollen germination and that ROPGEFs might be essential for bringing specificity to these signals.

Major comments:

1. Only one GEF11 mutant line, gef11-t1, was analyzed for germination ratio. It is presumptuous to conclude that GEF11 has no function in the pollen germination of Arabidopsis thaliana (line 241- line 242).

After the initial negative results, we did not focus on GEF11 further. Thus, we fully agree that it is presumptuous to make such strong statements about the role of GEF11 during pollen germination. We generated additional gef11 mutant alleles for this revision plan using CRISPR/Cas9 as no other suitable lines were available. Moreover, we now have additional higher-order mutants available to demonstrate the function of GEF11 during pollen germination. These additional lines were generated and confirmed and are growing right now. Thus, we will be able to implement new results addressing this point timely, allowing us to make a more founded statement about the function of GEF11 (see Response to Reviewer #2).

Minor comments:

1. In Figure 2A, pollen germination ratio was not provided for the single mutants gef8-c△3 and gef9-c△

This is due to the generation process of the CRISPR/Cas9 alleles. These alleles were generated by a construct mutating both genes simultaneously; thus, these mutants are unavailable as single mutant lines. Instead of separating these alleles by outcrossing, we included additional single mutant alleles for both GEFs with a similar deletion. As all these CRISPR/Cas9 mutants have a complete deletion of the GEF-ORF, we are sure about the loss of the according GEF function. Additional alleles account for possible unspecific effects.

In Figure 3D, the subcellular localization of GEF12GEF8C is fuzzy. Better imaging is needed.

We agree that the quality of these images is not ideal due to this specific line having less fluorescent signal. We screened for more lines of this construct and already performed more experiments. We will provide better images for this genotype.

In Figure 3E, it is intriguing that both GEF8-S518A and GEF8-S518D are not associated with the PM in germinating pollen grains. Does it mean that phosphorylation at S518 is not relevant to polar distribution of GEF8?

We also find this very intriguing as we did not expect this result. However, we interpret it slightly differently in the way that the S518 site is relevant for GEF polarisation, which might be conferred by RLK interaction. We think both mutant forms alter this potential association with RLKs, thus losing polarisation. We will include more imaging experiments of these constructs and additional lines to strengthen our results. Moreover, we generated lines to study these lines' functionality and complementation capacity, which will be included in a revised manuscript.

T-DNA insertion lines, gef11-t1 and gef12-t1, need to be verified by PCRs in Figure S3D.

Thanks for pointing this out. This control should be provided, and we will include the verification in the supplement.

Response to Reviewer #2:

Like Reviewer #2, we are also very intrigued by the biphasic accumulation of GEFs, as this is an entirely novel feature of this process. Like Reviewer #2, we also interpret this as an exploration and establishment phase, which could help us to understand how the pollen germination site is decided in species without aperture-dependent pollen germination.

Major comments:

1. In line 241, the authors conclude that GEF11 has no function in pollen germination. However, it is likely that GEF11 also plays a redundant role as GEF12 does. I recommend the authors check the phenotypes of gef11,gef12 double mutant and gef8,gef9,gef11 triple mutant to confirm that GEF11 has indeed no function. Otherwise, this conclusion should be better rephrased.

This point is well justified and similar to the comment of Reviewer #1. As stated before, we had to generate additional lines for this. We will analyse an additional gef11 allele, gef8/gef11 and gef9/11 double mutants, and gef9/11/12 triple mutants to address the function of GEF11 in more detail. The conclusions of the original manuscript will, of course, be adjusted according to the new results.

Although GEF12 is in the cytosol, the strong pollen germination defects in gef8,gef9,gef12 triple mutants do indicate a critical role of GEF12. Is it possible that GEFs could function in the cytosol? The authors can test this possibility by examining the rescuing ability of several constructs that express, for example, GEF12, GEF12(+GEF8C), GEF8(SA), or GEF8(SD) in gef8. The authors may not perform all of these rescue experiments, but some of the mentioned lines are already in hands. They could readily check the phenotypes.

We thank the Reviewer for this great point. This information is crucial to discriminate the function of the individual GEFs. We have generated new lines expressing some of the mentioned constructs in the gef8 background to address this. We now have lines that complement gef8 with GEF12, GEF12GEF8C, GEF8S518A, GEF8S518D, and GEF8ΔC. We are currently performing experiments which determine the functionality of these constructs, which will allow us to make more conclusive statements about the function of GEFs in the cytosol and how important the PRONE domain alone, or the membrane attachment of GEFs, is for their function.

The authors conclude that the C-terminus of GEF8 and GEF9 is necessary and sufficient for membrane localization because GEF8/9C can target GEF12 PRONE domain to the membrane. It is intriguing whether the C-terminus alone could confer membrane targeting ability. Currently, it is not fully understood how GEFs localize to the membrane. Examining the localization of GEF8/9C itself would help clarify this and improve our understanding of GEF regulation. Alternatively, the authors may discuss evidence that supports or disagrees with this possibility.

This is a good suggestion by the reviewer and indeed intriguing if the C-Terminus alone could confer membrane attachment. Meanwhile, we obtained plants expressing such constructs, showing that the C-terminus alone is insufficient for membrane attachment. This is not surprising, as these domains are largely disordered, and we suspect that the context of an adjacent PRONE domain is required to carry out this function. We will include our new results in the revised manuscript.

Minor comments:

1. The N- and C-terminus of GEF8 are predicted to inhibit complex formation. How is the prediction performed? Do the authors use monomer prediction or multimer prediction? Alphafold2 has a low accuracy in predicting non-conserved regions. How confident are the predicted inhibitory contacts?

We used multimer-prediction of Alphafold2 for the shown structures. However, we fully agree that the predicted structures of Alphafold have low accuracy in that regard, especially for disordered domains like this. We will provide confidence models and predicted aligned error (PAE) plots for this structure. Additionally, we will put our conclusions in a better perspective of these structure confidences and tone down our interpretations of this section.

Localization of ROPs and calcium reporter in Figure 4 appears to be variable. It would help clarify the specific effects on each reporter if the authors present these data more quantitatively.

We agree with the reviewer that some of the observations are variable. We will provide the data more quantitatively, including overviews of which percentage we observed the described phenomena and a more quantitative analysis of the strength and timing of signal accumulation (see also Response to Reviewer #3).

Response to Reviewer #3:

Major points:

1. One of my major points is that the manuscript is now mainly based on the observations of individual pollen grains. These are then subjected to well-performed image analysis approaches but still represent somewhat anecdotal evidence (Fig 1A, B, Fig 3C-E, etc). The analysis and (numerical) presentation of a more robust data sample (which I presume the authors have acquired) would strengthen the ms considerably. This goes beyond the Figs - e.g. in l. 164-165 authors state rather vaguely, "we observed that mCit-GEF8 and mCit-GEF9 accumulated at a defined region in the cell periphery, which strongly correlated with the future germination site." Here, I would appreciate the data showing the actual correlation, if every germinated pollen grain displays GEF8/9 accumulation, whether there is a population of pollen grains showing the GEF8/9 transient but not germinating, etc...

We very much appreciate the reviewer's comment, as this version of the manuscript indeed seems like we made our conclusions based on observations made from individual pollen. However, this is not the case. As the reviewer suspected, more data is available but not included in the manuscript. We have multiple observations for each of the shown constructs and only show a representative one. Furthermore, we imaged more pollen germination events of lines that showed variability and included additional lines for some constructs. We will provide a more quantitative analysis of the results to better represent the variability of the individual constructs, and we will adjust the manuscript accordingly (see comment 2).

Where the authors analyse multiple cells, we are still missing some info - e.g. it is not stated what the error bars in Fig 1C, D represents (SD, SEM, CI?), size of the sample, etc. In any case, it is evident that there is quite substantial variability in the data, which is understandable. Maybe the authors can plot the individual profile lines along the average? Plus, GEF9 seem to have the maximum pre-germination localisation at -5 min rather than -9 min.

We agree with the Reviewer that information is missing or not obviously stated. We will correct this for the revised manuscript. Moreover, we agree that the suggested way of showing the data would provide more information and allow a better representation of the results and their variability. This can be seen in the reviewer's interpretation of the results of GEF9. In this case, we see some variability in the timing of GEF9 accumulation, leading to the peak maximum shift. In a revised manuscript, we will, as suggested, show the data as individual lines, providing a better representation of the data. Moreover, we will include such representations for other used constructs to provide a general, more quantitative data analysis (see comment 1).

I know it is very challenging, but the ms would be much stronger with the in vivo imaging of pollen germination on stigmatic papillae (i) GEF8/9 in wt, (ii) gef8/9 double mutant. This would bring crucial data about the role of the GEF polar domain and its functional relation to pollination.

This would indeed be great to see. We put an effort into establishing such in vivo imaging experiments with our fluorescent markers. However, we cannot image these events in an in vivo setup (at least with our resources). This has two reasons: 1. The events are very fast and limited to a small region at the pollen-papilla contact side, which we have issues resolving optically and timely. 2. The used marker lines only have a low fluorescent level due to the native promoter, and stronger expression would lead to overexpression artefacts. In vitro, it is difficult to see the observed signal accumulation. In the in vivo situation, we are facing additional diffraction of the papilla cells, which would make the observation of GEF accumulation impossible with our microscopes.

The phylogeny presented in Fig S1 is only rudimental and not very interesting. Given the author's results, I would love to see if GEF8/9 orthologs also exist in species with defined pollen apertures (where establishing a dynamic site makes little sense). The authors touch on this (L409-411), but it would deserve better analysis and discussion.

We agree with the reviewer that studying GEF function/accumulation in species with aperture-dependent germination would be interesting. However, we can not conclude functional orthologs in other species based on phylogeny. Such phylogenetic analyses were done, for example, by Kim et al. (BMC Plant Biology, 2020, doi: 10.1186/s12870-020-2298-5). The issue is that all Arabidopsis pollen-expressed GEFs form a closed phylogenetic group without allowing the interpretation of which rice homolog is the functional ortholog of the respective Arabidopsis GEF (this is the same for maize). Thus, such phylogenetic analyses are not conclusive, and they would require experimental data to prove orthology. However, we agree that this point can be interpreted and discussed better, and we will include this in the revised manuscript.

I am not entirely convinced by the authors' interpretation of rather strange S518 mutation data. Could S518A mutation affect overall GEF8 structure/stability?

We were also suspicious about these results, as they were unexpected (see also Response to Reviewer #1). To confirm these results, we made additional lines for these constructs, double-checked that the constructs were correct and made more observations for both GEF8S18A and GEF8S18D. Additionally, we started investigating the functionality of these constructs and have this data available timely. Preliminary results suggest that the constructs are partial to fully functional compared to the WT GEF8, arguing against these mutations' effect on structure or stability. We will include more data for these constructs in a revised manuscript to allow a more conclusive interpretation of these unexpected observations.

Although the authors cannot observe the localisation of ROPs in the plasma membrane, they see the apparent accumulation of active ROP marker CRIB4 there - implying that ROPs must localise to the pollen PM at the germination site. This discrepancy should be solved or at least discussed more.

The reviewer is correct in that we cannot observe ROP accumulation but rather the accumulation of ROP activity (as seen by CRIB4). This is in line with the observation made by Xiang et al. (2023, Plant Physiology, doi: 10.1093/plphys/kiad196), which also cannot find ROP accumulation. We are convinced that ROPs are present at the plasma membrane of the pollen germination site, but no accumulation is observable. We believe this is due to a high mobility of ROPs and that no accumulation is required, as only a few ROPs are sufficient to activate downstream signals. We will discuss these results in more detail in a revised manuscript to better explain the observed discrepancy.

Given that calcium oscillates very rapidly in pollen and pollen tubes (with frequency ~6-20s), the profound, long-term changes in calcium levels reported by the authors can hardly be referred to as oscillations. The phenomenon observed should again be analysed using a bigger sample.

We agree that the terminology is not good, as it suggests similarities to the oscillations found in pollen tubes. Thus, we will change the revised manuscript and refer to the changes in Ca2+ levels as “elevations”. Moreover, we will provide a more quantitative analysis and a bigger sample size, as stated in Response to Reviewer #2.

Minor points:

1. In Fig 1F, GEF12 also seems to be polarly localised to the future site.

The chosen sample is not ideal, as it looks like GEF12 would also slightly accumulate. However, as seen in the quantification of this cell, GEF12 does not significantly accumulate at the pollen germination site, and we never observed any accumulation of GEF12 that is comparable to GEF8 or GEF9. We will include another sample of this colocalisation in the revised manuscript to avoid misinterpretation of the data.

It is difficult to make any assumptions based on the AlphaFold2 predictions without showing their confidence assessments (e.g., PAE plots). The authors state this themselves in the discussion (L. 447-449).

As the Response to Reviewer #2 stated, we will include structures with confidence values and PAE plots in the supplement. We additionally tone down our interpretation of these structure predictions to make clear that these structures should be interpreted carefully.

On one hand the authors repeatedly state that pollen GEFs do act in a redundant manner (and provide some evidence for it), on the other hand the absence of an in vivo phenotype for single and double knockout lines and only mild phenotype for a triple ko line does suggest a level of redundancy. This should be rephrased.

We agree that this is not clearly phrased. In a revised version, we will change the manuscript to indicate which type and level of redundancy are described. We will discriminate between genetic redundancy, as seen in the mild in vivo effects, and non-redundant molecular function, as observed by protein localisation.

Reviewer #1 (Public Review):

Summary:

The novel advance by Wang et al is in the demonstration that, relative to a standard extinction procedure, the retrieval-extinction procedure more effectively suppresses responses to a conditioned threat stimulus when testing occurs just minutes after extinction. The authors provide some solid evidence to show that this "short-term" suppression of responding involves engagement of the dorsolateral prefrontal cortex.

Strengths:

Overall, the study is well-designed and the results are potentially interesting. There are, however, a few issues in the way that it is introduced and discussed. Some of the issues concern clarity of expression/communication. However, others relate to a theory that could be used to help the reader understand why the results should have come out the way that they did. More specific comments and questions are presented below.

Weaknesses:

INTRODUCTION & THEORY

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

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

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

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

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

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

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

https://pubmed.ncbi.nlm.nih.gov/32580869/<br /> https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7115860/

CLARIFICATIONS, ELABORATIONS, EDITS

(5) The Abstract was not easy to follow:

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

5B. "First, we demonstrate that memory reactivation prevents the return of fear shortly after extinction training in contrast to the memory reconsolidation effect which takes several hours to emerge and such a short-term amnesia effect is cue independent (Study 1, N = 57 adults)."<br /> ***The phrasing here could be improved for clarity: "First, we demonstrate that the retrieval-extinction protocol prevents the return of fear shortly after extinction training (i.e., when testing occurs just min after the end of extinction)." Also, cue-dependence of the retrieval-extinction effect was assessed in study 2.

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

5D. "...we directly manipulated brain activities in the dorsolateral prefrontal cortex and found that both memory retrieval and intact prefrontal cortex functions were necessary for the short-term fear amnesia."<br /> ***This could be edited to better describe what was shown: E.g., "...we directly manipulated brain activities in the dorsolateral prefrontal cortex and found that intact prefrontal cortex functions were necessary for the short-term fear amnesia after the retrieval-extinction protocol."

5E. "The temporal scale and cue-specificity results of the short-term fear amnesia are clearly dissociable from the amnesia related to memory reconsolidation, and suggest that memory retrieval and extinction training trigger distinct underlying memory update mechanisms."<br /> ***The pattern of results when testing occurred just minutes after the retrieval-extinction protocol was different from that obtained when testing occurred 24 hours after the protocol. Describing this in terms of temporal scale is unnecessary, and suggesting that memory retrieval and extinction trigger different memory update mechanisms is not obviously warranted. The results of interest are due to the combined effects of retrieval+extinction and there is no sense in which different memory update mechanisms should be identified with retrieval (mechanism 1) and extinction (mechanism 2).

5F. "These findings raise the possibility of concerted memory modulation processes related to memory retrieval..."<br /> ***What does this mean?

(6) "...suggesting that the fear memory might be amenable to a more immediate effect, in addition to what the memory reconsolidation theory prescribes..."<br /> ***What does it mean to say that the fear memory might be amenable to a more immediate effect?

(7) "Parallel to the behavioral manifestation of long- and short-term memory deficits, concurrent neural evidence supporting memory reconsolidation theory emphasizes the long-term effect of memory retrieval by hypothesizing that synapse degradation and de novo protein synthesis are required for reconsolidation."<br /> ***This sentence needs to be edited for clarity.

(8) "previous behavioral manipulations engendering the short-term declarative memory effect..."<br /> ***What is the declarative memory effect? It should be defined.

(9) "The declarative amnesia effect emerges much earlier due to the online functional activity modulation..."<br /> ***Even if the declarative memory amnesia effect had been defined, the reference to online functional activity modulation is not clear.

(10) "However, it remains unclear whether memory retrieval might also precipitate a short-term amnesia effect for the fear memory, in addition to the long-term prevention orchestrated by memory consolidation."<br /> ***I found this sentence difficult to understand on my first pass through the paper. I think it is because of the phrasing of memory retrieval. That is, memory retrieval does NOT precipitate any type of short-term amnesia for the fear memory: it is the retrieval-extinction protocol that produces something like short-term amnesia. Perhaps this sentence should also be edited for clarity.

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

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

(12) "We hypothesize that the labile state triggered by the memory retrieval may facilitate different memory update mechanisms following extinction training, and these mechanisms can be further disentangled through the lens of temporal dynamics and cue-specificities."<br /> ***What does this mean? The first part of the sentence is confusing around the usage of the term "facilitate"; and the second part of the sentence that references a "lens of temporal dynamics and cue-specificities" is mysterious. Indeed, as all rats received the same retrieval-extinction exposures in Study 2, it is not clear how or why any differences between the groups are attributed to "different memory update mechanisms following extinction".

(13) "In the first study, we aimed to test whether there is a short-term amnesia effect of fear memory retrieval following the fear retrieval-extinction paradigm."<br /> ***Again, the language is confusing. The phrase, "a short-term amnesia effect" implies that the amnesia itself is temporary; but I don't think that this implication is intended. The problem is specifically in the use of the phrase "a short-term amnesia effect of fear memory retrieval." To the extent that short-term amnesia is evident in the data, it is not due to retrieval per se but, rather, the retrieval-extinction protocol.

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

DATA

(15) Points about data:

15A. The eight participants who were discontinued after Day 1 in study 1 were all from the no-reminder group. Can the authors please comment on how participants were allocated to the two groups in this experiment so that the reader can better understand why the distribution of non-responders was non-random (as it appears to be)?

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

15C. "Post hoc t-tests showed that fear memories were resilient after regular extinction training, as demonstrated by the significant difference between fear recovery indexes of the CS+ and CS- for the no-reminder group (t26 = 7.441, P < 0.001; Fig. 1e), while subjects in the reminder group showed no difference of fear recovery between CS+ and CS- (t29 = 0.797, P = 0.432, Fig. 1e)."<br /> ***Is the fear recovery index shown in Figure 1E based on the results of the 1st test trial only? How can there have been a "significant difference between fear recovery indexes of the CS+ and CS- for the no-reminder group" when the difference in responding to the CS+ and CS- is used to calculate the fear recovery index shown in 1E? What are the t-tests comparing exactly, and what correction is used to account for the fact that they are applied post-hoc?

15D. "Finally, there is no statistical difference between the differential fear recovery indexes between CS+ in the reminder and no reminder groups (t55 = -2.022, P = 0.048; Fig. 1c, also see Supplemental Material for direct test for the test phase)."<br /> ***Is this statement correct - i.e., that there is no statistically significant difference in fear recovery to the CS+ in the reminder and no reminder groups? I'm sure that the authors would like to claim that there IS such a difference; but if such a difference is claimed, one would be concerned by the fact that it is coming through in an uncorrected t-test, which is the third one of its kind in this paragraph. What correction (for the Type 1 error rate) is used to account for the fact that the t-tests are applied post-hoc? And if no correction, why not?

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

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

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

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

(17) "These results are consistent with previous research which suggested that people with better capability to resist intrusive thoughts also performed better in motivated dementia in both declarative and associative memories."<br /> ***Which parts of the present results are consistent with such prior results? It is not clear from the descriptions provided here why thought control ability should be related to the present findings or, indeed, past ones in other domains. This should be elaborated to make the connections clear.

Reviewer #3 (Public Review):

SUMMARY

Wang et al. have addressed how acquired fear and extinction memories evolve over time. Using a retrieval-extinction procedure in healthy humans, they have investigated the recovery of fear memories 30-60 minutes., 6 hours, and 24 hours after the retrieval-extinction phase. They have addressed this research question through 3 different experiments which included manipulations of the reminder cue, the time interval, and brain activity. Together, the studies suggest that early on after retrieval-extinction (30-60 min. later), retrieval-extinction may lead to an attenuation of fear recovery (after reinstatement) for all fear cues, as well as the non-reminded ones. Study 3 moreover suggests that this effect may depend on normal dlPFC function. In addition, the paper also contains data in line with prior findings suggesting that a 6-hour interval does not benefit from the reminder cue, and that a 24-hour interval does, and specifically for the reminded fear cue. The latter findings are seen as evidence of fear memory reconsolidation.

STRENGTHS

(1) The paper combines three related human fear conditioning studies, each with decent sample sizes. The authors are transparent about the fact that they excluded many participants and about which conditions they belonged to.

(2) The effect that this paper investigates (short-term fear memory after a retrieval-extinction procedure) has not been studied extensively, thus making it a relevant topic.

(3) The application of brain stimulation as a means to study causal relationships is interesting and goes beyond the purely behavioral or pharmacological interventions that are often used in human fear conditioning research. Also, the use of an active control stimulation is a strength of the study.

WEAKNESSES

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

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

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

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

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

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

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

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

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

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

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

Author response:

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

Reviewer #1

Summary:

In this paper, the authors performed molecular dynamics (MD) simulations to investigate the molecular basis of the association of alpha-synuclein chains under molecular crowding and salt conditions. Aggregation of alpha-synuclein is linked to the pathogenesis of Parkinson's disease, and the liquid-liquid phase separation (LLPS) is considered to play an important role in the nucleation step of the alpha-synuclein aggregation. This paper re-tuned the Martini3 coarse-grained force field parameters, which allows long-timescale MD simulations of intrinsically disordered proteins with explicit solvent under diverse environmental perturbation. Their MD simulations showed that alpha-synuclein does not have a high LLPS-forming propensity, but the molecular crowding and salt addition tend to enhance the tendency of droplet formation and therefore modulate the alpha-synuclein aggregation. The MD simulation results also revealed important intra- and inter-molecule conformational features of the alpha-synuclein chains in the formed droplets and the key interactions responsible for the stability of the droplets. These MD simulation data add biophysical insights into the molecular mechanism underlying the association of alpha-synuclein chains, which is important for understanding the pathogenesis of Parkinson's disease.

Strengths:

(1) The re-parameterized Martini 3 coarse-grained force field enables the large-scale MD simulations of the intrinsically disordered proteins with explicit solvent, which will be useful for a more realistic description of the molecular basis of LLPS.

(2) This paper showed that molecular crowding and salt contribute to the modulation of the LLPS through different means. The molecular crowding minimally affects surface tension, but adding salt increases surface tension. It is also interesting to show that the aggregation pathway involves the disruption of the intra-chain interactions arising from C-terminal regions, which potentially facilitates the formation of inter-chain interactions.

We thank the reviewer for pointing out the strengths of our study.

Weaknesses:

(1) Although the authors emphasized the advantage of the Martini3 force field for its explicit description of solvent, the whole paper did not discuss the water's role in the aggregation and LLPS.

We thank the reviewer for pointing this out. We agree that we have not explored or discussed the role of water in aS aggregation or LLPS. We would like to convey that we would like to explore that in detail in a separate study altogether. However we have updated the “Discussion” section with the following lines to convey to the readers the importance water plays in aggregation and LLPS of aS.

Page 24: “The significance of the solvent in alpha-synuclein (αS) aggregation remains underexplored. Recent studies [26, 55] underscore the pivotal role of water as a solvent in LLPS. It suggests that comprehending the solvent’s role, particularly water, is essential for attaining a deeper grasp of the thermodynamic and physical aspects of αS LLPS and aggregation. By delving into the solvent’s contribution, researchers can uncover additional factors influencing αS aggregation. Such insights hold the potential to advance our comprehension of protein aggregation phenomena, crucial for devising strategies to address diseases linked to protein misfolding and aggregation, notably Parkinson’s disease. Future investigations focusing on elucidating the interplay between αS, solvent (especially water), and other environmental elements could yield valuable insights into the mechanisms underlying LLPS and aggregation. Ultimately, this could aid in the development of therapeutic interventions or preventive measures for Parkinson’s and related diseases.”

(2) This paper discussed the effects of crowders and salt on the surface tension of the droplets.

The calculation of the surface tension relies on the droplet shape. However, for the formed clusters in the MD simulations, the typical size is <10, which may be too small to rigorously define the droplet shape. As shown in previous work cited by this paper [Benayad et al., J. Chem. Theory Comput. 2021, 17, 525−537], the calculated surface tension becomes stable when the chain number is larger than 100.

We appreciate the insightful feedback from the reviewer. However, we would like to emphasize that the αS droplets exhibit a highly liquid-like behavior, characterized by frequent exchanges of chains between the dense and dilute phases, alongside a slow aggregation process. In the study by Benayad et al. (2020, JCTC) [ref. 30], FUS-LCD was the protein of choice at concentrations in the (mM) range. FUS-LCD is known to undergo very rapid LLPS at concentrations lower than 100 (μM) where for αS the critical concentration for LLPS is 500 (μM) and undergoes slower aggregation than FUS. Moreover, the diffusion constant of αS inside newly formed droplets (no liquid to solid phase transition has occurred) has been estimated to be 0.23-0.58 μm2/s (Ray et al, 2020, Nat. Comm.). The value of diffusion constant for FUS-LCD inside LLPS droplets has been estimated to be 0.17 μm2/s (Murthy et al. 2023, Nat. Struct. and Mol. Biol.). These prove that αS forms droplets that are less viscous than that formed by FUS-LCD. This dynamic nature impedes the formation of large droplets in the simulations, making it challenging to rigorously calculate surface tension from interfacial width, which, in turn, necessitates the computation of g(r) between water and the droplet.

Furthermore, it's essential to note that our primary aim in calculating surface tension was not to determine its absolute value. Rather, we aimed to compare surface tensions obtained for the three distinct environments explored in this study. Hence, our primary objective is to compare the distributions of surface tensions rather than focusing solely on the mean values obtained. The distributions shown in Figure 4a clearly show a trend which we have stated in the article.

(3) In this work, the Martini 3 force field was modified by rescaling the LJ parameters \epsilon and \sigma with a common factor \lambda. It has not been very clearly described in the manuscript why these two different parameters can be rescaled by a common factor and why it is necessary to separately tune these two parameters, instead of just tuning the coefficient \epsilon as did in a previous work [Larsen et al., PLoS Comput Biol 16: e1007870].

We thank the reviewer for the comment. We think that the distance of the first hydration layer also should have an impact on aggregation/LLPS. Here we are scaling both the epsilon and sigma. A higher epsilon of water-protein interactions mean higher the energy required for removal of water molecules (dehydration) when a chain goes from the dilute to the dense phase. A higher sigma on the other hand means that the hydration shell will also be at a larger distance making dehydration easier. Moreover, tuning both (either by same or different parameter) required a change of the overall protein-water interaction by only 1%, thereby requiring only considerably minimal change in forcefield parameters (compared to the case where only epsilon is being tuned which required 6-10% change in epsilon from its original values.) . Thus we think one of the ways of tuning water-protein interactions which requires minimal retuning of Martini 3 is by optimizing both epsilon and sigma. However whether a single scaling parameter is good enough requires further exploration and is outside the scope of the current study. More importantly it would introduce another free parameter into the system and the lesser the number of free parameters, the better. For this study, a single parameter sufficed as depicted in Figure 9. To inform the readers of why we chose to scale both sigma and epsilon, we have added the following in the main text:

Page 25-26: “Increasing the ϵ value of water-protein interactions results in a higher energy demand for removing water molecules (dehydration) as a chain transitions from the dilute to the dense phase. Conversely, a higher σ value implies that the hydration shell will be at a greater distance, facilitating dehydration if a chain moves into the dilute phase. Therefore, adjusting water-protein interactions based on the protein’s single-chain behavior may not significantly influence the protein’s phase behavior. Furthermore, fine-tuning both ϵ and σ parameters only requires a minimal change in the overall protein-water interaction (1%). As a result, this adjustment minimally alters the force field parameters.”

(4) Both the sizes and volume fractions of the crowders can affect the protein association. It will be interesting to perform MD simulations by adding crowders with various sizes and volume fractions. In addition, in this work, the crowders were modelled by fullerenes, which contribute to protein aggregation mainly by entropic means as discussed in the manuscript. It is not very clear how the crowder effect is sensitive to the chemical nature of the crowders (e.g., inert crowders with excluded volume effect or crowders with non-specific attractive interactions with proteins, etc) and therefore the force field parameters.

We thank the reviewer for a potential future direction. In this investigation our main focus was to simulate the inertness features of crowders only, to ensure that only entropic effect of the crowders are explored. Although this study focuses on the factors that enable aS to form an aggregates/LLPS under different environmental conditions, it would be interesting to explore in a systematic way the mechanism of action of crowders of varying shapes, sizes and interactions. Therefore we added the following lines in the “Discussion” section to let the readers know that this is also a future prospect of investigation.

Page 22: “Under physiological conditions, crowding effects emerge prominently. While crowders are commonly perceived to be inert, as has been considered in this investigation, the morphology, dimensions, and chemical interactions of crowding agents with αS in both dilute and dense phases may potentially exert considerable influence on its LLPS. Hence, a comprehensive understanding through systematic exploration is another avenue that warrants extensive investigation.”

Reviewer #1 (Recommendations For The Authors):

(1) Figure S1. The title of the figure and the description in the figure caption are inconsistent?

We thank the reviewer for the comment and we have updated the article with the correct caption.

(2) Page 14, line 3, the authors may want to provide more descriptions of the "ms1", "ms2", and "ms3" for better understanding.

We are grateful to the reviewer for pointing this out. We have added a line describing in brief what “ms1”, “ms2” and “ms3” represent. It reads “Subsequent to the investigation, we utilize three representative conformations, each corresponding to one of the macrostates. We designate these macrostates as 1 (ms1), 2 (ms2), and 3 (ms3) (Figure S7)” (Page 28)

(3) Page 20, the authors may want to briefly explain how the normalized Shannon entropy was calculated.

We thank the reviewer for pointing this out. This is plain Shannon Entropy and the word “normalized” should not have been there. To avoid confusion we have provided the equation we have used to calculate the Shannon entropy (Eq 8) (Page 21).

Reviewer #2 (Public Review):

In the manuscript "Modulation of α-Synuclein Aggregation Amid Diverse Environmental Perturbation", Wasim et al describe coarse-grained molecular dynamics (cgMD) simulations of α-Synuclein (αS) at several concentrations and in the presence of molecular crowding agents or high salt. They begin by bench-marking their cgMD against all-atom simulations by Shaw. They then carry 2.4-4.3 µs cgMD simulations under the above-noted conditions and analyze the data in terms of protein structure, interaction network analysis, and extrapolated fluid mechanics properties. This is an interesting study because a molecular scale understanding of protein droplets is currently lacking, but I have a number of concerns about how it is currently executed and presented.

We thank the reviewer for finding our study interesting.

(1) It is not clear whether the simulations have reached a steady state. If they have not, it invalidates many of their analysis methods and conclusions.

We have used the last 1 μs (1.5-2.5 1 μs) from each simulation for further analysis in this study. To understand whether the simulations have reached steady state or not, we plot the time profile of the concentration of the protein in the dilute phase for all three cases.

Author response image 1.

Except for the scenario of only αS (Figures a and b), the rest show very steady concentrations across various sections of the trajectory (Figures c-f). The larger sudden fluctuations observed inFigures a and b are due to the fact that only αS undergo very slow spontaneous aggregation and owing to the fact that the dense phase itself is very fluxional, addition/removal of a few chains to/from the dense to dilute phase register themselves as large fluctuations in the protein concentration in the dilute phase. For the other two scenarios (Figures c-f) aggregation has been accelerated due to the presence of crowders/salt. This causes larger aggregates to be formed. Therefore addition/removal of one or two chains does not significantly affect the concentration and we do not see such sudden large jumps. In summary, the large jumps seen in Figures a and b are due to slow, fluxional aggregation of pure αS and finite size effects. However as these still are only fluctuations, we posit that the systems have reached steady states. This claim is further supported by the following figure where the time profile of a few useful system wide macroscopic properties show no change between 1.5-2.5 µs.

We also have added a brief discussion in the Methods section (Page 29-30) with these figures in the Supplementary Information.

Author response image 2.

“In this study, we utilized the final 1 µs from each simulation for further analysis. To ascertain whether the simulations have achieved a steady state, we plotted the time profile of protein concentration in the dilute phase for all three cases. Except for minor intermittent fluctuation involving only αS in neat water (Figures S8a and S8b), the remaining cases exhibit notably stable concentrations throughout various segments of the trajectory (Figures S8 c-f). The relatively higher fluctuations observed in Figures S8a and b stem from the slow, spontaneous aggregation of αS alone, compounded by the inherently ambiguous nature of the dense phase.

Consequently, the addition or removal of a few chains from the dense to the dilute phase results in significant fluctuations in protein concentration within the dilute phase. Conversely, in the other two scenarios (Figures S8c-f), aggregation is expedited by the presence of crowders/salt, leading to the formation of larger aggregates. Consequently, the addition or removal of one or two chains has negligible impact on concentration, thereby mitigating sudden large jumps. In summary, the conspicuous jumps depicted in Figures S8a and b arise from the gradual, fluctuating aggregation of pure αS and finite size effects. However, since these remain within the realm of fluctuations, we assert that the systems have indeed reached steady states. This assertion is bolstered by the subsequent figure, where the time profile of several pertinent system-wide macroscopic properties reveals no discernible change between 1.5-2.5 µs (Figures S9).”

(2) The benchmarking used to validate their cgMD methods is very minimal and fails to utilize a large amount of available all-atom simulation and experimental data.

We disagree with the reviewer on this point. We have cited multiple previous studies [26, 27] that have chosen Rg as a metric of choice for benchmarking coarse-grained model and have used a reference (experimental or otherwise) to tune Martini force fields. Majority of the notable literature where Rg was used as a benchmark during generation of new coarse-grained force fields are works by Dignon et al. (PLoS Comp. Biol.) [ref. 25], Regy et al (Protein Science. 2021) [ref. 26], Joseph et al.(Nature Computational Science. 2021) [ref. 27] and Tesei et al (Open Research Europe, 2022) [ref. 28]. From a polymer physics perspective, tuning water-protein interactions is simply changing the solvent characteristics for the biopolymer and Rg has been generally considered a suitable metric in the case of coarse-grained model. Moreover we try to match the distribution of the Rg rather than only the mean value. This suggests that at a single molecule level, the cgMD simulations at the optimum water of water-protein interactions would allow the protein to sample the conformations present in the reference ensemble. We use the extensively sampled 70 μs all-atom data from DE Shaw Research to obtain the reference Rg distribution. Also we perform a cross validation by comparing the fraction of bound states in all-atom and cgMD dimer simulations which also seem to corroborate well with each other at optimum water-protein interactions. To let the readers understand the rationale behind choosing Rg we have added a section in the Methods section (Page 25) that explains why Rg is plausibly a good metric for tuning water-protein interactions in Martini 3, at least when dealing with IDPs.

Our optimized model is further supported by the FRET experiments by Ray et al. [6]. They found that interchain NAC-NAC interactions drive LLPS. Residue level contact maps obtained from our simulations also show decreased intrachain NAC-NAC interactions with an increased interchain NAC-NAC interactions inside the droplet. This corroborates well with the experimental observations and furthermore validates the metrics we have used for optimization of the water-protein interactions. However the comparison with the FRET data by Ray et al. was not present earlier and we have added the following lines in the updated draft.

Page17: “Thus we observed that increased inter-chain NAC-NAC regions facilitate the formation of αS droplets which also have previously been seen from FRET experiments on αS LLPS

droplets[6].”

(3) They also miss opportunities to compare their simulations to experimental data on aSyn protein droplets.

We thank the reviewer for pointing this out. We have tried to compare the results from our simulations to existing experimental FRET data on αS. Please see the previous response where we have described our comparison with FRET observations.

(4) Aspects such as network analysis are not contextualized by comparison to other protein condensed phases.

For a proper comparison between other protein condensed phases, we would require the position phase space of such condensates which is not readily available. Therefore we tried to explain it in a simpler manner to paint a picture of how αS forms an interconnecting network inside the droplet phase.

(5) Data are not made available, which is an emerging standard in the field.

We thank the reviewer for mentioning this. We have provided the trajectories between 1.5-2.5 μs, which we used for the analysis presented in the article, via a zenodo repository along with other relevant files related to the simulations (https://zenodo.org/records/10926368).

Firstly, it is not clear that these systems are equilibrated or at a steady state (since protein droplets are not really equilibrium systems). The authors do not present any data showing time courses that indicate the system to be reaching a steady state. This is problematic for several of their data analysis procedures, but particularly in determining free energy of transfer between the condensed and dilute phases based on partitioning.

We have addressed this concern as stated previously in the response. We have updated the article accordingly.

Secondly, the benchmarking that they perform against the 73 µs all-atom simulation of aSyn monomer by Shaw and coworkers provides only very crude validation of their cgMD models based on reproducing Rg for the monomer. The authors should make more extensive comparisons to the specific conformations observed in the DE Shaw work. Shaw makes the entire trajectory publicly available. There are also a wealth of experimental data that could be used for validation with more molecular detail. See for example, NMR and FRET data used to benchmark Monte Carlo simulations of aSyn monomer (as well as extensive comparisons to the Shaw MD trajectory) in Ferrie at al: A Unified De Novo Approach for Predicting the Structures of Ordered and Disordered Proteins, J. Phys. Chem. B 124 5538-5548 (2020)

DOI:10.1021/acs.jpcb.0c02924

I note that NMR measurements of aSyn in liquid droplets are available from Vendruscolo: Observation of an α-synuclein liquid droplet state and its maturation into Lewy body-like assemblies, Journal of Molecular Cell Biology, Volume 13, Issue 4, April 2021, Pages 282-294, https://doi.org/10.1093/jmcb/mjaa075.

In addition, there are FRET studies by Maji: Spectrally Resolved FRET Microscopy of α-Synuclein Phase-Separated Liquid Droplets, Methods Mol Biol 2023:2551:425-447. doi: 10.1007/978-1-0716-2597-2_27.

So the authors are missing opportunities to better validate the simulations and place their structural understanding in greater context. This is just based on my own quick search, so I am sure that additional and possibly better experimental comparisons can be found.

We have performed a comparison with existing FRET measurements by Ray et al. (2020) as discussed in a previous response and also updated the same in the article. The doi (10.1007/978-1-0716-2597-2_27) provided by the reviewer is however for a book on Methods to characterize protein aggregates and does not contain any information regarding the observations from FRET experiments. The other doi (https://doi.org/10.1093/jmcb/mjaa075) for the article from Vendrusculo group does not contain information directly relevant to this study. Moreover NMR measurements cannot be predicted from cgMD since full atomic resolution is lost upon coarse-graining of the protein . A past literature survey by the authors found very little scientific literature on molecular level characterization of αS LLPS droplets.

Thirdly, the small word network analysis is interesting, but hard to contextualize. For instance, the 8 Å cutoff used seems arbitrary. How does changing the cutoff affect the value of S determined? Also, how does the value of S compare to other condensed phases like crystal packing or amyloid forms of aSyn?

The 8 Å cutoff is actually arbitrary since a distance based clustering always requires a cutoff which is empirically decided. However 8 Å is quite large compared to other cutoffs used for distance based clustering. For example in ref 26, 5 Å was used as a cutoff for calculation of protein clusters. Larger cutoffs will lead to sparser network structures. However we used the same cutoff for all distance based clustering which makes the networks obtained comparable. We wanted to perform a comparison among the networks formed by αS under different environmental conditions.

Fourthly, I see no statement on data availability. The emerging standard in the computational field is to make all data publicly available through Github or some similar mechanism.

We thank the reviewer for pointing this out and we have provided the raw data between 1.5-2.5 μs for each scenario along with other relevant files via a zenodo repository (https://zenodo.org/records/10926368).

Finally, on page 16, they discuss the interactions of aSyn(95-110), but the sequence that they give is too long (seeming to contain repeated characters, but also not accurate). aSyn(95-110) = VKKDQLGKNEEGAPQE. Presumably this is just a typo, but potentially raises concerns about the simulations (since without available data, one cannot check that the sequence is accurate) and data analysis elsewhere.

This indeed is a typographical error. We have updated the article with the correct sequence. The validity of the simulations can be verified from the data we have shared via the zenodo repository (https://zenodo.org/records/10926368).

Author response:

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

Reviewer #1 (Public Review): 

Summary:

In this manuscript, Fister et. al. investigate how amputational and burn wounds affect sensory axonal damage and regeneration in a zebrafish model system. The authors discovered that burn injury results in increased peripheral axon damage and impaired regeneration. Convincing experiments show altered axonal morphology and increased Ca2+ fluxes as a result of burn damage. Further experimental proof supports that early removal of the burnt tissue by amputation rescues axonal damage. Burn damage was also shown to markedly increase keratinocyte migration and increase localized ROS production as measured by the dye Pfbsf. These responses could be inhibited by Arp 2/3 inhibition and isotonic treatment. 

Strengths: 

The authors use state-of-the-art methods to study and compare transection and burn-induced tissue damage. Multiple experimental approaches (morphology, Ca2+ fluxing, cell membrane labeling) confirm axonal damage and impaired regeneration time. Furthermore, the results are also accompanied by functional response tests of touch sensitivity. This is the first study to extend the role of tissue-damage-related osmotic exposure beyond wound closure and leukocyte migration to a novel layer of pathology: axonal damage and regeneration. 

Weaknesses: 

The conclusions of the paper claiming a link between burn-induced epithelial cell migration, spatial redox signaling, and sensory axon regeneration are mainly based on correlative observations. Arp 2/3 inhibition impairs cell migration but has no significant effect on axon regeneration and restoration of touch sensitivity. 

We agree with the reviewer. We have tried many experiments to address this question. The data show that Arp 2/3 inhibition with CK666 is an effective way to inhibit initial keratinocyte migration. However, later migration still proceeds. What is interesting is that just inhibition of the early migration is sufficient to restore localized ROS production in the wound area in the first  hour post-burn, even if this is not sufficient to prevent ROS accumulation over time. There is also a trend toward improved sensory neuron function late after this early treatment. However, this is not statistically significant. We think it is likely that both migration and tissue scale ROS influence the regeneration defect of sensory neurons after burn. The data using isotonic solution supports this conclusion. We have tried many other ways to limit keratinocyte migration including depletion of talin and expression of a dominant negative Rac in basal epithelial cells, but these treatments were not compatible with survival of the fish after burn.

Pharmacological or genetic approaches should be used to prove the role of ROS production by directly targeting the known H2O2 source in the system: DUOX. 

We agree that pharmacologic or genetic approaches to directly manipulate ROS production would provide substantial support to the hypothesis that ROS, along with keratinocyte migration, is a main factor contributing to poor burn outcomes. To address this, we first tried using a morpholino to deplete DUOX. However, the combination of DUOX morpholino and burn injury was lethal to larvae. We also used pharmacologic inhibition of ROS production using DPI (Diphenyleneiodonium). With this treatment, ROS is inhibited for only the first hour post-burn as treatment is lethal for longer periods of time. Burned larvae have marginally improved axon density and touch sensitivity, suggesting the importance of ROS in burn outcomes, however it was not statistically significant. It is likely that an increased effect would be observed with longer treatment, but treatment for more than 1 hour was toxic. We have added a supplemental figure with this new DPI data.

While the authors provide clear and compelling proof that osmotic responses lie at the heart of the burn-induced axonal damage responses, they did not consider the option of further exploring any biology related to osmotic cell swelling. Could osmotic ATP release maybe play a role through excitotoxicity? Could cPLA2 activation-dependent eicosanoid production relate to the process? Pharmacological tests using purinergic receptor inhibition or blockage of eicosanoid production could answer these questions. 

We agree that the role of osmotic cell swelling in the burn response is an interesting avenue for future study. However, we make use of isotonic treatment in this study specifically for its effect on keratinocyte migration and broad-scale wound healing. As a result, we feel that pursuing the biology of this swelling phenomenon is outside the scope of this paper.

The authors provide elegant experiments showing that early removal of the burnt tissue can rescue damage-induced axonal damage, which could also be interpreted in an osmotic manner: tail fin transections could close faster than burn wounds, allowing for lower hypotonic exposure time. Axonal damage and slow regeneration in tail fin burn wounds could be a direct consequence of extended exposure time to hypotonic water. 

We have done experiments using FM dye to test how long it takes burn and transection wounds to close (shown below). In these experiments, dye entry into wounded tissue is used as a readout of wound closure. Dye is only able to enter wounded tissue when the epithelial barrier is disrupted. Our data reveal that transections take approximately 10 minutes to fully close, while burns take approximately 20 minutes to close.

Author response image 1.

To test if this difference in wound closure time would have an effect on axon outcomes, we repeated, but slightly modified, the dual-wound experiment. We increased the amount of time the burn condition was exposed to hypotonic conditions by 10 additional minutes (by transecting burned tissue at 15 minutes post burn, shortly before closure) and compared axon outcomes to the 5 mpw control transection. These results show there was no difference in axon regeneration or function when secondary transection was performed at 5 or 15 minutes post burn, suggesting that increased exposure to hypotonic solution is not the reason for defects in axon outcomes after burn injury.

Author response image 2.

Reviewer #2 (Public Review): 

This is an interesting study in which the authors show that a thermal injury leads to extensive sensory axon damage and impaired regrowth compared to a mechanical transection injury. This correlates with increased keratinocyte migration. That migration is inhibited by CK666 drug treatment and isotonic medium. Both restrict ROS signalling to the wound edge. In addition, the isotonic medium also rescues the regrowth of sensory axons and recovery of sensory function. The findings may have implications for understanding non-optimal re-innervation of burn wounds in mammals. 

The interpretation of results is generally cautious and controls are robust. 

Here are some suggestions for additional discussion: 

The study compares burn injury which produces a diffuse injury to a mechanical cut injury which produces focal damage. It would help the reader to give a definition of wound edge in the burn situation. Is the thermally injured tissue completely dead and is resorbed or do axons have to grow into damaged tissue? The two-cut model suggests the latter. Also giving timescales would help, e.g. when do axons grow in relation to keratinocyte movement? An introductory cartoon might help. 

We thank the reviewer for these insightful comments and questions. The burn wound is defined as the area that is directly damaged as a result of increased heat (labeled by FM dye entry), and the burn wound edge as the first line of healthy cells adjacent to the burned cells. These definitions have been added to the text to clarify the areas referenced. Recent experiments lead us to believe the wound area is composed almost completely of dead cells, but we are currently working to discover the fate of these dead cells as well as the wound adjacent cells that migrate to the wound edge after burn. As a result, we do not know whether axons grow into damaged tissue or if the damaged tissue is extruded, but we do see growth cone formation within a few hours after wounding suggesting the axons are actively trying to regenerate after a burn.

Could treatment with CK666 or isotonic solution influence sensory axons directly, or through other non-keratinocyte cell types, such as immune cells? 

We have done experiments looking at the density of caudal fin innervation in CK666, isotonic, or DPI treated fins. The axon density is unchanged in all these treatments compared to control treated larvae, so we do not believe these treatments affect axon health homeostatically. These data have been added to supplemental figure 3. Additionally, one of the benefits of the larval zebrafish burn model is the simplicity of the system – the epidermis is primarily composed of sensory axons, mesenchymal cells and keratinocytes. The burn environment is proinflammatory so it does promote immune cell recruitment, but we do not believe the immune cells are interacting directly with sensory axons besides clearing axonal debris. Previous papers by our lab have shown that peak immune cell recruitment occurs at 6 hpw, but they localize to the damaged tissue in the burn area and not the wound edge.

Reviewer #3 (Public Review): 

Fister and colleagues use regeneration of the larval zebrafish caudal fin to compare the effects of two modes of tissue damage-transection and burn-on cutaneous sensory axon regeneration. The authors found that restoration of sensory axon density and function is delayed following burn injury compared to transection. 

The authors hypothesized that thermal injury triggers signals within the wound microenvironment that impair sensory neuron regeneration. The authors identify differences in the responses of epithelial keratinocytes to the two modes of injury: keratinocytes migrate in response to burn but not transection. Inhibiting keratinocyte migration with the small-molecule inhibitor of Arp2/3 (CK666) resulted in decreased production of reactive oxygen species (ROS) at early, but not late, time points. Preventing keratinocyte migration by wounding in isotonic media resulted in increased sensory function 24 hours after burn. 

Strengths of the study include the beautiful imaging and rigorous statistical approaches used by the authors. The ability to assess both axon density and axon function during regeneration is quite powerful. The touch assay adds a unique component to the paper and strengthens the argument that burns are more damaging to sensory structures and that different treatments help to ameliorate this. 

A weakness of the study is the lack of genetic and cell-autonomous manipulations. Additional comparisons between transection and burns, in particular with manipulations that specifically modulate ROS generation or cell migration without potentially confounding effects on other cell types or processes would help to strengthen the manuscript.

The use of genetic and cell-autonomous approaches would strengthen our study, however, we were unable to do this due to the lethality of these genetic approaches (or cell autonomous approaches). Basal epithelial migration is necessary for embryonic development. We attempted to circumvent this by generation of larvae transiently expressing a dominant-negative form of Rac, a protein crucial to the migratory process. The chimeric expression of the dominant negative Rac was either damaging to the larvae or the mosaicism was too low to observe any effects on migration phenotype.

We also attempted a genetic approach to manipulate ROS production, as discussed above. We found that the DUOX morpholino was lethal to burned larvae. Finally, we attempted pharmacological inhibition of ROS production using the inhibitor DPI (Diphenyleneiodonium). With this treatment, burned larvae have marginally improved axon density and touch sensitivity, suggesting that dampening ROS may improve outcome. The DPI data have been added to the manuscript.

In terms of framing their results, the authors refer to "sensory neurons" and "sensory axons" throughout the text - it should be made clear what type of neuron(s)/axon(s) are being visualized/assayed. Along these lines, a broader discussion of how burn injuries affect sensory function in other systems - and how the authors' results might inform our understanding of these injury responses - would be beneficial to the reader. 

In summary, the authors have established a tractable vertebrate system to investigate different sensory axon wound healing outcomes in vivo that may ultimately allow for the identification of improved treatment strategies for human burn patients. Although the study implicates differences in keratinocyte migration and associated ROS production in sensory axon wound healing outcomes, the links between these processes could be more rigorously established. 

The inconsistency between “neuron” and “axon” has been noted and the text has been corrected accordingly. “Neuron” is used when referring to the cell as a whole, while “axon” is used when referring to the sensory processes in the caudal fin. We added information about burn in the introduction as suggested: “While epithelial tissue is well adapted to repair from mechanical damage, burn wounds heal poorly. Thermal injury results in chronic pain and lack of sensation in the affected tissue, suggesting that an abnormal sensory neuron response contributes to burn wound pathophysiology.”

We thank the reviewer’s for their comments.

Recommendations For The Authors:

Reviewer #1 (Recommendations For The Authors): 

Suggested experiments: 

(1) ROS measurements with the dye Pfbsf should be validated with more established ROS probes such as HyPer. 

Pfbsf has been used previously as a readout of ROS production, and its use is documented in zebrafish (Maeda et al., Angew Chem Int Ed Engl, 2004, and Niethammer et al, Nature, 2009). These sources have been added as references when introducing Pfbsf to provide context for its use. The probe was validated and compared to HyPer in Niethammer’s 2009 paper. In our hands, we have used both probes and have similar results with tail transection.

(2) To better support claims on ROS and H2O2 playing a central role in mediating axonal damage, the authors should consider pharmacological approaches such as rescue experiments with H2O2 and experiments using inhibitors such as DPI ar apocynin. 

While the above reagents and drugs have limitations and non-specific side effects, more convincing proof could result from genetic approaches including experiments on DOUX knockdown or knockout lines. 

To further dissect the role of ROS in the burn response, we conducted experiments using DPI, a potent ROS inhibitor that is well-documented in the literature. We found that 20 uM treatment of DPI (1 hour pretreatment, 1 hour post-burn) marginally improved axon density when quantified 24 hpw. Any higher dose, when in combination with a burn, proved to be lethal. Longer treatment with DPI was also not tolerated.

In addition to experiments with DPI, we attempted to burn larvae that were injected with DUOX morpholino. The combined use of burn and DUOX MO was lethal. We have dampened the conclusions and include the new data with the DPI in the revised manuscript.

Minor corrections: 

(1)A phrase/expression in the abstract is confusing: isotonic treatment does not "induce osmotic regulation". Cells exposed to hypo- or hypertonicity will respond by regulatory volume decrease or increase, respectively. Isotonic treatment maintains homeostasis. 

We appreciate this point and agree with the distinction. Revisions have been made in the text accordingly.

(2) Figures 4E and 5E would be better to show as an average of multiple experiments with statistical significance. 

The purpose of figures 4E and 5E are to demonstrate changes in fluorescence intensity and localization of ROS using the representative time series shown in 4D and 5D. The figure legend has been updated accordingly.

Reviewer #2 (Recommendations For The Authors): 

Figure 3D How can one distinguish between the two cellular elements that randomly meet or that there is actual coordination? Can the interactions be quantified? It is also unclear what the authors mean by "sensory neuron movement". The authors show that the neuronal cell bodies stay in their position, so only the axons change position. Do they do this by growth, i.e. the neuronal growth cones follow the keratinocytes or do keratinocytes displace the axon shafts? 

We have included supplemental movies that address this question in the new uploaded document. Figure 3D is comprised of still images taken from supplemental movie 2, which is a timelapse of keratinocytes/axons moving together after a burn injury.  This movie clearly shows keratinocytes and their ensheathed axons moving simultaneously, so keratinocytes are mechanically pulling sensory axon shafts with them. We have revised the text to say axon movement, not sensory neuron movement.

Over the time course of axonal movement (1 hour post-burn), it is not possible that neuronal growth cones contribute to movement, as this is too slow – previous work by other labs has shown that it takes several hours for axons to fully regenerate into amputated tissue, with movement not even noticeable until about 3 hours post-wound (Rieger and Sagasti, PLOS Biology, 2011).

Regarding the second point, “neuron” vs. “axon” is an inconsistency in the text that has been corrected. “Neuron” is used when referring to the cell as a whole, “axon” is used when referring to the processes that innervate the caudal fin. The axons are physically pulled along with keratinocytes as they migrate after burn application. From our observations, growth cones appear closer to the wound site after the movement has stopped.

Figure 4G It is surprising that the visual differences in the distribution of values are not statistically significant. 

The distribution of values in 4G was large and that is why there is no statistically-significant difference – we were also surprised at this result. We did all statistics with a statistician and this included rigorous criteria for significance.

Figure 4H The images seem to show a difference, whereas the quantification does not. I suggest choosing more representative images. 

Figure 4H has been updated to include a more representative image of axon patterning with CK666 treatment.

Figure 6A The text states that axon damage in the control and isotonic condition is comparable, yet in the image, it appears that the damage in the isotonic treatment at 0 hpw is more distal. 

This is a good observation that we consistently see in isotonic-treated fish after burn. Axon damage localizes more proximally in isotonic-treated samples because the keratinocytes distal to the notochord are likely dead, and the axons innervating those cells are likely immediately destroyed upon burn application. As a result, the distal axons are not present to express GCaMP. We believe isotonic treatment allows keratinocytes to live slightly longer, so axon damage is therefore prevented for longer. This is also the focus of continuing work to further understand the burn microenvironment.

Finally, the materials section could mention bias mitigation measures, e.g. withholding the treatment condition from the experimenter in the touch test. 

We minimized bias in experiments whenever possible, and the conservative statistical measures that were applied to our data further reduce the likelihood of false significance.

Reviewer #3 (Recommendations For The Authors): 

- Line numbers would have facilitated reviewer feedback. 

- Supplementary movies were missing in the submission. 

The lack of supplementary movies upon submission was a mistake and the movies have been uploaded along with the revised manuscript.

Introduction: 

- Pg. 3: "In response to tissue damage, sensory neurons undergo rapid and localized axonal degeneration 4,5." Not sure reference 4 (Reyes et al) is appropriate here as this study was not in the context of tissue damage. 

We have revised this section as suggested by the reviewer.

Results: 

- The expected expression pattern/localization of several transgenes was unclear. Please clearly state what cell type(s) each should label. For example, pg. 5 - "We next sought to further investigate sensory neuron function in burned tissue. For this, we assessed wound-induced axonal damage using zebrafish larvae that express the calcium probe GCaMP." Where is GCaMP expressed? 

The manuscript has been updated to include expression patterns for the included transgenes – in this mentioned case, GCaMP is expressed in neurons under the pan-neuronal Elavl3 promoter.

- Introducing the GCaMP labeling could use some clarification. Pg. 5 - "As shown previously by other groups, GCaMP labels degenerating neurons in real time35." This is confusing. Do the authors mean that GCaMP increases immediately prior to Wallerian degeneration as shown by Vargas et al. (PMID: 26558774)? 

Sustained elevated calcium levels are associated with axon damage. Previous work from other labs has shown that calcium influx follows axon injury (Ziv and Spira, EJN 1993, Adalbert et al., Neuroscience 2012). In these experiments, whenever there are CGaMP-positive punctae, this indicates axon damage. We have revised the manuscript to address this critique.

The Elavl3-GCaMP5 transgenic line will label when calcium levels increase in neurons. However, given the parameters used for imaging in our study (20x magnification, 100 ms exposure, and collection speed every 30 seconds for timelapses), we believe that only sufficiently large increases in calcium that are indicative of cell damage, and not physiological function, are being visualized.

- Figure 1E - Are these panels images of the same fish? Please specify in the legend. 

Figure 1E is comprised of one transected and one burned larva each, live-imaged over the course of six hours. The legend has been updated to include this information.

- Figure 1F - How was the damage area measured? Consider doing this measurement over time to match Figure 1E. 

Axon damage area measurements were performed similar to axon density measurements – maximum intensity z-projected confocal images of the caudal fin were generated using FIJI. For all experiments, the caudal fin area posterior to the notochord was outlined using the Polygon tool and measured to obtain a total surface area ROI. Axon fragments inside the outlined area were manually thresholded so all fragments posterior to the notochord were labeled and no saturated pixels were present, and an area measurement of these thresholded pixels was taken. We have added a section describing these measurements in the Methods section under “Axon damage quantification.”

- Pg. 5 - When introducing the ngn1 MO - please state the expected phenotype and cite the appropriate background literature_._ 

The ngn1 morpholino was cited in the Methods section with the appropriate literature (Cornell and Eisen, Development, 2002), from which we got the morpholino sequence. We thank the reviewer for pointing out the need for more introduction and clarification in the main text, so the ngn1 morpholino has been discussed in greater depth and cited in the main text as well using the same citation.

- The two-wound model is an elegant approach but could be more clearly described in the main text. 

An improved explanation of the two-wound experiment has been added to the text.

- For Figure 3, it would be helpful to have a schematic of the anatomy illustrating the relative positions of axons and epidermal cell types. 

- Figure 3C - should an additional control here be transected? Given that the krt4:lifeact transgene labels both layers of the epidermis, how were the superficial and basal keratinocytes separated? Interpretation of this section should be carefully worded. The authors state that "...suggesting that the superficial keratinocytes are being pulled by the motile basal keratinocytes" (pg.7 ) but isn't another possibility that the superficial cells are stationary? 

It is correct that the krt4:lifeact transgene labels both layers of keratinocytes, which together span 20-30 microns. These layers were separated from the same z-stack collected by confocal imaging. The first z-slice and last z-slice of the same stack were separated using FIJI and pseudocolored to appear as different colors. This clarification has been added to the Methods.

Prior observations with the krt4:lifeact and krt4:utrch (figure 3A) transgenic lines reveal that both keratinocyte layers will move distally after burn application.

- Pg. 7 - "The axons of sensory neurons are ensheathed within actin-rich channels running through basal keratinocytes 50,51." ref 51 is a C. elegans paper which does not have basal keratinocytes.

This was in error. The correct reference has replaced reference 51 (O’brien, J Comp. Neurol., 2012), in which electron microscopy is used to document the development of two layers of epithelial cells that also ensheath sensory neurons in a protective manner similar to glial cells in the central nervous system.

- Figures S1E and F - the authors state that RB and DRG soma don't move. However, it was unclear from the figure panels and legend whether the authors imaged neurons that actually innervate the caudal fin (rather than some other region of the animal). Please clarify. For comparison, Fig S1F needs a pre-injury image to be meaningful. 

The imaged cell bodies were those in the posterior trunk region, which are responsible for innervating the posterior sections of the fish including the caudal fin. From our observations, there was no movement of neuronal cell bodies after the burn.

- Figure 5 title - can the authors clarify what aspect of this figure relates to "sustained epidermal damage" 

The figure 5 title has been updated in response to the reviewer comments.

- Figure 6 - is touch sensitivity really "restored" as the authors suggest? Alternatively, sensitivity may never be lost in isotonic treatment. Or the loss may be delayed? 

We have modified the text accordingly by updating our phrasing – “restored” has been replaced with “improved” to indicate benefit over time.

- Can the authors further disentangle the effects of keratinocyte migration, ROS, and isotonic treatment on axon regeneration? For example, would the addition of CK666 to the Isotonic +1 hpw treatment improve axon regeneration? Can the authors directly manipulate ROS signaling (e.g., through exogenous addition of H2O2 or duox1 MO) to alter regeneration outcomes in their wounding assays? 

See the comments above.

- Figure 6 title - consider removing or clarifying the word "excessive" here 

The title has been revised according to the reviewer suggestion.

- hpw vs hpb were used inconsistently throughout the text 

The manuscript has been revised to use “hpw” when referring to the timeframe after injury application.

Methods: 

- Zebrafish transgenics are missing allele names 

References: 

- Many mistakes were noted in this section e.g., journal names missing, wrong authors, typos, DOIs misformatted 

The references section has been corrected to use formatting consistent with APA citation and eLife preferred guidelines.

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Referee #1

Evidence, reproducibility and clarity

Review, 3D SIM + AO, Wang and coworkers

In this manuscript, Wang and coworkers report an upright 3D SIM system with adaptive optics (AO) correction. They demonstrate that AO improves imaging into thick 3D samples, including Drosophila larval brain. They also explore the use of remote focusing with their setup. The authors clearly demonstrate a gain with AO, and we are convinced that the microscope they build offers some utility over existing state of the art, particularly in samples thicker than a single cell. That said, we have concerns with the manuscript that we would like to see addressed before recommending publication:

• Given the emphasis on super-resolution imaging deep inside a sample, we were surprised to see no mention of other forms of structured illumination that allow super-resolution imaging in samples thicker than a single cell. These include the 'spot-scanning' implementations of SIM that offer better imaging at depth by virtue of pinholes, and include MSIM, iSIM, and rescan confocal technologies. The two-photon / AO implementation of iSIM seems particularly germane, e.g. https://pubmed.ncbi.nlm.nih.gov/28628128/ Please consider citing these works, as they help place the existing work into context.
• As we're sure the authors appreciate, besides aberrations, a major additional obstacle to 3D SIM in thick tissues is the presence of out-of-focus background. Indeed, this point was mentioned by Gustafsson in his classic 2008 paper on 3D SIM (https://pubmed.ncbi.nlm.nih.gov/18326650/): 'The application area of three-dimensional structured illumination microscopy overlaps with that of confocal microscopy, but the two techniques have different and complementary strengths. Structured illumination microscopy offers higher effective lateral resolution, because it concentrates much of the excitation light at the very highest illumination angles, which are most effective for encoding high-resolution information into the observed data, whereas confocal microscopy spreads out its illumination light more or-less uniformly over all available angles to form a focused beam. For very thick and compactly fluorescent samples, however, confocal microscopy has an advantage in that its pinhole removes out-of focus light physically. Structured illumination microscopy is quite effective at removing out-of-focus light computationally, because it is not subject to the missing-cone problem, but computational removal leaves behind the associated shot noise. Therefore confocal microscopy may be preferable on very thick and dense samples, for which the in-focus information in a conventional microscope image would be overwhelmed by out-of-focus light, whereas structured illumination microscopy may be superior in a regime of thinner or sparser samples.' This point is not mentioned at all in the manuscript, yet we are certain it is at least partially responsible for the residual image artifacts the authors mention. Please discuss the problem of out of focus light on 3D samples, particularly with an eye to the 'spot-scanning' papers mentioned above.
• The authors use a water dipping lens, yet they image into samples that are mounted on coverslips, i.e. they use a dipping lens to image through a coverslip: see attached pdf for reference

This almost certainly introduces spherical aberration, which the authors seem to observe: see attached pdf for reference

We find this troubling, as it seems that in the process of building their setup, the authors have made a choice of objective lens that introduces aberrations - that they later correct. At the very least, this point needs to be acknowledged in the manuscript (or please correct us if we're wrong) - as it renders the data in Figs. 3-4 somewhat less compelling than if the authors used an objective lens that allowed correction through a coverglass, e.g. a water dipping lens with a correction collar. In other words, in the process of building their AO setup, the authors have introduced system aberrations that render the comparison with 3D SIM somewhat unfair. Ideally the authors would show a comparison with an objective lens that can image through a glass coverslip. - The authors tend to include numbers for resolution without statistics. This renders the comparisons meaningless in my opinion; ideally every number would have a mean and error bar associated with it. We have included specific examples in the minor comments below. - In Fig. 5, after the 'multipoint AO SIM', the SNR in some regions seems to decrease after AO: see attached pdf for reference

Please comment on this issue.

• Please provide timing costs for the indirect AO methods used in the paper, so the reader understands how this time compares to the time required for taking a 3D SIM stack. In a similar vein, the authors in Lines 213-215, mention a 'disproportionate measurement time' when referring to the time required for AO correction at each plane - providing numbers here would be very useful to a reader, so they can judge for themselves what this means. What is the measurement time, why is it so long, and how does it compare to the time for 3D SIM? It would also be useful to provide a comparison between the time needed for AO correction at each (or two) planes without remote focusing (RF) vs. with RF, so the reader understands the relative temporal contributions of each part of the method. We would suggest, for the data shown in Fig. 5, to report a) the time to acquire the whole stack without AO (3D SIM only); b) the time to acquire the data as shown; c) the time to acquire the AO stack without RF. This would help bolster the case for remote focusing in general; as is we are not sure we buy that this is a capability worth having, at least for the data shown in this paper.
• Some further discussion on possibly extending the remote focusing range would be helpful. We gather that limitations arose from an older model of the DM being used, due to creep effects. We also gather from the SI that edge effects at the periphery of the DM was also problematic. Are these limitations likely non-issues with modern DMs, and how much range could one reasonably expect to achieve as a result? We are wondering if the 10 um range is a fundamental practical limitation or if in principle it could be extended with commercial DMs.

Minor comments

• The paper mentions Ephys multiple times, even putting micromanipulators into Fig. 1 - although it is not actually used in this paper. If including in Figure 1, please make it clear that these additional components are aspirational and not actually used in the paper.
• The abstract mentions '3D SIM microscopes', 'microscopes' redundant as the 'm' in 'SIM' stands for 'microscope'.
• 'fast optical sectioning', line 42, how can optical sectioning be 'fast'? Do they mean rapid imaging with optical sectinong?
• line 59, 'effective imaging depth may be increased to some extent using silicone immersion objectives', what about water immersion objectives? We would guess these could also be used.
• line 65 - evidence for 'water-dipping objectives are more sensitive to aberrations' ? Please provide citation or remove. They are certainly more prone to aberrations if used with a coverslip as done here.
• 'fast z stacks' is mentioned in line 103. How fast is fast?
• line 116 'we imaged 100 nm diameter green fluorescent beads'. Deposited on glass? Given that this paper is about imaging deep this detail seems worth specifying in the main text.
• lines 127-130, when describing changes in the bead shape with numbers for the FWHM, please provide statistics - quoting single numbers for comparison is almost useless and we cannot conclude that there is a meaningful improvement without statistics.
• In the same vein, how can we understand that remote focus actually improves the axial FWHM of the widefield bead? Is this result repeatable, or it just noise?
• line 155, 'Because of the high spatial information...' -> 'Because of the high resolution spatial information...'
• When quoting estimated resolution #s from microtubules (lines 158-163) similarly please provide statistics as for beads.
• It seems worth mentioning the mechanism of AO correction (i.e. indirect sensing) in the main body of the text, not just the methods.
• How long do the AO corrections take for the datasets in the paper?
• Were the datasets in Fig. 2-4 acquired with remote focusing, or in conventional z stack mode? Please clarify this point in the main text and the figure captions.
• It would be helpful when showing z projections in Figs. 3-5 to indicate the direction of increasing depth (we assume this is 'down' due to the upright setup, but this would be good to clarify)
• line 174, 'showed significant improvements in both intensity and contrast after reconstruction' - we see the improvements in contrast and resolution, it is harder to appreciate improvements in intensity. Perhaps if the authors showed some line profiles or otherwise quantified intensity this would be easier to appreciate.
• line 195 'reduced artefacts' due to AO. We would agree with this statement - the benefit from AO is obvious, and yet there are still artefacts. If the authors could clarify what these (residual) artefacts are, and their cause (out of focus light, uncorrected residual aberrations, etc) this would be helpful for a reader that is not used to looking at 3D SIM images.
• Line 197, 'expected overall structure', please clarify what is expected about the structure and why.
• Line 199, what is a 'pseudo structure'?
• Fig. 4B, 'a resolution of ~200 nm is retained at depth', please clarify how this estimate was obtained, ideally with statistics.
• Fig. 4D, please comment on the unphysical negative valued intensities in Fig. 4D, ideally explaining their presence in the caption. It would also be helpful to highlight where in the figure these plots arise, so the reader can visually follow along.
• Line 245, 'rapid mitosis'. What does rapid mean, i.e. please provide the expected timescale for mitosis.
• For the data in Fig. 6, was remote refocusing necessary?
• What is the evidence for 'reduced residual aberrations', was a comparative stack taken without AO? In general we feel that the results shown in Fig. 6 would be stronger if there were comparative results shown without AO (or remote focusing).
• Line 350, 'incorporation of denoising algorithms' - citations would be helpful here.
• Line 411, 'All three were further developed and improved' - vague, how so?
• Sensorless AO description; how many Zernike modes were corrected?
• Multi-position aberration correction. Was the assumption of linearity in the Zernike correction verified or met? Why is this a reasonable assumption?
• Fig. S1B is not useful; if the idea is to give a visual impression of the setup, we would recommend providing more photos with approximate distances indicated so that the reader has a sense of the scale of the setup. As is - it looks like a photograph of some generic optical setup.
• SI pattern generation - 'the maximum achievable reconstruction resolution was only slightly reduced to about 95% of the theoretical maximum'. We don't understand this sentence, as the resolution obtained on the 100 nm beads is considerably worse than 95% of the theoretical maximum. Or do the authors mean 95% of the theoretical maximum given their pitch size of 317 nm for green and 367 nm for red? SI Deformable mirror calibration

'spanning the range [0.1, 0.9]' - what are the units here?

What are the units in Fig. S5C, S5D?

It would be useful to define 'warmup' also in the caption of SI Fig. S6A. SI Remote Focusing, 'four offsets, {-5 mm, -2.5 mm, 2.5 mm, 5 mm}...' are the units mm or um? '...whereas that of the 10 beads was...' here, do the authors mean the position of the beads derived from the movement of the piezo stage, as opposed to the remote focusing? The authors refer to the 'results from Chapter 3.2'. What are they talking about? Do they mean a supplementary figure, or earlier supplementary results? In general, we found the discussion in this paragraph difficult to follow. Supplementary Fig. 9 seems to be not referred to anywhere in the text. - Since the paper emphasizes 3D SIM, OTFs along the axial direction would also be useful to show, in addition to the lateral OTFs shown in Fig. 2D. - When the sample is moved by the piezo, the axial phase of the 3D-SIM illumination pattern is stable as the sample is scanned through the illumination pattern. When remote focusing is performed, the sample is always stable so the axial phase of the 3D-SIM illumination pattern is presumably changing with remote focusing. Can the authors clarify if the 3D SIM illumination pattern is scanned when remote focusing is applied, or is the intensity pattern stable in z? - In Supplementary Fig. 9, primary spherical is referred to twice, both at index 11 and 22. The latter is presumably secondary spherical? - we do not understand the x axis label, in Fig. S4D, is it really [0, 50, 50, 50] as written? see attached pdf for reference

Referee Cross-Commenting

I don't have much to add; the other reviewers raise good points and I think it would be good if the authors could respond to their feedback in a revised manuscript.

Significance

Nearly all fluorescence images deteriorate as a function of depth. Methods to ameliorate this depth-dependent degradation are thus of great practical value, as they improve the information content of images and thus (hopefully) biological insight. In this work, the authors develop a method to improve super-resolution imaging (3D SIM) at depth, by combining it with adaptive optics.

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Reply to the reviewers

Reviewer #1:

This study provides negative in vivo evidence for the use of two PERK inhibitors and of TUDCA for the treatment of Sli1-related Marinesco-Sjögren syndrome (MSS).

Overall, the manuscript reports a substantial amount of work and the study could be published in its present format. The experiments are well described in terms of methodology and appropriate analysis has been applied. Claims are proportionate and not overstated

I would have only minor comments related to some clarifications that the authors could make in the present manuscript and a suggestion for experiments that could improve the manuscript.

First, although this is not my expertise, the in vitro analysis of CHOP luciferase assays suggests that very high concentrations, in particular of TUDCA, are needed to observe an effect. The authors may wish to clarify their opinion and whether this could be the reason why in vivo they have been unable to obtain any inhibition of the PERK pathway.

The reviewer is correct in pointing out that high concentrations of trazodone, DBM and TUDCA were required to inhibit the PERK pathway in the CHOP::luciferase reporter cell lines. However, as we state in the Discussion, we do not think that their lack of effect in vivo was due to insufficient drug levels, since woozy mice were treated with trazodone, DBM or TUDCA according to dose regimens and administration routes that have proved effective in other neurodegenerative disease mouse models. Moreover, our analysis did not find major differences in drug bioavailability between mice with the woozy genetic background (CXB5/ByJ) and C57BL/6J mice in which these drugs had shown neuroprotective effects (see also the response to the next point).

Second, it seems to me that when measuring the Trazodone metabolism there is a difference between acute and chronic treatment. It would be worth discussing what the authors make of that and what is more relevant (I assume chronic) to the disease model outcome.

We realized that the nomenclature used in Figures 6 and 7 was confusing, leading the reader to think there were differences in trazodone levels between chronically and acutely treated mice.

The experiment shown in Figure 6 was designed to test whether there were differences in trazodone pharmacokinetics and metabolism between mice of the woozy strain, which have the CXB5/ByJ genetic background, and C57BL/6J mice in which trazodone had shown neuroprotective effects in previous studies. In contrast, Figure 7 illustrates the levels of trazodone and m-CPP in control and woozy mice (both of which have the CXB5/ByJ genetic background) that had been chronically treated with trazodone for 5 weeks. These are the same animals as in Figure 3, as we state in Figure 7 legend. Therefore one should compare the levels of trazodone and m-CPP in Figure 7 with those of the "woozy" group (CXB5/ByJ genetic background) in Figure 6. This comparison shows that trazodone and m-CPP levels are comparable after chronic and acute (6h) treatment.

To avoid confusion, we have changed the mouse nomenclature. We have renamed the control group of mice as "CT" (previously "WT") throughout the text and figures. In Figure 6, we have used CXB5/ByJ instead of "woozy" to emphasize the comparison between the different genetic backgrounds (CXB5/ByJ vs C57BL/6J). Finally, we have replaced the colors of symbols in Figure 7 in order to match those of Figure 3. We have also made the description and discussion of these results clearer in the revised manuscript.

With respect to the experiments a simple and informative addition would be the evaluation of the PERK pathway in mice treated with TUDCA, as this is missing.

The effect of TUDCA treatment on the PERK pathway is shown in Figure 5, where we measured CHOP mRNA levels in Purkinje cells microdissected from mice treated with 0.4% TUDCA in the chow, and in Figure 9C and D, where we measured the percentage of CHOP-immunopositive Purkinje cells in the cerebellum of same groups of mice by immunohistochemistry.

Figure 10 illustrates the results of an additional experiment in which woozy mice were treated with 500 mg/kg TUDCA intraperitoneally (ip), to test whether this alternative dosing regimen was any better. Like the treatment per os, TUDCA ip had no beneficial effect on motor dysfunction. Therefore we deemed it unnecessary to check the effect on PERK pathway inhibition in this group of mice.

A more difficult but potentially more interesting line of investigation is that of searching for potential actions of Trazodone that are PERK independent and might be responsible for the partial rescue observed in the beam walking test, which is much more sensitive and specific than rotarod, so worth considering. Assuming authors want to go down this route and add significance to their study my suggestion would be an unbiased RNA seq from the brain samples they already have. However, this is a suggestion to steer the study towards a more positive outcome and it is not necessary to support their current conclusions.

We agree with the reviewer that it would be interesting to investigate the mechanism by which trazodone slightly ameliorated the motor performance of woozy mice in the beam walking test. In the Discussion, we speculated that this could be due to an effect of trazodone on cerebellar serotonergic neurotransmission, which would require electrophysiological investigations to demonstrate. Of course, other mechanisms may also be operative, which RNA seq may help identify, as the reviewer suggests. However, this would be a complex and lengthy investigation, the results of which would not change the main conclusions of the present paper. We plan to explore this line of investigation in a future study.

Reviewer #2:

Lavigna et al. described the effect of Trazodone in Marinesco-Sjögren syndrome model mice. Although the results are somewhat disappointing, this research has provided fundamental evidence for the future development of MSS therapeutics. There are few minor comments to further improve the manuscript

Major comment<br /> P14<br /> "Trazodone metabolism to m-CPP was slightly impaired in woozy mice compared to C57BL/6J mice. This was evident from the m-CPP/trazodone ratio, calculated on the AUC0-t in the plasma, which was 0.34 in woozy and 0.67 in C57BL/6J mice."

Why was the concentration different between WT and woozy mice? Which organ mainly contributes to the metabolism of trazodone? Is the function of this target organ different between WT and woozy mice?<br /> Similar to trazodone, m-CPP clearance from plasma was slightly faster in woozy than in C57BL/6J mice.<br /> Is m-CPP eliminated via the kidney? Or liver? Why is there a difference? Does SIL1 functions in liver or kidney? Needs discussion. This is the same for brain m-CPP levels.

As explained in the response to the second comment of reviewer #1, "woozy" in Figure 6 referred to mice with the CXB5/ByJ genetic background, and in this experiment we compared trazodone pharmacokinetics and metabolism between CXB5/ByJ and C57BL/6J mice. We have modified the nomenclature of Figure 6 and the Results to make this clear.

Trazodone undergoes extensive hepatic metabolism, and only a small percentage is excreted unchanged in the urine. Metabolism involves hydroxylation, oxidation and dealkylation reactions, forming in particular the 5HT-active metabolite m-CPP (by CYP3A4). This and other metabolites are mainly excreted in urine, as conjugates [1-3]. The slight differences in trazodone pharmacokinetics and metabolism between the CXB5/ByJ and C57BL6/J mice shown in Figure 6 is not attributable to loss of SIL1 function, since both groups of mice carried wild-type Sil1 alleles, but is most likely due to subtle differences between the two strains, for example in the binding to plasma proteins, metabolic enzymes, transporters and/or the excretion processes. The available data do not allow to clarify this issue.

The main point, however, is that no major differences were found in the plasma and brain concentrations of trazodone between these two strains of mice, which could have explained the lack of efficacy of trazodone in woozy mice, as we now further stress in the revised Discussion.

Minor comments

P3 L5 mutation should be variant.

This has been changed.

P4 L1 eIF2a-P should be phosphorylated eIF2α (p-eIF2α). The reviewer prefers (p-eIF2α) than (eIF2α-p) throughout the manuscript.

There is no standard rule for indicating phosphorylated proteins, and phosphorylated eIF2α is referred to in various ways in different papers, with the "p" in capital or lowercase, preceding or following the protein name, separated by a dash or not. We would prefer to maintain the current nomenclature for consistency with our previous publications, unless the Editor deems otherwise.

P9 L11 M-CPP should be fully spelled out the first time it appears. m-Chlorophenylpiperazine (m-CPP)

M-CPP is spelled out the first time it appears in the Material and Methods, subheading Drug treatments and bioanalysis.

Please explain the difference between the expected function of trazodone and its metabolite m-CPP. Why m-CPP is not effective.

Based on the observation that mice of the woozy strain had lower brain levels of m-CPP than C57BL6/J mice (Figure 6), we hypothesized that the lack of effect of trazodone in woozy mice could be due to m-CPP mediating the PERK signaling inhibitory activity of trazodone. However, experiments in CHOP::luciferase reporter cells demonstrated that m-CPP does not inhibit PERK signaling (Figure 2D). The precise mechanism by which trazodone inhibits PERK signaling is not known [4], which makes it difficult to speculate why its main metabolite, m-CPP, does not exhibit this activity.

P11 L3 Fig. 3 Fig. 3A and B?

Yes, we specifically refer to panels A and B of Figure 3. We have indicated this in the revised manuscript.

P11 L6 at 7 weeks of age?

We have re-done the statistical analysis by two-way ANOVA and reported the results in the legend to Figure 3. There is a significant difference between vehicle- and trazodone-treated woozy mice in the number of missteps when the two groups are compared globally. No statistically significant difference in the number of missteps is detected at specific time points by post-hoc analysis. There is no statistically significant difference between vehicle- and trazodone-treated woozy mice in the time to traverse the beam. The Results section has been revised accordingly.

P12 L17 ~4 times, 4 times? Please state the exact value.

Done.

Figure 7 Why are brain m-CPP levels higher than plasma levels? Is trazodone metabolized in brain tissue?

Trazodone is extensively metabolized in the liver through Cytochrome P450 (Rotzinger et al., 1999). It is well documented that m-CPP readily passes the blood-brain barrier, much better than the parent compound, explaining its high brain levels [2].

P19 L7 ISRIB; please fully spell out the first time it appears.

Done.

References

1. Rotzinger S, Bourin M, Akimoto Y, Coutts RT, Baker GB (1999) Metabolism of some “second”- and “fourth”-generation antidepressants: iprindole, viloxazine, bupropion, mianserin, maprotiline, trazodone, nefazodone, and venlafaxine. Cell Mol Neurobiol 19:427– 442. https://doi.org/10.1023/a:1006953923305
2. Caccia S, Ballabio M, Samanin R, Zanini MG, Garattini S (1981) (--)-m-Chlorophenyl- piperazine, a central 5-hydroxytryptamine agonist, is a metabolite of trazodone. J Pharm Pharmacol 33:477–478. https://doi.org/10.1111/j.2042-7158.1981.tb13841.x
3. DeVane CL, Boulton DW, Miller LF, Miller RL (1999) Pharmacokinetics of trazodone and its major metabolite m-chlorophenylpiperazine in plasma and brain of rats. Int J Neuropsychopharm 2:17–23. https://doi.org/10.1017/S1461145799001303
4. Halliday M, Radford H, Zents KAM, Molloy C, Moreno JA, Verity NC, Smith E, Ortori CA, Barrett DA, Bushell M, Mallucci GR (2017) Repurposed drugs targeting eIF2alpha-P-mediated translational repression prevent neurodegeneration in mice. Brain 140:1768– 1783. https://doi.org/10.1093/brain/awx074

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Reply to the reviewers

Author responses

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

In their manuscript, Dutta and colleagues compared the meiotic recombination landscapes between five budding yeast species. In the first part of the work, the authors constructed a high-resolution map of meiotic recombination events in Kluyveromyces lactis supported by high-quality genome assemblies for two strains of this yeast. Then, partially repeating their CO and NCO mapping strategy, they compared a number of meiotic recombination parameters between the five species (sometimes three, depending on the quality of the data for each species). They particularly focused on key parameters for meiotic recombination, such as crossover interference and homeostasis and obligate crossover. Although the analysis is interesting, it is underdeveloped in many places and lacks the general conclusions regarding the evolution of recombination and the broader perspective that would be expected from a comparison of these phenomena in budding yeasts.

[R] Tackling the evolution of recombination is ambitious. Here, with a dataset of five species, it is hard to draw strong evolutionary conclusions besides the variations in the crossover (CO) landscapes and the control of CO formation that we observed, which is already significant. The multiple losses of CO interference that we describe here may constitute our strongest evolutionary conclusion. It potentially underscores the minor evolutionary advantage associated to CO interference at least in budding yeasts. In this context, we changed the title to be more factual and updated the text to better highlight the significance and implications of our findings.

Major comments:

The authors indicate that the distribution of hotspots and coldspots is not preserved between species, but this finding is not properly documented. I think it would be useful to include recombination maps in a main figure for all species (or at least for S. cerevisiae, K. lactis and L. waltii) with the elements highlighted. This will allow for a visual illustration of the variability in the recombination landscape between the studied species. [R] The genomes of the species show blocks of synteny but overall, they are not collinear and therefore, it is not possible to have a direct comparison of the recombination maps. In our previous work, we have highlighted the non-conservation of CO hotspots between S. cerevisiae, L. kluyveri and L. waltii (Brion et al. 2017; Dutreux et al. 2023). Briefly, we retrieved conserved syntenic blocks in L. kluyveri and L. waltii genomes containing at least two S. cerevisiae orthologs associated with one hotspot. L. waltii shares only five out of the 92 S. cerevisiae crossover hotspots (RHO5, SLS1, GYP6, OLE1 and MRPL8), while L. kluyveri shares only one. L. waltii and L. kluyveri share no crossover hotspots. In addition, our current study shows that none of the K. lactis hotspot is conserved in any of the four other species (response figure 1 and new supplementary figure S11).

Response Figure 1. Density of crossovers along the genome using a 5 kb window in the S. cerevisiae genome (Mancera et al. 2008; Oke et al. 2014; Krishnaprasad et al. 2015 combined dataset). Horizontal dotted green line represents crossover hotspot significance threshold. Solid spheres represent the conserved CO hotspots with either L. kluyveri (red) or L. waltii (blue). None of the 92 S. cerevisiae crossover hotspot is conserved in L. lactis.

Although analyses analogous to those presented in Fig. S5 had already been published in other comparisons of the recombination landscape in yeast (e.g. Dutreux et al., 2023), I think that Figs. S5A and S5B are worth to be presented in the main figures (not supplementary data). In many species of eukaryotes, the detection of NCOs is practically impossible, therefore only results for COs are presented. Therefore, it is perhaps also worth discussing the fact that the relationship applies to all recombination events and not only COs, and therefore is related to the regulation of DSBs frequency and not individual DSBs repair pathways.

[R] Figures S5A-B are now included in the main figure, Figure 2B. The association holds true for all total recombination (CO+NCO) events as well, new supplementary figure S6A.

The authors find that CO coldspots were associated with DNA repair genes. Unfortunately, an equivalent analysis was not performed for all recombination events (CO + NCO). I presume this approach is based on the belief that COs are more mutagenic than NCOs. However, recent studies in humans suggest that, at least in mammals, meiotic DSBs themselves are mutagenic, regardless of the pathway used for their repair (Hinch et al., Science 2023). Therefore, I would suggest repeating the analysis also considering NCOs (although I am aware that the picture of NCOs may be incomplete). I would also like to see some graphical representation of the analysis. Is it possible to perform a classic analysis of coldspots/hotspot enrichment in relation to gene ontology?

[R] As suggested, we performed the analysis to independently detect coldspots for all recombination events (CO+NCO). Based on a threshold of

In relation to the previous point - it may be worth repeating this type of analysis also for other yeasts used in this study, or at least for S. cerevisiae, to be able to consider the extent to which this relationship is universal and dependent on the meiotic DSB repair pathway.

[R] The analysis regarding the CO coldspots has been performed in the other species as well. As mentioned in the main text, although some overlap between CO coldspots and DNA repair genes has been observed in the other species as well, we observed a significant enrichment in K. lactis only, maybe because the dataset is larger than in the other species.

In Fig. S7, the point where WGD occurred is marked in the wrong place, or at least that is what the sentence in the text says ("The Lachancea and Kluyveromyces species branched from the Saccharomyces lineage more than 100 million years ago, before to the ancestral whole-genome duplication (WGD) event specific of the S. cerevisiae lineage").

[R] We regret the oversight and have corrected the figure.

The result presented in Fig. S8 is interesting and should be shown in the main figures. Perhaps it would be worth adding an illustration illustrating simple versus complex COs.

[R] The old Figure S8 is now a part of main Figure 2C-D with the illustrations describing the CO types.

The last part of the results includes an analysis of the evolutionary rates of the ZMM genes. In the discussion, the authors should also refer the results of this analysis to the previous analysis of the overrepresentation of DNA repair genes in recombination coldspots. I understand that ZMM are not DNA repair proteins in the strict sense, but I think it is worth familiarizing readers with the authors' view on this matter. Moreover, I would suggest showing where MLH1 and MLH3 are located on the plot in Fig. 6 (especially the meiosis-specific MLH3), whether the selection pressure acts on them as on ZMM proteins, or rather as on DNA repair proteins. Showing the SLX4 and MUS81 would also be interesting.

[R] Figure 6 has been updated as suggested and now shows the Mlh1, Mlh3, Slx4 and Mus81 dN/dS values for the three species.

I feel like the discussion is underdeveloped. I missed a deeper summary of the comparison between meiotic recombination among the tested budding yeasts in the context of the presence and absence of functional ZMM. Even the title of the work is not properly developed in the manuscript text. The analysis shows that it is not the presence of a functional ZMM pathway or its lack that introduces differences between the individual recombination landscapes, although ZMM determines the presence of proper CO interference. With the caveat that for L. kluyveri it is basically unknown whether it has a functional ZMM or not. Maybe confirming the lack of expression of some ZMM genes in meiosis of this species would answer the question of how it should be treated?

[R] We agree with this reviewer that our original title was imprecise, so we changed it to be more factual, emphasizing on the multiple losses of crossover interference in budding yeasts. As stated above, it potentially underscores the minor/negligible evolutionary advantage associated to CO interference at least in budding yeasts. From there, it is hard to draw deeper conclusions since the actual roles/functions of CO interference are still under debate, notably in yeasts where the CO frequency tends to be high. We improved the discussion to better highlight these points.

We also agree that a deeper characterization of the ZMM factors persisting in the non-Saccharomyces yeasts would be informative, but we believe it is beyond the scope of the current manuscript and more suitable for a follow up work. However, our recent publication about L. kluyveri (Legrand et al 2024) shows that Zip3 is properly expressed in meiosis and behaves as in S. cerevisiaesince it is located at DSB sites. Furthermore, we have unpublished transcriptomic data (Response Figure 2) showing that all the ZMM genes from L. kluyveri are specifically induced in meiosis (fold increase >16 at least compared to pre-sporulation conditions). Therefore, so far, although the level of CO interference in L. kluyveri is minimal, there is no indication that the ZMM genes are mis regulated.

Response Figure 2. Transcriptomic data showing that all the ZMM genes from L. kluyveri are specifically induced in meiosis (Unpublished data from Llorente Lab, CRCM, Marseille).

Minor comments:

In general, Figure captions are imprecise, many of them lack clear information explaining what is depicted. Authors should remember that figure legends should be self-sufficient. [R] The figure legends have been updated and are now self-sufficient.

In the revised manuscript, I would suggest placing figure numbers on the figures and using line numbering, which would facilitate the reception of the work and possible reference to its individual elements in the review.

[R] We regret the omission. Figure numbers, Line numbers and Page numbers have been added.

Reviewer #1 (Significance (Required)):

The study provides a new insight into the variation in recombination landscape within budding yeast species with a special emphasis on crossover control. This includes also de novo assemblies of Kluyveromyces lactis genome and high-resolution tetrad-based maps of meiotic recombination events. Previously, recombination maps of different yeast species were compared, however this study focuses on budding yeasts, some of which lost ZMM pathway and differ in some crossover parameters, like interference and homeostasis. Although the analysis is interesting, it lacks the general conclusions regarding the evolution of recombination and the broader perspective that would be expected from a comparison of these phenomena in budding yeasts.

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

This paper describes the genome-wide mapping of meiotic recombination in non-Saccharomyces yeast, Kluyveromyces lactis. By using heterologous parental strains, the authors mapped crossovers (COs) and noncrossovers (NCOs) on the genome of K. lactis which lacks proteins necessary for CO formation such as S. cerevisiae, mammals and plants. This is an extension of previous works by the authors' group which mapped CO and NCO in different yeast, Lachancea kluyveri and L. waltii by a similar approach. The authors found that CO frequencies in K. lactis are much lower than those in S. cerevisiae and COs showed weaker interference, which facilitates the non-random distribution of COs along a chromosome. Overall, the experiments and informatic analyses have been done in good quality and the results are convincing. The paper provides additional new information on the landscape of meiotic recombination in different yeast species. These results are of great interest to researchers in the field of meiotic recombination and evolution of meiosis. There are some issues that the authors may be able to address before the publication.

Major points: While the authors noted that K. lactic shows the loss of a pro-CO factors (ZMM protein), Spo16, and Msh5 (due to the introduction of an in-frame stop codon), it still possesses other proteins such as Zip1, Zip2, Zip3, Zip4/Spo22, Mer3, and Msh4. It is still likely that these pro-CO factors control CO formation (and interference) in this yeast. It would be nice for the authors to study whether the knockout of these genes is dispensable for CO formation and interference in meiosis. A similar analysis should be done for L. kluyveri which retains all ZMM genes, but this is clearly out of the scope of this paper.

[R] The question of the functions of the remaining ZMM factors is indeed interesting and related to point #8 from reviewer 1 (please see above). Although this is beyond the scope of our work, we would like to refer here to work from Amy McQueen's lab using L. lactis Zip1 in S. cerevisiae (Voelkel-Meiman 2015). This study shows that L. lactis Zip1 does not allow synaptonemal complex assembly in S. cerevisiae but allows CO formation independently of the Msh4/5 complex but that depend on Zip2/4/Spo16 and Mlh1/3 for their resolution. Overall, these results suggests that L. lactis Zip1 at least retained ancestral functions shared with S. cerevisiae Zip1. However, it is not possible to conclude if the lack of full complementation of L. lactis Zip1 in S. cerevisiae comes from functional divergence or simply by the inability of L. lactis Zip1 to function properly in a heterologous context.

Minor points:

No page number, no main Figure number. It is hard to review this paper. [R] We regret the oversight. Figure numbers, Line numbers and Page numbers have been added.

References: In some cases, in the Introduction, the authors referred to review papers such as Pyatnitskaya et al. (2019) for ZMM proteins while in the other parts, they referred to original papers; for example, three papers for Mlh1-Mlh3. If the number of references is not limited, original papers should be cited in the text.

[R] We regret this omission. Original papers have now been included in the citations.

Figure 3A, page 9, second paragraph: When the authors compared CO and NCO densities, it would be nice to show P-values for the comparison.

[R] p-values have now been added to the updated figure.

Please show a ratio of CO to NCO in each yeast in Figure 3B in the second paragraph of page 9 in the main text.

[R] The ratios have now been included in the figure for both the CO:NCO ratios and CO:corrected_NCO ratios, in the main text and figure legends.

Figure S5 and page 7, the first paragraph and page 9, third paragraph: CO/NCO densities (negative correlation to chromosome sizes) in S. cerevisiae should be checked with or without short chromosomes (I, III, and VI), which show very unique regulation of meiotic DSB formation (see Murakami et al. Nature 2020).

[R] Even excluding the small chromosomes, the size dependent trend persists for S. cerevisiae and S. paradoxus.

Table S7: Please add the S. cerevisiae gene name such as ZIP1 next to S. cerevisiae orthologs such as YDR285W. Moreover, please explain the column in detail or clarify the data. What does "meiosis" mean here? For example, YJL074C is SMC3, which is expressed in mitosis as well as in meiosis. The same is true for YGL163C, which is RAD54, which plays a minor role in meiosis, but plays a critical in mitotic DSB repair.

[R] We corrected Table S7 as desired by systematically including the standardized gene names.

The Gene Ontology (GO) annotation is a statement about the function of a particular gene. It offers a structured framework and a comprehensive set of concepts to describe the functions of gene products across all organisms. It is specifically crafted to support the computational representation of biological systems. In our specific case, we only looked at genes with the gene ontology annotation "meiosis". Together, these statements comprise a "snapshot" of current biological knowledge and is by no means absolute. This has been detailed in the supplementary Table S7.

Reviewer #2 (Significance (Required)):

This study provides the landscape of meiotic recombination in non-Saccharomyces yeast, Kluyveromyces lactis. The genome-wide recombination map in K. lactis shows lower crossover frequencies with weaker crossover interference than those in S. cerevisiae. Overall, the experiments and informatic analyses have been done in good quality and the results are convincing. The paper provides additional new information on the landscape of meiotic recombination in different yeast species, particularly in terms of the evolution of meiotic recombination. These results are of great interest to researchers in the field of meiotic recombination and evolution of meiosis.

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

Dutta et al. have compiled a genome-wide meiotic recombination map for Kluyveromyces lactis and compared it to a compilation of meiotic recombination maps for four other species, two of which (Lachancea kluyveri and Lachancea waltii), like K. lactis, predate the genome duplication event that produced the other two (Saccharomyces cerevisiae and S. paradoxus). Meiosis in many species studied (including metazoans and plants) shows control over the number and distribution of crossovers, which are critical for faithful chromosome segregation during meiosis. This takes the form of crossover interference, where crossovers are spaced more evenly than expected by chance, and crossover homeostasis, where many fewer chromosomes lack a crossover than is expected by chance. While both of the post-duplication species show both crossover interference and homeostasis, none of the pre-duplication species show crossover homeostasis, and crossover interference is very weak. In two cases (K. lactis and L. waltii), this can be explained by mutational loss of a few of the genes (called the ZMM genes) that promote meiotic crossovers in many species. However, L. kluyveribehavior cannot be explained in this way. Recombination hotspots are present but are not shared between the pre-duplication species or between the pre- and post-duplication species, perhaps not surprising for species that diverged more that 100 million years ago. Overall, this work will be a useful contribution to our understanding of the different possible flavors of meiotic recombination mechanisms and control that are possible (and, one might add, promote long-term species viability). A) Evaluation, reproducibility and clarity The work presented in this paper is straightforward and unimpeachable and will largely be of interest to those studying meiotic recombination, be it mechanistic studies or studies of the implications for population genetics. The analysis is technically correct, although there are some aspects where a slightly different emphasis should be considered (see comments below). However, the data and the analysis could stand as they currently are, without further revision.

Suggestions are below. 1. (trivial) it would have been useful if pages and lines were numbered.

[R] We regret the oversight. Figure numbers, Line numbers and Page numbers have been added.

"Across the 205 meioses...". In general, it would be desirable to apply compensation for the fact that NCOs and COs are differently detected. Since, in K. lactis, 35% of COs are not accompanied by detectable gene conversion, it seems reasonable to apply a correction to measured NCOs here and throughout the paper, regardless of the species. For example, if one assumes that 35% of NCOs are not detected, how does this affect estimates of chromosomes that do not appear to have undergone interhomolog recombination? Estimates of CO/NCO bias? In a similar vein, if the CO event is not considered (just the conversion events associated with it), how does this affect measures of conversion tract lengths in COs and NCOs?

[R] We thank the reviewer for this suggestion. We have performed the correction for the NCO estimates as described in Mancera et al. 2008, on a per tetrad basis across all the species. The fraction of missed NCOs were 7%, 34%, 30%, 23% and 25% respectively for S. paradoxus, S. cerevisiae, K. lactis, L. waltii and L. kluyveri. The fraction of missed NCOs depend upon the parental marker density. In addition, we performed the CO:NCO bias analysis both with the detected and the corrected NCO frequencies and the trends remain unchanged (Now included in figure 3). Finally, we refrain from using the corrected NCO frequencies while reporting the NCO frequencies (Table 1, main text) to maintain uniformity with our previous work and since, these corrections do not alter any results.

It might be useful to report recombination event frequencies in terms of events/chromosome, as this, rather than event/unit distance, is functionally more relevant. In the same vein, it might be useful to consider total event homeostasis, in addition to just crossover homeostasis.

[R] This has been updated as suggested. .

An interesting observation is that two of the three pre-duplication species clearly at one time had a full complement of ZMM genes but lost some due to mutation. Have there ever been attempts to detect either synaptonemal complex or axial elements in these species?

[R] This is related to point #8 from reviewer 1 and to the major point of reviewer 2 (please see above).

To our knowledge, cytological observations of synaptonemal complex (SC) or axial elements have been performed in L. kluyverionly by us and the SC is clearly visible (Legrand et al 2024).

However, it is key to remind here that K. lactis axis protein encoding genes HOP1 and RED1 have been cloned by the Roeder's lab by functional complementation of S. cerevisiae corresponding mutants, supporting the functional conservation of these genes (Smith and Roeder 2000). Finally, as mentioned above, K. lactis Zip1 retained at least some function of the ancestral Zip1 protein that are also shared by the S. cerevisiae protein (Voelkel-Meiman 2015).

The observation of elevated evolutionary rates in ZMM genes is also intriguing, but it would help if "dN/dS ratio" was defined.

[R] It is now defined in the text.

The observation of frequent E0 chromosomes is taken to suggest efficient achiasmate segregation; has the "corrected" NCO frequency been considered? Do the different frequencies of E0 chromosomes predict the different spore viabilities seen between species?

[R] E0 is not predictive at all of the spore viability as we have shown in previous studies (see L. kluyveri - Brion et al. 2017, L. waltii-Dutreux et al. 2023). In addition, this has been shown is S. cerevisiae as well (Nishant et al. 2009).

Figure 3A-what would this look like if it were plotted as "Events per chromosome" rather than per megabase?

[R] We changed the figure (now figure 2A) and plotted as events per chromosome to show the variability of events at the chromosome level.

Figure legends tend to be unreasonably terse, which makes figures more difficult to interpret.

[R] This has been updated as suggested.

Author response:

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

eLife assessment:

This useful modeling study explores how the biophysical properties of interneuron subtypes in the basolateral amygdala enable them to produce nested oscillations whose interactions facilitate functions such as spike-timing-dependent plasticity. The strength of evidence is currently viewed as incomplete because of insufficient grounding in prior experimental results and insufficient consideration of alternative explanations. This work will be of interest to investigators studying circuit mechanisms of fear conditioning as well as rhythms in the basolateral amygdala.

We disagree with the overall assessment of our paper. The current reviews published below focus on two kinds of perceived inadequacies. Reviewer 1 (R1) was concerned that the fear conditioning paradigm used in the model is not compatible with some of the experiments we are modeling. The reviewer helpfully suggested in the Recommendations for the Authors some papers, which R1 believed exposed this incompatibility. In our reading, those data are indeed compatible with our hypotheses, as we will explain in our reply. Furthermore, the point raised by R1 is an issue for the entire field. We will suggest a solution to that issue based on published data.

Reviewer 2 (R2) said that there is no evidence that the BLA is capable of producing, by itself, the rhythms that have been observed during fear conditioning in BLA and, furthermore, that the paper we cited to support such evidence, in fact, refutes our argument. We believe that the reasoning used by reviewer 2 is wrong and that the framework of R2 for what counts as evidence is inadequate. We spell out our arguments below in the reply to the reviewers.

Finally, we believe this work is of interest far beyond investigators studying fear conditioning. The work shows how rhythms can create the timing necessary for spike-timing-dependent plasticity using multiple time scales that come from multiple different kinds of interneurons found both in BLA and, more broadly, in cortex. Thus, the work is relevant for all kinds of associative learning, not just fear conditioning. Furthermore, it is one of the first papers to show how rhythms can be central in mechanisms of higher-order cognition.

Reviewer #1

We thank Reviewer 1 for his kind remarks about our first set of responses and their understanding of the importance of the work. There was only one remaining point to be addressed:

Deficient in this study is the construction of the afferent drive to the network, which does elicit activities that are consistent with those observed to similar stimuli. It still remains to be demonstrated that their mechanism promotes plasticity for training protocols that emulate the kinds of activities observed in the BLA during fear conditioning.

It is true that some fear conditioning protocols involve non-overlapping US and CS, raising the question of how plasticity happens or whether behavioral effects may happen without plasticity. This is an issue for the entire field (Sun et al., F1000Research, 2020). Several papers (Quirk, Repa and LeDoux, 1995; Herry et al, 2007; Bordi and Ledoux 1992) show that the pips in auditory fear conditioning increase the activity of some BLA neurons: after an initial transient, the overall spike rate is still higher than baseline activity. The question remains as to whether the spiking is sustained long enough and at a high enough rate for STDP to take place when US is presented sometime after the stop of the CS.

Experimental recordings cannot speak to the rate of spiking of BLA neurons during US due to recording interference from the shock. However, evidence seems to suggest that ECS activity should increase during the US due to the release of acetylcholine (ACh) from neurons in the basal forebrain (BF) (Rajebhosale et al., 2024). Pyramidal cells of the BLA robustly express M1 muscarinic ACh receptors (Muller et al., 2013; McDonald and Mott, 2021) and M1 receptors target spines receiving glutamatergic input (McDonald et al., 2019). Thus, ACh from BF should elicit a long-lasting depolarization in pyramidal cells. Indeed, the pairing of ACh with even low levels of spiking of BLA neurons results in a membrane depolarization that can last 7 – 10 s (Unal et al., 2015). This implies that the release of ACh can affect the consequences of the CS in successive trials. This should include higher spiking rates and more sustained activity in the ECS neurons after the first presentation of US, thus ensuring a concomitant activation of ECS and fear (F) neurons necessary for STDP to take place. Hence, we suggest that a solution to the problem raised by R1 may be solved by considering the role of ACh release by BF. To the best of our knowledge, there is nothing in the literature that contradicts this potential solution. The model we have may be considered a “minimal” model that puts in by hand the higher frequency due to the cholinergic drive without explicitly modeling it. As R1 says, it is important for us to give the motivation of that higher frequency; in the next revision, we will be explicit about how the needed adequate firing rate can come about without an overlap of CS and US in any given trial.

Reviewer #2

The authors of this study have investigated how oscillations may promote fear learning using a network model. They distinguished three types of rhythmic activities and implemented an STDP rule to the network aiming to understand the mechanisms underlying fear learning in the BLA.

After the revision, the fundamental question, namely, whether the BLA networks can or cannot intrinsically generate any theta rhythms, is still unanswered. The author added this sentence to the revised version: "A recent experimental paper, (Antonoudiou et al., 2022), suggests that the BLA can intrinsically generate theta oscillations (3-12 Hz) detectable by LFP recordings under certain conditions, such as reduced inhibitory tone." In the cited paper, the authors studied gamma oscillations, and when they applied 10 uM Gabazine to the BLA slices observed rhythmic oscillations at theta frequencies. 10 uM Gabazine does not reduce the GABA-A receptor-mediated inhibition but eliminates it, resulting in rhythmic populations burst driven solely by excitatory cells. Thus, the results by Antonoudiou et al., 2022 contrast with, and do not support, the present study, which claims that rhythmic oscillations in the BLA depend on the function of interneurons. Thus, there is still no convincing evidence that BLA circuits can intrinsically generate theta oscillations in intact brain or acute slices. If one extrapolates from the hippocampal studies, then this is not surprising, as the hippocampal theta depends on extra-hippocampal inputs, including, but not limited to the entorhinal afferents and medial septal projections (see Buzsaki, 2002). Similarly, respiratory related 4 Hz oscillations are also driven by extrinsic inputs. Therefore, at present, it is unclear which kind of physiologically relevant theta rhythm in the BLA networks has been modelled.

Reviewer 2 (R2) says “the fundamental question, namely, whether the BLA networks can or cannot intrinsically generate any theta rhythms, is still unanswered.” In our revision, we cited (Antonoudiou et al., 2022), who showed that BLA can intrinsically generate theta oscillations (3-12 Hz) detectable by LFP recordings. R2 pointed out that this paper produces such theta under conditions in which the inhibition is totally removed. R2 then states that the resulting rhythmic populations burst at theta “are driven solely by excitatory cells. Thus, the results by (Antonoudiou et al., 2022) contrast with, and do not support, the present study, which claims that rhythmic oscillations in the BLA depend on the function of interneurons. Thus, there is still no convincing evidence that BLA circuits can intrinsically generate theta oscillations in intact brain or acute slices.”

This reasoning of R2 is faulty. With all GABAergic currents omitted, the LFP is composed of excitatory currents and intrinsic currents. Our model of the LFP includes all synaptic and membrane currents. In our model, the high theta comes from the spiking activity of the SOM cells, which increase their activity if the inhibition from VIP cells is removed. We are including a new simulation, which models the activity of the slice in the presence of kainate (as done in Antonoudiou et al., 2022), providing additional excitation to the network. If the BLA starts at high excitation, our model produces an ongoing gamma in the VIP cells that suppress SOM cells and allows a PING gamma to form between PV and F cells; with Gabazine (modeled as the removal of all the GABAergic synapses), this PING is no longer possible and so the gamma rhythm disappears. As expected, the simulation shows that the model produces theta with Gabazine; the model also shows that a PING rhythm is produced without Gabazine, and that this rhythm goes away with Gabazine because PING requires feedback inhibition (see Author response image 1). Thus, the theta increase with Gabazine in the (Antonoudiou et al., 2022) paper can be reproduced in our model, so that paper does support the model.

Author response image 1.

Spectral properties of the BLA network without (black) versus with Gabazine (magenta). Power spectra of the LFP proxy, which is the linear sum of AMPA, GABA (only present in the absence of Gabazine, D-, NaP-, and H-currents. Both power spectra are represented as mean and standard deviation across 10 network realizations. Bottom: inset between 35 and 50 Hz.

Nevertheless, we agree that this paper alone is not sufficient evidence that the BLA can produce a low theta. We have recently learned of a new paper (Bratsch-Prince et al., 2024) that is directly related to the issue of whether the BLA by itself can produce low theta, and in what circumstances. In this study, intrinsic BLA theta is produced in slices with ACh stimulation (without needing external glutamate input) which, in vivo, would be produced by the basal forebrain (Rajebhosale et al., eLife, 2024) in response to salient stimuli. The low-theta depends on muscarinic activation of CCK interneurons, a group of interneurons that overlaps with the VIP neurons in our model (Krabbe 2017; Mascagni and McDonald, 2003).

We suspect that the low theta produced in (Bratsch-Prince et al., 2024) is the same as the low theta in our model. We do not explicitly include ACh modulation of BLA in our paper, but in current work with experimentalists, we aim to show that ACh is essential to the theta by activating the BLA VIP cells. In our re-revised version, we will discuss Bratsch-Prince et al., 2024 and its connection to our hypothesis that the theta oscillations can be produced within the BLA.

Note that we have already included a paragraph stating explicitly that our hypothesis in no way contradicts the idea that inputs to the BLA may include theta oscillations. Indeed, the following paragraphs in the revised paper describe the complexity of trying to understand the origin of brain rhythms in vivo. R2 did not appear to take this complexity, and the possible involvement of neuromodulation, into account in their current position that the theta rhythms cannot be produced intrinsically in the BLA.

From revised paper: “Where the rhythms originate, and by what mechanisms. A recent experimental paper, (Antonoudiou et al. 2022), suggests that the BLA can intrinsically generate theta oscillations (3-12 Hz) detectable by LFP recordings under certain conditions, such as reduced inhibitory tone. They draw this conclusion in mice by removing the hippocampus, which can volume conduct to BLA, and noticing that other nearby brain structures did not display any oscillatory activity. Our model also supports the idea that intrinsic mechanisms in the BLA can support the generation of the low theta, high theta, and gamma rhythms.

Although the BLA can produce these rhythms, this does not rule out that other brain structures also produce the same rhythms through different mechanisms, and these can be transmitted to the BLA. Specifically, it is known that the olfactory bulb produces and transmits the respiratory-related low theta (4 Hz) oscillations to the dorsomedial prefrontal cortex, where it organizes neural activity (Bagur et al., 2021). Thus, the respiratory-related low theta may be captured by BLA LFP because of volume conduction or through BLA extensive communications with the prefrontal cortex. Furthermore, high theta oscillations are known to be produced by the hippocampus during various brain functions and behavioral states, including during spatial exploration (Vanderwolf, 1969) and memory formation/retrieval (Raghavachari et al., 2001), which are both involved in fear conditioning. Similarly to the low theta rhythm, the hippocampal high theta can manifest in the BLA. It remains to understand how these other rhythms may interact with the ones described in our paper.”

We believe our current paper is important to show how detailed biophysical modeling can unearth the functional implications of physiological details (such as the biophysical bases of rhythms), which are often (indeed, usually) ignored in models, and why rhythms may be essential to some cognitive processes (including STDP). Indeed, for evaluating our paper it is necessary to go back to the purpose of a model, especially one such as ours, which is “hypothesis/data driven”. The hypotheses of the model serve to illuminate the functional roles of the physiological details, giving meaning to the data. Of course, the hypotheses must be plausible, and we think that the discussion above easily clears that bar. Hypotheses should also be checked experimentally, and a model that explains the implications of a hypothesis, such as ours, provides motivation for doing the hard work of experimental testing. We think that R1 understands this and has been very helpful.

—————

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

eLife assessment

This useful modeling study explores how the biophysical properties of interneuron subtypes in the basolateral amygdala enable them to produce nested oscillations whose interactions facilitate functions such as spike-timing-dependent plasticity. The strength of evidence is currently viewed as incomplete because the relevance to plasticity induced by fear conditioning is viewed as insufficiently grounded in existing training protocols and prior experimental results, and alternative explanations are not sufficiently considered. This work will be of interest to investigators studying circuit mechanisms of fear conditioning as well as rhythms in the basolateral amygdala. 

Most of our comments below are intended to rebut the sentence: “The strength of evidence is currently viewed as incomplete because the relevance to plasticity induced by fear conditioning is viewed as insufficiently grounded in existing training protocols and prior experimental results, and alternative explanations are not sufficiently considered”. 

We believe this work will be interesting to investigators interested in dynamics associated with plasticity, which goes beyond fear learning. It will also be of interest because of its emphasis on the interactions of multiple kinds of interneurons that produce dynamics used in plasticity, in the cortex (which has similar interneurons) as well as BLA. We note that the model has sufficiently detailed physiology to make many predictions that can be tested experimentally. Details are below in the answer to reviewers.

Reviewer #1 (Public Comments):  

(1) … the weakness is that their attempt to align with the experimental literature (specifically Krabbe et al. 2019) is performed inconsistently. Some connections between cell types were excluded without adequate justification (e.g. SOM+ to PV+). 

In order to constrain our model, we focused on what is reported in (Krabbe et al., 2019) in terms of functional connectivity instead of structural connectivity. Thus, we included only those connections for which there was strong functional connectivity. For example, the SOM to PV connection is shown to be small (Krabbe et al., 2019, Supp. Fig. 4, panel t). We also omitted PV to SOM, PV to VIP, SOM to VIP, VIP to excitatory projection neurons; all of these are shown in (Krabbe et al. 2019, Fig. 3 (panel l), and Supp. Fig. 4 (panels m,t)) to have weak functional connectivity, at least in the context of fear conditioning. 

We reply with more details below to the Recommendations for the Authors, including new text.

(2) The construction of the afferent drive to the network does not reflect the stimulus presentations that are given in fear conditioning tasks. For instance, the authors only used a single training trial, the conditioning stimulus was tonic instead of pulsed, the unconditioned stimulus duration was artificially extended in time, and its delivery overlapped with the neutral stimulus, instead of following its offset. These deviations undercut the applicability of their findings.  

Regarding the use of a single long presentation of US rather than multiple presentations (i.e., multiple trials): in early versions of this paper, we did indeed use multiple presentations. We were told by experimental colleagues that the learning could be achieved in a single trial. We note that, if there are multiple presentations in our modeling, nothing changes; once the association between CS and US is learned, the conductance of the synapse is stable. Also, our model does not need a long period of US if there are multiple presentations.  

We agree that, in order to implement the fear conditioning paradigm in our in-silico network, we made several assumptions about the nature of the CS and US inputs affecting the neurons in the BLA and the duration of these inputs. A Poisson spike train to the BLA is a signal that contains no structure that could influence the timing of the BLA output; hence, we used this as our CS input signal. We also note that the CS input can be of many forms in general fear conditioning (e.g., tone, light, odor), and we wished to de-emphasize the specific nature of the CS. The reference mentioned in the Recommendations for authors, (Quirk, Armony, and LeDoux 1997), uses pulses 2 seconds long. At the end of fear conditioning, the response to those pulses is brief. However, in the early stages of conditioning, the response goes on for as long as the figure shows. The authors do show the number of cells responding decreases from early to late training, which perhaps reflects increasing specificity over training. This feature is not currently in our model, but we look forward to thinking about how it might be incorporated. Regarding the CS pulsed protocol used in (Krabbe et al., 2019), it has been shown that intense inputs (6kHz and 12 kHz inputs) can lead to metabotropic effects that last much longer than the actual input (200 ms duration) (Whittington et al., Nature, 1995). Thus, the effective input to the BLA may indeed be more like Poisson.

Our model requires the effect of the CS and US inputs on the BLA neuron activity to overlap in time in order to instantiate fear learning. Despite paradigms involving both overlapping (delay conditioning, where US coterminates with CS (Lindquist et al., 2004), or immediately follows CS (e.g., Krabbe et al., 2019)) and non-overlapping (trace conditioning) CS/US inputs existing in the literature, we hypothesized that concomitant activity in CS- and US-encoding neuron activity should be crucial in both cases. This may be mediated by the memory effect, as suggested in the Discussion of our paper, or by metabotropic effects as suggested above, or by the contribution from other brain regions. We will emphasize in our revision that the overlap in time, however instantiated, is a hypothesis of our model. It is hard to see how plasticity can occur without some memory trace of US. This is a consequence of our larger hypothesis that fear learning uses spiketiming-dependent plasticity; such a hypothesis about plasticity is common in the modeling literature. 

We reply with more details below to the Recommendations for the Authors, including new text.

Reviewer #1 (Recommendations For The Authors): 

Major points: 

(1) This paper draws extensively from Krabbe et al. 2019, but it does not do so consistently. The paper would be strengthened if it tried to better match the circuit properties and activations.

Specifically: 

a. Krabbe found that PV interneurons were comparably activated by the US (see Supp Fig 1). Your model does not include that. The basis for the Krabbe 2019 claim that PV US responses are weaker is that they have a slightly larger proportion of cells inhibited by the US, but this is not especially compelling. In addition, their Fig 2 showed that VIP and SOM cells receive afferents from the same set of upstream regions. 

b. The model excluded PV-SOM connections, but this does not agree with Krabbe et al. 2019, Table 2. PV cells % connectivity and IPSC amplitudes were comparable to those from VIP interneurons. 

c. ECS to PV synapses are not included. This seems unlikely given the dense connectivity between PV interneurons and principal neurons in cortical circuits and the BLA (Woodruff and Sah 2007 give 38% connection probability in BLA). 

We thank the Reviewer for raising these points, which allow us to clarify how we constrained our model and to do more simulations. Specifically: 

a. (Wolff et al., Nature, 2014), cited by (Krabbe et al. 2018), reported that PV and SOM interneurons are on average inhibited by the US during the fear conditioning. However, we agree that (Krabbe et al., 2019) added to this by specifying that PV interneurons respond to both CS+ and US, although the fraction of US-inhibited PV interneurons is larger. As noted by the Reviewer, in the model we initially considered the PV interneurons responding only to CS+ (identified as “CS” in our manuscript). For the current revision, we ran new simulations in which the PV interneuron receives the US input, instead of CS+. It turned out that this did not affect the results, as shown in the figure below: all the network realizations learn the association between CS and fear. In the model, the PING rhythm between PV and F is the crucial component for establishing fine timing between ECS and F, which is necessary for learning. Having PV responding to the same input as F, i.e., US, facilitates their entrainment in PING and, thus, successful learning. 

As for afferents of VIP and SOM from upstream regions, in (Krabbe et al., 2019) is reported that “[…] BLA SOM interneurons receive a different array of afferent innervation compared to that of VIP and PV interneurons, which might contribute to the differential activity patterns observed during fear learning.” Thus, in the model, we are agnostic about inputs to SOM interneurons; we modeled them to fire spontaneously at high theta.

To address these points in the manuscript, we added some new text in what follows:

(1) New Section “An alternative network configuration characterized by US input to PV, instead of CS, also learns the association between CS and fear” in the Supplementary information:

“We constrained the BLA network in Fig. 2 with CS input to the PV interneuron, as reported in (Krabbe et al., 2018). However, (Krabbe et al., 2019) notes that a class of PV interneurons may be responding to US rather than CS. Fig. S3 presents the results obtained with this variation in the model (see Fig. 3 A,B for comparison) and shows that all the network realizations learn the association between CS and fear. In the model, the PING rhythm between PV and F is the crucial component for establishing fine timing between ECS and F, which is necessary for learning. Having PV responding to the same input as F, i.e., US, facilitates their entrainment in PING and, thus, successful fear learning.

We model the VIP interneuron as affected by US; in addition, (Krabbe et al. 2019) reports that a substantial proportion of them is mildly activated by CS. Replacing the US by CS does not change the input to VIP cells, which is modeled by the same constant applied current. Thus, the VIP CS-induced activity is a bursting activity at low theta, similar to the one elicited by US in Fig. 2.”

(2) Section “With the depression-dominated plasticity rule, all interneuron types are needed to provide potentiation during fear learning” in Results: “Finally, since (Krabbe et al., 2019) reported that a fraction of PV interneurons are affected by US, we have also run the simulations for single neuron network with the PV interneuron affected by US instead of CS. In this case as well, all the network realizations are learners (see Fig. S3). ”

(3) Section “Conditioned and unconditioned stimuli” in Materials and Methods: “To make Fig. S3, we also considered a variation of the model with PV interneurons affected by US, instead of CS, as reported in (Krabbe et al. 2019).”

b. Re the SOM to PV connection: As reported in the reply to the public reviews, we considered the prominent functional connections reported in (Krabbe et al., 2019), instead of structural connections. That is, we included only those connections for which there was strong functional connectivity. For example, the SOM to PV connection is shown to be small (Supp. Fig. 4, panel t, in (Krabbe et al., 2019)). We also omitted PV to SOM, PV to VIP, SOM to VIP, and VIP to excitatory projection neurons; all of these are shown in (Krabbe et al. 2019, Fig. 3 (panel l), and Supp. Fig. 4 (panels m,t)) to have weak functional connectivity, at least in the context of fear conditioning.

In order to clarify this point, in Section “Network connectivity and synaptic currents” in Materials and Methods, we now say:

“We modeled the network connectivity as presented in Fig. 2B, derived from the prominent functional, instead of structural, connections reported in (Krabbe et al., 2019).”

c. Re the ECS to PV synapses: We thank the Reviewer for the reference provided; as the Reviewer says, the ECS to PV synapses are not included. Upon adding this connection in our network, we found that, unlike the connection suggested in part a above, introducing these synapses would, in fact, change the outcome. Thus, the omission of this connection must be considered an implied hypothesis. Including those synapses with a significant strength would alter the PING rhythm created by the interactions between F and PV, which is crucial for ECS and F fine timing. Thanks very much for showing us that this needs to be said. Our hypothesis does not contradict the dense connections mentioned by the Reviewer; such dense connectivity does not mean that all pyramidal cells connect to all interneurons. This hypothesis may be taken as a prediction of the model.

The absence of this connection is now discussed at the end of a new Section of the Discussion entitled “Assumptions and predictions of the model”, which reads as follows:

“Finally, the model assumes the absence of significantly strong connections from the excitatory projection cells ECS to PV interneurons, unlike the ones from F to PV. Including those synapses would alter the PING rhythm created by the interactions between F and PV, which is crucial for ECS and F fine timing. We note that in (Woodruff and Sah, 2007) only 38% of the pyramidal cells are connected to PV cells. The functional identity of the connected pyramidal cells is unknown. Our model suggests that successful fear conditioning requires F to PV connections and that ECS to PV must be weak or absent.”

(2) Krabbe et al. 2019 and Davis et al. 2017 were referenced for the construction of the conditioned and unconditioned stimulus pairing protocol. The Davis citation is not applicable here because that study was a contextual, not cued, fear conditioning paradigm. Regarding Krabbe, the pairing protocol was radically different from what the authors used. Their conditioned stimulus was a train of tone pips presented at 0.9 Hz, which lasted 30 s, after which the unconditioned stimulus was presented after tone offset. The authors should determine how their network behaves when this protocol is used. Also, note that basolateral amygdala responses to tone stimuli are primarily brief onset responses (e.g. Quirk, Armony, and LeDoux 1997), and not the tonic activation used in the model.  

We replied to this point in our responses to the Reviewer’s Public Comments as follows:

“We agree that, in order to implement the fear conditioning paradigm in our in-silico network, we made several assumptions about the nature of the CS and US inputs affecting the neurons in the BLA and the duration of these inputs. A Poisson spike train to the BLA is a signal that contains no structure that could influence the timing of the BLA output; hence, we used this as our CS input signal. We also note that the CS input can be of many forms in general fear conditioning (e.g., tone, light, odor), and we wished to de-emphasize the specific nature of the CS. The reference mentioned in the Recommendations for authors, (Quirk, Armony, and LeDoux 1997), uses pulses 2 seconds long. At the end of fear conditioning, the response to those pulses is brief. However, in the early stages of conditioning, the response goes on for as long as the figure shows. The authors do show the number of cells responding decreases from early to late training, which perhaps reflects increasing specificity over training. This feature is not currently in our model, but we look forward to thinking about how it might be incorporated. Regarding the CS pulsed protocol used in (Krabbe et al., 2019), it has been shown that intense inputs (6kHz and 12 kHz inputs) can lead to metabotropic effects that last much longer than the actual input (200 ms duration) (Whittington et al., Nature, 1995). Thus, the effective input to the BLA may indeed be more like

Poisson.”

Current answer to the Reviewer:

There are several distinct issues raised by the Reviewer in the more detailed critique. We respectfully disagree that the model is not applicable to context-dependent fear learning where the context acts as a CS, though we should have been more explicit. Specifically, our CS input can describe both the cue and the context. We included the following text in the Results section “Interneuron rhythms provide the fine timing needed for depression-dominated STDP to make the association between CS and fear”:

“In our simulations, the CS input describes either the context or the cue in contextual and cued fear conditioning, respectively. For the context, the input may come from the hippocampus or other non-sensory regions, but this does not affect its role as input in the model.”

The second major issue is whether the specific training protocols used in the cited papers need to be exactly reproduced in the signals received by the elements of our model; we note that there are many transformations that can occur between the sensory input and the signals received by the BLA. In the case of auditory fear conditioning, a series of pips, rather than individual pips, are considered the CS (e.g., (Stujenske et al., 2014; Krabbe et al. 2019)). Our understanding is that a single pip does not elicit a fear response; a series of pips is required for fear learning. This indicates that it is not the neural code of a single pip that matters, but rather the signal entering the amygdala that incorporates any history-dependent signaling that could lead to spiking throughout the sequence of pips.  Also, as mentioned above, intense inputs at frequencies about 6kHz and 12kHz can lead to metabotropic effects that last much longer than each brief pip (~200 ms), thus possibly producing continuous activity in neurons encoding the input. Thus, we believe that our use of the Poisson spike train is reasonable. 

However, we are aware that the activity of neurons encoding CS can be modulated by the pips: neurons encoding auditory CS display a higher firing rate when each pip is presented and a Poisson-like spike train between pips (Herry et al., Journal of Neuroscience, 2007). Here we confirm that potentiation is present even in the presence of the fast transient response elicited by the pips. We said in the original manuscript that there is learning for a Poisson spike train CS input at ~50 Hz; this describes the neuronal activity in between pips. For the revision, we asked whether learning is preserved when CS is characterized by higher frequencies, which would describe the CS during and right after each pip. We show in the new Fig. S4 that potentiation is ensured for a range of CS frequencies. The figure shows the learning speed as a function of CS and US frequencies. For all the CS frequencies considered, i) there is learning, ii) learning speed increases with CS frequency. Thus, potentiation is present even when pips elicit a faster transient response.

To better specify this in the manuscript, 

We added the following sentences in the Results section “With the depressiondominated plasticity rule, all interneuron types are needed to provide potentiation during fear learning”: 

“We note that the CS and US inputs modeled as independent Poisson spike trains represent stimuli with no structure. Although we have not explicitly modeled pulsating pips, as common in auditory fear conditioning (e.g., (Stujenske 2014; Krabbe 2019)), we show in Fig. S4 that potentiation can be achieved over a relatively wide range of gamma frequencies. This indicates that overall potentiation is ensured if the gamma frequency transiently increases after the pip.”

We added the section “The full network potentiates for a range of CS frequencies“ and figure S4 in the Supplementary Information:

We included in Materials and Methods “Conditioned and unconditioned stimuli” the following sentences:

“Finally, for Fig.S4, we considered a range of frequencies for the CS stimulus. To generate the three Poisson spike trains with average frequencies from 48 to 64 Hz in Fig. S4, we set 𝜆 = 800, 1000, 1200.”

Finally, to address the comment about the need for CS and US overlapping in time to instantiate fear association, we added the following text in the Results section “Assumptions and predictions of the model”:

“Finally, our model requires the effect of the CS and US inputs on the BLA neuron activity to overlap in time in order to instantiate fear learning. Despite paradigms involving both overlapping (delay conditioning, where US co-terminates with CS (e.g., (Lindquist et al., 2004)), or immediately follows CS (e.g., Krabbe et al., 2019)) and non-overlapping (trace conditioning) CS/US inputs exist, we hypothesized that concomitant activity in CS- and US-encoding neuron activity should be crucial in both cases. This may be mediated by the memory effect due to metabotropic effects (Whittington et al., Nature, 1995) as suggested above, or by the contribution from other brain regions (see section “Involvement of other brain structures” in the Discussion). The fact that plasticity occurs with US memory trace is a consequence of our larger hypothesis that fear learning uses spike-timing-dependent plasticity; such a hypothesis about plasticity is common in the modeling literature.”

(3) As best as I could tell, only a single training trial was used in this study. Fair enough, especially given that fear learning can occur with a single trial. However, most studies of amygdala fear conditioning have multiple trials (~5 or more). How does the model perform when multiple trials are given?  

The association between CS and fear acquired after one trial, i.e., through a potentiated ECS to F connection, is preserved in the presence of multiple trials.  Indeed, the association would be weakened or erased (through depression of the ECS to F connection) only if ECS and F did not display good fine timing, i.e., F does not fire right after ECS most of the time. However, the implemented circuit supports the role of interneurons in providing the correct fine timing, thus preventing the association acquired from being erased.  

In the second paragraph of the Results section “With the depression-dominated plasticity rule, all interneuron types are needed to provide potentiation during fear learning”, we made the above point by adding the following text:

“We note that once the association between CS and fear is acquired, subsequent presentations of CS and US do not weaken or erase it: the interneurons ensure the correct timing and pauses in ECS and F activity, which are conducive for potentiation.”

(4) The LFP calculations are problematic. First, it is unclear how they were done. Did the authors just take the transmembrane currents they included and sum them, or were they scaled by distance from the 'electrode' and extracellular conductivity (as one would derive from the Laplace equation)? Presumably, the spatial arrangement of model neurons was neglected so distance was not a factor. 

Second, if this is the case, then the argument for excluding GABAergic conductances seems flawed. If the spatial arrangement of neurons is relevant to whether to include or exclude GABAergic conductances, then wouldn't a simulation without any spatial structure not be subject to the concern of laminar vs. nuclear arrangement? 

Moreover, to the best I can tell, the literature the authors use to justify the exclusion of

GABAergic currents does not make the case for a lack of GABAergic contribution in non-laminar structures. Instead, those studies only argue that in a non-laminar structure, AMPA currents are detectable, not that GABA cannot be detected. Thus, the authors should either include the GABAergic currents when calculating their simulated LFP, or provide a substantially better argument or citation for their exclusion. 

We thank the Reviewer for pointing this out; this comment helped us rethink how to model the LFP. The origin of the LFP signal in BLA has not been fully determined, but factors thought to be important include differences in the spatial extension of the arborization in excitatory and inhibitory neurons, in the number of synaptic boutons, and spatial distributions of somata and synapses (Lindén et al 2011; Łęski 2013; Mazzoni et al. 2015). In the first version of the manuscript, we excluded the GABAergic currents because it is typically assumed that they add very little to the extracellular field as the inhibitory reversal potential is close to the resting membrane potential. For the revision, we re-ran the simulations during pre and post fear conditioning and we modeled the LFP as the sum of the AMPA, GABA and NaP-/H-/D- currents. With this new version of the LFP, we added a new Fig. 6 showing that there is a significant increase in the low theta power, but not in the high theta power, with fear learning (Fig. 6 C, D, E). This increase in the low theta power was mainly due to the AMPA currents created by the newly established connection from ECS to F, which allowed F to be active after fear conditioning in response to CS. 

However, as the Reviewer mentioned, our network has no spatial extent: neurons are modeled as point cells. Thus, our current model does not include the features necessary to model some central aspects of the LFP. Despite that, our model does clearly demonstrate how rhythmic activity in the spike timing of neurons within the network changes due to fear learning (Fig. 6B). The spiking outputs of the network are key components of the inputs to the LFP, and thus we expect the rhythms in the spiking to be reflected in more complex descriptions of the LFP. But we also discovered that different LFP proxies provide different changes in rhythmic activity comparing pre- and post-fear learning; although we have no principled way to choose a LFP proxy, we believe that the rhythmic firing is the essential finding of the model.

We have added the following to the manuscript:

(1) In the new version of Fig. 6, we present the power spectra of the network spiking activity (panel B), along with the power spectra of the LFP proxy that includes the GABA, AMPA, and NaP-/H-/D- currents (panels C, D, E). 

(2) We modified the conclusion of the Results section entitled “Increased low-theta frequency is a biomarker of fear learning” by saying:

“In this section, we explore how plasticity in the fear circuit affects the network dynamics, comparing after fear conditioning to before. We first show that fear conditioning leads to an increase in low theta frequency power of the network spiking activity compared to the pre-conditioned level (Fig. 6 A,B); there is no change in the high theta power. We also show that the LFP, modeled as the linear sum of all the AMPA, GABA, NaP-, D-, and H- currents in the network, similarly reveals a low theta power increase and no significant variation in the high theta power (Fig. 6 C,D,E). These results reproduce the experimental findings in (Davis et al., 2017), and (Davis et al., 2017), and Fig 6 F,G show that the low theta increase is due to added excitation provided by the new learned pathway. The additional unresponsive ECS and F cells in the network were included to ensure we had not biased the LFP towards excitation. Nevertheless, although both the AMPA and GABA currents contribute to the power increase in the low theta frequency range (Fig. 6F), the AMPA currents show a dramatic power increase relative to the baseline (the average power ratio of AMPA and GABA post- vs pre-conditioning across 20 network realizations is 3*103 and 4.6, respectively). This points to the AMPA currents as the major contributor to the low theta power increase. Specifically, the newly potentiated AMPA synapse from ECS to F ensures F is active after fear conditioning, thus generating strong currents in the PV cells to which it has strong connections (Fig. 6G). Finally, the increase in power is in the low theta range because ECS and F are allowed to spike only during the active phase of the low theta spiking VIP neurons. We have also explored another proxy for the LFP (see Supplementary Information and Fig. S6).”

In the Supplementary Information, we included a figure and some text in the new section entitled “A higher low theta power increase emerges in LFP approximated with the sum of the absolute values of the currents compared to their linear sum”:

“Given that our BLA network comprises a few neurons described as single-compartment cells with no spatial extension and location, the LFP cannot be computed directly from our model’s read-outs. In the main text, we choose as an LFP proxy the linear sum of the AMPA, GABA, and P-/H-/D-currents. We note that if the LFP is modeled as the sum of the absolute value of the currents, as suggested by (Mazzoni et al. 2008; Mazzoni et al. 2015), an even higher low theta power increase arises after fear conditioning compared to the linear sum. Differences in the power spectra also arise if other LFP proxies (e.g., only AMPA currents, only GABA currents) are considered. A principled description of an LFP proxy would require modeling the three-dimensional BLA anatomy, including that of the interneurons VIP and SOM; this is outside the scope of the current paper. (See (Feng et al. 2019) for a related project in the BLA.)”

(3) We updated the Materials and Methods section “Local field potentials and spectral analysis” to explain how we compute the LFP in the revised manuscript: 

“We considered as an LFP proxy as the linear sum of all the AMPA, GABA, NaP, D, and H currents in the network. The D-current is in the VIP interneurons, and NaP-current and H-current are in SOM interneurons.”

Although it is beyond the scope of the current work, an exploration of the most accurate proxy of the LFP in the amygdala is warranted. Such a study could be accomplished by adopting a similar approach as in (Mazzoni et al., 2015), where several LFP proxies based on point-neuron leaky-integrate and fire neuronal network were compared with a “groundtruth” LFP obtained in an analogous realistic three-dimensional network model. 

To explicitly mention this issue in the paper, we add a paragraph in the “Limitations and caveats” section in the Discussion, which reads as follows:

“LFPs recorded in the experiments are thought to be mainly created by transmembrane currents in neurons located around the electrode and depend on several factors, including the morphology of the arborization of contributing neurons and the location of AMPA and GABA boutons (Katzner et al. 2009; Lindén et al 2011; Łęski 2013; Mazzoni et al. 2015). Since our model has no spatial extension, we used an LFP proxy; this proxy was shown to reflect the rhythmic output of the network, which we believe to be the essential result (for more details see Results “Increased low-theta frequency is a biomarker of fear learning”, and Supplementary Information “A higher low theta power increase emerges in LFP approximated with the sum of the absolute values of the currents compared to their linear sum”).”

(4)     We have removed the section “Plasticity between fear neuron and VIP slows down overall potentiation” in Results and sections “Plasticity between the fear neuron (F) and VIP slows down overall potentiation” and “Plastic F to VIP connections further increase lowtheta frequency power after fear conditioning” in the Supplementary Information. This material is extraneous since we are using a new proxy for LFP.

Minor points: 

(1) In Figure 3C, the y-axis tick label for 0.037 is written as "0.37."

We thank the reviewer for finding this typo; we fixed it.

(2) Figure 5B is unclear. It seems to suggest that the added ECS and F neurons did not respond to either the CS or UCS. Is this true? If so, why include them in the model? How would their inclusion change the model behavior? 

It is correct that the added ECS and F neurons did not respond to the CS or US (UCS); they are constructed to be firing at 11 Hz in the absence of any connections from other cells.  These cells were included to be part of our computation of the LFP.  Specifically, adding in those cells would make the LFP take inhibition into account more, and we wanted to make sure that were not biasing our computation away from the effects of inhibition.  As shown in the paper (Fig. 6B), even with inhibition onto these non-responsive cells, the LFP has the properties claimed in the paper concerning the changes in the low theta and high-theta power, because the LFP is dominated by new excitation rather than the inhibition. 

First, in the Results section “Network with multiple heterogeneous neurons can establish the association between CS and fear”, we commented on the added ECS and F neurons that do not respond to either CS or US by saying the following:

“The ECS cells not receiving CS are inhibited by ongoing PV activity during the disinhibition window (Fig. 5B); they are constructed to be firing at 11 Hz in the absence of any connections from other cells. The lack of activity in those cells during fear conditioning implies that there is no plasticity from those ECS cells to the active F. Those cells are included for the calculation of the LFP (see below in “Increased low-theta frequency is a biomarker of fear learning”.)”

Furthermore, we add the following sentence in the Results section “Increased low-theta frequency is a biomarker of fear learning”: 

“The additional unresponsive ECS and F cells in the network were included to ensure we had not biased the LFP towards excitation.”

(3) Applied currents are given as current densities, but these are difficult to compare with current levels observed from whole-cell patch clamp recordings. Can the currents be given as absolute levels, in pA/nA. 

In principle, it is possible to connect current densities with absolute levels, as requested. However, we note that the number of cells in models is orders of magnitude smaller than the number being modeled. It is common in modeling to adjust physiological parameters to achieve the qualitative properties that are important to the model, rather than trying to exactly match particular recordings.

We added to the Methods description why we choose units per unit area, rather than absolute units. 

“All the currents are expressed in units per area, rather than absolute units, to avoid making assumptions about the size of the neuron surface.”

(4) Regarding: "We note that the presence of SOM cells is crucial for plasticity in our model since they help to produce the necessary pauses in the excitatory projection cell activity. However, the high theta rhythm they produce is not crucial to the plasticity: in our model, high theta or higher frequency rhythms in SOM cells are all conducive to associative fear learning. This opens the possibility that the high theta rhythm in the BLA mostly originates in the prefrontal cortex and/or the hippocampus (Stujenske et al., 2014, 2022)." The chain of reasoning in the above statement is unclear. The second sentence seems to be saying contradictory things. 

We agree that the sentence was confusing; thank you for pointing it out. We have revised the paragraph to make our point clearer. The central points are: 1) having the SOM cells in the BLA is critical to the plasticity in the model, and 2) these cells may or may not be the source of the high theta observed in the BLA during fear learning.

We deleted from the discussion the text reported by the Reviewer, and we added the following one to make this point clearer:

“We note that the presence of SOM cells is crucial for plasticity in our model since they help to produce the necessary pauses in the excitatory projection cell activity. The BLA SOM cells do not necessarily have to be the only source of the high theta observed in the BLA during fear learning; the high theta detected in the LFP of the BLA also originates from the prefrontal cortex and/or the hippocampus (Stujenske et al., 2014, 2022).”

(5) Regarding: "This suggests low theta power change is not just an epiphenomenon but rather a biomarker of successful fear conditioning." Not sure this is the right framing for the above statement. The power of the theta signal in the LFP reflects the strengthening of connections, but it itself does not have an impact on network activity. Moreover, whether something is epiphenomenal is not relevant to the question of whether it can serve as a successful biomarker. A biomarker just needs to be indicative, not causal. 

We intended to say why the low theta power change is a biomarker in the sense of the Reviewer. That is: experiments have shown that, with learning, the low theta power increases. The modeling shows in addition that, when learning does not take place, the low power does not increase. That means that the low theta power increases if and only if there is learning, i.e., the change in low theta power is a biomarker. To make our meaning clearer, we have changed the quoted sentences to read: 

“This suggests that the low theta power change is a biomarker of successful fear conditioning: it occurs when there is learning and does not occur when there is no learning.”

Reviewer #2 (Public Comments): 

We thank the Reviewer for raising these interesting points. Below are our public replies and the changes we made to the manuscript to address the Reviewer’s objections.

(1) Gamma oscillations are generated locally; thus, it is appropriate to model in any cortical structure. However, the generation of theta rhythms is based on the interplay of many brain areas therefore local circuits may not be sufficient to model these oscillations.

Moreover, to generate the classical theta, a laminal structure arrangement is needed (where neurons form layers like in the hippocampus and cortex)(Buzsaki, 2002), which is clearly not present in the BLA. To date, I am not aware of any study which has demonstrated that theta is generated in the BLA. All studies that recorded theta in the BLA performed the recordings referenced to a ground electrode far away from the BLA, an approach that can easily pick up volume conducted theta rhythm generated e.g., in the hippocampus or other layered cortical structure. To clarify whether theta rhythm can be generated locally, one should have conducted recordings referenced to a local channel (see Lalla et al., 2017 eNeuro). In summary, at present, there is no evidence that theta can be generated locally within the BLA. Though, there can be BLA neurons, firing of which shows theta rhythmicity, e.g., driven by hippocampal afferents at theta rhythm, this does not mean that theta rhythm per se can be generated within the BLA as the structure of the BLA does not support generation of rhythmic current dipoles. This questions the rationale of using theta as a proxy for BLA network function which does not necessarily reflect the population activity of local principal neurons in contrast to that seen in the hippocampus.

In both modeling and experiments, a laminar structure does not seem to be needed to produce a theta rhythm. A recent experimental paper, (Antonoudiou et al. 2022), suggests that the BLA can intrinsically generate theta oscillations (3-12 Hz) detectable by LFP recordings under certain conditions, such as reduced inhibitory tone. The authors draw this conclusion by looking at mice ex vivo slices. The currents that generate these rhythms are in the BLA, since the hippocampus was removed to eliminate hippocampal volume conduction and other nearby brain structures did not display any oscillatory activity. Also, in the modeling literature, there are multiple examples of the production of theta rhythms in small networks not involving layers; these papers explain the mechanisms producing theta from non-laminated structures (Dudman et al., 2009, Kispersky et al., 2010, Chartove et al. 2020).  We are not aware of any model description of the mechanisms of theta that do require layers.

We added the following text in the introduction of the manuscript to make this point clearer:  “A recent rodent experimental study (Antonoudiou et al. 2022) suggests that BLA can intrinsically generate theta oscillations (3-12 Hz).”

(2) The authors distinguished low and high theta. This may be misleading, as the low theta they refer to is basically a respiratory-driven rhythm typically present during an attentive state (Karalis and Sirota, 2022; Bagur et al., 2021, etc.). Thus, it would be more appropriate to use breathing-driven oscillations instead of low theta. Again, this rhythm is not generated by the BLA circuits, but by volume conducted into this region. Yet, the firing of BLA neurons can still be entrained by this oscillation. I think it is important to emphasize the difference.

Many rhythms of the nervous system can be generated in multiple parts of the brain by multiple mechanisms. We do not dispute that low theta appears in the context of respiration; however, this does not mean that other rhythms with the same frequencies are driven by respiration. Indeed, in the response to question 1 above, we showed that theta can appear in the BLA without inputs from other regions. In our paper, the low theta is generated in the BLA by VIP neurons. Using intrinsic currents known to exist in VIP neurons (Porter et al., 1998), modeling has shown that such neurons can intrinsically produce a low theta rhythm. This is also shown in the current paper. This example is part of a substantial literature showing that there are multiple mechanisms for any given frequency band. 

To elaborate more on this in the manuscript, we added the following new section in the discussion:

“Where the rhythms originate, and by what mechanisms. A recent experimental paper, (Antonoudiou et al. 2022), suggests that the BLA can intrinsically generate theta oscillations (3-12 Hz) detectable by LFP recordings under certain conditions, such as reduced inhibitory tone. They draw this conclusion in mice by removing the hippocampus, which can volume conduct to BLA, and noticing that other nearby brain structures did not display any oscillatory activity. Our model also supports the idea that intrinsic mechanisms in the BLA can support the generation of the low theta, high theta, and gamma rhythms. 

Although the BLA can produce these rhythms, this does not rule out that other brain structures also produce the same rhythms through different mechanisms, and these can be transmitted to the BLA. Specifically, it is known that the olfactory bulb produces and transmits the respiratory-related low theta (4 Hz) oscillations to the dorsomedial prefrontal cortex, where it organizes neural activity (Bagur et al., 2021). Thus, the respiratory-related low theta may be captured by BLA LFP because of volume conduction or through BLA extensive communications with the prefrontal cortex. Furthermore, high theta oscillations are known to be produced by the hippocampus during various brain functions and behavioral states, including during spatial exploration (Vanderwolf, 1969) and memory formation/retrieval (Raghavachari et al., 2001), which are both involved in fear conditioning. Similarly to the low theta rhythm, the hippocampal high theta can manifest in the BLA. It remains to understand how these other rhythms may interact with the ones described in our paper.”

We also note that the presence of D-currents in the BLA VIP interneurons should be confirmed experimentally, and that the ability of VIP interneurons to generate the BLA low theta rhythm constitutes a prediction of our computational model. These points are specified in the first paragraph in the Discussion entitled “Assumptions and predictions of the model”:

“The interneuron descriptions in the model were constrained by the electrophysiological properties reported in response to hyperpolarizing currents (Sosulina et al., 2010). Specifically, we modeled the three subtypes of VIP, SOM, and PV interneurons displaying bursting behavior, regular spiking with early spike-frequency adaptation, and regular spiking without spike-frequency adaptation, respectively. Focusing on VIP interneurons, we were able to model the bursting behavior by including the D-type potassium current. This current is thought to exist in the VIP interneurons in the cortex (Porter et al., 1998), but whether this current is also found in the VIP interneurons the BLA is still unknown. Similarly, we endowed the SOM interneurons with NaP- and H-currents, as the OLM cells in the hippocampus. Due to these currents, the VIP and SOM cells are able to show  low- and high-theta oscillations, respectively. The presence of these currents and the neurons’ ability to exhibit oscillations in the theta range during fear conditioning and at baseline in BLA, which are assumptions of our model, should be tested experimentally.”

(3) The authors implemented three interneuron types in their model, ignoring a large fraction of GABAergic cells present in the BLA (Vereczki et al., 2021). Recently, the microcircuit organization of the BLA has been more thoroughly uncovered, including connectivity details for PV+ interneurons, firing features of neurochemically identified interneurons (instead of mRNA expression-based identification, Sosulina et al., 2010), synaptic properties between distinct interneuron types as well as principal cells and interneurons using paired recordings. These recent findings would be vital to incorporate into the model instead of using results obtained in the hippocampus and neocortex. I am not sure that a realistic model can be achieved by excluding many interneuron types.

The interneurons and connectivity that we used were inspired by the functional connectivity reported in (Krabbe et al., 2019) (see above answer to Reviewer #1). As reported in (Vereczki et al., 2021), there are multiple categories and subcategories of interneurons; that paper does not report on which ones are essential for fear conditioning. We did use all the highly represented categories of the interneurons, except NPYcontaining neurogliaform cells.

The Reviewer says “I am not sure that a realistic model can be achieved by excluding many interneuron types”. We agree with the Reviewer that discarding the introduction of other interneurons subtypes and the description of more specific connectivity (soma-, dendrite-, and axon-targeting connections) may limit the ability of our model to describe all the details in the BLA. However, this work represents a first effort towards a biophysically detailed description of the BLA rhythms and their function. As in any modeling approach, assumptions about what to describe and test are determined by the scientific question; details postulated to be less relevant are omitted to obtain clarity. The interneuron subtypes we modeled, especially VIP+ and PV+, have been reported to have a crucial role in fear conditioning (Krabbe et al., 2019). Other interneurons, e.g. cholecystokinin and SOM+, have been suggested as essential in fear extinction. Thus, in the follow-up of this work to explain fear extinction, we will introduce other cell types and connectivity. In the current work, we have achieved our goals of explaining the origin of the experimentally found rhythms and their roles in the production of plasticity underlying fear learning. Of course, a more detailed model may reveal flaws in this explanation, but this is science that has not yet been done.

We elaborate more on this in a new section in the Discussion entitled “Assumptions and predictions of the model”. The paragraph related to this point reads as follows:

“Our model, which is a first effort towards a biophysically detailed description of the BLA rhythms and their functions, does not include the neuron morphology, many other cell types, conductances, and connections that are known to exist in the BLA; models such as ours are often called “minimal models” and constitute the majority of biologically detailed models. Such minimal models are used to maximize the insight that can be gained by omitting details whose influence on the answers to the questions addressed in the model are believed not to be qualitatively important. We note that the absence of these omitted features constitutes hypotheses of the model: we hypothesize that the absence of these features does not materially affect the conclusions of the model about the questions we are investigating. Of course, such hypotheses can be refuted by further work showing the importance of some omitted features for these questions and may be critical for other questions. Our results hold when there is some degree of heterogeneity of cells of the same type, showing that homogeneity is not a necessary condition.”

(4) The authors set the reversal potential of GABA-A receptor-mediated currents to -80 mV. What was the rationale for choosing this value? The reversal potential of IPSCs has been found to be -54 mV in fast-spiking (i.e., parvalbumin) interneurons and around -72 mV in principal cells (Martina et al., 2001, Veres et al., 2017).

A GABA-A reversal potential around -80 mV is common in the modeling literature (Jensen et al., 2005; Traub et al., 2005; Kumar et al., 2011; Chartove et al., 2020). Other computational works of the amygdala, e.g. (Kim et al., 2016), consider GABA-A reversal potential at -75 mV based on the cortex (Durstewitz et al., 2000). The papers cited by the reviewer have a GABA-A reversal potential of -72 mV for synapses onto pyramidal cells; this is sufficiently close to our model that it is not likely to make a difference. For synapses onto PV+ cells, the papers cited by the reviewer suggest that the GABA-A reversal potential is -54 mV; such a reversal potential would lead these synapses to be excitatory instead of inhibitory. However, it is known (Krabbe et al., 2019; Supp. Fig. 4b) that such synapses are in fact inhibitory. Thus, we wonder if the measurements of Martina and Veres were made in a condition very different from that of Krabbe. For all these reasons, we consider a GABA-A reversal potential around -80 mV in amygdala to be a reasonable assumption.

In section “Network connectivity and synaptic currents” in “Materials and Methods” we provided references to motivate our choice of considering a GABA-A reversal potential around -80 mV:

“The GABAa current reversal potential (𝐸!) is set to −80        𝑚𝑉, as common in the modeling literature (Jensen et al., 2005; Traub et al., 2005; Kumar et al., 2011; Chartove et al., 2020).”

(5) Proposing neuropeptide VIP as a key factor for learning is interesting. Though, it is not clear why this peptide is more important in fear learning in comparison to SST and CCK, which are also abundant in the BLA and can effectively regulate the circuit operation in cortical areas.

Other peptides seem to be important in overall modulation of fear, but VIP is especially important in the first part of fear learning, the subject of our paper. Re SST: we hypothesize that SST interneurons are critical in fear extinction and preventing fear generalization, but not to initial fear learning. The peptide of the CCK neurons, which overlap with VIP cells, has been proposed to promote the switch between fear and safety states after fear extinction (Krabbe al. 2018). Thus, these other peptides are likely more important for other aspects of fear learning.  

In the Discussion, we have added:

“We hypothesize that SST peptide is critical in fear extinction and preventing fear generalization, but not to initial fear learning. Also, the CCK peptide has been proposed to promote the switch between fear and safety states after fear extinction (Krabbe al. 2018).”

Reviewer #2 (Recommendations For The Authors): 

We note that Reviewer #2’s Recommendations For The Authors have the same content as the Public Comments. Thus, the changes to the manuscript we implemented above address also the private critiques listed below.

(1) As the breathing-driven rhythm is a global phenomenon accompanying fear state, one might restrict the analysis to this oscillation. The rationale beyond this restriction is that the 'high' theta in the BLA has an unknown origin (since it can originate from the ventral hippocampus, piriform cortex etc.). 

In response to point 4 made by Reviewer 1 (Recommendations for the Authors) (p. 13), referring to high theta in the BLA, we previously wrote: 1) having the SOM cells in the BLA is critical to the plasticity in the model, and 2) these cells may or may not be the source of the high theta observed in the BLA during fear learning.

In the Public Critiques, Reviewer 2 relates the respiratory rhythm to the low theta. We answered this point in point 2 of the Reviewer’s Public Comments (at p. 15).

(2) I would include more interneurons in the network model incorporating recent findings. 

This point was answered in our response to point 3 of the Reviewer’s Public Comments.

(3) The reversal potential for GABA-A receptor-mediated currents would be good to set to measured values. In addition, I would use AMPA conductance values that have been measured in the BLA. 

We addressed this objection in our response to point 4 of the Reviewer’s Public Comments.

Reviewer #3 (Public comments):

Weaknesses: 

(1) The main weakness of the approach is the lack of experimental data from the BLA to constrain the biophysical models. This forces the authors to use models based on other brain regions and leaves open the question of whether the model really faithfully represents the basolateral amygdala circuitry. 

(2) Furthermore, the authors chose to use model neurons without a representation of the morphology. However, given that PV+ and SOM+ cells are known to preferentially target different parts of pyramidal cells and given that the model relies on a strong inhibition form SOM to silence pyramidal cells, the question arises whether SOM inhibition at the apical dendrite in a model representing pyramidal cell morphology would still be sufficient to provide enough inhibition to silence pyramidal firing.

3) Lastly, the fear learning relies on the presentation of the unconditioned stimulus over a long period of time (40 seconds). The authors justify this long-lasting input as reflecting not only the stimulus itself but as a memory of the US that is present over this extended time period. However, the experimental evidence for this presented in the paper is only very weak.

We are repeating here the answers we gave in response to the public comments, adding further relevant points.

(1) Our neurons were constrained by electrophysiology properties in response to hyperpolarizing currents in the BLA (Sosulina et al., 2010). We can reproduce these electrophysiological properties by using specific membrane currents known to be present in similar neurons in other brain regions (D-current in VIP interneurons in the cortex, and NaP- and H-currents in OLM/SOM cells in the hippocampus). Also, though a much more detailed description of BLA interneurons was given in (Vereczki et al., 2021), it is not clear that this level of detail is relevant to the questions that we were asking, especially since the experiments described were not done in the context of fear learning.

(2) It is true that we did not include the morphology, which undoubtedly makes a difference to some aspects of the circuit dynamics. Furthermore, it is correct that the model relies on a strong inhibition from SOM and PV to silence the excitatory projection neurons. We agree that the placement of the SOM inhibition on the pyramidal neurons can make a difference on some aspects of the circuit behavior. We are assuming that the inhibition from the SOM cells can inhibit the pyramidal cells firing, which can be seen as a hypothesis of our model. It is well known that VIP cells disinhibit pyramidal cells through inhibition of SOM and PV cells (Krabbe et al. 2019); hence, this hypothesis is generally believed. This choice of parameters comes from using simplified models: it is standard in modeling to adjust parameters to compensate for simplifications.

Re points 1) and 2), in a new paragraph (“Assumptions and predictions of the model”) in the Discussion reported in response to Reviewer #2 (public comments)’s point 3, we stated that modeling requires the omission of many details to bring out the significance of other details.

(3) 40 seconds is the temporal interval we decided to use to present the results. In the Results, we also showed that there is learning over a shorter interval of time (15 seconds) where CS and US/memory of US should both be present. Thus, our model requires 15 seconds over a single or multiple trials for associative learning to be established. We included references to additional experimental papers to support our reasoning in the last paragraph of section “Assumptions and predictions of the model” in the Discussion, also reported in response to Reviewer #1 point 2 (Recommendations for the Authors). We said there that some form of memory or overlap in the activity of the excitatory projection neurons is necessary for spike-timing-dependent plasticity.

The authors achieved the aim of constructing a biophysically detailed model of the BLA not only capable of fear learning but also showing spectral signatures seen in vivo. The presented results support the conclusions with the exception of a potential alternative circuit mechanism demonstrating fear learning based on a classical Hebbian (i.e. non-depression-dominated) plasticity rule, which would not require the intricate interplay between the inhibitory interneurons. This alternative circuit is mentioned but a more detailed comparison between it and the proposed circuitry is warranted.

Our model accounts for the multiple rhythms observed in the context of fear learning, as well as the known involvement of multiple kinds of interneurons. We did not say explicitly enough why our complicated model may be functionally important in ways that cannot be fulfilled with a simpler model with the non depression-dominated Hebbian rule. To explain this, we have added the following in the manuscript discussion: 

“Although fear learning can occur without the depression-dominated rule, we hypothesize that it is necessary for other aspects of fear learning and regulation. That is, in pathological cases, there can be overgeneralization of learning. We hypothesize that the modulation created by the involvement of these interneurons is normally used to prevent such overgeneralization. However, this is beyond the scope of the present paper.”

We have also written an extra paragraph about generalization in the Discussion “Synaptic plasticity in our model”:

“With the classical Hebbian plasticity rule, we show that learning can occur without the involvement of the VIP and SOM cells. Although fear learning can occur without the depressiondominated rule, we hypothesize that the latter is necessary for other aspects of fear learning and regulation. Generalization of learning can be pathological, and we hypothesize that the modulation created by the involvement of VIP and SOM interneurons is normally used to prevent such overgeneralization. However, in some circumstances, it may be desirable to account for many possible threats, and then a classical Hebbian plasticity rule could be useful. We note that the involvement or not of the VIP-SOM circuit has been implicated when there are multiple strategies for solving a task (Piet et al., 2024). In our situation, the nature of the task (including reward structure) may determine whether the learning rule is depression-dominated and therefore whether the VIP-SOM circuit plays an important role.”

Reviewer #3 (Recommendations For The Authors): 

We thank the Reviewer for all the recommendations. We replied to each of them below.

In general, there are some inconsistencies in the naming (e.g. sometimes you write PV sometimes PV+,...), please use consistent abbreviations throughout the manuscript. You also introduce some of the abbreviations multiple times. 

We modified the manuscript to remove all the inconsistencies in the naming. 

Introduction: 

- In the last section you speak about one recent study but actually cite two articles. 

We removed the reference to (Perrenoud and Cardin, 2023), which is a commentary on the Veit et al. article.

Results: 

- 'Brain rhythms are thought to be encoded and propagated largely by interneurons' What do you mean by encoded here? 

We agree with the Reviewer that the verb “to encode” is not accurate. We modified the sentence as follows:

“Brain rhythms are thought to be generated and propagated largely by interneurons”.

- The section 'Interneurons interact to modulate fear neuron output' could be clearer. Start with describing the elements of the circuit, then the rhythms in the baseline. 

We reorganized the section as follows:

“Interneurons interact to modulate fear neuron output. Our BLA network consists of interneurons, detailed in the previous section, and excitatory projection neurons (Fig. 2A). Both the fear-encoding neuron (F), an excitatory projection neuron, and the VIP interneuron are activated by the noxious stimulus US (Krabbe et al., 2019). As shown in Fig. 2A (top, right), VIP disinhibits F by inhibiting both SOM and PV, as suggested in (Krabbe et al., 2019). We do not include connections from PV to SOM and VIP, nor connections from SOM to PV and VIP, since those connections have been shown to be significantly weaker than the ones included (Krabbe et al., 2019). The simplest network we consider is made of one neuron for each cell type. We introduce a larger network with some heterogeneity in the last two sections of the Results.

Fig. 2A (bottom) shows a typical dynamic of the network before and after the US input onset, with US modeled as a Poisson spike train at ~50 Hz; the network produces all the rhythms originating from the interneurons alone or through their interactions with the excitatory projection neurons (shown in Fig. 1). Specifically, since VIP is active at low theta during both rest and upon the injection of US, it then modulates F at low theta cycles via SOM and PV. In the baseline condition, the VIP interneuron has short gamma bursts nested in low theta rhythm. With US onset, VIP increases its burst duration and the frequency of low theta rhythm. These longer bursts make the SOM cell silent for long periods of each low theta cycle, providing F with windows of disinhibition and contributing to the abrupt increase in activity right after the US onset. Finally, in Fig. 2A, PV lacks any external input and fires only when excited by F. Thanks to their reciprocal interactions, PV forms a PING rhythm with F, as depicted in Fig.1C.”

- Figure 3C: The lower dashed line has the tick label '0.37' which should read '0.037'. 

We fixed it.

- The section describing the network with multiple neurons could be clearer, especially, it is not really clear how these different ECS and F neurons receive their input. 

We answered the same objection in the reply to Reviewer #1 in point 2 under “minor issues.”

Discussion: 

- The paragraph 'It has also been suggested that ventral tegmental area has a role in fear expression (Lesas et al.,2023). Furthermore, it has been reported that the prelimbic cortex (PL) modulates the BLA SOM cells during fear retrieval, and the latter cells are crucial to discriminate non-threatening cues when desynchronized by the PL inputs (Stujenske et al., 2022).' is merely stating facts but I don't see how they relate to the presented work. 

We thank the Reviewer for pointing out that this was confusing. What we meant to emphasize was that later stages of fear conditioning and extinction appear to require more than the BLA. We specifically mention the discrimination of non-threatening cues at the end of the paragraph, which now reads as follows:

“Other brain structures may be involved in later stages of fear responsiveness, such as fear extinction and prevention of generalization. It has been reported that the prelimbic cortex (PL) modulates the BLA SOM cells during fear retrieval, and the latter cells are crucial to discriminate non-threatening cues when desynchronized by the PL inputs (Stujenske et al., 2022). Brain structures such as the prefrontal cortex and hippocampus have been documented to play a crucial role also in fear extinction, the paradigm following fear conditioning aimed at decrementing the conditioned fearful response through repeated presentations of the CS alone. As reported by several studies, fear extinction suppresses the fear memory through the acquisition of a distinct memory, instead of through the erasure of the fear memory itself (Harris et al., 2000; Bouton, 2002; Trouche et al., 2013; Thompson et al., 2018). Davis et al., 2017 found a high theta rhythm following fear extinction that was associated with the suppression of threat in rodents. Our model can be extended to include structures in the prefrontal cortex and the hippocampus to further investigate the role of rhythms in the context of discrimination of non-threatening cues and extinction. We hypothesize that a different population of PV interneurons plays a crucial role in mediating competition between fearful memories, associated with a low theta rhythm, and safety memories, associated with a high theta rhythm; supporting experimental evidence is in (Lucas et al., 2016; Davis et al., 2017; Chen et al., 2022).”

- The comparison to other models BLA is quite short and seems a bit superficial. A more indepth comparison seems warranted. 

We thank the reviewer for suggesting that a more in-depth comparison between our and other models in the literature would improve the manuscript. We rewrote entirely the first paragraph of that section. The new content reads as follows:

“Comparison with other models. Many computational models that study fear conditioning have been proposed in the last years; the list includes biophysically detailed models (e.g., (Li 2009; Kim et al., 2013a)), firing rate models (e.g., Krasne 2011; Ball 2012; Vlachos 2011), and connectionist models (e.g., Moustafa 2013; Armony 1997; Edeline 1992) (for a review see (Nair et al., 2016)). Both firing rate models and connectionist models use an abstract description of the interacting neurons or regions. The omission of biophysical details prevents such models from addressing questions concerning the roles of dynamics and biophysical details in fear conditioning, which is the aim of our model.  There are also biophysically detailed models (Li 2009; Kim 2013; Kim 2016; Feng 2019), which differ from ours in both the physiology included in the model and the description of how plastic changes take place.  One main difference in the physiology is that we differentiated among types of interneurons, since the fine timing produced for the latter was key to our use of rhythms to produce spike-time dependent plasticity. The origin of the gamma rhythm (but not the other rhythms) was investigated in Feng et al 2019, but none of these papers connected the rhythms to plasticity.

The most interesting difference between our work and that in (Li 2009; Kim 2013; Kim 2016) is the modeling of plasticity.  We use spike-time dependent plasticity rules.  The models in (Li 2009; Kim 2013; Kim 2016) were more mechanistic about how the plasticity takes place, starting with the known involvement of calcium with plasticity.  Using a hypothesis about back propagation of spikes, the set of papers together come up with a theory that is consistent with STDP and other instantiations of plasticity (Shouval 2002a; Shouval 2002b).  For the purposes of our paper, this level of detail, though very interesting, was not necessary for our conclusions.  By contrast, in order for the rhythms and the interneurons to have the dynamic roles they play in the model, we needed to restrict our STDP rule to ones that are depression-dominated.  Our reading of (Shouval 2002) suggests to us that such subrules are possible outcomes of the general theory.  Thus, there is no contradiction between the models, just a difference in focus; our focus was on the importance of the much-documented rhythms (Seidenbecher et al., 2003; Courtin et al., 2014b; Stujenske et al., 2014; Davis et al., 2017) in providing the correct spike timing.  We showed in the Supplementary Information (“Classical Hebbian plasticity rule, unlike the depression-dominated one, shows potentiation even with no strict pre and postsynaptic spike timing”) that if the STDP rule was not depression dominated, the rhythms need not be necessary.  We hypothesize that the necessity of strict timing enforced by the depression-dominated rule may foster the most appropriate association with fear at the expense of less relevant associations.”

- The paragraph 'This could happen among some cells responding to weaker sensory inputs that do not lead to pre-post timing with fear neurons. This timing could be modified by the "triconditional rule", as suggested in (Grewe et al., 2017).' is not very clear. What exactly is 'this' in the first sentence referring to? If you mention the 'tri-conditional rule' here, please briefly explain it and how it would solve the issue at hand here.  

We apologize that the sentence reported was not sufficiently clear. “This” refers to “depression”. We meant that, in our model, depression during fear conditioning happens every time there is no pre-post timing between neurons encoding the neutral stimuli and fear cells; poor pre-post timing can characterize the activity of neurons responding to weaker sensory inputs and does not lead to associative learning. We modified that paragraph as follows:

“The study in (Grewe et al., 2017) suggests that associative learning resulting from fear conditioning induces both potentiation and depression among coactive excitatory neurons; coactivity was determined by calcium signaling and thus did not allow measurements of fine timing between spikes. In our model, we show how potentiation between coactive cells occurs when strict pre-post spike timing and appropriate pauses in the spiking activity arise. Depression happens when one or both of these components are not present. Thus, in our model, depression represents the absence of successful fear association and does not take part in the reshaping of the ensemble encoding the association, as instead suggested in (Grewe et al., 2017). A possible follow-up of our work involves investigating how fear ensembles form and modify through fear conditioning and later stages. This follow-up work may involve using a tri-conditional rule, as suggested in (Grewe et al. 2017), in which the potential role of neuromodulators is taken into account in addition to the pre- and postsynaptic neuron activity; this may lead to both potentiation and depression in establishing an associative memory.”

- In the limitations and caveats section you mention that the small size of the network implies that they represent a synchronous population. What are the potential implications for the proposed rhythm-dependent mechanism? What are your expectations for larger networks? 

We apologize if we were not adequately clear. We are guessing that the Reviewer thought we meant the entire population was synchronous, which it is not. We meant that, when we use a single cell to represent a subpopulation of cells of that type, that subpopulation is effectively synchronous. For larger networks in which each subtype is represented by many cells, there can be heterogeneity within each subtype. We have shown in the paper that the basic results still hold under some heterogeneity; however, they may fail if the heterogeneity is too large.

We mentioned in a new section named “Assumptions and predictions of the model” in response to point 3 made by Reviewer #2.

- The discussion is also missing a section on predictions/new experiments that can be derived from the model. How can the model be confirmed, what experiments/results would break the model? 

To answer this question, we put in a new section in the Discussion entitled “Assumptions and predictions of the model”. The first paragraph of this section is in the reply to Reviewer #2 point 2; the second paragraph is in the reply to Reviewer #2 point 3; the last paragraph is in the Reply to Reviewer #1 point c; the rest of the section reads as follows:

“Our study suggests that all the interneurons are necessary for associative learning provided that the STDP rule is depression-dominated. This prediction could be tested experimentally by selectively silencing each interneuron subtype in the BLA: if the associative learning is hampered by silencing any of the interneuron subtypes, this validates our study. Finally, the model prediction could be tested indirectly by acquiring more information about the plasticity rule involved in the BLA during associative learning. We found that all the interneurons are necessary to establish fear learning only in the case of a depression-dominated rule. This rule ensures that fine timing and pauses are always required for potentiation: interneurons provide both fine timing and pauses to pyramidal cells, making them crucial components of the fear circuit. 

The modeling of the interneurons assumes the involvement of various intrinsic currents; the inclusion of those currents can be considered hypotheses of the model. Our model predicts that blockade of D-current in VIP interneurons (or silencing VIP interneurons) will both diminish low theta and prevent fear learning. Finally, the model assumes the absence of significantly strong connections from the excitatory projection cells ECS to PV interneurons, unlike the ones from F to PV. Including those synapses would alter the PING rhythm created by the interactions between F and PV, which is crucial for fine timing between ECS and F needed for LTP.”

We would like to thank you and the reviewers for your thoughtful comments that assisted us to improve the manuscript. We carefully followed the reviewers’ recommendations and provide a detailed point-by-point account of our responses to the comments. 

Please find below the important changes in the updated manuscript.

(1) We changed the title according to the comments provided by reviewer #1.

(2) We edited the introduction, results, and discussion to improve the link between the objectives of the study, the findings, and their discussion, as reviewer #2 recommended.

(3) We clarified the link between camouflage and fitness, which is now presented as a hypothesis, as reviewer #1 suggested.

(4) We added new analyses and figures in the main text and in the supplementary materials to better emphasize sex differences in landing force, foraging strategies and hunting success, following reviewer #1 suggestion.

(5) According to reviewer #2 comments, we edited the results adding key information about methods to help the reader understand the findings without reading the Methods section.

(6) We added important details about the model selection approach along with a discussion of the low R-square values reported in our analyses on hunting success, as reviewer #2 suggested.

eLife assessment 

This fundamental work substantially advances our understanding of animals' foraging behaviour, by monitoring the movement and body posture of barn owls in high resolution, in addition to assessing their foraging success. With a large dataset, the evidence supporting the main conclusions is convincing. This work provides new evidence for motion-induced sound camouflage and has broad implications for understanding predator-prey interactions. 

Public Reviews: 

Reviewer #1 (Public Review): 

In this paper, Schalcher et al. examined how barn owls' landing force affects their hunting success during two hunting strategies: strike hunting and sit-and-wait hunting. They tracked tens of barn owls that raised their nestlings in nest boxes and utilized high-resolution GPS and acceleration loggers to monitor their movements. In addition, camcorders were placed near their nest boxes and used to record the prey they brought to the nest, thus measuring their foraging success. 

This study generated a unique dataset and provided new insights into the foraging behavior of barn owls. The researchers discovered that the landing force during hunting strikes was significantly higher compared to the sit-and-wait strategy. Additionally, they found a positive relationship between landing force and foraging success during hunting strikes, whereas, during the sit-and-wait strategy, there was a negative relationship between the two. This suggests that barn owls avoid detection by generating a lower landing force and producing less noise. Furthermore, the researchers observed that environmental characteristics affect barn owls' landing force during sit-and-wait hunting. They found a greater landing force when landing on buildings, a lower landing force when landing on trees, and the lowest landing force when landing on poles. The landing force also decreased as the time to the next hunting attempt decreased. These findings collectively suggest that barn owls reduce their landing force as an acoustic camouflage to avoid detection by their prey. 

The main strength of this work is the researchers' comprehensive approach, examining different aspects of foraging behavior, including high-resolution movement, foraging success, and the influence of the environment on this behavior, supported by impressive data collection. The weakness of this study is that the results only present a partial biological story contained within the data. The focus is on acoustic camouflage without addressing other aspects of barn owls' foraging strategy, leaving the reader with many unanswered questions. These include individual differences, direct measurements of owls' fitness, a detailed analysis of the foraging strategy of males and females, and the collective effort per nest box. However, it is possible that these data will be published in a separate paper. 

We greatly appreciate your recognition of the comprehensive approach and extensive data collection. Our primary objective was to study the role of acoustic camouflage. Nonetheless, the manuscript now includes a detailed analysis of the foraging strategy and hunting success of males and females (lines 164-225).

The results presented support the authors' conclusion that lower landing force during sit-andwait hunting increases hunting success, likely due to a decreased probability of detection by their prey, resulting in acoustic camouflage. The authors also argue that hunting success is crucial for survival, and thus, acoustic camouflage has a direct link to fitness. While this statement is reasonable, it should be presented as a hypothesis, as no direct evidence has been provided here.

Thank you for the comment. We agree and thus have edited the language accordingly.  

However, since information about nestling survival is typically monitored when studying behavior during the breeding period, the authors' knowledge of the effect of acoustic camouflage on owls' fitness can probably be provided. Furthermore, it will be interesting to further examine the foraging strategies used by different individuals during foraging, the joint foraging success of both males and females within each nest box, and the link between landing force and foraging success if the data are available.

We are currently writing a manuscript on these topics. We are aware that several scientific questions regarding the foraging ecology of the barn owl still need our attention. Regarding the link between landing force and foraging success, we believe that our revised manuscript addresses this specific topic, please see specific responses below.

However, even without this additional analysis on survival, this paper provides an unprecedented dataset and the first measurement of landing force during hunting in the wild. It is likely to inspire many other researchers currently studying animal foraging behavior to explore how animals' movements affect foraging success.

Reviewer #2 (Public Review): 

Summary: 

The authors provide new evidence for motion-induced sound camouflage and can link the hunting approach to hunting success (detailing the adaptation and inferring a fitness consequence). 

Strengths: 

Strong evidence by combining high-resolution accelerometer data with a ground-truthed data set on prey provisioning at nest boxes. A good set of co-variates to control for some of the noise in the data provides some additional insights into owl hunting attempts. 

Weaknesses: 

There is a disconnect between the hypotheses tested and the results presented, and insufficient detail is provided on the statistical approach. R2 values of the presented models are very small compared to the significance of the effect presented. Without more detail, it is impossible to assess the strength of the evidence.

In the revised manuscript, we changed the way results are presented and we improved the link between the hypotheses and the results. The R2 values are indeed small. It is however important to keep in mind that we are assessing the outcome of one specific behavior (i.e. landing force during sit-and-wait hunts) on hunting success in a wild environment, where many complex ecological interactions likely influence hunting success. Nonetheless, the coefficients (as reported in the results) show that for every 1 N increase in landing force, there is a 15% reduction in hunting success, which is substantial. In the discussion we also note that 50 Hz is a relatively low sampling frequency for estimating the peak ground reaction force. We have gone back over the presentation of our results and made our discussion more nuanced to acknowledge this aspect. 

We have also added a detailed description about our model selection process in the methods section and provide a model selection table for each analysis in the supplementary materials.

The authors seem to overcome persisting challenges associated with the validation and calibration of accelerometer data by ground-truthing on-board measures with direct observations in captivity, but here the methods are not described any further and sample sizes (2 owls - how many different loggers were deployed?) might be too small to achieve robust behavioural classifications.

Thank you for the comment. Details of our methods of behavioural identification are provided in lines 385 – 429. There are two reasons why our results should not be limited by the sample size. First, we used the temporal sequence of changes in acceleration, and rates of change in acceleration data, which make the methods robust to individual differences in acceleration values. Furthermore, our methods for behavioural identification were not based on machine learning. Instead, we use a Boolean based approach (as described in Wilson et al. 2018. MEE), which is more robust to small differences in absolute values that might occur e.g. in relation to slight changes in device position. 

Recommendation for the authors:

Reviewer #1 (Recommendations For The Authors): 

Comment 1. This study provides new insights into animals' foraging behavior and will probably inspire other researchers to examine foraging behavior in such high resolution.

We hope so, thank you.

Comment 2. However, it is necessary to describe better the measured landing force and the hunting strike and perching behavior so the readers can understand these methods when reading the results (and without reading the Methods).

We have now changed the text in the “Results” to help the reader understand the key methods while reading the results.

Comment 3. In addition, make sure you use the same terminology for hunting strategies during the entire paper and especially in all figures and corresponding result descriptions.

We now use consistent terminology throughout the text and figures. We hope that this is now clear in the revised manuscript.

Comment 4. In addition, although I find your statement about the link between acoustic camouflage and fitness reasonable, it should be described as a hypothesis or examined if you want to keep the direct link statement. I believe showing a direct link can add an additional outstanding aspect to this paper, but I also understand that it can be addressed in a separate paper.

We agree that the relationship between hunting success and barn owl fitness is an important topic, but it necessitates a consideration of both hunting strategies, including hunting on the wing, which extends beyond the limits of our current study. Indeed, our primary objective was to conduct a detailed examination of the interplay between acoustic camouflage and the success of the sit-and-wait technique.

However, we have edited the manuscript to explicitly describe the link between acoustic camouflage and fitness as a hypothesis. We believe this adjustment provides a more accurate representation of our approach. We hope this clarifies the specific emphasis of our work and its contribution to the understanding of barn owl hunting behavior.

Here are my detailed comments about the paper: 

Comment 5. Title: Consider changing the title to "Acoustic camouflage predicts hunting success in a wild predator." 

We would like to thank you for your nice proposition. However, we opted for a different title, which is now “Landing force reveals new form of motion-induced sound camouflage in a wild predator”.

Comment 6. Line 91-93: Please provide additional information about the collected dataset, including: 

Description of the total period of observations, an average and standard deviation of perching and hunting attempt events per individual per night, number of foraging trips per individual per night, details about the geographic location and characteristics of the habitat, season, and reproductive state. 

The revised manuscript now includes detailed information about the collected dataset (i.e. study area, reproductive state, etc…). “We used GPS loggers and accelerometers to record high resolution movement data during two consecutive breeding seasons (May to August in 2019 and 2020) from 163 wild barn owls (79 males and 84 females) breeding in nest boxes across a 1,000 km² intensive agricultural landscape in the western Swiss plateau.” Results section, lines 79 – 82

Details about the number of foraging trips per individuals and per night are now presented in the results: “Sexual dimorphism in body mass was marked among our sampled individuals. Males were lighter than females (84 females, average body mass: 322 ± 22.6 g; 79 males, average body mass 281 ± 16.5 g, Fig S6) and provided almost three times more prey per night than females (males: 8 ± 5 prey per night; females: 3 ± 3 prey per night; Fig.S7). Males also displayed higher nightly hunting effort than females (Males: 46 ± 16 hunting attempts per night, n= 79; Females: 25 ± 11 hunting attempts per nights, n=84; Fig. 3A, Fig S8). However, females were more likely to use a sit and wait strategy than males (females: 24% ± 15%, males: 13% ± 10%, Fig.S9). As a result, the number of perching events per night was similar between males and females (Females: 76 ± 23 perching events per nights; Males: 69 ± 20 perching events per night; Fig S8).” (lines 165 – 174) 

Comment 7. In addition, state if the information describes breeding pairs of males and females and provides statistics on the number of tracked pairs and the number of nest boxes.

The revised manuscript now includes a description of the number of tracked breeding pairs and the number of nest boxes. “Of these individuals, 142 belonged to pairs for which data were recovered from both partners (71 pairs in total, 40 in 2019, 31 in 2020). The remaining 21 individuals belonged to pairs with data from one partner (11 females and 1 male in 2019; 4 females and 5 males in 2020).” (lines 82 – 85.)

Comment 8. Line 93: Briefly define the term "landing force" and explain how it was measured (and let the reader know that there is a detailed description in the Methods).

We now include a brief definition of the “landing force” along with a brief explanation of how it was measured in the results section. “We extracted the peak vectoral sum of the raw acceleration during each landing and converted this to ground reaction force (hereafter “landing force”, in Newtons) using measurements of individual body mass (see methods for detailed description).” (lines 92 – 95).

Comment 9. Line 94: All definitions, including "pre-hunting force," need to be better described in the Results section.

Thank you for this suggestion. We now provided a better description of those key definitions directly in the results section: 

Measurement of landing force: “Barn owls employing a sit-and-wait strategy land on multiple perches before initiating an attack, with successive landings reducing the distance to the target prey (Fig. 2C). 

We used the acceleration data to identify 84,855 landings. These were further categorized into perching events (n = 56,874) and hunting strikes (n = 27,981), depending whether barn owls were landing on a perch or attempting to strike prey on the ground (Fig. 1A and B, see methods for specific details on behavioral classification).” (lines 88 – 95)

Pre-hunt perching force predicts hunting success: “Finally, we analyzed whether the landing force in the last perching event before each hunting attempt (i.e. pre-hunt perching force) predicted variation in hunting success” (lines 229 – 230)

Comment 10. Line 102: Remove "Our analysis of 27,981 hunting strikes showed that" and add "n = 27,981" after the statistics. You have already stated your sample size earlier. There is no need to emphasize it again, although your sample size is impressive.

We modified the text in the results section as suggested.

Comment 11. Line 104: The results so far suggest that the difference in landing force between males and females is an outcome of their different body masses. However, it is not clear what is the reason for the difference in the number of hunting strike attempts between males and females (Lines 104-106). Can you compare the difference in landing force between males and females with similar body mass (females from the lower part of the distribution and males from the upper part)? Is there still a difference?

Thank you, following your comment we made some new analyses that clarified the situation around landing force involved in perching and hunting strike events between sexes. But firstly, we wanted to clarify why there is a difference in number of hunting attempts between males and females. During the breeding season, females typically perform most of the incubation, brooding, and feeding of nestlings in the nest, while the male primarily hunts food for the female and chicks. The female supports the male providing food in a very irregular way, and this changes from pair to pair (paper in prep.). The differences in number of hunting attempts between males and females reflects this asymmetry in food provisioning between sexes during this specific period. We specified this in the revised version of the manuscript (lines 164 – 174). 

We also provide a new analysis to investigate sex differences in mass-specific landing force (force/body mass). We found that males and females produce similar force per unit of body mass during perching events. This demonstrates that the overall higher perching force in females (see Fig. 4C in the manuscript) is therefore driven by their higher body mass. (lines 194 – 199)

Comment 12. Line 154: I believe Boonman et al. (2018) is relevant to this part of the discussion. Boonman, Arjan, et al. found that barn owl noise during landing and taking off is worth considering. ["The sounds of silence: barn owl noise in landing and taking off."

Behavioral Processes 157 (2018): 484-488.]

We now cited this paper in the discussion.

Comment 13. Line 164: Your results do not directly demonstrate a link to fitness, although they potentially serve as a proxy for fitness (add a reference). However, you might have information regarding nestlings' survival - that will provide a direct link for fitness. Change your statement or add the relevant data.

We appreciated your feedback, and we adjusted the language accordingly.

Comment 14. Line 213: If the poles are closer to the ground - is it possible that the higher trees and buildings serve for resting and gathering environmental information over greater distances? For example, identifying prey at farther distances or navigating to the next pole?

Yes, this is indeed the most likely explanation for the fact that owls land more on buildings and trees than on poles until the last period (about 6 minutes) before hunting. In these last minutes, barn owls preferentially use poles, as we showed in figure 2B. The revised manuscript now includes this explanation in the discussion (lines 269 – 284).

Comment 15. Line 250: The product "AXY-Trek loggers" does not appear on the Technosmart website (there are similar names, but not an exact match). Are you sure this is the correct name of the tracking device you used? 

Thank you for pointing out this detail that we missed. The device we used is now called "AXY-Trek Mini" (https://www.technosmart.eu/axy-trek-mini/). We have corrected this error directly in the revised manuscript.

Comment 16. Line 256: Please explain how the devices were recovered. Did you recapture the animals? If so, how? Additionally, replace "after approximately 15 days" with the exact average and standard deviation. Furthermore, since you have these data, please state the difference in body mass between the two measurements before and after tagging.

The birds were recaptured to recover the devices. Adults barn owls were recaptured at their nest sites, again using automatic sliding traps that are activated when birds enter the nest box. The statement "after approximately 15 days" was replaced by the exact mean and standard deviation, which were 10.47 ± 2.27 days. Those numbers exclude five individuals from the total of 163 individuals included in this study. They could not be recaptured in the appropriate time window but were re-encountered when they initiated a second clutch later in the season (4 individuals) or a new clutch the year after (1 individual).

We integrated this previously missing information in the revised manuscript (lines 370 – 372).

Comment 17. Line 259: What was the resolution of the camera? What were the recording methods and schedule? How did you analyze these data? 

The resolution was set to 3.1 megapixel. Motion sensitive camera traps were installed at the entrance to each nest box throughout the period when the barn owls were wearing data loggers, and each movement detected triggered the capture of three photos in bursts. The photos recorded were not analyzed as such for this study, but were used to confirm each supply of prey, which had previously been detected from the accelerometer data. We added these details in the revised manuscript (lines 377 – 380)

Comment 18_1. Figure 1: 

Panel A) Include the sex of the described individual. 

The sex of the described individual is now included in the figure caption.

Comment 18_2. It would be interesting to show these data for both males and females from the same nest box (choose another example if you don't have the data for this specific nest box). 

Although we agree that showing tracks of males and females from the same nest is very interesting, the purpose of this figure was to illustrate our data annotation process and we believe that adding too many details on this figure will make it appear messy. However, the revised manuscript now includes a new figure (Fig. 3A) which shows simultaneous GPS tracks of a male and a female during a complete night, with detailed information about perching and hunting behaviors.

Comment 18_3. Add the symbol of the nest box to the legend. 

Done

Comment 18_4. Provide information about the total time of the foraging trip in the text below. 

The duration of the illustrated foraging trip has been included in the figure caption.

Comment 18_5. To enhance the figure’s information on foraging behavior, consider color coding the trajectory based on time and adding a background representing the landscape. Since this paper may be of interest to researchers unfamiliar with barn owl foraging behavior, it could answer some common questions. 

For similar reasons explained in our answer above (Comment 18_2), we would rather keep this figure as clean as possible. However, we followed your recommendations and included these details in the new Figure 3 described above. In this new figure, GPS tracks are color coded according to the foraging trip number and includes a background representing the landscape. To provide even more detail about the landscape, we added another figure in the supplementary materials (Fig. S2) which provides illustration of barn owls foraging ground and nest site that we think might be of interest for people unfamiliar with barn owls.

Comment 18_6. Inset panels) provide a detailed description of the acceleration insert panels. 

Done

Comment 18_7. Color code the acceleration data with different colors for each axis, add x and y axes with labels, and ensure the time frame on the x-axis is clear. How was the self-feeding behavior verified (should be described in the methods section)? 

We kept both inset panels as simple as possible since they serve here as examples, but a complete representation of these behaviors (with time frame, different colors and labels) is provided in the supplementary materials (figure S3). We included this statement in the figure caption and added a reference to the full representations from the supplementary materials: 

In the Figure caption: “Inset panels show an example of the pattern of the tri-axial acceleration corresponding to both nest-box return and self-feeding behaviors (but see Fig S3for a detailed representation of the acceleration pattern corresponding to each behavior).” 

In the Method section: “Self-feeding was evident from multiple and regular acceleration peaks in the surge and heave axes (resulting in peaks in VeDBA values > 0.2 g and < 0.9 g, Fig.S3D), with each peak corresponding to the movement of the head as the prey was swallowed whole.”.

Comment 18_8. Panel B) Note in the caption that you refer to the acceleration z-axis.

We believe that keeping the statement “the heave acceleration…” in the figure caption is more informative than referring to the “z-axis” as it describes the real dimension to which we are referring. The use of the x, y and z axes can be misleading as they can be interchanged depending on the type and setting of recorders used.

Comment 18_9. Present the same time scale for both hunting strategies to facilitate comparison. You can achieve this by showing only part of the flight phase before perching. 

Done

Comment 18_10. Panel C) Presenting the data for both hunting strategy and sex would provide more comprehensive information about the results and would be relatively easy to implement. 

We agree with your comment. We present the differences in landing force for both landing contexts and sexes in the new Figure 3 as well as in the supplementary materials (Figure S10) of this revised manuscript.

Comment 19. Figure 2: Please provide an explanation of the meaning of the circles in the figure caption.  

Done

Comment 20. Figure 3: 

Panel A) It is unclear how the owl illustration is relevant to this specific figure, unlike the previous figures where it is clear. Also, suggest removing the upper black line from the edge of the figure or add a line on the right side. 

Done (now in Figure 2).

Panel B) "Density" should be capitalized. 

Done

Panel C) Add a scale in meters, and it would be helpful to include an indication of time before hunting for each data point. 

Done

Comment 21. Figure S1: Mark the locations of the nest boxes and ensure that trajectories of different individuals and sexes can be identified. 

The purpose of this figure was to show the spatial distribution of the data. We think that adding nest locations and coloring the paths according to individuals and/or sex will make the figure less clear. However, the new Figure 3 highlights those details.

Comment 22. Figure S2: Show the pitch angle similarly to how you showed the acceleration axes, and explain what "VeDBA" stands for. Provide a description of the perching behavior, clearly indicating it on the figure. Add axes (x, y, z) to the illustration of the acceleration explanation. 

We edited this figure (now figure S3) to show the pitch angle and provide an explanation of what “VeDBA” stands for in the figure caption. The figure caption now also provides a better description of the perching behavior. For the axes (i.e. X, Y, Z), we prefer to refer to the heave, surge, and sway as this is more informative and refers to what is usually reported in studies working with tri-axial accelerometers.

Comment 23. Table S1: Improve the explanation in the caption and titles of the table. 

Done

Reviewer #2 (Recommendations For The Authors): 

Comment 1. From the public review and my assessment there, the authors can be assured that I thoroughly enjoyed the read and am looking forward to seeing a revised and improved version of this paper. 

We thank the reviewer for this comment. We revised the manuscript according to their comments.

Comment 2. In addition to my major points stated above, I would like to add the following recommendations: 

The manuscript is overall well written, but it uses a very pictorial language (a little as if we were in a David Attenborough documentary) that I find inappropriate for a research paper (especially in the abstract and introduction, "remarkable" (2x), "sophisticated" (are there any unsophisticated adaptations? We are referring to something under selection after all) etc.

We appreciated that you found the paper overall well written, and we understand the comment about pictorial language. We therefore slightly changed the text to make sure that the adjective used to describe adaptive strategies are not over-emphasized.

Comment 3. Abstract 

"While the theoretical benefits of predator camouflage are well established, no study has yet been able to quantify its consequences for hunting success." - This claim is actually not fully true: 

Nebel Carina, Sumasgutner Petra, Pajot Adrien and Amar Arjun 2019: Response time of an avian prey to a simulated hawk attack is slower in darker conditions, but is independent of hawk colour morph. Soc. open sci.6:190677 

We edited our claim to specify that the consequences of predator camouflage on hunting success has never been quantified in natural conditions and cited the reference in the introduction.

Comment 4. Line 23. Rephrase to: "We used high-resolution movement data to quantify how barn owls (Tyto alba) conceal their approach when using a sit-and-wait strategy, as well as the power exerted during strikes." 

We edited this sentence in the abstract, as suggested.

Comment 5. Results 

There is a disconnect between the objectives outlined at the end of the introduction and the following results that should be improved. 

The authors state: "Using high-frequency GPS and accelerometer data from wild barn owls (Tyto alba), we quantify the landing dynamics of this sit-and-wait strategy to (i) examine how birds adjust their landing force with the behavioral and environmental context and (ii) test the extent to which the magnitude of the predator cue affects hunting success." But one of the first results presented are sex differences. 

This is a fair point. We have now changed our statement in the end of the introduction as well as the order of the results to improve the link between the objectives outlined in the introduction and the way result are presented. 

Comment 6. At this stage, the reader does not even know yet that we are presented with a size-dimorphic species that also has very different parental roles during the breeding season. This should be better streamlined, with an extra paragraph in the introduction. And these sex differences are then not even discussed, so why bring them up in the first place (and not just state "sex has been fitted as additional co-variate to account for the size-dimorphism in the species" without further details). 

We edited the way the objectives are outlined in the introduction to cover the size dimorphism (lines 70 – 76). We also completely changed the way the sex differences are presented in the results, including a new analysis that we believe provides a better comprehensive understanding of barn owl foraging behavior (lines 164 – 206). Finally, we added a new paragraph in the discussion to consider those results (lines 319 – 339).

Comment 7. It is not clear to me where and how high-resolution GPS data were used? The results seem to concentrate on ACC – why GPS was used and how it features should be foreshadowed in a few lines in the introduction. I definitively prefer having the methods at the end of a manuscript, but with this structure, it is crucial to give the reader some help to understand the storyline. 

GPS data were used to validate some behavioral classifications (prey provisioning for example), but most importantly they were used to link each landing event with perch types. We edited the text in the result section to clarify where GPS and/or ACC data were used.

Comment 8. Discussion 

Move the orca example further down, where more detail can be provided to understand the evidence. 

After our extensive edits in the discussion, we felt this example was interrupting the flow. We now cite this study in the introduction. 

Comment 9. Size dimorphism and evident sex differences are not discussed. 

The revised manuscript now includes a new paragraph in the discussion in which sex differences are discussed (lines 319 – 339).

Comment 10. Be more precise in the terminology used (for example, land use seems to be interchangeable with habitat characteristics?). 

We modified “land use” with “habitat data” in the revised manuscript.

Comment 11. Methods 

Please provide a justification for the very high weight limit (5%; line 256). This limit is outdated and does not fulfill the international standard of 3% body weight. I assume the ethics clearance went through because of the short nature of the study (i.e., the birds were not burdened for life with the excess weight? But a line is needed here or under the ethics considerations to clarify this). 

The 5% weight limit was considered acceptable due to the short deployment period, and we now edited the ethics statement to emphasize this point. However, it is important to note that there is no real international standard, with both 3% and 5% weight limits being commonly used. Both limits are arbitrary and the impact of a fixed mass on a bird varies with species and flight style. All owls survived and bred similarly to the non-tagged individuals in the population (lines 373 – 376 & lines 558 – 561)

EDITORIAL COMMENT: We strongly encourage you to provide further context and clarification on this issue, as suggested by the Reviewer. On a related point, the ethics statement refers to GPS loggers, rather than GPS and ACC devices; we encourage you to clarify wording here.

Thank you for highlighting this point that indeed needed some clarifications.

Although we have used the terminology "GPS recorders", the authorization granted by the Swiss authorities for this study effectively covers the entire tracking system, which combines both GPS and ACC recorders in the same device. We have therefore changed the wording used in the ethics statement to avoid any misunderstanding (lines 373 – 376 & lines 558 – 561)

Comment 12. Please provide more information on the model selection approach, what does "Non-significant terms were dropped via model simplification by comparing model AIC with and without terms." mean? Did the authors use a stepwise backward elimination procedure (drop1 function)? Or did they apply a complete comparison of several candidate models? I think a model comparison approach rather than stepwise selection would be more informative, as several rather than only one model could be equally probable. This might also improve model weights or might require a model averaging procedure - current reported R2values are very small and do not seem to support the results well. 

We apologize for the lack of details about this important aspect of the statistical analysis. We applied an automated stepwise selection using the dredge function from the R package “MuMin”, therefore applying a complete comparison of several candidate models. The final models were chosen as the best models since the number of candidate models within ∆AIC<2 was relatively low in each analysis and thus a model averaging was not appropriate here. We edited the methods section to ensure clarity, and added model selection tables for each analysis, ranked according to AICc scores, in the supplementary materials (lines 532 – 552)

In addition, we agree that the reported R-squared values in our analyses are quite low, specifically regarding the influence of pre-hunt perching force on hunting success (cond R2 = 0.04). Nonetheless, landing impact still has a notable effect size (an increase of 1N reduces hunting success by 15%). The reported values are indicative of the inherent complexity in studying hunting behavior in a wild setting where numerous variables come into play. We specifically investigated the hypothesis that the force involved during pre-hunt landings, and consequently the emitted noise, influences the success of the next hunting attempt in wild barn owls. Factors such as prey behavior and micro-habitat characteristics surrounding prey (such as substrate type and vegetation height) are most likely to be influential but hard, or nearly impossible, to model. We now cover this in a more nuanced way in the discussion (lines 266 – 268)

Comment 13. Please explain why BirdID was nested in NightID - this is not clear to me.

Probably here there is a misunderstanding because we wrote that we nested NightID in BirdID (and not BirdID in NightID). 

Comment 14. I hope the final graphs and legends will be larger, they are almost impossible to read. 

We enlarged the graphs and legends as much as possible to improve readability. However, looking at the graphs in the published version they seem clear and readable.

Comment 15. Figure S1: Does "representation" mean the tracks don't show all of the 163 owls? If so, be precise and tell us how many are illustrated in the figure. 

Figure S1 represent the tracks for each of the 163 barn owls used in the study. We changed the terminology used in the figure caption to avoid any misunderstanding.

Comment 16. Figure S4: Please adjust the y-axis to a readable format. 

Done

Author response:

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

Response to Reviewer #1 comments:

(1) SY1 aggregation enhances (in terms of number of aggregates) when Sphingolipid biosynthesis is blocked.

a. Line no 132-133: I agree that there is circumstantial evidence that the maturation pathway of SY1 IB is perturbed by knocking down sphingolipid biosynthesis. However, to prove this formally, a time course of IB maturation needs to be reported in the knock-down strains.

Please see Figure 2-figure supplement 1 for the time course of SY1 IB maturation in the knock-down strains. We have added the result to the manuscript, please see lines 129-131on page 5 in the revised version.

b. It will be good to have formal evidence that sphingolipids are indeed downregulated when these genes are downregulated (knocked down).

This issue has been clearly evidenced in previous reports, and we have added the appropriate references in the main text. For example, down-regulation of LCB1 or SPT in yeast decreased sphingolipid levels by Huang et al (https://doi.org/10.1371/journal.pgen.1002493). According to the report from Tafesse FG, et al (https://doi.org/10.1371/journal.ppat.1005188), in mammalian cells in which Sptlc2 was knocked down by CRISPR/Cas9, sphingolipid and glucosylceramide production is almost completely blocked. In addition, the levels of sphingosine, sphingomyelin, and ceramide were significantly lower compared to control cells. Please see lines 143-144 on pages 6 and lines 232-233 on pages 9 in the revised version.

(2) In a normal cell (where sphingolipid biosynthesis is not hampered), the aggregate of SY1 (primarily the Class I aggregate) is localized only on the mitochondrial endomembrane system. These results have been published for other aggregation-prone proteins and are partly explained in the literature. However, their role in the context of maturation is relatively unclear. The authors however provide no strong evidence to show if mitochondria are preferentially involved in any of the stages of IB maturation. Specifically:

a. Line 166-167: It is not clear from Figure 4B that this is indeed the case. Only the large IB seems to colocalize in all three panels (Class I, 2, 3) with Mitotracker. The smaller IBs in 2 and 3 do not show any obvious co-localization. It is also possible that they do co-localize, but it is not clear from the images. I would appreciate it if the authors either provide stronger evidence (better image) or revise this statement. This point is crucial in some claims made later in the manuscript. (pls see comment #5A).

Based on the reviewer's suggestion, we replaced the images in Figure 4B. In addition, we added the 3D reconstruction results of the interrelationship between Class 3 and Mitotracker in Figure 4-figure supplement 1B, to further show their relationship.

(3) The localization is due to the association of SY1 (aggregates) with mitochondrial proteins like Tom70, Tim44 etc. There are some critical points (that can strengthen the manuscript) that are not addressed here. Primarily, the important role of mitochondria in the context of toxicity is neglected. Although the authors have mentioned in the discussion that it was not their main focus, I believe that this is the novel part of the manuscript and this part is potentially a beautiful addition to literature. The questions I found unanswered are:

a. Is the localization completely lost upon deleting these genes? I see only a partial loss in shape/localization. This is not properly explained in the manuscript. The shape of the IB seems to remain intact while the localization is slightly altered. This indicates that even when sphingolipid is present, SY1 localization is dictated by the (lipid-raft embedded) proteins. Interestingly, it shows that even in the absence of mitochondrial localization the shape of the aggregates is not altered in these deletion strains! How do the authors explain this if mitochondrial surface sphingolipids are important for IB maturation? (the primary screen found that sphingolipid biosynthesis promotes the formation of Class I IBs).

We agree that mutation in one mitochondrial binding protein only a partial loss in shape/localization, and we have replaced “association” with “surrounding” in the manuscript. Please see lines 163-166 on page 6 in the revised version. In mutants that interact with SY1, we counted the proportion of Class 3 aggregates formed by SY1 and found an increase in the proportion of SY1 Class 3 aggregates in the deletion mutants compared to controls, partially lost interaction of SY1 with mitochondria has effect on shape of aggregates, as detailed in line 184 on page 7 and Figure 4-figure supplement 1D. We think that SY1 interactions with mitochondrial proteins are important for the localization of SY1 IB in mitochondria, whereas sphingolipids play an important role in facilitating the formation of Class 1 IBs from Class 3 aggregates.

b. What happens to the toxicity when the aggregates are not localized on mitochondria?

We thank the reviewer for the comments, however to investigate this issue, since a single mutant can only partially affect the phenotype, it may be necessary to construct groups of mutants of different genes to observe the effect, which we will further elucidate in our future studies. What we want to show in this work is that SY1 achieves binding to mitochondria by interacting with these mitochondrial proteins.

c. It is important to note that sphingolipids may affect the whole process indirectly by altering pathways involved in protein quality control or UPR. UPR may regulate the maturation of IBs. It is therefore important to test if any of the effects seen could be of direct consequence.

We agree with the reviewer's comments, but there was no significant enrichment for protein quality control or UPR-related pathways in our genome-wide screen, so it is unlikely that sphingolipids indirectly cause maturation of IBs by affecting these two pathways. We addressed this issue in our discussion. Please see lines 325-328 on page 12 in the revised version.

d. In Figure 4D, the authors find SY1 when they pull down Tom70, Tom37 or Tim44. Tim44 is a protein found in the mitochondrial matrix, how do the authors explain that this protein is interacting with a protein outside the mitochondrial outer membrane?

This interaction could be potentially due to that some of the soluble SY1 enter the mitochondrial matrix and interact with Tim44.

e. Is it possible that the authors are immunoprecipitating SY1 since IBs have some amount of unimported mitochondrial proteins in aggregates formed during proteotoxic stress (https://doi.org/10.1073/pnas.2300475120) (Liu et al. 2023).

Our Co-IP experiments were performed in the soluble state supernatant, so mitochondrial proteins in aggregates were not detected.

f. Line 261 (Discussion): Does deletion of Tom70 or one of the anchors increase Class III aggregation and increase toxicity? Without this, it is hard to say if mitochondria are involved in detoxification.

We thank the reviewer for the comments, please see our response to comment 3b.

(4) This fuels the loss of mitochondrial function.

a. Line 218-219: Although the change is significant, the percentage change is very slight. Is this difference enough to be of physiological relevance in mitochondrial function? In our hands, the DCF fluorescence is much more variable.

We agree with the reviewer that there is a small difference (but significant). To which extend such a difference be of physiological relevance in mitochondrial function need to be further investigated.

b. Is SY1-induced loss of mitochondrial function less in knockouts of Tom70 or the other ones found to be important for localizing the SY1 aggregate to mitochondria?

We examined mitochondrial membrane potential (indicated by Rho 123 fluor intensity) in tom70Δ, tom37Δ and control his3Δ strains and found that the knocking out of Tom70 or Tom37 reduced the mitochondrial toxicity caused by SY1 expression. Please see lines 212-214 on page 8 in the revised version, and Figure 5-figure supplement 2.

(5) Mitochondrial function is further abrogated when there is a block in sphingolipid biosynthesis.

a. Myriosin acted like the deletion strains that showed less structured aggregates. There were more aggregates (Class 3) but visually they seemed to be spread apart. The first comment (#2A) on aggregate classes and their interaction with mitochondria may become relevant here.

According to a recent review article (https://doi.org/10.3389/fcell.2023.1302472), sphingolipids are present in the mitochondrial membrane, bind to many mitochondrial proteins and have emerged as key regulators of mitochondrial morphology, distribution and function. Dysregulation of sphingolipid metabolism in mitochondria disrupts many mitochondrial processes, leading to mitochondrial fragmentation, impaired bioenergetics and impaired cellular function. Myriocin treatment, which affects sphingolipid metabolism, causes mitochondria to become more fragmented, which may explain why the aggregates appear visually spread apart. Regarding the interaction with mitochondria, we counted the proportion of SY1 aggregates surrounded by mitochondria after treatment with myriocin, and the results were not significantly different compared to the control. Please see lines 168-169 on page 6 in the revised version, and Figure 4-figure supplement 1C.

(6) A similar phenomenon is conserved in mammalian cell lines.

a. Line 225-226: Did the authors confirm that this was the only alteration in the genome? Or did they complement the phenotype, genetically?

We performed SPTLC2 gene complementation experiments in knockout cell lines and found that SPTLC2 gene complementation was able to reduce the number of cells forming IBs and the percentage of dispersed irregular IBs compared to controls. Please see lines 240-242 on page 9 in the revised version, and Figure 6-figure supplement 2B.

b. Line 241-245: One of the significant phenotypes observed by downregulating sphingolipid biosynthesis in yeast and mammalian cells, was the increase in the number of aggregates. This is not shown in myriocin treatment in mammalian cells. This needs to be shown to the main concordance with the original screen and the data presented with the KO mammalian cell line.

Please see Figure 7-figure supplement 1A for the data on the proportion of cells forming SY1 IBs after myriocin treatment in mammalian cells, and myriocin treatment in mammalian cells was the same as in the KO mammalian cell line.

Minor Comments:

Line 273-275: How is this statement connected to the previous statement? Was it observed that aggregate fusion was advantageous to the cells?

Yes, aggregate/oligomer fusion is advantageous to the cells, and we have modified the previous statement. Please see line 280 on page 10 in the revised version.

Line 293-294: I am not sure I understand this statement.

We have modified this statement. Please see lines 302-303 on page 11 in the revised version.

Line 295-296: But the authors have commented at multiple places that mitochondria detoxify the cell from SY1 aggregates. I find this link fascinating and worth investigating. Most of the current work has some known links in literature (not everything). The mitochondrial connection being the most fascinating one.

We have removed this sentence. We have added a validation experiment for the role of mitochondrial activity in SY1 IB maturation in the revised version.

Line 318: Do the authors mean: The open question is...

Thanks to the reviewer, we have corrected it.

Response to Reviewer #2 comments:

I recommend considering live cell microscopy to analyze whether sphingolipid-dependent formation of SY1 IB takes place at the mitochondrial outer membrane. The IBs could also be produced at other membranes and then transported to the mitochondrial outer membrane for storage.

As shown in Figure 4A, SY1 IB primarily interacts with mitochondria.

I recommend analyzing whether mitochondrial activity is needed for sphingolipid-dependent SY1 IB formation. Are these IBs localized to mitochondrial membrane solely as scaffold or are these organelles needed to provide the energy for driving IB formation in concert with sphingolipids? This point could be addressed with rho0 strains lacking mitochondrial DNA.

We thank the reviewer for this recommendation. We expressed SY1 protein in BY4741 rho0 strain as suggested and found that the maturation and mitochondrial surrounding state of SY1 IB was not affected by mitochondrial activity. Please see lines 185-187 on page 7 in the revised version, and Figure 4-figure supplement 1E and 1F.

The authors should be more precise in the statistical methods used in their study (method, pre-/post-tests, number of replicates...).

We thank the reviewer for the comment and we have provided a more precise description of the statistical methods. Please see lines 531-534 on page 19 and figure legends in the revised version.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

This manuscript aims at a quantitative model of how visual stimuli, given as time-dependent light intensity signals, are transduced into electrical currents in photoreceptors of macaque and mouse retina. Based on prior knowledge of the fundamental biophysical steps of the transduction cascade and a relatively small number of free parameters, the resulting model is found to fairly accurately capture measured photoreceptor currents under a range of diverse visual stimuli and with parameters that are (mostly) identical for photoreceptors of the same type.

Furthermore, as the model is invertible, the authors show that it can be used to derive visual stimuli that result in a desired, predetermined photoreceptor response. As demonstrated with several examples, this can be used to probe how the dynamics of phototransduction affect downstream signals in retinal ganglion cells, for example, by manipulating the visual stimuli in such a way that photoreceptor signals are linear or have reduced or altered adaptation. This innovative approach had already previously been used by the same lab to probe the contribution of photoreceptor adaptation to differences between On and Off parasol cells (Yu et al, eLife 2022), but the present paper extends this by describing and testing the photoreceptor model more generally and in both macaque and mouse as well as for both rods and cones.

Strengths:

The presentation of the model is thorough and convincing, and the ability to capture responses to stimuli as different as white noise with varying mean intensity and flashes with a common set of model parameters across cells is impressive. Also, the suggested approach of applying the model to modify visual stimuli that effectively alter photoreceptor signal processing is thought-provoking and should be a powerful tool for future investigations of retinal circuit function. The examples of how this approach can be applied are convincing and corroborate, for example, previous findings that adaptation to ambient light in the primate retina, as measured by responses to light flashes, mostly originates in photoreceptors.

Weaknesses:

In the current form of the presentation, it doesn't become fully clear how easily the approach is applicable at different mean light levels and where exactly the limits for the model inversion are at high frequency. Also, accessibility and applicability by others could be strengthened by including more details about how parameters are fixed and what consensus values are selected.

Thank you - indeed a central goal of writing this paper was to provide a tool that could be easily used by other laboratories. We have clarified and expanded four points in this regard: (1) we have stated more clearly that mean light levels are naturally part of inversion process, and hence the approach can be applied across a broad range of light levels (lines 292-297); (2) we have expanded our analysis of the high frequency limits to the inversion and added that expanded figure to the main text (new Fig 5); (3) we have included additional detail about our calibration procedures, including our calibration code, to facilitate transfer to other labs; and, (4) we have detailed the procedure for identification of consensus parameters (line 172-182, 191-199 and Methods section starting on line 831).

Reviewer #2 (Public Review):

Summary:

This manuscript proposes a modeling approach to capture nonlinear processes of photocurrents in mammalian (mouse, primate) rod and cone photoreceptors. The ultimate goal is to separate these nonlinearities at the level of photocurrent from subsequent nonlinear processing that occurs in retinal circuitry. The authors devised a strategy to generate stimuli that cancel the major nonlinearities in photocurrents. For example, modified stimuli would generate genuine sinusoidal modulation of the photocurrent, whereas a sinusoidal stimulus would not (i.e., because of asymmetries in the photocurrent to light vs. dark changes); and modified stimuli that could cancel the effects of light adaptation at the photocurrent level. Using these modified stimuli, one could record downstream neurons, knowing that any nonlinearities that emerge must happen post-photocurrent. This could be a useful method for separating nonlinear mechanisms across different stages of retinal processing, although there are some apparent limitations to the overall strategy.

Strengths:

(1) This is a very quantitative and thoughtful approach and addresses a long-standing problem in the field: determining the location of nonlinearities within a complex circuit, including asymmetric responses to different polarities of contrast, adaptation, etc.

(2) The study presents data for two primary models of mammalian retina, mouse, and primate, and shows that the basic strategy works in each case.

(3) Ideally, the present results would generalize to the work in other labs and possibly other sensory systems. How easy would this be? Would one lab have to be able to record both receptor and post-receptor neurons? Would in vitro recordings be useful for interpreting in vivo studies? It would be useful to comment on how well the current strategy could be generalized.

We agree that generalization to work in other laboratories is important, and indeed that was a motivation for writing this as a methods paper. The key issue in such generalization is calibration. We have expanded our discussion of our calibration procedures and included that code as part of the github repository associated with the paper. Figure 10 (previously Figure 9) was added to illustrate generalization. We believe that the approach we introduce here should generalize to in vivo conditions. We have expanded the text on these issues in the Discussion (sections starting on line 689 and 757).

Weaknesses:

(1) The model is limited to describing photoreceptor responses at the level of photocurrents, as opposed to the output of the cell, which takes into account voltage-dependent mechanisms, horizontal cell feedback, etc., as the authors acknowledge. How would one distinguish nonlinearities that emerge at the level of post-photocurrent processing within the photoreceptor as opposed to downstream mechanisms? It would seem as if one is back to the earlier approach, recording at multiple levels of the circuit (e.g., Dunn et al., 2006, 2007).

Indeed the current model is limited to a description of rod and cone photocurrents. Nonetheless, the transformation of light inputs to photocurrents can be strongly nonlinear, and such nonlinearities can be difficult to untangle from those occurring late in visual processing. Hence, we feel that the ability to capture and manipulate nonlinearities in the photocurrents is an important step. We have expanded Figure 10 to show an additional example of how manipulation of nonlinearities in phototransduction can give insight into downstream responses. We have also noted in text that an important next step would be to include inner segment mechanisms (section starting on line 661); doing so will require not only characterization of the current-to-voltage transformation, but also horizontal cell feedback and properties of the cone output synapse.

(2) It would have been nice to see additional confirmations of the approach beyond what is presented in Figure 9. This is limited by the sample (n = 1 horizontal cell) and the number of conditions (1). It would have been interesting to at least see the same test at a dimmer light level, where the major adaptation mechanisms are supposed to occur beyond the photoreceptors (Dunn et al., 2007).

We have added an additional experiment to this figure (now Figure 10) which we feel nicely exemplifies the approach. The approach that we introduce here really only makes sense at light levels where the photoreceptors are adapting; at lower light levels the photoreceptors respond near-linearly, so our “modified” and “original” stimuli as in Figure 10 (previously Figure 9) would be very similar (and post-phototransduction nonlinearities are naturally isolated at these light levels).

Reviewer #3 (Public Review):

Summary:

The authors propose to invert a mechanistic model of phototransduction in mouse and rod photoreceptors to derive stimuli that compensate for nonlinearities in these cells. They fit the model to a large set of photoreceptor recordings and show in additional data that the compensation works. This can allow the exclusion of photoreceptors as a source of nonlinear computation in the retina, as desired to pinpoint nonlinearities in retinal computation. Overall, the recordings made by the authors are impressive and I appreciate the simplicity and elegance of the idea. The data support the authors' conclusions but the presentation can be improved.

Strengths:

-  The authors collected an impressive set of recordings from mouse and primate photoreceptors, which is very challenging to obtain.

-  The authors propose to exploit mechanistic mathematical models of well-understood phototransduction to design light stimuli that compensate for nonlinearities.

-  The authors demonstrate through additional experiments that their proposed approach works.

Weaknesses:

-  The authors use numerical optimization for fitting the parameters of the photoreceptor model to the data. Recently, the field of simulation-based inference has developed methods to do so, including quantification of the uncertainty of the resulting estimates. Since the authors state that two different procedures were used due to the different amounts of data collected from different cells, it may be worthwhile to rather test these methods, as implemented e.g. in the SBI toolbox (https://joss.theoj.org/papers/10.21105/joss.02505). This would also allow them to directly identify dependencies between parameters, and obtain associated uncertainty estimates. This would also make the discussion of how well constrained the parameters are by the data or how much they vary more principled because the SBI uncertainty estimates could be used.

Thank you - we have improved how we describe and report parameter values in several ways. First, the previous text erroneously stated that we used different fitting procedures for different cell types - but the real difference was in the amount of data and range of stimuli we had available between rods and cones. The fitting procedure itself was the same for all cell types. We have clarified this along with other details of the model fitting both in the main text (lines 121-130) and in the Methods (section starting on line 832). We also collected parameter values and estimates of allowed ranges in two tables. Finally, we used sloppy modeling to identify parameters that could covary with relatively small impact on model performance; we added a description of this analysis to the Methods (section starting on line 903).

-  In several places, the authors refer the reader to look up specific values e.g. of parameters in the associated MATLAB code. I don't think this is appropriate, important values/findings/facts should be in the paper (lines 142, 114, 168). I would even find the precise values that the authors measure interesting, so I think the authors should show them in a figure/table. In general, I would like to see also the average variance explained by different models summarized in a table and precise mean/median values for all important quantities (like the response amplitude ratios in Figures 6/9).

We have added two tables with these parameters values and estimates of allowable ranges. We also added points to show the mean (and SD) across cells to the population figures and added those numerical values to the figure legends throughout.

-  If the proposed model is supposed to model photoreceptor adaptation on a longer time scale, I fail to see why this can be an invertible model. Could the authors explain this better? I suspect that the model is mainly about nonlinearities as the authors also discuss in lines 360ff.

For the stimuli that we use we see little or no contribution of slow adaptation in phototransduction. We have expanded the description of this point in the text and referred to Angueyra et al (2022) which looks at this issue in more detail for primate cones (paragraph starting on line 280).

-  The important Figures 6-8 are very hard to read, as it is not easy to see what the stimulus is, the modified stimulus, the response with and without modification, what the desired output looks like, and what is measured for part B. Reworking these figures would be highly recommended.

We have reworked all of the figures to make the traces clearer.

-  If I understand Figure 6 correctly, part B is about quantifying the relative size of the response to the little first flash to the little second flash. While clearly, the response amplitude of the second flash is only 50% for the second flash compared to the first flash in primate rod and cones in the original condition, the modified stimulus seems to overcompensate and result in 130% response for the second flash. How do the authors explain this? A similar effect occurs in Figure 9, which the authors should also discuss.

Indeed, in those instances the modified stimulus does appear to overcompensate. We suspect this is due to differences in sensitivity of the specific cells probed for these experiments and those used in the model construction. We now describe this limitation in more detail (lines 524-526). A similar point comes up for those experiments in which we speed the photoreceptor responses (new FIgure 9B), and we similarly note that the cells used to test those manipulations differed systematically from those used to fit the model (lines 558-560).

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

I only have a few minor questions and suggestions for clarification.

It hasn't become fully clear to me how general the model is when different mean light levels (on long-time scales) are considered. Are there slow adaptation processes not captured in the model that affect model performance? And how should one go about setting the mean light level when, for example, probing ganglion cells with a stimulus obtained through model inversion? Should it work to add an appropriate DC component to the current that is provided as input to the inverted model? (Presumably, deriving a stimulus and then just adding background illumination should not work, or could this be a good approximation, given a steady state that is adapted to the background?)

We have clarified in the main text that slow adaptation does not contribute substantially to responses to the range of stimuli we explored (lines 281-289). We have also clarified that the stimulus in the model inversion is specified in isomerizations per second - so the mean value of the stimulus is automatically included in the model inversion (lines 293-298).

Furthermore, a caveat for the model inversion seems to be the potential amplification of high-frequency noise. The suggested application of a cutoff temporal frequency seems appropriate, but data are shown only for a few example cells. Is this consistent across cells? (Given that performance between, e.g., mouse cones can vary considerably according to Fig. 4B?) I would also like to suggest moving the corresponding Supplemental Figure (4.1) into the main part of the manuscript, as it seems quite important.

We have added population analysis to the new Figure 5 (which was Figure 4 - Figure Supplement 1). We have also clarified that the amplification of high frequency noise is an issue only when we try to apply model inversion to measured stimuli. When we use model inversion to identify stimuli that elicit desired responses, the target responses are computed from a linear model that has no noise, so this is not a concern in applications like those in Figures 6-10.

Also, could the authors explain more clearly what the effect of the normalization of the estimated stimulus by the power of the true stimulus is? Does this simply reduce power at high frequency or also affect frequencies below the suggested cutoff (where the stimulus reconstruction should presumably be accurate even without normalization)?

Indeed this normalization reduces high frequency power and has little impact on low frequencies where the inversion is accurate; this is now noted in the text (line 363). As for amplification of high frequency noise (previous comment), the normalization by the stimulus power is only needed when inverting measured responses (i.e. responses with noise) and is omitted when we are identifying stimuli that elicit desired responses (e.g. in Figures 6-10).

While the overall performance of the model to predict photoreceptor currents is impressive, it seems that particular misses occur for flashes right after a step in background illumination and for the white-noise responses at low background illumination (e.g. Figure 1B). Is that systematic, and if so what might be missing in the model?

Indeed the model (at least with fixed parameters across stimuli) appears to systematically miss a few aspects of the photoreceptor responses. These include the latency of the response to a bright flash and the early flashes in the step + flash protocol in Figure 1B. Model errors for the variable mean noise stimulus (Figure 2) showed little dependence on time even when responses were sorted by mean light level and by previous mean level. Model errors did not show a clear systematic dependence on light level; this likely reflects, at least in part, the use of mean-square-error to identify model parameters. We have expanded our discussion of these systematic errors in the text (lines 164-166).

I was also wondering whether this is related to the fact that in Figure 9B, the gain in the modified condition is actually systematically higher when there is more background light. Do the authors think that this could be a real effect or rather an overcompensation from the model? (By the way, is it specified what "Delta-gain" really is, i.e., ratio or normalized difference?)

We suspect this is an issue with the sensitivity of the specific cells for which we did these experiments (i.e. variability in the gamma parameter between cells). This sensitivity varies between cells, and such variations are likely to place the strongest limitation on our ability to use this approach to manipulate responses in different retinas. We now note those issues in the Results (lines 523-526, 557-559 and 591-593) with reference to Figures 9 (previously Figure 8) and 10 (previously Figure 9), and describe this limitation more generally in the Discussion (section starting on line 649). We have also changed delta-gain to response ratio, which seemed more intuitive.

Maybe I missed this, but it seems that the parameter gamma is fitted in a cell-type-specific fashion (e.g. line 163), but then needs to be fixed for held-out cells. How was this done? Is there much variability of gamma between cells?

There is variability in gamma between cells, and this likely explains some of systematic differences between data and model (see above and Methods, lines 902-903). For the consensus models in Figure 2B, gamma was allowed to vary for each cell while the remaining consensus model parameters were fixed. Gamma was set equal to the mean value across cells for model inversion (i.e. for all of the analyses in Figures 4-10). We have described the fitting procedure in considerably more detail in the revised Methods (starting on line 832).

For completeness, it would be nice to have the applied consensus model parameters in the manuscript rather than just in the Matlab code (especially since the code has not been part of the submission). Also, some notes on how the numerical integration of the differential equations was done would be nice (time step size?).

We have added tables with consensus parameters and estimates of the sensitivity of model predictions to each parameter. We have also added additional details about the numerical approaches (including the time step) to Methods.

Similarly, it would be nice to explicitly see the relationships that are used to fix certain model parameters (lines 705ff). And can the constants k and n (lines 709-710) be assumed identical for different species and receptor types?

We have added more details to the model fitting to the methods, including the use of steady-state conditions to hold certain parameters fixed (lines 862 and 866). We are not aware of any direct comparisons of k and n across species and receptor types. We have noted that model performance was not improved by modest changes in these parameters (due to compensation by other model parameters). More generally, we have explained how some parameters trade for others and hence the logic of fixing some even when exact values were not available.

For the previous measurements of m and beta (lines 712-713), is there a reference or source?

We have added references for these values.

Did the authors check for differences in the model parameters between cone types (e.g., S vs. M)?

We did not include S cones here. They are harder to record from and collecting a fairly large data set across a range of stimuli would be challenging. Our previous work shows that S cones have slower responses than L and M cones, and this would certainly be reflected in differences in model parameters. We have noted this in the text (Methods, line 808-810).

For the stated flash responses time-to-peak (lines 183-184), is this for a particular light intensity with no background illumination?

Those are flashes from darkness - now noted in the text.

Figure 2 - Supplement 1 doesn't have panel labels A and B, unlike the legend.

Fixed - thank you.

Reviewer #2 (Recommendations For The Authors):

(1) Fig. 2B - for some cells, the consensus model seems to fit better than the individual model. How is this possible?

This was mostly an error on our part (we inadvertently included responses to more stimuli in fitting the individual models, which slightly hampered their performance). Even with this correction, however, a few cells remain for which the consensus model outperforms and individual model. We believe this is because there is more data to constrain model parameters for the consensus models (since they are fit to all cells at the same time), and that can compensate for improvements associated with customizing parameters to specific cells.

(2) Fig. 2 Supplement 1, it would be useful to see a blow-up of the data in an inset, as in Fig. 2B.

Thanks - added.

(3) Line 400 - this paragraph could include additional quantification and statistics to back up claims re 'substantially reduced', 'considerably lower'.

We quantify that in the next sentence by computing the mean-square-error between responses and sinusoidal fits (also in Figure 7B, which now includes statistics as well). We have made that connection more direct in the text.

(4) Maybe a supplement to Fig. 8 could show the changes to the stimulus required to alter the kinetics in both directions - to give more insight into part B., especially.

Good suggestion - we have added the stimuli to all of the panels of the figure (now Figure 9).

(5) Fig. 8B - in 'Speed response up' condition - there seems to be error in the model for the decay time of the response - especially for the 'original' condition, which is not quantified in 8C. Was it generally difficult to predict responses to flashes?

That seems largely to reflect that the cells used for those experiments had faster initial kinetics than the average cells (responses to the control traces are also faster than model predictions in these cells - black traces in Figure 9B). We have added this to the text.

(6) Line 678, possibly notes that 405 nm equally activates S and M photopigments in mice, since most of the cones co-express the two photopigments (Rohlich et al., 1994; Applebury et al., 2000; Wang et al., 2011).

Thanks - we have added this (lines 827-829).

(7) The discussion could include a broader description of the various approaches to identifying nonlinearities within retinal circuitry, which include (incomplete list): recording at multiple levels of the circuit (e.g., Kim and Rieke 2001; Rieke, 2001; Baccus and Meister, 2002; Dunn et al., 2006; 2007; Beaudoin et al., 2007; Baccus et al., 2008); recording currents vs. spiking responses in a ganglion cell (e.g., Kim and Rieke, 2001; Zaghloul et al., 2005; Cui et al., 2016); neural network modeling approaches (e.g., Maheswaranathan et al., 2023); optogenetic approaches to studying filtering/nonlinear behavior at synapses (e.g., Pottackal et al., 2020; 2021).

Good suggestion - we have added this to the final paragraph of the Discussion.

Reviewer #3 (Recommendations For The Authors):

-  I am personally not a fan of the style: "... as Figure 4A shows..." or comparable and much prefer a direct "We observe that X is the case (Figure 4A)". If the authors agree, they may want to revise their paper in this way.

We have revised the text to avoid the “... as Figure xx shows” construction. We have retained multiple instances which follow a “Figure xx shows that …” construction (which is both active rather than passive and does not use a personal pronoun).

-  I am not a fan of the title. Light-adaption clamp caters only to a very specialized audience.

We have changed the title to “Predictably manipulating photoreceptor light responses to reveal their role in downstream visual responses.”

-  The parameter fitting procedure should not only be described in Matlab code, but in the paper.

Thanks - we have expanded this in the Methods considerably (section starting on line 832).

-  The authors should elaborate on why different fitting procedures were used.

We did not describe that issue clearly. The fitting procedures used across cells were identical, but we had different data available for different cell types due to experimental limitations. We have substantially revised that part of the main text to clarify this issue (paragraph starting on line 121).

-  The authors state in line 126 that the input stimulus is supposed to mimic eye movements mouse, monkey, or human? Please clarify.

Thanks - we have changed this sentence to “abrupt and frequent changes in intensity that characterize natural vision.”

-  Please improve the figure style. For example, labels should be in consistent capitalization and ideally use complete words (e.g. Figure 2B, 4B, and others).

We have made numerous small changes in the figures to make them more consistent.

-  Is the fraction of variance calculated on held-out-data? Linear models should be added to Figure 2B.

The fraction of variance explained was not calculated on held out data because of limitations in the duration of our recordings. Given the small number of free parameters, and the ability of the model to capture held out cells, we believe that the model generalizes well. We have added a supplemental figure with linear model performance (Figure 2 - Figure Supplement 2).

-  Fig. 9A is lacking bipolar cell and amacrine cell labels. Currently, it looks like HC is next to the BC in the schematic.

Thanks - we have updated that figure (now Figure 10A)

-  Maybe I am misunderstanding something, but it seems like the linear model prediction shown in Figure 2A for the rod could be easily improved by scaling it appropriately. Is this impression correct or why not?

We have clarified how the linear model is constructed (by fitting the linear model to low contrast responses of the full model at the mean stimulus intensity). We also added a supplemental figure, following the suggestion above, showing the linear model performance when a free scaling factor is included for each cell.

-  The verification experiment in Fig. 5 is only anecdotal and is elaborated only in Figure 6. If I am not mistaken, this does not necessitate its own figure/section but could rather be merged.

We have kept this figure separate (now Figure 6) as we felt that it was important to highlight the approach in general in a figure before getting into quantification of how well it works.

-  Figure 5 right is lacking labels. What is red and grey?

Thanks for catching that - labels are added now.

-  The end of the Discussion is slightly unusual. Did some text go missing?

Thanks - we have rearranged the Discussion so as not to end on Limitations.

-  There is a bonus figure at the end which seems also not to belong in the manuscript.

Thanks - the bonus figure is removed now.

-  The methods should also describe briefly what kind of routines were used in the Matlab code, e.g. gradient descent with what optimizer?

We’ve added that information as well.

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Reply to the reviewers

We thank the reviewers for their positive assessment of our manuscript. We agree that there are some further experiments suggested by the reviewers that would enhance our study. We have highlighted further proposed experimental work in bold for clarity.

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

1. EVIDENCE, REPRODUCIBILITY AND CLARITY Summary: The Matrix 2 (M2) protein of influenza A virus (IAV) is a single pass transmembrane protein known to act as a tetrameric ion channel that is important for both viral entry and egress. The paper by Figueras-Nova et al. entitled "Caspase cleavage of Influenza A virus M2 disrupts M2-LC3 interaction and regulates virion production" reports on the regulation of IAV virion production through a regulatory interplay between a caspase cleavage site and a LC3 interacting region (LIR) motif in M2. In its C-terminal cytoplasmic tail the IAV M2 protein contains a C-terminal LIR motif interacting with LC3. The authors show that this LIR motif is preceded by a functional caspase cleavage motif cleaved predominantly by caspase-6, with some contribution from caspase-3: The motif 82-SAVD-85 directs cleavage after the aspartate (D) at position 85. The cleavage leads to loss of the remaining C terminal sequence from amino acid 86 to 97. The core LIR motif 91-FVSI-94 LIR motif is then lost from M2 which can no longer bind LC3. As previously described by the same group using point mutations in the LIR motif (Ref 12.), loss of a functional LIR., here by caspase- mediated deletion of the LIR, affects the virion production and inhibits filamentous budding. LC3B lipidation is increased upon treatment with a caspase inhibitor. The authors show for the first time that LC3 is included into IAV virions via binding to M2. Furthermore, they also report a co-crystal structure of the M2 C terminus (aa 70-97), containing the caspase cleavage site and LIR, and LC3B (aa 3-125) adding new insights into this interaction and showing that the caspase cleavage site is in a flexible region N-terminal to the LIR. This work shows how caspase cleavage may modulate LC3B lipidation, trafficking to the plasma membrane, incorporation of LC3B in the virions, filamentous budding and virion production (viral titer).

Major comments: The findings reported here are very well supported by the data shown. This is a very clearly written paper with well described and nicely visualized results that are accompanied by adequate statistical analyses.

We thank the reviewer for their assessment of our manuscript.

The authors report a new way the LC3B binding to the C-terminal tail of the M2 proteins is regulated and suggest that this is an adaptation the virus has made to adjust virion production to host cell status by hijacking the function of host caspases. They show that the caspase cleavage motif is evolutionary conserved and use that as an argument. Perhaps it could be discussed if it also could be an argument that the host protects itself against a too massive virion production as this could be too detrimental to the host? Would it not also be an evolutionary advantage to the virus in the long run by avoiding killing the host?

This is an interesting point. We agree there could be advantage for the virus not to overproduce virions under certain circumstances. Consistent with this caspase-6 deficient mice had increased mortality in response to IAV PR8 infection, and presented and increase in viral spread in the lungs (Zheng, 2021; doi: 10.1016/j.cell.2020.03.040). This is also relevant for the comments made by Reviewer 2. The manuscript will be updated to include a discussion of this point.

A question I may raise which is optional as it may be too much work to address as part of this study is if the reported regulation of LC3B binding has any role in regulating the ion channel function of the M2 tetramer?

It is well established that there is no impact of distal C-terminal truncations on M2 ion channel activity (Cady et al., 2009, doi: 10.1021/bi9008837 Schnell and Chou, doi: 10.1038/nature06531; Nguyen et al., 2008, doi: 10.1021/bi801315m; Tobler et al., 1999, doi: 10.1128/jvi.73.12.9695-9701.1999). This is also consistent with data from our lab (Ulferts et al., 2021, doi: 10.1016/j.celrep.2021.109899, Beale et al., 2014, doi: 10.1016/j.chom.2014.01.006) as well as others (Ren et al., 2015, doi: 10.1128/JVI.00576-15) showing the effects of the LIR motif and the proton channel are distinct. We appreciate the reviewer suggesting further work here as optional, but there is already compelling evidence to show there is no substantial effect of the LIR motif on ion channel activity. (See also Reviewer 2 points 4 and 5).

Minor comments: Delete "with" in line 145.

This will be changed in the updated manuscript.

Line 217: It should be written more specifically how "cells were surface stained with M2"

The protocol for surface staining of M2 will be explained in more detail in the updated manuscript.

1. SIGNIFICANCE

This is a very well performed study with a sound experimental strategy and well performed assays with clear results increasing our insight into the interplay between the Influenza A virus and host cells. Although caspase mediated cleavage of the autophagy receptor and signaling scaffold protein p62 (Ref. 25), removing the LIR and LC3-binding, has been reported before I consider this study as novel in reporting this type of regulation of LC3 binding. The cleavage of p62 deletes a large part of the protein while here it is a "clean" deletion of the LIR sequence representing a conceptual advance of regulation of LC3 binding. The study also reports for the first time on LC3B incorporated into virions. The effects on trafficking to the plasma membrane and viral budding and virion production are similar to those reported before (Ref. 12) using viruses with point mutations crippling the LIR motif. This research will be of interested to all studying virus- host interaction and to the autophagy field both as a non autophagic role of LC3B, and as a regulatory mechanism of LIR-LC3B interactions involving the irreversible caspase cleavage-mediated deletion of the LIR motif.

We thank the reviewer for this assessment of our manuscript.

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

The influenza A virus (IAV) M2 protein is small transmembrane protein which plays a role in virus entry and egress. In a previous study, Beale et al. (2014) identified an LC3-interacting region (LIR) in the M2 cytoplasmic domain that was found to recruit the LC3B protein to the plasma membrane. Recombinant IAV harboring mutations in the LIR motif showed reduced particle stability and lost filamentous morphology.

In the present study, Figueras-Novoa et al. show that the LIR motif is removed in response to activation of cellular caspases. The authors demonstrate that in in IAV-infected THP-1 cells M2 is partially cleaved at the motif (82)SAVD(85)¯A by caspase 6. Caspase inhibitors abolished cleavage, and a mutant virus harboring the D85A substitution was found to be resistant to caspase action. A crystal structure of purified M2 C- terminus and LC3B revealed that the caspase cleavage site lies in a flexible region that is accessible to caspases.

Mutant virus encoding a truncated M2 protein (M2D86-97) was unable to interact with LC3, in accordance with the absence of the LIR motif. The M2D86-97 mutant showed reduced lipidation of LC3, while enhanced lipidation of LC3 was observed when wild-type virus-infected cells were treated with caspase inhibitors. The authors also observed that cell surface transport of M2D86-97 but not M2-D85A was impaired. However, in purified virus particles a mix of cleaved and uncleaved M2 was detected. The authors also demonstrated that lipidated LC3B was present in purified virions of wild-type virus particles but even more abundant in M2-D85A virions. Finally, M2D86-97 mutants produced significantly less infectious particles compared to wild-type virus while the D85A cleavage mutant replicated to similar titers than wt virus.

Based on these findings the authors concluded that caspases regulate the interaction of M2 protein with LC3 which impacts virion production. Specifically, they propose that caspase-mediated removal of the LIR motif may enable a switch between filamentous and non-filamentous budding in response to depletion of cellular resources. However, the authors were unable to rescue a filamentous IAV with a truncated M2 protein and therefore could not provide direct proof for their guess.

While the data are sound and presented well, they do not support the conclusions of the authors.

1. To the authors opinion, the conserved caspase cleavage site in the M2 protein might provide an evolutionary advantage for the virus. However, the M2-D85A mutation has no effect on viral replication, so the biological significance of why M2 needs to be cleaved at all is unclear. The conclusion that caspase-induced M2 cleavage is a fine-tuning mechanism of IAV has not been supported by experiments.

We thank the reviewer for the assessment of our data. We think the reviewer is specifically objecting to the phrase “We conclude that this highly conserved interaction and cleavage act as a regulatory mechanism exploited by IAV to fine-tune virion production in different cellular contexts.” This is a reasonable inference from our results, but we accept that it is not proven. We will change the wording to make it clear this has not been definitively demonstrated.

1. The finding that the permanently truncated IAV M2 mutant virus was substantially attenuated does not necessarily mean that abrogation of M2-LC3 interaction was responsible for this attenuation. As the M2 protein plays a role in virus budding at the plasma membrane (recruitment of M1 protein, induction of membrane curvature, membrane scission), the impaired transport of the truncated M2 protein might already explain that the virus was attenuated and that incorporation of the protein into the viral envelope was reduced.

We will confirm this further with additional experiments using LIR mutants. Recapitulating the plasma membrane transport defect of truncated M2 with LIR mutants including the newly characterised M2D87A and M2D88A mutants and a more severe mutant with a FVSI_AAAA substitution would strongly imply this truncation mutant phenotype is due to the lack of LIR motif.

1. It is also not clear whether the loss of the C-terminal 11 amino acids may have affected the interaction of the M2 protein with other proteins such as TRAPPC6A-delta (Zhu et al., 2017).

This is a reasonable point, however Zhu et al., 2017 (https://doi.org/10.1128/jvi.01757-16) reported that the interaction with TRAPPC6A retains M2 intracellularly. If the phenotype observed with our truncation was due to the loss of interaction with TRAPPC6A, the opposite phenotype would be observed (more M2 in the plasma membrane with the truncated M2∆86-97 mutant). To address this point directly we will attempt to rescue an M2 mutant virus that has disrupted the reported TRAPPC6A binding site and assess M2 plasma membrane localization.

The authors did not rule out whether the truncation of the M2 protein by 11 amino acids would have an effect on proton channel activity. Proton channel activity, however, might be important to preserve the metastable conformation of HA in the secretory pathway and might be also important for virus uncoating.

M2D86-97 induced less LC3 lipidation than wild-type M2 or the D85A mutant. The remaining lipidation was attributed to the ion channel activity of the M2 protein. Can the authors rule out that the truncation of the M2 protein led to reduced ion channel activity which in turn led to reduced LC3B lipidation?

We have addressed points 4 and 5 in response to Reviewer 1.

The suggested role of caspase cleavage as a regulatory switch between filamentous and spherical virions (lines 304- 313) is highly speculative as long as the authors do not provide any experimental proof for it. The authors indicated that they were unable to rescue filamentous IAV with M2D86-97. However, would it be possible to use caspase inhibitors to test their hypothesis?

We acknowledge that M2∆86-97 could not be rescued in a filamentous background. The use of caspase inhibitors would only increase the amount of full length M2 present, and does not provide an alternative strategy for increasing the proportion of truncated M2. However, since M2∆86-97 mutant could not be rescued, we will attempt to rescue additional LIR motif mutants to address this point. In particular, D87A and D88A mutants could be generated in a MUd background, as well as the F91S mutant.

The authors used only the PR8 strain for their studies, a highly cell culture-adapted strain with spherical morphology. Are the findings obtained with this strain are also valid for others IAV strains?

As we highlight in Figure 2I, both the caspase cleavage motif and LIR motif are highly conserved in human IAV strains. PR8 was used as it is the reverse genetic system in use and approved for use in the lab. We will attempt to address this by testing whether other IAV strains we are able to obtain also undergo caspase mediated cleavage of M2. If possible, we will obtain recent clinical isolates to show cleavage of M2 in a strain that has not adapted to cell culture.

1. The authors mainly used the THP-1 cells for their studies, a human macrophage-like cell line. However, human IAV mostly replicate in epithelial cells of the respiratory tract and cause only abortive infections of macrophages. Why did the authors choose this cell line? Can the findings obtained with this cell line be translated to epithelial cells of the airways?

THP-1 cells are widely used for the study of caspase activity. However, we also show M2 cleavage in MDCK cells and HAP1 cells. PR8 infection of A549 cells does not induce significant amounts of cell death in the infection time points used and, as caspase activation is linked to cell death, we did not observe M2 cleavage in this cell type. We will attempt to infect some epithelial cell types to confirm this phenotype.

1. Minor issues:

2. Fig. 1C: There seem to be quite some differences in the cleavage efficiency of M2 between panels A, B, C, and D? Any explanations?

Different cell types (THP-1 cells and HAP1 cells) are used for the experiments mentioned above, which accounts for the different amount of M2 cleavage.

• Fig. 1: Panel E: The labeling of the first amino acids as aa 76 seems to be wrong!

We thank the reviewer for pointing this out, this will be corrected in the updated manuscript.

Line 147: ...caspase mediated disruption of the M2-LC3 interaction (Fig 2A-B). Should be Fig. 2A-C.

This sentence was referring to Figure 2A-B, as it refers to LC3B lipidation and not the coIP. This sentence will be changed in the text to reflect the intended meaning.

• Growth kinetics of the various mutant viruses are missing?

__We will provide growth kinetics for the relevant mutants _(M2D85A and M2∆86-97).___

• Line 195: The authors speculate that aa85 is important for viral fitness: That should be demonstrated!

This speculation is based on the very strong conservation of D85 in human IAV strains. The importance of D85 in viral fitness (permitting cleavage of M2) is only likely to be directly demonstrable in transmission models (for example ferrets) which is not feasible or justifiable.

Reviewer #2 (Significance (Required)):

Authors concluded that caspases regulate the interaction of M2 protein with LC3 which impacts virion production. Specifically, they propose that caspase-mediated removal of the LIR motif may enable a switch between filamentous and non-filamentous budding in response to depletion of cellular resources. However, the authors were unable to rescue a filamentous IAV with a truncated M2 protein and therefore could not provide direct proof for their guess. +<br /> +

• As stated in the response to the comments above, we will attempt to rescue LIR mutant viruses (____D87A and D88A) in a MUd background which would provide further support for our hypothesis. Our data has significance for the understanding of the cell biology of influenza infection as commented on by Reviewers 1 and 3.

• • Reviewer #3 (Evidence, reproducibility and clarity (Required)): Summary : In this article, the authors identify a caspase cleavage site in the influenza A virus (IAV) Matrix 2 protein (M2) that leads to a truncated form of M2 deleted from its C-term LC3-interacting region (LIR). This cleaved form of M2 is seen and accumulates starting at 12 hours post-infection. IAV expressing M2 delta 86-97 mutant, corresponding to cleaved M2, seems to disrupt LC3B localization to cell plasma membrane upon infection. The authors also show that the IAV M2 delta 86-97 has a reduced viral titer compared to IAV WT. Overall the data are quite exciting where the authors identify the specific caspase responsible for the cleavage and show the residues of M2 necessary for LC3 interaction. However, some of the data showing the consequence of the cleavage for viral replication could be better clarified.

We thank Reviewer 3 for their kind comments and we propose further experiments to clarify the consequences of cleavage.

Major comments: - In Fig3A-B, the authors seek to demonstrate that the localization of M2 to the plasma membrane requires LIR motif. However, the representative images for cell infected with the delta 86-97 mutant show relatively few cell are expressing M2 raising questions of the infectivity of this mutant virus or if the overall expression of M2 in this assay is less for the delta 86-97 mutant. The authors should consider first quantifying the ratio of M2 cell surface staining over total M2 staining and second re-evaluate the representative images chosen.

__We will include more examples of permeabilised cells in which comparable numbers of cells are M2 positive between mutants. We will also include high-content microscopy based quantification to support this. __To clarify, we confirm that the quantification of M2 intensity in the plasma membrane is carried out relative to the number of M2 positive cells, as the reviewer agrees is the most accurate way. To avoid confusion, we will update figure legends to describe more accurately the quantification process. A comparison between surface M2 and total M2 cannot be done on an individual cell basis, as once cells are permeabilized (to look for internal M2), robust differentiation between surface and internal M2 is difficult. The above clarification and additional data should provide the necessary support for our conclusions.

• In fig3E, it is unclear what is being quantified in the graph as the legend and text lines 222-223 mention that spot intensity was measured but the y axis indicates LC3 relocalization intensity. Given LC3 is punctated particularly in the cytosol, It is unclear which spots of LC3 they are referring to. Based on the images shown, using a graph with LC3 surface staining as performed for M2 would clarify the data. The authors should clarify the reporting of these data in the results section. Additionally, the images of the control non-infected cells should be added to 3C.

We agree with the reviewer on this point. The figure will be updated to describe more accurately what is being quantified. Additionally, images for uninfected cells in 3C will be added.

• The data in Fig4 and FigS3 need to be strengthened to be conclusive. The volcano plot in FigS3A indicates that there is more LC3B and IAV proteins in M2 D85A than M2delta86-97. However in Fig4E, both LC3 I and LC3 II are increased in virions M2 delta 86-97 compared to M2 D85A which is opposite to the authors' conclusions in lines 244-245. In other words, the total amount of lipidated LC3 is higher in virions from IAV M2 without LIR motif than M2 with LIR. LC3II/I ratio in fig4F would suggest in virions containing M2 with LIR motif, LC3B II may be preferentially incorporated compared to virions containing M2 without LIR, which incorporates both LC3B I and LC3B II. Since this is a critical point made by the authors, performing a co-immunoprecipitation of M2 D58A and M2delta86-97 in the particles and then assessing for binding of LC3 I or II would bolster their conclusions.

Figure 4F quantifies the ratio of LC3II to LC3I in infectious particles. Another two repeats used to quantify this ratio will be shown in addition, with a better representation of increased amounts of lipidated LC3II in M2D85A infectious particles, as well as an increased LC3II/LC3I ration in said particles when compared to M2∆86-97. Because of the low yield acquired from the purification of IAV virions, performing an IP would be difficult. Even if this were technically feasible it would not prove that M2 is binding LC3 inside the virion – we do not make this claim in our paper, merely that LC3B can be detected in the purified viral particles. We will clarify this point in the revised manuscript.

• In Fig4J, even if statistically significant, the PFU difference between M2 D85A and M2 delta86-97 is minimal, performing growth curve assay would help appreciate this difference over time. In Thp1 cells, as the authors show caspase cleavage of M2 at time point 12h 14h 16hpi etc... (fig1), they should also show PFU data at these same time points for M2 mutant D85A compared to WT and M2 delta 86-97.

We agree with the reviewer and indeed this was a point we attempted to make in our manuscript: Figure 4J shows a statistically significant difference between the titers. However, in the text we state that, even though statistically significant, the difference is much smaller than in other titer quantifications performed. Given the nature of a plaque assay, differences of less than a log fold cannot be considered as definitively indicating biological significance. We will clarify this in a revised manuscript. We will also provide the relevant growth kinetics (as per response to Reviewer 2).

• The title of Fig4 and FigS3 and in text line 226 should be changed as M2 incorporation into virions is not shown and not described in the text. Plus, in figS3B, the authors show that between the M2 mutants, there is no difference in the abundance of M2 and other viral proteins compared to M1.

The title of Figures 4 and S3 will be changed to more accurately reflect all of the points made by the figure.

• In the image shown in Fig4H the number of plaques is higher for M2delta86-97 even though the size in smaller than M2 WT. Could the authors clarify in the text of the results section how they quantify PFU in their plaque assay and if they used a size criterion when quantifying the number of plaques?

The images of plaques are taken at different dilutions, with the M2∆86-97 image belonging to two dilutions lower than the M2WT image. We will include the calculation used for PFU/mL, which does not take into account plaque size. Furthermore, images of the whole plate, showing plaqued serial dilutions will be shown.

• In fig3B, the legend indicates 8 hpi but on the graphs it is 9 hpi.

We thank the reviewer for pointing out this mistake. Both should read 8 hpi, this will be corrected in the new manuscript.

Reviewer #3 (Significance (Required)):

The authors demonstrated that IAV M2 binding to LC3 is regulated by caspase cleavage. The authors clearly identify the cleavage site and the caspase involved: caspase 6. The cleaved form of M2 seems relevant to IAV infection as it is accumulating after 12hpi. Using a M2 mutant D85A that cannot be cleaved by caspase 6 and truncated M2 mutant delta86-97 mimicking caspase cleaved M2, the authors are able to elegantly address the role of M2 cleavage. However, the importance of M2 caspase cleavage on IAV infection is not demonstrated. Eventually, addressing the impact of the caspase cleavage of M2 LIR motif on autophagy or CASM would be interesting. - Advance: conceptual. - Audience: basic research, specialized in virology, specialized in autophagy. - Field of expertise: virology, autophagy.

We agree with the reviewer that we have made a conceptual advance in our understanding of the cell biology of influenza A virus infection. We have also determined the structure of the terminal part of the M2 tail in complex with LC3B. The biological importance of the phenotypes we show are most likely in transmission of the virus between hosts, which for IAV would require animal experiments outside the scope of this study. We have demonstrated regulation of the LIR motif by caspase cleavage in a variety of ways, using cell biological and biochemical methods. IAV is a very significant human and animal pathogen, and we believe we have made an important advance in describing a host-pathogen interaction of relevance for viral egress.

Author response:

Reviewer #1 (Public Review):

Weaknesses:

There are some minor weaknesses.

Notably, there are not a lot of new insights coming from this paper. The structural comparisons between MCC and PCC have already been described in the literature and there were not a lot of significant changes (outside of the exo- to endo- transition) in the presence vs. absence of substrate analogues.

We agree that the structures of the human MCC and PCC holoenzymes are similar to their bacterial homologs. That is due to the conserved sequences and functions of MCC and PCC across different species.

There is not a great deal of depth of analysis in the discussion. For example, no new insights were gained with respect to the factors contributing to substrate selectivity (the factors contributing to selectivity for propionyl-CoA vs. acetyl-CoA in PCC). The authors state that the longer acyl group in propionyl-CoA may mediate stronger hydrophobic interactions that stabilize the alpha carbon of the acyl group at the proper position. This is not a particularly deep analysis and doesn't really require a cryo-EM structure to invoke. The authors did not take the opportunity to describe the specific interactions that may be responsible for the stronger hydrophobic interaction nor do they offer any plausible explanation for how these might account for an astounding difference in the selectivity for propionyl-CoA vs. acetyl-CoA. This suggests, perhaps, that these structures do not yet fully capture the proper conformational states.

We appreciate this comment. Unfortunately, in the cryo-EM maps of the PCC holoenzymes, the acyl groups were not resolved (fig. S6), so we were unable to analyze the specific interactions between the acyl-CoAs and PCC. We will discuss this limitation in our revised manuscript.

The authors also need to be careful with their over-interpretation of structure to invoke mechanisms of conformational change. A snapshot of the starting state (apo) and final state (ligand-bound) is insufficient to conclude *how* the enzyme transitioned between conformational states. I am constantly frustrated by structural reports in the biotin-dependent enzymes that invoke "induced conformational changes" with absolutely no experimental evidence to support such statements. Conformational changes that accompany ligand binding may occur through an induced conformational change or through conformational selection and structural snapshots of the starting point and the end point cannot offer any valid insight into which of these mechanisms is at play.

Point accepted. We will revise our manuscript to use "conformational differences" instead of "conformational changes" to describe the differences between the apo and ligand-bound states.

Reviewer #2 (Public Review):

Comments and questions to the manuscripts:

I'm quite impressed with the protein purification and structure determination, but I think some functional characterization of the purified proteins should be included in the manuscript. The activity of enzymes should be the foundation of all structures and other speculations based on structures.

We appreciate this comment. However, since we purified the endogenous BDCs and the sample we obtained was a mixture of four BDCs, the enzymatic activity of this mixture cannot accurately reflect the catalytic activity of PCC or MCC holoenzyme. We will acknowledge this limitation in the discussion section of our revised manuscript.

In Figure 1B, the structure of MCC is shown as two layers of beta units and two layers of alpha units, while there is only one layer of alpha units resolved in the density maps. I suggest the authors show the structures resolved based on the density maps and show the complete structure with the docked layer in the supplementary figure.

We appreciate this comment. We have shown the cryo-EM maps of the PCC and MCC holoenzymes in fig. S8 to indicate the unresolved regions in these structures. The BC domains in one layer of MCCα in the MCC-apo structure were not resolved. However, we think it would be better to show a complete structure in Fig. 1 to provide an overall view of the MCC holoenzyme. We will revise Fig. 1B and the figure legend to clearly point out which domains were not resolved in the cryo-EM map and were built in the structure through docking.

In the introduction, I suggest the author provide more information about the previous studies about the structure and reaction mechanisms of BDCs, what is the knowledge gap, and what problem you will resolve with a higher resolution structure. For example, you mentioned in line 52 that G437 and A438 are catalytic residues, are these residues reported as catalytic residues or this is based on your structures? Has the catalytic mechanism been reported before? Has the role of biotin in catalytic reactions revealed in previous studies?

Point accepted. It was reported that G419 and A420 in S. coelicolor PCC, corresponding to G437 and A438 in human PCC, were the catalytic residues (PMID: 15518551). The same study also reported the catalytic mechanism of the carboxyl transfer reaction. The role of biotin in the BDC-catalyzed carboxylation reactions has been extensively studied (PMIDs: 22869039, 28683917). We will include these information in the introduction section of our revised manuscript.

In the discussion, the authors indicate that the movement of biotin could be related to the recognition of acyl-CoA in BDCs, however, they didn't observe a change in the propionyl-CoA bound MCC structure, which is contradictory to their speculation. What could be the explanation for the exception in the MCC structure?

We appreciate this comment. We do not have a good explanation for why we did not observe a change in the propionyl-CoA bound MCC structure. It is noteworthy that neither acetyl-CoA nor propionyl-CoA is the natural substrate of MCC. Recently, a cryo-EM structure of the human MCC holoenzyme in complex with its natural substrate, 3-methylcrotonyl-CoA, has been resolved (PDB code: 8J4Z). In this structure, the binding site of biotin and the conformation of the CT domain closely resemble that in our acetyl-CoA-bound MCC structure. Therefore, the movement of biotin induced by acetyl-CoA binding mimics that induced by the binding of MCC's natural substrate, 3-methylcrotonyl-CoA, indicating that in comparison with propionylCoA, acetyl-CoA is closer to 3-methylcrotonyl-CoA regarding its ability to bind to MCC. We will discuss this possibility in our revised manuscript.

In the discussion, the authors indicate that the selectivity of PCC to different acyl-CoA is determined by the recognition of the acyl chain. However, there are no figures or descriptions about the recognition of the acyl chain by PCC and MCC. It will be more informative if they can show more details about substrate recognition in Figures 3 and 4.

We appreciate this comment. Unfortunately, in the cryo-EM maps of the PCC holoenzymes, the acyl groups were not resolved (fig. S6), so we were unable to analyze the specific interactions between the acyl-CoAs and PCC. We will discuss this limitation in our revised manuscript.

How are the solved structures compared with the latest Alphafold3 prediction?

Since AlphaFold3 was not released when our manuscript was submitted, we did not compare the solved structures with the AlphaFold3 predictions. We have now carried out the predictions using Alphafold3. Due to the token limitation of the AlphaFold3 server, we can only include two α and six β subunits of human PCC or MCC in the prediction. The overall assembly patterns of the Alphafold3-predicted structures are similar to that of the cryo-EM structures. The RMSDs between PCCα, PCCβ, MCCα, and MCCβ in the apo cryo-EM structures and those in the AlphaFold3-predicted structures are 7.490 Å, 0.857 Å, 7.869 Å, and 1.845 Å, respectively. The PCCα and MCCα subunits adopt an open conformation in the cryo-EM structures but adopt a closed conformation in the AlphaFold-3 predicted structures, resulting in large RMSDs.