Author response:

The following is the authors’ response to the original reviews

eLife Assessment

This valuable study investigates prey capture by archer fish, showing that even though the visuomotor behavior unfolds very rapidly (within 40-70 ms), it is not hardwired; it can adapt to different simulated physics and different prey shapes. Although there was agreement that the model system, experimental design, and main hypothesis are certainly interesting, opinions were divided on whether the evidence supporting the central claims is incomplete. A more rigorous definition and assessment of "reflex speed", more detailed evidence of stimulus control, and a more detailed analysis of individual subjects could potentially increase confidence in the main conclusions.

Thank you very much. There are several points that we had to absolutely make sure that they are very well understood. (1) Explaining in the best possible way the experiment with a fly sliding on top of a glass plate. Here, the virtual ballistic landing point can be calculated using simple high school physics. It turns out that this is where the fish turn to – even though the fly is not falling at all. Once this is understood it becomes clear that we can precisely measure latency and accuracy of the C-start turns. In the new version we explain this essential aspect in more detail and add an extra Figure (new Figure 2). This may, perhaps, help readers to notice this important background (previously covered in Fig. 1C). (2) The full experimental evidence that the VR method works is presented in more detail and all measurements necessary will be clear after the new Figure 2. They will however not be clear if this Figure is ignored. (3) We have rewritten the manuscript to make it easier to understand what we wanted to show, why we needed VR to proceed and why the archerfish highspeed decision lent itself so readily to tackle the problem. (4) We emphasize the importance of speed-accuracy tradeoffs in standard decision-making and also include data on the absence of such a relation in the archerfish highspeed decisions.

So, in summary, we have emphasized what we wanted to show and what we did not want to show, we have rewritten the text to make it easier for future readers and we have tried to add more guidance to the figures. We do hope very much that the beauty of the quite unexpected findings is more easily visible to those who take the trouble of actually reading the paper.  

Public Reviews:

Reviewer #1 (Public review):

Summary:

The authors test whether the archerfish can modulate the fast response to a falling target. By manipulating the trajectory of the target, they claim that the fish can modulate the fast response. While it is clear from the result that the fish can modulate the fast response, the experimental support for the argument that the fish can do it for a reflex-like behavior is inadequate.

Please note that we have not simply tested whether archerfish can 'modulate the fast response'. We quantitatively test specific hypotheses on the rules used by the fish. For this the accuracy of the decisions is analyzed with respect to specific points that can be calculated precisely in each of the experiments. These points are shown on the figures and in the movies that were meant to illustrate this important aspect. We had to make sure that the way we calculate the predicted point(s) is made as clear as possible in the text. We added more text and separated the fundamentally important aspects in a separate Figure 2 to make it more difficult to overlook the fundamental aspects that lay the foundation for everything that follows.

Strengths:

Overall, the question that the authors raised in the manuscript is interesting.

Thank you and we do hope very much that, with our revision, you will see the beauty of the findings.

Weaknesses:

(1) The argument that the fish can modulate reflex-like behavior relies on the claim that the archerfish makes the decision in 40 ms. There is little support for the 40 ms reaction time. The reaction time for the same behavior in Schlegel 2008, is 6070 ms, and in Tsvilling 2012 about 75 ms, if we take the half height of the maximum as the estimated reaction time in both cases. If we take the peak (or average) of the distribution as an estimation of reaction time, the reaction time is even longer. This number is critical for the analysis the authors perform since if the reaction time is longer, maybe this is not a reflex as claimed. In addition, mentioning the 40 ms in the abstract is overselling the result. The title is also not supported by the results.

Although the minimum latency is indeed 40 ms (it can be slightly less: e.g., see the evidence in the paper, for instance the plots in the new Fig. 4) the paper's statements are not dependent on a specific number. Even if minimum latency was 100 ms (which it is not) the speed of the response and the absence of a speedaccuracy relation (now shown directly in Fig. 4) is what is of importance. To show this we have completely rewritten large parts of the manuscript.

(2) A critical technical issue of the stimulus delivery is not clear. The frame rate is 120 FPS and the target horizontal speed can be up to 1.775 m/s. This produces a target jumping on the screen 15 mm in each frame. This is not a continuous motion. Thus, the similarity between the natural system where the target experiences ballistic trajectory and the experiment here is not clear. Ideally, another type of stimulus delivery system is needed for a project of this kind that requires fast-moving targets (e.g. Reiser, J. Neurosci.Meth. 2008). In addition, the screen is rectangular and not circular, so in some directions, the target vanishes earlier than others. It must produce a bias in the fish response but there is no analysis of this type.

Please note that the new Fig. 3 (former Fig. 2) reports all the evidence that is needed to just show this and in a way that could in no way have been better. We have rewritten the text to explain what needs to be shown experimentally in order to be able to proceed, what critical tests were done and what results were obtained. We also add a short comment on another unsuccessful attempt that we have tried before.

(3) The results here rely on the ability to measure the error of response in the case of a virtual experiment. It is not clear how this is done since the virtual target does not fall. How do the authors validate that the fish indeed perceives the virtual target as the falling target? Since the deflection is at a later stage of the virtual trajectory, it is not clear what is the actual physics that governs the world of the experiment. Overall, the experimental setup is not well designed.

Understanding this aspect is essential. If the glass plate experiment is not thoroughly understood (new Fig. 2 with new text to emphasize that this is absolutely essential) nothing that follows makes any sense, including what is meant by the statement that the decision could be hardwired to ballistic motion.

Reviewer #2 (Public review):

Summary:

This manuscript studies prey capture by archer fish, which observe the initial values of motion of aerial prey they made fall by spitting on them, and then rapidly turn to reach the ballistic landing point on the water surface. The question raised by the article is whether this incredibly fast decision-making process is hardwired and thus unmodifiable or can be adjusted by experience to follow a new rule, namely that the landing point is deflected from a certain amount of the expected ballistic landing point. The results show that the fish learn the new rule and use it afterward in a variety of novel situations that include height, side, and speed of the prey, and which preserve the speed of the fish's decision. Moreover, a remarkable finding presented in this work is the fact that fish that have learned to use the new rule can relearn to use the ballistic landing point for an object based on its shape (a triangle) while keeping simultaneously the 'deflected rule' for an object differing in shape (a disc); in other words, fish can master simultaneously two decisionmaking rules based on the different shape of objects.

Strengths:

The manuscript relies on a sophisticated and clever experimental design that allows changing the apparent landing point of a virtual prey using a virtual reality system. Several robust controls are provided to demonstrate the reliability and usefulness of the experimental setup.

Overall, I very much like the idea conveyed by the authors that even stimuli triggering apparently hardwired responses can be relearned in order to be associated with a different response, thus showing the impressive flexibility of circuits that are sometimes considered mediating pure reflexive responses.

Thank you so much for this precise assessment of what we have shown!

This is the case - as an additional example - of the main component of the Nasanov pheromone of bees (geraniol), which triggers immediate reflexive attraction and appetitive responses, and which can, nevertheless, be learned by bees in association with an electric shock so that bees end up exhibiting avoidance and the aversive response of sting extension to this odorant (1), which is a fully unnatural situation, and which shows that associative aversive learning is strong enough to override preprogrammed responding, thus reflecting an impressive behavioral flexibility.

That's very interesting, thanks and we are very happy to mention this important study in the revised version.

Weaknesses:

As a general remark, there is some information that I missed and that is mandatory in the analysis of behavioral changes.

Firstly, the variability in the performances displayed. The authors mentioned that the results reported come from 6 fish (which is a low sample size). How were the individual performances in terms of consistency? Were all fish equally good in adjusting/learning the new rule? How did errors vary according to individual identity? It seems to me that this kind of information should be available as the authors reported that individual fish could be recognized and tracked (see lines 620-635) and is essential for appreciating the flexibility of the system under study.

Secondly, the speed of the learning process is not properly explained. Admittedly, fish learn in an impressive way the new rule and even two rules simultaneously; yet, how long did they need to achieve this? In the article, Figure 2 mentions that at least 6 training stages (each defined as a block of 60 evaluated turn decisions, which actually shows that the standard term 'Training Block' would be more appropriate) were required for the fish to learn the 'deflected rule'. While this means 360 trials (turning starts), I was left with the question of how long this process lasted. How many hours, days, and weeks were needed for the fish to learn? And as mentioned above, were all fish equally fast in learning? I would appreciate explaining this very important point because learning dynamics is relevant to understanding the flexibility of the system.

First, it is very important to keep the question in mind that we wanted to clarify: Does the system have the potential to re-tune the decisions to other non-ballistic relations between the input variables and the output? This would have been established if one fish was found capable of doing that. We have rewritten the introduction and discussion to specifically say what our aim was. We feel that the paper is already extremely long and difficult to understand (even after we tried very hard in this revision to explain everything in detail and as good as we could), requires the establishment of a method whose success was really unexpected and finding a degree of plasticity that we did not expect at all. We also have added a section in the discussion stating what we can, and we cannot say given the number of fish examined. For instance, we do not know if there are differences in the speed at which the different individuals mastered the new rules and if social learning could play a role to speed up the acquisition. That is a brilliant idea and we are very interested in checking this - but we wanted to stick with the (quite ambitious) goal of the present study.

Reference:

(1) Roussel, E., Padie, S. & Giurfa, M. Aversive learning overcomes appetitive innate responding in honeybees. Anim Cogn 15, 135-141, doi:10.1007/s10071011-0426-1 (2012).

Thanks for this reference!

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Minor comments:

(1) What is the difference between Reinel, J. Exp. Bio. 2016 and the current study?

Clearly in that study all objects were strictly falling ballistically, and latency and accuracy of the turn decisions were determined when the initial motion was not only horizontal but had an additional vertical component of speed. The question of that study was if the need to account to an additional variable (vertical speed) in the decision would affect its latency or accuracy. The study showed that also then archerfish rapidly turn to the later impact point. It also showed that accuracy and latency were not changed by the added degree of freedom.

(2) How do Figures 2 F and G demonstrate that an accurate start is possible?

See above.

(3) Figure 4 is hard to follow, it is not clear what is presented and how it supports the claim that the new rule is represented in a way that allows immediate generalization.

Yes, this is not at all an easy experiment. Briefly, fish were re-trained at only one height level and then are tested at other levels. The strategy is as in the experiments Schuster et al. 2004 Current Biology, Vol. 14, 1565–1568, Figure 5. We have changed text and Figure (new Figure 5) to show how the predictions were reached.

Reviewer #2 (Recommendations for the authors):

Minor remarks

Lines 88-90: I was surprised to see that in this section, the authors did not mention the speed-accuracy trade-off off which has inspired numerous experiments in animal behavior (1). This could be used to back their point, namely, that speed comes with an apparent cost of a loss in accuracy.

Yes, that is a crucial aspect that was completely missing even though it demonstrates a key aspect of 'standard' versus some 'highspeed' decisions! We definitely had to include it and also to show, directly under the conditions of our present experiments (in the new Fig. 4) the absence of a significant speedaccuracy relation for the archerfish highspeed decisions! Thank you very much for emphasizing this crucial aspect!

Lines 182-184: Specify that this situation corresponds to the hatched bar in Figure (this can be specified in the caption of the figure, where the bar is not mentioned).

Thanks!

Lines 187-188: here and elsewhere (e.g. lines 224-225, etc), the error made by the fish is presented in cm (see Figure 2 where the inset shows how the error was computed). I wonder if it would not be more appropriate to present it in terms of the angular difference between the trajectory made by the fish and the food delivery location.

Angles could also be used, but because of the large variation in initial distances (that we wanted to make sure that the fish had to capture a rule, allowing them to respond from various distances) another measure was used that we found somehow more natural: it is simply how close a fish would get to the landing point if it continued in the direction assumed after the turn. Although we describe how we defined accuracy we did not discuss why this measure was used in this (and many previous studies). We are very happy to add this. Please also note that running all tests based on angular errors (which we also have done throughout to ensure that the conclusions are independent on an arbitrary measure of the error) leads to no different conclusion. We have added a brief explanation in the text and in the new Fig. 2.

Lines 299-323: Is it my impression or did fish have more trouble in generalizing their learned rule to the condition untrained larger height (see for instance red curves in Figures 4 D, E, G)? Could the authors elaborate on this point?

We changed the code to make this more clear. The red curves (before marked A to highlight impact point option A) correspond to the errors to the ballistic impact point without deflection, so what would have to be compared are the black curves (marked P to highlight the virtual impact point that should be chosen had the fish immediately generated to the untrained conditions). We have rewritten the text and the labels in the Figure (now Figure 5) to illustrate the predictions and to name them in more helpful ways and so that they can't be confused with panel labels. At any rate, what needs to be compared, to check the idea, are the black curves, and these are not statistically different between both heights (p=0.525, Mann-Whitney). Interestingly, none of the black curves from all panels (D-G) differ (p>0.3).

Line 559: if we are speaking here about luminance contrast, it should read 'Michelson Contrast' rather than 'Michelsen Contrast'.

Absolutely, thanks!

References

(1) Chittka, L., Skorupski, P. & Raine, N. E. Speed-accuracy tradeoffs in animal decision making. Trends Ecol Evol 24, 400-407, doi:10.1016/j.tree.2009.02.010 (2009).

An excellent paper that helps to stress our main question

Author response:

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

Reviewer #1 (Public review):

Summary:

In this manuscript, Pakula et al. explore the impact of reactive oxygen species (ROS) on neonatal cerebellar regeneration, providing evidence that ROS activates regeneration through Nestin-expressing progenitors (NEPs). Using scRNA-seq analysis of FACS-isolated NEPs, the authors characterize injury-induced changes, including an enrichment in ROS metabolic processes within the cerebellar microenvironment. Biochemical analyses confirm a rapid increase in ROS levels following irradiation and forced catalase expression, which reduces ROS levels, and impairs external granule layer (EGL) replenishment post-injury.

Strengths:

Overall, the study robustly supports its main conclusion and provides valuable insights into ROS as a regenerative signal in the neonatal cerebellum.

Comments on revisions:

The authors have addressed most of the previous comments. However, they should clarify the following response:

*"For reasons we have not explored, the phenotype is most prominent in these lobules, that is why they were originally chosen. We edited the following sentence (lines 578-579):

First, we analyzed the replenishment of the EGL by BgL-NEPs in vermis lobules 3-5, since our previous work showed that these lobules have a prominent defect."*

It has been reported that the anterior part of the cerebellum may have a lower regenerative capacity compared to the posterior lobe. To avoid potential ambiguity, the authors should clarify that "the phenotype" and "prominent defect" refer to more severe EGL depletion at an earlier stage after IR rather than a poorer regenerative outcome. Additionally, they should provide a reference to support their statement or indicate if it is based on unpublished observations.

Our comment does not refer to a more severe EGL depletion at an earlier stage. There is instead poorer regeneration of the anterior region. The irradiation approach used provides consistent cell killing of GCPs across the cerebellum. This can be seen in Fig. 1c, e, g, i in our previous publication: Wojcinski, et al. (2017) Cerebellar granule cell replenishment post-injury by adaptive reprogramming of Nestin+ progenitors. Nature Neuroscience, 20:1361-1370). Also, Fig 2e, g, k, m in the paper shows that by P5 and P8, posterior lobule 8 recovers better than anterior lobules 1-5.

Reviewer #2 (Public review):

Summary:

The authors have previously shown that the mouse neonatal cerebellum can regenerate damage to granule cell progenitors in the external granular layer, through reprogramming of gliogenic nestin-expressing progenitors (NEPs). The mechanisms of this reprogramming remain largely unknown. Here the authors used scRNAseq and ATACseq of purified neonatal NEPs from P1-P5 and showed that ROS signatures were transiently upregulated in gliogenic NEPs ve neurogenic NEPs 24 hours post injury (P2). To assess the role of ROS, mice transgenic for global catalase activity were assessed to reduce ROS. Inhibition of ROS significantly decreased gliogenic NEP reprogramming and diminished cerebellar growth post-injury. Further, inhibition of microglia across this same time period prevented one of the first steps of repair - the migration of NEPs into the external granule layer. This work is the first demonstration that the tissue microenvironment of the damaged neonatal cerebellum is a major regulator of neonatal cerebellar regeneration. Increased ROS is seen in other CNS damage models, including adults, thus there may be some shared mechanisms across age and regions, although interestingly neonatal cerebellar astrocytes do not upregulate GFAP as seen in adult CNS damage models. Another intriguing finding is that global inhibition of ROS did not alter normal cerebellar development.

Strengths:

This paper presents a beautiful example of using single cell data to generate biologically relevant, testable hypotheses of mechanisms driving important biological processes. The scRNAseq and ATACseq analyses are rigorously conducted and conclusive. Data is very clearly presented and easily interpreted supporting the hypothesis next tested by reduce ROS in irradiated brains.

Analysis of whole tissue and FAC sorted NEPS in transgenic mice where human catalase was globally expressed in mitochondria were rigorously controlled and conclusively show that ROS upregulation was indeed decreased post injury and very clearly the regenerative response was inhibited. The authors are to be commended on the very careful analyses which are very well presented and again, easy to follow with all appropriate data shown to support their conclusions.

Weaknesses:

The authors also present data to show that microglia are required for an early step of mobilizing gliogenic NEPs into the damaged EGL. While the data that PLX5622 administration from P0-P5 or even P0-P8 clearly shows that there is an immediate reduction of NEPs mobilized to the damaged EGL, there is no subsequent reduction of cerebellar growth such that by P30, the treated and untreated irradiated cerebella are equivalent in size. There is speculation in the discussion about why this might be the case. Additional experiments and tools are required to assess mechanisms. Regardless, the data still implicate microglia in the neonatal regenerative response, and this finding remains an important advance.

As stated previously, the suggested follow up experiments while relevant are extensive and considered beyond the scope of the current paper.

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

In this manuscript, Pakula et al. explore the impact of reactive oxygen species (ROS) on neonatal cerebellar regeneration, providing evidence that ROS activates regeneration through Nestin-expressing progenitors (NEPs). Using scRNA-seq analysis of FACS-isolated NEPs, the authors characterize injury-induced changes, including an enrichment in ROS metabolic processes within the cerebellar microenvironment. Biochemical analyses confirm a rapid increase in ROS levels following irradiation, and forced catalase expression, which reduces ROS levels, and impairs external granule layer (EGL) replenishment post-injury.

Strengths:

Overall, the study robustly supports its main conclusion and provides valuable insights into ROS as a regenerative signal in the neonatal cerebellum.

Weaknesses:

(1) The diversity of cell types recovered from scRNA-seq libraries of sorted Nes-CFP cells is unexpected, especially the inclusion of minor types such as microglia, meninges, and ependymal cells. The authors should validate whether Nes and CFP mRNAs are enriched in the sorted cells; if not, they should discuss the potential pitfalls in sampling bias or artifacts that may have affected the dataset, impacting interpretation.

In our previous work, we thoroughly assessed the transgene using RNA in situ hybridization for Cfp, immunofluorescent analysis for CFP and scRNA-seq analysis for Cfp transcripts (Bayin et al., Science Adv. 2021, Fig. S1-2)(1), and characterized the diversity within the NEP populations of the cerebellum. Our present scRNA-seq data also confirms that Nes transcripts are expressed in all the NEP subtypes. A feature plot for Nes expression has been added to the revised manuscript (Fig 1E), as well as a sentence explaining the results. Of note, since this data was generated from FACS-isolated CFP+ cells, the perdurance of the protein allows for the detection of immediate progeny of Nes-expressing cells, even in cells where Nes is not expressed once cells are differentiated. Finally, oligodendrocyte progenitors, perivascular cells, some rare microglia and ependymal cells have been demonstrated to express Nes in the central nervous system; therefore, detecting small groups of these cells is expected (2-4). We have added the following sentence (lines 391-394):

“Detection of Nes mRNA confirmed that the transgene reflects endogenous Nes expression in progenitors of many lineages, and also that the perdurance of CFP protein in immediate progeny of Nes-expressing cells allowed the isolation of these cells by FACS (Figure 1E)”.

(2) The authors should de-emphasize that ROS signaling and related gene upregulation exclusively in gliogenic NEPs. Genes such as Cdkn1a, Phlda3, Ass1, and Bax are identified as differentially expressed in neurogenic NEPs and granule cell progenitors (GCPs), with Ass1 absent in GCPs. According to Table S4, gene ontology (GO) terms related to ROS metabolic processes are also enriched in gliogenic NEPs, neurogenic NEPs, and GCPs.

As the reviewer requested, we have de-emphasized that ROS signaling is preferentially upregulated in gliogenic NEPs, since we agree with the reviewer that there is some evidence for similar transcriptional signatures in neurogenic NEPs and GCPs. We added the following (lines 429-531):

“Some of the DNA damage and apoptosis related genes that were upregulated in IR gliogenic-NEPs (Cdkn1a, Phlda3, Bax) were also upregulated in the IR neurogenic-NEPs and GCPs at P2 (Supplementary Figure 2B-E).”

And we edited the last few sentences of the section to state (lines 453-459):

“Interestingly, we did not observe significant enrichment for GO terms associated with cellular stress response in the GCPs that survived the irradiation compared to controls, despite significant enrichment for ROS signaling related GO-terms (Table S4). Collectively, these results indicate that injury induces significant and overlapping transcriptional changes in NEPs and GCPs. The gliogenic- and neurogenic-NEP subtypes transiently upregulate stress response genes upon GCP death, and an overall increase in ROS signaling is observed in the injured cerebella.”

(3) The authors need to justify the selection of only the anterior lobe for EGL replenishment and microglia quantification.

We thank the reviewers for asking for this clarification. Our previous publications on regeneration of the EGL by NEPs have all involved quantification of these lobules, thus we think it is important to stay with the same lobules. For reasons we have not explored, the phenotype is most prominent in these lobules, that is why they were originally chosen. We edited the following sentence (lines 578-579):

“First, we analyzed the replenishment of the EGL by BgL-NEPs in vermis lobules 3-5, since our previous work showed that these lobules have a prominent defect.”

(4) Figure 1K: The figure presents linkages between genes and GO terms as a network but does not depict a gene network. The terminology should be corrected accordingly.

We have corrected the terminology and added the following (lines 487-489):

“Finally, linkages between the genes in differentially open regions identified by ATAC-seq and the associated GO-terms revealed an active transcriptional network involved in regulating cell death and apoptosis (Figure 1K).”

(5) Figure 1H and S2: The x-axis appears to display raw p-values rather than log10(p.value) as indicated. The x-axis should ideally show -log10(p.adjust), beginning at zero. The current format may misleadingly suggest that the ROS GO term has the lowest p-values.

Apologies for the mistake. The data represents raw p-values and the x-axis has been corrected.

(6) Genes such as Ppara, Egln3, Foxo3, Jun, and Nos1ap were identified by bulk ATAC-seq based on proximity to peaks, not by scRNA-seq. Without additional expression data, caution is needed when presenting these genes as direct evidence of ROS involvement in NEPs.

We modified the text to discuss the discrepancies between the analyses. While some of this could be due to the lower detection limits in the scRNA-seq, it also highlights that chromatin accessibility is not a direct readout for expression levels and further analysis is needed. Nevertheless, both scRNA-seq and ATAC-seq have identified similar mechanisms, and our mutant analysis confirmed our hypothesis that an increase in ROS levels underlies repair, further increasing the confidence in our analyses. Further investigation is needed to understand the downstream mechanisms. We added the following sentence (lines 478-481):

“However, not all genes in the accessible areas were differentially expressed in the scRNA-seq data. While some of this could be due to the detection limits of scRNA-seq, further analysis is required to assess the mechanisms of how the differentially accessible chromatin affects transcription.”

(7) The authors should annotate cell identities for the different clusters in Table S2.

All cell types have been annotated in Table S2.

(8) Reiterative clustering analysis reveals distinct subpopulations among gliogenic and neurogenic NEPs. Could the authors clarify the identities of these subclusters? Can we distinguish the gliogenic NEPs in the Bergmann glia layer from those in the white matter?

Thank you for this clarification. As shown in our previous studies, we can not distinguish between the gliogenic NEPs in the Bergmann glia layer and the white matter based on scRNA-seq, but expression of the Bergmann glia marker Gdf10 suggests that a large proportion of the cells in the Hopx+ clusters are in the Bergmann glia layer. The distinction within the major subpopulations that we characterized (Hopx-, Ascl1-expressing NEPs and GCPs) are driven by their proliferative/maturation status as we previously observed. We have included a detailed annotation of all the clusters in Table S2, as requested and a UMAP for mKi57 expression in Fig 1E. We have clarified this in the following sentence (lines 383-385):

“These groups of cells were further subdivided into molecularly distinct clusters based on marker genes and their cell cycle profiles or developmental stages (Figure 1D, Table S2).”

(9) In the Methods section, the authors mention filtering out genes with fewer than 10 counts. They should specify if these genes were used as background for enrichment analysis. Background gene selection is critical, as it influences the functional enrichment of gene sets in the list.

As requested, the approach used has been added to the Methods section of the revised paper. Briefly, the background genes used by the goseq function are the same genes used for the probability weight function (nullp). The mm8 genome annotation was used in the nullp function, and all annotated genes were used as background genes to compute GO term enrichment. The following was added (lines 307-308):

“The background genes used to compute the GO term enrichment includes all genes with gene symbol annotations within mm8.”

(10) Figure S1C: The authors could consider using bar plots to better illustrate cell composition differences across conditions and replicates.

As suggested, we have included bar plots in Fig. S1D-F.

(11) Figures 4-6: It remains unclear how the white matter microglia contribute to the recruitment of BgL-NEPs to the EGL, as the mCAT-mediated microglia loss data are all confined to the white matter.

We have thought about the question and had initially quantified the microglia in the white matter and the rest of the lobules (excluding the EGL) separately. However, there are very few microglia outside the white matter in each section, thus it is not possible to obtain reliable statistical data on such a small population. We therefore did not include the cells in the analysis. We have added this point in the main text (line 548).

“As a possible explanation for how white matter microglia could influence NEP behaviors, given the small size of the lobules and how the cytoarchitecture is disrupted after injury, we think it is possible that secreted factors from the white matter microglia could reach the BgL NEPs. Alternatively, there could be a relay system through an intermediate cell type closer to the microglia.” We have added these ideas to the Discussion of the revised paper (lines 735-738).

Reviewer #2 (Public review):

Summary:

The authors have previously shown that the mouse neonatal cerebellum can regenerate damage to granule cell progenitors in the external granular layer, through reprogramming of gliogenic nestin-expressing progenitors (NEPs). The mechanisms of this reprogramming remain largely unknown. Here the authors used scRNAseq and ATACseq of purified neonatal NEPs from P1-P5 and showed that ROS signatures were transiently upregulated in gliogenic NEPs ve neurogenic NEPs 24 hours post injury (P2). To assess the role of ROS, mice transgenic for global catalase activity were assessed to reduce ROS. Inhibition of ROS significantly decreased gliogenic NEP reprogramming and diminished cerebellar growth post-injury. Further, inhibition of microglia across this same time period prevented one of the first steps of repair - the migration of NEPs into the external granule layer. This work is the first demonstration that the tissue microenvironment of the damaged neonatal cerebellum is a major regulator of neonatal cerebellar regeneration. Increased ROS is seen in other CNS damage models including adults, thus there may be some shared mechanisms across age and regions, although interestingly neonatal cerebellar astrocytes do not upregulate GFAP as seen in adult CNS damage models. Another intriguing finding is that global inhibition of ROS did not alter normal cerebellar development.

Strengths:

This paper presents a beautiful example of using single cell data to generate biologically relevant, testable hypotheses of mechanisms driving important biological processes. The scRNAseq and ATACseq analyses are rigorously conducted and conclusive. Data is very clearly presented and easily interpreted supporting the hypothesis next tested by reduce ROS in irradiated brains.

Analysis of whole tissue and FAC sorted NEPS in transgenic mice where human catalase was globally expressed in mitochondria were rigorously controlled and conclusively show that ROS upregulation was indeed decreased post injury and very clearly the regenerative response was inhibited. The authors are to be commended on the very careful analyses which are very well presented and again, easy to follow with all appropriate data shown to support their conclusions.

Weaknesses:

The authors also present data to show that microglia are required for an early step of mobilizing gliogenic NEPs into the damaged EGL. While the data that PLX5622 administration from P0-P5 or even P0-P8 clearly shows that there is an immediate reduction of NEPs mobilized to the damaged EGL, there is no subsequent reduction of cerebellar growth such that by P30, the treated and untreated irradiated cerebella are equivalent in size. There is speculation in the discussion about why this might be the case, but there is no explanation for why further, longer treatment was not attempted nor was there any additional analyses of other regenerative steps in the treated animals. The data still implicate microglia in the neonatal regenerative response, but how remains uncertain.

Recommendations for the authors:

Reviewer #2 (Recommendations for the authors):

This is an exemplary manuscript.

The methods and data are very well described and presented.

I actually have very little to ask the authors except for an explanation of why PLX treatment was discontinued after P5 or P8 and what other steps of NEP reprogramming were assessed in these animals? Was NEP expansion still decreased at P8 even in the presence of PLX at this stage? Also - was there any analysis attempted combining mCAT and PLX?

We agree with the reviewer that a follow up study that goes into a deeper analysis of the role of microglia in GCP regeneration and any interaction with ROS signaling would interesting. However, it would require a set of tools that we do not currently have. We did not have enough PLX5622 to perform addition experiments or extend the length of treatment. Plexxikon informed us in 2021 that they were no longer manufacturing PLX5622 because they were focusing on new analogs for in vivo use, and thus we had to use what we had left over from a completed preclinical cancer study. We nevertheless think it is important to publish our preliminary results to spark further experiments by other groups.

References

(1) Bayin N. S. Mizrak D., Stephen N. D., Lao Z., Sims P. A., Joyner A. L. Injury induced ASCL1 expression orchestrates a transitory cell state required for repair of the neonatal cerebellum. Sci Adv. 2021;7(50):eabj1598.

(2) Cawsey T, Duflou J, Weickert CS, Gorrie CA. Nestin-Positive Ependymal Cells Are Increased in the Human Spinal Cord after Traumatic Central Nervous System Injury. J Neurotrauma. 2015;32(18):1393-402.

(3) Gallo V, Armstrong RC. Developmental and growth factor-induced regulation of nestin in oligodendrocyte lineage cells. The Journal of neuroscience : the official journal of the Society for Neuroscience. 1995;15(1 Pt 1):394-406.

(4) Huang Y, Xu Z, Xiong S, Sun F, Qin G, Hu G, et al. Repopulated microglia are solely derived from the proliferation of residual microglia after acute depletion. Nat Neurosci. 2018;21(4):530-40.

Author response:

The following is the authors’ response to the previous reviews

Reviewer #1 (Public review):

Jin et al. investigated how the bacterial DNA damage (SOS) response and its regulator protein RecA affects the development of drug resistance under short-term exposure to beta-lactam antibiotics. Canonically, the SOS response is triggered by DNA damage, which results in the induction of error-prone DNA repair mechanisms. These error-prone repair pathways can increase mutagenesis in the cell, leading to the evolution of drug resistance. Thus, inhibiting the SOS regulator RecA has been proposed as means to delay the rise of resistance.

In this paper, the authors deleted the RecA protein from E. coli and exposed this ∆recA strain to selective levels of the beta-lactam antibiotic, ampicillin. After an 8h treatment, they washed the antibiotic away and allowed the surviving cells to recover in regular media. They then measured the minimum inhibitory concentration (MIC) of ampicillin against these treated strains. They note that after just 8 h treatment with ampicillin, the ∆recA had developed higher MICs towards ampicillin, while by contrast, wild-type cells exhibited unchanged MICs. This MIC increase was also observed subsequent generations of bacteria, suggesting that the phenotype is driven by a genetic change.

The authors then used whole genome sequencing (WGS) to identify mutations that accounted for the resistance phenotype. Within resistant populations, they discovered key mutations in the promoter region of the beta-lactamase gene, ampC; in the penicillin-binding protein PBP3 which is the target of ampicillin; and in the AcrB subunit of the AcrAB-TolC efflux machinery. Importantly, mutations in the efflux machinery can impact the resistances towards other antibiotics, not just beta-lactams. To test this, they repeated the MIC experiments with other classes of antibiotics, including kanamycin, chloramphenicol, and rifampicin. Interestingly, they observed that the ∆recA strains pre-treated with ampicillin showed higher MICs towards all other antibiotic tested. This suggests that the mutations conferring resistance to ampicillin are also increasing resistance to other antibiotics.

The authors then performed an impressive series of genetic, microscopy, and transcriptomic experiments to show that this increase in resistance is not driven by the SOS response, but by independent DNA repair and stress response pathways. Specifically, they show that deletion of the recA reduces the bacterium's ability to process reactive oxygen species (ROS) and repair its DNA. These factors drive accumulation of mutations that can confer resistance towards different classes of antibiotics. The conclusions are reasonably well-supported by the data, but some aspects of the data and the model need to be clarified and extended.

Strengths:

A major strength of the paper is the detailed bacterial genetics and transcriptomics that the authors performed to elucidate the molecular pathways responsible for this increased resistance. They systemically deleted or inactivated genes involved in the SOS response in E. coli. They then subjected these mutants the same MIC assays as described previously. Surprisingly, none of the other SOS gene deletions resulted an increase in drug resistance, suggesting that the SOS response is not involved in this phenotype. This led the authors to focus on the localization of DNA PolI, which also participates in DNA damage repair. Using microscopy, they discovered that in the RecA deletion background, PolI co-localizes with the bacterial chromosome at much lower rates than wild-type. This led the authors to conclude that deletion of RecA hinders PolI and DNA repair. Although the authors do not provide a mechanism, this observation is nonetheless valuable for the field and can stimulate further investigations in the future.

In order to understand how RecA deletion affects cellular physiology, the authors performed RNA-seq on ampicillin-treated strains. Crucially, they discovered that in the RecA deletion strain, genes associated with antioxidative activity (cysJ, cysI, cysH, soda, sufD) and Base Excision Repair repair (mutH, mutY, mutM), which repairs oxidized forms of guanine, were all downregulated. The authors conclude that down-regulation of these genes might result in elevated levels of reactive oxygen species in the cells, which in turn, might drive the rise of resistance. Experimentally, they further demonstrated that treating the ∆recA strain with an antioxidant GSH prevents the rise of MICs. These observations will be useful for more detailed mechanistic follow-ups in the future.

Weaknesses:

Throughout the paper, the authors use language suggesting that ampicillin treatment of the ∆recA strain induces higher levels of mutagenesis inside the cells, leading to the rapid rise of resistance mutations. However, as the authors note, the mutants enriched by ampicillin selection can play a role in efflux and can thus change a bacterium's sensitivity to a wide range of antibiotics, in what is known as cross-resistance. The current data is not clear on whether the elevated "mutagenesis" is driven ampicillin selection or by a bona fide increase in mutation rate.

Furthermore, on a technical level, the authors employed WGS to identify resistance mutations in the treated ampicillin-treated wild-type and ∆recA strains. However, the WGS methodology described in the paper is inconsistent. Notably, wild-type WGS samples were picked from non-selective plates, while ΔrecA WGS isolates were picked from selective plates with 50 μg/mL ampicillin. Such an approach biases the frequency and identity of the mutations seen in the WGS and cannot be used to support the idea that ampicillin treatment induces higher levels of mutagenesis.

Finally, it is important to establish what the basal mutation rates of both the WT and ∆recA strains are. Currently, only the ampicillin-treated populations were reported. It is possible that the ∆recA strain has inherently higher mutagenesis than WT, with a larger subpopulation of resistant clones. Thus, ampicillin treatment might not in fact induce higher mutagenesis in ∆recA.

Comments on revisions:

Thank you for responding to the concerns raised previously. The manuscript overall has improved.

We sincerely thank the reviewer for raising this important point. In our initial submission, we acknowledge that our mutation analysis was based on a limited number of replicates (n=6), which may not have been sufficient to robustly distinguish between mutation induction and selection. In response to this concern, we have substantially expanded our experimental dataset. Specifically, we redesigned the mutation rate validation experiment by increasing the number of biological replicates in each condition to 96 independent parallel cultures. This enabled us to systematically assess mutation frequency distributions under four conditions (WT, WT+ampicillin, ΔrecA, ΔrecA+ampicillin), using both maximum likelihood estimation (MLE) and distribution-based fluctuation analysis (new Figure 1F, 1G, and Figure S5).

These expanded datasets revealed that:

(1) While the estimated mutation rate was significantly elevated in ΔrecA+ampicillin compared to ΔrecA alone (Fig. 1G),

(2) The distribution of mutation frequencies in ΔrecA+ampicillin was highly skewed with evident jackpot cultures (Fig. 1F), and

(3) The observed pattern significantly deviated from Poisson expectations, which is inconsistent with uniform mutagenesis and instead supports clonal selection from an early-arising mutational pool (Fig. S5).

Importantly, these new results do not contradict our original conclusions but rather extend and refine them. The previous evidence for ROS-mediated mutagenesis remains valid and is supported by our GSH experiments, transcriptomic analysis of oxidative stress genes, and DNA repair pathway repression. However, the additional data now indicate that ROS-induced variants are not uniformly induced after antibiotic exposure but are instead generated stochastically under the stress-prone ΔrecA background and then selectively enriched upon ampicillin treatment.

Taken together, we now propose a two-step model of resistance evolution in ΔrecA cells (new Figure 5):

Step i: RecA deficiency creates a hypermutable state through impaired repair and elevated ROS, increasing the probability of resistance-conferring mutations.

Step ii: β-lactam exposure acts as a selective bottleneck, enriching early-arising mutants that confer resistance.

We have revised both the Results and Discussion sections to clearly articulate this complementary relationship between mutational supply and selection, and we believe this integrated model better explains the observed phenotypes and mechanistic outcomes.

Reviewer #2 (Public review):

This study aims to demonstrate that E. coli can acquire rapid antibiotic resistance mutations in the absence of a DNA damage response. The authors employed a modified Adaptive Laboratory Evolution (ALE) workflow to investigate this, initiating the process by diluting an overnight culture 50-fold into an ampicillin selection medium. They present evidence that a recA- strain develops ampicillin resistance mutations more rapidly than the wild-type, as indicated by the Minimum Inhibitory Concentration (MIC) and mutation frequency. Whole-genome sequencing of recA- colonies resistant to ampicillin showed predominant inactivation of genes involved in the multi-drug efflux pump system, contrasting with wild-type mutations that seem to activate the chromosomal ampC cryptic promoter. Further analysis of mutants, including a lexA3 mutant incapable of inducing the SOS response, led the authors to conclude that the rapid evolution of antibiotic resistance occurs via an SOS-independent mechanism in the absence of recA. RNA sequencing suggests that antioxidative response genes drive the rapid evolution of antibiotic resistance in the recA- strain. They assert that rapid evolution is facilitated by compromised DNA repair, transcriptional repression of antioxidative stress genes, and excessive ROS accumulation.

Strengths:

The experiments are well-executed and the data appear reliable. It is evident that the inactivation of recA promotes faster evolutionary responses, although the exact mechanisms driving this acceleration remain elusive and deserve further investigation.

Weaknesses:

Some conclusions are overstated. For instance, the conclusion regarding the LexA3 allele, indicating that rapid evolution occurs in an SOS-independent manner (line 217), contradicts the introductory statement that attributes evolution to compromised DNA repair.

We thank the reviewer for this insightful observation, which highlights a central conceptual advance of our study. Our data indeed indicate that resistance evolution in ΔrecA occurs independently of canonical SOS induction (as shown by the lack of resistance in lexA3, dpiBA, and translesion polymerase mutants), yet is clearly associated with impaired DNA repair capacity (e.g., downregulation of polA, mutH, mutY).

This apparent “contradiction” reflects the dual role of RecA: it functions both as the master activator of the SOS response and as a key factor in SOS-independent repair processes. Thus, the rapid resistance evolution in ΔrecA is not due to loss of SOS, but rather due to the broader suppression of DNA repair pathways that RecA coordinates, which elevates mutational load under stress (This point is discussed in further detail in our response to Reviewer 1).

The claim made in the discussion of Figure 3 that the hindrance of DNA repair in recA- is crucial for rapid evolution is at best suggestive, not demonstrative. Additionally, the interpretation of the PolI data implies its role, yet it remains speculative.

We appreciate this comment and would like to respectfully clarify that our conclusion regarding the role of DNA repair impairment is supported by several independent lines of mechanistic evidence.

First, our RNA-seq analysis revealed transcriptional suppression of multiple DNA repair genes in ΔrecA cells following ampicillin treatment, including polA (DNA Pol I) and the base excision repair genes mutH, mutY, and mutM (Fig. 4K). This indicates that multiple repair pathways, including those responsible for correcting oxidative DNA lesions, are downregulated under these conditions.

Second, we observed a significant reduction in DNA Pol I protein expression as well as reduced colocalization with chromosomal DNA in ΔrecA cells, suggesting impaired engagement of repair machinery (Fig. 3C-E). These phenotypes are not limited to transcriptional signatures but extend to functional protein localization.

Third, and most importantly, resistance evolution was fully suppressed in ΔrecA cells upon co-treatment with glutathione (GSH), which reduces ROS levels. As GSH did not affect ampicillin killing (Fig. 4J), these findings suggest that mutagenesis and thus the emergence of resistance requires both ROS accumulation and the absence of efficient repair.

Therefore, we believe these data go beyond correlation and demonstrate a mechanistic role for DNA repair impairment in driving stress-associated resistance evolution in ΔrecA. We have revised the Discussion to emphasize the strength of this evidence while avoiding overstatement.

In Figure 2A table, mutations in amp promoters are leading to amino acid changes.

We thank the reviewer for spotting this inconsistency. Indeed, the ampC promoter mutations we identified reside in non-coding regulatory regions and do not result in amino acid substitutions. We have corrected the annotation in Fig. 2A and clarified in the main text that these mutations likely affect gene expression through transcriptional regulation, rather than protein sequence alteration.

The authors' assertion that ampicillin significantly influences persistence pathways in the wild-type strain, affecting quorum sensing, flagellar assembly, biofilm formation, and bacterial chemotaxis, lacks empirical validation.

We thank the reviewer for pointing this out. In the original version, we acknowledged transcriptional enrichment of genes related to quorum sensing, flagellar assembly, and chemotaxis in the wild-type strain upon ampicillin treatment. However, as we did not directly assess persistence phenotypes (e.g., biofilm formation or persister levels), we agree that such functional inferences were not fully supported. We have revised the relevant statements to focus solely on transcriptomic changes and have removed language suggesting direct effects on persistence pathways.

Figure 1G suggests that recA cells treated with ampicillin exhibit a strong mutator phenotype; however, it remains unclear if this can be linked to the mutations identified in Figure 2's sequencing analysis.

We appreciate the reviewer’s comment. This point is discussed in further detail in our response to Reviewer 1.

Reviewer #3 (Public review):

In the present work, Zhang et al investigate involvement of the bacterial DNA damage repair SOS response in the evolution of beta-lactam drug resistance evolution in Escherichia coli. Using a combination of microbiological, bacterial genetics, laboratory evolution, next-generation, and live-cell imaging approaches, the authors propose short-term (transient) drug resistance evolution can take place in RecA-deficient cells in an SOS response-independent manner. They propose the evolvability of drug resistance is alternatively driven by the oxidative stress imposed by accumulation of reactive oxygen species and compromised DNA repair. Overall, this is a nice study that addresses a growing and fundamental global health challenge (antimicrobial resistance).

Strengths:

The authors introduce new concepts to antimicrobial resistance evolution mechanisms. They show short-term exposure to beta-lactams can induce durably fixed antimicrobial resistance mutations. They propose this is due to comprised DNA repair and oxidative stress. Antibiotic resistance evolution under transient stress is poorly studied, so the authors' work is a nice mechanistic contribution to this field.

Weaknesses:

The authors do not show any direct evidence of altered mutation rate or accumulated DNA damage in their model.

We appreciate the reviewer’s comment. This point is discussed in further detail in our response to Reviewer 1.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

I would like to suggest two minor changes to the text.

(1) Re. WGS data.

The authors write in their response "We appreciate your concern regarding potential inconsistencies in the WGS methodology. However, we would like to clarify that the primary aim of the WGS experiment was to identify the types of mutations present in the wild type and ΔrecA strains after treatment of ampicillin, rather than to quantify or compare mutation frequencies. This purpose was explicitly stated in the manuscript.

I think the source of my confusion stemmed from this part in the text:

"In bacteria, resistance to most antibiotics requires the accumulation of drug resistance associated DNA mutations developed over time to provide high levels of resistance (29). To verify whether drug resistance associated DNA mutations have led to the rapid development of antibiotic resistance in recA mutant strain, we..."

I would change the phrase "verify whether drug resistance associated DNA mutations have led to the rapid development of antibiotic resistance in recA mutant strain" to "identify the types of mutations present in the wild type and ΔrecA strains after treatment of ampicillin." This would explicitly state what the sequencing was for (ie. ID-ing mutations). The current phrase can give the impression that WGS was used to validate rapid or high mutagenesis.

Thanks for this suggestion. We have revised this description to “In bacteria, resistance to most antibiotics requires the accumulation of drug resistance associated DNA mutations that can arise stochastically and, under stress conditions, become enriched through selection over time to confer high levels of resistance (33). Having observed a non-random and right-skewed distribution of mutation frequencies in ΔrecA isolates following ampicillin exposure, we next sought to determine whether specific resistance-conferring mutations were enriched in ΔrecA isolates following antibiotic exposure.”

(2) Re. whether the mutations are "induced" or "pre-existing."

The authors write:

"We appreciate your detailed feedback on the language used to describe our data. We understand the concern regarding the use of the term "induced" in relation to beta-lactam exposure. To clarify, we employed not only beta-lactam antibiotics but also other antibiotics, such as ciprofloxacin and chloramphenicol, in our experiments (data not shown). However, we observed that beta-lactam antibiotics specifically induced the emergence of resistance or altered the MIC in our bacterial populations. If resistance had pre-existed before antibiotic exposure, we would expect other antibiotics to exhibit a similar selective effect, particularly given the potential for cross-resistance to multiple antibiotics."

I think it is important to discuss the negative data for the other antibiotics (along with the other points made in your Reviewer response) in the main text.

This point is discussed in further detail in our response to Reviewer 1 (Public Review).

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public reviews):

(1) A cartoon paradigm of the HFD treatment window would be a helpful addition to Figure 1. Relatedly, the authors might consider qualifying MHFD as 'lactational MHFD.' Readers might miss the fact that the exposure window starts at birth.

This is a good suggestion. The MHFD-L model has been used previously (e.g. Vogt et al. 2014). We have included a cartoon of the MHFD-L model and the PLX treatments to Figure 4, which we feel helps the readers and thank the reviewer for the suggestion.

(2) More details on the modeling pipeline are needed either in Figure 1 or text. Of the ~50 microglia that were counted (based on Figure 1J), were all 50 quantified for the morphological assessments? Were equal numbers used for the control and MHFD groups? Were the 3D models adjusted manually for accuracy? How much background was detected by IMARIS that was discarded? Was the user blind to the treatment group while using the pipeline? Were the microglia clustered or equally spread across the PVN?

In response to this suggestion, we have expanded the description of the image analysis routine in the methods. The analysis focused on detailed changes in microglial morphology as opposed to overall changes in microglia throughout the PVH as a whole. Accordingly, we applied anatomically matched ROIs to the PVH for the measurements. As described in the methods, the Imaris Filaments tool was used to visualize microglia fully contained within a tissue section and a mask derived from the 3D model for these cells was used to isolate them for further analysis, thereby separating these cells from interstitial labeling corresponding to parts of cell processes or other labeling not associated with selected cells. There was no formal “background subtraction.” This was an error in the previous version of the manuscript and we have revised the methods to reflect the process actually used. The images were segmented (to enhance signal to noise for 3D rendering), and then a Gaussian filter was applied to improve edge detection, which facilitates the morphological measurements.

(3) Suggest toning back some of the language. For example: "...consistent with enhanced activity and surveillance of their immediate microenvironment" (Line 195) could be "...perhaps consistent with...". Likewise, "profound" (Lines 194, 377) might be an overstatement.

Revisions have been made to both the Introduction and Discussion to modulate our representation of this controversial issue.

(4) Representative images for AgRP+ cells (quantified in Figure 2J) are missing. Why not a co-label of Iba1+/AgRP+ as per Figure 1, 3? Also, what was quantified in Figure 2J - soma? Total immunoreactivity?

Because the density of AgRP labeling does not change in the ARH we omitted the red channel image (AgRP labeling) to highlight the similarity of the microglial morphology. To address the reviewer’s concerns, in the revised figure we have reconstituted the figure with both the green (microglial) and red (AgRP) channels depicted.

Figure 2J displays the numbers of AgRP neurons counted in the ARH in selected R01s through the ARH. The Methods section has been revised to include the visualization procedure used for the cell counts.

(5) For the PLX experiment:

a) "...we depleted microglia during the lactation period" (Line 234). This statement suggests microglia decreased from the first injection at P4 and throughout lactation, which is inaccurate. PLX5622 effects take time, upwards of a week. Thus, if PLX5622 injections started at P4, it could be P11 before the decrease in microglia numbers is stable. Moreover, by the time microglia are entirely knocked down, the pups might be supplementing some chow for milk, making it unclear how much PLX5622 they were receiving from the dam, which could also impact the rate at which microglia repopulation commences in the fetal brain. Quantifying microglia across the P4-P21 treatment window would be helpful, especially at P16, since the PVN AgRP microglia phenotypes were demonstrated and roughly when pups might start eating some chow. b) I am surprised that ~70% of the microglia are present at P21. Does this number reflect that microglia are returning as the pups no longer receive PLX5622 from milk from the dam? Does it reflect the poor elimination of microglia in the first place?

This is an important point and have revised the first sentence in section 2.3 to clarify the PLX treatment logic and added a cartoon to Fig. 4 to show the treatment timeline. The PLX5622 was not administered to the dams but daily to the pups. We also agree with the interpretation that PLX5622 depleted numbers of microglia, as supported by the microglial cell counts, rather than effected a complete elimination and have made revisions to clarify this distinction. Although mice were weighed at weaning, cellular measurements were only made in mice perfused at P55.

(6) Was microglia morphology examined for all microglia across the PVN? It is possible that a focus on PVNmpd microglia would reveal a stronger phenotype? In Figure 4H, J, AgRP+ terminals are counted in PVN subregions - PVNmpd and PVNpml, with PVNmpd showing a decrease of ~300 AgRP+ terminals in MHFD/Veh (rescued in MHFD/PLX5622). In Figure 1K, AgRP+ terminals across what appears to be the entire PVN decrease by ~300, suggesting that PVNmpd is driving this phenotype. If true, then do microglia within the PVNmpd display this morphology phenotype?

We have revised the description of the analysis procedures to clarify these points. All measurements were made in user defined, matched regions of interest according to morphological features of the PVH. No measurements were made that included the entire PVH and we revised the Methods section to improve clarity.

(7) What chow did the pups receive as they started to consume solid food? Is this only a MHFD challenge, or could the pups be consuming HFD chow that fell into the cage?

The pups were weaned onto the same normal chow diet that the dams received prior to MHFD-L treatment. The cages were inspected daily and minimal HFD spillage was observed, although we cannot rule out with certainty any contribution of the pups directly consuming the HFD. We have edited Methods section 5.2 for clarity.

(8) Figure 5: Does internalized AgRP+ co-localize with CD68+ lysosomes? How was 'internalized' determined?

This important point has been clarified by revisions to the Methods section.

(9) Different sample sizes are used across experiments (e.g., Figure 4 NCD n=5, MHFD n=4). Does this impact statistical significance?

Sample size does impact power of ANOVA with larger samples reducing the chance of errors. ANOVA is generally robust in the face of moderate departures from the assumption of equal sample sizes and equal variance such as we experienced in the PLX5622 experiment. Here we used t-tests to detect differences in a single variable between two groups and two-way ANOVA to compare treatment by diet and treatment changes in the PLX5622 studies. Additional detail has been added to the Methods section to clarify this point.

Reviewer #2 (Public reviews):

(1) Under chow-fed conditions, there is a decrease in the number of microglia in the PVH and ARH between P16 and P30, accompanied by an increase in complexity/volume. With the exception of PVH microglia at P16, this maturation process is not affected by MHFD. This "transient" increase in microglial complexity could also reflect premature maturation of the circuit.

This is an interesting possibility that requires future investigation (see response to Recommended Suggestions, above).

(2) The key experiment in this paper, the ablation of microglia, was presumably designed to prevent microglial expansion/activation in the PVH of MHFD pups. However, it also likely accelerates and exaggerates the decrease in cell number during normal development regardless of maternal diet. Efforts to interpret these findings are further complicated because microglial and AgRP neuronal phenotypes were not assessed at earlier time points when the circuit is most sensitive to maternal influences.

We agree that evaluations of microglia and hypothalamic circuits at many more time points would indeed be informative (see comments above).

(3) Microglial loss was induced broadly in the forebrain. Enhanced AgRP outgrowth to the PVH could be caused by actions elsewhere, such as direct effects on AgRP neurons in the ARH or secondary effects of changes in growth rates.

A local effect of microglia in the ARH that affects growth of AgRP axons remains a distinct possibility that deserves a targeted examination (see response to Recommended Suggestions, above).

(4) Prior publications from the authors and other groups support the idea that the density of AgRP projections to the PVH is primarily driven by factors regulating outgrowth and not pruning. The failure to observe increased engulfment of AgRP fibers by PVH microglia is therefore not surprising. The possibility that synaptic connectivity is modulated by microglia was not explored.

Synaptic pruning and regulation of axon targeting are not mutually exclusive processes and microglia may participate in both. Here we evaluated innervation of the PVH, which is sensitive to MHFD-L exposure, and engulfment of AgRP terminals by microglia, which does appear to be altered by MHFD-L. Given previous observations of terminal engulfment by microglia in other brain regions in response to environmental changes (e.g. prolonged stress) it is not unreasonable to expect this outcome in the offspring of MHFD-L dams.  In future work it will be important to profile multiple cell types in the PVH for microglial dependent and MHFDL-sensitive changes in targeting of AgRP axons. Equally important is a full characterization of postsynaptic changes in PVH neurons.

Reviewer #3 (Public reviews):

There was no attempt to interrogate microglia in different parts of the hypothalamus functionally. Morphology alone does not reflect a potential for significant signaling alterations that may occur within and between these and other cell types.

The authors should discuss the limitations of their approach and findings and propose future directions to address them.

We agree that evaluations of microglia and hypothalamic circuits at many more time points that include analyses of multiple regions would indeed be informative. We have added statements to the manuscript that address the limitations of our experimental approach and suggest future studies that will extend understanding of underlying mechanisms beyond those investigated here.

Recommendations for the authors:

Reviewing Editors Comments:

(1) The Abstract is 405 words and should be shortened to less than 200 words.  

The abstract has been edited to 200 words.

(2) The authors might consider raising the question in the Introduction of whether reduced AgRP innervation of the PVN in MHFD-treated mice is due to decreased axonal growth, enhanced microglial-mediated pruning, or a combination of both. The potential effects on axonal growth should be given more consideration.

This is an important point that we agree deserves additional consideration in the manuscript. Our past work has focused on leptin’s ability to influence axonal targeting of PVH neurons by AgRP and PPG neurons through a cell-autonomous mechanism and our conclusion is that leptin primarily induces axon growth. Because in this study our design did not focus on changes in axon growth over time but on regional changes in microglia and their interactions with AgRP terminals we did not want to divert attention from our logic in the introduction by highlighting multiple mechanisms. However, we have added a brief mention in the Introduction and have expanded consideration of axonal growth effects to the Discussion. Distinguishing between microglia’s role in synaptic density or axon targeting in this pathway is an important goal of future work.

(3) Line 37, a high-fat diet should be defined here as HFD and used consistently thereafter. Note that "high-fat-diet exposure" requires two hyphens.

The suggested revisions have been made throughout the manuscript.

(4) Line 38 and elsewhere, MHFD does not adequately describe the treatment being limited to the lactation period, perhaps MLHFD would be better or just LHFD (because the pups can't lactate).

The suggested revisions have been made throughout the manuscript, and we have used MHFD-L to describe maternal consumption of a high-fat diet that is restricted to the lactation period.

(5) Line 110, leptin-deficient mice (add hyphen).

(6) Line 183, NCD should be defined.

The suggested revisions have been made throughout the manuscript.

(7) Lines 237- 238, it is not clear what is widespread in the rostral forebrain. Is it the loss of microglia? What is the dividing point between the rostral and caudal forebrain? Were microglia depleted in the caudal forebrain too?

We have revised this section of the manuscript to focus the description on the hypothalamus alone and specify that the reduction in microglial density is not restricted to the PVH.  

(8) Line 245, microglial-mediated effects (add hyphen).

(9) Line 247, vehicle-treated mice (add hyphen).

The suggested revisions have been made throughout the manuscript.

(10) Line 457, when referring to genes, the approved gene name should be used in italics, AgRP should be Agrp (italics).

The suggested revision has been made throughout the manuscript.

(11) Line 459, the name of the Syn-Tom mice in the Key Resource table, Methods, and Text should be consistent. It would be best to use the formal name of the Ai34 line of mice on the JAX website.

The suggested revisions have been made throughout the manuscript.

(12) Figure 1G H, and I um should have Greek micro; Fig. 1J and K, Replace # with Number. The same suggestions apply to all the other figures.

Both the manuscript and figures have been revised in accordance with this recommendation.

(13) Figures 4 G, H, I and J. and Figures 5 M and O. The font size is too small to see well.

Fonts have been changed in the figures to improve visibility.

Reviewer #1 (Recommendations for the authors):

(1) Figures are out of order in the text. For example, Figure 1A is followed next by the results for Figure 1J instead of Figure 1B.

We regret that the organization of figure panels makes for awkward matching for the reader as they proceed through the text. We designed the figures to facilitate comparisons between cellular responses and differences in labeling. After evaluating a reorganization, we decided to maintain the original panel configurations, but have revised the text to more closely follow the presentation of cellular features in the figures.

(2) Figure 1B.: All images lack scale bars.

(3) Line 433 - 'underlie' is spelled wrong.

(4) Rosin et al should be 2019 and not 2018.

These corrections have been implemented in the revised text and figures.

(5) The statement that "the effects of MHFD on microglial morphology in the PVH of offspring display both temporal and regional specificity, which correspond to a decrease in the density of AgRP inputs to the PVH" (Line 196) needs clarification, as the phrase "regional specificity" has not been substantiated in this section even though it is discussed later.

We agree with this comment and have revised section 2.1 to more closely match the data presented to this point in the manuscript.

Reviewer #2 (Recommendations for the authors):

(1) The claim of "spatial specificity" in the effects of MHFD on microglia is based on an increase in the complexity/volume of microglia at P16 in the PVH that was not seen in the ARH or BNST. The transient nature of the effect raises several questions: Does the effect on the PVH represent premature maturation?

This is an interesting suggestion. However, given how little is known about microglial maturation in the hypothalamus it is difficult to address. It is indeed possible that microglia mature at different rates in each AgRP target, and that MHFD-L exposure alters the rate of maturation in some regions but not others. This will require a great deal more analysis of both microglia and ARH projections to understand fully (see below).

(2) To support their central claim that microglia in the PVH "sculpt the density of AgRP inputs to the PVH" the authors report effects on Iba1+ cells in the PVH of chow-fed dams at P55, body weight at P21, and AgRP projections in the PVH at an unspecified age. It is hard to understand what is happening across "normal" development in chow-fed dams since the number of Iba1+ cells decreases from ~50 to ~25 between P16 and P30 (Figure 1), but then increases to >60 cells at P55 (Figure 4). Given the large fluctuations in microglial population across time, analyzing the same parameters (i.e. microglial number/morphology in the ARH and PVH, AgRP neuronal number in the ARH, and fiber density in the PVH, and body weight) across time points before, during and after the critical period in chow and MHFD conditions would be very helpful.

The time points we evaluated were chosen to be during and after the previously determined critical period for development of AgRP projections to the PVH, which were then compared with adults (which were all P55) to assess longevity of the effects. We have incorporated revisions to improve the clarity of when measurements were assessed, and treatments implemented. Defining the cellular dynamics of microglia across time remains a major challenge for the field and will certainly be informed by future studies with additional time points, as well as by in vivo imaging studies focused on regions identified here. Although such studies are beyond the scope of the present work, their completion would advance our current understanding of how microglia respond to nutritional changes during development of feeding circuits.

(3) As microglia are also ablated in the ARH, direct effects on AgRP neurons or indirect effects via changes in growth rates could also contribute to increased AgRP fiber density in the PVH. In support of the first possibility, postnatal microglial depletion increases the number of AgRP neurons (Sun, et al. 2023).

We agree with the suggestion, also raised by the Reviewing Editor, which has been addressed briefly in the Introduction, and in more detail by revisions to the Discussion section.

(4) The failure to assess alpha-MSH fibers in the same animals was a missed opportunity. They are also affected by MHFD but likely involve a distinct mechanism (Vogt, et al 2014).

Given the paired interest in POMC neurons and AgRP neurons I understand the reviewer’s comment. We chose to focus solely on AgRP neurons because we do not currently have a way to genetically target axonal labeling exclusively to POMC neurons due to the shared precursor origin of POMC neurons and a percentage of NPY neurons in the ARH, as shown by Lori Zeltser’s laboratory. Moreover, the elegant work by Vogt et al. focused on responses of POMC neurons in the MHFD-L model. However, it certainly remains possible that microglia in the PVH interact with terminals derived from POMC neurons, as well as with terminals derived from other afferent populations of neurons.

(5) All statistical analyses involved unpaired t-tests. Two-way ANOVAs should be used to assess the effects of age and HFD and interactions between these factors.

We used t-tests to detect differences in a single variable between two groups and two-way ANOVA to compare treatment by diet and treatment changes in the PLX5622 studies.  Additional detail has been added to the Methods section and information added to the figure legend for Fig. 4 to clarify this point.

Reviewer #3 (Recommendations for the authors):

I suggest exploring the deeper characterization of the microglia in various parts of the hypothalamus in different conditions. This could include cytokine assessment or spatial transcriptomic.

We agree that a great deal more work is needed to improve our understanding of how microglia impact hypothalamic development more broadly and to identify underlying molecular mechanisms. We are hopeful that the data presented here will motivate additional study of microglial dynamics in multiple hypothalamic regions, as well as detailed studies of cellular signaling events for factors derived from MHFD-L dams that impact neural development in the hypothalamus.

Author response:

Public Reviews:

Reviewer #1 (Public review):

Summary:

n this manuscript, the authors used a leucine/pantothenate auxotrophic strain of Mtb to screen a library of FDA-approved compounds for their antimycobacterial activity and found significant antibacterial activity of the inhibitor semapimod. In addition to alterations in pathways, including amino acid and lipid metabolism and transcriptional machinery, the authors demonstrate that semapimod treatment targets leucine uptake in Mtb. The work presents an interesting connection between nutrient uptake and cell wall composition in mycobacteria.

Strengths:

The link between the leucine uptake pathway and PDIM is interesting but has not been characterized mechanistically. The authors discuss that PDIM presents a barrier to the uptake of nutrients and shows binding of the drug with PpsB. However it is unclear why only the leucine uptake pathway was affected.

We observe interference of L-leucine, but not of pantothenate, uptake in mc2 6206 strain upon semapimod treatment. At present, we do not have any clue whether PDIM presents a barrier exclusively to the uptake of L-leucine. Further studies may shed a light on underlying mechanism(s) by which L-leucine uptake is modulated by this small molecule.

We still do not know what PpsB actually does for amino acid uptake - is it a transporter?

By BLI-Octet we do not find any interaction between L-leucine and PpsB. Therefore, we doubt that PpsB is a transporter of L-leucine.

Does semapimod binding affect its activity?

Our study suggests that semapimod treatment alters PDIM architecture which becomes restrictive to L-leucine. However, at present the exact mechanism is not clear. Further studies are required to thoroughly examine the effect of semapimod on Mtb PpsB activity and alterations in PDIM by mass spectrometry.

Does the auxotrophic Mtb have lower PDIM levels compared to wild-type Mtb?

As per the published report by Mulholland et al, and by vancomycin susceptibility phenotype in our study, both the strains appear to have comparable PDIM levels.

The authors show an interesting result where they observed antibacterial activity of semapimod against H37Rv only in vivo and not in vitro. Why do the authors think this is the basis of this observation? It is possible semapimod has an immunomodulatory effect on the host since leucine is an essential amino acid in mice. The authors could check pro-inflammatory cytokine levels in infected mouse lungs with and without drug treatment.

Semapimod inhibits production of proinflammatory cytokines such as TNF-α, IL-1β, and IL-6, which would indeed help pathogen establish chronic infection. However, a significant reduction in bacterial loads in lungs and spleen upon semapimod treatment despite inhibition of proinflammatory cytokines clearly indicates bacterial dependence on host-derived exogenous leucine during intracellular growth.

The authors show that the semapimod-resistant auxotroph lacks PDIM. The conclusions would be further strengthened by including validations using PDIM mutants, including del-ppsB Mtb and other genes of the PDIM locus, whether in vivo this mutant would be more susceptible (or resistant) to semapimod treatment.

PDIM is a virulence factor, and plays an important role in the intracellular survival of the TB pathogen. Mtb strains lacking PDIM are expected to show attenuated growth during infection, even without semapimod treatment. In such a case, it might be difficult to draw any conclusions about the effect of semapimod against PDIM(-) strains in vivo.

Prolonged subculturing can introduce mutations in PDIM, which can be overcome by supplementing with propionate (Mullholland et al, Nat Microbiol, 2024). Did the authors also supplement their cultures with propionate? It would be interesting to see what mutations would result in Semr strains with propionate supplementation along with prolonged semapimod treatment.

Considering the fact that extensive subculturing may result in loss of PDIM, we avoided prolonged subculturing of bacteria. As presented in Fig. 6b, the WT bacteria retain PDIM. While performing the initial screening of drugs, we did not anticipate such phenotype, and hence bacteria were cultured in regular 7H9-OADS medium without propionate supplementation.

A comprehensive future study would help examining the effect of propionate on generation of semapimod resistant mutants in Mtb mc2 6206.

Weaknesses:

I have summarized the limitations above in my comments. Overall, it would be helpful to provide more mechanistic details to study the connection between leucine uptake and PDIM.

Reviewer #2 (Public review):

Summary

This important study uncovers a novel mechanism for L-leucine uptake by M. tuberculosis and shows that targeting this pathway with 'Semapimod' interferes with bacterial metabolism and virulence. These results identify the leucine uptake pathway as a potential target to design new anti-tubercular therapy.

Strengths

The authors took numerous approaches to prove that L-leucine uptake of M. tuberculosis is an important physiological phenomenon and may be effectively targeted by 'Semapimod'. This study utilizes a series of experiments using a broad set of tools to justify how the leucine uptake pathway of M. tuberculosis may be targeted to design new anti-tubercular therapy.

Weaknesses

The study does not explain how L-leucine is taken up by M. tuberculosis, leaving the mechanism unclear. Even though 'Semapimod' binds to the PpsB protein, the relevant connection between changes in PDIM and amino acid transport remains incomplete.

While Leucine uptake involves specific transporters in other bacteria, such transport system is not known in Mtb. By screening small molecule inhibitors, we came across a molecule, semapimod, which selectively kills the leucine auxotroph (mc2 6206), but not the WT Mtb. To understand the underlying mechanism of differential susceptibility of the WT and auxotrophic strains to this molecule, we evaluated the effect of restoration of leuCD and panCD expression on susceptibility of the auxotrophic strain to semapimod. Interestingly, our results demonstrated that upon endogenous expression of leuCD genes, mc2 6206 strain becomes resistant to killing by semapimod. In contrast, no effect of panCD expression was observed on semapimod susceptibility of mc2 6206. These findings were further substantiated by gene expression analysis of semapimod treated mc2 6206, which exhibits differential regulation of a set of genes that are altered upon leucine depletion in Mtb as well as in other bacteria. Overall results thus provide first evidence of perturbation of L-leucine uptake by semapimod treatment of the leucine auxotroph.

To further gain mechanistic insights into the effect of semapimod on leucine uptake in Mtb, we generated the semapimod resistant strain which exhibits point mutation in 4 genes including ppsB. Interestingly, overexpression of wild-type ppsB, but not of other genes, restored susceptibility of the resistant bacteria to semapimod. Our observations that semapimod interacts with PpsB, and semapimod resistant strain accumulates mutation in PpsB resulting in loss of PDIM together support the involvement of cell-wall PDIM in regulation of L-leucine transport in Mtb.

As mentioned above, we anticipate that semapimod treatment brings about certain modifications in PDIM which becomes more restrictive to L-leucine. A comprehensive future study will be helpful to examine the effect of semapimod on Mtb physiology.

Also, the fact that the drug does not function on WT bacteria makes it a weak candidate to consider its usefulness for a therapeutic option.

We agree that semapimod is not an appropriate drug candidate against TB owing to its inhibitory effect on production of proinflammatory cytokines such as TNF-α, IL-1β, and IL-6 that help pathogen establish chronic infection. However, a significant reduction in bacterial loads in lungs and spleen upon semapimod treatment despite inhibition of proinflammatory cytokines clearly indicates bacterial dependence on host-derived exogenous leucine during intracellular growth. Therefore targeting L-leucine uptake can be a novel therapeutic strategy against TB.

Reviewer #3 (Public review):

Agarwal et al identified the small molecule semapimod from a chemical screen of repurposed drugs with specific antimycobacterial activity against a leucine-dependent strain of M. tuberculosis. To better understand the mechanism of action of this repurposed anti-inflammatory drug, the authors used RNA-seq to reveal a leucine-deficient transcriptomic signature from semapimod challenge. The authors then measured a decreased intracellular concentration of leucine after semapimod challenge, suggesting that semapimod disrupts leucine uptake as the primary mechanism of action. Unexpectedly, however, resistant mutants raised against semapimod had a mutation in the polyketide synthase gene ppsB that resulted in loss of PDIM synthesis. The authors believe growth inhibition is a consequence of decreased accumulation of leucine as a result of an impaired cell wall and a disrupted, unknown leucine transporter. This study highlights the importance of branched-chain amino acids for M. tuberculosis survival, and the chemical genetic interactions between semapimod and ppsB indicate that ppsB is a conditionally essential gene in a medium depleted of leucine.

The conclusions regarding the leucine and PDIM phenotypes are moderately supported by experimental data. The authors do not provide experimental evidence to support a specific link between leucine uptake and impaired PDIM production. Additional work is needed to support these claims and strengthen this mechanism of action.

As mentioned above, overall results from this study provide first evidence of perturbation of L-leucine uptake by semapimod treatment of the leucine auxotroph. Our observations that semapimod interacts with PpsB, and semapimod resistant strain accumulates mutation in PpsB resulting in loss of PDIM together support the involvement of cell-wall PDIM in regulation of L-leucine transport in Mtb.

As hitherto mentioned, it appears that semapimod treatment brings about certain modifications in PDIM which becomes restrictive to L-leucine. Future studies are required to gain detailed mechanistic insights into the effect of semapimod on Mtb physiology.

Since leucine uptake and PDIM synthesis are important concepts of the manuscript, experiments would benefit from exploring other BCAAs to know if the phenotypes observed are specific to leucine, and adding additional strains to the 2D TLC experiments to provide confidence in the absence of the PDIM band.

We thank the peer reviewer for this suggestion. We would be happy to analyse the effect of semapimod on the level of other amino acids including BCAA by mass spectrometry.

The intriguing observation that wild-type H37Rv is resistant to semapimod but the leucine-auxotroph is sensitive should be further explored. If the authors are correct and semapimod does inhibit leucine uptake through a specific transporter or disrupted cell wall (PDIM synthesis), testing semapimod activity against the leucine-auxotroph in various concentrations of BCAAs could highlight the importance of intracellular leucine. H37Rv is still able to synthesize endogenous leucine and is able to circumvent the effect of semapimod.

We thank the peer reviewer for this suggestion. We would explore the possibility of analysing the effect of increasing concentrations of BCAAs on mc2 6206 susceptibility to semapimod.

Author response:

Public Reviews:

Reviewer #1 (Public review):

Summary:

The manuscript finds a negative relationship between tuberculin skin test-induced type I interferon activity with chest X-ray tuberculosis severity in humans. This evidence is between incomplete and solid. It needs a bioinfomatics/transcriptomics reviewer to make a more insightful judgement. The manuscript demonstrates a convincing role for Stat2 in controlling Mycobacterium marinum infection in zebrafish embryos, incomplete data are presented linking reduced leukocyte recruitment to the infection susceptibility phenotype.

Strengths:

(1) An interesting analysis of TST response correlated with chest X-ray pathology.

(2) Novel data on a protective role for Stat2 in a natural host-mycobacterial species infection pairing.

We appreciate the reviewer’s positive comments.

Weaknesses:

(1) The transcriptional modules are very large sets of genes that do not present a clear picture of what is actually being measured relative to other biological pathways.

The transcriptional module analysis is a major strength of our approach. These gene signatures are derived from independent experiments, most of which have been previously published/validated [1,2]. To clarify, they represent co-regulated gene sets downstream of signalling pathways. Increased number of genes in these modules increases their combinatorial specificity for a given biological pathway. In the human data, they serve as orthogonal validation for the bioinformatic analysis showing enrichment of the type I IFN pathway among TST transcriptome genes that are negatively correlated with radiographic disease severity in pulmonary TB (see Figure 2). Importantly, our modules confirm the relationship with type I IFN signalling (see Figure 2E) by discriminating from type II IFN signalling, which is not statistically significantly correlated with radiographic TB severity (see Figure S6C-E).

(2) The link between infection-Stat2-leukocyte recruitment and containment of infection is plausible, but lacks a specific link to the first part of the manuscript.

For clarification, the first part of the study seeks to identify immune response pathways that relate to severity of human disease, leading to the identification of type I IFN signalling. Since the human data are limited to an observational analysis in which we cannot test causality, the second part of our study uses a genetically tractable experimental model to test the hypothesis that type I IFN signalling is host-protective and explore possible mechanisms for a beneficial effect. This leads to the observation that type I IFN responses contribute to early myeloid cell recruitment to the site of infection, that has previously been shown to be crucial for containment of mycobacterial infection in zebrafish larvae. We will further evaluate the introduction and results sections to ensure a clear link between the human and zebrafish work.

Major concerns

(1) Line 158: The two transcriptional modules should be placed in the context of other DEG patterns. The macrophage type I interferon module, in particular, is quite large (361 genes). Can this be made more granular in terms of type I IFN ligands and STAT2-dependent genes?

We respectfully disagree with this comment. For clarification, the 360 gene module reflects the zebrafish larval response to IFNphi1 protein [3]. Type I IFNs are known to induce hundreds of interferon stimulated genes [4]. As explained above, the size of the modules increases specificity for a given signalling pathway. In this case, we are most interested in discriminating type I and type II IFN signalling pathways that represent very different upstream biological processes. The discrimination we achieve with our modular approach is a major advance over previous reports of gene signatures in TB that do not discriminate between the two pathways. In this study, we did not discriminate between signalling downstream of type I IFN ligands and STAT2, consistent with existing literature showing that type I IFN signalling is STAT2 dependent [5,6].

(2) The ifnphi1 injection into mxa:mCherry stat2 crispants is a nice experiment to demonstrate loss of type I IFN responsiveness. Further data is required to demonstrate if important mycobacterial control pathways (IFNy, TNF, il6?, etc) are intact in stat2 crispants before being able to conclude that these phenotypes are specific to type I IFN.

Thank you for the positive comment. We acknowledge this point and will attempt to evaluate whether pro-inflammatory cytokine responses are intact in stat2 CRISPants by qPCR or bulk RNAseq. However, these experiments may prove inconclusive because of the limited sensitivity in this approach.

Reviewer #2 (Public review):

Summary:

This study shows that type I interferon (IFN-I) signaling helps protect against mycobacterial infection. Using human gene expression data and a zebrafish model, the authors find that reduced IFN-I activity is linked to more severe disease. They also show that zebrafish lacking the IFN-I signaling gene stat2 are more vulnerable to infection due to poor macrophage migration. These results suggest a protective role for IFN-I in mycobacterial disease, challenging previous findings from other animal models.

Strengths:

Strengths of the manuscript include the use of human clinical samples to support relevance to disease, along with a genetically tractable zebrafish model that enables mechanistic insight.

We welcome the reviewer’s positive summary of our study.

Weaknesses:

(1) The manuscript presents intriguing human data showing an inverse correlation between IFN-I gene signatures and TB disease, but the findings remain correlative and may be cohort-specific. Given that the skin is not a primary site of TB and is relatively immunotolerant, the biological relevance of downregulated IFN-I-related genes in this tissue to systemic or pulmonary TB is unclear.

We agree with the reviewer that the observational human data are correlative. That is precisely why we extend the study to undertake mechanistic studies in a genetically tractable animal model, using M. marinum infection of zebrafish larvae. In the introduction, we already provide a detailed rationale for the strengths of the TST model to study human immune responses to a standardised mycobacterial challenge. This approach mitigates against the confounding of heterogeneity in bacterial burden and sampling different stages of the natural history of infection in conventional observational human studies. Therefore, the application of the TST is a major strength of this study. We do not understand the context in which the reviewer suggests the skin is immunotolerant. In the present study and previous work we provide molecular level analysis of the TST as a robust cell mediated immune response that reflects molecular perturbation in granuloma from the site of pulmonary TB disease 1.

(2) The reliance on stat2 CRISPants in zebrafish offers a limited view of IFN-I signaling. Including additional crispant lines targeting other key regulators (e.g., ifnar1, tyk2, irf3, irf7) would strengthen the interpretation and clarify whether the observed effects reflect broader IFN-I pathway disruption.

We respectfully disagree with this comment. Our objective was to test the role of type I IFN signalling in M. marinum infection of zebrafish. We show that stat2 deletion effectively disrupts type I IFN signalling (Figure S8). Therefore, we do not see a compelling rationale to evaluate other molecules in the signalling pathway.

(3) The conclusion that IFN-I is protective contrasts with established findings from murine and non-human primate models, where IFN-I is often detrimental. While the authors highlight species differences, the lack of functional human data and reliance on M. marinum in zebrafish limit the translational relevance. A more balanced discussion addressing these discrepancies would improve the manuscript.

We acknowledge that our findings contrast with the prevailing view in published literature to date. We will further review the discussion to see how we can elaborate on the potential strengths and weaknesses of different experimental approaches, which may underpin these discrepancies.

(4) Quantification of bacterial burden using fluorescence intensity alone may not accurately reflect bacterial viability. Complementary methods, such as qPCR for bacterial DNA, would provide a more robust assessment of antimicrobial activity.

We and others have previously validated the use of the quantitative measures of fluorescence, used here as a measure of bacterial load [7,8]. Importantly, our measurements do not rely purely on the total fluorescence signal, but also measures of dissemination of infection, for which we see consistent findings. It is also widely recognised that DNA measurements do not necessarily correlate well with bacterial viability. Therefore, we respectfully disagree that a PCR-based approach will add substantial value to our existing analysis.

(5) Finally, the authors should clarify whether impaired macrophage recruitment in stat2 crispants results from defects in chemotaxis, differentiation, or survival, and address discrepancies between their human blood findings and prior studies.

We acknowledge that these are important questions. Our data show that stat2 disruption does not impact total macrophage numbers at baseline (Figure 4A,B) and therefore do not support any effect of Stat2 signalling on steady state macrophage survival or differentiation. The downregulation of macrophage mpeg1 expression in M. marinum infection precludes long-term follow-up of these cells in the context of infection [9]. Therefore, we cannot currently test the hypothesis that Stat2 signalling may influence death of macrophages recruited to the site of infection or make them more susceptible to the cytopathic effects of direct mycobacterial infection. We will attempt to confirm using short-term time-lapse imaging that cellular migration to the site of hindbrain M. marinum infection is reduced in stat2 deficient zebrafish. On the strength of what is possible to test and the established role of type I IFNs in induction of several chemokines [10,11], the most likely effect is that Stat2 signalling increases recruitment through chemokine production. We are exploring the possibility of testing changes to the chemokine profile in stat2 CRISPants by qPCR or bulk RNAseq, but these experiments may prove inconclusive because of the limitations of sensitivity in this approach.

We recognize that our finding of no relationship between peripheral blood type I IFN activity and severity of human TB contrasts with that of previous studies. As stated in the discussion, the most likely explanation for this is our use of transcriptional modules which reflect exclusive type I IFN responses. The signatures used in other studies include both type I and type II IFN inducible genes and therefore also reflect IFN gamma driven responses.

Reviewer #3 (Public review):

Summary:

In this manuscript, the authors presented an interesting study providing an insight into the role of Type-I interferon responses in tuberculosis (TB) pathogenesis by combining transcriptome analysis of PBMCs and TST from tuberculosis patients. The zebrafish model was used to identify the changes in the innate immune cell population of macrophages and neutrophils. The findings suggested that Type-I interferon signatures inversely correlated with disease severity in the TST transcriptome data. The authors validated the observations by CRISPR-mediated disruption of stat2 (a critical transcription factor for type I interferon signaling) in zebrafish larvae, showing increased susceptibility to M. marinum infection. Traditionally, type-I interferon responses have been viewed as detrimental in mycobacterial infections, with studies suggesting enhanced susceptibility in certain mouse models. The study tried to identify and further characterize the understanding of the role of type-I interferons in TB.

Strengths:

Traditionally, type-I interferon responses have been viewed as detrimental in mycobacterial infections, with studies suggesting enhanced susceptibility in certain mouse models. The study tried to further understand the role of type-I interferons in TB pathogenesis.

We thank the reviewer for their summary.

Weaknesses:

Though the study showed an inverse correlation of Type-I interferon with radiological features of TB, the molecular mechanism is largely unexplored in the study, which is making it difficult to understand the basis of the results shown in the manuscript by the authors.

We respectfully disagree with this comment. The observations in the human data lead to the hypothesis that type I IFN responses may be host-protective, which we then test specifically in the zebrafish model, and explore candidate mechanisms, focussing on myeloid cell recruitment to the site of infection.

References

(1) Bell, L.C.K., Pollara, G., Pascoe, M., Tomlinson, G.S., Lehloenya, R.J., Roe, J., Meldau, R., Miller, R.F., Ramsay, A., Chain, B.M., et al. (2016). In Vivo Molecular Dissection of the Effects of HIV-1 in Active Tuberculosis. PLoS Pathog. 12, e1005469. https://doi.org/10.1371/journal.ppat.1005469.

(2) Pollara, G., Turner, C.T., Rosenheim, J., Chandran, A., Bell, L.C.K., Khan, A., Patel, A., Peralta, L.F., Folino, A., Akarca, A., et al. (2021). Exaggerated IL-17A activity in human in vivo recall responses discriminates active tuberculosis from latent infection and cured disease. Sci. Transl. Med. 13, eabg7673. https://doi.org/10.1126/scitranslmed.abg7673.

(3) Levraud, J.-P., Jouneau, L., Briolat, V., Laghi, V., and Boudinot, P. (2019). IFN-Stimulated Genes in Zebrafish and Humans Define an Ancient Arsenal of Antiviral Immunity. J. Immunol. Baltim. Md 1950 203, 3361–3373. https://doi.org/10.4049/jimmunol.1900804.

(4) Schoggins, J.W. (2019). Interferon-Stimulated Genes: What Do They All Do? Annu. Rev. Virol. 6, 567–584. https://doi.org/10.1146/annurev-virology-092818-015756.

(5) Blaszczyk, K., Nowicka, H., Kostyrko, K., Antonczyk, A., Wesoly, J., and Bluyssen, H.A.R. (2016). The unique role of STAT2 in constitutive and IFN-induced transcription and antiviral responses. Cytokine Growth Factor Rev. 29, 71–81. https://doi.org/10.1016/j.cytogfr.2016.02.010.

(6) Begitt, A., Droescher, M., Meyer, T., Schmid, C.D., Baker, M., Antunes, F., Knobeloch, K.-P., Owen, M.R., Naumann, R., Decker, T., et al. (2014). STAT1-cooperative DNA binding distinguishes type 1 from type 2 interferon signaling. Nat. Immunol. 15, 168–176. https://doi.org/10.1038/ni.2794.

(7) Stirling, D.R., Suleyman, O., Gil, E., Elks, P.M., Torraca, V., Noursadeghi, M., and Tomlinson, G.S. (2020). Analysis tools to quantify dissemination of pathology in zebrafish larvae. Sci. Rep. 10, 3149. https://doi.org/10.1038/s41598-020-59932-1.

(8) Takaki, K., Davis, J.M., Winglee, K., and Ramakrishnan, L. (2013). Evaluation of the pathogenesis and treatment of Mycobacterium marinum infection in zebrafish. Nat. Protoc. 8, 1114–1124. https://doi.org/10.1038/nprot.2013.068.

(9) Benard, E.L., Racz, P.I., Rougeot, J., Nezhinsky, A.E., Verbeek, F.J., Spaink, H.P., and Meijer, A.H. (2015). Macrophage-expressed perforins mpeg1 and mpeg1.2 have an anti-bacterial function in zebrafish. J. Innate Immun. 7, 136–152. https://doi.org/10.1159/000366103.

(10) Lehmann, M.H., Torres-Domínguez, L.E., Price, P.J.R., Brandmüller, C., Kirschning, C.J., and Sutter, G. (2016). CCL2 expression is mediated by type I IFN receptor and recruits NK and T cells to the lung during MVA infection. J. Leukoc. Biol. 99, 1057–1064. https://doi.org/10.1189/jlb.4MA0815-376RR.

(11) Buttmann, M., Merzyn, C., and Rieckmann, P. (2004). Interferon-beta induces transient systemic IP-10/CXCL10 chemokine release in patients with multiple sclerosis. J. Neuroimmunol. 156, 195–203. https://doi.org/10.1016/j.jneuroim.2004.07.016.

Author response:

Reviewer #1:

Lipid transfer proteins (LTPs) play a crucial role in the intramembrane lipid exchange within cells. However, the molecular mechanisms that govern this activity remain largely unclear. Specifically, the way in which LTPs surmount the energy barrier to extract a single lipid molecule from a lipid bilayer is not yet fully understood. This manuscript investigates the influence of membrane properties on the binding of Ups1 to the membrane and the transfer of phosphatidic acid (PA) by the LTP. The findings reveal that Ups1 shows a preference for binding to membranes with positive curvature. Moreover, coarse-grained molecular dynamics simulations indicate that positive curvature decreases the energy barrier associated with PA extraction from the membrane. Additionally, lipid transfer assays conducted with purified proteins and liposomes in vitro demonstrate that the size of the donor membrane significantly impacts lipid transfer efficiency by Ups1-Mdm35 complexes, with smaller liposomes (characterized by high positive curvature) promoting rapid lipid transfer.

This study offers significant new insights into the reaction cycle of phosphatidic acid (PA) transfer by Ups1 in mitochondria. Notably, the authors present compelling evidence that, alongside negatively charged phospholipids, positive membrane curvature enhances lipid transfer - an effect that is particularly relevant at the mitochondrial outer membrane. The experiments are technically robust, and my primary feedback pertains to the interpretation of specific results.

(1) The authors conclude from the lipid transfer assays (Figure 5) that lipid extraction is the rate-limiting step in the transfer cycle. While this conclusion seems plausible, it should be noted that the authors employed high concentrations of Ups1-Mdm35 along with less negatively charged phospholipids in these reactions. This combination may lead to binding becoming the rate-limiting factor. The authors should take this point into consideration. In this type of assay, it is challenging to clearly distinguish between binding, lipid extraction, and membrane dissociation as separate processes.

We thank the reviewer for the constructive and positive evaluation of our manuscript. We agree that, while our data support the interpretation that the rate-limiting step occurs at the donor membrane, it is difficult to dissect in our assay which of the individual steps at the donor membrane - such as binding of Ups1, lipid extraction into the binding pocket, or dissociation of Ups1 - is rate-limiting. Nevertheless, although we cannot exclude contributions from membrane binding or dissociation, several observations suggest that lipid extraction is a rate-limiting step under our experimental conditions.

The acceptor membrane has a similar lipid composition to the donor membrane (in tendency, the donor membrane is even a bit richer in binding-promoting lipids). If binding was ratelimiting, similar constraints would be expected at the acceptor membrane during lipid insertion. However, this is not observed.

Regarding dissociation, if this step were rate-limiting, one would expect similar constraints to be evident at the acceptor vesicles as well. Nevertheless, membrane dissociation might be mechanistically coupled to lipid extraction and thus difficult to evaluate as an independent step.

Based on our data and the considerations described above, we suggest that lipid extraction is the dominant rate-limiting step at the donor membrane under our conditions. However, we agree that a clear separation of these individual steps is not possible with the current experimental design. We will revise the corresponding passage to clarify that the rate-limiting step occurs at the donor membrane and, based on our observations, likely involves lipid extraction. Future studies aiming on dissecting these steps, will be important for elucidating the mechanism and regulation of Ups1-mediated lipid transfer both in vitro and in vivo.

(2) The authors should discuss that variations in the size of liposomes will also affect the distance between them at a constant concentration, which may affect the rate of lipid transfer. Therefore, the authors should determine the average size and size distribution of liposomes after sonication (by DLS or nanoparticle analyzer, etc.)

We agree that variations in liposome size will influence the average distance between vesicles at a given lipid concentration, which may in turn affect the rate of lipid transfer. As suggested, we will include DLS measurements to characterize the size distribution of our different liposome preparations.

Our setup was designed to keep the total membrane surface area comparable across conditions. This approach ensures a comparable overall binding capacity for Ups1 and enables the comparison of membrane binding and lipid extraction from different membranes. However, we agree that vesicle spacing, which is affected by liposome size at constant lipid concentration, could potentially influence certain steps in the transfer process, such as the time required for Ups1 to travel between donor and acceptor membranes. Whether this intermembrane travel time contributes to rate limitation is indeed an interesting question, and we will address this point through further discussion in the revised manuscript.

Investigating such effects in our current experimental system would require altering the vesicle concentration, which would in turn change the total membrane surface area and introduce additional variables. Nevertheless, exploring the influence of vesicle spacing and intermembrane distance on lipid transfer represents a promising direction for future studies aimed at dissecting the rate-limiting steps of the transfer cycle.

(3) The authors use NBD-PA in the lipid transfer assays. Does the size of the donor liposomes affect the transfer of NBD-PA and DOPA similarly? Since NBD-labeled lipids are somewhat unstable within lipid bilayers (as shown by spontaneous desorption in Figure 5B), monitoring the transfer of unlabeled PA in at least one setting would strengthen the conclusion of the swap experiments.

Ups1-mediated transfer of PA has been demonstrated both by mass spectrometry analysis of donor and acceptor vesicles (Connerth et al., 2012) and by NBD-fluorescence-based lipid transfer assays (Lu et al., 2020; Miliara et al., 2015; Miliara et al., 2019; Miliara et al., 2023; Potting et al., 2013; Watanabe et al., 2015). The fluorescence-based approach has been the most widely applied across multiple studies and has enabled detailed analysis of various aspects of lipid transfer by Ups1. It has been used to investigate mutants of key structural elements—such as the lipid-binding pocket and the α2–loop region. It has also been used to analyze fusion constructs between Ups1 and Mdm35, the influence of Mdm35 variants, and competition with excess Mdm35. Additionally, by comparing the transfer of NBD-labeled PA and NBD-labeled PS, this assay has provided insights into the determinants of the lipid specificity of Ups1. Hence, our experiments are based on the standard assay used to analyse lipid transfer in the field and thus can be corralated with the majority of published data.

Nevertheless, we agree that it is important to keep in mind that NBD labeling may alter the biophysical properties of lipids and, consequently, affect their transfer efficiency. Moreover, NBD-labeled lipids are not suitable for comparing the transfer efficiency of different PA species, as the label itself may mask differences in acyl chain composition. Therefore, it will be valuable to establish complementary methods that do not rely on NBD-labeled PA. We aim to develop these non-standard methods for possible inclusion in the present study, but even if not fully implemented at this stage, they will certainly form an important part of future investigations.

(4) The present study suggests that membrane domains with positive curvature at the outer membrane may serve as starting points for lipid transport by Ups1-Mdm35. Is anything known about the mechanisms that form such structures? This should be discussed in the text.

The origin of positively curved membrane domains is indeed highly relevant in the context of our findings, and while not the primary focus of this work, we will place more emphasis on discussing how such curvature may arise. Mechanisms include the action of curvature-generating proteins, asymmetric lipid composition and curvature induced at membrane contact sites. We have so far included examples of proteins in the outer mitochondrial membrane that are expected to influence curvature in their vicinity, and we will expand on this aspect and other contributing factors more thoroughly in the revised text.

Reviewer #2:

Summary:

Lipid transfer between membranes is essential for lipid biosynthesis across different organelle membranes. Ups1-Mdm35 is one of the best-characterized lipid transfer proteins, responsible for transferring phosphatidic acid (PA) between the mitochondrial outer membrane (OM) and inner membrane (IM), a process critical for cardiolipin (CL) synthesis in the IM. Upon dissociation from Mdm35, Ups1 binds to the intermembrane space (IMS) surface of the OM, extracts a PA molecule, re-associates with Mdm35, and moves through the aqueous IMS to deliver PA to the IM. Here, the authors analyzed the early steps of this PA transfer - membrane binding and PA extraction - using a combination of in vitro biochemical assays with lipid liposomes and purified Ups1-Mdm35 to measure liposome binding, lipid transfer between liposomes, and lipid extraction from liposomes. The authors found that membrane curvature, a previously overlooked property of the membrane, significantly affects PA extraction but not PA insertion into liposomes. These findings were further supported by MD simulations.

Strengths:

The experiments are well-designed, and the data are logically interpreted. The present study provides an important basis for understanding the mechanism of lipid transfer between membranes.  

Weaknesses:

The physiological relevance of membrane curvature in lipid extraction and transfer still remains open.

We thank the reviewer for the constructive feedback on our work. We agree that the physiological relevance of membrane curvature in lipid extraction and transfer remains an open question. Our data show that Ups1 binding to native-like OM membranes under physiological pH conditions is curvature-dependent, supporting the idea that this mechanism may optimize lipid transfer in vivo. While the intricate biophysical basis of this behaviour can only be dissected in vitro, these findings offer valuable insight into how curvature may functionally regulate Ups1 activity in the cellular context. To directly test this, it will be important in future studies to identify Ups1 mutants that lack curvature sensitivity and assess their performance in vivo, which will help clarify the physiological importance of this mechanism.

Reviewer #3:

The manuscript by Sadeqi et al. studies the interactions between the mitochondrial protein Ups1 and reconstituted membranes. The authors apply synthetic liposomal vesicles to investigate the role of pH, curvature, and charge on the binding of Ups1 to membranes and its ability to extract PA from them. The manuscript is well wrifen and structured. With minor exceptions, the authors provide all relevant information (see minor points below) and reference the appropriate literature in their introduction. The underlying question of how the energy barrier for lipid extraction from membranes is overcome by Ups1 is interesting, and the data presented by the authors could offer a valuable new perspective on this process. It is also certainly a challenging in vitro reconstitution experiment, as the authors aim to disentangle individual membrane properties (e.g., curvature, charge, and packing density) to study protein adsorption and lipid transfer. I have one major suggestion and a few minor ones that the authors might want to consider to improve their manuscript and data interpretation:

Major Comments:

The experiments are performed with reconstituted vesicles, which are incubated with recombinant protein variants and quantitatively assessed in flotation and pelleting assays. According to the Materials and Methods section, the lipid concentration in these assays is kept constant at 5 µM. However, the authors change the size of the vesicles to tune their curvature. Using the same lipid concentration but varying vesicle sizes results in different total vesicle concentrations. Moreover, larger vesicles (produced by freeze-thawing and extrusion) tend to form a higher proportion of multilamellar vesicles, thus also altering the total membrane area available for binding. Could these differences in the experimental system account for the variation in binding? To address this, the authors would need to perform the experiments either under saturation (excess protein) conditions or find an experimental approach to normalize for these differences.

We thank the reviewer for the constructive and positive comments. We agree that, since the total number of lipids was kept constant, the number of vesicles varied with vesicle size in our experiments. However, the setup was specifically designed to maintain a comparable total membrane surface area across conditions, ensuring a comparable number of available binding sites for Ups1. Because membrane surface area decreases with the square of the vesicle radius, keeping vesicle number constant would have led to a marked reduction in binding surface. Our approach was therefore aimed at preserving comparable binding capacity while varying membrane curvature.

With respect to multilamellarity, we thank the reviewer for addressing this important point. As described above, we aimed to maintain a constant total membrane surface area across all conditions to ensure an equal number of potential binding sites. We agree that multilamellarity in large liposomes could restrict accessibility to part of the membrane surface. However, we see in our experiments that even when the total membrane surface area of the small liposomes is reduced to one quarter of the standard amount, binding to the small liposomes remained stronger than to the larger liposomes at the higher concentration. This strongly indicates that restricted accessibility cannot account for the curvature-specific effect observed. Nonetheless, we will further address this aspect experimentally and in the discussion of the revised manuscript.

References

Connerth, M., Tatsuta, T., Haag, M., Klecker, T., Westermann, B., & Langer, T. (2012). Intramitochondrial transport of phosphatidic acid in yeast by a lipid transfer protein. Science, 338(6108), 815-818. https://doi.org/10.1126/science.1225625 

Lu, J., Chan, C., Yu, L., Fan, J., Sun, F., & Zhai, Y. (2020). Molecular mechanism of mitochondrial phosphatidate transfer by Ups1. Commun Biol, 3(1), 468. https://doi.org/10.1038/s42003-020-01121-x 

Miliara, X., Garnef, J. A., Tatsuta, T., Abid Ali, F., Baldie, H., Perez-Dorado, I., Simpson, P., Yague, E., Langer, T., & Mafhews, S. (2015). Structural insight into the TRIAP1/PRELI-like domain family of mitochondrial phospholipid transfer complexes. EMBO Rep, 16(7), 824-835. https://doi.org/10.15252/embr.201540229 

Miliara, X., Tatsuta, T., Berry, J. L., Rouse, S. L., Solak, K., Chorev, D. S., Wu, D., Robinson, C. V., Mafhews, S., & Langer, T. (2019). Structural determinants of lipid specificity within Ups/PRELI lipid transfer proteins. Nat Commun, 10(1), 1130. https://doi.org/10.1038/s41467-019-09089-x 

Miliara, X., Tatsuta, T., Eiyama, A., Langer, T., Rouse, S. L., & Mafhews, S. (2023). An intermolecular hydrogen bonded network in the PRELID-TRIAP protein family plays a role in lipid sensing. Biochim Biophys Acta Proteins Proteom, 1871(1), 140867. https://doi.org/10.1016/j.bbapap.2022.140867 

Posng, C., Tatsuta, T., Konig, T., Haag, M., Wai, T., Aaltonen, M. J., & Langer, T. (2013). TRIAP1/PRELI complexes prevent apoptosis by mediating intramitochondrial transport of phosphatidic acid. Cell Metab, 18(2), 287-295. https://doi.org/10.1016/j.cmet.2013.07.008 

Watanabe, Y., Tamura, Y., Kawano, S., & Endo, T. (2015). Structural and mechanistic insights into phospholipid transfer by Ups1-Mdm35 in mitochondria. Nat Commun, 6, 7922. https://doi.org/10.1038/ncomms8922

Author response:

The following is the authors’ response to the original reviews

Reviewer #1:

(1) 8 molar urea not only denatures proteins but also denatures DNA. Obviously, this does not affect the ChIP, since antibodies often recognize small linear epitopes and the proteins are crosslinked. However, under high urea conditions the BUR elements should be rendered single-stranded, and one wonders whether this has any effect on the procedure. The authors should alert the reader of these circumstances.

Thank you for raising this important question about the effects of 8M urea. We have added a brief paragraph explaining this point in the revised manuscript. Despite common misconceptions, 8M urea by itself does not actively convert double-stranded DNA to single-stranded DNA. For this conversion to occur, a heat denaturation step is required. Once DNA is heat-denatured to become single-stranded, urea can maintain this configuration. This is why the addition of 8M urea to acrylamide gel electrophoresis is a standard method for analyzing single-stranded oligonucleotides, but the DNA must first be denatured by heat (Summer et al., J. Vis. Exp. (32), e1485, DOI : 10.3791/1485). This is clearly described in published work comparing the status of DNA with and without heat treatment in an 8M urea-containing buffer (Hegedus et al., Nucl.Acids Res. 2009 (doi:10.1093/nar/gkp539).

We have additional evidence supporting this conclusion in the context of our urea ultracentrifugation experiment. Both crosslinked and un-crosslinked genomic DNA purified by 8M urea centrifugation can be digested with restriction enzymes, which indicates that the DNA remains double-stranded. For instance, we previously published SATB1 ChIP-3C results using Sau3A-digested DNA after urea purification. In the current paper, we used HindIII to digest urea-purified DNA for urea4C-seq. The BUR reference map can also be generated after restriction digestion of urea-purified DNA and isolating and sequencing SATB1-bound restriction fragments in vitro. If genomic DNA were denatured by 8M urea ultracentrifugation, we would not have been able to digest it with restriction enzymes to obtain these results.

We have now added a sentence noting that SATB1 is a double-stranded DNA-binding protein that does not bind to single-stranded DNA, as we have previously shown (Dickinson et al., 1992, Ref 32).

(2) An important conclusion is that urea-ChIP reveals direct DNA binding events, whereas standard ChIP shows indirect binding (which is stripped off by urea). I do not see any evidence for direct binding. At low resolution, predicted BUR elements are enriched in domains where SATB-1 is mapped by urea-ChIP. A statement like 'In a zoomed-in view, covering a 430 kb region, SATB1 sites identified from urea ChIP-seq precisely coincided with BUR peaks' is certainly not correct: most BUR peaks do not show significant SATB-1 binding. The randomly chosen regions shown in Figure 4 – Supplement 1 show how poor the overlap of SATB-1 and BURs is; indeed, they show that SATB-1 binds DNA mostly at non-BUR sites. I see Figure 2D, but such cumulative plots can be highly biased by very few cases. I suggest showing these data in heat maps instead.

We believe there may be some confusion regarding the interpretation of our figures. Looking at Track 3 (BUR reference map, RED peaks) and urea SATB1 Tracks 4 and 5 (replicas from two independent experiments) in Fig. 2B, the SATB1 peaks detected by urea ChIP-seq do indeed coincide with BUR peaks. In the revised manuscript, we have provided a further ‘zoomed-in’ view to better illustrate this point and also provided the underlying BUR sequence from one of these SATB1-bound regions (Figure 2—supplement figure 1).

It is true that many more BURs exist than SATB1-bound BURs, especially in gene-poor regions where BURs are clustered. However, from the perspective of SATB1-bound peaks, the majority of these coincide with BURs, as shown by both deepTools analyses and new heatmap, as suggested (Figure 2E, and Figure 7—supplement figure 3).

The results from our genome-wide quantitative analyses using deepTools to compare peaks from urea SATB1 ChIP-seq data and the BUR reference map shown in Supplementary Tables 1 and 2 are consistent with the heatmap analyses.

We must apologize for an error in the scaling of the y-axis in Figure 4-supplement figure 1 that likely contributed to some confusion. We have corrected our mistake in the revised manuscript. As we were preparing our figures, when placed in the figure and axes relabeled for legibility, the BUR reference peaks were mislabeled on their y-axis. In the figure the peaks were erroneously labeled on a scale of 0.1-1 read counts/million reads, but the data shown is actually scaled at 0.1 to 2 read counts per million reads. Unfortunately, we did not realize this error and, using the figure as a guide for scaling, provided urea SATB1 ChIP-seq peaks at a scale of 0.1-1 read counts/million reads to match the mislabeled BUR reference track. This had the effect of reducing the signal/noise in the SATB1 ChIP-seq data (Figure 1). We have now standardized the y-axis for fair comparison using a scaling of the y-axis at 0.1-2 for all tracks.  This will more clearly show that there are indeed more BUR peaks than SATB1-bound sites, consistent with our quantitative analysis.

We hope that these clarifications as well as the added heatmaps and binding site example allay the concerns about the specificity and overlap of SATB1 binding on BURS.

(3) In Figure 6C 'peaks' are compared. However, looking at Figure 4 - Supplement 1 again it is clear that peak calling can yield a misleading impression. Figure 6D suggests that there are more BURs than SATB-1 peaks but this is not true from looking at the browser.

We thank the reviewer for this observation. As noted in our response to point 2 above, the inconsistent y-axis scaling in Figure 4-supplement figure 1 created a misleading impression, which we have corrected in the revised manuscript. When properly displayed with consistent y-axis scaling, the browser view aligns with our quantitative data showing that there are indeed many more BURs than SATB1-bound sites. As mentioned under 2 above, we have performed genome-wide quantitative analysis by deepTools (Supplementary Tables 1 and 2) to confirm the results shown by bar graphs in Fig. 6C, 6D and Fig. 2D. 

In Figure 6C, the bars show the percentage of SATB1-bound peaks in each cell type (denominator) that overlap with confirmed BUR sites in the BUR reference map (numerator). In Figure 6D, we show the percentage of total BUR sites in the BUR reference map (denominator) that are bound by SATB1 from urea ChIP-seq (numerator). To avoid any confusion, we have added brief subtitles to Figures 6C and 6D in the revised manuscript.

(4) An important conclusion is that urea-ChIP reveals direct DNA binding events, whereas standard ChIP shows indirect binding (which is stripped off by urea). I do not yet see any evidence for direct binding. It cannot be excluded that the binding is RNA-mediated. The authors mention in passing that urea-ChIP material still contains (specific!) RNA. Given that this is a new procedure, the authors should document the RNA content of urea-ChIP and RNase-treat their samples prior to ChIP to monitor an RNA contribution.

Thank you for raising this important point. The direct binding of SATB1 to BURs is well-established in our previous work. Indeed, this was the main motivation to explore the reason for the lack of evidence for genome-wide SATB1 binding to BURs in the DNA-binding profile by standard ChIP-seq. This has been a major point of confusion for us for many years.

SATB1 was originally identified through a search for mammalian proteins that could recognize BURs specifically and not just any A+T-rich sequence. The Satb1 gene was originally cloned by an expression cDNA library and encoded SATB1 protein bound the BUR probe but not a mutated AT-rich BUR (control) probe.  Subsequent experiments confirmed that SATB1 specifically binds to many BURs without requiring additional factors. Furthermore, SATB1 recognizes BURs by binding in the minor groove of double-stranded DNA, presumably recognizing the altered phosphate backbone structure of BUR DNA, rather than accessing nucleotide bases (Dickinson et al, 1992).

We do agree with the reviewer, however, that there is a possibility that RNA can redirect SATB1 to different subsets of BURs and/or to interact indirectly with different regulatory regions depending on cell type or developmental stage. Although urea ultracentrifugation clearly separates most RNA (found in the middle region of the tube) from genomic DNA (pelleted at the bottom) (de Belle et al., 1998), upon crosslinking cells, a small quantity of RNA is found co-pelleted with DNA (our recent unpublished results). This RNA, tightly associated with crosslinked chromatin, may have some impact on SATB1 function.

Based on our preliminary data, we are currently planning to study the impact of RNA using RNase A as well as by targeting specific RNAs employing an anti-sense approach. We believe that thoroughly addressing the impact of RNA warrants a full paper, including the potential roles of specific non-coding RNAs in SATB1 function, and thus is beyond the scope of the current paper. However, we have now added discussion of this important point in the manuscript.

(5) An important aspect of the model is that SATB1 tethers active genes to inactive LADs. However, in the 4C experiment the BUR elements used to anchor the looping are both in the accessible, active chromatin domain. If the authors want to maintain their statement, they must show a 4C result that connects the 2 distinct domains and transverses A/B domain boundaries. Currently, the data only show a looping within accessible chromatin.

We appreciate REVIEWER 1 for bringing up the important point that our model could potentially be interpreted as “SATB1 tethers active genes to inactive LADs.” Since we describe that BURs are enriched in LADs and that SATB1 binds a subset of BURs, readers may assume that we aim to demonstrate, through urea 4C-seq, that SATB1 tethers active genes to transcriptionally-inactive LADs (via BURs). However, this is not our intention in the model (Figure 8). In the experiment we designed for our present study,  we selected BUR-1 and BUR-2 as viewpoints from a non-LAD gene-rich region (inter-LAD). Because these BURs are bound by SATB1, it indicates that these BURs are part of the “hard-to-access” SATB1-rich subnuclear structure, which resists extraction, in contrast to accessible chromatin. Thus, we illustrate in the model that BURs anchored to the SATB1-rich nuclear substructure make contact with accessible chromatin over long distances in a SATB1-dependent manner. Therefore, we do not intend to conclude that SATB1 mediates interactions between LADs and inter-LADs (accessible chromatin) from our current study: this would be a topic for future research. In the original model in the submitted manuscript, we used the terms “inaccessible” and “accessible.” In the revised version, we clarified this in the model by changing “inaccessible” to “SATB1-rich subnuclear structure” and carefully revised  the text in the Figure 8 legend to clarify the model. 

At this time, we do not know exactly how LADs and SATB1 nuclear architecture are related spatially and functionally. While LADs are mapped as genomic domains in proximity to Lamin B1 by LaminB1-DamID, BURs are mapped at ~300-500 bp resolution by urea ChIP-seq. To gain further insight into this important question, a large body of DNA-FISH and immunoDNA-FISH experiments will be required, comparing different cell types to see whether and how specific BURs move between LADs and SATB1 nuclear architecture. Such experiments may benefit from testing the Gabrg1 and Gabra2 loci, where many BURs are anchored to SATB1 in neurons but not in thymocytes, for instance.  This is included in Discussion in the revised manuscript.

Regarding the reviewer's second point about showing more extended domains for 4C interactions, we would like to highlight that Figure 5—supplement figure 3 in our submitted manuscript addresses this concern. This figure shows that BUR-interactions extend to multiple gene-rich regions across intervening gene-poor regions. Interestingly, BUR-1 and BUR-2 interactions skip a transcriptionally silent gene-rich region containing olfactory receptor genes but interact with subsequent gene-rich regions containing active genes. These data demonstrate that BUR-interactions do indeed traverse A- and B-compartment boundaries.  In the revised manuscript (in Figure 5—supplement figure 3), we newly added a Lamin B1-DamID (thymocyte) track.  Comparing with LADs, BUR-1 interactions occur mostly in non-LAD regions. Some minor overlap with LADs was detected in high resolution views (not shown). Future experiments testing BUR viewpoints that reside within LADs are required to assess whether SATB1 mediates interactions between B and A compartments.

(6) The description of the urea-co-immunoprecipitation experiment (Figure 3C) could be improved to make it unequivocally clear that co-binding to chromatin is tested, not protein-protein interaction (which is destroyed by urea).

Thank you for this helpful suggestion. We have revised the text in the manuscript by stating “Distinct from protein-protein co-immunoprecipitation (co-IP) using whole cell or nuclear extracts, we examined the direct co-binding status on chromatin in vivo of SATB1 and CTCF or cohesin by urea ChIP-Western”.

Reviewer #2:

(1) Since SATB1 has been described to interact with beta-catenin, I wonder if the authors have looked at TCF4/TCF7l2 binding patterns and their potential overlap with SATB1 binding patterns. This might appear a trivial request. However, uncontrolled WNT signalling is a major feature of cancer undergoing metastasis - a process that the authors have earlier associated with unscheduled SATB1 expression in triple-negative breast cancer.

We thank the reviewer for highlighting this important point about the potential relationship between SATB1 and TCF4/TCF7l2 binding patterns. Based on published observations with other factors (Rad21, CTCF, BRG1, RUNX) that show substantial overlap with SATB1 in standard ChIP-seq peaks(Kakugawa et al., Cell Rep 19, 1176-1188 (2017). DOI: 10.1016/j.celrep.2017.04.038. Poterlowicz et al., PLoS Genet, 2017 DOI: 10.1371/journal.pgen.1006966), we would anticipate that TCF4 might also show significant overlap with SATB1. An important question is whether the DNA binding profile of TCF4 depends on SATB1.

We have not yet generated ChIP-seq data for TCF4 in the presence and absence of SATB1, but we agree that such experiments could provide important insights into cancer progression as well as brain function. This represents an interesting direction for future work. We have added this point in our discussion based on your kind suggestion.

(2) The CTCF sizes indicated in the western blot analyses of Figures 3C and Figure 3 - supplement figure 2 do not display the normal size, which is around 130 kDa. Either the issue is erroneous marking or a so-called salt effect to slow the migration in the gel. Alternatively, it reflects a slower migrating form of CTCF generated by for example PARylation (by PARP1) that is known to approach 180 kDa. It would be useful if the authors could clarify this minor issue.

We appreciate the reviewer pointing out this discrepancy. As the reviewer correctly noted, CTCF can appear at a higher molecular weight due to post-translational modifications such as PARylation and O-GlcNAcylation, which alter its migration during electrophoresis.

Upon re-examination of our raw data for Figure 3—supplement figure 2A, we discovered that the marker lane for the CTCF panel was broken, and the 150kDa band was erroneously assigned. This led to the 150kDa marker being placed below the CTCF migration position, which is clearly an error. We thank the reviewer for bringing this to our attention.

We have checked our other data and consistently observe CTCF migrating below the 150kDa band, similar to the pattern shown on the Abcam website for the antibody we used (ab128873) (Figure 2). For Figure 3-supplement figure 2, we will use a marker lane from a parallel gel with identical composition and run time to correctly indicate the molecular weight. We havealso corrected the marker position in Figure 3C.

Reviewing Editor (Recommendations for the authors):

(1) The introduction states that urea ChIP-seq is "unbiased", which is difficult to unambiguously determine and therefore might be an overstatement. Maybe the authors could consider rephrasing.

We agree with the reviewer's assessment and have rephrased our description of the urea ChIP-seq method to avoid using the term "unbiased."

(2) The authors propose that in standard ChIP, most SATB1 is in the insoluble fraction. This seems easy to test and demonstrating it may help to further clarify the differences between the protocols.

We appreciate this suggestion and would like to clarify our description. What we stated in the manuscript was:

"We envision that SATB1 bound to inaccessible nuclear regions may be lost in the insoluble fraction."

This refers specifically to a subpopulation of SATB1 that is bound to the high-salt extraction-resistant nuclear substructure, not to the total SATB1 protein. We also noted elsewhere in the manuscript that:

"SATB1 proteins are found in high salt-resistant fraction as well as salt-extracted fraction (40). Thus, it is possible that soluble SATB1 may associate with open chromatin."

Our unpublished results show that SATB1 proteins exist in at least two distinct forms based on protein mobility: SATB1 with high mobility and another with very low or no mobility. While we have identified the SATB1 domain responsible for each of these distinct mobility patterns, we have not yet identified biochemical differences that would allow us to distinguish them conclusively. Therefore, an experiment to test the distribution of SATB1 in soluble versus insoluble fractions would show SATB1 in both fractions but would not necessarily provide information about the functional significance of these different populations. We believe this is an important area for future research and are working to develop tools to specifically distinguish and characterize SATB1 in the soluble versus insoluble fractions.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public Review):

Summary:

This work studies representations in a network with one recurrent layer and one output layer that needs to path-integrate so that its position can be accurately decoded from its output. To formalise this problem, the authors define a cost function consisting of the decoding error and a regularisation term. They specify a decoding procedure that at a given time averages the output unit center locations, weighted by the activity of the unit at that time. The network is initialised without position information, and only receives a velocity signal (and a context signal to index the environment) at each timestep, so to achieve low decoding error it needs to infer its position and keep it updated with respect to its velocity by path integration.

The authors take the trained network and let it explore a series of environments with different geometries while collecting unit activities to probe learned representations. They find localised responses in the output units (resembling place fields) and border responses in the recurrent units. Across environments, the output units show global remapping and the recurrent units show rate remapping. Stretching the environment generally produces stretched responses in output and recurrent units. Ratemaps remain stable within environments and stabilise after noise injection. Low-dimensional projections of the recurrent population activity forms environment-specific clusters that reflect the environment's geometry, which suggests independent rather than generalised representations. Finally, the authors discover that the centers of the output unit ratemaps cluster together on a triangular lattice (like the receptive fields of a single grid cell), and find significant clustering of place cell centers in empirical data as well.

The model setup and simulations are clearly described, and are an interesting exploration of the consequences of a particular set of training requirements - here: path integration and decodability. But it is not obvious to what extent the modelling choices are a realistic reflection of how the brain solves navigation. Therefore it is not clear whether the results generalize beyond the specifics of the setup here.

Strengths:

The authors introduce a very minimal set of model requirements, assumptions, and constraints. In that sense, the model can function as a useful 'baseline', that shows how spatial representations and remapping properties can emerge from the requirement of path integration and decodability alone. Moreover, the authors use the same formalism to relate their setup to existing spatial navigation models, which is informative.

The global remapping that the authors show is convincing and well-supported by their analyses. The geometric manipulations and the resulting stretching of place responses, without additional training, are interesting. They seem to suggest that the recurrent network may scale the velocity input by the environment dimensions so that the exact same path integrator-output mappings remain valid (but maybe there are other mechanisms too that achieve the same).

The clustering of place cell peaks on a triangular lattice is intriguing, given there is no grid cell input. It could have something to do with the fact that a triangular lattice provides optimal coverage of 2d space? The included comparison with empirical data is valuable, although the authors only show significant clustering - there is no analysis of its grid-like regularity.

First of all, we would like to thank the reviewer for their comprehensive feedback, and their insightful comments. Importantly, as you point out, our goal with this model was to build a minimal model of place cell representations, where representations were encouraged to be place-like, but free to vary in tuning and firing locations. By doing so, we could explore what upstream representations facilitate place-like representations, and even remapping (as it turned out) with minimal assumptions. However, we agree that our task does not capture some of the nuances of real-world navigation, such as sensory observations, which could be useful extensions in future work. Then again, the simplicity of our setup makes it easier to interpret the model, and makes it all the more surprising that it learns many behaviors exhibited by real world place cells.

As to the distribution of phases - we also agree that a hexagonal arrangement likely reflects some optimal configuration for decoding of location.

And we agree that the symmetry within the experimental data is important; we have revised analyses on experimental phase distributions, and included an analysis of ensemble grid score, to quantify any hexagonal symmetries within the data.

Weaknesses:

The navigation problem that needs to be solved by the model is a bit of an odd one. Without any initial position information, the network needs to figure out where it is, and then path-integrate with respect to a velocity signal. As the authors remark in Methods 4.2, without additional input, the only way to infer location is from border interactions. It is like navigating in absolute darkness. Therefore, it seems likely that the salient wall representations found in the recurrent units are just a consequence of the specific navigation task here; it is unclear if the same would apply in natural navigation. In natural navigation, there are many more sensory cues that help inferring location, most importantly vision, but also smell and whiskers/touch (which provides a more direct wall interaction; here, wall interactions are indirect by constraining velocity vectors). There is a similar but weaker concern about whether the (place cell like) localised firing fields of the output units are a direct consequence of the decoding procedure that only considers activity center locations.

Thank you for raising this point; we absolutely agree that the navigation task is somewhat niche. However, this was a conscious decision, to minimize any possible confounding from alternate input sources, such as observations. In part, this experimental design was inspired by the suggestion that grid cells support navigation/path integration in open-field environments with minimal sensory input (as they could, conceivably do so with no external input). This also pertains to your other point, that boundary interactions are necessary for navigation. In our model, using boundaries is one solution, but there is another way around this problem, which is conceivably better: to path integrate in an egocentric frame, starting from your initial position. Since the locations of place fields are inferred only after a trajectory has been traversed, the network is free to create a new or shifted representation every time, independently of the arena. In this case, one might have expected generalized solutions, such as grid cells to emerge. That this is not the case, seems to suggest that grid cells may somehow not be optimal for pure path integration, or at the very least, hard to learn (but may still play a part, as alluded to by place field locations). We have tried to make these points more evident in the revised manuscript.

As for the point that the decoding may lead to place-like representations, this is a fair point. Indeed, we did choose this form of decoding, inspired by the localized firing of place cells, in the hope that it would encourage minimally constrained, place-like solutions. However, compared to other works (Sorscher and Xu) hand tuning the functional form of their place cells, our (although biased towards centralized tuning curves) allows for flexible functional forms such as the position of the place cell centers, their tuning width, whether or not it is center-surround activity, and how they should tune to different environments/rooms. This allows us to study several features of the place cell system, such as remapping and field formation. We have revised to make this more clear in the model description.

The conclusion that 'contexts are attractive' (heading of section 2) is not well-supported. The authors show 'attractor-like behaviour' within a single context, but there could be alternative explanations for the recovery of stable ratemaps after noise injection. For example, the noise injection could scramble the network's currently inferred position, so that it would need to re-infer its position from boundary interactions along the trajectory. In that case the stabilisation would be driven by the input, not just internal attractor dynamics. Moreover, the authors show that different contexts occupy different regions in the space of low-dimensional projections of recurrent activity, but not that these regions are attractive.

We agree that boundary interactions could facilitate the convergence of representations after noise injection. We did try to moderate this claim by the wording “attractor-like”, but we agree that boundaries could confound this result. We have therefore performed a modified noise injection experiment, where we let the network run for an extended period of time, before noise injection (and no velocity signal), see Appendix Velocity Ablation in the revised text. Notably, representations converge to their pre-scrambled state after noise injection, even without a velocity signal. However, place-like representations do not converge for all noise levels in this case, possibly indicating that boundary interactions do serve an error-correcting function, also. Thank you for pointing this out.

As for the attractiveness of contexts, we agree that more analyses were required to demonstrate this. We have therefore conducted a supplementary analysis where we run the trained network with a mismatch in context/geometry, and demonstrate that the context signal fixes the representation, up to geometric distortions.

The authors report empirical data that shows clustering of place cell centers like they find for their output units. They report that 'there appears to be a tendency for the clusters to arrange in hexagonal fashion, similar to our computational findings'. They only quantify the clustering, but not the arrangement. Moreover, in Figure 7e they only plot data from a single animal, then plot all other animals in the supplementary. Does the analysis of Fig 7f include all animals, or just the one for which the data is plotted in 7e? If so, why that animal? As Appendix C mentions that the ratemap for the plotted animal 'has a hexagonal resemblance' whereas other have 'no clear pattern in their center arrangements', it feels like cherrypicking to only analyse one animal without further justification.

Thank you for pointing this out; we agree that this is not sufficiently explained and explored in the current version. We have therefore conducted a grid score analysis of the experimental place center distributions, to uncover possible hexagonal symmetries. The reason for choosing this particular animal was in part because it featured the largest number of included cells, while also demonstrating the most striking phase distribution, while including all distributions in the supplementary. Originally, this was only intended as a preliminary analysis, suggesting non-uniformity in experimental place field distributions, but we realize that these may all provide interesting insight into the distributional properties of place cells.

We have explained these choices in the revised text, and expanded analyses on all animals to showcase these results more clearly.

Reviewer #2 (Public Review):

Summary:

The authors proposed a neural network model to explore the spatial representations of the hippocampal CA1 and entorhinal cortex (EC) and the remapping of these representations when multiple environments are learned. The model consists of a recurrent network and output units (a decoder) mimicking the EC and CA1, respectively. The major results of this study are: the EC network generates cells with their receptive fields tuned to a border of the arena; decoder develops neuron clusters arranged in a hexagonal lattice. Thus, the model accounts for entorhinal border cells and CA1 place cells. The authors also suggested the remapping of place cells occurs between different environments through state transitions corresponding to unstable dynamical modes in the recurrent network.

Strengths:

The authors found a spatial arrangement of receptive fields similar to their model's prediction in experimental data recorded from CA1. Thus, the model proposes a plausible mechanisms to generate hippocampal spatial representations without relying on grid cells. This result is consistent with the observation that grid cells are unnecessary to generate CA1 place cells.

The suggestion about the remapping mechanism shows an interesting theoretical possibility.

We thank the reviewer for their kind feedback.

Weaknesses:

The explicit mechanisms of generating border cells and place cells and those underlying remapping were not clarified at a satisfactory level.

The model cannot generate entorhinal grid cells. Therefore, how the proposed model is integrated into the entire picture of the hippocampal mechanism of meｍory processing remains elusive.

We appreciate this point, and hope to clarify: From a purely architectural perspective, place-like representations are generated by linear combinations of recurrent unit representations, which, after training, appear border-like. During remapping, the network is simply evaluated/run in different geometries/contexts, which, it turns out, causes the network to exhibit different representations, likely as solutions to optimally encoding position in the different environments. We have attempted to revise the text to make some of these interpretations more clear. We have also conducted a supplementary analysis to demonstrate how representations are determined by the context signal directly, which helps to explain how recurrent and output units form their representations.

We also agree that our model does not capture the full complexity of the Hippocampal formation. However, we would argue that its simplicity (focusing on a single cell type and a pure path integration task), acts as a useful baseline for studying the role of place cells during spatial navigation. The fact that our model captures a range of place cell behaviors (field formation, remapping and geometric deformation) without grid cells also point to several interesting possibilities, such that grid cells may not be strictly necessary for place cell formation and remapping, or that border cells may account for many of the peculiar behaviors of place cells. However, we wholeheartedly agree that including e.g. sensory information and memory storage/retrieval tasks would prove a very interesting extension of our model to more naturalistic tasks and settings. In fact, our framework could easily accommodate this, e.g. by decoding contexts/observations/memories from the network state, alongside location.

Reviewer #3 (Public Review):

Summary:

The authors used recurrent neural network modelling of spatial navigation tasks to investigate border and place cell behaviour during remapping phenomena.

Strengths:

The neural network training seemed for the most part (see comments later) well-performed, and the analyses used to make the points were thorough.

The paper and ideas were well explained.

Figure 4 contained some interesting and strong evidence for map-like generalisation as environmental geometry was warped.

Figure 7 was striking, and potentially very interesting.

It was impressive that the RNN path-integration error stayed low for so long (Fig A1), given that normally networks that only work with dead-reckoning have errors that compound. I would have loved to know how the network was doing this, given that borders did not provide sensory input to the network. I could not think of many other plausible explanations... It would be even more impressive if it was preserved when the network was slightly noisy.

Thank you for your insightful comments! Regarding the low path integration error, there is a slight statistical signal from the boundaries, as trajectories tend to turn away from arena boundaries. However, we agree, that studying path integration performance in the face of noise would make for a very interesting future development.

Weaknesses:

I felt that the stated neuroscience interpretations were not well supported by the presented evidence, for a few reasons I'll now detail.

First, I was unconvinced by the interpretation of the reported recurrent cells as border cells. An equally likely hypothesis seemed to be that they were positions cells that are linearly encoding the x and y position, which when your environment only contains external linear boundaries, look the same. As in figure 4, in environments with internal boundaries the cells do not encode them, they encode (x,y) position. Further, if I'm not misunderstanding, there is, throughout, a confusing case of broken symmetry. The cells appear to code not for any random linear direction, but for either the x or y axis (i.e. there are x cells and y cells). These look like border cells in environments in which the boundaries are external only, and align with the axes (like square and rectangular ones), but the same also appears to be true in the rotationally symmetric circular environment, which strikes me as very odd. I can't think of a good reason why the cells in circular environments should care about the particular choice of (x,y) axes... unless the choice of position encoding scheme is leaking influence throughout. A good test of these would be differently oriented (45 degree rotated square) or more geometrically complicated (two diamonds connected) environments in which the difference between a pure (x,y) code and a border code are more obvious.

Thank you for pointing this out. This is an excellent point, that we agree could be addressed more rigorously. Note that there is no position encoding in our model; the initial state of the network is a vector of zeros, and the network must infer its location from boundary interactions and context information alone. So there is no way for positional information to leak through to the recurrent layer directly. However, one possible reason for the observed symmetry breaking, is the fact that the velocity input signal is aligned with the cardinal directions. To investigate this, we trained a new model, wherein input velocities are rotated 45 degrees relative to the horizontal, as you suggest. The results, shown and discussed in appendix E (Learned recurrent representations align with environment boundaries), do indicate that representations are tuned to environment boundaries, and not the cardinal directions, which hopefully improves upon this point.

Next, the decoding mechanism used seems to have forced the representation to learn place cells (no other cell type is going to be usefully decodable?). That is, in itself, not a problem. It just changes the interpretation of the results. To be a normative interpretation for place cells you need to show some evidence that this decoding mechanism is relevant for the brain, since this seems to be where they are coming from in this model. Instead, this is a model with place cells built into it, which can then be used for studying things like remapping, which is a reasonable stance.

This is a great point, and we agree. We do write that we perform this encoding to encourage minimally constrained place-like representations (to study their properties), but we have revised to make this more evident.

However, the remapping results were also puzzling. The authors present convincing evidence that the recurrent units effectively form 6 different maps of the 6 different environments (e.g. the sparsity of the code, or fig 6a), with the place cells remapping between environments. Yet, as the authors point out, in neural data the finding is that some cells generalise their co-firing patterns across environments (e.g. grid cells, border cells), while place cells remap, making it unclear what correspondence to make between the authors network and the brain. There are existing normative models that capture both entorhinal's consistent and hippocampus' less consistent neural remapping behaviour (Whittington et al. and probably others), what have we then learnt from this exercise?

Thanks for raising this point! We agree that this finding is surprising, but we hold that it actually shows something quite important: that border-type units are sufficient to create place-like representations, and learns several of the behaviors associated with place cells and remapping (including global remapping and field stretching). In other words, a single cell type known to exist upstream of place cells is sufficient to explain a surprising range of phenomena, demonstrating that other cell types are not strictly necessary. However, we agree that understanding why the boundary type units sometimes rate remap, and whether that can be true for some border type cells in the brain (either directly, or through gating mechanisms) would be important future developments. Related to this point, we also expanded upon the influence of the context signal for representation selection (appendix F)

Concerning the relationship to other models, we would argue that the simplicity of our model is one of its core strengths, making it possible to disentangle what different cell types are doing. While other models, including TEM, are highly important for understanding how different cell types and brain regions interact to solve complex problems, we believe there is a need for minimal, understandable models that allows us to investigate what each cell type is doing, and this is where we believe our work is important. As an example, our model not only highlights the sufficiency of boundary-type cells as generators of place cells, its lack of e.g. grid cells also suggest that grid cells may not be strictly necessary for e.g. open-field/sensory-deprived navigation, as is often claimed.

One striking result was figure 7, the hexagonal arrangement of place cell centres. I had one question that I couldn't find the answer to in the paper, which would change my interpretation. Are place cell centres within a single clusters of points in figure 7a, for example, from one cell across the 100 trajectories, or from many? If each cluster belongs to a different place cell then the interpretation seems like some kind of optimal packing/coding of 2D space by a set of place cells, an interesting prediction. If multiple place cells fall within a single cluster then that's a very puzzling suggestion about the grouping of place cells into these discrete clusters. From figure 7c I guess that the former is the likely interpretation, from the fact that clusters appear to maintain the same colour, and are unlikely to be co-remapping place cells, but I would like to know for sure!

This is a good point, and you are correct: one cluster tends to correspond to one unit. To make this more clear, we have revised Fig. 7, so that each decoded center is shaded by unit identity, which makes this more evident. And yes, this is, seemingly in line with some form of optimal packing/encoding of space, yes!

I felt that the neural data analysis was unconvincing. Most notably, the statistical effect was found in only one of seven animals. Random noise is likely to pass statistical tests 1 in 20 times (at 0.05 p value), this seems like it could have been something similar? Further, the data was compared to a null model in which place cell fields were randomly distributed. The authors claim place cell fields have two properties that the random model doesn't (1) clustering to edges (as experimentally reported) and (2) much more provocatively, a hexagonal lattice arrangement. The test seems to collude the two; I think that nearby ball radii could be overrepresented, as in figure 7f, due to either effect. I would have liked to see a computation of the statistic for a null model in which place cells were random but with a bias towards to boundaries of the environment that matches the observed changing density, to distinguish these two hypotheses.

Thanks for raising this point. We agree that we were not clear enough in our original manuscript. We included additional analyses in one animal, to showcase one preliminary case of non-uniform phases. To mitigate this, we have performed the same analyses for all animals, and included a longer discussion of these results (included in the supplementary material). We have also moderated the discussion on Ripley’s H to encompass only non-uniformity, and added a grid score analysis to showcase possible rotational symmetries in the data. We hope this gets our findings across more clearly

Some smaller weaknesses:

- Had the models trained to convergence? From the loss plot it seemed like not, and when including regularisors recent work (grokking phenomena, e.g. Nanda et al. 2023) has shown the importance of letting the regularisor minimise completely to see the resulting effect. Else you are interpreting representations that are likely still being learnt, a dangerous business.

Longer training time did not seem to affect representations. However, due to the long trajectories and statefulness involved, training was time-intensive and could become unstable for very long training. We therefore stopped training at the indicated time.

- Since RNNs are nonlinear it seems that eigenvalues larger than 1 doesn't necessarily mean unstable?

This is a good point; stability is not guaranteed. We have updated the text to reflect this.

- Why do you not include a bias in the networks? ReLU networks without bias are not universal function approximators, so it is a real change in architecture that doesn't seem to have any positives?

We found that bias tended to have a detrimental effect on training, possibly related to the identity initialization used (see e.g. Le et al. 2015), and found that training improved when biases were fixed to zero.

- The claim that this work provided a mathematical formalism of the intuitive idea of a cognitive map seems strange, given that upwards of 10 of the works this paper cite also mathematically formalise a cognitive map into a similar integration loss for a neural network.

We agree that other works also provide ways of formalizing this concepts. However, our goal by doing so was to elucidate common features across these seemingly disparate models. We also found that the concept of a learned and target map made it easier to come up with novel models, such as one wherein place cells are constructed to match a grid cell label.

Aim Achieved? Impact/Utility/Context of Work

Given the listed weaknesses, I think this was a thorough exploration of how this network with these losses is able to path-integrate its position and remap. This is useful, it is good to know how another neural network with slightly different constraints learns to perform these behaviours. That said, I do not think the link to neuroscience was convincing, and as such, it has not achieved its stated aim of explaining these phenomena in biology. The mechanism for remapping in the entorhinal module seemed fundamentally different to the brain's, instead using completely disjoint maps; the recurrent cell types described seemed to match no described cell type (no bad thing in itself, but it does limit the permissible neuroscience claims) either in tuning or remapping properties, with a potentially worrying link between an arbitrary encoding choice and the responses; and the striking place cell prediction was unconvincingly matched by neural data. Further, this is a busy field in which many remapping results have been shown before by similar models, limiting the impact of this work. For example, George et al. and Whittington et al. show remapping of place cells across environments; Whittington et al. study remapping of entorhinal codes; and Rajkumar Vasudeva et al. 2022 show similar place cell stretching results under environmental shifts. As such, this papers contribution is muddied significantly.

Thank you for this perspective; we agree that all of these are important works that arrive at complementary findings. We hold that the importance of our paper lies in its minimal nature, and its focus on place cells, via a purpose-built decoding that enables place-like representations. In doing so, we can point to possibly under explored relationships between cell types, in particular place cells and border cells, while challenging the necessity of other cell types for open-field navigation (i.e. grid cells). In addition, our work points to a novel connection between grid cells, place cells and even border cells, by way of the hexagonal arrangement of place unit centers. However, we agree that expanding our model to include more biologically plausible architectures and constraints would make for a very interesting extension in the future.

Thank you again for your time, as well as insightful comments.  

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Even after reading Methods 5.3, I found it hard to understand how the ratemap population vectors that produce Fig 3e and Fig 5 are calculated. It's unclear to me how there can be a ratemap at a single timestep, because calculating a ratemap involves averaging the activity in each location, which would take a whole trajectory and not a single timestep. But I think I've understood from Methods 5.1 that instead the ratemap is calculated by running multiple 'simultaneous' trajectories, so that there are many visited locations at each timestep. That's a bit confusing because as far as I know it's not a common way to calculate ratemaps in rodent experiments (probably because it would be hard to repeat the same task 500 times, while the representations remain the same), so it might be worth explaining more in Methods 5.3.

We understand the confusion, and have attempted to make this more clear in the revised manuscript. We did indeed create ratemaps over many trajectories for time-dependent plots, for the reasons you mentioned. We also agree that this would be difficult to do experimentally, but found it an interesting way to observe convergence of representations in our simulated scenario.

Fig 3b-d shows multiple analyses to support output unit global remapping, but no analysis to support the claim that recurrent units remap by rate changes. The examples in Fig 3ai look pretty convincing, but it would be useful to also have a more quantitative result.

We agree, and only showed that units turn off/become silent using ratemaps. We have therefore added an explicit analysis, showcasing rate remapping in recurrent units (see appendix G; Recurrent units rate remap)

Reviewer #2 (Recommendations For The Authors):

Some parts of the current manuscript are hard to follow. Particularly, the model description is not transparent enough. See below for the details.

Major comments:

(1) Mathematical models should be explained more explicitly and carefully. I had to guess or desperately search for the definitions of parameters. For instance, define the loss function L in eq.(1). Though I can assume L represents the least square error (in A.8), I could not find the definition in Model & Objective. N should also be defined explicitly in equation (3). Is this the number of output cells?

Thank you for pointing this out, we have revised to make it more clear.

(2) In Fig. 1d, how were the velocity and context inputs given to individual neurons in the network? The information may be described in the Methods, but I could not identify it.

This was described in the methods section (Neural Network Architecture and Training), but we realize that we used confusing notation, when comparing with Fig. 1d. We have therefore changed the notation, and it should hopefully be clearer now. Thanks for pointing out this discrepancy.

(3) I took a while to understand equations (3) and (4) (for instance, t is not defined here). The manuscript would be easier to read if equations (5) and (6) are explained in the main text but not on page 18 (indeed, these equations are just copies of equations 3 and 4). Otherwise, the authors may replace equations (3) and (4) with verbal explanations similar to figure legend for Fig. 1b.

(4) Is there any experimental evidence for uniformly strong EC-to-CA1 projections assumed in the non-trainable decoder? This point should be briefly mentioned.

Thank you for raising this point. The decoding from EC (the RNN) to CA1 (the output layer) consists of a trainable weight matrix, and may thus be non-uniform in magnitude. The non-trainable decoding acts on the resulting “CA1” representation only. We hope that improvements to the model description also makes this more evident.  

(5) The explanation of Fig. 3 in the main text is difficult to follow because subpanels are explained in separate paragraphs, some of which are very short, as short as just a few lines.

This presentation style makes it difficult to follow the logical relationships between the subpanels. This writing style is obeyed throughout the manuscript but is not popular in neuroscience.

Thanks for pointing this out, we have revised to accommodate this.

(6) Why do field centers cluster near boundaries? No underlying mechanisms are discussed in the manuscript.

This is a good point; we have added a note on this; it likely reflects the border tuning of upstream units.

(7) In Fig. 4, the authors presented how cognitive maps may vary when the shape and size of open arenas are modified. The results would be more interesting if the authors explained the remapping mechanism. For instance, on page 8, the authors mentioned that output units exhibit global remapping between contexts, whereas recurrent units mainly rate remapping.

Why do such representational differences emerge?

We agree! Thanks for raising this point. We have therefore expanded upon this discussion in section 2.4.

(8) In the first paragraph of page 10, the authors stated ".. some output units display distinct field doubling (see both Fig. 4c), bottom right, and Fig. 4d), middle row)". I could not understand how Fig. 4d, middle row supports the argument. Similarly, they stated "..some output units reflect their main boundary input (with greater activity near one boundary)." I can neither understand what the authors mean to say nor which figures support the statement. Please clarify.

This is a good point, there was an identifier missing; we have updated to refer to the correct “magnification”. Thanks!

(9) The underlying mechanism of generating the hexagonal representation of output cells remains unclear. The decoder network uses a non-trainable decoding scheme based on localized firing patterns of output units. To what extent does the hexagonal representation depend on the particular decoding scheme? Similarly, how does the emergence of the hexagonal representation rely on the border representation in the upstream recurrent network? Showing several snapshots of the two place representations during learning may answer these questions.

This is an interesting point, and we have added some discussion on this matter. In particular, we speculate whether it’s an optimal configuration for position reconstruction, which is demanded by the task and thus highly likely dependent on the decoding scheme. We have not reached a conclusive method to determine the explicit dependence of the hexagonal arrangement on the choice of decoding scheme. Still, it seems this would require comparison with other schemes. In our framework, this would require changing the fundamental operation of the model, which we leave as inspiration for future work. We have also added additional discussion concerning the relationship between place units, border units, and remapping in our model. As for exploring different training snapshots, the model is randomly initialized, which suggests that earlier training steps should tend to reveal unorganized/uninformative phase arrangements, as phases are learned as a way of optimizing position reconstruction. However, we do call for more analysis of experimental data to determine whether this is true in animals, which would strongly support this observation. We also hope that our work inspires other models studying the formation and remapping of place cells, which could serve as a starting point for answering this question in the future.

(10) Figure 7 requires a title including the word "hexagonal" to make it easier to find the results demonstrating the hexagonal representations. In addition, please clarify which networks, p or g, gave the results shown here.

We agree, and have added it!

Minor comments:

(11) In many paragraphs, conclusions appear near their ends. Stating the conclusion at the beginning of each paragraph whenever possible will improve the readability.

We have made several rewrites to the manuscript, and hope this improves readability.

(12) Figure A4 is important as it shows evidence of the CA1 spatial representation predicted by the model. However, I could not find where the figure is cited in the manuscript. The authors can consider showing this figure in the main text.

We agree, and we have added more references to the experimental data analyses in the main text, as well as expanded this analysis.

(13) The main text cites figures in the following format: "... rate mapping of Fig. 3a), i), boundary ...." The parentheses make reading difficult.

We have removed the overly stringent use of double parentheses, thanks for letting us know.

(14) It would be nice if the authors briefly explained the concept of Ripley's H function on page 14.

Yes, we have added a brief descriptor.

Author response:

The following is the authors’ response to the previous reviews

Review 1:

Weaknesses:

The weaknesses of the study also stem from the methodological approach, particularly the use of whole-brain Calcium imaging as a measure of brain activity. While epilepsy and seizures involve network interactions, they typically do not originate across the entire brain simultaneously. Seizures often begin in specific regions or even within specific populations of neurons within those regions. Therefore, a whole-brain approach, especially with Calcium imaging with inherited limitations, may not fully capture the localized nature of seizure initiation and propagation, potentially limiting the understanding of Galanin's role in epilepsy.

We agree with the reviewers that the whole brain imaging approach is both a strength and a weakness. This manuscript and our previously published paper (Hotz et al., 2022) show indeed that the seizures have a initiation point and spread throughout the brain, interestingly affecting the telencephalon last. Localized seizure initiation was not the scope of this manuscript, however also here we would have to rely on imaging techniques. Using cell type specific drivers for specific neuronal subpopulation are an interesting approach, but outside of the scope of this study. An interesting approach would also include a more detailed analysis of glia in the context of epilepsy.

Furthermore, Galanin's effects may vary across different brain areas, likely influenced by the predominant receptor types expressed in those regions. Additionally, the use of PTZ as a "stressor" is questionable since PTZ induces seizures rather than conventional stress. Referring to seizures induced by PTZ as "stress" might be a misinterpretation intended to fit the proposed model of stress regulation by receptors other than Galanin receptor 1 (GalR1).

We also agree, that a more regional approach, after having more reliable information on the expression domains of the different galanin receptors, including more information on their respective role, is an important future research direction.

The description of the EAAT2 mutants is missing crucial details. EAAT2 plays a significant role in the uptake of glutamate from the synaptic cleft, thereby regulating excitatory neurotransmission and preventing excitotoxicity. Authors suggest that in EAAT2 knockout (KO) mice galanin expression is upregulated 15-fold compared to wild-type (WT) mice, which could be interpreted as galanin playing a role in the hypoactivity observed in these animals.

However, the study does not explore the misregulation of other genes that could be contributing to the observed phenotype. For instance, if AMPA receptors are significantly downregulated, or if there are alterations in other genes critical for brain activity, these changes could be more important than the upregulation of galanin. The lack of wider gene expression analysis leaves open the possibility that the observed hypoactivity could be due to factors other than, or in addition to, galanin upregulation.

We are in the process of preparing a manuscript describing a more detailed gene expression study of this and a chemically induced seizure model. Surprisingly we did not observe strong effects on glutamate receptor related genes. This does not preclude and indeed we deem it likely that additional factors play a role, e.g. other neuropeptides.

Moreover, the observation that in double KO mice for both EAAT2 and galanin there was little difference in seizure susceptibility compared to EAAT2 KO mice alone further supports the idea that galanin upregulation might not be the reason to the observed phenotype. This indicates that other regulatory mechanisms or gene expressions might be playing a more pivotal role in the manifestation of hypoactivity in EAAT2 mutants.

Yes, we agree that galanin is likely not the only player. This warrants further investigations.

These methodological shortcomings and conceptual inconsistencies undermine the perceived strengths of the study, and hinders understanding of Galanin's role in epilepsy and stress regulation.

Review 2:

Previous concerns about sex or developmental biological variables were addressed, as their model's seizure phenotype emerges rapidly and long prior to the establishment of zebrafish sexual maturity. However, in the course of re-review, some additional concerns (below) were detected that, if addressed, could further improve the manuscript. These concerns relate to how seizures were defined from the measurement of fluorescent calcium imaging data. Overall, this study is important and convincing, and carries clear value for understanding the multifaceted functions that neuronal galanin can perform under homeostatic and disease conditions.

We are pleased that we could dispel the initial concerns.

Additional Concerns:

- The authors have validated their ability to measure behavioral seizures quantitatively in their 2022 Glia paper but the information provided on defining behavioral seizures was limited. The definition of behavioral seizure activity is not expanded upon in this paper, but could provide detail about how the behavioral seizures relate to a seizure detected via calcium imaging.

In this paper we indeed do not address behavioral seizures but focus completely on neuronal seizures as defined in the material and methods section (“seizures were defined as calcium fluctuations reaching at least 100% of ΔF/F0 in the whole brain.”). Epileptic seizures in zebrafish, either evoked by pharmacological means or the result of genetic mutations, evoke stereotyped locomotor behavior in zebrafish as described in multiple publications (e.g. Baraban et al., 2005, Berghmans et al., 2007, Baxendale et al., 2012 and references therein).

- Related to the previous point, for the calcium imaging, the difference between an increase in fluorescence that the authors think reflects increased neuronal activity and the fluorescence that corresponds to seizures is not very clear. This detail is necessary because exactly when the term "seizure" describes a degree of increased activity can be difficult to distinguish objectively.

In our material and methods section, we describe our working definition of a seizure. Seizures are easily distinguished from increased activity by being synchronized.

- The supplementary movies that were added were very useful, but raised some questions. For example, what brain regions were pulsating? What areas seemed to constantly exhibit strong fluorescence and was this an artifact? It seemed that sometimes there was background fluorescence in the body. Perhaps an anatomical diagram could be provided for the readers. In addition, there were some movies with much greater fluorescence changes - are these the seizures? These are some reasons for our request for clarified definitions of the term "seizure".

The ”pulsating” (or “flickering”) brain activity is spontaneous neuronal activity. Some areas may appear to be more active, probably by a denser packing of neurons and intrinsically more spontaneous neuronal activity. However, since we only use normalized data, this does not affect our measurements.

- While it is not critical to change, I will again note the possible confusion that the use of the word "sedative" in this context may cause. However, I do understand this is a stylistic choice.

- Supplementary Figure 1B: the N values along the x-axis appear to have been duplicated and the duplications are offset and overlapping with one another by mistake.

Thank you for pointing this out. We have corrected the figure accordingly.

Review 3:

(1) Although the relationship between galanin and brain activity during interictal or seizure-free periods was clear, the revised manuscript still lacks mechanistic insight in the role of galanin during seizure-like activity induced by PTZ.

We agree that the mechanistic role of galanin still needs to be defined. The role is more complex that we expected, mainly due to its negative feedback properties. A complete mechanistic understanding will require a number of additional studies and is unfortunately outside of the scope of this manuscript.

(2) The revised manuscript continues to heavily rely on calcium imaging of different mutant lines. Confirmation of knockouts has been provided with immunostaining in a new supplementary figure. Additional methods could strengthen the data, translational relevance, and interpretation (e.g., acute pharmacology using galanin agonists or antagonists, brain or cell recordings, biochemistry, etc).

Cell recordings and biochemistry is challenging in the small larval zebrafish brain. We deem the genetic manipulations that we describe to be more informative than pharmacological experiments due to specificity issues.

Author response

eLife Assessment

The authors investigated KLF Transcription Factor 16 (KLF16) as an inhibitor of osteogenic differentiation, which plays a critical role in bone development, metabolism and repair. The results of the study are valuable as they could help to facilitate future research on the regulation of osteogenesis in vitro and in vivo. However, the evidence overall is incomplete, as validation by knockout mouse models would help to strengthen the conclusions.

We appreciate the editors’ evaluation and recognition of the importance of our research. The primary goal and value of our study is to provide robust bioinformatics analyses of 20 independent iPSC lines, which can lead to the identification of novel genes involved in osteogenic differentiation. The identification of KLF16 serves to illustrate this goal. A thorough analysis of the function of any single gene both in vitro and in vivo is beyond the initial scope of this study. To validate KLF16’s inhibitory role in osteogenic differentiation, we provided evidence showing overexpression of Klf16 suppressed osteogenic differentiation in vitro, and Klf16^+/- mice exhibited enhanced bone mineral content and density in vivo.

Public Reviews:

Reviewer #1 (Public review):

Summary:

In this manuscript, Ru and colleagues investigated regulatory gene interactions during osteogenic differentiation. By profiling transcriptomic changes during mesenchymal stem cell differentiation, they identified KLF16 as a key transcription factor that inhibits osteogenic differentiation and mineralization. It was found that overexpression of KLF16 suppressed osteogenesis in vitro, while Klf16^+/- mice exhibited enhanced bone density, underscoring its regulatory role in bone formation.

Strengths:

(1) Bioinformatics is strong and comprehensive.

(2) Identification of KLF16 in osteoblast differentiation is exciting and innovative.

We appreciate the reviewer’s comments on our bioinformatic analyses of MSC osteogenic differentiation and the identification of KLF16 as a new osteogenesis regulator. The differentiation of iPSC-derived MSCs to OBs serves as a valuable model for investigating gene expression and regulatory networks in osteogenic differentiation. This study provides insights into the complex and dynamic regulation of the transcriptomic landscape in osteogenic differentiation and supplies a foundational resource for additional investigation into normal bone formation and the mechanisms underlying pathological conditions.

Weaknesses:

(1) The mechanism of KLF16 function is not studied.

(2) Studies of KLF16 in bone development, from both in vitro and in vivo perspectives, are descriptive.

Our study aims to apply rigorous bioinformatic analyses of 20 iPSC lines to identify novel genes involved in osteogenic differentiation. With this strategy, we successfully identified KLF16 as a regulator of osteogenic differentiation. We validated this with both in vitro and in vivo models even though we had limited availability of Klf16 knockout mice when the study was conducted. We demonstrated that overexpression of Klf16 suppressed osteogenesis in vitro, while Klf16^+/- mice exhibited increased bone mineral density, trabecular number, and cortical bone area, highlighting its role in bone formation. With these mice now available, further investigation into the mechanism of KLF16's function is possible.

(3) Findings in bioinformatics analysis are mostly redundant with previous studies in the field, and can be simplified.

We compared our bulk RNA-seq data with our previously published single-cell RNA-seq (scRNA-seq) data generated from iPSC-induced cells during osteogenic differentiation (Housman et al., 2022). The purpose is to corroborate the expression patterns of the genes we focused on during osteogenic differentiation. We found similar differential expression patterns in a pseudobulk analysis of the scRNA-seq data, even though there are significant differences between these two studies, including: cell culture conditions, sequencing approaches (bulk vs. single cell), goals of the studies (key TF drivers of osteoblast differentiation vs. mapping differentiation stages and inter-species gene programs in human and chimp), and findings (identification of TFs vs. identification of interspecific regulatory differences) .

Importantly, we performed network analyses to identify key transcription factors, which were not redundant with previous studies. We constructed a transcription factor regulatory network analysis during human osteogenic differentiation, and identified a network organized into five interactive modules. The most exciting finding was the identification of KLF16 as one of the strongest regulators in Module 5 (Figure 3), which previously was not demonstrated to be involved in bone formation. We also demonstrated known TF genes regulating osteogenic differentiation in these modules, and performed gene ontology (GO) and reactome pathway (RP) analyses for regulatory functions and pathways specific to each module. To clarify that our findings do not overlap with previous studies, we will revise the manuscript focusing on Module 5 and simplify the description of the bioinformatics analysis as the reviewer suggested.

Reviewer #2 (Public review):

In their manuscript with the title "Integrated transcriptomic analysis of human induced pluripotent stem cell (iPSC)-derived osteogenic differentiation reveals a regulatory role of KLF16", Ru et al. have analyzed the gene expression changes during the osteogenic differentiation of iPSC-derived mesenchymal stem/stromal cells into preosteoblasts and osteoblasts. As part of the computational analyses, they have investigated the transcription factor regulatory network mediating this differentiation process, which has also led to the identification of the transcription factor KLF16. Overexpression experiments in vitro and the analysis of heterozygous KLF16 knockout mice in vivo indicate that KLF16 is an inhibitor of osteogenic differentiation.

The integrated analysis of iPSC bulk transcriptomic data is a major strength of the study, and it is also great that the authors provide deeper functional characterization of the transcription factor KLF16, one of the newly identified candidate regulators of osteogenic differentiation.

We appreciate the reviewer’s summary and comments on the strength of our bioinformatic analyses of iPSC/MSC osteogenic differentiation and the deep functional characterization of the KLF16, as well as the novelty of our findings.

However, characterization of KLF16 expression in the mouse and validation of the knockout model are currently lacking. Alternative explanations for the mutant phenotype should be considered to improve the strength of the conclusions.

If all issues can be addressed, the study would provide an important resource for the field that would facilitate future research on the regulation of osteogenesis in vitro and in vivo, with potential implications for preclinical and clinical research as well as bioengineering.

We appreciate the reviewer’s valuable suggestions. Klf16 is highly expressed in mandibular, maxillary and tail mesenchyme at embryonic Day 12 (D'Souza et al., 2002), indicating its role in early bone development. We will further characterize the expression of Klf16 in mice, especially in the developing bones.

We identified Klf16 as a potential regulator of osteogenic differentiation, and then validated this with both in vitro and in vivo models. Overexpression of Klf16 suppressed osteogenesis in vitro, and Klf16^+/- mice showed increased bone mineral content and density, indicating its regulatory role in bone formation. We agree with the reviewer that the bone phenotypes of Klf16 knockout mice potentially can be affected by other factors in addition to osteogenic differentiation. As both bone formation and resorption are critical for bone development, we evaluated osteoclastogenesis in the Klf16^+/- mice by analyzing the expression of osteoclast marker CALCR and regulator RANKL in the femurs of the Klf16^+/- mice. Neither CALCR nor RANKL decreased in the bone of Klf16^+/- mice, indicating that osteoclastogenesis is not decreased; therefore, increased bone mineral content and density in the mutant mice is more likely attributed to enhanced bone formation rather than reduced resorption by osteoclasts. Additionally, we will discuss other alternative explanations for the bone phenotypes of Klf16 knockout mice as suggested by the reviewer.

References

D'Souza, U. M., Lammers, C.-H., Hwang, C. K., Yajima, S. and Mouradian, M. M. (2002). Developmental expression of the zinc finger transcription factor DRRF (dopamine receptor regulating factor). Mechanisms of Development 110, 197-201.

Housman, G., Briscoe, E. and Gilad, Y. (2022). Evolutionary insights into primate skeletal gene regulation using a comparative cell culture model. PLOS Genetics 18, e1010073-e1010073.

Author response:

The following is the authors’ response to the original reviews

We thank all the reviewers for their time and valuable feedback, which helped us improve our manuscript. Based on the comments, we have made several critical changes to the revised manuscript.

(1) We have changed our threshold for detecting freezing epochs from 1 cm/s to 0 cm/s in this revised manuscript. This change allows us to capture periods when animals are completely still on the treadmill, better matching the "true freezing" behavior seen in freely moving set-ups. We have added a new supplementary video (Supplementary Video 2) that better demonstrates the freezing response we observe. All results and figures in the revised manuscript reflect this updated threshold (Figure 2-6, Supplementary Figures 16, Tables 1-6). Our main findings remain robust, demonstrating that freezing serves as a reliable conditioned response in our paradigms, comparable to freely moving animals. Specifically, freezing behavior increased reliably in the fear-conditioned environment following CFC across all paradigms. We have also added data from a no-shock control group (Supplementary Figure 2) which, when compared to the conditioned group, shows that freezing responses in the conditioned group result from fear conditioning rather than immobility. We do observe other avoidance behaviors unique to our treadmill-based task— such as hesitation, backward movement, and slow crawls. These conditioned behaviors are captured through a separate metric: the time taken to complete a lap.

(2) As suggested by the reviewers, we have separately analyzed fear discrimination and extinction dynamics across recall days (Supplementary Figures 2, 5 and 6, Table 1-6). To assess fear discrimination, we use within-group comparisons to evaluate how well animals differentiate between the two VRs across days. For extinction, we use within-VR comparisons to examine freezing dynamics over time. Freezing across recall days is compared to baseline freezing (pre-conditioning) using a Linear Mixed Effects model (Tables 1-6), with recall days as fixed effects and mouse as a random effect, using baseline freezing as the reference.

(3) We have expanded the behavioral dataset in Paradigm 1 to investigate the effect of shock amplitude on the conditioned fear response (Supplementary Figure 2 C-E). Consistent with findings in freely moving animals, our data show that increasing shock intensity from 0.6 mA to 1.0 mA leads to stronger freezing. For the revised manuscript, we specifically increased the sample size in the 0.6 mA group (n = 8) in Paradigm 1, as this intensity is used in Paradigm 3. These additional data demonstrate that combining a lower shock amplitude with shorter inter-shock intervals and retaining the tail-coat during recall can enhance freezing, suggesting that these parameters help compensate for lower shock intensity.

(4) We have added more sample sizes to the imaging dataset (now n = 8, Figures 7-8).

Finally, we acknowledge that many aspects of this paradigm still require optimization. The headfixed CFC paradigm is in its early stages compared to the decades of research dedicated to understanding fear learning parameters in freely moving CFC paradigms. While there are numerous parameters that could be tested—both those identified through our own discussions and those raised by the reviewers—it is not feasible for a single lab to conduct a full evaluation of all the possible factors that could influence CFC in the head-fixed prep. A key limitation is that our approach requires robust navigation behavior in the VR without rewards, which requires weeks of training per mouse. It also necessitates larger sample sizes at the outset as not all animals will make it through our behavioral criteria required for CFC. Another important consideration is scalability. Unlike freely moving CFC paradigms, which allow parallel testing of many animals with minimal pre-training, the VR-CFC setup requires several weeks of behavior training and involves a more complex integration of hardware and software to accurately track behavior in virtual space. The number of VR rigs that can be operated simultaneously in a single lab is often limited, making high-throughput testing more challenging. These factors mean that the testing of a single parameter in a group of animals requires approximately 3–4 months to complete. Despite these constraints, we are committed to continue refining this paradigm over time. With this manuscript, our main aim was to provide a detailed framework, initial parameters, and evidence for conditioned behavior in the head-fixed preparation. By doing so, we hope to facilitate the adoption of this paradigm by researchers interested in studying the neural correlates of learning and memory using multiphoton imaging and stimulation techniques. This approach enables investigations that are not possible in freely moving animals, while the presence of freezing as a conditioned response allows for direct comparisons to the extensive body of work done in freely moving paradigms. Moving forward, we anticipate that optimizing this paradigm and identifying the key parameters that drive learning will be a collaborative, community-led effort.

Public Reviews:

Reviewer #1 (Public review):

The authors set out to develop a contextual fear learning (CFC) paradigm in head-fixed mice that would produce freezing as the conditioned response. Typically, lick suppression is the conditioned response in such designs, but this (1) introduces a potential confounding influence of reward learning on neural assessments of aversion learning and (2) does not easily allow comparison of head-fixed studies with extensive previous work in freely moving animals, which use freezing as the primary conditioned response.

The first part of this study is a report on the development and outcomes of 3 variations of the CFC paradigm in a virtual reality environment. The fundamental design is strong, with headfixed mice required to run down a linear virtual track to obtain a water reward. Once trained, the water reward is no longer necessary and mice will navigate virtual reality environments. There are rigorous performance criteria to ensure that mice that make it to the experimental stage show very low levels of inactivity prior to fear conditioning. These criteria do result in only 40% of the mice making it to the experimental stage, but high rates of activity in the VR environment are crucial for detecting learning-related freezing. It is possible that further adjustments to the procedure could improve attrition rates.

We acknowledge that further adjustments to the procedure could improve attrition rates, and we will continue to work on improving the paradigm.

Paradigm versions 1 and 2 vary the familiarity of the control context while paradigm versions 2 and 3 vary the inter-shock interval. Paradigm version 1 is the most promising, showing the greatest increase in conditioned freezing (~40%) and good discrimination between contexts (delta ~15-20%). Paradigm version 2 showed no clear evidence of learning - average freezing at recall day 1 was not different than pre-shock freezing. First-lap freezing showed a difference, but this single-lap effect is not useful for many of the neural circuit questions for which this paradigm is meant to facilitate. Also, the claim that mice extinguished first-lap freezing after 1 day is weak. Extinction is determined here by the loss of context discrimination, but this was not strong to begin with. First-lap freezing does not appear to be different between Recall Day 1 and 2, but this analysis was not done.

This is an important point. Following reviewer suggestions, we have replotted our figures for all paradigms to show within-VR freezing (see Supplementary Figures 2, 5 and 6) as the appropriate method for quantifying fear extinction across days. Using an LME model (Tables 16), we quantify freezing during recall days against baseline freezing levels measured before fear conditioning within each VR. In Paradigm 2, while some fear discrimination persists across days, extinction does occur rapidly. After the first lap in the CFC VR, we observed no significant differences in freezing compared to the baseline. These results are shown in the revised Supplementary Figure 5, and the revised text is in lines 393-399.

Paradigm version 3 has some promise, but the magnitude of the context discrimination is modest (~10% difference in freezing). Thus, further optimization of the VR CFC will be needed to achieve robust learning and extinction. This could include factors not thoroughly tested in this study, including context pre-exposure timing and duration and shock intensity and frequency.

We acknowledge that many aspects of this paradigm still need optimization, as virtual reality CFC is in its early stages, and we have not explored all of the parameter space. We describe above the reasoning for this. However, for this revised version of the paper we have added new behavioral data (Supplementary Figure 2 C-E) showing that increasing shock intensities from 0.6 mA to 1 mA enhances freezing, both in the first lap and on average. There are of course many other parameters that are likely important, like the ones pointed out here by the reviewer, but exploring the entire parameter space will take many years and will likely require many labs. The purpose of this paper is to show that VR-CFC fundamentally works and is a starting point from which the field can build on. We have now pointed out in the introduction (lines 54-58) and discussion (lines 730-737, 810-814) that there remains significant scope for improving this paradigm and optimizing parameters in the future.

The second part of the study is a validation of the head-fixed CFC VR protocol through the demonstration that fear conditioning leads to the remapping of dorsal CA1 place fields, similar to that observed in freely moving subjects. The results support this aim and largely replicate previous findings in freely moving subjects. One difference from previous work of note is that VR CFC led to the remapping of the control environment, not just the conditioning context. The authors present several possible explanations for this lack of specificity to the shock context, further underscoring the need for further refinement of the CFC protocol before it can be widely applied. While this experiment examined place cell remapping after fear conditioning, it did not attempt to link neural activity to the learned association or freezing behavior.

This is an interesting observation. We think that the remapping observed in the control context likely occurred due to the absence of reward in a previously rewarded environment. Our prior work has demonstrated that removal of reward causes increased remapping (Krishnan et al., 2022, Krishnan and Sheffield, 2023). In other words, the continued presence of reward within an environment stabilizes CA1 place fields. The Moita et al. (2004) paper, which showed remapping only in the fear conditioned context and not in the control context, provided rats with food pellets throughout the experimental session in both the control and conditioned context— likely to increase exploration necessary for identifying place cells. The presence of reward in the Moita et al experiment could explain the minimal remapping observed in their control context compared to our control context which lacked reward. Another possibility could lie in the differences in the intervals between place cell activity recordings in our study and that of Moita et al. While Moita et al. separated their recordings by just one hour, our recordings were separated by a full day, with a sleep period in between. The absence of sleep and the shorter time interval between conditioning and retrieval sessions in their study could explain the minimal remapping observed by Moita et al. compared to our findings. We have now addressed this discrepancy explicitly in lines 596-606.

Although we agree with the reviewer that it would be informative to perform analysis of how neural activity correlates with freezing responses, we think this warrants its own stand-alone manuscript as the neural dynamics and methods to appropriately analyze them are complicated. We are in the midst of analyzing this data further and will present these findings in a separate publication.

In summary, this is an important study that sets the initial parameters and neuronal validation needed to establish a head-fixed CFC paradigm that produces freezing behaviors. In the discussion, the authors note the limitations of this study, suggest the next steps in refinement, and point to several future directions using this protocol to significantly advance our understanding of the neural circuits of threat-related learning and behavior.

Reviewer #2 (Public review):

Summary:

In this manuscript, Krishnan et al devised three paradigms to perform contextual fear conditioning in head-fixed mice. Each of the paradigms relied on head-fixed mice running on a treadmill through virtual reality arenas. The authors tested the validity of three versions of the paradigms by using various parameters. As described below, I think there are several issues with the way the paradigms are designed and how the data are interpreted. Moreover, as Paradigm 3 was published previously in a study by the same group, it is unclear to me what this manuscript offers beyond the validations of parameters used for the previous publication. Below, I list my concerns point-by-point, which I believe need to be addressed to strengthen the manuscript.

Major comments

(1) In the analysis using the LME model (Tables 1 and 2), I am left wondering why the mice had increased freezing across recall days as well as increased generalization (increased freezing to the familiar context, where shock was never delivered). Would the authors expect freezing to decrease across recall days, since repeated exposure to the shock context should drive some extinction? This is complicated by the analysis showing that freeing was increased only on retrieval day 1 when analyzing data from the first lap only. Since reward (e.g., motivation to run) is removed during the conditioning and retrieval tests, I wonder if what the authors are observing is related to decreased motivation to perform the task (mice will just sit, immobile, not necessarily freezing per se). I think that these aspects need to be teased out.

This is an important point and we agree teasing out a lack of motivation versus fearful freezing would be useful. To address the possibility that reduced motivation to run without reward could contribute to the observed freezing behavior, we have now included a no-shock control group in the revised manuscript (n = 7; Supplementary Figure 2A-B, H–I). These control mice experienced the same protocol, including the wearing of a tail coat, but did not receive any shocks. We observed no increases in freezing across days in these controls, confirming that the increased freezing in the Familiar context of our experimental group stems from fear conditioning rather than the removal of reward from a previously rewarded context. If reduced motivation from reward removal were the primary driver, similar freezing patterns would have emerged in the no-shock controls. We have added lines 248-261 in the revised manuscript, discussing this point, and we thank the reviewer for motivating us to do this experiment and analysis.

That said, the precise mechanisms underlying the fear generalization observed in the nonconditioned context—particularly its emergence during later recall days—remain unclear. Studies in freely moving animals have shown that fear memories initially specific to the conditioned context can become generalized with repeated exposures, which may be occurring here (Biedenkapp & Rudy, 2007; Wiltgen & Silva, 2007). Alternatively, it is possible that the combination of fear conditioning and the removal of expected reward contributes to a delayed generalization effect. This may reflect a limitation of our approach, which relies on reward to motivate initial training. As noted by another reviewer, we have now addressed this potential drawback of reward-based training in the discussion (see lines 809-817). Clearly, unique factors specific to the head-fixed VR paradigm may contribute to this phenomenon. Understanding the mechanisms underlying fear generalization in the head-fixed VR CFC paradigm will be a valuable direction for future research.

(2) Related to point 1, the authors actually point out that these changes could be due to the loss of the water reward. So, in line 304, is it appropriate to call this freezing? I think it will be very important for the authors to exactly define and delineate what they consider as freezing in this task, versus mice just simply sitting around, immobile, and taking a break from performing the task when they realize there is no reward at the end.

As noted in point 1 above, we have added a no-shock control group (n = 7; Supplementary Figure 2A-B, H–I) to determine whether the observed freezing was driven by fear conditioning or by reduced motivation to run in the absence of reward. The absence of increased freezing in these controls supports the interpretation that the behavior in the conditioned group is fearrelated. In future studies, incorporating additional physiological measures—such as heart rate monitoring—could further help distinguish fear-related freezing from other forms of immobility.

(3) In the second paradigm, mice are exposed to both novel and (at the time before conditioning) neutral environments just before fear conditioning. There is a big chance that the mice are 'linking' the memories (Cai et al 2016) of the two contexts such that there is no difference in freezing in the shock context compared to the neutral context, which is what the authors observe (Lines 333-335). The experiment should be repeated such that exposure to the contexts does not occur on the conditioning day.

This is an interesting idea. However, if memory linking were driving the observed freezing patterns, we would expect to see similarly reduced fear discrimination across all three paradigms, as mice experience both contexts sequentially in each case. However, this effect appears to be specific to Paradigm 2, suggesting this may be due to other factors. We agree it would be informative to eliminate pre-conditioning exposure to both environments—to assess whether this improves fear discrimination and helps clarify the potential contribution of memory linking. This is something we plan to do in future studies that are beyond the scope of this initial paper on VR-CFC.

(4) On lines 360-361, the authors conclude that extinction happens rapidly, within the first lap of the VR trial. To my understanding, that would mean that extinction would happen within the first 5-10 seconds of the test (according to Figure S1E). That seems far too fast for extinction to occur, as this never occurs in freely behaving mice this quickly.

We agree with the reviewer that extinction in Paradigm 2 appears to occur relatively rapidly.

However, the average time to complete the first lap in the fear-conditioned context in Paradigm 2 is 25.68 ± 5.55 seconds (as stated in line 384), indicating that extinction occurs within approximately the first 30 seconds of context exposure—not within 5–10 seconds. This is specific to Paradigm 2 and does not happen in either of the other paradigms, as shown in Supplementary Figure 4. For clarification, Figure S1E pertains to baseline running in Paradigm 1 and does not apply to Paradigm 2.

As the reviewer points out, even at 30 seconds, extinction seems to be happening more quickly in Paradigm 2 than seen in freely moving setups. This may be due to a key structural difference in our setup. The VR-CFC task is organized into discrete trials, with mice being teleported back to the start after reaching the end of the virtual track. Completing a full lap without receiving a shock could serve as a clear signal that the threat is no longer present within the environment as the completion of a lap means that the animals have surveyed all locations within the environment. This structure could accelerate extinction compared to freely moving setups, where animals take longer to explore their complete environment due to the lack of discrete trials. Although this is true for all our paradigms, the accelerated extinction seen in paradigm 2 versus 1 and 3 may be driven by other factors. As noted by the reviewers, other task parameters—such as context pre-exposure timing, shock intensity, and conditioning duration— are likely to play a role in shaping extinction dynamics. These factors warrant further investigation, and we plan to explore them in future studies to better understand the conditions influencing extinction in the VR-CFC paradigm.

(5) Throughout the different paradigms, the authors are using different shock intensities. This can lead to differences in fear memory encoding as well as in levels of fear memory generalization. I don't think that comparisons can be made across the different paradigms as too many variables (including shock intensity - 0.5/0.6mA can be very different from 1.0 mA) are different. How can the authors pinpoint which works best? Indeed, they find Paradigm 3 'works' better than Paradigm 2 because mice discriminate better between the neutral and shock contexts. This can definitely be driven by decreased generalization from using a 0.6mA shock in Paradigm 3 compared to 1.0 mA shock in Paradigm 2.

The reviewer brings up important points here. We have now added new data evaluating 0.6 mA shocks in Paradigm 1 (Supplementary Figure 2A–E, n=8). These data show that 1.0 mA shocks produced stronger conditioned responses and greater fear discrimination compared to 0.6 mA. Our goal in Paradigm 3 was to begin with a lower shock intensity and assess whether additional modifications—specifically the shorter ISI and retention of the tail-coat during recall—could enhance fear conditioning. Surprisingly, despite the weaker shock intensity, Paradigm 3 resulted in improved discrimination and freezing behavior relative to Paradigm 2. We have now clarified this point in the manuscript (lines 466-470), and we interpret this outcome as evidence that the shorter ISIs and contextual cue continuity (tail-coat) likely play a more significant role in enhancing learning and recall. However, as noted in the text (lines 511-514), further testing is needed to determine the individual contributions of each parameter to successful VR-CFC. Fully optimizing the parameter settings will take additional time and resources, and we aim to continually refine the parameter space in the future, as has been done over the years for freely moving animals.

(6) There are some differences in the calcium imaging dataset compared to other studies, and the authors should perform additional testing to determine why. This will be integral to validating their head-fixed paradigm(s) and showing they are useful for modeling circuit dynamics/behaviors observed in freely behaving mice. Moreover, the sample size (number of mice) seems low.

The one notable difference between our imaging study and that done in freely moving animals is that we observed remapping of place cells in the control context. In contrast, Moita et al. (2004) reported more stable place fields in the control context. A key distinction is that their study included rewards in the control context, which may have contributed to the spatial stability. We now discuss this difference in the manuscript (lines 599-605).

It should be noted that there are many key distinctions among paradigms that study neural activity during fear conditioning in freely moving animals. These include varying exposure times to environments (1–6 days), the time interval between neural activity recordings, and the use of food rewards during the experiment stages in freely moving animals to encourage exploration for place cell identification. Although freely moving paradigms that investigate fear conditioning and place cells are heterogeneous, we were encouraged by the replication of several key findings. This validates VR-based CFC as a viable tool for neural circuit investigations. While future work will include more thorough analyses, our current findings demonstrate the paradigm's effectiveness for modeling circuit dynamics and behavior. We have now expanded our dataset, which includes four additional mice, further corroborating these original findings.

(7) It appears that the authors have already published a paper using Paradigm 3 (Ratigan et al 2023). If they already found a paradigm that is published and works, it is unclear to me what the current manuscript offers beyond that initial manuscript.

The reviewer is correct that we have published a paper using Paradigm 3. However, this manuscript goes beyond that one and provides a much more comprehensive description and fundamental analysis of the behavior and experimental parameters regarding VR-CFC, allowing the research community to adapt our paradigm reproducibly. While Ratigan et al. (2023) offered only a minimal description of behavior and included just Paradigm 3, we present two additional paradigms along with neuronal validation using hippocampal place cells. We have now explicitly stated this in the introduction (lines 50-55).

(8) As written, the manuscript is really difficult to follow with the averages and standard error reported throughout the text. This reporting in the text occurred heterogeneously throughout the text, as sometimes it was reported and other times it was not. Cleaning this reporting up throughout the paper would greatly improve the flow of the text and qualitative description of the results.

We completely agree with this point and have now cleaned up the text, leaving details only in a few places we felt were important.

Reviewer #3 (Public review):

Summary:

Krishnan et al. present a novel contextual fear conditioning (CFC) paradigm using a virtual reality (VR) apparatus to evaluate whether conditioned context-induced freezing can be elicited in head-fixed mice. By combining this approach with two-photon imaging, the authors aim to provide high-resolution insights into the neural mechanisms underlying learning, memory, and fear. Their experiments demonstrate that head-fixed mice can discriminate between threat and non-threat contexts, exhibit fear-related behavior in VR, and show context-dependent variability during extinction. Supplemental analyses further explore alternative behaviors and the influence of experimental parameters, while hippocampal neuron remapping is tracked throughout the experiments, showcasing the paradigm's potential for studying memory formation and extinction processes.

Strengths:

Methodological Innovation: The integration of a VR-based CFC paradigm with real-time twophoton imaging offers a powerful, high-resolution tool for investigating the neural circuits underlying fear, learning, and memory.

Versatility and Utility: The paradigm provides a controlled and reproducible environment for studying contextual fear learning, addressing challenges associated with freely moving paradigms.

Potential for Broader Applications: By demonstrating hippocampal neuron remapping during fear learning and extinction, the study highlights the paradigm's utility for exploring memory dynamics, providing a strong foundation for future studies in behavioral neuroscience.

Comprehensive Data Presentation: The inclusion of supplemental figures and behavioral analyses (e.g., licking behaviors and variability in extinction) strengthens the manuscript by addressing additional dimensions of the experimental outcomes.

Weaknesses:

Characterization of Freezing Behavior: The evidence supporting freezing behavior as the primary defensive response in VR is unclear. Supplementary videos suggest the observed behaviors may include avoidance-like actions (e.g., backing away or stopping locomotion) rather than true freezing. Additional physiological measurements, such as EMG or heart rate, are necessary to substantiate the claim that freezing is elicited in the paradigm.

To strengthen our claim that freezing is a conditioned response in this task, we have taken three key steps:

(1) We adjusted our freezing detection threshold from 1 cm/s to near 0 cm/s to capture only periods where the animal is virtually motionless on the treadmill. We validated this approach in Figure 2, particularly in the zoomed-in track position trace in Figure 2A, which clearly shows that the identified freezing epochs correspond to no change in track position. All analyses and figures have been updated to reflect this more stringent threshold.

(2) We have added a no-shock control group in the revised manuscript (n = 7; Supplementary Figure 2A-B, H–I) where mice experienced the same protocol, including wearing a tail-coat, but received no shocks. These mice showed no increases in freezing behavior, which further demonstrates that the increased freezing we observe is a result of fear conditioning.

(3) We have added a new supplementary video (Supplementary Video 2) that better illustrates the freezing behavior in our task.

That said, we fully agree with the reviewer that freezing is not the only defensive response observed. Other behaviors—such as hesitation, backward movement, and slowing down—also emerge that are unique to our treadmill-based paradigm. We chose to focus on freezing in this manuscript to align with convention in freely moving fear conditioning studies and to facilitate direct comparisons. We agree that additional physiological measurements (e.g., EMG or heart rate) would provide further validation and could help distinguish between different forms of defensive responses. We view this as an important future direction and plan to incorporate such measures in upcoming studies. We highlight this in the results section (lines 175-179, 262-268) and in the discussion (lines 739-750).

Analysis of Extinction: Extinction dynamics are only analyzed through between-group comparisons within each Recall day, without addressing within-group changes in behavior across days. Statistical comparisons within groups would provide a more robust demonstration of extinction processes.

This is an important distinction and we have now added figures (Supplementary Figures 2H-I, 5C-D, 6C-D) showing within-VR behavior across Recall days, along with statistical comparisons and a description of the extinction process based on these results.

Low Sample Sizes: Paradigm 1 includes conditions with very low sample sizes (N=1-3), limiting the reliability of statistical comparisons regarding the effects of shock number and intensity.

Increasing sample sizes or excluding data from mice that do not match the conditions used in Paradigms 2 and 3 would improve the rigor of the analysis.

While we included all conditions in Figure 2 for completeness, we have separated these conditions in Supplementary Figure 2 to ensure clarity. This allows researchers interested in this paradigm to see the approximate range of conditioned responses observed across different parameters. When comparing Paradigm 1 with Paradigms 2 and 3, we have only used data from 1mA, 6 shocks condition.

Potential Confound of Water Reward: The authors critique the use of reward in conjunction with fear conditioning in prior studies but do not fully address the potential confound introduced by using water reward during the training phase in their own paradigm.

We agree this is a point that needs discussion. We have now noted the limitation of using water rewards during training in the discussion section, particularly its effect on the animal’s motivation in the long term and on place cell activity (lines 814-820).

Recommendations for the authors

Reviewer #1 (Recommendations for the authors):

I suggest changing "3 paradigms" to "3 versions of a CFC paradigm," as the paradigm is fundamentally the same, but parameters were adjusted towards finding an optimal protocol.

We have changed this phrasing where applicable.

Figure S2: There appear to be different sets of shock parameters for different mice, most with an n of 1 or 2. This is not reliable for making a decision for optimal shock parameters and should not be discussed in that way until a full-powered comparison is completed. Also, the N adds up to 19, yet only 18 are described as being included in the study.

We thank the reviewer for this important point. We agree that the current study is not powered to definitively identify optimal parameter settings. We have been careful not to interpret it in that way in the text. Rather, we adopted a commonly used starting point from the freely moving literature—1 mA with six shocks—as our initial condition (lines 196-199). To provide context for others interested in pursuing this work, we have presented a range of conditioned responses from different parameter combinations to illustrate potential variability. In most cases, these data are intended for illustrative purposes only and are not meant to support firm conclusions. We agree that a systematic and fully powered investigation of each parameter would be highly valuable, and we plan to pursue this in future work (and hope other labs contribute to this goal, too), much like the iterative optimizations performed in freely moving paradigms over time.

We thank the reviewer for catching the sample size discrepancy and have now corrected it.

The number of animals for the no-shock condition should be included.

Thank you. We have now included this.

A possible explanation for the lower fear and poorer discrimination in versions 2 and 3 could be that 10 min pre-exposure to the CFC context on day -1 led to latent inhibition. Shorter (or eliminated) pre-exposure may improve outcomes.

We agree that the exposure time is a parameter that we should explore. We have highlighted this in the discussion (lines 729-736) as a parameter that is worth testing in the future.

For analysis of extinction, it is best to establish this within condition - is freezing to the CFC context significantly reduced compared with initial recall and similar to pre-training freezing? By using discrimination as your index of extinction, increases in control context freezing/inactivity can eliminate context discrimination without the conditioned response of freezing actually undergoing extinction.

This is a good point, and we have now included analysis and conclusions based on a within-VR comparison for the analysis of fear extinction (Supplementary Figures 2H-I, 5C-D, 6C-D).

Reviewer #3 (Recommendations for the authors):

Clarification of Treadmill Shape: The manuscript describes the treadmill as "spherical" throughout. However, based on representative images and videos, the treadmill appears cylindrical. This discrepancy should be clarified to ensure consistency between the text and visuals.

The reviewer is correct that the treadmill is cylindrical, and this was an error on our part. We have corrected it throughout.

Figure and Legend Labeling: To improve clarity, all figures and their legends should be explicitly labeled with the corresponding paradigm (1, 2, or 3) to facilitate interpretation.

We have now added a label on all figures that clarifies which Paradigm the figures are referring to. We have also explicitly added this to the figure legends.

Objective Language: Subjective language, such as "since we wanted animals to" (Line 850), should be revised to reflect an objective tone (e.g., "to allow animals to"). Similarly, phrases like "We believe" (Line 896) should be avoided to maintain an unbiased presentation.

We have removed subjective language from our text.

Placement of Future Directions: Speculations on future experimental plans, such as the use of sex as a biological variable (Lines 895-903), should be included in the Discussion section rather than the Methods. Additionally, remarks about the responsiveness of female mice to tail shocks should be moved to the main text for proper contextualization.

We have moved these lines as suggested by the reviewer.

Author response:

The following is the authors’ response to the previous reviews

Public Reviews:

Reviewer #1 (Public review):

Summary:

In this manuscript, Guo and colleagues used a cell rounding assay to screen a library of compounds for inhibition of TcdB, an important toxin produced by Clostridioides difficile. Caffeic acid and derivatives were identified as promising leads, and caffeic acid phenethyl ester (CAPE) was further investigated.

Strengths:

Considering the high morbidity rate associated with C. difficile infections (CDI), this manuscript presents valuable research in the investigation of novel therapeutics to combat this pressing issue. Given the rising antibiotic resistance in CDI, the significance of this work is particularly noteworthy. The authors employed a robust set of methods and confirmatory tests, which strengthen the validity of the findings. The explanations provided are clear, and the scientific rationale behind the results is well-articulated. The manuscript is extremely well written and organized. There is a clear flow in the description of the experiments performed. Also, the authors have investigated the effects of CAPE on TcdB in careful detail, and reported compelling evidence that this is a meaningful and potentially useful metabolite for further studies.

Weaknesses:

The authors have made some changes in the revised version. However, many of the changes were superficial, and some concerns still need to be addressed. Important details are still missing from the description of some experiments. Authors should carefully revise the manuscript to ascertain that all details that could affect interpretation of their results are presented clearly. For instance, authors still need to include details of how the metabolomics analyses were performed. Just stating that samples were "frozen for metabolomics analyses" is not enough. Was this mass-spec or NMR-based metabolomics. Assuming it was mass-spec, what kind? How was metabolite identity assigned, etc? These are important details, which need to be included. Even in cases where additional information was included, the authors did not discuss how the specific way in which certain experiments were performed could affect interpretation of their results. One example is the potential for compound carryover in their experiments. Another important one is the fact that CAPE affects bacterial growth and sporulation. Therefore, it is critical that authors acknowledge that they cannot discard the possibility that other factors besides compound interactions with the toxin are involved in their phenotypes. As stated previously, authors should also be careful when drawing conclusions from the analysis of microbiota composition data, and changes to the manuscript should be made to reflect this. Ascribing causality to correlational relationships is a recurring issue in the microbiome field. Again, I suggest authors carefully revise the manuscript and tone down some statements about the impact of CAPE treatment on the gut microbiota.

Thanks for your constructive suggestion. We have carefully revised the manuscript according to your suggestions.

Reviewer #2 (Public review):

I appreciate the author's responses to my original review. This is a comprehensive analysis of CAPE on C. difficile activity. It seems like this compound affects all aspects of C. difficile, which could make it effective during infection but also make it difficult to understand the mechanism. Even considering the authors responses, I think it is critical for the authors to work on the conclusions regarding the infection model. There is some protection from disease by CAPE but some parameters are not substantially changed. For instance, weight loss is not significantly different in the C. difficile only group versus the C. difficile + CAPE group. Histology analysis still shows a substantial amount of pathology in the C. difficile + CAPE group. This should be discussed more thoroughly using precise language.

Thanks for your constructive suggestion. We have carefully revised the manuscript according to your suggestions.

Reviewer #3 (Public review):

Summary:

The study is well written, and the results are solid and well demonstrated. It shows a field that can be explored for the treatment of CDI

Strengths:

Results are really good, and the CAPE shows a good and promising alternative for treating CDI.

Weaknesses:

Some references are too old or missing.

Comments on revisions:

I have read your study after comments made by all referees, and I noticed that all questions and suggestions addressed to the authors were answered and well explained. Some of the minor and major issues related to the article were also solved. I am satisfied with all the effort given by the authors to improve their manuscript.

Thanks again for your review.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

The legend of Figure 3SB is incorrect. It should read "Growth curves of C. difficile BAA-1870 in the presence of varying concentrations of CAPE (0-64 µg/mL)". Also, there is something wrong with the symbols in this figure. I suspect what is happening is that the symbols for the concentrations of 32 and 64 µg/mL are superimposing, but this is a problem because the lower line looks like a closed circle, which is supposed to represent the condition where no CAPE was added. The authors should change the symbols to allow clear distinction between each of the conditions.

Thanks for your constructive suggestion. We have modified the panel and figure legend in Figure 3SB. The concentrations of 32 μg/mL and 64 μg/mL are quite similar, which makes it challenging to differentiate between the corresponding data points on the graph. To enhance clarity, we have utilized distinct colors to help distinguish these closely valued lines as effectively as possible.

Since the authors observed a significant effect of CAPE on both bacterial growth and spore production, their discussion and conclusions need to reflect the fact that the effects observed can no longer be attributed solely to toxin inhibition.

Thanks for your comments. We have modified the corresponding description according to your suggestions.

In lines 43-45, authors state that "CAPE treatment of C. difficile-challenged mice induces a remarkable increase in the diversity and composition of the gut microbiota (e.g., Bacteroides spp.)". It is still unclear to this reviewer why mention Bacteroides between parentheses. Does this mean that there was an increase in the abundance of Bacteroides? If that is the case this needs to be stated more clearly.

Thanks for your comments. Treatment with CAPE indeed significantly increased the abundance of Bacteroides spp. in the gut microbiota (Figure 7H-J). However, to avoid ambiguity in the abstract, we have chosen to delete the specific mention of Bacteroides spp. within the parentheses.

The modifications made to lines 132-135 still do not address my concern. Authors stated in the manuscript that "compounds that were not bound to TcdB were removed". But how was this done? This needs to be clearly explained in the manuscript. In the response to reviewers document, authors state that this was done through centrifugation. But given that the goal here is to separate excess of small molecule from a protein target, just stating that centrifugation was used is not enough. Did the authors use ultracentrifugation? What were the conditions employed. This is critical so that the reader can assess the degree of compound carryover that may have occurred. Also, authors need to clearly acknowledge the caveats of their experimental design by stating that they cannot rule out the contribution of compound carryover to their results.

Thanks for your comments. We employed ultrafiltration centrifugal partition to remove the unbound small molecule compounds. Due to the large molecular weight of TcdB, approximately 270 kDa, we selected a 100 kDa molecular weight cutoff ultrafiltration membrane. The centrifugation was performed at 4000 g for 5 min to eliminate the compounds that did not bind to TcdB. We have incorporated the relevant methods and discussed the potential impacts on the respective sections of the manuscript.

In line 142, authors added the molar concentration of caffeic acid, as requested. Although this helps, it is even more important that molar concentrations are added every time a compound concentration is mentioned. For instance, just 2 lines down there is another mention of a compound concentration. It would be informative if authors also added molar concentrations here and throughout the manuscript.

Thanks for your comments. In our initial test design, we have utilized the concentration unit of μg/mL. However, during the conversion to μM using the dilution method, some values do not result in neat, whole numbers. For instance, the conversion of 32 μg/mL of caffeic acid phenyl ethyl ester yields 112.55 μM, which appears somewhat irregular when expressed in this manner.

Line 277. For the sake of clarity, I would strongly suggest that authors use the term "control mice" instead of "model mice".

Thanks for your comments. We have modified “model mice” to “control mice” throughout the manuscript.

In line 302, the word taxa should not be capitalized. I capitalized it in my original comments simply to draw attention to it.

Thanks for your comments. We have modified this word.

In the section starting in line 318, authors still need to include details of how the metabolomics analyses were performed. Just stating that samples were "frozen for metabolomics analyses" is not enough. Was this mass-spec or NMR-based metabolomics. Assuming it was mass-spec, what kind? How was metabolite identity assigned? Etc, etc. These are important details, which need to be included.

Thanks for your comments. We have added some metabolomics methods in the corresponding section.

In line 338, the authors misunderstood my original comment. This sentence should read "...the final product of purine degradation, were markedly decreased in mice after...".

Thanks for your comments. We have modified this sentence.

Panels of figure 3 are still incorrectly labeled. The secondary structure predictions are shown in A and C, not A and B as is currently stated in the legend.

Thanks for your comments. We have modified the figure legend in Figure 3.

About Figure 5C, I think the authors for the clarification, but this explanation should be included in the figure legend.

Thanks for your comments. We have added the relevant information to the figure legend.

Author response:

The following is the authors’ response to the previous reviews

Reviewer #1 (Public Review):

Batra, Cabrera and Spence et al. present a model which integrates histone posttranslational modification (PTM) data across cell models to predict gene expression with the goal of using this model to better understand epigenetic editing. This gene expression prediction model approach is useful if a) it predicts gene expression in specific cell lines b) it predicts expression values rather than a rank or bin, c) if it helps us to better understand the biology of gene expression or d) it helps us to understand epigenome editing activity. Problematically for point a) and b) it is easier to directly measure gene expression than to measure multiple PTMs and so the real usefulness of this approach mostly relates to c) and d).

We appreciate this point from Reviewer #1 and the instructive comments and helpful feedback on our study. We designed our approach keeping in mind that the primary use case is to understand how epigenome editing would affect gene expression.

Other approaches have been published that use histone PTM to predict expression (e.g. PMID 27587684, 36588793). Is this model better in some way? No comparisons are made although a claim is made that direct comparisons are difficult. I appreciate that the authors have not used the histone PTM data to predict gene expression levels of an "average cell" but rather that they are predicting expression within specific cell types or for unseen cell types. Approaches that predict expression levels are much more useful whereas some previous approaches have only predicted expressed or not expressed or a rank order or bin-based ranking. The paper does not seem to have substantial novel insights into understanding the biology of gene expression.

We thank Reviewer #1 again for this insightful comment. We have included citations for a series of papers (PMIDs: 27587684, 30147283, 36588793) that performed gene expression prediction using histone PTM data. However, each of these methods performs classification of gene expression as opposed to predicting the actual gene expression value via regression. Additionally, the referenced studies all work with Roadmap Epigenomics read-depth data as opposed to p-values obtained from the ENCODE pipelines, making it difficult to make direct comparisons. We outline in the Discussion section that by creating a comprehensive dataset of epigenome editing outcomes, which include quantification of histone PTMs before and after in situ 1 perturbations, will improve our understanding of the effects of dCas9-p300 on gene expression and assist in the design of gRNAs for achieving fine-tuned control over gene expression levels. In this revised version of our study, we have also added new data (Figure 3 – figure supplement 3) to further benchmark our model against others.

The approach of using this model to predict epigenetic editor activity on transcription is interesting and to my knowledge novel although only examined in the context of a p300 editor. As the author point out the interpretation of the epigenetic editing data is convoluted by things like sgRNA activity scoring and to fully understand the results likely would require histone PTM profiling and maybe dCas9 ChIP-seq for each sgRNA which would be a substantial amount of work.

We agree with the Reviewer and view these experiments as important components of future studies.

Furthermore from the model evaluation of H3K9me3 is seems the model is performing modestly for other forms of epigenetic or transcriptional editing- e.g. we know for the best studied transcriptional editor which is CRISPRi (dCas9-KRAB) that recruitment to a locus is associated with robust gene repression across the genome and is associated with H3K9me3 deposition by recruitment of KAP1/HP1/SETDB1 (PMID: 35688146, 31980609, 27980086, 26501517).

This is an interesting point. We have included new data (Figure 4 – figure supplement 1), that quantifies how sensitive the trained gene expression model is to perturbations in H3K9me3. Indeed our data suggests that the model predictions are sensitive to perturbations in H3K9me3. For instance, there is a clear decrease and a gradual increase as the position where the perturbation is performed moves from upstream to downstream of the TSS. Additionally, the magnitude of the predicted fold-change is a function of how much the H3K9me3 is perturbed and hence the magnitude of change would be even higher if the perturbation magnitude is increased. However, this precise magnitude is hard to estimate In the absence of experimental perturbation data for H3K9me3. Leveraging our model in combination with KRAB-based CRISPRi is an exciting and important aspect of future studies.

One concern overall with this approach is that dCas9-p300 has been observed to induce sgRNA independent off target H3K27Ac (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8349887/ see Figure S5D) which could convolute interpretation of this type of experiment for the model.

This remains an excellent point and indeed, we and others have observed that dCas9-p300 can result in off-target H3K27ac levels (both increased and suppressed) across the genome. Our study focused on p300, because the molecule is one of the few known proteins that can catalyze H3K27ac in the human genome, and H3K27ac remains a proxy for active genomic regulatory elements. Nevertheless, any off target activity of dCas9-p300 could certainly convolute our analyses. We have included language to address this caveat in our discussion.

Reviewer #2 (Public review):

Summary:

The authors build a gene expression model based on histone post-translational modifications, and find that H3K27ac is correlated with gene expression. They proceed to perturb H3K27ac at 13 gene promoters in two cell types, and measure gene expression changes to test their model.

We remain appreciative of the constructive feedback and input from Reviewer #2 on our manuscript.

Strengths:

The combination of multiple methods to model expression, along with utilizing 6 histone datasets in 13 cell types allowed the authors to build a model that correlates between 0.7-0.79 with gene expression. They use dCas9-p300 fusions to perturb H3K27ac and monitor gene expression to test their model. Ranked correlations of the HEK293 data showed some support for the predictions after perturbation of H3K27ac.

Weaknesses:

The perturbation of 5 genes in K562 with perturb-seq data shows a modest correlation of ~0.5 and isn't included in the main figures. The authors are then left to speculate reasons why the outcome of epigenome editing doesn't fit their predictions, which highlights the limited value in the current version of this method.

We agree with the reviewer’s suggestion and highlight in our conclusion that generating epigenome editing data across a variety of cell types and across many genes will help uncover the underlying mechanisms of gene expression modulation.

As mentioned before, testing genes that were not expressed being most activated by dCas9-p300 weaken the correlations vs. looking at a broad range of different gene expression as the original model was trained on.

We appreciate this comment from Reviewer #2. We note that the data generated from this dCas9-p300 perturb-seq experiment used gRNAs from a pre-existing library published previously (PMID: 37034704). While this library enabled deeper interrogation of dCas9-p300 driven effects compared to our previous revision, the gRNAs in this library were designed against genes associated with haploinsufficiency in neuronal cell types, and which were generally lowly-expressed in K562 cells. Further, we restricted our analysis here to promoter-proximal gRNAs (as opposed to enhancer-targeted gRNAs in the library), focusing our scope even more so. Thus the genes ultimately used for analysis are enriched for low expression.

If the authors want this method to be used to predict outcomes of epigenome editing, expanding to dCas9-KRAB and other CRISPRa methods (SAM and VPR) would be useful. Those datasets are published and could be analyzed for this manuscript.

This is an exciting suggestion from Reviewer #2. We agree, and view this as a component of future work in this area.

The authors don't compare their method to other prediction methods.

In this revised version of our study, we have also added new data (Figure 3 – figure supplement 3) to further benchmark our model against others. These data demonstrate that our CNN model outperforms existing approaches across multiple cell types.

Recommendations for the authors:

Reviewer #2 (Recommendations for the authors):

Looking at the individual genes in K562 shows a random looking range of predictions and observed, with the exception of Bcl11A which is one of two genes in this set of 5 that are not expressed. I will repeat my earlier comment, that epigenome editing and CRISPRa methods generally show the most upregulation with the lowest expressed genes. I speculate that plotting endogenous expression vs. outcome (assuming using all gRNAs within a reasonable and similar distance to TSS) would produce a correlation of -0.5 or greater and be as useful as this method.

We agree, and believe that this demonstrates more work is needed in this emerging research area.

The methods describe Perturb-seq analysis but not the bench experiments.

We have added the bench methods related to our Perturb-seq experiments to our revised manuscript under the Experimental Methods section in the Appendix.

I don't understand why the authors can't compare to other methods as that is fairly standard in new prediction papers. I get that others used REMC vs. ENCODE, and were rank or binary based, but the authors could use REMC data and/or convert their data to ranked or binary and still compare. Lacking that it's hard to judge this manuscript.

We have added benchmarking against existing methods as Figure 3 – figure supplement 3.

Author response:

The following is the authors’ response to the previous reviews

Our revised manuscript thoroughly addresses all comments and suggestions raised by the reviewers, as detailed in our point-by-point response. To strengthen our findings, we have conducted additional in vivo experiments to evaluate the presence of fibro-adipogenic progenitors (FAPs) at different time points during HO formation in control and BYL719-treated mice. Our results indicate that BYL719 reduces the accumulation of FAPs and promotes muscle fiber regeneration in vivo. We have also expanded our discussion on BYL719’s effects on mTOR signaling, further clarifying key points raised by Reviewer #1, and have addressed all minor comments.

Additionally, in response to Reviewer #2, we have employed an orthogonal and complementary approach using a new model. We conducted chondrogenic differentiation experiments with murine MSCs expressing either ACVR1wt or ACVR1^R206H. qPCR analysis of chondrogenic gene markers (Sox9, Acan, Col2a1) demonstrates that Activin A enhances their expression in ACVR1^R206H cells, whereas BYL719 strongly suppresses their expression, regardless of ACVR1 mutational status. These new data further confirm that BYL719 effectively inhibits genes involved in ossification and osteoblast differentiation, independent of the ACVR1 mutation. We have also expanded our discussion to further clarify points raised by Reviewer #2 and have addressed all remaining minor comments.

Below, we provide a detailed point-by-point response to the reviewers’ comments:

Rreviewer #1:

Point 1: In this revised manuscript, the authors clearly showed that BYL719 suppressed the proliferation and differentiation of murine myoblasts, C2C12 cells, in addition to human MSCs in vitro. Furthermore, BYL719 decreased migratory activity in vitro in monocytes and macrophages without suppressing proliferation. Overall, these data suggested that BYL719 is not a specific chemical compound for cell types or signaling pathways as mentioned in the manuscript by the authors themselves. Therefore, it was still unclear how to explain the molecular mechanisms in inhibition of HO by the compound in a specific signaling pathway in a specific cell type, MSCs, contradicting many other possibilities. The authors should add logical explanations in the manuscript.

Regarding its selectivity, BYL719 is a potent and highly selective inhibitor of PI3Kα. It has been demonstrated in multiple studies and in several in vitro kinase assay panels (Furet et al. PMID: 23726034, Fritsch et al. PMID: 24608574). The IC50 or Kd values for BYL719 against PI3Kα were at least 50 times lower than for most of other kinases tested. Moreover, BYL719 is also highly selective for PI3Kα (IC50 = 4.6 nmol/L) compared to other class I PI3K (PI3Kβ (IC50 = 1,156 nmol/L), PI3Kδ (IC50 = 290 nmol/L), PI3Kγ (IC50 = 250 nmol/L)) (Fritsch et al). Consistent with these data, we show that, at the concentrations tested, BYL719 does not have a direct effect on any kinase receptor within the TGF-b superfamily, including ACVR1 or ACVR1^R206H.

Rather than blocking ACVR1 kinase activity, in our manuscript we provide evidence that BYL719 has the potential to inhibit osteochondroprogenitor specification and prevent an exacerbated inflammatory response in vivo (Valer et al., 2019a PMID: 31373426, and this manuscript) through different mechanisms, such as (i) increasing SMAD1/5 degradation, (ii) reducing transcriptional responsiveness to BMPs and Activin, (iii) blocking non-canonical ACVR1 responses such as the activation of AKT/mTOR. All these defined molecular mechanisms contribute to suppress HO in vitro and in vivo, as we report and explain throughout the manuscript. Selective PI3Kα inhibition is at the core of the different molecular pathways described. As such, PI3Kα blockade inhibits the phosphorylation of GSK3 and compromises SMAD1 protein stability, thereby altering canonical responsiveness and osteochondroprogenitor specification (Gamez et al PMID: 26896753; Valer et al PMID: 31373426). Moreover, PI3Kα blockade downregulates Akt/mTOR signalling, which is critical for FOP and non‐genetic (trauma induced) HO in preclinical models (Hino et al, 2017 PMID: 28758906; Hino et al. PMID: 30392977). Finally, PI3Kα inhibition hampers a number of proinflammatory pathways, thereby limiting the expression of pro-inflammatory cytokines, reducing the proliferation of monocytes, macrophages and mast cells, and partially blocking the migration of monocytes. As we suggest in the discussion of the manuscript, this effect likely causes a poor recruitment of monocytes and macrophages at injury sites and throughout the in vivo ossification process.

Noteworthy, in our manuscript we do not refer to a “specific chemical compound for cell types”. Rather, in the Discussion we write “the administration of BYL719 prevented an exacerbated inflammatory response in vivo, possibly due to specific effects observed on immune cell populations.” This sentence did not intend to imply that BYL719 only affects these specific cell types, but aimed to emphasize the effects observed on those cell populations, even though systemic BYL719 may affect all populations. We rephrased it to “the administration of BYL719 prevented an exacerbated inflammatory response in vivo, possibly due to the effects observed on immune cell populations.” to provide a clearer message as suggested by the reviewer. We thank the reviewer for these questions and hope that these explanations and changes in the text improve the clarity of the message.

Mesenchymal stem/stromal cells (MSCs) are osteochondroprogenitor cells that can follow distinct differentiation paths. In this study, we use these cells as an in vitro model for the study of osteochondrogenitor specification. MSCs, and induced MSCs (iMSCs), have been widely used as in vitro cellular models of osteochondroprogenitor specification for the analysis of markers, signaling, modulation, and differentiation potential or capacity. Their use as models for this purpose has been extensively studied in wild type MSCs, and in the presence of FOP mutations (Boeuf and Richter PMID: 20959030; Schwartzl et al. PMID: 37923731).

Point 2: Related to comment #1, the effects of BYL719 on the proliferation and differentiation of fibro-adipogenic cells in skeletal muscle, which are potential progenitor cells of HO, should be important to support the claim of the authors.

We have performed additional in vivo experiments to assess the presence of fibro-adipogenic precursors (FAPs) at different time-points during HO formation in control and BYL719-treated in the mouse model of heterotopic ossification. We analyzed the number of fibro-adipogenic progenitor (FAPs) during the progression of the HO. These data are shown in the new Figure3-Figure Supplement 1. We demonstrate that BYL719 reduces the number of PDGFRA+ cells (FAPs, red) throughout the ossification process in vivo. Moreover, now we also show an enlargement of the diameter of myofibers (labelled with wheat germ agglutinin, green) when animals were treated with BYL719, indicating improved muscle regeneration and further validating the data reported as supplementary figures that were added in the first revision of this manuscript.

Point 3: BYL719 inhibited signaling through not only ACVR1-R206H and ACVR1-Q207D but also wild type ACVR1 and suppressed the chondrogenic differentiation of parental MSCs regardless of the expression of wild type or mutant ACVR1. Again, these findings suggest that BYL719 inhibits HO through a multiple and nonspecific pathway in multiple types of cells in vivo. The authors are encouraged to explain logically the use of bone marrow-derived MSCs to examine the effects of BYL719.

As detailed in main point 1, we consider that the main target, molecular mechanisms and inhibited pathways by BYL719 are specific and well characterised in other research articles and further defined in this manuscript, including the generation of PI3Ka deficient mice in an FOP background, that undoubtedly demonstrates an essential role for PI3Ka in ACVR1-driven heterotopic ossification in vivo. Altogether, we are confident that BYL719 inhibits HO through multiple and specific pathways that arise from the PI3Kα inhibition. As a systemically administrated drug, BYL719 affects the multiple types of cells in vivo that express PI3Kα. It is well known that PI3Kα is exquisitely required for chondrogenesis and osteogenesis (Zuscik et al. PMID; Gamez et al PMID: 26896753 1824619). Accordingly, throughout the manuscript we refrain from suggesting a specific effect on ACVR1-R206H cells but instead an inhibitory effect on cell number and differentiation regardless on the ACVR1 form expressed.

Similarly, as detailed in main point 1, MSCs and hiPSCs have been extensible used as in vitro cellular models of osteochondroprogenitor specification for the analysis of markers, signaling, modulation, and differentiation potential or capacity (Barruet et al., PMID: 28716551; Kan et al., PMID: 39308190).

Point 4: BYL719 clearly inhibits an mTOR pathway. Is there a possibility that BYL719 suppresses HO by inhibiting mTOR rather than PI3K? The authors are encouraged to show the unique role of PI3K in BYL719-suppressed HO formation.

As clarified above, BYL719 is a potent and selective inhibitor of PI3Kα, with minimal off-target inhibition against other kinases, as it has been demonstrated in multiple studies and in several in vitro kinase assay panels. In the same study, while IC50 of BYL719 against PI3Kα was (IC50 = 4.6 nmol/L), IC50 against mTOR was (IC50= >9,100 nmol/L), indicating that it was not directly inhibited. mTOR is one of the well-known pathways that are activated downstream of PI3K. Therefore, there is no surprise that blocking PI3Kα will block mTOR signalling. This potential effect was already demonstrated in previous publications (Valer et al., 2019a PMID: 31373426) and discussed throughout the first revision. We consider that the additive effect of mTOR inhibition and other molecular mechanisms downstream of PI3Kα, including reduced SMAD1/5 protein levels, contribute to the in vivo HO inhibition by BYL719.

Reviewer #2:

Point 1: It is also important to note that, in most of the data, there is no significant difference between cells with wild-type ACVR1 and those with the R206H mutation. The authors demonstrated that ACVR1 is not a target of BYL719 based on NanoBRET assay data, suggesting that BYL719's effect is not specific to FOP cells, even though they used an FOP mouse model to show in vivo effects.

The main effect of R206H mutation is the gain of function in response to Activin A. For most of the responses to other ACVR1 ligands (e.g. BMP6/7), we observe a slightly increased response in the presence of the mutation (which is consistent with previous research, usually labelling RH as a “weak activating mutant” unless Activin A is added (Song et al., PMID: 20463014)). Therefore, as expected, most of the differences between WT and RH mutant cells can be observed mostly upon Activin A addition, as observed, for example, in Figure 3 of our manuscript.

We agree with the reviewer that, at the concentrations used, BYL719 does not specifically target FOP cells. However, we believe that it targets downstream pathways of PI3Kα inhibition that are essential for osteochondrogenic specification, regardless of mutation status. This therapeutic strategy aligns with other experimental drugs, including Palovarotene (validated for FOP) and Garetosmab and Saracatinib (in advanced clinical trials), which target Activin A function, ACVR1 activity, or osteochondrogenic differentiation irrespective of the mutant allele. Unlike these molecules, BYL719 has been chronically administered to patients (including children) without major side effects (Gallagher et al.; PMID: 38297009), further supporting its potential for safe long-term use.

The authors should consider that the effect of Activin A on R206H cells is not identical to that of BMP6 on WT cells. If the authors aim to identify the target of BYL719 in FOP cells, they should compare R206H cells treated with Activin A/BYL719 to WT cells treated with BMP6/BYL719.

We use Activin A and BMP6, both high-affinity ACVR1 ligands, to demonstrate, as observed in figure 6, that PI3Kα inhibition can inhibit the expression of genes within GO terms ossification and osteoblast differentiation. It is important to note, however, that Activin A canonical signaling receptor is ACVR1B. Since BYL719 blocks the induction of a heterotopic ossification gene expression signature common to Activin A and BMP6, in the context of the FOP mutation R206H, our results indicate that BYL719 inhibition affects a signaling pathway downstream of ACVR1, activated by either BMP6 (wild type receptor, relevant for non-genetic heterotopic ossifications) or Activin (R206H mutant receptor, relevant for FOP).

We consider that the comparison (RH ACTA BYL vs WT BMP6 BYL) would provide confounding results raised from intrinsic model differences in basal expression programs (WT vs RH), and differences in the quantitative level of signaling of the different ligands at these specific doses. First, if we only consider SMAD1/5 signaling, Activin A and BMP6 won’t have identical signaling, and differences will arise from the strength of that signaling. Secondly, in the suggested comparison we would find, mostly, all the differential gene expression promoted by Activin A canonical signaling through type I receptors ACVR1B/ALK4 in complex with ACVR2A or ACVR2B, promoting SMAD2/3 activation (in addition to the altered signaling that ACVR1-R206H could promote). Examples of differential response in pSMAD1/5 in ACVR1-WT or RH with BMP ligands and R206H with Activin A ligand, and examples of pSMAD2/3 canonical signaling in R206H cells have been described in Ramachandran et al, PMID: 34003511; Hatsell et al., PMID: 26333933).

Point 2: The interpretation of the data in the new Figure 5 is inappropriate. Based on the expression levels of SOX9, COL2A1, and ACAN, it is unclear whether the effect of BYL719 is due to the inhibition of differentiation or proliferation. The addition of Activin A showed no difference between ACVR1/WT and ACVR1/R206H cells, suggesting that these cells did not accurately replicate the FOP condition.

To gain consistency in our manuscript, we decided to use an orthogonal and complementary approach in a completely new model. We performed new experiments of chondrogenic differentiation using murine MSCs from UBC-Cre-ERT2/ACVR1^R206H knock-in mice. These cells, when treated with 4OH-tamoxifen, express the intracellular exons of human ACVR1^R206H in the murine Acvr1 locus. Therefore, we can compare differentiation of wild type and R206H MSCs isolated form the same mice. We initiated the chondrogenic differentiation assay from confluent cells to minimize changes in cell proliferation throughout the process. These new results are shown in the new Figure 5F. Mutant (RH) cells display an enhanced chondrogenic response to activin A compared to wild type cells. The treatment with BYL719 decreased the expression of chondrogenic markers irrespective of the mutational status of ACVR1 in the cells, further supporting our previous results in this manuscript and published article (Valer et al., 2019a PMID: 31373426).

Point 3: The additional investigation of RNA-seq data provided useful information but was insufficient to fully address the purpose of this study. The authors should identify downregulated genes by comparing WT cells treated with Activin A/BYL719 and Activin A alone and then compare these identified genes with those shown in Figure 5E. Additionally, they should compare R206H cells treated with Activin A/BYL719 to WT cells treated with BMP6/BYL719. These comparisons will clarify whether there are FOP-specific BYL719-regulated genes.

We thank the reviewer for considering that RNAseq data provides useful information. As already discussed in our answer above, our results indicate that regardless of the ligand (Activin A or BMP6) and regardless of the ACVR1 mutation (WT, relevant for non-genetic heterotopic ossifications or RH, relevant for FOP), BYL719 can inhibit the expression of the genes relevant to endochondral ossification. In our opinion, this is a very relevant conclusion of this study.

We have deeply considered the strategy proposed by the reviewer, comparing “WT cells treated with Activin A/BYL719 and Activin A alone and then compare these identified genes with those shown in Figure 5E” and/or comparing “R206H cells treated with Activin A/BYL719 to WT cells treated with BMP6/BYL719”. While we have discussed why we do not consider appropriate the first comparison proposed, there are a number of reasons why we are not confident that the second comparison would provide a straightforward conclusion.

Regarding the second suggested comparison already in Main point 1, we consider that it would provide confounding results due to all the arguments detailed in Main point 1. Regarding the first suggested comparison, we also consider that it would provide confounding results. There are several reasons why we do not consider that the genes only found in the RH comparison can be confidently considered genes that are only affected by BYL719 in RH cells.

First, the effect of BYL719 in an osteogenic-prone sample (for example, RH-ActA) is higher than the effect that we can observe in absence of this activation (for example, WT-ActA), as observed in the higher number of significantly downregulated genes in RH ActA BYL vs RH ActA comparison, compared to WT ActA BYL vs WT ActA. Similar results are observed in figure 3C, where the expressions of the genes are significantly inhibited in RH ActA compared to RH ActA BYL. This inhibition is not significantly observed in in WT ActA compared to WT ActA BYL because the osteogenic expression of these genes is already very weak in the absence of ACVR1 R206H. This weak signaling of pSMAD1/5 in the absence of osteogenic signaling (RH without ligand or, especially, WT with Activin A) has already been described (Ramachandran et al. MID: 34003511). Therefore, even though the inhibition is present in both comparisons, as observed in figure 6C, the extent of the observed effect is different. Second, we are comparing a different number of DEGs for each comparison between them. If we compare the 67 downregulated genes from one comparison and 38 downregulated genes from the other comparison, the unequal list size may inflate the number of unique genes in the group with more downregulated genes. To prove these concerns, we performed the comparison that the reviewer suggested and we found, for example, that amongst the 38 differentially downregulated ossification genes in (WT_ActA_BYL vs WT_ActA) and 67 differentially downregulated ossification genes in (RH_ActA_BYL vs RH_ActA), 39 genes were only found in the RH comparison, while 10 were only found in the WT comparison, and 28 were found in both.

These effects are present, for example, when studying the ID genes, well-known downstream mediators of BMP signaling. In this case, ID1 is downregulated in both comparisons, while ID2, ID3, and ID4, are downregulated only in the RH-group, despite the fact that all ID1, ID2, ID3, and ID4 are similarly regulated and increase their expression with similar time curves upon BMP signaling activation (Yang et al., PMID: 23771884). Therefore, we consider that the comparisons proposed will not help us to identify specific BYL719-regulated genes relevant for FOP and/or ACVR1 R206H signaling. Again, we consider that BYL719 effect is not specific of FOP cells. Our results show that regardless of the ligand (Activin A or BMP6) and regardless of the ACVR1 mutation (WT, relevant for non-genetic heterotopic ossifications or RH, relevant for FOP), BYL719 can inhibit the expression of the genes linked to ossification and osteoblast differentiation, which could be important for the treatment of FOP and non-genetic heterotopic ossifications.

Point 4: The data in Figure 7 are not relevant to the aim of this study because the cell lines used in these experiments did not have ACVR1/R206H mutations. The authors mentioned that BMP6 is a ligand for ACVR1 and, therefore, these experiments reflect the situation of inflammatory cells in FOP. This is inappropriate and not rational. As mentioned above, the effect of Activin A on FOP cells is not identical to the effect of BMP6 in wild-type cells. The data in Figure 7 indicated that the effect of BYL719 is unrelated to the presence of BMP6, clearly demonstrating that these experiments are not related to the activation of ACVR1. In the gene expression analyses, almost all genes showed no changes with the addition of BMP6. Only TGF and CCL2 showed upregulation in THP1 cells, and the treatment with BYL719 failed to inhibit the effect of BMP6, suggesting that these experiments merely demonstrate the effect of BYL719 on inflammatory cells irrespective of the presence of the HO signal.

We consider that Figure 7 is relevant to the aim of this study. As shown in Fig. 8, treatment of FOP mice with BYL719 led to a decreased recruitment of immune cells within the FOP lesions, suggesting a direct effect of BYL719 in immune cells. This is very relevant for the FOP pathology, since flare-ups have been linked with inflammatory episodes since the very early characterization of the disease (Mejias-Rivera et al., PMID: 38672135). Given the technical difficulties to transduce THP1, RAW264 and HMC1 cell lines with lentiviral particles carrying ACVR1 R206H, we decided to partially recapitulate ACVR1 R206H activation with recombinant BMP6 and to test the effect of BYL719 in these conditions. In these models, we found that BYL719 inhibited the expression of key genes driving immune cell activation, in a cell-type and ligand independent manner. To clarify this rationale, we have swapped Figures 7 and 8 and adjusted our conclusions accordingly. We have softened our interpretations, emphasizing the absence of the ACVR1 R206H mutant receptor in these experiments.

Author response:

The following is the authors’ response to the original reviews

Reviewer 1 (Public review)

Summary

The results offer compelling evidence that L5-L5 tLTD depends on presynaptic NMDARs, a concept that has previously been somewhat controversial. It documents the novel finding that presynaptic NMDARs facilitate tLTD through their metabotropic signaling mechanism.

We thank Reviewer 1 for their kind words and thoughtful feedback!

Strengths

The experimental design is clever and clean. The approach of comparing the results in cell pairs where NMDA is deleted either presynaptically or postsynaptically is technically insightful and yields decisive data. The MK801 experiments are also compelling.

We are very grateful for this kind feedback!

Weaknesses

No major weaknesses were noted by this reviewer.

We were happy to see that Reviewer 1 had no concerns in the Public Review. We address their Recommendations here below.

Reviewer #1 (Recommendations for the authors):

There is one minor issue that the authors might want to address. In Figure 6C, the average time course of the controls (blue symbols) shows a clear decline in the baseline. The rate of this decline appears to be similar to the initial decline rate observed after inducing tLTD.

Sorry, the x-axis was truncated so the first data points were not visible. We fixed Fig 6C as well as 6G, which suffered from the same problem.

Reviewer 2 (Public review)

Summary

The study characterized the dependence of spike-timing-dependent long-term depression (tLTD) on presynaptic NMDA receptors and the intracellular cascade after NMDAR activation possibly involved in the observed decrease in glutamate probability release at L5-L5 synapses of the visual cortex in mouse brain slices.

We are grateful for Reviewer 2’s thoughtful and detailed feedback!

Strengths

The genetic and electrophysiological experiments are thorough. The experiments are well-reported and mainly support the conclusions. This study confirms and extends current knowledge by elucidating additional plasticity mechanisms at cortical synapses, complementing existing literature.

We were thrilled to see that the reviewer thinks our experiments are “thorough”, “well-reported” and they “mainly support the conclusions”!

Weaknesses

While one of the main conclusions (preNMDARs mediating presynaptic LTD) is resolved in a very convincing genetic approach, the second main conclusion of the manuscript (non-ionotropic preNMDARs) relies on the use of a high concentration of extracellular blockers (MK801, 2 mM; 7-clorokinurenic acid: 100 microM), but no controls for the specific actions of these compounds are shown.

We thank the reviewer for calling our genetic approach “very convincing”!

Regarding the pharmacological controls: for MK-801, we deliberately used a high extracellular concentration in the mM-range to match the intracellular concentrations used both in our own experiments and in prior studies (Berretta and Jones, 1996; Brasier and Feldman, 2008; Buchanan et al., 2012; Corlew et al., 2007; Humeau et al., 2003; Larsen et al., 2011; Rodríguez-Moreno et al., 2011; Rodríguez-Moreno and Paulsen, 2008). Our goal was to isolate the variable of application site (internal vs. external) while keeping concentration constant. If we had used the lower, more conventional µM-range extracellular concentrations (e.g., Huettner and Bean, 1988; Kemp et al., 1988; Tovar and Westbrook, 1999), differences in outcome might have reflected differences in drug efficacy rather than localization — particularly since failure to observe an effect at low concentrations would be hard to interpret.

We now clarify this rationale in the revised manuscript (lines 578-585).

As for 7-chlorokynurenic acid (7-CK), the 100 µM concentration we used is standard for effectively blocking the glycine-binding site of NMDARs (e.g., Nabavi et al., 2013).

We also added two supplementary figures to show the effects of washing in MK-801 and 7-CK. In MK-801, responses are stable at low frequency (clarified in the manuscript lines 155-157 and Supp Fig 1 caption text). However, 7-CK suppresses responses appreciably, which takes time to stabilize. We clarify in the revised manuscript that in 7-CK experiments, we waited for this stabilization before inducing tLTD (lines 167-172 and Supp Fig 2 caption text). This additional suppression is consistent with 7-CK also acting as a potent competitive inhibitor of L-glutamate transport into synaptic vesicles (Bartlett et al., 1998).

In addition, no direct testing for ions passing through preNMDAR has been performed.

Sorry for being unclear, we have previously tested directly for ions passing through preNMDARs. For example, we showed blockade with Mg²⁺ before (Abrahamsson et al., 2017; Wong et al., 2024), and we showed preNMDAR Ca²⁺ supralinearities before (Abrahamsson et al., 2017; Buchanan et al., 2012). To improve the manuscript, we clarified the text accordingly (lines 140-141).

It is not known if the results can be extrapolated to adult brain as the data were obtained from 11-18 days-old mice slices, a period during which synapses are still maturing and the cortex is highly plastic.

Thank you, this is a good point. We address this point in the revised manuscript (lines 428-432). While our study focuses on the early postnatal period (P11–P18), when plasticity mechanisms are prominent and synaptic maturation is ongoing, we agree that extrapolation to the adult brain should be made with caution.

Reviewer #2 (Recommendations for the authors):

Points 1-3 were also found in the Public Review so are not addressed again here.

(4) Results seem to be obtained in the absence of inhibition blocking and the role of inhibition in tLTD is not described. It should be indicated whether present results are obtained with or without the functional inhibitory synapse activation. If GABAergic synapses are not blocked authors need to show what happens when this inhibition is blocked.

We agree that extracellular stimulation can inadvertently recruit inhibitory circuits. However, in our paired whole-cell recordings, synaptic responses are always subthreshold and exclusively reflect the direct connection between the two recorded neurons (Chou et al., 2024; Song et al., 2005). Under these conditions, inhibitory synapses are not activated, and we therefore did not apply GABAergic blockers. We thank the reviewer for raising this, which is now clarified in the Methods (lines 539-541) of the revised manuscript.

(5) In some figures, the number of experiments seems to be low, and this number of experiments might be increased (Figures 1C, 3C, 4B).

We acknowledge that the number of experiments in these figures is modest, but these recordings are technically demanding, and the data are carefully curated. Importantly, the observed effects were statistically significant, indicating that the sample sizes were sufficient. We also note that concerns about statistical power are typically more critical in the case of negative or null results, whereas our findings were positive.

(6) The discussion is detailed but it is not clear that the activation of JNK2 needs to be achieved by a non-ionotropic action of NMDAR as activation after ionotropic NMDAR activation has been described in the literature. This point needs to be clarified and expanded.

Sorry that we were unclear on this point. We clarified this on lines 371-372 of the manuscript.

(7) Adding a cartoon/schematic summarizing the proposed mechanism for tLTD would help the reading of the manuscript.

We appreciate this suggestion and agree that a schematic would be helpful. However, we prefer to hold off on including one at this stage, as aspects of the underlying mechanism — particularly the role of CB1 receptors in presynaptic pyramidal cells (Sjöström et al., 2003) — are currently under active investigation in a separate project. To avoid potentially misleading oversimplifications, we would prefer to revisit a summary schematic once these uncertainties have been resolved.

Minor:

(1) Concentration of compounds is recommended to be included in the figures or in the text. This would make it easy to follow the results.

We appreciate the suggestion. However, we avoid repeating concentrations to emphasize that conditions are consistent unless otherwise stated. All compound concentrations are clearly listed in the Methods and remain unchanged across experiments. We believe this streamlined approach avoids redundancy while keeping the results clear.

(2) In some figures, failures in synaptic transmission can be observed (and changes after tLTD). The authors may analyse changes in a number of failures in synaptic transmission after tLTD as an additional indication of a presynaptic expression of this form of tLTD. PPR may also be included in all figures.

While failures in synaptic transmission are occasionally visible, we chose to focus on CV analysis, which is mathematically equivalent to failure rate analysis, as both rely on the same underlying variability in synaptic responses (Brock et al., 2020). Provided failures are reliably extracted (which requires sufficient signal-to-noise), CV and failure rate analyses should yield consistent conclusions.

In contrast, PPR analysis is not mathematically equivalent to CV analysis and may offer complementary insights into presynaptic mechanisms. However, the presence of preNMDARs complicates the use of paired-pulse stimulation during baseline: preNMDARs enhance release during high-frequency activity (Abrahamsson et al., 2017; Sjöström et al., 2003; Wong et al., 2024), so repeated stimulation can suppress synaptic responses when preNMDARs are blocked, potentially confounding interpretation. For this reason, we limited PPR analysis to Figures 5 and 6, where conditions were appropriate.

Admittedly, our manuscript was previously not clear on when we did paired-pulse stimulation and when we did not. We have clarified this in the revised manuscript (lines 548- 551 and lines 569-574).

(3) Discussion: Line 363-64, hippocampal (SC-CA1 synapses) results exist where postsynaptic MK801 blocks presynaptic tLTD, this may be added here and in the references.

While we acknowledge that postsynaptic MK-801 has been shown to block presynaptic tLTD at hippocampal SC–CA1 synapses, we note that the hippocampus is part of the archicortex, whereas our study focuses on neocortical circuits, as highlighted in the manuscript title. Given the substantial anatomical and functional differences between these regions, we prefer to keep our discussion focused on the neocortex to maintain conceptual coherence.

(4) Discussion: While authors indicate "non-ionotropic" they do not discuss whether this action can be named properly "metabotropic" and whether G-proteins may be in fact needed for this action. The authors may briefly discuss this point.

We previously referred to non-ionotropic NMDAR signaling as “metabotropic,” but reconsidered after discussions with colleagues, including Juan Lerma, who pointed out that the term typically implies G-protein coupling, which has not been definitively shown in this context. While the term “metabotropic” is used inconsistently in the literature (Heuss and Gerber, 2000; Heuss et al., 1999) — sometimes broadly to indicate non-ion flow signaling — we prefer to avoid potential confusion and therefore use “non-ionotropic” unless and until G-protein involvement is clearly demonstrated. We clarified this on lines 423-427 of the Discussion.

(5) Page 19, line 451 NMDR needs to be corrected to NMDAR.

Thanks! This was corrected.

Reviewer 3 (Public review)

Summary

In this manuscript, "Neocortical Layer-5 tLTD Relies on Non-Ionotropic Presynaptic NMDA Receptor Signaling", Thomazeau et al. seek to determine the role of presynaptic NMDA receptors and the mechanism by which they mediate expression of frequency-independent timing-dependent long-term depression (tLTD) between layer-5 (L5) pyramidal cells (PCs) in the developing mouse visual cortex. By utilizing sophisticated methods, including sparse Cre-dependent deletion of GluN1 subunit via neonatal iCre-encoding viral injection, in vitro quadruple patch clamp recordings, and pharmacological interventions, the authors elegantly show that L5 PC->PC tLTD is (1) dependent on presynaptic NMDA receptors, (2) mediated by non-ionotropic NMDA receptor signaling, and (3) is reliant on JNK2/Syntaxin-1a (STX1a) interaction (but not RIM1αβ) in the presynaptic neuron. The study elegantly and pointedly addresses a long-standing conundrum regarding the lack of frequency dependence of tLTD.

We thank the reviewer for calling our methods “sophisticated” and our study “elegant”! We appreciate the kind feedback!

Strengths

The authors did a commendable job presenting a very polished piece of work with high-quality data that this Reviewer feels enthusiastic about. The manuscript has several notable strengths. Firstly, the methodological approach used in the study is highly sophisticated and technically challenging and successfully produced high-quality data that were easily accessible to a broader audience. Secondly, the pharmacological interventions used in the study targeted specific players and their mechanistic roles, unveiling the mechanism in question step-by-step. Lastly, the manuscript is written in a well-organized manner that is easy to follow. Overall, the study provides a series of compelling evidence that leads to a clear illustration of mechanistic understanding.

We are elated that the reviewer described our study with words such as “polished”, “high-quality”, “sophisticated”, and “compelling”!

Minor comments

(1) For the broad readership, a brief description of JNK2-mediated signaling cascade underlying tLTD, including its intersection with CB1 receptor signaling may be desired.

Thank you, this is a great suggestion for improving clarity. We briefly address this point in the revised manuscript (lines 360-363).

(2) The authors used juvenile mice, P11 to P18 of age. It is a typical age range used for plasticity experiments, but it is also true that this age range spans before and after eye-opening in mice (~P13) and is a few days before the onset of the classical critical period for ocular dominance plasticity in the visual cortex. Given the mechanistic novelty reported in the study, can authors comment on whether this signaling pathway may be age-dependent?

Thanks, Reviewer 2 also raised this point. In the revised manuscript, we discuss this point (lines 428-432).

Reviewer #3 (Recommendations for the authors):

(1) Minor typos: page 4 line 101: sensitivity -> sensitive.

We fixed this typo.

(2) Page 15 line 333: sensitivity -> sensitive.

We fixed this typo.

(3) Minor aesthetic suggestion: On the scale bars for all examples, LTP and LTD data are easily confused with the letter L. I'd suggest flipping them left to right.

We thank the reviewer for the suggestion. We flipped the scale bars in all figures.

References

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Bartlett, R.D., Esslinger, C.S., Thompson, C.M., and Bridges, R.J. 1998. Substituted quinolines as inhibitors of L-glutamate transport into synaptic vesicles. Neuropharmacology 37: 839-846

Berretta, N., and Jones, R.S. 1996. Tonic facilitation of glutamate release by presynaptic N-methyl-D-aspartate autoreceptors in the entorhinal cortex. Neuroscience 75: 339-344.

Brasier, D.J., and Feldman, D.E. 2008. Synapse-specific expression of functional presynaptic NMDA receptors in rat somatosensory cortex. J Neurosci 28: 2199-2211

Brock, J.A., Thomazeau, A., Watanabe, A., Li, S.S.Y., and Sjöström, P.J. 2020. A Practical Guide to Using CV Analysis for Determining the Locus of Synaptic Plasticity. Frontiers in Synaptic Neuroscience 12:11 10.3389/fnsyn.2020.00011

Buchanan, K.A., Blackman, A.V., Moreau, A.W., Elgar, D., Costa, R.P., Lalanne, T., Tudor Jones, A.A., Oyrer, J., and Sjöström, P.J. 2012. Target-Specific Expression of Presynaptic NMDA Receptors in Neocortical Microcircuits. Neuron 75: 451-466

Chou, C.Y.C., Wong, H.H.W., Guo, C., Boukoulou, K.E., Huang, C., Jannat, J., Klimenko, T., Li, V.Y., Liang, T.A., Wu, V.C., and Sjöström, P.J. 2024. Principles of visual cortex excitatory microcircuit organization. The Innovation 6: 1-11

Corlew, R., Wang, Y., Ghermazien, H., Erisir, A., and Philpot, B.D. 2007. Developmental switch in the contribution of presynaptic and postsynaptic NMDA receptors to long-term depression. J Neurosci 27: 9835-9845

Heuss, C., and Gerber, U. 2000. G-protein-independent signaling by G-protein-coupled receptors. Trends in Neurosciences 23: 469-475

Heuss, C., Scanziani, M., Gähwiler, B.H., and Gerber, U. 1999. G-protein-independent signaling mediated by metabotropic glutamate receptors. Nature Neuroscience 2: 1070-1077

Huettner, J.E., and Bean, B.P. 1988. Block of N-methyl-D-aspartate-activated current by the anticonvulsant MK-801: selective binding to open channels. PNAS 85: 1307-1311.

Humeau, Y., Shaban, H., Bissière, S., and Lüthi, A. 2003. Presynaptic induction of heterosynaptic associative plasticity in the mammalian brain. Nature 426: 841-845

Kemp, J.A., Foster, A.C., Leeson, P.D., Priestley, T., Tridgett, R., Iversen, L.L., and Woodruff, G.N. 1988. 7-Chlorokynurenic acid is a selective antagonist at the glycine modulatory site of the N-methyl-D-aspartate receptor complex. PNAS 85: 6547-6550

Larsen, R.S., Corlew, R.J., Henson, M.A., Roberts, A.C., Mishina, M., Watanabe, M., Lipton, S.A., Nakanishi, N., Perez-Otano, I., Weinberg, R.J., and Philpot, B.D. 2011. NR3A-containing NMDARs promote neurotransmitter release and spike timing-dependent plasticity. Nat Neurosci 14: 338-344

Nabavi, S., Kessels, H.W., Alfonso, S., Aow, J., Fox, R., and Malinow, R. 2013. Metabotropic NMDA receptor function is required for NMDA receptor-dependent long-term depression. PNAS 110: 4027-4032

Rodríguez-Moreno, A., Kohl, M.M., Reeve, J.E., Eaton, T.R., Collins, H.A., Anderson, H.L., and Paulsen, O. 2011. Presynaptic induction and expression of timing-dependent long-term depression demonstrated by compartment-specific photorelease of a use-dependent NMDA receptor antagonist. J Neurosci 31: 8564-8569

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Author response:

The following is the authors’ response to the original reviews

Reviewer 1 (Public review):

(1) “It is likely that metabolism changes ex vivo vs in vivo, and therefore stable isotope tracing experiments in the explants may not reflect in vivo metabolism.”

We agree with the reviewer that metabolic changes may differ ex vivo versus in vivo. We now state: “Lastly, an important caveat to our study is that metabolism changes ex vivo versus in vivo, and thus, in the future, in vivo studies can be performed to assess metabolic changes.” (lines 591-593).

(2) “The retina at P0 is composed of both progenitors and differentiated cells. It is not clear if the results of the RNA-seq and metabolic analysis reflect changes in the metabolism of progenitors, or of mature cells, or changes in cell type composition rather than direct metabolic changes in a specific cell type.”

We have clarified that the metabolic changes may be in RPCs or in other retinal cell types on lines 149-152: “Since these measurements were performed in bulk, and the ratio of RPCs to differentiated cells declines as development proceeds, it is not clear whether glycolytic activity is temporally regulated within RPCs or in other retinal cell types.”

However, since we mined a single cell (sc) RNA-seq dataset, we are able to attribute gene expression specifically within RPCs (Figure 1).

(3) “The biochemical links between elevated glycolysis and pH and beta-catenin stability are unclear. White et al found that higher pH decreased beta-catenin stability (JCB 217: 3965) in contrast to the results here. Oginuma et al found that inhibition of glycolysis or beta-catenin acetylation does not affect beta-catenin stability (Nature 584:98), again in contrast to these results. Another paper showed that acidification inhibits Wnt signaling by promoting the expression of a transcriptional repressor and not via beta-catenin stability (Cell Discovery 4:37). There are also additional papers showing increased pH can promote cell proliferation via other mechanisms (e.g. Nat Metab 2:1212). It is possible that there is organ-specificity in these signaling pathways however some clarification of these divergent results is warranted.”

We have added the information and references brought up by the reviewer in our discussion (lines 529-549 and 570-574). We have also suggested future experiments to further analyse our system in line with the studies now referenced (lines 580-589).

(4) The gene expression analysis is not completely convincing. E.g. the expression of additional glycolytic genes should be shown in Figure 1. It is not clear why Hk1 and Pgk1 are specifically shown, and conclusions about changes in glycolysis are difficult to draw from the expression of these two genes. The increase in glycolytic gene expression in the Pten-deficient retina is generally small.

We have expanded the list of glycolytic genes analysed, in modified Figure 1B, and expanded the description of these results on lines 156-166.

(5) Is it possible that glycolytic inhibition with 2DG slows down the development and production of most newly differentiated cells rather than specifically affecting photoreceptor differentiation?

We added a comment to this effect to the discussion: “It is possible that glycolytic inhibition with 2DG slows down the development and production of most newly differentiated cells rather than specifically affecting photoreceptor differentiation, which we could assess in the future.“ (lines 600-603).

(6) “Likewise the result that an increase in pH from 7.4 to 8.0 is sufficient to increase proliferation implies that pH regulation may have instructive roles in setting the tempo of retinal development and embryonic cell proliferation. Similarly, the results show that acetate supplementation increases proliferation (I think this result should be moved to the main figures).”

We have added the acetate data to main Figure 7E.

We added a supplemental data table that was inadvertently not included in our last submission. Figure 2– Data supplement 1.

Reviewer #2 (Recommendations for the authors):

Major points

(1) Assuming that increased glycolysis gets RPCs to exit from the proliferative stage earlier, the total number of retinal cells, notably that of the rod photoreceptors, should be reduced since the pool of proliferating cells is depleted earlier. Is that really the case for a mature retina? To address this question, the authors should perform quantifications of photoreceptors at a stage where most developmental cell death has concluded (i.e. at P14 or later; Young, J. Comp. Neurol. 229:362-373, 1984) and check whether or not there are more or less photoreceptors present.

We have previously quantified numbers of each cell type in Pten RPC-cKO retinas, and as suggested by the reviewer, there are fewer rod photoreceptors at P7 (Tachibana et al. 2016. J Neurosci 36 (36) 9454-9471) and P21 (Hanna et al. 2025. IOVS. Mar 3;66(3):45). We have edited the following sentence: “Using cellular birthdating, we previously showed that Pten-cKO RPCs are hyperproliferative and differentiate on an accelerated schedule between E12.5 and E18.5, yet fewer rod photoreceptors are ultimately present in P7 (Tachibana et al., 2016) and P21 (Hanna et al., 2025) retinas, suggestive of a developmental defect. (lines 184-187).

(2) Figure 1B, 1H: On what data are these two figures based? The plots suggest that a high-density time series of gene expression and rod photoreceptor birth was performed, yet it is not clear where and how this was done. The authors should provide the data, plot individual data points, and, if applicable perform a statistical analysis to support their idea that glycolytic gene expression (as a surrogate for glycolysis) overlaps in time with rod photoreceptor birth (Figure 1B) and that in Pten KO the glycolytic gene expression is shifted forward in time (Figure 1H). If the data required to construct these plots (min. 5 data points, min 3 repeats each) does not exist or cannot be generated (e.g. from reanalysis of previously published datasets), then these graphs should be removed.

We have removed the previous Figure 1B and Figure 1H.

(3) Figure 2E: Which PKM isozyme was analyzed here? Does the genetic analysis allow us to distinguish between PKM1 and PKM2? Since PKM governs the key rate-limiting step of glycolysis but was not significantly upregulated, does this not contradict the authors' main hypothesis? If PKM at some point was inhibited (see also below comment to Figure 5) one would expect an accumulation of glycolytic intermediates, including phosphoenolpyruvate. Was such an effect observed?

The data in Figure 2E is bulk RNA-seq data. Since there is only a single Pkm gene that is alternatively spliced, the RNA-sequencing data cannot distinguish between the four PK isozymes that arise from alternative splicing. Specifically, we used Illumina NextSeq 500 for sequencing of 75bp Single-End reads that will sequence transcripts for alternatively spliced Pkm1 and Pkm2 mRNAs, which carry a common 3’end. We added a statement to this effect: “However, since we employed 75 bp single-end sequencing, we could not distinguish between alternatively spliced Pkm1 and Pkm2 mRNAs.“ (lines 215-216).

We have not performed metabolic analyses of glycolytic intermediates, but we have proposed such a strategy as an important avenue of investigation for future studies in the Discussion: “Lastly, an important caveat to our study is that metabolism changes ex vivo versus in vivo, and thus, in the future, in vivo studies can be performed to assess metabolic changes.” (lines 591-593).

(4) Figure 3 and materials & methods: For the retinal explant cultures, was the RPE included in the cultured explants? If so, how can the authors distinguish drug effects on neuroretina and RPE? If the RPE was not included, then the authors should discuss how the missing RPE - neuroretina interaction could have influenced their results.

We remove the RPE from the retinal explants, as indicated in the Methods section. The RPE is a metabolic hub that allows transport of nutrients for the retina, so in the absence of the RPE, there is not an immediate source of energy, such as glucose, to the retina. However, the media (DMEM) contains 25 mM glucose to replace the RPE as an energy source, and we now show that RPCs express GLUT1, which allows uptake of glucose (see new Figure 3A).

We added the following sentence “P0 explants were mounted on Nucleopore membranes and cultured on top of retinal explant media, providing a source of nutrients, growth factors and glucose. “(lines 241-243).

(5) Figure 3: It seems rather odd that, if glycolysis was so important for retinal proliferation, differentiation, and metabolism in general, the inhibition of glycolysis with 2DG should not produce a strong degeneration. However, since 2DG competes with glucose, and must be used at nearly equimolar concentration to block glycolysis in a meaningful way, it is possible that the 2DG concentration used simply was not high enough to substantially inhibit glycolysis. Since the inhibitory effect of 2DG depends on the glucose concentration, the authors should measure and provide the concentration of glucose in the explant culture medium. This value should be given either in results or materials and methods.

We recently published a manuscript showing that 2DG treatments at the same concentrations employed in this study are effective at reducing lactate production in the developing retina in vivo, which is the expected effect of reduced glycolysis (Hanna et al. 2025. IOVS). However, in this study, we did not observe an impact on cell survival.

We do not agree that it is necessary to measure glucose in the media since the anti-proliferative effect of 2DG is well known, and we are working in the effective range established by multiple groups. We have clarified that we are in the effective range by adding the following sentences: “2DG is typically used in the range of 5-10 mM in cell culture studies and in general, has anti-proliferative effects. To test whether 2DG treatment was in the effective range, explants were exposed to BrdU, which is incorporated into S-phase cells, for 30 minutes prior to harvesting. 2DG treatment resulted in a dose-dependent inhibition of RPC proliferation as evidenced by a reduction in BrdU⁺ cells (Figure 3D), indicating that our treatment was in the effective range.” (lines 246-251).

(6) Figure 3F: The authors use immunostaining for cleaved, activated caspase-3 to assess the amount of apoptotic cell death. However, there are many different possible mechanisms for neuronal cells to die, the majority of which are caspase-independent. To assess the amount of cell death occurring, the authors should perform a TUNEL assay (which labels apoptotic and non-apoptotic forms of cell death; Grasl-Kraupp et al., Hepatology 21:1465-8, 1995), quantify the numbers of TUNEL-positive cells in the retina, and compare this to the numbers of cells positive for activated caspase-3.

We agree with the reviewer that there are more ways for a cell to die than just apoptosis, and TUNEL would pick up dying cells that may undergo apoptosis or necrosis, for example, our data with cleaved caspase-3, an executioner protease for apoptosis, provides us with clear evidence of cell death in our different conditions. Since this manuscript is not focused on cell death pathways, we have not performed the additional TUNEL assay.

(7) Figure 4F and 4I: At post-natal day P7 the rod outer segments (OSs) only just start to grow out and the characteristic, rhodopsin-filled disk stacks are not yet formed. To test whether the PFKB3 gain-of function or the Pten KO has a marked effect on OS formation and length, the authors should perform the same tests on older, more mature retina at a time when rod OS show their characteristic disk structures (e.g. somewhere between P14 to P30). The same applies to the 2DG inhibition on the Pten KO retina.

The precocious differentiation of rod outer segments observed in P7 Pten-cKO retinas does not persist in adulthood, and instead reflects a developmental acceleration. Indeed, we found that in Pten cKO retinas at 3-, 6- and 12-months of age, rod and cone photoreceptors degenerate, and cone outer segments are shorter (Hanna et al., 2025; Tachibana et al., 2016). These data demonstrate that Pten is required to support rod and cone survival.

(8) Figure 5: Lowering media pH is a rather coarse and untargeted intervention that will have multiple metabolic consequences independent of PKM2. It is thus hardly possible to attribute the effects of pH manipulation to any specific enzyme. To assess this and possibly confirm the results obtained with low pH, the authors should perform a targeted inhibition experiment, for instance using Shikonin (Chen et al., Oncogene 30:4297-306, 2011), to selectively inhibit PKM2. If the retinal explant cultures contained the RPE, an additional question would be how the changes in RPE would alter lactate flux and metabolization between RPE and neuroretina (see also question 4 above).

We have reframed the rationale for the pH manipulation experiments, highlighting the importance of pH in cell fate specification, and indicating that the aggregation of PKM2 is only one possible effect of lower pH.

We wrote: “Given that altered glycolysis influences intracellular pH, which in turn controls cell fate decisions, we set out to assess the impact of manipulating pH on cell fate selection in the retina. One of the expected impacts of lowering pH was the aggregation of PKM2, a rate-limiting enzyme for glycolysis, which aggregates in reversible, inactive amyloids (Cereghetti et al., 2024).” (lines 362-366). 

We have also added a discussion point “Whether pH manipulations also impact the stability of other retinal proteins, such as PKM2, can be further investigated in the future using specific PKM2 inhibitors, such as Shikonin (Chen et al., 2011). (lines 545-547).

(9) Figure 5G: As for Figure 3F, the authors should perform TUNEL assays to assess the number of cells dying independent of caspase-3.

Please see response to point 6.

(10) Figure 7E: In the figure legend "K" should read "E". From the figure and the legend, it is not clear to which cell type this diagram should refer. This must be specified. Importantly, the insulin-dependent glucose-transporter 4 (GLUT4) highlighted in Figure 7E, while expressed on inner retinal vasculature endothelial cells, is not expressed in retinal neurons. What GLUTs exactly are expressed in what retinal neurons may still be to some extent contentious (cf. Chen et al., elife, https://doi.org/10.7554/eLife.91141.3 ; and reviewer comments therein), yet RPE cells clearly express GLUT1, photoreceptors likely express GLUT3, Müller glia cells may express GLUT1, while horizontal cells likely express GLUT2 (Yang et al., J Neurochem. 160:283-296, 2022).’

We have removed this summary schematic for simplicity.

(11) Materials and methods: The retinal explant culture system must be described in more detail. Important questions concern the use of medium and serum for which the providers, order numbers, and batch/lot numbers (whichever is applicable) must be given. The glucose concentration in the medium (including the serum content) should be measured. A key concern is whether the explants were cultivated submerged into the medium - this would prevent sufficient oxygenation and drive metabolism towards glycolysis (i.e. the Pasteur effect) - or whether they were cultivated on top of the liquid medium, at the interface between air and liquid (i.e. a situation that would favor OXPHOS).

We have added further detail to the methods section for the explant assay (lines 686-689). We cultured the retinal explants on membranes on top of the media, which is the standard methodology in the field and in our laboratory (Cantrup et al., 2012; Tachibana et al., 2016; Touahri et al., 2024). Typically, RPCs undergo aerobic glycolysis, meaning that even in the presence of oxygen, they still prefer glycolysis rather than OXPHOS. We demonstrated that 2DG blocks RPC proliferation when treated with 2DG, indicating that RPCs are indeed favoring glycolysis in our assay system.

(12) A point the authors may want to discuss additionally is the potential relevance of their data for the pathogenesis of human diseases, especially early developmental defects such as they occur in oxygen-induced retinopathy of prematurity.

We would like to thank the reviewer for their valuable comment. Given that retinopathy of prematurity (ROP) is primarily vascular in nature, and we have not investigated vascular defects in this study, we have elected not to add a discussion of ROP to our manuscript.

Minor points

(1) Please add a label indicating the ages of the retina to images showing the entire retina (i.e. "P7"; e.g. in Figures 1F, 3, 4D, 5, etc.).

Figure 1:

1D: E18.5 indicated at the bottom of the two panels

1F – P0 is indicated at the bottom of the two panels.

Figure 3C-H: P0 explant stage and days of culture indicated

Figure 4D: E12.5 BrdU and P7 harvest date indicated

Figure 5C-H: P0 explant stage and days of culture indicated

Figure 7A-E: P0 explant stage and days of culture indicated

(2) The term Ctnnb1 should be introduced also in the abstract.

We now state that Ctnnb1 encodes for b-catenin in the abstract.

(3) Line 249: "...remaining..." should probably read "...remained...".

Changed (now line 260).

(4) Line 381: The sentence "...correlating with the propensity of some RPCs to continue to proliferate while others to differentiate.", should probably be rewritten to something like "...correlating with the propensity of some RPCs to continue to proliferate while others differentiate.".

We have corrected this sentence.

(5) The structure of the discussion might benefit from the introduction of subheadings.

We have introduced subheadings.

Reviewer #3 (Recommendations for the authors):

(1) Figure 1H shows the kinetics of rod photoreceptor production as accelerated, but does not represent the fact that fewer rods are ultimately produced, which appears to be the case from the data. If so, the Pten cKO curve should probably be lower than WT to reflect that difference.

We have removed this graph (as per Reviewer #2, point 2).

(2) KEGG analysis also showed that the HIF-1 signaling pathway is altered in the Pten cKO retina. What is the significance of that, and is it related to metabolic dysregulation? It has been shown that lactate can promote vessel growth, which initiates at birth in the mouse retina.

We have added some information on HIF-1 to the Discussion. “The increased glycolytic gene expression in Pten-cKO retinas is likely tied to the increased expression of hypoxia-induced-factor-1-alpha (Hif1a), a known target of mTOR signaling that transcriptionally activates Slc1a3 (GLUT1) and glycolytic genes (Hanna et al., 2022). Indeed, mTOR signaling is hyperactive in Pten-cKO retinas (Cantrup et al., 2012; Tachibana et al., 2016; Tachibana et al., 2018; Touahri et al., 2024), and likewise, in Tsc1-cKO retinas, which also increase glycolysis via HIF-1A (Lim et al., 2021).” (lines 489-494).

Cantrup, R., Dixit, R., Palmesino, E., Bonfield, S., Shaker, T., Tachibana, N., Zinyk, D., Dalesman, S., Yamakawa, K., Stell, W. K., Wong, R. O., Reese, B. E., Kania, A., Sauve, Y., & Schuurmans, C. (2012). Cell-type specific roles for PTEN in establishing a functional retinal architecture. PLoS One, 7(3), e32795. https://doi.org/10.1371/journal.pone.0032795

Cereghetti, G., Kissling, V. M., Koch, L. M., Arm, A., Schmidt, C. C., Thüringer, Y., Zamboni, N., Afanasyev, P., Linsenmeier, M., Eichmann, C., Kroschwald, S., Zhou, J., Cao, Y., Pfizenmaier, D. M., Wiegand, T., Cadalbert, R., Gupta, G., Boehringer, D., Knowles, T. P. J., Mezzenga, R., Arosio, P., Riek, R., & Peter, M. (2024). An evolutionarily conserved mechanism controls reversible amyloids of pyruvate kinase via pH-sensing regions. Dev Cell. https://doi.org/10.1016/j.devcel.2024.04.018

Chen, J., Xie, J., Jiang, Z., Wang, B., Wang, Y., & Hu, X. (2011). Shikonin and its analogs inhibit cancer cell glycolysis by targeting tumor pyruvate kinase-M2. Oncogene, 30(42), 4297-4306. https://doi.org/10.1038/onc.2011.137

Hanna, J., Touahri, Y., Pak, A., David, L. A., van Oosten, E., Dixit, R., Vecchio, L. M., Mehta, D. N., Minamisono, R., Aubert, I., & Schuurmans, C. (2025). Pten Loss Triggers Progressive Photoreceptor Degeneration in an mTORC1-Independent Manner. Invest Ophthalmol Vis Sci, 66(3), 45. https://doi.org/10.1167/iovs.66.3.45

Tachibana, N., Cantrup, R., Dixit, R., Touahri, Y., Kaushik, G., Zinyk, D., Daftarian, N., Biernaskie, J., McFarlane, S., & Schuurmans, C. (2016). Pten Regulates Retinal Amacrine Cell Number by Modulating Akt, Tgfbeta, and Erk Signaling. J Neurosci, 36(36), 9454-9471. https://doi.org/10.1523/JNEUROSCI.0936-16.2016

Touahri, Y., Hanna, J., Tachibana, N., Okawa, S., Liu, H., David, L. A., Olender, T., Vasan, L., Pak, A., Mehta, D. N., Chinchalongporn, V., Balakrishnan, A., Cantrup, R., Dixit, R., Mattar, P., Saleh, F., Ilnytskyy, Y., Murshed, M., Mains, P. E., Kovalchuk, I., Lefebvre, J. L., Leong, H. S., Cayouette, M., Wang, C., Sol, A. D., Brand, M., Reese, B. E., & Schuurmans, C. (2024). Pten regulates endocytic trafficking of cell adhesion and Wnt signaling molecules to pattern the retina. Cell Rep, 43(4), 114005. https://doi.org/10.1016/j.celrep.2024.114005

Author response:

Public Reviews:

Reviewer #1 (Public review):

Summary:

The paper describes the cryoEM structure of RAD51 filament on the recombination intermediate. In the RAD51 filament, the insertion of a DNA-binding loop called the L2 loop stabilizes the separation of the complementary strand for the base-pairing with an incoming ssDNA and the non-complementary strand, which is captured by the second DNA-binding channel called the site II. The molecular structure of the RAD51 filament with a recombination intermediate provides a new insight into the mechanism of homology search and strand exchange between ssDNA and dsDNA.

Strengths:

This is the first human RAD51 filament structure with a recombination intermediate called the D-loop. The work has been done with great care, and the results shown in the paper are compelling based on cryo-EM and biochemical analyses. The paper is really nice and important for researchers in the field of homologous recombination, which gives a new view on the molecular mechanism of RAD51-mediated homology search and strand exchange.

Weaknesses:

The authors need more careful text writing. Without page and line numbers, it is hard to give comments.

We would like to thank the reviewer for their kind words of appreciation of our work.

Reviewer #2 (Public review):

Summary:

Homologous recombination (HR) is a critical pathway for repairing double-strand DNA breaks and ensuring genomic stability. At the core of HR is the RAD51-mediated strand-exchange process, in which the RAD51-ssDNA filament binds to homologous double-stranded DNA (dsDNA) to form a characteristic D-loop structure. While decades of biochemical, genetic, and single-molecule studies have elucidated many aspects of this mechanism, the atomic-level details of the strand-exchange process remained unresolved due to a lack of atomic-resolution structure of RAD51 D-loop complex.

In this study, the authors achieved this by reconstituting a RAD51 mini-filament, allowing them to solve the RAD51 D-loop complex at 2.64 Å resolution using a single particle approach. The atomic resolution structure reveals how specific residues of RAD51 facilitate the strand exchange reaction. Ultimately, this work provides unprecedented structural insight into the eukaryotic HR process and deepens the understanding of RAD51 function at the atomic level, advancing the broader knowledge of DNA repair mechanisms.

Strengths:

The authors overcame the challenge of RAD51's helical symmetry by designing a minifilament system suitable for single-particle cryo-EM, enabling them to resolve the RAD51 D-loop structure at 2.64 Å without imposed symmetry. This high resolution revealed precise roles of key residues, including F279 in Loop 2, which facilitates strand separation, and basic residues on site II that capture the displaced strand. Their findings were supported by mutagenesis, strand exchange assays, and single-molecule analysis, providing strong validation of the structural insights.

Weaknesses:

Despite the detailed structural data, some structure-based mutagenesis data interpretation lacks clarity. Additionally, the proposed 3′-to-5′ polarity of strand exchange relies on assumptions from static structural features, such as stronger binding of the 5′-arm-which are not directly supported by other experiments. This makes the directional model compelling but contradicts several well-established biochemical studies that support a 5'-to-3' polarity relative to the complementary strand (e.g., Cell 1995, PMID: 7634335; JBC 1996, PMID: 8910403; Nature 2008, PMID: 18256600).

Overall:

The 2.6 Å resolution cryoEM structure of the RAD51 D-loop complex provides remarkably detailed insights into the residues involved in D-loop formation. The high-quality cryoEM density enables precise placement of each nucleotide, which is essential for interpreting the molecular interactions between RAD51 and DNA. Particularly, the structural analysis highlights specific roles for key domains, such as the N-terminal domain (NTD), in engaging the donor DNA duplex.

This structural interpretation is further substantiated by single-molecule fluorescence experiments using the KK39,40AA NTD mutant. The data clearly show a significant reduction in D-loop formation by the mutant compared to wild-type, supporting the proposed functional role of the NTD observed in the cryoEM model.

However, the strand exchange activity interpretation presented in Figure 5B could benefit from a more rigorous experimental design. The current assay measures an increase in fluorescence intensity, which depends heavily on the formation of RAD51-ssDNA filaments. As shown in Figure S6A, several mutants exhibit reduced ability to form such filaments, which could confound the interpretation of strand exchange efficiency. To address this, the assay should either: (1) normalize for equivalent levels of RAD51-ssDNA filaments across samples, or (2) compare the initial rates of fluorescence increase (i.e., the slope of the reaction curve), rather than endpoint fluorescence, to better isolate the strand exchange activity itself.

Based on the structural features of the D-loop, the authors propose that strand pairing and exchange initiate at the 3'-end of the complementary strand in the donor DNA and proceed with a 3'-to-5' polarity. This conclusion, drawn from static structural observations, contrasts with several well-established biochemical studies that support a 5'-to-3' polarity relative to the complementary strand (e.g., Cell 1995, PMID: 7634335; JBC 1996, PMID: 8910403; Nature 2008, PMID: 18256600). While the structural model is compelling and methodologically robust, this discrepancy underscores the need for further experiments.

We would like to thank the reviewer for highlighting the importance of our findings to our understanding of the mechanism of homologous recombination.

We agree with the reviewer that the reduced filament-forming ability of some of the RAD51 mutants complicates a straightforward interpretation of their strand-exchange assay. Interestingly, the RAD51 mutants that appear most impaired are the esDNA-capture mutants that do not contact the ssDNA in the structure of the pre-synaptic filament. However, the RAD51 NTD mutants, that display the most severe defect in strand-exchange, have a near-WT filament forming ability.

The reviewer correctly points out that the polarity of strand exchange by RecA and RAD51 is an extensively researched topic that has been characterised in several authoritative studies. In our paper, we simply describe the mechanistic insights obtained from the structural D-loop models of RAD51 (our work) and RecA (Yang et al, PMID: 33057191).The structures illustrate a very similar mechanism of D-loop formation that proceeds with opposite polarity of strand exchange for RAD51 and RecA. Comparison of the D-loop structures for RecA and RAD51 provides an attractive explanation for the opposite polarity, as caused by the different positions of their dsDNA-binding domains in the filament structure. We agree with the reviewer that further investigation will be needed for an adequate rationalisation of the available evidence. We will mention the relevant literature in the revised version of the manuscript.

Reviewer #3 (Public review):

Summary:

Built on their previous pioneer expertise in studying RAD51 biology, in this paper, the authors aim to capture and investigate the structural mechanism of human RAD51 filament bound with a displacement loop (D-loop), which occurs during the dynamic synaptic state of the homologous recombination (HR) strand-exchange step. As the structures of both pre- and post-synaptic RAD51 filaments were previously determined, a complex structure of RAD51 filaments during strand exchange is one of the key missing pieces of information for a complete understanding of how RAD51 functions in the HR pathway. This paper aims to determine the high-resolution cryo-EM structure of RAD51 filament bound with the D-loop. Combined with mutagenesis analysis and biophysical assays, the authors aim to investigate the D-loop DNA structure, RAD51-mediated strand separation and polarity, and a working model of RAD51 during HR strand invasion in comparison with RecA.

Strengths:

(1) The structural work and associated biophysical assays in this paper are solid, elegantly designed, and interpreted.  These results provide novel insights into RAD51's function in HR.

(2) The DNA substrate used was well designed, taking into consideration the nucleotide number requirement of RAD51 for stable capture of donor DNA. This DNA substrate choice lays the foundation for successfully determining the structure of the RAD51 filament on D-loop DNA using single-particle cryo-EM.

(3) The authors utilised their previous expertise in capping DNA ends using monomeric streptavidin and combined their careful data collection and processing to determine the cryo-EM structure of full-length human RAD51 bound at the D-loop in high resolution. This interesting structure forms the core part of this work and allows detailed mapping of DNA-DNA and DNA-protein interaction among RAD51, invading strands, and donor DNA arms (Figures 1, 2, 3, 4). The geometric analysis of D-loop DNA bound with RAD51 and EM density for homologous DNA pairing is also impressive (Figure S5). The previously disordered RAD51's L2-loop is now ordered and traceable in the density map and functions as a physical spacer when bound with D-loop DNA. Interestingly, the authors identified that the side chain position of F279 in the L2_loop of RAD51_H differs from other F279 residues in L2-loops of E, F, and G protomers. This asymmetric binding of L2 loops and RAD51_NTD binding with donor DNA arms forms the basis of the proposed working model about the polarity of csDNA during RAD51-mediated strand exchange.

(4) This work also includes mutagenesis analysis and biophysical experiments, especially EMSA, single-molecule fluorescence imaging using an optical tweezer, and DNA strand exchange assay, which are all suitable methods to study the key residues of RAD51 for strand exchange and D-loop formation (Figure 5).

Weaknesses:

(1) The proposed model for the 3'-5' polarity of RAD51-mediated strand invasion is based on the structural observations in the cryo-EM structure. This study lacks follow-up biochemical/biophysical experiments to validate the proposed model compared to RecA or developing methods to capture structures of any intermediate states with different polarity models.

(2) The functional impact of key mutants designed based on structure has not been tested in cells to evaluate how these mutants impact the HR pathway.

The significance of the work for the DNA repair field and beyond:

Homologous recombination (HR) is a key pathway for repairing DNA double-strand breaks and involves multiple steps. RAD51 forms nucleoprotein filaments first with 3' overhang single-strand DNA (ssDNA), followed by a search and exchange with a homologous strand. This function serves as the basis of an accurate template-based DNA repair during HR. This research addressed a long-standing challenge of capturing RAD51 bound with the dynamic synaptic DNA and provided the first structural insight into how RAD51 performs this function. The significance of this work extends beyond the discovery of biology for the DNA repair field, into its medical relevance. RAD51 is a potential drug target for inhibiting DNA repair in cancer cells to overcome drug resistance. This work offers a structural understanding of RAD51's function with the D-loop and provides new strategies for targeting RAD51 to improve cancer therapies.

We thank the reviewer for their positive comments on the significance of our work. Concerning the proposed polarity of strand exchange based on our structural finding, please see our reply to the previous reviewer; we agree with the reviewer that further experimentation will be needed to reach a settled view on this.

Testing the functional effects of the RAD51 mutants on HR in cells was not an aim of the current work but we agree that it would be a very interesting experiment, which would likely provide further important insights into the mechanism of strand exchange at the core of the HR reaction.

Author response:

The following is the authors’ response to the original reviews

Reviewer 1:

(1) The initial high accumulation by all cells followed by the emergence of a sub-population that has reduced its intracellular levels of tachyplesin is a key observation and I agree with the authors' conclusion that this suggests an induced response to the AMP is important in facilitating the bimodal distribution. However, I think the conclusion that upregulated efflux is driving the reduction in signal in the "low accumulator" subpopulation is not fully supported. Steady-state amounts of intracellular fluorescent AMP are determined by the relative rates of influx and efflux and a decrease could be caused by decreasing influx (while efflux remained unchanged), increasing efflux (while influx remained unchanged), or both decreasing influx and increasing efflux. Given the transcriptomic data suggest possible changes in the expression of enzymes that could affect outer membrane permeability and outer membrane vesicle formation as well as efflux, it seems very possible that changes to both influx and efflux are important. The "efflux inhibitors" shown to block the formation of the low accumulator subpopulation have highly pleiotropic or incompletely characterised mechanisms of action so they also do not exclusively support a hypothesis of increased efflux.

We agree with the reviewer that the emergence of low accumulators after 30 min in the presence of extracellular tachyplesin-NBD (Figure 4A) could be due to either decreased influx while efflux remained unchanged, increased efflux while influx remained unchanged, or both decreasing influx and increasing efflux. Increased proteolytic activity or increased secretion of OMVs could also play a role.

We have now acknowledged that “Reduced intracellular accumulation of tachyplesin-NBD in the presence of extracellular tachyplesin-NBD could be due to decreased drug influx, increased drug efflux, increased proteolytic activity or increased secretion of OMVs.” (lines 313-315).

However, the emergence of low accumulators after 60 min in the absence of extracellular tachyplesin-NBD in our efflux assays (Figure 4C) cannot be due to decreased influx while efflux remained unchanged because of the absence of extracellular tachyplesin-NBD. We acknowledge that in our original manuscript we did not explicitly state that the efflux assays reported in Figure 4C-D were performed in the absence of tachyplesin-NBD in the extracellular environment. We have now clarified this point in our manuscript, we have added illustrations in Figure 4A, 4C-D and we have also carried out efflux assays using ethidium bromide (EtBr) to further support our conclusions about the primary role played by efflux in reducing tachyplesin accumulation in low accumulators. We have added the following paragraphs to our revised manuscript:

“Next, we performed efflux assays using ethidium bromide (EtBr) by adapting a previously described protocol [62]. Briefly, we preloaded stationary phase E. coli with EtBr by incubating cells at a concentration of 254 µM EtBr in M9 medium for 90 min. Cells were then pelleted and resuspended in M9 to remove extracellular EtBr. Single-cell EtBr fluorescence was measured at regular time points in the absence of extracellular EtBr using flow cytometry. This analysis revealed a progressive homogeneous decrease of EtBr fluorescence due to efflux from all cells within the stationary phase E. coli population (Figure S13A). In contrast, when we performed efflux assays by preloading cells with tachyplesin-NBD (46 μg mL^-1 or 18.2 μM), followed by pelleting and resuspension in M9 to remove extracellular tachyplesin-NBD, we observed a heterogeneous decrease in tachyplesin-NBD fluorescence in the absence of extracellular tachyplesin-NBD: a subpopulation retained high tachyplesin-NBD fluorescence, i.e. high accumulators; whereas another subpopulation displayed decreased tachyplesin-NBD fluorescence, 60 min after the removal of extracellular tachyplesin-NBD (Figure 4B). Since these assays were performed in the absence of extracellular tachyplesin-NBD, decreased tachyplesin-NBD fluorescence could not be ascribed to decreased drug influx or increased secretion of OMVs in low accumulators, but could be due to either enhanced efflux or proteolytic activity in low accumulators.

Next, we repeated efflux assays using EtBr in the presence of 46 μg mL^-1 (or 20.3 µM) extracellular tachyplesin-1. We observed a heterogeneous decrease of EtBr fluorescence with a subpopulation retaining high EtBr fluorescence (i.e. high tachyplesin accumulators) and another population displaying reduced EtBr fluorescence (i.e. low tachyplesin accumulators, Figure S14B) when extracellular tachyplesin-1 was present. Moreover, we repeated tachyplesin-NBD efflux assays in the presence of M9 containing 50 μg mL^-1 (244 μM) carbonyl cyanide m-chlorophenyl hydrazone (CCCP), an ionophore that disrupts the proton motive force (PMF) and is commonly employed to abolish efflux and found that all cells retained tachyplesin-NBD fluorescence (Figure S15B). However, it is important to note that CCCP does not only abolish efflux but also other respiration-associated and energy-driven processes [63].

Taken together, our data demonstrate that in the absence of extracellular tachyplesin, stationary phase E. coli homogeneously efflux EtBr, whereas only low accumulators are capable of performing efflux of intracellular tachyplesin after initial tachyplesin accumulation. In the presence of extracellular tachyplesin, only low accumulators can perform efflux of both intracellular tachyplesin and intracellular EtBr. However, it is also conceivable that besides enhanced efflux, low accumulators employ proteolytic activity, OMV secretion, and variations to their bacterial membrane to hinder further uptake and intracellular accumulation of tachyplesin in the presence of extracellular tachyplesin.”

These amendments can be found on lines 316-350 and in the new Figure S13 and Figure 4. We have also carried out more tachyplesin-NBD accumulation assays using single and double gene-deletion mutants lacking efflux components, please see Response 3 to reviewer 2 and the data reported in Figure 4B.

(2) A conclusion of the transcriptomic analysis is that the lower accumulating subpopulation was exhibiting "a less translationally and metabolically active state" based on less upregulation of a cluster of genes including those involved in transcription and translation. This conclusion seems to borrow from well-described relationships referred to as bacterial growth laws in which the expression of genes involved in ribosome production and translation is directly related to the bacterial growth (and metabolic) rate. However, the assumptions that allow the formulation of the bacterial growth laws (balanced, steady state, exponential growth) do not hold in growth arrest. A non-growing cell could express no genes at all or could express ribosomal genes at a very low level, or efflux pumps at a high level. The distribution of transcripts among the functional classes of genes does not reveal anything about metabolic rates within the context of growth arrest - it only allows insight into metabolic rates when the constraint of exponential growth can be assumed. Efflux pumps can be highly metabolically costly; for example, Tn-Seq experiments have repeatedly shown that mutants for efflux pump gene transcriptional repressors have strong fitness disadvantages in energy-limited conditions. There are no data presented here to disprove a hypothesis that the low accumulators have high metabolic rates but allocate all of their metabolic resources to fortifying their outer membranes and upregulating efflux. This could be an important distinction for understanding the vulnerabilities of this subpopulation. Metabolic rates can be more directly estimated for single cells using respiratory dyes or pulsed metabolic labelling, for example, and these data could allow deeper insight into the metabolic rates of the two subpopulations. My main recommendation for additional experiments to strengthen the conclusions of the paper would be to attempt to directly measure metabolic or translational activity in the high- and low-accumulating populations. I do not think that the transcriptomic data are sufficient to draw conclusions about this but it would be interesting to directly measure activity. Otherwise, it might be reasonable to simply soften the language describing the two populations as having different activity levels. They do seem to have different transcriptional profiles, and this is already an interesting observation.

We agree with the reviewer that it might be misleading to draw conclusions on bacterial metabolic states solely based on transcriptomic data. We have therefore removed the statement “low accumulators displayed a less translationally and metabolically active state”. We have instead stated the following: “Our transcriptomics analysis showed that low tachyplesin accumulators downregulated protein synthesis, energy production, and gene expression processes compared to high accumulators”. Moreover, we have employed the membrane-permeable redox-sensitive dye C₁₂-resazurin, which is reduced to the fluorescent C₁₂-resorufin in metabolically active cells, to obtain a more direct estimate of the metabolic state of low and high accumulators of tachyplesin. We have added the following paragraph reporting our new data:

“Our transcriptomics analysis also showed that low tachyplesin accumulators downregulated protein synthesis, energy production, and gene expression compared to high accumulators. To gain further insight on the metabolic state of low tachyplesin accumulators, we employed the membrane-permeable redox-sensitive dye, resazurin, which is reduced to the highly fluorescent resorufin in metabolically active cells. We first treated stationary phase E. coli with 46 μg mL^-1 (18.2 μM) tachyplesin-NBD for 60 min, then washed the cells, and then incubated them in 1 μM resazurin for 15 min and measured single-cell fluorescence of resorufin and tachyplesin-NBD simultaneously via flow cytometry. We found that low tachyplesin-NBD accumulators also displayed low fluorescence of resorufin, whereas high tachyplesin-NBD accumulators also displayed high fluorescence of resorufin (Figure S16), suggesting lower metabolic activity in low tachyplesin-NBD accumulators.”

These amendments can be found on lines 398-408 and in Figure S16.

(3) The observation that adding nutrients to the stationary phase cultures pushes most of the cells to the "high accumulator" state is presented as support of the hypothesis that the high accumulator state is a higher metabolism/higher translational activity state. However, it is important to note that adding nutrients will cause most or all of the cells in the population to start to grow, thus re-entering the familiar regime in which bacterial growth laws apply. This is evident in the slightly larger cell sizes seen in the nutrient-amended condition. In contrast to stationary phase cells, growing cells largely do not exhibit the bimodal distribution, and they are much more sensitive to tachyplesin, as demonstrated clearly in the supplement. Growing cells are not necessarily the same as the high-accumulating subpopulation of non-growing cells.

Following the reviewer’s suggestion, we are no longer using the nutrient supplementation data to support the hypothesis that high accumulators possess higher metabolism or translational activity.

The nutrient supplementation data is now only used to investigate whether tachyplesin-NBD accumulation and efficacy can be increased, and not to show that high tachyplesin-NBD accumulators are more metabolically or translationally active.

Furthermore, our previous statement “Our data suggests that such slower-growing subpopulations might display lower antibiotic accumulation and thus enhanced survival to antibiotic treatment.” has now been removed from the discussion.

(4) It might also be worth adding some additional context around the potential to employ efflux inhibitors as therapeutics. It is very clear that obtaining sufficient antimicrobial drug accumulation within Gram-negative bacteria is a substantial barrier to effective treatments, and large concerted efforts to find and develop therapeutic efflux pump inhibitors have been undertaken repeatedly over the last 25 years. Sufficiently selective inhibitors of bacterial efflux pumps with appropriate drug-like properties have been challenging to find and none have entered clinical trials. Multiple psychoactive drugs have been shown to impact efflux in bacteria but usually using concentrations in the 10-100 uM range (as here). Meanwhile, the Ki values for their human targets are usually in the sub- to low-nanomolar range. The authors rightly note that the concentration of sertraline they have used is higher than that achieved in patients, but this is by many orders of magnitude, and it might be worth expanding a bit on the substantial challenge of finding efflux inhibitors that would be specific and non-toxic enough to be used therapeutically. Many advances in structural biology, molecular dynamics, and medicinal chemistry may make the quest for therapeutic efflux inhibitors more fruitful than it has been in the past but it is likely to remain a substantial challenge.

We agree with this comment and we have now added the following statement:

“This limitation underscores the broader challenge of identifying EPIs that are both effective and minimally toxic within clinically achievable concentrations, while also meeting key therapeutic criteria such as broad-spectrum efficacy against diverse efflux pumps, high specificity for bacterial targets, and non-inducers of AMR [117]. However, advances in biochemical, computational, and structural methodologies hold the potential to guide rational drug design, making the search for effective EPIs more promising [118]. Therefore, more investigation should be carried out to further optimise the use of sertraline or other EPIs in combination with tachyplesin and other AMPs.”

This amendment can be found on lines 535-542.

(5) My second recommendation is that the transcriptomic data should be made available in full and in a format that is easier for other researchers to explore. The raw data should also be uploaded to a sequence repository, such as the NCBI Geo database or the EMBL ENA. The most useful format for sharing transcriptomic data is a table (such as an excel spreadsheet) of transcripts per million counts for each gene for each sample. This allows other researchers to do their own analyses and compare expression levels to observations from other datasets. When only fold change data are supplied, data cannot be compared to other datasets at all, because they are relative to levels in an untreated control which are not known. The cluster analysis is one way of gaining insight into biological function revealed by transcriptional profile, but it can hide interesting additional complexities. For example, rpoS is named as one of the transcription-associated genes that are higher in the high accumulator subpopulation and evidence of generally increased activity. But RpoS is the stress sigma factor that drives much lower levels of expression generally than the housekeeping sigma factor RpoD, even though it recognises many of the same promoters (and some additional stress-specific promoters). Therefore, increased RpoS occupancy of RNAP would be expected to result in overall lower levels of transcription. However, it is also true that the transcript level for the rpoS gene is a particularly poor indicator of expression - rpoS is largely post-transcriptionally regulated. More generally, annotations are always evolving and key functional insights related to each gene might change in the future, so the results are a more durable resource if they are presented in a less analysed form as well as showing the analysis steps. It can also be important to know which genes were robustly expressed but did not change, versus genes that were not detected.

Sequencing data associated with this study have now been uploaded and linked under NCBI BioProject accession number PRJNA1096674 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1096674).

We have added this link to the methods under subheading “Accession Numbers” on lines 858-860. Additionally, transcripts per million counts for each gene for each sample have been added to the Figure 3 - Source Data file as requested by the reviewer.

(6) In the introduction, the susceptibility of AMP efficacy to resistance mechanisms is discussed:

"However, compared to small molecule antimicrobials, AMP resistance genes typically confer smaller increases in resistance, with polymyxin-B being a notable exception 7, 8. Moreover, mobile resistance genes against AMPs are relatively rare, and horizontal acquisition of AMP resistance is hindered by phylogenetic barriers owing to functional incompatibility with the new host bacteria9, again with plasmid-transmitted polymyxin resistance being a notable exception."

It seems worth pointing out that polymixins are the only AMPs that can reasonably be compared with small molecule antibiotics in terms of resistance acquisition since they are the only AMPs that have been widely used as drugs and therefore had similar chances to select for resistance among diverse global microbial populations.

We have now clarified that we are referring to laboratory evolutionary analyses of resistance towards small molecule antibiotics and AMPs (Spohn et al., 2019) and that polymyxins are the only AMPs that have been used in antibiotic treatment to date.

We have added the following statement to address this point:

“Bacteria have developed genetic resistance to AMPs, including proteolysis by proteases, modifications in membrane charge and fluidity to reduce affinity, and extrusion by AMP transporters. However, compared to small molecule antimicrobials, AMP resistance genes typically confer smaller increases in resistance in experimental evolution analyses, with polymyxin-B and CAP18 being notable exceptions [8]. Moreover, mobile resistance genes against AMPs are relatively rare and horizontal acquisition of AMP resistance is hindered by phylogenetic barriers owing to functional incompatibility with the new host bacteria [9]. Plasmid-transmitted polymyxin resistance constitutes a notable exception [10], possibly because polymyxins are the only AMPs that have been in clinical use to date [9].”

This amendment can be found on lines 57-65.

(7) In the description of Figure 4, " tachyplesin monotherapy" is mentioned. It is not really appropriate to describe the treatment of a planktonic culture of bacteria in a test tube as a therapy since there is no host that is benefitting.

We have now replaced “tachyplesin monotherapy” with “tachyplesin treatment”.

(8) In the discussion, it is stated that " tachyplesin accumulates intracellularly only in bacteria that do not survive tachyplesin exposure" but this is clearly not true. All bacteria accumulate tachyplesin intracellularly initially, but if the bacteria are non-growing during the exposure, some of them are able to reduce their intracellular levels. The fraction of survivors is roughly correlated with the fraction of bacteria that do not maintain high intracellular levels of tachyplesin and that do not stain with propidium iodide, but for any given cell it seems that there is no clear point at which a high intracellular level of tachyplesin means that it will definitely not survive.

We have now clarified this statement as follows: “We show that after an initial homogeneous tachyplesin accumulation within a stationary phase E. coli population, tachyplesin is retained intracellularly by bacteria that do not survive tachyplesin exposure, whereas tachyplesin is retained only in the membrane of bacteria that survive tachyplesin exposure.”

This amendment can be found on lines 443-446.

(9) Also in the discussion: " Our data suggests that such slower-growing subpopulations might display lower antibiotic accumulation and thus enchanced [sic] survival to antibiotic treatment." This does not really relate to the results here because the bimodal distributions were primarily studied in the absence of growth. In the LB/exponential growth situations where the population was growing but a very small subpopulation of low accumulators was observed, no measurements were made to indicate subpopulation growth rates.

We have now removed this statement from the manuscript.

(10) In discussion, L-Ara4N appears to be referred to as both positively charged and negatively charged; this should be clarified.

We have now clarified that L-Ara4N is positively charged.

This amendment can be found on line 496.

(11) Discussion of TF analysis seems to overstate what is supported by the evidence. The correlation of up- and downregulated genes with previously described TF regulons (probably measured in very different conditions) does not really demonstrate TF activity. This could be measured directly with additional experiments but in the absence of those experiments claims about detecting TF activity should probably be avoided. The attempts to directly demonstrate the importance of those transcription factors to the observed accumulation activity were not successful.

We have now removed from the discussion the previous paragraph related to the TF analysis. We have also modified the results section reported the TF analysis as follows: “Next, we sought to infer transcription factor (TF) activities via differential expression of their known regulatory targets [61]. A total of 126 TFs were inferred to exhibit differential activity between low and high accumulators (Data Set S4). Among the top ten TFs displaying higher inferred activity in low accumulators compared to high accumulators, four regulate transport systems, i.e. Nac, EvgA, Cra, and NtrC (Figure S12). However, further experiments should be carried out to directly measure the activity of these TFs.”

Finally, we have also moved the TFs’ data from Figure 3 to Figure S12 in the Supplementary information.

These amendments can be found on lines 288-293.

(12) When discussing the possibility of nutrient supplementation versus efflux inhibition as a potential therapeutic strategy, it could be noted that nutrient supplementation cannot be done in many infection contexts. The host immune system and host/bacterial cell density control nutrient access.

We have now added the following statement: “Moreover, nutrient supplementation as a therapeutic strategy may not be viable in many infection contexts, as host density and the immune system often regulate access to nutrients [3]”.

These amendments can be found on lines 553-555.

Reviewer 2:

(1) Some questions regarding the mechanism remain. One shortcoming of the setup of the transcriptomics experiment is that the tachyplesin-NBD probe itself has antibiotic efficacy and induces phenotypes (and eventually cell death) in the ´high accumulator´cells. This makes it challenging to interpret whether any differences seen between the two groups are causative for the observed accumulation pattern or if they are a consequence of differential accumulation and downstream phenotypic effects.

We agree with the reviewer and we have now acknowledged that “tachyplesin-NBD has antibiotic efficacy (see Figure 2) and has an impact on the E. coli transcriptome (Figure 3). Therefore, we cannot conclude whether the transcriptomic differences reported between low and high accumulators of tachyplesin-NBD are causative for the distinct accumulation patterns or if they are a consequence of differential accumulation and downstream phenotypic effects.”

These amendments can be found on lines 283-287.

(2) It would be relevant to test and report the MIC of sertraline for the strain tested, particularly since in Figure 4G an initial reduction in CFUs is observed for sertraline treatment, which suggests the existence of biological effects in addition to efflux inhibition.

We have now measured the MIC of sertraline against E. coli BW25113 finding the MIC value to be 128 μg mL^-1 (418 µM). This value is more than four times higher compared to the sertraline concentration employed in our study, i.e. 30 μg mL^-1 (98 μM).

These amendments can be found on lines 389-391 and data has been added to Figure 4 – Source Data.

(3) The role of efflux systems is further supported by the finding that efflux pump inhibitors sensitize E. coli to tachyplesin and prevent the occurrence of the tolerant ´low accumulator´ subpopulations. In principle, this is a great way of validating the role of efflux pumps, but the limited selectivity of these inhibitors (CCCP is an uncoupling agent, and for sertraline direct antimicrobial effects on E. coli have been reported by Bohnert et al.) leaves some ambiguity as to whether the synergistic effect is truly mediated via efflux pump inhibition. To strengthen the mechanistic angle of the work analysis of tachyplesin-NBD accumulation in mutants of the identified efflux components would be interesting.

We have now performed tachyplesin-NBD accumulation assays using 28 single and 4 double E. coli BW25113 gene-deletion mutants of efflux components and transcription factors regulating efflux. While for the majority of the mutants we recorded bimodal distributions of tachyplesin-NBD accumulation similar to the distribution recorded for the E. coli BW25113 parental strain (Figure 4B and Figure S13), we found unimodal distributions of tachyplesin-NBD accumulation constituted only of high accumulators for both DqseB and DqseBDqseC mutants as well as reduced numbers of low accumulators for the DacrADtolC mutant (Figure 4B). Considering that the AcrAB-TolC tripartite RND efflux system is known to confer genetic resistance against AMPs like protamine and polymyxin-B [29,30] and that the quorum sensing regulators qseBC might control the expression of acrA [64] , these data further corroborate the hypothesis that low accumulators can efflux tachyplesin and survive treatment with this AMP.

These amendments can be found on lines 351-361, in the new Figure 4B and in the new Figure S14.

Moreover, we have also carried out further efflux assays with both ethidium bromide and tachyplesin-NBD to further demonstrate the role of efflux in reduced accumulation of tachyplesin as well as acknowledging that other mechanisms (i.e reduced influx, increased protease activity or increased secretion of OMVs) could play an important role, please see Response 1 to Reviewer 1.

(4) The authors imply that protease could contribute to the low accumulator mechanism. Proteases could certainly cleave and thus inactivate AMPs/tachyplesin, but would this effect really lead to a reduction in fluorescence levels since the fluorophore itself would not be affected by proteolytic cleavage?

We agree with the reviewer that nitrobenzoxadiazole (NBD) might not be cleaved by proteases that inactivate tachyplesin and other AMPs. Therefore, inactivation of tachyplesin by proteases might not affect cellular fluorescence levels unless efflux of NBD is possible following the cleavage of tachyplesin-NBD. We have therefore removed the statement “Conversely, should efflux or proteolytic activities by proteases underpin the functioning of low accumulators, we should observe high initial tachyplesin-NBD fluorescence in the intracellular space of low accumulators followed by a decrease in fluorescence due to efflux or proteolytic degradation.” We have now stated the following: “Low accumulators displayed an upregulation of peptidases and proteases compared to high accumulators, suggesting a potential mechanism for degrading tachyplesin (Table S1 and Data Set S3).”

These amendments can be found on lines 280-282.

(5) To facilitate comparison with other literature (e.g. papers on sertraline) it would be helpful to state compound concentrations also as molar concentrations.

We have now added the molar concentrations alongside all instances where concentrations are stated in μg mL^-1.

(6) The authors tested a series of efflux pump inhibitors and found that CCCP and sertraline prevented the generation of the low accumulator subpopulation, whereas other inhibitors did not. An overview and discussion of the known molecular targets and mode of action of the different selected inhibitors could reveal additional insights into the molecular mechanism underlying the synergy with tachyplesin.

We have now added molecular targets and mode of action of the different inhibitors where known. “Moreover, we repeated tachyplesin-NBD efflux assays in the presence of M9 containing 50 μg mL^-1 (244 μM) carbonyl cyanide m-chlorophenyl hydrazone (CCCP), an ionophore that disrupts the proton motive force (PMF) and is commonly employed to abolish efflux and found that all cells retained tachyplesin-NBD fluorescence (Figure S15B). However, it is important to note that CCCP does not only abolish efflux but also other respiration-associated and energy-driven processes [63].” And “Interestingly, M9 containing 30 µg mL^-1 (98 μM) sertraline (Figure 4D and S15C), an antidepressant which inhibits efflux activity of RND pumps, potentially through direct binding to efflux pumps [65] and decreasing the PMF [66], or 50 µg mL^-1 (110 μM) verapamil (Figure S15D), a calcium channel blocker that inhibits MATE transporters [67] by a generally accepted mechanism of PMF generation interference [68,69], was able to prevent the emergence of low accumulators. Furthermore, tachyplesin-NBD cotreatment with sertraline simultaneously increased tachyplesin-NBD accumulation and PI fluorescence levels in individual cells (Figure 4E and F, p-value < 0.0001 and 0.05, respectively). The use of berberine, a natural isoquinoline alkaloid that inhibits MFS transporters [70] and RND pumps [71], potentially by inhibiting conformational changes required for efflux activity [70], and baicalein, a natural flavonoid compound that inhibits ABC [72] and MFS [73,74] transporters, potentially through PMF dissipation [75], prevented the formation of a bimodal distribution of tachyplesin accumulation, however displayed reduction in fluorescence of the whole population (Figure S15E and F). Phenylalanine-arginine beta-naphthylamide (PAbN), a synthetic peptidomimetic compound that inhibits RND pumps [76] through competitive inhibition [77], reserpine, an indole alkaloid that inhibits ABC and MFS transporters, and RND pumps [78], by altering the generation of the PMF [69], and 1-(1-naphthylmethyl)piperazine (NMP), a synthetic piperazine derivative that inhibits RND pumps [79], through non-competitive inhibition [80], did not prevent the emergence of low accumulators (Figure S15G-I).”

These amendments can be found on lines 337-342 and 367-385.

(7) Page 8. The term ´medium accumulators´ for a 1:1 mix of low and high accumulators is misleading.

We have now replaced the term “medium accumulators” with “a 1:1 (v/v) mixture of low and high accumulators”.

These amendments to the description can be found on lines 238-239.

(8) Figure 3. It may be more appropriate to rephrase the title of the figure to ´biological processes associated with low tachyplesin accumulation´ (rather than ´facilitate accumulation´). The same applies to the section title on page 8.

We have amended the title of Figure 3 as requested by the reviewer.

(9) The fact that the low accumulation phenotype depends on the growth media and conditions and can be prevented by nutrients is highly relevant. I would encourage the authors to consider showing the corresponding data in the main manuscript rather than in the SI.

We have created a new Figure 5, displaying the impact of the nutritional environment and bacterial growth phase on both tachyplesin-NBD accumulation and efficacy.

(10) In the discussion the authors state´ Heterogeneous expression of efflux pumps within isogenic bacterial populations has been reported 29,32,33,67-69. However, recent reports have suggested that efflux is not the primary mechanism of antimicrobial resistance within stationary-phase bacteria 31,70.´. In light of the authors´ findings that the response to tachyplesin is induced by exposure and is not pre-selected, could they speculate on why this specific response can be induced in stationary, but not exponential cells? Could there be a combination of pre-existing traits and induced responses at play? Could e.g. the reduced growth rate/metabolism in these cells render these cells less susceptible to the intracellular effects of tachyplesin and slow down the antibiotic efficacy, giving the cells enough time to mount additional protective responses that then lead to the low accumulation phenotype?

We have now acknowledged that it is conceivable that other pre-existing traits of low accumulators also contribute to reduced tachyplesin accumulation. For example, reduced protein synthesis, energy production and gene expression in low accumulators could slow down tachyplesin efficacy, giving low accumulators more time to mount efflux as an additional protective response.

“As our accumulation assay did not require the prior selection for phenotypic variants, we have demonstrated that low accumulators emerge subsequent to the initial high accumulation of tachyplesin-NBD, suggesting enhanced efflux as an induced response. However, it is conceivable that other pre-existing traits of low accumulators also contribute to reduced tachyplesin accumulation. For example, reduced protein synthesis, energy production, and gene expression in low accumulators could slow down tachyplesin efficacy, giving low accumulators more time to mount efflux as an additional protective response.”

This amendment can be found on lines 482-489.

(11) In the abstract: Is it true that low accumulators ´sequester´ the drug in their membrane? In my understanding ´sequestering´ would imply that low accumulators would bind higher levels of tachyplesin-NBD in their membrane compared to high accumulators (and thereby preventing it from entering the cells). According to Figure 1 J, K, it rather seems that the fluorescent signal around the membrane is also stronger in high accumulators.

We have now removed the sentence “low accumulators sequester the drug in their membrane” from the abstract. We have instead stated: “These phenotypic variants display enhanced efflux activity to limit intracellular peptide accumulation.”

These amendments can be found on lines 34-35.

Reviewer 3:

(1) The authors' claims about high efflux being the main mechanism of survival are unconvincing, given the current data. There can be several alternative hypotheses that could explain their results, such as lower binding of the AMP, lower rate of internalization, metabolic inactivity, etc. It is unclear how efflux can be important for survival against a peptide that the authors claim binds externally to the cell. The addition of efflux assays would be beneficial for clear interpretations. Given the current data, the authors' claims about efflux being the major mechanism in this resistance are unconvincing (in my humble opinion). Some direct evidence is necessary to confirm the involvement of efflux. The data with CCCP in Figure 4C can only indicate accumulation, not efflux. The authors are encouraged to perform direct efflux assays using known methods (e.g., PMIDs 20606071, 30981730, etc.). Figure 4A: The data does not support the broad claims about efflux. First, if the peptide is accumulated on the outside of the outer membrane, how will efflux help in survival? The dynamics shown in 4A may be due to lower binding, lower entry, or lower efflux. These mechanisms are not dissected here. Second, the heterogeneity can be preexisting or a result of the response to this stress. Either way, whether active efflux or dynamic transcriptomic changes are responsible for these patterns is not clear. Direct efflux assays are crucial to conclude that efflux is a major factor here.

This important comment is similar in scope to the first comment of reviewer 1 and it is partly due to the fact that we had not clearly explained our efflux assays reported in Figure 4 in the original manuscript. We kindly refer this reviewer to our extensive response 1 to reviewer 1 and corresponding amendments on lines 316-350 and in the new Figure S13 and Figure 4 (reported in the response 1 to reviewer 1 above), where we have now fully addressed this reviewer’s and reviewer 1 concerns, as well as performing new experiments following their important suggestions and the methods described in PMIDs 20606071 suggested by this reviewer.

(2) The fluorescent imaging experiments can be conducted in the presence of externally added proteases, such as proteinase K, which has multiple cleavage sites on tachyplesin. This would ensure that all the external peptides (both free and bound) are removed. If the signal is still present, it can be concluded that the peptide is present internally. If the peptide is primarily external, the authors need to explain how efflux could help with externally bound peptides. Figure 1J-K: How are the authors sure about the location of the intensity? The peptide can be inside or outside and still give the same signal. To prove that the peptide is inside or outside, a proteolytic cleavage experiment is necessary (proteinase K, Arg-C proteinase, clostripain, etc.).

We thank the reviewer for this important suggestion.

We have now performed experiments where stationary phase E. coli was incubated in 46 μg mL^-1 (18.2 μM) tachyplesin-NBD in M9 for 60 min. Next, cells were pelleted and washed to remove extracellular tachyplesin-NBD and then incubated in either M9 or 20 μg mL^-1 (0.7 μΜ) proteinase K in M9 for 120 min. We found that the fluorescence of low accumulators decreased over time in the presence of proteinase K; in contrast, the fluorescence of high accumulators did not decrease over time in the presence of proteinase K. These data therefore suggest that tachyplesin-NBD is present only on the cell membrane of low accumulators and both on the membrane and intracellularly in high accumulators.

Moreover, confocal microscopy using tachyplesin-NBD along with the membrane dye FM™ 4-64FX further confirmed that tachyplesin-NBD is present only on the cell membrane of low accumulators and both on the membrane and intracellularly in high accumulators.

These amendments can be found on lines 173-179, lines 188-192 and in the new Figures S4 and S6.

(3) Further genetic experiments are necessary to test whether efflux genes are involved at all. The genetic data presented by the authors in Figure S11 is crucial and should be further extended. The problem with fitting this data to the current hypothesis is as follows: If specific efflux pumps are involved in the resistance mechanism, then single deletions would cause some changes to the resistance phenotype, and the data in Figure S11 would look different. If there is redundancy (as is the case in many efflux phenotypes), the authors may consider performing double deletions on the major RND regulators (for example, evgA and marA). Additionally, the deletion of pump components such as TolC (one of the few OM components) and adaptors (such as acrA/D) might also provide insights. If the peptide is present in the periplasm, then deletions involving outer components would become important.

This important comment is similar in scope to the third comment of reviewer 2. We have now performed tachyplesin-NBD accumulation assays using 28 single and 4 double E. coli BW25113 gene-deletion mutants of efflux components and transcription factors regulating efflux. While for the majority of the mutants we recorded bimodal distributions of tachyplesin-NBD accumulation similar to the distribution recorded for the E. coli BW25113 parental strain (Figure 4B and Figure S13), we found unimodal distributions of tachyplesin-NBD accumulation constituted only of high accumulators for both DqseB and DqseBDqseC mutants as well as reduced numbers of low accumulators for the DacrADtolC mutant.

These amendments can be found on lines 351-361, in the new Figure 4B and in the new Figure S14, please also see our response to comment 3 of reviewer 2.

(4) Line numbers would have been really helpful. Please mention the size of the peptide (length and spatial) for readers.

We have now added line numbers to the revised manuscript. The length and molecular weight of tachyplesin-1 have now been added on lines 75.

(5) Figure S4 is unclear. How were the low accumulators collected? What prompted the low-temperature experiment? The conclusion that it accumulates at the outer membrane is unjustified. Where is the data for high accumulators?

We have now corrected the results section to state that tachyplesin-NBD accumulates on the cell membranes, rather than at the outer membrane of E. coli cells.

These amendments can be found on lines 178 and 190.

We would like to clarify that in Figure S4 we compare the distribution of tachyplesin-NBD single-cell fluorescence at low temperature versus 37 °C across the whole stationary phase E. coli population, we did not collect low accumulators only.

The low-temperature experiment was prompted by a previous publication paper (Zhou Y et al. 2015: doi: 10.1021/ac504880r. Epub 2015 Mar 24. PMID: 25753586) that showed non-specific adherence of antimicrobials to the bacterial surface occurs at low temperatures and that passive and active transport of antimicrobials across the membrane is significantly diminished. Additionally, there are previous reports that suggest low temperatures inhibit post-binding peptide-lipid interactions, but not the primary binding step (PMID: 16569868; PMCID: PMC1426969; PMID: 3891625; PMCID: PMC262080).

Therefore, the low-temperature experiment was performed to quantify the fluorescence of cells due to non-specific binding. This quantification allowed us to deduce that fluorescence levels of high accumulators are above the measured non-specific binding fluorescence (measured in the low-temperature experiment for the whole stationary phase E. coli population) is the result of intracellular tachyplesin-NBD accumulation. In contrast, the comparable fluorescence levels between all the cells in the low-temperature experiment and the low accumulator subpopulation at 37 °C suggest that tachyplesin-NBD is predominantly accumulated on the cell membranes of low accumulators instead of intracellularly.

Please also see our response to comment 2 above for further evidence supporting that tachyplesin-NBD accumulates only on the cell membranes of low accumulators and both on the cell membranes and intracellularly in low accumulators.

(6) Figure S5: Describe the microfluidic setup briefly. Why did the distribution pattern change (compared to Figure 1A)? Now, there are more high accumulators. Does the peptide get equally distributed between daughter cells?

We have now added a brief description of the microfluidic setup on lines 182-184.

The difference in the abundance of low and high accumulators between the microfluidics and flow cytometry measurements is likely due to differences in cell density, i.e. a few cells per channel vs millions of cells in a tube. A second major difference is that tachyplesin-NBD is continuously supplied in the microfluidic device for the entire duration of the experiment, therefore, the extracellular concentration of tachyplesin-NBD does not decrease over time. In contrast, tachyplesin-NBD is added to the tube only at the beginning of the experiment, therefore, the extracellular concentration of tachyplesin-NBD likely decreases in time as it is accumulated by the bacteria. The relative abundance of low and high accumulators changes with the extracellular concentration of tachyplesin-NBD as shown in Figure 1A.

We have added a sentence to acknowledge this discrepancy on lines 186-187.

No instances of cell division were observed in stationary phase E. coli in the absence of nutrients in all microfluidics assays. Therefore, we cannot comment on the distribution of tachyplesin-NBD across daughter cells.

(7) How did the authors conclude this: "tachyplesin accumulation on the bacterial membrane may not be sufficient for bacterial eradication"? It is completely unclear to this reviewer.

We presented this hypothesis at the end of the section “Tachyplesin accumulates primarily in the membranes of low accumulators” as a link to the following section “Tachyplesin accumulation on the bacterial membranes is insufficient for bacterial eradication” where we test this hypothesis. For clarity, we have now moved this sentence to the beginning of the section “Tachyplesin accumulation on the bacterial membranes is insufficient for bacterial eradication”.

(8) What is meant by membrane accumulation? Outside, inside, periplasm? Where? Figure 2H conclusions are unjustified. Bacterial killing with many antibiotics is associated with membrane damage, which is an aftereffect of direct antibiotic action. How can the authors state that "low accumulators primarily accumulate tachyplesin-NBD on the bacterial membrane, maintaining an intact membrane, strongly contributing to the survival of the bacterial population"? This reviewer could not find justifications for the claims about the location of the accumulation or cells actively maintaining an intact membrane. Also, PI staining reports damage both membranes.

Based on the experiments that we have carried out after this reviewer’s suggestions, please see response 2 above, it is likely that tachyplesin-NBD is present only on the bacterial surface, i.e. in or on the outer membrane of low accumulators, considering that their fluorescence decreases during treatment with proteinase K. However, to take a more conservative approach we have now written on the cell membranes throughout the manuscript, i.e. either the outer or the inner membrane.

We have also rephrased the statement reported by the reviewer as follows:

“Taken together with PI staining data indicating membrane damage caused by high tachyplesin accumulation, these data demonstrate that low accumulators, which primarily accumulate tachyplesin-NBD on the bacterial membranes, maintain membrane integrity and strongly contribute to the survival of the bacterial population in response to tachyplesin treatment.”

These amendments can be found on lines 228-232.

(9) Figure 3: The findings about cluster 2 and cluster 4 genes do not correlate logically. If the cells are in a metabolically low active state, how are the cells getting enough energy for active efflux and membrane transport? This scenario is possible, but the authors must confirm the metabolic activity by measuring respiration rates. Also, metabolically less-active cells may import a lower number of peptides to begin with. That also may contribute to cell survival. Additionally, lowered metabolism is a known strategy of antibiotic survival that is distinctly different from efflux-mediated survival.

Following this reviewer’s comment and comment 2 of reviewer 1, we have now carried out further experiments to estimate the metabolic activity of low and high accumulators. Please see our response to comment 2 of reviewer 1 above.

(10) Figure S10: How did the authors test their hypothesis that cardiolipin is involved in the binding of the peptide to the membrane? The transcriptome data does not confirm it. Genetic experiments are necessary to confirm this claim.

We would like to clarify that we have not set out to test the hypothesis that cardiolipin is involved in the binding of tachyplesin-NBD. We have only stated that cardiolipin could bind tachyplesin due to its negative charge. We have now cited two previous studies that suggest that tachyplesin has an increased affinity for lipids mixtures containing either cardiolipin (Edwards et al. ACS Inf Dis 2017) or PG lipids (Matsuzaki et al. BBA 1991), i.e. the main constituents of cardiolipins.

These amendments can be found on lines 264-267.

(11) Figure 4B-F: There are several controls missing. For Sertraline treatment, the authors must test that the metabolic profile, transcriptomic changes, or import of the peptide are not responsible for enhanced survival. CCCP will not only abolish efflux but also many other respiration-associated or all other energy-driven processes.

Figure 4D presents data acquired in efflux assays in the absence of extracellular tachyplesin-NBD. Therefore, altered tachyplesin-NBD import cannot contribute to the lack of formation of the low accumulator subpopulation.

We have now acknowledged that it is conceivable that increased tachyplesin efficacy is due to metabolic and transcriptomic changes induced by sertraline.

These amendments can be found on lines 396-397.

We have also acknowledged that CCCP does not only abolish efflux but also other respiration-associated and energy-driven processes.

These amendments can be found on lines 341-342.

Author response:

The following is the authors’ response to the previous reviews

Public Reviews:

Reviewer #1 (Public review):

Summary:

This is a very well-written paper presenting interesting findings related to the recovery following the end-Permian event in continental settings, from N China. The finding is timely as the topic is actively discussed in the scientific community. The data provides additional insights into the faunal, and partly, floral global recovery following the EPE, adding to the global picture.

Strengths:

The conclusions are supported by an impressive amount of sedimentological and paleontological data (mainly trace fossils) and illustrations.

We thank Reviewer #1 for the positive assessments.

Weaknesses: [eliminated in revision]

We thank Reviewer #1.

Reviewer #2 (Public review):

Summary:

The authors made a thorough revision of the manuscript, strengthening the message. They also considered all the comments made by the reviewers and provided appropriate and convincing arguments.

Strengths:

The revised manuscript clarifies all the major points raised by the reviewers, and the way the information is presented (in the text, figures and tables) is clear.

We thank Reviewer #2 for the positive comments on our work.

Weaknesses:

The authors provided an appropriate and convincing rebuttal regarding the potential weakness I pointed out in the first review of the manuscript. Therefore, I do not see any major issue in their work.

Introduction

(1) P. 2, L. 32: Replace "to migrated" with "to migrate".

Revised as suggested.

(2) P. 3, L. 43-44: We recently published a review article on the tetrapod terrestrial record from the Central European Basin, showing that Olenekian tetrapod faunas (and ichnofaunas) were already quite rich and diverse. Article: https://doi.org/10.1016/j.earscirev.2025.105085

Yes, we have read this paper. This summary is very important for the understanding of the biotic recovery after the PTME, especially in the early stage. We have added the new result in our manuscript.

(3) P. 3, L. 57: Replace "recovered terrestrial ecosystems in tropical" with "recovered tropical terrestrial ecosystems".

Revised as suggested.

Results and Discussion

(4) P. 6, L. 118: Replace "declined" with "decline".

Revised as suggested.

(5) P. 7, L. 131: Replace "microbial" with "microbially".

Revised as suggested.

Conclusions

(6) P. 11, L. 224: Replace "as little as" with "as early as".

Revised as suggested.

(7) P. 11, L. 227: Replace "not only results in" with "not only result in".

Revised as suggested.

(8) 11, L. 230: Replace "suggesting" with "suggest".

Revised as suggested.

Reviewer #3 (Public review):

Summary:

This manuscript by Guo and colleagues features the documentation and interpretation of three successions of continental to marginal marine deposits spanning the P/T transition and their respective ichnofaunas. Based on these new data inferences concerning end-Permian mass extinction and Triassic recovery in the tropical realm are discussed.

Strengths:

The manuscript is well-written and organized and includes a large amount of new lithological and ichnological data that illuminate ecosystem evolution in a time of large-scale transition. The lithological documentations, facies interpretations, and ichnotaxonomic assignments look okay (with a few exceptions).

We thank Reviewer #3 for the positive assessments.

Weaknesses:

Weaknesses: [all eliminated in revision]

We thank Reviewer #3.

Author response:

The following is the authors’ response to the original reviews

Public Reviews: 

Reviewer #1 (Public review): 

Summary: 

The authors found that IL-1b signaling is pivotal for hypoxemia development and can modulate NETs formation in LPS+HVV ALI model.  

Strengths: 

They used IL1R1 ko mice and proved that IL1R1 is involved in ALI model proving that IL1b signalling leads towards ARDS. In addition, hypothermia reduces this effect, suggesting a therapeutic option.  

We thank the Reviewer for recognizing the strengths of our study and their positive feedback.

Weaknesses: 

(1) IL1R1 binds IL1a and IL1b. What would be the role of IL1a in this scenario? 

Thank you for asking this question. We have addressed this in our previous paper (Nosaka et al. Front Immunol 2020;11; 207) where we used  anti-IL-1a and IL-1a KO mice (Nosaka et al. Front Immunol 2020;11; 207) in our model and found that neither anti-IL-1a treated mice nor IL-1a KO mice were protected. Thus, IL-1b plays a role in inducing hypoxemia during LPS+HVV but not IL-1a. We will now add this point in our revised manuscript discussion.

(2) The authors depleted neutrophils using anti-Ly6G. What about MDSCs? Do these latter cells be involved in ARDS and VILI?  

Anti-Ly6G neutrophils depletion may potentially affect G-MDSCs as well (Blood Adv 2022 Jul 29;7(1):73–86), however, we have not looked directly at G-MDSCs.  If these cells were depleted we would have expected to see an increase in inflammation, which we did not.   Instead, anti-Ly6G treated mice were protected. Thus, we can not comment on any presumed role of G-MDSCs in LPS+HVV induced severe ALI model that we used.  

(3) The authors found that TH inhibited IL-1β release from macrophages led to less NETs formation and albumin leakage in the alveolar space in their lung injury model. A graphical abstract could be included suggesting a cellular mechanism.  

Thanks for summarizing our findings and the suggestion. Unfortunately, eLIFE does not publish a graphical abstract.  

(4) If Macrophages are responsible for IL1b release that via IL1R1 induces NETosis, what happens if you deplete macrophages? what is the role of epithelial cells?  

Previous studies have found that macrophage depletion is protective in several models of ALI (Eyal. Intensive Care Med. 2007;33:1212–1218., Lindauer.  J Immunol. 2009;183:1419–1426.), and other researchers have found that airway epithelial cells did not contribute to IL-1β secretion (Tang. PLoS ONE. 2012;7:e37689.). We have previously reported that epithelial cells produce IL-18 without LPS priming signal during LPS+HVV (Nosaka et al. Front Immunol 2020;11; 207). Thus, IL-18 is not sufficient to induce Hypoxemia as Saline+HVV treated mice do not develop hypoxemia (Nosaka et al. Front Immunol 2020;11; 207). We will now add this point to the revised discussion of the manuscript.

Reviewer #2 (Public review): 

Summary: 

The manuscript by Nosaka et al is a comprehensive study exploring the involvement of IL1beta signaling in a 2-hit model of lung injury + ventilation, with a focus on modulation by hypothermia. 

Strengths: 

The authors demonstrate quite convincingly that interleukin 1 beta plays a role in the development of ventilator-induced lung injury in this model, and that this role includes the regulation of neutrophil extracellular trap formation. The authors use a variety of in vivo animal-based and in vitro cell culture work, and interventions including global gene knockout, cell-targeted knockout and pharmacological inhibition, which greatly strengthen the ability to make clear biological interpretations. 

We thank the Reviewer for their positive feedback 

Weaknesses: 

A primary point for open discussion is the translatability of the findings to patients. The main model used, one of intratracheal LPS plus mechanical ventilation is well accepted for research exploring the pathogenesis and potential treatments for acute respiratory distress syndrome (ARDS). However, the interpretation may still be open to question - in the model here, animals were exposed to LPS to induce inflammation for only 2 hours, and seemingly displayed no signs of sickness, before the start of ventilation. This would not be typical for the majority of ARDS patients, and whether hypothermia could be effective once substantial injury is already present remains an open question. The interaction between LPS/infection and temperature is also complicated - in humans, LPS (or infection) induces a febrile, hyperthermic response, whereas in mice LPS induces hypothermia (eg. Ganeshan K, Chawla A. Nat Rev Endocrinol. 2017;13:458-465). Given this difference in physiological response, it is therefore unclear whether hypothermia in mice and hypothermia in humans are easily comparable. Finally, the use of only young, male animals such as in the current study has been typical but may be criticised as limiting translatability to people. 

Therefore while the conclusions of the paper are well supported by the data, and the biological pathways have been impressively explored, questions still remain regarding the ultimate interpretations.  

We agree with the reviewer that at two hours post LPS, there is only minimal pulmonary inflammation at that time (Dagvadorj et al Immunity 42, 640–653). This is a limitation to the experimental model we used in our study. Additionally, as the reviewer pointed out that LPS induces hyperthermia in human, but it is also well-established that physiological hypothermia occurs in humans with severe infections and sepsis (Baisse. Am J Emerg Med. 2023 Sep: 71: 134-138., Werner.  Am J Emerg Med. 2025 Feb;88:64-78.). Therefore, the difference between human and mouse responses to sepsis or infections may be more nuanced.  Furthermore, it is important to distinguish between physiological hypothermia (just <36°C) and therapeutic hypothermia (typically 32-34°C). We will add to the discussion whether hypothermia serves as a protective response, and the transition from normothermia to hyperthermia could have detrimental effects. We only used young male mice in our study as the Reviewer points out; we will also add this point to the revised discussion as a limitation of our study.

Recommendations for the authors: 

(i) With hypothermia, metabolic activity would be expected to be reduced and therefore presumably impact on CO2/pH. These may have an impact on outcomes from ventilation, so could the authors include this data and discuss as appropriate? 

We have now included these data in Suppl Fig 6.  While we observed significant differences in blood pH and  PaCO₂ in Hypothermia treatment group, these values remained within clinically normal range (PaCO₂ : 35 - 45 mmHg, pH : 7.35 - 7.45). Neither Alkalosis (PaCO₂ < 35 mmHg , pH> 7.45) nor Acidosis (PaCO₂ > 45 mmHg, pH < 7.35) was observed.

(ii) It is noticeable that there are quite large differences in experimental numbers between groups - typically 7-12, 5-12 in Figure 2. How were these N determined? For example is there a reason why there is apparently N = 8 for BALF neutrophils in the saline + HVV group (Figure 1c) but N = 12 for LPS + HVV group? Did any animals die during any of the protocols for example? 

We conducted experiments with 4 mice per experiment (2 mice per group x2  or 4 mice per group) for ventilation experiments, and pooled data from 5-6 independent experiments or 3-4 independent experiments, respectively. No mouse mortality was observed (unless otherwise noted). However, in the severe ARDS group, some mice were dehydrated by the endpoint of experiments, preventing blood or BALF collections. As a result sample sizes were unequal in some case. Nevertheless, no data were selectively excluded.

(iii) Discussion - On page 13 you refer to data involving Cl-amidine administration. This does not seem to be related to any experiments reported in the manuscript. 

We apology for this mistake and have removed it.

(iv) Methods - authors state that BALF was obtained after 150 minutes of ventilation, yet the experiments apparently lasted for 180 minutes. Presumably this is an error? 

We apology for this inconsistency.  We collected blood for measuring blood gas at 30 min and 150 min after ventilation. However, mice were kept on ventilator 30 min longer, and then mice were euthanized and BALF were collected.  Thus, BALF were collected at 180 min, 30 minutes after the final blood draw. We have corrected the methods in revised manuscript.  

(v) Statistical methods - authors state that sometimes Mann-Whitney U-test was used and sometimes unpaired t-test, presumably reflecting that some data were normally distributed and some were not. Could the authors please describe the tests used to confirm distribution of data. 

We have clarified which stattistcal methods were used in our revised manuscript. 

Briefly, Normality within the groups was assessed using the Shapiro-Wilk and KolmogorovSmirnov tests. Three-way ANOVA (Figure 1B; Supplemental Figure 1B-D; Supplemental Figure 6), one-way ANOVA (Supplemental Figure 4D-E; Supplemental Figure 5C), and two-way ANOVA were performed for data with more than two groups, followed by Tukey's post hoc test. Some groups analyzed by two-way ANOVA in Figure 1 and Supplemental Figure 1 failed the normality tests due to zero values (analyte not detected by ELISA) or the relatively small sample size, as samples were distributed across multiple measurements. However, the primary group of interest, LPS+HVV, showed significant differences from other groups with consistently low P-values in most datasets, supporting the decision to retain the ANOVA analyses. For comparisons between two groups, the Mann-Whitney U test was used when one or both groups failed the Shapiro-Wilk normality test, while the unpaired Student's t-test was applied to the remaining normally distributed data.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public review):

Summary:

The manuscript presents a significant and rigorous investigation into the role of CHMP5 in regulating bone formation and cellular senescence. The study provides compelling evidence that CHMP5 is essential for maintaining endolysosomal function and controlling mitochondrial ROS levels, thereby preventing the senescence of skeletal progenitor cells.

Strengths:

The authors demonstrate that the deletion of Chmp5 results in endolysosomal dysfunction, elevated mitochondrial ROS, and ultimately enhanced bone formation through both autonomous and paracrine mechanisms. The innovative use of senolytic drugs to ameliorate musculoskeletal abnormalities in Chmp5-deficient mice is a novel and critical finding, suggesting potential therapeutic strategies for musculoskeletal disorders linked to endolysosomal dysfunction.

Weaknesses:

The manuscript requires a deeper discussion or exploration of CHMP5's roles and a more refined analysis of senolytic drug specificity and effects. This would greatly enhance the comprehensiveness and clarity of the manuscript.

We thank the reviewer for these insightful comments. In the revised manuscript, we have expanded the discussion of the distinct roles of CHMP5 in different cell types. Specifically, we add the following sentences (Lines 433-439 in the combined manuscript):

“Also, a previous study by Adoro et al. did not detect endolysosomal abnormalities in Chmp5 deficient developmental T cells [1]. Since both osteoclasts and T cells are of hematopoietic origin, and meanwhile osteogenic cells and MEFs, which show endolysosomal abnormalities after CHMP5 deficiency, are of mesenchymal origin, it turns out that the function of CHMP5 in regulating endolysosomal pathway could be cell lineage-specific, which remains clarified in future studies.”

In addition, we tested another senolytic drug Navitoclax (ABT-263), which is a BCL-2 family inhibitor and induces apoptosis of senescent cells, in Chmp5^Ctsk mice. Micro-CT analysis showed that ABT-263 could also improve periskeletal bone overgrowth in Chmp5^Ctsk mice (Fig. 5F). Furthermore, we have also discussed the potential off-target effects of senolytic drugs in Chmp5^Ctsk mice in the revised manuscript. Specifically, we added the following paragraph (Lines 441-451):

“Furthermore, it is unclear whether the effect of senolytic drugs in Chmp5^Ctsk mice involves targeting osteoclasts other than osteogenic cells, as osteoclast senescence has not yet been evaluated. However, the efficacy of Q + D in targeting osteogenic cells, which is the focus of the current study, was confirmed in Chmp5^Dmp1 mice (Fig. 5C-E). Additionally, Q + D caused a higher cell apoptotic ratio in Chmp5^Ctsk compared to wild-type periskeletal progenitors in ex vivo culture (Fig. 5A), demonstrating the effectiveness of Q + D in targeting osteogenic cells in the Chmp5^Ctsk model. Furthermore, an alternative senolytic drug ABT-263 could also ameliorate periskeletal bone overgrowth in Chmp5^Ctsk mice (Fig. 5F). Together, these results confirm that osteogenic cell senescence is responsible for the bone overgrowth in Chmp5^Ctsk and Chmp5^Dmp1 mice, and senolytic treatments are effective in alleviating these skeletal disorders.”

Reviewer #2 (Public review):

Summary:

The authors try to show the importance of CHMP5 for skeletal development.

Strengths:

The findings of this manuscript are interesting. The mouse phenotypes are well done and are of interest to a broader (bone) field.

Weaknesses:

The mechanistic insights are mediocre, and the cellular senescence aspect poor.

In total, it has not been shown that there are actual senescent cells that are reduced after D+Qtreatment. These statements need to be scaled back substantially.

We thank the reviewer for these suggestive comments. We have added additional results to strengthen the senescent phenotypes of Chmp5-deficient skeletal progenitor cells, including significant enrichment of the SAUL_SEN_MAYO geneset (positively correlated with cell senescence) and the KAMMINGA_SENESCENCE geneset (negatively correlated with cell senescence) at the transcriptional level by GSEA analysis of RNA-seq data (Fig. S3C), and the increase of γH2Ax⁺;GFP⁺ cells at periskeletal overgrowth in Chmp5^Ctsk;Rosa26^26mTmG/+ mice vs. the periosteum of Chmp5^Ctsk/+;Rosa26^26mTmG/+ control mice (Fig. 3E). These results further advocate for the senescent phenotypes of Chmp5-deficient skeletal progenitors.

Furthermore, the combination of Q + D caused a higher cell apoptotic ratio in Chmp5^Ctsk vs. wildtype periskeletal progenitors in ex vivo culture (Fig. 5A), suggesting their effectiveness in targeting periskeletal progenitor cell senescence in Chmp5^Ctsk mice. Furthermore, we tested an alternative senolytic drug ABT-263, which is an inhibitor of the BCL-2 family and induces apoptosis of senescent cells, in Chmp5^Ctsk mice, and ABT-263 could also alleviate periskeletal bone overgrowth in Chmp5^Ctsk mice (Fig. 5F). Together, these results demonstrate that osteogenic cell senescence is responsible for abnormal bone overgrowth in Chmp5-deficient mice and that senolytic drugs are effective in improving these skeletal disorders.

Reviewer #3 (Public review):

Summary:

In this study, Zhang et al. reported that CHMP5 restricts bone formation by controlling endolysosomemitochondrion-mediated cell senescence. The effects of CHMP5 on osteoclastic bone resorption and bone turnover have been reported previously (PMID: 26195726), in which study the aberrant bone phenotype was observed in the CHMP5-ctsk-CKO mouse model, using the same mouse model, Zhang et al., report a novel role of CHMP5 on osteogenesis through affecting cell senescence. Overall, it is an interesting study and provides new insights in the field of cell senescence and bone.

Strengths:

Analyzed the bone phenotype OF CHMP5-periskeletal progenitor-CKO mouse model and found the novel role of senescent cells on osteogenesis and migration.

Weaknesses:

(1) There are a lot of papers that have reported that senescence impairs osteogenesis of skeletal stem cells. In this study, the author claimed that Chmp5 deficiency induces skeletal progenitor cell senescence and enhanced osteogenesis. Can the authors explain the controversial results?

Different skeletal stem cell populations in time and space have been identified and reported [2-6]. The present study shows that Chmp5 deficiency in periskeletal (Ctsk-Cre) and endosteal (Dmp1-Cre) osteogenic cells causes cell senescence and aberrant bone formation. Although cell senescence during aging can impair the osteogenesis of marrow stromal cells (MSCs), which contributes to diseases with low bone mass such as osteoporosis, aging can also increase heterotopic ossification or mineralization in musculoskeletal soft tissues such as ligaments and tendons [7]. Notably, the abnormal periskeletal bone overgrowth in Chmp5^Ctsk mice was mainly mapped to insertion sites of tendons and ligaments on the bone (Fig. 1A and E), consistent with changes during aging. More broadly, aging can also cause abnormal ossification or mineralization in other body tissues, such as the heart valve [8, 9]. These different results reflect an aberrant state of ossification or mineralization in musculoskeletal tissues and throughout the body during aging. Based on the reviewer’s comment, we have discussed these results in the revised manuscript. Specifically, we add the following paragraph (Lines 453-462 in the combined manuscript):

“Notably, aging is associated with decreased osteogenic capacity in marrow stromal cells, which is related to conditions with low bone mass, such as osteoporosis. Rather, aging is also accompanied by increased ossification or mineralization in musculoskeletal soft tissues, such as tendons and ligaments [7]. In particular, the abnormal periskeletal overgrowth in Chmp5^Ctsk mice was predominantly mapped to insertion sites of tendons and ligaments on the bone (Fig. 1A and E), which is consistent with changes during aging and suggests that mechanical stress at these sites could contribute to the aberrant bone growth. These results suggest that skeletal stem/progenitor cells at different sites of musculoskeletal tissues could demonstrate different, even opposite outcomes in osteogenesis, due to cell senescence.”

(2) Co-culture of Chmp5-KO periskeletal progenitors with WT ones should be conducted to detect the migration and osteogenesis of WT cells in response to Chmp5-KO-induced senescent cells. In addition, the co-culture of WT periskeletal progenitors with senescent cells induced by H2O2, radiation, or from aged mice would provide more information.

In the present study, the increased proliferation and osteogenesis of CD45-;CD31-;GFP- periskeletal progenitors were shown as paracrine mechanisms of Chmp5-deficient periskeletal progenitors to promote bone overgrowth in Chmp5^Ctsk mice (Figs. 4F, G, and S4C-E). According to the reviewer’s suggestion, we have carried out the coculture experiment and the coculture of Chmp5^Ctsk with wild-type skeletal progenitors could promote osteogenesis of wild-type cells (Fig. S4B), which further supports the paracrine effect of Chmp5-deficient periskeletal progenitors.

In addition, the cause and outcome of cell senescence could be highly heterogeneous, and different causes of cell senescence can cause significantly distinct, even opposite outcomes. Although the coculture experiments of WT periskeletal progenitors with senescent cells induced by H2O2, radiation, or from aged mice are very interesting, these are beyond the scope of the current study.

(3) Many EVs were secreted from Chmp5-deleted periskeletal progenitors, compared to the rarely detected EVs around WT cells. Since EVs of BMSCs or osteoprogenitors show strong effects of promoting osteogenesis, did the EVs contribute to the enhanced osteogenesis induced by Chmp5defeciency? Author’s response:

This is an interesting question. Although we did not separately test the effect of EVs from Chmp5-deficient periskeletal progenitors on the osteogenesis of WT skeletal progenitors, the CD45-;CD31-;GFP- skeletal progenitor cells from Chmp5^Ctsk mice have an increased capacity of osteogenesis compared to corresponding cells from control animals (Figs. 4G and S4D). Also, the coculture of Chmp5-deficient with wild-type skeletal progenitors could enhance the osteogenesis of wild-type cells (Fig. S4B). These results suggest that EVs from Chmp5-deficient periskeletal progenitors could promote osteogenesis of neighboring WT skeletal progenitors. The specific functions of EVs of Chmp5-deficient periskeletal progenitors in regulating osteogenesis will be further investigated in future studies.

(4) EVs secreted from senescent cells propagate senescence and impair osteogenesis, why do EVs secreted from senescent cells induced by Chmp5-defeciency have opposite effects on osteogenesis?

The question is similar to comments #1 and #3 from this reviewer. First, the manifestations (including the secretory phenotype) and outcomes of cell senescence could be highly heterogeneous depending on inducers, tissue and cell contexts, and other factors such as “time”. Different causes of cell senescence could lead to different manifestations and outcomes, which have been discussed in the manuscript (Lines 381-383). Similarly, as mentioned above, skeletal stem/progenitor cells at different sites of musculoskeletal tissues could also demonstrate distinct, even opposite outcomes, as a result of cell senescence (Line 453-462). Second, CD45-;CD31-;GFP- periskeletal progenitor cells from Chmp5^Ctsk;Rosa26^26mTmG/+ mice have an increased capacity of proliferation and osteogenesis compared to corresponding cells from control animals (Figs. 4F, G and S4C-E). Furthermore, the conditioned medium of Chmp5-deficient skeletal progenitors promoted the proliferation of ATDC5 cells (Fig. 4E) and the coculture of Chmp5^Ctsk and wild-type periskeletal progenitors could enhance the osteogenesis of wild-type cells (Fig. S4B). Taken together, these results show paracrine actions of Chmp5-deficient periskeletal progenitors in promoting aberrant bone growth in Chmp5 conditional knockout mice. We also refer the reviewer to our responses to comments #1 and #3.

(5) The Chmp5-ctsk mice show accelerated aging-related phenotypes, such as hair loss and joint stiffness. Did Ctsk also label cells in hair follicles or joint tissue?

This is an interesting question. Although we did not check the expression of CHMP5 in hair follicles, which is outside the scope of the present study, the result in Fig. 1E showed the expression of Ctsk in joint ligaments, tendons, and their insertion sites on the bone (Lines 108-111). Notably, the periskeletal bone overgrowth in Chmp5^Ctsk mice was mainly mapped to insertion sites of ligaments and tendons on the bone, which have been discussed in the revised manuscript (Lines 456-460).

(6) Fifteen proteins were found to increase and five proteins to decrease in the cell supernatant of Chmp5^Ctsk periskeletal progenitors. How about SASP factors in the secretory profile?

The SASP phenotype and related factors of senescent cells could be highly heterogeneous depending on inducers, cell types, and timing of senescence [10, 11]. Most of the proteins we identified in the secretome analysis have previously been reported in the secretory profile of osteoblasts or involved in the regulation of osteogenesis. Although we were interested in changes in common SASP factors, such as cytokines and chemokines, the experiment did not detect these factors, probably due to their small molecular weights and the technical limitations of the mass-spec analysis. We have clarified this in the revised manuscript. Specifically, we add the following sentences (Lines 258-261):

“Notably, the secretome analysis did not detect common SASP factors, such as cytokines and chemokines, in the secretory profile of Chmp5^Ctsk periskeletal progenitors, probably due to their small molecular weights and the technical limitations of the mass-spec analysis.”

(7) D+Q treatment mitigates musculoskeletal pathologies in Chmp5 conditional knockout mice. In the previously published paper (CHMP5 controls bone turnover rates by dampening NF-κB activity in osteoclasts), inhibition of osteoclastic bone resorption rescues the aberrant bone phenotype of the Chmp5 conditional knockout mice. Whether the effects of D+Q on bone overgrowth is because of the inhibition of bone resorption?

This is an important question. We have discussed the potential off-target effect of senolytic drugs in Chmp5^Ctsk mice in the revised manuscript. Specifically, we add the following paragraph (Lines 441451):

“Furthermore, it is unclear whether the effect of senolytic drugs in Chmp5^Ctsk mice involves targeting osteoclasts other than osteogenic cells, as osteoclast senescence has not yet been evaluated. However, the efficacy of Q + D in targeting osteogenic cells, which is the focus of the current study, was confirmed in Chmp5^Dmp1 mice (Fig. 5C-E). Additionally, Q + D caused a higher cell apoptotic ratio in Chmp5^Ctsk compared to wild-type periskeletal progenitors in ex vivo culture (Fig. 5A), demonstrating the effectiveness of Q + D in targeting osteogenic cells in the Chmp5^Ctsk model. Furthermore, an alternative senolytic drug ABT-263 could also ameliorate periskeletal bone overgrowth in Chmp5^Ctsk mice (Fig. 5F). Together, these results confirm that osteogenic cell senescence is responsible for the bone overgrowth in Chmp5^Ctsk and Chmp5^Dmp1 mice and senolytic treatments are effective in alleviating these skeletal disorders.”

(8) The role of VPS4A in cell senescence should be measured to support the conclusion that CHMP5 regulates osteogenesis by affecting cell senescence.

We thank the reviewer for this suggestion. The current study mainly reports the function of CHMP5 in the regulation of skeletal progenitor cell senescence and osteogenesis. The roles of VPS4A in cell senescence and skeletal biology will be further explored in future studies. We have discussed this in the revised manuscript. Specifically, we add the following sentence (Lines 407-409):

“The roles of VPS4A in regulating musculoskeletal biology and cell senescence should be further explored in future studies.”

(9) Cell senescence with markers, such as p21 and H2AX, co-stained with GFP should be performed in the mouse models to indicate the effects of Chmp5 on cell senescence in vivo.

According to the reviewer’s suggestion, we have already performed immunostaining of γH2AX and colocalization with GFP in Chmp5^Ctsk;Rosa26^26mTmG/+ and Chmp5^Ctsk/+;Rosa26^26mTmG/+ mice. The results showed that there are more γH2AX+;GFP+ cells in the periskeletal overgrowth in Chmp5^Ctsk;Rosa26^26mTmG/+ mice compared to the periosteum of Chmp5^Ctsk/+;Rosa26^26mTmG/+ control animals. Because the γH2AX staining could stand as one of the critical results supporting the senescent phenotype of Chmp5-deficient periskeletal progenitors. We have added these results to Fig. 3E and put Fig. 3F in the original manuscript into Fig. S3E due to the space limitation in Figure 3. In sum, these results further enrich the senescent manifestations of Chmp5-deficient periskeletal progenitors.

(10) ADTC5 cell as osteochondromas cells line, is not a good cell model of periskeletal progenitors.

Maybe primary periskeletal progenitor cell is a better choice.

ATDC5 cells are typically used as a chondrocyte progenitor cell line. However, our previous study showed that ATDC5 cells could also be used as a reasonable cell model for periskeletal progenitors [12], which was mentioned in the manuscript (Lines 202-204). In addition, the results of ATDC5 cells were also verified in primary periskeletal progenitor cells in this study.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Despite the robust experimental framework and intriguing findings, there are several areas that require further attention to enhance the manuscript's overall quality and clarity:

(1) The manuscript could benefit from a more in-depth discussion of the tissue-specific roles of CHMP5, particularly in addressing why CHMP5 deficiency results in distinct outcomes in osteogenic cells as opposed to other cell types, such as osteoclasts. Expanding the discussion would greatly enhance the comprehensiveness and clarity of the manuscript.

Based on the reviewer’s suggestion, we have expanded the discussion of the distinct roles of CHMP5 in different cell types. Specifically, we state (Lines 433-439):

“Also, a previous study by Adoro et al. did not detect endolysosomal abnormalities in _Chmp5_deficient developmental T cells [1]. Since both osteoclasts and T cells are of hematopoietic origin, and meanwhile osteogenic cells and MEFs, which show endolysosomal abnormalities after CHMP5 deficiency, are of mesenchymal origin, it turns out that the function of CHMP5 in regulating the endolysosomal pathway could be cell lineage-specific, which remains clarified in future studies.”

(2) Given that Figures 1 and 2 suggest that the absence of Chmp5 (CHMP5Ctsk & CHMP5Dmp1) leads to disordered proliferation or mineralization of bone or osteoblasts, the manuscript should delve deeper into the potential links between these findings and aging-related processes, such as age-associated fibrosis. Providing clearer explanations and discussion on these connections would help present a more cohesive understanding of the results in the context of aging.

We thank the reviewer for this favorable suggestion. A feature of aging is heterotopic ossification or mineralization in musculoskeletal soft tissues, including tendons and ligaments [7]. Notably, the abnormal periskeletal bone formation in Chmp5^Ctsk mice in this study was mostly mapped to the insertion sites of tendons and ligaments on the bone (Fig. 1A and E), which is consistent with changes during aging and suggests that mechanical stress at these sites could be a contributor to periskeletal overgrowth. We have discussed these results in the revised manuscript. Specifically, we add the following paragraph (Lines 453-462):

“Notably, aging is associated with decreased osteogenic capacity in marrow stromal cells, which is related to conditions with low bone mass, such as osteoporosis. Rather, aging is also accompanied by increased ossification or mineralization in musculoskeletal soft tissues, such as tendons and ligaments [7]. In particular, the abnormal periskeletal overgrowth in Chmp5^Ctsk mice was predominantly mapped to the insertion sites of tendons and ligaments on the bone (Fig. 1A and E), which is consistent with changes during aging and suggests that mechanical stress at these sites could contribute to the aberrant bone growth. These results suggest that skeletal stem/progenitor cells at different sites of musculoskeletal tissues could demonstrate different, even opposite outcomes in osteogenesis, due to cell senescence.”

(3) The manuscript would be improved by a more refined analysis in Figures 3 and 5, particularly in relation to the use of senolytic drugs. Furthermore, a detailed discussion of the specificity and potential off-target effects of quercetin and dasatinib treatments in Chmp5-deficient mice would strengthen the therapeutic claims of these drugs.

In Figure 3, we have added additional experiments and results to strengthen the senescent phenotypes of Chmp5-deficient periskeletal progenitors, including significant enrichment of the SAUL_SEN_MAYO geneset (positively correlated with cell senescence) and the KAMMINGA_SENESCENCE geneset (negatively correlated with cell senescence) at the transcriptional level by GSEA analysis of RNA-seq data (Fig. S3F), and an increase of γH2AX+;GFP+ cells at the site of periskeletal overgrowth in Chmp5^Ctsk;Rosa26^26mTmG/+ mice compared to the periosteum of Chmp5^Ctsk/+;Rosa26^26mTmG/+ control mice (Fig. 3E). These results further enrich the senescent molecular manifestations of Chmp5-deficient periskeletal progenitors.

In Figure 5, we used an alternative senolytic drug ABT-263 to treat Chmp5^Ctsk mice, and this antisenescence treatment could also alleviate periskeletal bone overgrowth in this mouse model (Fig. 5F). Furthermore, we have also discussed the potential off-target effects of senolytic drugs in Chmp5^Ctsk mice. Specifically, we add the following paragraph (Lines 441-451):

“Furthermore, it is unclear whether the effect of senolytic drugs in Chmp5^Ctsk mice involves targeting osteoclasts other than osteogenic cells, as osteoclast senescence has not yet been evaluated. However, the efficacy of Q + D in targeting osteogenic cells, which is the focus of the current study, was confirmed in Chmp5^Dmp1 mice (Fig. 5C-E). Additionally, Q + D caused a higher cell apoptotic ratio in Chmp5^Ctsk compared to wild-type periskeletal progenitors in ex vivo culture (Fig. 5A), demonstrating the effectiveness of Q + D in targeting osteogenic cells in the Chmp5^Ctsk model. Furthermore, an alternative senolytic drug ABT-263 could also ameliorate periskeletal bone overgrowth in Chmp5^Ctsk mice (Fig. 5F). Together, these results confirm that osteogenic cell senescence is responsible for the bone overgrowth in Chmp5^Ctsk and Chmp5^Dmp1 mice and senolytic treatments are effective in alleviating these skeletal disorders.”

(4) The manuscript could be further enhanced by providing more details into how CHMP5 specifically regulates VPS4A protein levels. Notably, this is a central aspect of the paper linking CHMP5 to endolysosomal dysfunction.

We thank the reviewer for this important suggestion. One of the novel findings of this study is that CHMP5 regulates the protein level of VPS4A without affecting its RNA transcription. The mechanism of CHMP5 in the regulation of VPS4A protein will be reported in a separate study. However, we have discussed the potential mechanism in the manuscript (Lines 399-409). Specifically, we state:

“However, the mechanism of CHMP5 in the regulation of the VPS4A protein has not yet been studied. Since CHMP5 can recruit the deubiquitinating enzyme USP15 to stabilize IκBα in osteoclasts by suppressing ubiquitination-mediated proteasomal degradation [13], it is also possible that CHMP5 stabilizes the VPS4A protein by recruiting deubiquitinating enzymes and regulating the ubiquitination of VPS4A, which needs to be clarified in future studies. Notably, mutations in the VPS4A gene in humans can cause multisystemic diseases, including musculoskeletal abnormalities [14] (OMIM: 619273), suggesting that normal expression and function of VPS4A are important for musculoskeletal physiology. The roles of VPS4A in regulating musculoskeletal biology and cell senescence should be further explored in future studies.”

(5) The discussion section could be enriched by more thoroughly integrating the current findings with previous studies on CHMP5, particularly those exploring its role in osteoclast differentiation and NF-κB signaling.

The comment is similar to comment #1 of this reviewer. We have expanded the discussion of the distinct functions of CHMP5 in osteoclasts and osteogenic cells (Lines 424-439). We also refer the reviewer to our response to comment #1.

(6) Figure S4 D is incorrectly arranged and should be revised accordingly.

Sorry for the confusion. We have added additional annotations to make the images clearer. Now it is Fig. S4E in the revised manuscript.

Reviewer #2 (Recommendations for the authors):

(1) Abstract A clinical perspective or at least an outline is desirable.

The clinical importance of the findings of this study in understanding and treating musculoskeletal disorders of lysosomal storage diseases has been highlighted at the end of the abstract (Line 38).

(2) Introduction Header missing.

The protein name is BCL2, not Bcl2.

These have been corrected in the revised manuscript (Lines 41, 66).

(3) Results

The mouse phenotype experiments are well done.

Hmga1, Hmga2, Trp53, Ets1, and Txn1 are no typical senescence-associated genes. How about

Cdkn2a and Cdkn1a? These could easily be highlighted in Figure 3B.

Hmga1, Hmga2, Trp53, Ets1, and Txn1 are within the geneset of Reactome Cellular Senescence. Notably, only the protein levels of CDKN2A (p16) and CDKN1A (p21) showed significant changes (Fig. 3D) and the mRNA levels of Cdkn2a and Cdkn1a did not show significant changes according to RNAseq data. We have added the result of Cdkn2a and Cdkn1a mRNA levels to Fig. S3D in the revised manuscript. Also, we add the following sentences in the text (Lines 193-195):

“However, the mRNA levels of Cdkn2a (p16) and Cdkn1a (p21) did not show significant changes according to the RNA-seq analysis (Fig. S3D).”

Figure 3C: Which gene set was used for SASP?

The SASP geneset in Fig. 3C was from the Reactome database. We have clarified this in the figure legend of Fig. 3 in the revised manuscript (Line 1013).

The symptom "joint stiffness/contracture" could also be due to skeletal abnormalities related to Chmp5Ctsk.

Joint stiffness/contracture during aging is mainly the result of heterotopic ossification or mineralization in musculoskeletal soft tissues, including ligaments, tendons, joint capsules, and their insertion sites on the bone. Notably, the periskeletal bone overgrowth in Chmp5^Ctsk mice was mainly mapped to the insertion sites of tendons, ligaments, and joint capsules on the bone, which are consistent with changes during aging. These results have been discussed in the revised manuscript (Lines 456-460).

Overall, cellular senescence needs at least Cdkn2a and/or Cdkn1a and another marker, i.e. SenMayo or telomere-associated foci or senescence-associated distortion of satellites.

We have run GSEA with the SenMayo geneset and the result is added in Fig. S3F in the revised manuscript. Also, we ran another geneset KAMMINGA_SENESCENCE which includes genes downregulated in cell senescence. Both genesets are significantly enriched in Chmp5-deficient periskeletal progenitors based on RNA-seq data (Fig. S3F).

In addition, we also performed immunostaining for another senescence marker γH2AX and the results showed that there are more γH2AX+;GFP+ cells in periskeletal overgrowth in Chmp5^Ctsk;Rosa26^mTmG/+ mice compared to the periosteum of Chmp5^Ctsk/+;Rosa26^26mTmG/+ control animals (Fig. 3E).

Together, these results further support the senescent phenotypes of Chmp5-deficient periskeletal progenitors.

For Figure 4A: What is the NES?

The value of NES has been added in Fig. 4A.

The existence of vesicles does not necessarily indicate more SASP. Author’s response:

We agree with the reviewer that the secretion of extracellular vesicles is not directly correlated with the SASP. In this study, the increased secretory vesicles around Chmp5^Ctsk periskeletal progenitors represent a secretory phenotype of Chmp5-deficient periskeletal progenitors and have paracrine effects in the abnormal bone growth in Chmp5 conditional knockout mice as shown in Figs. 4 and S4.

The Chmp5-deficient cells COULD promote the proliferation and osteogenesis of other progenitors, but they might as well not. And if this is through the SASP, is completely unresolved.

CD45^-;CD31^-;GFP^- periskeletal progenitor cells from Chmp5^Ctsk;Rosa26^26mTmG/+ mice showed an increased capacity of proliferation and osteogenesis compared to the corresponding cells from control animals (Figs. 4F, G, and S4C-E). Also, the conditioned medium of Chmp5-deficient skeletal progenitors promoted the proliferation of ATDC5 cells (Fig. 4E). In addition, the coculture of Chmp5^Ctsk and wild-type periskeletal progenitors could enhance the osteogenesis of wild-type cells (Fig. S4B). These results demonstrate the paracrine actions of Chmp5-deficient periskeletal progenitors in promoting aberrant bone growth in Chmp5^Ctsk and Chmp5^Dmp1 mice. However, factors that mediate the paracrine effects of Chmp5-deficient periskeletal progenitors remain further clarified in future studies.

This has been mentioned in the revised manuscript (Lines 263-265).

Figure 5C: The time points are not labelled.

The time point of 16 weeks was mentioned in the Method section and now it has been added in the legend of Fig. 5C (Line 1063).

Figure B: Was the bone's overall thickness quantified?

In Fig. 5B, bone morphology in Chmp5^Ctsk mice is irregular and difficult to quantify. Therefore, we did not qualify the overall bone thickness in these animals. However, the thickness of the cortical bone was measured by micro-CT analysis in Chmp5^Dmp1 mice after treatment with Q + D (Fig. 5E). Also, we have added the image of the gross femur thickness of Chmp5^Dmp1 mice before and after treatment with Q + D in Fig. 5E.

It needs to be demonstrated that the actual cell number was reduced after D+Q treatment.

The Q + D treatment caused a higher cell apoptotic ratio in Chmp5^Ctsk vs. wild-type skeletal progenitors in ex vivo culture (Fig. 5A), suggesting its effectiveness in targeting the senescent periskeletal progenitors.

Figure 7A: What is the NES?

The value of NES has been added in Fig. 7A.

Reviewer #3 (Recommendations for the authors):

(1) The WB analysis should be quantified in the Figure 3D.

In Fig. 3D, the numbers above the lanes of p16 and p21 are the results of the quantification of the band intensity after normalization by β-Actin, which has been indicated in the Figure legend (Lines 10151017).

(2) The osteoblast detection should be measured with antibody against osteocalcin.

This comment did not specify what result the reviewer was referring to. However, most of the experiments in this study were performed in primary skeletal progenitor cells or cell lines. Osteoblasts were not specifically involved in the current study.

(3) Co-culture of Chmp5-KO periskeletal progenitors with WT ones should be conducted to detect the migration and osteogenesis of WT cell in response to Chmp5-KO induced senescent cells. In addition, co-culture of WT periskeletal progenitors with senescent cells induced by H2O2, radiation, or from aged mice would provide more information.

This comment is the same as comment #2 in the Public Reviews of this Reviewer. We already carried out the coculture experiment of Chmp5-deficient and wild-type periskeletal progenitors and the result was added in Fig. S4B. We refer the reviewer to our response to comment #2 in the Public Reviews for more details.

(4) D+Q treatment mitigates musculoskeletal pathologies in Chmp5 conditional knockout mice. In the previously published paper (CHMP5 controls bone turnover rates by dampening NF-κB activity in osteoclasts), inhibition of osteoclastic bone resorption rescues the aberrant bone phenotype of the Chmp5 conditional knockout mice. Is the effect of D+Q on bone overgrowth because of the inhibition of bone resorption?

This comment is the same as comment #7 in the Public Reviews of this Reviewer, where we already address this question.

(5) The role of VPS4A in cell senescence should be measured to support the conclusion that CHMP5 regulates osteogenesis through affecting cell senescence.

This comment is the same as comment #8 in the Public Reviews of this Reviewer. We refer the reviewer to our response to that comment.

(6) Cell senescence with the markers, such as p21 and H2AX, co-stained with GFP should be performed in the mouse models to indicate the effects of Chmp5 on cell senescence in vivo.

This comment is the same as comment #9 in the Public Reviews of this Reviewer. We have performed immunostaining of γH2AX and colocalization with GFP in Chmp5^Ctsk;Rosa26^26mTmG/+ mice and Chmp5^Ctsk/+;Rosa26^26mTmG/+ mice. The results showed that there were more γH2AX+;GFP+ cells at the site of periskeletal overgrowth in Chmp5^Ctsk;Rosa26^26mTmG/+ mice compared to the periosteum of Chmp5^Ctsk/+;Rosa26^26mTmG/+ control mice (Fig. 3E). We also refer the reviewer to our response to comment #9 in Public Reviews.

(7) ADTC5 cell as osteochondromas cells line, is not a good cell model of periskeletal progenitors.

Maybe primary periskeletal progenitor cell is a better choice.

This comment is the same as comment #10 in the Public Reviews of this Reviewer. Our previous study showed that ATDC5 cells could be used as a reasonable cell model for periskeletal progenitors [12]. Also, most of the results of ATDC5 cells in the current study were verified in primary periskeletal progenitors.

References

(1) Adoro S, Park KH, Bettigole SE, Lis R, Shin HR, Seo H, et al. Post-translational control of T cell development by the ESCRT protein CHMP5. Nat Immunol. 2017;18(7):780-90. doi: 10.1038/ni.3764. PubMed PMID: 28553951.

(2) Kassem M, Bianco P. Skeletal stem cells in space and time. Cell. 2015;160(1-2):17-9. doi: 10.1016/j.cell.2014.12.034. PubMed PMID: 25594172.

(3) Chan CKF, Gulati GS, Sinha R, Tompkins JV, Lopez M, Carter AC, et al. Identification of the Human Skeletal Stem Cell. Cell. 2018;175(1):43-56 e21. doi: 10.1016/j.cell.2018.07.029. PubMed PMID: 30241615.

(4) Debnath S, Yallowitz AR, McCormick J, Lalani S, Zhang T, Xu R, et al. Discovery of a periosteal stem cell mediating intramembranous bone formation. Nature. 2018;562(7725):133-9. Epub 20180924. doi: 10.1038/s41586-018-0554-8. PubMed PMID: 30250253; PubMed Central PMCID: PMCPMC6193396.

(5) Mizuhashi K, Ono W, Matsushita Y, Sakagami N, Takahashi A, Saunders TL, et al. Resting zone of the growth plate houses a unique class of skeletal stem cells. Nature. 2018;563(7730):254-8. doi: 10.1038/s41586-018-0662-5. PubMed PMID: 30401834; PubMed Central PMCID: PMCPMC6251707.

(6) Zhang F, Wang Y, Zhao Y, Wang M, Zhou B, Zhou B, et al. NFATc1 marks articular cartilage progenitors and negatively determines articular chondrocyte differentiation. Elife. 2023;12. Epub 20230215. doi: 10.7554/eLife.81569. PubMed PMID: 36790146; PubMed Central PMCID: PMCPMC10076019.

(7) Dai GC, Wang H, Ming Z, Lu PP, Li YJ, Gao YC, et al. Heterotopic mineralization (ossification or calcification) in aged musculoskeletal soft tissues: A new candidate marker for aging. Ageing Res Rev. 2024;95:102215. Epub 20240205. doi: 10.1016/j.arr.2024.102215. PubMed PMID: 38325754.

(8) Mohler ER, 3rd, Adam LP, McClelland P, Graham L, Hathaway DR. Detection of osteopontin in calcified human aortic valves. Arterioscler Thromb Vasc Biol. 1997;17(3):547-52. doi: 10.1161/01.atv.17.3.547. PubMed PMID: 9102175.

(9) Mohler ER, 3rd, Gannon F, Reynolds C, Zimmerman R, Keane MG, Kaplan FS. Bone formation and inflammation in cardiac valves. Circulation. 2001;103(11):1522-8. doi: 10.1161/01.cir.103.11.1522. PubMed PMID: 11257079.

(10) Paramos-de-Carvalho D, Jacinto A, Saude L. The right time for senescence. Elife. 2021;10. Epub 2021/11/11. doi: 10.7554/eLife.72449. PubMed PMID: 34756162; PubMed Central PMCID: PMCPMC8580479.

(11) Wiley CD, Campisi J. The metabolic roots of senescence: mechanisms and opportunities for intervention. Nat Metab. 2021;3(10):1290-301. Epub 2021/10/20. doi: 10.1038/s42255-021-00483-8. PubMed PMID: 34663974; PubMed Central PMCID: PMCPMC8889622.

(12) Ge X, Tsang K, He L, Garcia RA, Ermann J, Mizoguchi F, et al. NFAT restricts osteochondroma formation from entheseal progenitors. JCI Insight. 2016;1(4):e86254. doi: 10.1172/jci.insight.86254. PubMed PMID: 27158674; PubMed Central PMCID: PMCPMC4855520.

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Author response:

eLife Assessment

This manuscript introduces a useful protein-stability-based fitness model for simulating protein evolution and unifying non-neutral models of molecular evolution with phylogenetic models. The model is applied to four viral proteins that are of structural and functional importance. The justification of some hypotheses regarding fitness is incomplete, as well as the evidence for the model's predictive power, since it shows little improvement over neutral models in predicting protein evolution.

We thank for the constructive comments that helped improve our study. Regarding the comment about justification of fitness, we will include in the revised manuscript additional information to support the relevance of modeling protein evolution accounting for protein folding stability. We agree that increasing the parameterization of the developed birth-death model is interesting, if it does not lead to overfitting. The model presented considers the fitness of protein variants to determine their reproductive success through the corresponding birth and death rates, varying among lineages, and it is biologically meaningful and technically correct (Harmon 2019). Following a suggestion of the first reviewer to allow variation of the global birth-death rate among lineages, we will additionally incorporate this aspect into the model and evaluate its performance with the data for the evaluation of the models. The integration of structurally constrained substitution models of protein evolution, as Markov models, into the birth-death process was made following standards approaches of molecular evolution in population genetics (Yang 2006; Carvajal-Rodriguez 2010; Arenas 2012; Hoban, et al. 2012) and we will provide more information about it in the revised manuscript. Regarding the predictive power, our study showed good accuracy in predicting the real folding stability of forecasted protein variants. On the other hand, predicting the exact sequences proved to be more challenging, indicating needs in the field of substitution models of molecular evolution. Altogether, we believe our findings provide a significant contribution to the field, as accurately forecasting the folding stability of future real proteins is fundamental for predicting their protein function and enabling a variety of applications. Additionally, we implemented the models into a freely available computer framework, with detailed documentation and diverse practical examples.

Public Reviews:

Reviewer #1 (Public review):

Summary:

Ferreiro et al. present a method to simulate protein sequence evolution under a birth-death model where sequence evolution is constrained by structural constraints on protein stability. The authors then use this model to explore the predictability of sequence evolution in several viral structural proteins. In principle, this work is of great interest to molecular evolution and phylodynamics, which have struggled to couple non-neutral models of sequence evolution to phylodynamic models like birth-death. Unfortunately, though, the model shows little improvement over neutral models in predicting protein evolution, and this ultimately appears to be due to fundamental conceptual problems with how fitness is modeled and linked to the phylodynamic birth-death model.

We thank the reviewer for the positive comments about our work.

Regarding predictive power, the study showed a good accuracy in predicting the real folding stability of forecasted protein variants under a selection model, but not under a neutral model. However, predicting the exact sequences was more challenging. For example, amino acids with similar physicochemical properties can result in similar folding stability while differ in the specific sequence, more accurate substitution models of molecular evolution are required in the field. We consider that forecasting the folding stability of future real proteins is an important advancement in forecasting protein evolution, given the essential role of folding stability in protein function and its variety of applications. Regarding the conceptual concerns related to fitness modeling, we clarify this issue in detail in our responses to the specific comments below.

Major concerns:

(1) Fitness model: All lineages have the same growth rate r = b-d because the authors assume b+d=1. But under a birth-death model, the growth r is equivalent to fitness, so this is essentially assuming all lineages have the same absolute fitness since increases in reproductive fitness (b) will simply trade off with decreases in survival (d). Thus, even if the SCS model constrains sequence evolution, the birth-death model does not really allow for non-neutral evolution such that mutations can feed back and alter the structure of the phylogeny.

We thank the reviewer for this comment that aims to improve the realism of our model. In the model presented (but see later for another model derived from the proposal of the reviewer and that we are now implementing into the framework and applying to the data used for the evaluation of the models), the fitness predicted from a protein variant is used to obtain the corresponding birth rate of that variant. In this way, protein variants with high fitness have high birth rates leading to overall more birth events, while protein variants with low fitness have low birth rates resulting in overall more extinction events, which has biological meaning for the study system. The statement “All lineages have the same growth rate r = b-d” in our model is incorrect because, in our model, b and d can vary among lineages according to the fitness. For example, a lineage might have b=0.9, d=0.1, r=0.8, while another lineage could have b=0.6, d=0.4, r=0.2. Indeed, the statement “this is essentially assuming all lineages have the same absolute fitness” is incorrect. Clearly, assuming that all lineages have the same fitness would not make sense, in that situation the folding stability of the forecasted protein variants would be similar under any model, which is not the case as shown in the results. In our model, the fitness affects the reproductive success, where protein variants with a high fitness have higher birth rates leading to more birth events, while those with lower fitness have higher death rates leading to more extinction events. This parameterization is meaningful for protein evolution because the fitness of a protein variant can affect its survival (birth or extinction) without necessarily affecting its rate of evolution. While faster growth rate can sometimes be associated with higher fitness, a variant with high fitness does not necessarily accumulate substitutions at a faster rate. Regarding the phylogenetic structure, the model presented considers variable birth and death events across different lineages according to the fitness of the corresponding protein variants, and this alters the derived phylogeny (i.e., protein variants selected against can go extinct while others with high fitness can produce descendants). We are not sure about the meaning of the term “mutations can feed back” in the context of our system. Note that we use Markov models of evolution, which are well-stablished in the field (despite their limitations), and substitutions are fixed mutations, which still could be reverted later if selected by the substitution model (Yang 2006). Altogether, we find that the presented birth-death model is technically correct and appropriate for modeling our biological system. Its integration with structurally constrained substitution (SCS) models of protein evolution, as Markov models, is correct following general approaches of molecular evolution in population genetics (Yang 2006; Carvajal-Rodriguez 2010; Arenas 2012; Hoban, et al. 2012). We will provide a more detailed description of the model in the revised manuscript.

Apart from these clarifications about the birth-death model used, we understand the point of the reviewer and following the suggestion we are now incorporating an additional birth-death model that accounts for variable global birth-death rate among lineages. Specifically, we are following the model proposed by Neher et al (2014), where the death rate is considered as 1 and the birth rate is modeled as 1 + fitness. In this model, the global birth-death rate varies among lineages. We are now implementing this model into the computer framework and applying it to the data used for the evaluation of the models. Preliminary results, which will be finally presented in the revised manuscript, indicate that this model yields similar predictive accuracy compared to the previous birth-death model. If this is confirmed, accounting for variability in the global birth-death rate does not appear to play a major role in the studied systems of protein evolution. We will present this additional birth-death model and its results in the revised manuscript.

(2) Predictive performance: Similar performance in predicting amino acid frequencies is observed under both the SCS model and the neutral model. I suspect that this rather disappointing result owes to the fact that the absolute fitness of different viral variants could not actually change during the simulations (see comment #1).

The study shows similar performance in predicting the sequences of the forecasted proteins under both the SCS model and the neutral model, but shows differences in predicting the folding stability of the forecasted proteins between these models. Indeed, as explained in the previous answer, the birth-death model accounts for variation in fitness among lineages, leading to differences among lineages in reproductive success. The new birth-death model that we are now implementing, which incorporates variation of the global birth-death rate among lineages, is producing similar preliminary results. In addition to these considerations, it is known that SCS models applied to phylogenetics (such as ancestral molecular reconstruction) can model protein evolution with high accuracy in terms of folding stability. However, inferring sequences (i.e., ancestral sequences) is considerably more challenging even for ancestral molecular reconstruction (Arenas, et al. 2017; Arenas and Bastolla 2020). The observed sequence diversity is much greater than the observed structural diversity (Illergard, et al. 2009; Pascual-Garcia, et al. 2010), and substitutions among amino acids with similar physicochemical properties can result in protein variants with similar folding stability but different specific amino acid sequences; further work is demanded in the field of substitution models of molecular evolution. We will expand the discussion of this aspect in the revised manuscript.

(3) Model assessment: It would be interesting to know how much the predictions were informed by the structurally constrained sequence evolution model versus the birth-death model. To explore this, the authors could consider three different models: 1) neutral, 2) SCS, and 3) SCS + BD. Simulations under the SCS model could be performed by simulating molecular evolution along just one hypothetical lineage. Seeing if the SCS + BD model improves over the SCS model alone would be another way of testing whether mutations could actually impact the evolutionary dynamics of lineages in the phylogeny.

In the present study, we compare the neutral model + birth-death (BD) with the SCS model + BD. Markov substitution models Q are applied upon an evolutionary time (i.e., branch length, t) and this allows to determine the probability of substitution events during that time period [P(t) = exp (Qt)]. This approach is traditionally used in phylogenetics to model the incorporation of substitutions over time. Therefore, to compare the neutral and SCS models, an evolutionary time is required, in this case it is provided by the birth-death process. The suggestions 1) and 2) cannot be compared without an underlined evolutionary history. However, comparisons in terms of likelihood, and other aspects, between models that ignore the protein structure and the implemented SCS models are already available in our previous studies based on coalescent simulations or given phylogenetic trees (Arenas, et al. 2013; Arenas, et al. 2015). There, SCS models produced proteins with more realistic folding stability than models that ignore evolutionary constraints from the protein structure, and those findings are consistent with the results from the present study where we explore the application of these models to forecasting protein evolution. We would like to emphasize that forecasting the folding stability of future real proteins is a significant and novel finding, folding stability is fundamental to protein function and has diverse implications. While accurately forecasting the exact sequences would indeed be ideal, this remains a challenging task with current substitution models. In this regard, we will discuss in the revised manuscript the need of developing more accurate substitution models.

(4) Background fitness effects: The model ignores background genetic variation in fitness. I think this is particularly important as the fitness effects of mutations in any one protein may be overshadowed by the fitness effects of mutations elsewhere in the genome. The model also ignores background changes in fitness due to the environment, but I acknowledge that might be beyond the scope of the current work.

This comment made us realize that more information about the features of the implemented SCS models should be included in the manuscript. In particular, the implemented SCS models consider a negative design based on the observed residue contacts in nearly all proteins available in the Protein Data Bank (Arenas, et al. 2013; Arenas, et al. 2015). This data is provided as an input file and it can be updated to incorporate new structures (see the framework documentation and the practical examples). Therefore, the prediction of folding stability is a combination of positive design (direct analysis of the target protein) and negative design (consideration of background proteins to reduce biases), thus incorporating background molecular diversity. This important feature was not sufficiently described in the manuscript, and we will add more details in the revised version. Regarding the fitness caused by the environment, we agree with the reviewer. This is a challenge for any method aiming to forecast evolution, as future environmental shifts are inherently unpredictable and may impact the accuracy of the predictions. Although one might attempt to incorporate such effects into the model, doing so risks overparameterization, especially when the additional factors are uncertain or speculative. We will include a discussion in the revised manuscript about our perspective on the potential effects of environmental changes on forecasting evolution.

(5) In contrast to the model explored here, recent work on multi-type birth-death processes has considered models where lineages have type-specific birth and/or death rates and therefore also type-specific growth rates and fitness (Stadler and Bonhoeffer, 2013; Kunhert et al., 2017; Barido-Sottani, 2023). Rasmussen & Stadler (eLife, 2019) even consider a multi-type birth-death model where the fitness effects of multiple mutations in a protein or viral genome collectively determine the overall fitness of a lineage. The key difference with this work presented here is that these models allow lineages to have different growth rates and fitness, so these models truly allow for non-neutral evolutionary dynamics. It would appear the authors might need to adopt a similar approach to successfully predict protein evolution.

We agree with the reviewer that robust birth-death models have been developed applying statistics and, in many cases, the primary aim of those studies is the development and refinement of the model itself. Regarding the study by Rasmussen and Stadler 2019, it incorporates an external evaluation of mutation events where the used fitness is specific for the proteins investigated in that study, which may pose challenges for users interested in analyzing other proteins. In contrast, our study takes a different approach. We implement a fitness function that can be predicted and evaluated for any type of protein (Goldstein 2013), making it broadly applicable. In addition, we provide a freely available and well-documented computational framework to facilitate its use. The primary aim of our study is not the development of novel or complex birth-death models. Rather, we aim to explore the integration of a standard birth-death model with structurally constrained substitution models for the purpose of predicting protein evolution. In the context of protein evolution, substitution models are a critical factor (Liberles, et al. 2012; Wilke 2012; Bordner and Mittelmann 2013; Echave, et al. 2016; Arenas, et al. 2017; Echave and Wilke 2017), and their combination with a birth-death model constitutes a first approximation upon which next studies can build to better understand this biological system. We will include these considerations in the revised manuscript.

Reviewer #2 (Public review):

Summary:

In this study, "Forecasting protein evolution by integrating birth-death population models with structurally constrained substitution models", David Ferreiro and co-authors present a forward-in-time evolutionary simulation framework that integrates a birth-death population model with a fitness function based on protein folding stability. By incorporating structurally constrained substitution models and estimating fitness from ΔG values using homology-modeled structures, the authors aim to capture biophysically realistic evolutionary dynamics. The approach is implemented in a new version of their open-source software, ProteinEvolver2, and is applied to four viral proteins from HIV-1 and SARS-CoV-2.

Overall, the study presents a compelling rationale for using folding stability as a constraint in evolutionary simulations and offers a novel framework and software to explore such dynamics. While the results are promising, particularly for predicting biophysical properties, the current analysis provides only partial evidence for true evolutionary forecasting, especially at the sequence level. The work offers a meaningful conceptual advance and a useful simulation tool, and sets the stage for more extensive validation in future studies.

We also thank this reviewer for the positive comments on our study. Regarding the predictive power, our results showed good accuracy in predicting the folding stability of the forecasted protein variants. However, predicting the specific sequences of these variants is more challenging. For example, forecasting in amino acids with similar physicochemical properties can result in different sequences but in similar folding stability. We believe that these findings are realistic and interesting as they indicate that while forecasting folding stability is feasible, forecasting the specific sequence evolution is more complex that one could anticipate.

Strengths:

The results demonstrate that fitness constraints based on protein stability can prevent the emergence of unrealistic, destabilized variants - a limitation of traditional, neutral substitution models. In particular, the predicted folding stabilities of simulated protein variants closely match those observed in real variants, suggesting that the model captures relevant biophysical constraints.

We agree with the reviewer and appreciate the consideration that forecasting the folding stability of future real proteins is a relevant finding. For instance, folding stability is fundamental for protein function and affects several other molecular properties.

Weaknesses:

The predictive scope of the method remains limited. While the model effectively preserves folding stability, its ability to forecast specific sequence content is not well supported.

It is known that structurally constrained substitution (SCS) models applied to phylogenetics (such as ancestral molecular reconstruction) can model protein evolution with high accuracy in terms of folding stability, while inferring sequences (i.e., ancestral sequences) remains considerably more challenging (Arenas, et al. 2017; Arenas and Bastolla 2020). The observed sequence diversity is much higher than the observed structural diversity (Illergard, et al. 2009; Pascual-Garcia, et al. 2010), and substitutions between amino acids with similar physicochemical properties can result in protein variants with similar folding stability but with different specific amino acid composition. We will expand the discussion of this aspect in the manuscript.

Only one dataset (HIV-1 MA) is evaluated for sequence-level divergence using KL divergence; this analysis is absent for the other proteins. The authors use a consensus Omicron sequence as a representative endpoint for SARS-CoV-2, which overlooks the rich longitudinal sequence data available from GISAID. The use of just one consensus from a single time point is not fully justified, given the extensive temporal and geographical sampling available. Extending the analysis to include multiple timepoints, particularly for SARS-CoV-2, would strengthen the predictive claims. Similarly, applying the model to other well-sampled viral proteins, such as those from influenza or RSV, would broaden its relevance and test its generalizability.

The evaluation of forecasting evolution using real datasets is complex due to several conceptual and practical aspects. In contrast to traditional phylogenetic reconstruction of past evolutionary events and ancestral sequences, forecasting evolution often begins with a variant that is evolved forward in time and requires a rough fitness landscape to select among possible future variants (Lässig, et al. 2017). Another concern for validating the method is the need to know the initial variant that gives rise to the corresponding forecasted variants, and it is not always known. Thus, we investigated systems where the initial variant, or a close approximation, is known, such as scenarios of in vitro monitored evolution. In the case of SARS-CoV-2, the Wuhan variant is commonly used as the starting variant of the pandemic. Next, since forecasting evolution is highly dependent on the used model of evolution, unexpected external factors can be dramatic for the predictions. For this reason, systems with minimal external influences provide a more controlled context for evaluating forecasting evolution. For instance, scenarios of in vitro monitored virus evolution avoid some external factors such as host immune response. Another important aspect is the availability of data at two (i.e., present and future) or more time points along the evolutionary trajectory, with sufficient genetic divergence between them to identify clear evolutionary signatures. Additionally, using consensus sequences can help mitigate effects from unfixed mutations, which should not be modeled by a substitution model of evolution. Altogether, not all datasets are appropriate to properly evaluate forecasting evolution. We will include these considerations in the revised manuscript.

Sequence comparisons based on the KL divergence require, at the studied time point, an observed distribution of amino acid frequencies among sites and an estimated distribution of amino acid frequencies among sites. In the study datasets, this is only the case for the HIV-1 MA dataset, which belongs to a previous study from one of us and collaborators where we obtained at least 20 independent sequences at each sampling point (Arenas, et al. 2016). We will provide additional information on this aspect in the manuscript.

Regarding the Omicron dataset, we used 384 curated sequences of the Omicron variant of concern to construct the study dataset and we believe that it is a representative sample. The sequence used for the initial time point was the Wuhan variant (Wu, et al. 2020), which is commonly assumed to be the origin of the pandemic in SARS-CoV-2 studies. As previously indicated, the use of consensus sequences is convenient to avoid variants with unfixed mutations. Regarding extending the analysis to other timepoints (other variants of concern), we kindly disagree because Omicron is the variant of concern with the highest genetic distance to the Wuhan variant, and a high genetic distance is required to properly evaluate the prediction method. We noted that earlier variants of concern show a small number of fixed mutations in the study proteins, despite the availability of large numbers of sequences in databases such as GISAID.

Additionally, we investigated the evolutionary trajectories of HIV-1 protease (PR) in 12 intra-host viral populations.

Next, following the proposal of the reviewer, we will incorporate the analysis of an additional viral dataset (probably influenza following the suggestion of the reviewer) to further assess the generalizability of the method. Still, as previously indicated, not all datasets are suitable for a proper evaluation of forecasting evolution. Factors such as the shape of the fitness landscape and the amount of genetic variation over time can influence the accuracy of predictions. We will present the results of the analysis of the new data in the revised manuscript.

It would also be informative to include a retrospective analysis of the evolution of protein stability along known historical trajectories. This would allow the authors to assess whether folding stability is indeed preserved in real-world evolution, as assumed in their model.

Our present study is not focused on investigating the evolution of the folding stability over time, although it provides this information indirectly at the studied time points. Instead, the present study shows that the folding stability of the forecasted protein variants is similar to the folding stability of the corresponding real protein variants for diverse viral proteins, which is an important evaluation of the method. Next, the folding stability can indeed vary over time in both real and modeled evolutionary scenarios, and our present study is not in conflict with this. In that regard, which is not the aim of our present study, some previous phylogenetic-based studies have reported temporal fluctuations in folding stability for diverse data (Arenas, et al. 2017; Olabode, et al. 2017; Arenas and Bastolla 2020; Ferreiro, et al. 2022).

Finally, a discussion on the impact of structural templates - and whether the fixed template remains valid across divergent sequences - would be valuable. Addressing the possibility of structural remodeling or template switching during evolution would improve confidence in the model's applicability to more divergent evolutionary scenarios.

This is an important point. For the datasets that required homology modeling (in several cases it was not necessary because the sequence was present in a protein structure of the PDB), the structural templates were selected using SWISS-MODEL, and we applied the best-fitting template. We will include additional details about the parameters of the homology modeling in the revised version. Indeed, our method assumes that the protein structure is maintained over the studied evolutionary time, which can be generally reasonable for short timescales where the structure is conserved (Illergard, et al. 2009; Pascual-Garcia, et al. 2010). Over longer evolutionary timescales, structural changes may occur, and in such cases, modeling the evolution of the protein structure would be necessary. To our knowledge, modeling the evolution of the protein structure remains a challenging task that requires substantial methodological developments. Recent advances in artificial intelligence, particularly in protein structure prediction from sequence, may offer promising tools for addressing this challenge. However, we believe that evaluating such approaches in the context of structural evolution would be difficult, especially given the limited availability of real data with known evolutionary trajectories involving structural change. In any case, this is probably an important direction for future research. We will include this discussion in the revised manuscript.

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Author response:

Reviewer #1 (Public review):

(1) The broader significance of the findings needs to be better articulated. While the authors emphasize that comparing adaptive traits in sympatry and allopatry provides insights into selective processes shaping reproductive isolation and coexistence, it is unclear what key conceptual or theoretical questions are being addressed. Are these patterns expected under certain evolutionary scenarios? Have they been empirically demonstrated in other systems? The authors should explicitly state the overarching research question, incorporate some predictions, and better contextualize their findings within the existing literature. If the results challenge or support previous work, that should be highlighted to strengthen the study's importance in a broader context.

We thank the reviewer for their valuable feedback. We understand that the framing of the results and the discussion did not allow to highlight the broader significance of our findings. In the revised version of the manuscript, we will explicitly mention the theoretical questions asked and our hypotheses in the introduction, and better compare our results to pre-existing examples from the literature.

(2) The motivation for studying visual signals and mate choice in allopatric populations (i.e., at the intraspecific level) is not well articulated, leaving their role in the broader narrative unclear. In particular, the rationale behind experiments 1, 2, and 3 is not well defined, as the authors have not made a strong case for the need for these intraspecific comparisons in the introduction. This issue is further compounded by the authors' primary focus on signal evolution in sympatry throughout both the results and the discussion. For instance, the divergence of iridescence in allopatry is a potentially interesting result. But the authors have not discussed its implications.

Overall, given that the primary conclusions are based on results and analyses in sympatry, the role of allopatric populations in shaping these conclusions needs to be better integrated and justified.

Without a stronger link between the comparative framework and the study's key takeaways, the use of allopatric populations feels somewhat peripheral rather than central to the study's aim.

Since the primary conclusions remain valid even without the allopatric comparisons, their inclusion requires a clearer rationale.

We recognize that the current manuscript places more emphasis on the sympatric Morpho population, and that the analysis and the discussion of the results regarding the allopatric Morpho population were underdeveloped. In the revised version, we plan to address this by (1) developing the rationale behind the male choice experiments performed on the allopatric population. We will argue that intraspecific comparison helps identify the traits involved in mate preference within species (iridescent color and/or wing pattern) and that those results can be compared to the interspecific mate choice results to identify the traits involved in species recognition. To explain the relevance of the comparison with the allopatric population, we will also (2) strengthen expectations on the effect of species interactions on the evolution of traits and mate recognition in sympatric populations vs. allopatric populations.

(3) While the authors demonstrate that iridescence is indistinguishable to predators in sympatry, they overstate the role of predation in driving convergence. The present study does not experimentally demonstrate that iridescence in this species has a confusion effect or contributes to evasive mimicry. Alternatively, convergence could result from other selective forces, such as signal efficacy due to environmental conditions, rather than being solely driven by predation.

We acknowledge that this study neither demonstrates that iridescence contributes to evasive mimicry nor that predation is the driver of the convergence in iridescence. We will tone down the interpretation of the results in the discussion and state that predation is not the only selective pressure that could have promoted a convergent evolution of iridescence in sympatric species, although this observation is consistent with the evasive mimicry hypothesis.

Reviewer #2 (Public review):

My only major comment concerns the authors' favoured explanation for aposematism (or evasive mimicry) for convergence among species, which is based upon the you-can't-catch-me hypothesis first presented by Young 1971. Although there is supporting work showing that iridescent-like stimuli are more difficult to precisely localize by a range of viewers, most of the evidence as applied to the Morpho system is circumstantial, and I'm not certain that there is widespread acceptance of this hypothesis. Given that the present study deals with closely-related (sub)species, one alternative explanation - a "null" hypothesis of sorts - is for a lack of divergence (from a common starting point) as opposed to evolutionary convergence per se. in other words, two subspecies are likely to retain ancestral character states unless there is selection that causes them to diverge. I feel that the manuscript would benefit from a discussion of this alternative, if not others. Signalling to predators could very well be involved in constraining the extent of convergence, but this seems a little premature to state as an up-front conclusion of this work. There is also the result of a *dorsal* wing manipulation by Vieira-Silva et al. 2024 (https://doi.org/10.1111/eth.13517), which seems difficult to reconcile in light of this explanation. Whereas this paper is cited by the authors, a more nuanced discussion of their experimental results would seem appropriate here.

We thank the reviewer for their constructive comments on our manuscript. We appreciate the reviewer’s concern regarding the way iridescence convergence between sympatric species is discussed in our manuscript, which aligns with similar concerns raised by Reviewer 1. We will improve the discussion on the different evolutionary forces that could have favored this convergent iridescent signal in sympatry to bring more nuance to the discussion.

Reviewer #3 (Public review):

First, when using allopatric and sympatric (sub)species pairs to test evolutionary hypotheses, replication is important. Ideally, multiple allopatric and sympatric (sub)species pairs are compared to avoid outlier (sub)species or pairs that lead to biased conclusions. Unfortunately, the current study compares 1 allopatric and 1 sympatric (sub)species pair, hence having poor (no) replication on the level of allopatric and sympatric (sub)species pairs.

We would like to thank the reviewer for their constructive feedbacks. We agree that replication is important to test evolutionary hypotheses and that our study lacks replication for allopatric and sympatric Morpho populations. Ideally, one would require several allopatric and sympatric replicates pointing respectively toward divergence and convergence of Morpho iridescence to conclude on the effect of species interaction in trait evolution. Our study is a first attempt at answering this question, covering few Morpho populations but proposing a broad assessment of iridescence and mate preference for those populations. We will make sure to mention this limitation more clearly in the revised version of our manuscript.

Second, chemical profiles were only measured for sympatric species and not for allopatric (sub)species, which limits the interpretation of this data. The allopatric (sub)species could have been measured as non-coexistence "control". If coexistence and convergence in wing colouration drives the evolution of alternative mate recognition signals, such alternative signals should not evolve/diverge for allopatric (sub)species where wing colouration is still a reliable mate recognition cue. More importantly, no details are provided on the quantification of butterfly chemical profiles, which is essential to understand such data. It is unclear how the chemical profiles were quantified and what data (concentrations, ratios, proportions) were used to perform NDMS and generate Figure 5 and the associated statistical tests.

We recognize that having the chemical profiles of the genitalia of the Morpho from the allopatric population would have made a stronger case arguing in favor of reinforcement acting on the divergence of the chemical compounds found on the genitalia of the sympatric Morpho species. Due to limited access to the biological material needed by the time of the chromatography, we could not test for lower divergence in the chemical profiles of allopatric Morpho butterflies. We will mention this limitation in the results, and clarify the protocol used to extract the chemical profiles, by mentioning the use of concentration data to generate Figure 5 and the associated statistical tests.

Third, throughout the discussion, the authors mention that their results support natural selection by predators on iridescent wing colouration, without measuring natural selection by predators or any other measure related to predation. It is unclear by what predators any of the butterfly species are predated on at this point.

We will mention in the next version of the manuscript previous predation experiments performed on Morpho and other butterflies showing evidence that birds can be predators for those species. Those observations lead us to test for the putative effect of predation on the evolution of their color pattern, without directly testing predatory rates. We will make sure this information is transparent in the revised manuscript.

To continue on the interpretation of the data related to selection on specific traits by specific selection agents: This study did not measure any form of selection or any selection agent. Hence, it is not known if iridescent wing colouration is actually under selection by predators and/or mates, if maybe other selection agents are involved or if these traits converge due to genetic correlations with other traits under selection. For example, Iridescent colouration in ground beetles has functions as antipredator defence but also thermo- and water regulation. None of these issues are recognized or discussed.

We acknowledge that the lack of discussion on alternative evolutionary forces involved in the evolution of iridescence has been highlighted by all reviewers. We will discuss how environmental factors, genetic factors or the correlation with others traits as explanatory variables might explain the convergent signal of iridescence found in sympatric Morpho species, and not only focus on the putative effect of predation.

Finally, some of the results are weakly supported by statistics or questionable methodology. Most notably, the perception of the iridescence coloration of allopatric subspecies by bird visual systems. Although for females, means and errors (not indicated what exactly, SD, SE or CI) are clearly above the 1 JND line, for males, means are only slightly above this line and errors or CIs clearly overlap with the 1 JND line. Since there is no additional statistical support, higher means but overlap of SD, SE or CI with the baseline provides weak statistical support for differences.

We thank the reviewer for bringing interpretation issues concerning the chromatic distances of allopatric Morpho species measured with a bird vision model. We will make sure to bring nuance to the interpretation of this graph, and clearly mention in the figure’s legend that the error bars represent the confidence intervals obtained after performing a bootstrap analysis.

Regarding the assortative mating experiment, the results are clearly driven by M. bristowi. For M. theodorus, females mate equally often with conspecifics (6 times) as with M. bristowi (5 times). For males, the ratio is slightly better (6 vs 3), but with such low numbers, I doubt this is statistically testable. Overall low mating for M. bristowi could indicate suboptimal experimental conditions, and hence results should be interpreted with care.

Regarding the wing manipulation experiment, M. theodorus does not show a preference when dummies with non-modified wings are presented and prefers non-modified dummies over modified dummies. This is acknowledged by the authors but not further discussed. Certainly, some control treatment for wing modification could have been added.

We recognize that the tetrad experiment results are mainly driven by M. bristowi’s behavior. This experiment would have benefited from more replicates. We will mention that the conclusions we draw for this experiment are mainly driven by male M. bristowi behavior, and that it is more difficult to test for assortative or disassortative mating in M. theodorus, adding more nuance to our interpretation. We will also make sure to discuss further the effect of wing modification in the discussion.

Overall, the fact that certain measurements only provide evidence for 1 of the 2 (sub)species (assortative mating, wing manipulation) or one sex of one of the species (bird visual systems) means overall interpretation and overgeneralization of the results to both allopatric or sympatric species should be done with care, and such nuances should ideally be discussed.

The aim of the authors, "to investigate the antagonistic effects of selective pressures generated by mate recognition and shared predation" has not been achieved, and the conclusions regarding this aim are not supported by the results. Nevertheless, the iridescence colour measurements are solid, and some of the behavioural experiments and chemical profile measurements seem to yield interesting results. The study would benefit from less overinterpretation of the results in the framework of predation and more careful consideration of methodological difficulties, statistical insecurities, and nuances in the results.

Overall, we would like to thank all reviewers for their thorough assessment of our work. We understand that the imbalance between mate choice data, visual model data and chemical data only give us a partial assessment of species recognition in Morpho butterflies, thus requiring more precision in the interpretation and the discussion of our results. We will implement all the comments made by the reviewers in the next version of our manuscript.

Author response:

Reviewer #1 (Public review):

Summary:

In this work, the authors have developed SPLASH+, a micro-assembly and biological interpretation framework that expands on their previously published reference-free statistical approach (SPLASH) for sequencing data analysis.

Thank you for this thorough overview of our work.

Strengths:

(1) The methodology developed by the authors seems like a promising approach to overcome many of the challenges posed by reference-based single-cell RNA-seq analysis methods.

Thank you for your positive comment on the potential of our approach to address the limitations of reference-based methods for scRNA-Seq analysis.

(2) The analysis of the RNU6 repetitive small nuclear RNA provides a very compelling example of a type of transcript that is very challenging to analyze with standard reference-based methods (e.g., most reads from this gene fail to align with STAR, if I understood the result correctly).

We thank the reviewer for their positive comment. We agree that the variation in RNU6 detected by SPLASH+ underscores the potential of our reference-free method to make discoveries in cases where reference-based approaches fall short.

Weaknesses:

(1) The manuscript presents a number of case studies from very diverse domains of single-cell RNA-seq analysis. As a result, the manuscript has been challenging to review, because it requires domain expertise in centromere biology, RNA splicing, RNA editing, V(D)J transcript diversity, and repeat polymorphisms.

We appreciate the reviewer’s effort in thoroughly evaluating this manuscript, especially given the broad range of biological domains discussed. Our main goal in presenting a wide range of applications was to highlight the key strength of the SPLASH+ framework: its ability to unify diverse biological discoveries within a single method that operates directly on sequencing reads.

(2) Although the paper focuses on SmartSeq2 full-length single-cell RNA-seq data analysis, the vast majority of single-cell RNA-seq data that is currently being generated comes from droplet-based methods (e.g., 10x Genomics) that sequence only the 3' or 5' ends of transcripts. As a result, it is unclear if SPLASH+ is also applicable to these types of data.

We thank the reviewer for this comment. Due to the specific data format of barcoded single-cell sequencing platforms such as 10x Genomics, extending the SPLASH framework to support 10x analysis required engineering a specialized preprocessing tool. We have addressed this in a recent work, which is now available as a preprint (https://doi.org/10.1101/2024.12.24.630263).

(3) The criteria used for the selection of the 10 'core genes' have not been sufficiently justified.

We chose these genes as SPLASH+ detected regulated splicing for them in nearly all tissues (18 out of 19)  analyzed in our study (i.e., identifying anchors classified as splicing anchors in those tissues). Our subsequent analysis showed that all these genes are involved in either splicing regulation or histone modification. We will further clarify this selection criterion in the revision. 

(4) It is currently unclear how the splicing diversity discovered in this paper relates to the concept of noisy splicing (i.e., there are likely many low-frequency transcripts and splice junctions that are unlikely to have a significant functional impact beyond triggering nonsense-mediated decay).

In our analysis, to ensure sufficient read coverage, we considered significant anchors supported by more than 50 reads and detected in over 10 cells. Additionally, our downstream analyses (including splicing analysis) are based on assembled sequences (compactors) generated through our micro-assembly step. This process effectively acts as a denoising step by filtering out sequences likely caused by sequencing errors or with very low read support. However, we agree that the detected splice variants have not been fully functionally characterized, and further functional experiments may be needed.

(5) The paper presents only a very superficial discussion of the potential weaknesses of the SPLASH+ method.

We discussed two potential limitations of SPLASH+ in the Conclusions section: (1) it is not suitable for differential gene expression analysis, and (2) although we provide a framework for interpreting and analyzing SPLASH results, further work is still needed to improve the annotation of calls lacking BLAST matches. We will add more discussion for these in the revision. 

(6) The cursory mention of metatranscriptome in the conclusion of the paper is confusing, as it might suggest the presence of microbial cells in sterile human tissues (which has recently been discredited in cancer, see e.g. https://www.science.org/content/article/journal-retracts-influential-cancer-microbiome-paper).

We will remove the mention of metatranscriptome in the revised manuscript.

Reviewer #2 (Public review):

The authors extend their SPLASH framework with single-cell RNA-seq in mind, in two ways. First, they introduce "compactors", which are possible paths branching out from an anchor. Second, they introduce a workflow to classify compactors according to the type of biological sequence variation represented (splicing, SNV, etc). They focus on simulated data for fusion detection, and then focus on analyzing the Tabula sapiens Smart-seq2 data, showing extensive results on alternative splicing analysis, VDJ, and repeat elements.

This is strong work with an impressive array of biological investigations and results for a methods paper. I have various concerns about terminology and comparisons, as follows (in a somewhat arbitrary order, apologies).

Thank you for this thorough overview of our work and your positive comment on the strength of our work.

(1) The discussion of the weaknesses of the consensus sequence approach of SPLASH is an odd way to motivate SPLASH+ in my opinion, in that SPLASH is not yet so widely used, so the baseline for SPLASH+ is really standard alignment-based approaches. It is fine to mention consensus sequence issues briefly, but it felt belabored.

We thank the reviewer and agree that the primary comparison for SPLASH+ is with reference-based methods. However, since SPLASH+ builds upon SPLASH, we also aimed to highlight the limitations of the consensus step in original SPLASH and how SPLASH+ addresses them. To maintain the main focus of the paper on comparison with reference-based methods and biological investigations, this discussion with consensus was provided in a Supplementary Figure. We will shorten this discussion in the revision.

(2) Regarding compactors reducing alignment cost: the comparison should really be between compactor construction and alignment vs read alignment (and maybe vs modern contig construction algorithms and alignment).

Since the SPLASH framework is fundamentally reference-free and does not require read alignment, we compared the number of sequence alignments for compactors to the total read alignments required by a reference-based method to show that while compactors are aligned to the reference, the number of alignments needed is still orders of magnitude less than a reference-based approach requiring alignment of all the reads.

(3) The language around "compactors" is a bit confusing, where the authors sometimes refer to the tree of possibilities from an anchor as a "compactor", and sometimes a compactor is a single branch. Presumably, ideally, compactors should be DAGs, not trees, i.e., they can connect back together. Perhaps the authors could comment on whether this matters/would be a valuable extension.

We thank the reviewer for their comment. We refer to each generated assembled sequence as “a compactor”, and we attempted to make this clear in the paper. We will review the text further to ensure this definition is clear in the revised version.

(4) The main oddness of the splicing analysis to me is not using cell-type/state in any way in the statistical testing. This need not be discrete cell types: psiX, for example, tested whether exonic PSI was variable with reference to a continuous gene expression embedding. Intuitively, such transcriptome-wide signal should be valuable for a) improving power and b) distinguishing cell-type intrinsic/"noisy" from cell-type specific splicing variation. A straightforward way of doing this would be pseudobulking cell types. Possibly a more sophisticated hierarchical model could be constructed also.

We appreciate the reviewer’s concern regarding SPLASH+ not using cell type metadata. SPLASH, which performs the core statistical inference in SPLASH+, is an unsupervised tool specifically designed to make biological discoveries without relying on metadata (such as cell type annotations in scRNA-Seq). This is particularly useful in scRNA-seq, where cell type labels could be missing, imprecise, or may miss important within-cell-type variation. As shown in the paper, even without using metadata, SPLASH+ demonstrated improved performance than both SpliZ and Leafcutter (two metadata-dependent tools) in terms of achieving higher concordance and identifying more differentially spliced genes. Regarding pseudobulking, as has been shown in the SpliZ paper (https://doi.org/10.1038/s41592-022-01400-x), pseudobulking requires multiple pseudobulked replicates per cell type for reliable inference, which is often not feasible in scRNA-seq settings, making such methods statistically suboptimal for single-cell studies. We will add a discussion on pseudobulking in the revision. 

(5) A secondary weakness is that some informative reads will not be used, for example, unspliced reads aligning to an alterantive exons. This relates to the broader weakness of SPLASH that it is blind to changes in coverage that are not linked to a specific anchor (which should be acknowledged somewhere, maybe in the Discussion). In the deeply sequenced SS2 data, this is likely not an issue, but might be more limiting in sparser data. A related issue is that coverage change indicative of, e.g., alternative TSS or TES (that do not also include a change in splice junction use) will not be detected. In fairness, all these weaknesses are shared by LeafCutter. It would be valuable to have a comparison to a more "traditional" splicing analysis approach (pick your favorite of rMATS, MISO, SUPPA).

We thank the reviewer for their comment. As noted in the Conclusion, the SPLASH framework is not designed for differential gene expression analysis, which relies on quantifying read coverage. Rather, it focuses on detecting differential sequence diversity arising from mechanisms like alternative splicing or RNA editing. We will clarify this limitation further in the revised Conclusion. 

Regarding splicing evaluation, we have performed extensive comparisons with two widely used and recent methods—SpliZ and Leafcutter—for both bulk and single-cell splicing analysis. While we appreciate the reviewer’s suggestion to include an additional method, given the current length of the paper and the fact that leafcutter has previously been shown to outperform rMATS, MAJIQ, and Cufflinks2

(https://www.nature.com/articles/s41588-017-0004-9), we believe the current comparisons provide sufficient support for the evaluation of the splicing detection by SPLASH+.

(6) "We should note that there is no difference between gene fusions and other RNA variants (e.g., RNA splicing) from a sequence assembly viewpoint". Maybe this is true in an abstract sense, but I don't think it is in reality. AS can produce hundreds of isoforms from the same gene, and be variable across individual cells. Gene fusions are generally less numerous/varied and will be shared across clonal populations, so the complexity is lower. That simplicity is balanced against the challenge that any genes could, in principle, fuse.

We selected the fusion benchmarking dataset solely to evaluate how well compactors reconstruct sequences. Since our goal was to assess the accuracy of reconstructed compactor sequences, we needed a benchmarking dataset with ground truth sequences, which this dataset provides. We had explained our main reason and purpose for selecting fusion dataset in the text, but we will clarify it further in the revision.

(7) For the fusion detection assessment, SPLASH+ is given the correct anchor for detection. This feels like cheating since this information wouldn't usually be available. Can the authors motivate this? Are the other methods given comparable information? Also, TPM>100 seems like a very high expression threshold for the assessment.

We agree with the reviewer that the fusion benchmarking dataset should not be used to assess the entire SPLASH+ framework. In fact, we did not use this dataset to evaluate SPLASH+; it was used exclusively to evaluate the performance of compactors as a standalone module. Specifically, we tested how well compactors can reconstruct fusion sequences when provided with seed sequences corresponding to fusion junctions. This aligns with our expectation from compactors in SPLASH+, that they should correctly reconstruct the sequence context for the detected anchors. As noted in our previous response, since our goal was to assess the accuracy of reconstructed compactor sequences, we required a benchmarking dataset with ground truth sequences, which this dataset provides. We will clarify this further in the revision.

We appreciate the reviewer’s concern that a TPM of 100 is high. In Figure 1C, we presented the full TPM distribution for fusions missed or detected by compactors. The 100 threshold was an arbitrary benchmark to illustrate the clear difference in TPM profiles between these two sets of fusions. We will clarify this point in the revised manuscript.

(8) Why are only 3'UTRs considered and not 5'? Is this because the analysis is asymmetric, i.e., only considering upstream anchors and downstream variation? If so, that seems like a limitation: how much additional variation would you find if including the other direction?

We thank the reviewer for their comment. SPLASH+ can, in principle, detect variation in 5’ UTR regions, as demonstrated by the variations observed in the 5’ UTRs of the genes ANPC16 and ARPC2. If sequence variation exists in the 5′ UTR, SPLASH+ can still detect it by identifying an anchor upstream of the variable region, as it directly parses sequencing reads to find anchors with downstream sequence diversity. Even when the variation occurs near the 5′ end of the 5′ UTR, SPLASH+ can still capture this diversity if the user selects a shorter anchor length.

(9) I don't find the theoretical results very meaningful. Assuming independent reads (equivalently binomial counts) has been repeatedly shown to be a poor assumption in sequencing data, likely due to various biases, including PCR. This has motivated the use of overdispersed distributions such as the negative Binomial and beta binomial. The theory would be valuable if it could say something at a specified level of overdispersion. If not, the caveat of assuming no overdispersion should be clearly stated.

We appreciate the reviewer’s comment. We will clarify this in the revised paper.

Author response:

The following is the authors’ response to the previous reviews

Public Reviews:

Reviewer #1 (Public review):

Summary:

The manuscript entitled "Phosphodiesterase 1A Physically Interacts with YTHDF2 and Reinforces the Progression of Non-Small Cell Lung Cancer" explores the role of PDE1A in promoting NSCLC progression by binding to the m6A reader YTHDF2 and regulating the mRNA stability of several novel target genes, consequently activating the STAT3 pathway and leading to metastasis and drug resistance.

Strengths:

The study addresses a novel mechanism involving PDE1A and YTHDF2 interaction in NSCLC, contributing to our understanding of cancer progression.

Reviewer #2 (Public review):

Summary

This revised manuscript investigates the role and the mechanism by which PDE1 impacts NSCLC progression. They provide evidence to demonstrate that PDE1 binds to m6A reader YTHDF2, in turn, regulating STAT3 signaling pathway through its interaction, promoting metastasis and angiogenesis.

Strength:

The study uncovers a novel PDE1A/YTHDF2/SOCS2/STAT3 pathway in NSCLC progression and the findings provide a potential treatment strategy for NSCLC patients with metastasis.

Weakness:

In discussion, it is stated in the revised version that "the role of YTHDF2 in PDE1A-driven tumor metastasis should be elucidated in future studies", however, given that physical interaction of PDE1A and YTHDF2 plays a critical role in PDE1A-mediated NSCLC metastasis, whether YTHDF2 mimicking the effect of PDE1A in metastasis will strength the manuscript.

Recommendations for the authors:

Reviewer #2 (Recommendations for the authors):

(1) In Figure 1A, the y-axis should be "IOD/Area" instead of "IDO/Area".

Figure 1A was revised as suggested.

(2) Figure 3A legend for (F) and (G) was switched.

Figure 3A legend was revised as suggested “(F-G) The mRNA (F) and protein (G) levels of indicated genes were determined in P3 and P0 NSCLC cells.”.

(3) The statistical analysis should be performed for Figure 3H.

Figure 3H was revised as suggested.

(4) Figure 4F, Y-axis has a typo for "vessels" and statistical analysis should be performed on this data.

Figure 4F was revised as suggested.

(5) Figure 6 E, typo for "migrated" on the y-axis.

Figure 6E was revised as suggested.

(6) Figure 7 C, typos for "expression" on y-aixs in both figures need to be fixed.

Figure 7C was revised as suggested.

(7) P-values for Figure 7B need to be stated.

Figure 7B was revised as suggested.

(8) m6A should be consistent throughout the manuscript.

m6A was consistent throughout the manuscript.

Author response:

The following is the authors’ response to the previous reviews

Public Reviews:

Reviewer #1 (Public review):

IKK is the key signaling node for inflammatory signaling. Despite the availability of molecular structures, how the kinase achieves its specificity remains unclear. This paper describes a dynamic sequence of events in which autophosphorylation of a tyrosine near the activate site facilitates phosphorylation of the serine on the substrate via a phosphor-transfer reaction. The proposed mechanism is conceptually novel in several ways, suggesting that the kinase is dual specificity (tyrosine and serine) and that it mediates a phospho-transfer reaction. While bacteria contain phosphorylation-transfer enzymes, this is unheard of for mammalian kinases. However, what the functional significance of this enzymatic activity might remain unaddressed.

The revised manuscript adequately addresses all the points I suggested in the review of the first submission.

Response: Authors thank the reviewer for their valuable comments and constructive criticisms for the betterment of the manuscript. We also thank them for appreciating our work. We agree with the reviewer that the functional significance of this particular enzymatic activity of IKK2 is yet to be fully realized. 

Reviewer #2 (Public review):

The authors investigate the phosphotransfer capacity of Ser/Thr kinase IkB kinase (IKK), a mediator of cellular inflammation signaling. Canonically, IKK activity is promoted by activation loop phosphorylation at Ser177/Ser181. Active IKK can then unleash NF-kB signaling by phosphorylating repressor IkBα at residues Ser32/Ser26. Noting the reports of other IKK phosphorylation sites, the authors explore the extent of autophosphorylation.

Semi-phosphorylated IKK purified from Sf9 cells, exhibits the capacity for further autophosphorylation. Anti-phosphotyrosine immunoblotting indicated unexpected tyrosine phosphorylation. Contaminating kinase activity was tested by generating a kinase-dead K44M variant, supporting the notion that the unexpected phosphorylation was IKK-dependent. In addition, the observed phosphotyrosine signal required phosphorylated IKK activation loop serines.

Two candidate IKK tyrosines were examined as the source of the phosphotyrosine immunoblotting signal. Activation loop residues Tyr169 and Tyr188 were each rendered non-phosphorylatable by mutation to Phe. The Tyr variants decreased both autophosphorylation and phosphotransfer to IkBα. Likewise, Y169F and Y188F IKK2 variants immunoprecipitated from TNFa-stimulated cells also exhibited reduced activity in vitro.

The authors further focus on Tyr169 phosphorylation, proposing a role as a phospho-sink capable of phosphotransfer to IkBα substrate. This model is reminiscent of the bacterial two-component signaling phosphotransfer from phosphohistidine to aspartate. Efforts are made to phosphorylate IKK2 and remove ATP to assess the capacity for phosphotransfer. Phosphorylation of IkBα is observed after ATP removal, although there are ambiguous requirements for ADP.

Strengths:

Ultimately, the authors draw together the lines of evidence for IKK2 phosphotyrosine and ATP-independent phosphotransfer to develop a novel model for IKK2-mediated phosphorylation of IkBα. The model suggests that IKK activation loop Ser phosphorylation primes the kinase for tyrosine autophosphorylation. With the assumption that IKK retains the bound ADP, the phosphotyrosine is conformationally available to relay the phosphate to IkBα substrate. The authors are clearly aware of the high burden of evidence required for this unusual proposed mechanism. Indeed, many possible artifacts (e.g., contaminating kinases or ATP) are anticipated and control experiments are included to address many of these concerns. The analysis hinges on the fidelity of pan-specific phosphotyrosine antibodies, and the authors have probed with two different anti-phosphotyrosine antibody clones. Taken together, the observations are thought-provoking, and I look forward to seeing this model tested in a cellular system.

Weaknesses:

Multiple phosphorylated tyrosines in IKK2 were apparently identified by mass spectrometric analyses. LC-MS/MS spectra are presented, but fragments supporting phospho-Y188 and Y325 are difficult to distinguish from noise. It is common to find non-physiological post-translational modifications in over-expressed proteins from recombinant sources. Are these IKK2 phosphotyrosines evident by MS in IKK2 immunoprecipitated from TNFa-stimulated cells? Identifying IKK2 phosphotyrosine sites from cells would be especially helpful in supporting the proposed model.

Authors thank the reviewer for their elaborate comments and constructive criticisms that helped enrich the manuscript. We also thank them for pointing out the critical points in the model. We agree with the reviewer that testing this model in a cellular system is required to bolster this concept. However, an appropriate cellular assay system to investigate and monitor this mode of phosphotransfer is still elusive. We agree with the reviewer’s concerns on the identification of Y188 and Y325 as potential phosphosites. They have been omitted in the current version and relevant changes have been incorporated. IKK2’s tyrosine phosphorylation status in cells is reported earlier. Although we have not analyzed IKK2 from TNF-a treated cells in this study, a different study of phospho-status of cellular IKK2 indicated tyrosine phosphorylation (Meyer et al 2013).   

Reviewer #3 (Public review):

Summary:

The authors investigate the kinase activity of IKK2, a crucial regulator of inflammatory cell signaling. They describe a novel tyrosine kinase activity of this well-studied enzyme and a highly unusual phosphotransfer from phosphorylated IKK2 onto substrate proteins in the absence of ATP as a substrate.

Strengths:

The authors provide an extensive biochemical characterization of the processes with recombinant protein, western blot, autoradiography, protein engineering and provide MS data now.

Weaknesses:

The identity and purity of the used proteins has improved in the revised work. Since the findings are so unexpected and potentially of wide-reaching interest - this is important. Similar specific detection of phospho-Ser/Thr vs phospho-Tyr relies largely on antibodies which can have varying degrees of specificity. Using multiple antibodies and MS improves the quality of the data.

Authors thank the reviewer for their crisp comments and constructive criticisms that helped improve the manuscript.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Generally, the paper is well written, but the first 4 figures are slow going and could be condensed to show the key points, so that reader gets to Figure 6 and 7 which contain the "meat" of the paper.

Specific points:

Several figures should be quantified and experimental reproducibility is not always clear.

I understand that Figure 3 shows that K44M abolishes both S32/26 phosphorylation and tyrosine phosphorylation, but not PEST region phosphorylation. This suggests that autophosphorylation is reflective of its known specific biological role in signal transduction. But I do not understand why "these results strongly suggest that IKK2-autophosphorylation is critical for its substrate specificity". That statement would be supported by a mutant that no longer autophosphorylates, and as a result shows a loss of substrate specificity, i.e. phosphorylates non-specific residues more strongly. Is that the case? Maybe Darwech et al 2010 or Meyer et al 2013 showed this? Later figures seem to address this point, so maybe this conclusion should be stated later in the paper.

Page 10: mentions DFG+1 without proper introduction. The Chen et al 2014 paper appears to inform the author's interest in Y169 phosphorylation, or is just an additional interesting finding? Does this publication belong in the Introduction or the Discussion?

To understand the significance of Figure 4D, we need a WT IKK2 control: or is there prior literature to cite?

This is relevant for the conclusion that Y169 phosphorylation is particularly important for S32 phosphorylation.

The cold ATP quenching experiment is nice for testing the model that Y169 functions as a phospho sink that allows for a transfer reaction. However, there is only a single timepoint and condition, which does not allow for a quantitative analysis. Furthermore, a positive control would make this experiment more compelling, and Y169F mutant should show that cold ATP quenching reduces the phosphorylation of IkBa.

Note after revision: I thank the authors for addressing these points. The manuscript is thereby improved.

We thank the reviewer for appreciating our efforts in addressing their concerns.

Reviewer #2 (Recommendations for the authors):

In the revisions, the authors provide LC-MS/MS spectra for putative phospho-Y325 and phospho-Y188. The details are hard to see at the scale provided, but the fragment ions for pY188 and pY325 peptides are unconvincing. Phospho-Y169, on the other hand, is much more credible. In addition, the revision rebuttal clarifies that Y188 would be packed into a catalytically important core, and Y188F is likely to disrupt the fold. Taken together, it seems doubtful that Y188 is subject to any significant autophosphorylation, and presenting the Y188F data (and discussion) seems like a distraction.

We agree with the reviewer’s concerns on the identification of Y188 and Y325 as potential phosphosites. They have been omitted in the current version and relevant sections in the manuscript text and figures have been edited.

Author response:

The following is the authors’ response to the original reviews

We thank the reviewers for the careful review of our manuscript. Overall, they were positive about our use of cutting-edge methods to identify six inversions segregating in Lake Malawi. Their distribution in ~100 species of Lake Malawi species demonstrated that they were differentially segregating in different ecogroups/habitats and could potentially play a role in local adaptation, speciation, and sex determination. Reviewers were positive about our finding that the chromosome 10 inversion was associated with sex-determination in a deep benthic species and its potential role in regulating traits under sexual selection. They agree that this work is an important starting point in understanding the role of these inversions in the amazing phenotypic diversity found in the Lake Malawi cichlid flock.

There were two main criticisms that were made which we summarize:

(1) Lack of clarity. It was noted that the writing could be improved to make many technical points clearer. Additionally, certain discussion topics were not included that should be.

We will rewrite the text and add additional figures and tables to address the issues that were brought up in a point-by-point response. We will improve/include (1) the nomenclature to understand the inversions in different lineages, (2) improved descriptions for various genomic approaches, (3) a figure to document the samples and technologies used for each ecogroup, and 4) integration of LR sequences to identify inversion breakpoints to the finest resolution possible.

(2) We overstate the role that selection plays in the spread of these inversions and neglect other evolutionary processes that could be responsible for their spread.

We agree with the overarching point. We did not show that selection is involved in the spread of these inversions and other forces can be at play. Additionally, there were concerns with our model that the inversions introgressed from a Diplotaxodon ancestor into benthic ancestors and incomplete lineage sorting or balancing selection (via sex determination) could be at play. Overall, we agree with the reviewers with the following caveats. 1. Our analysis of the genetic distance between Diplotaxodons and benthic species in the inverted regions is more consistent with their spread through introgression versus incomplete lineage sorting or balancing selection. 2. Further the role of these inversions is likely different in different species. For example, the inversion of 10 and 11 play a role in sex determination in some species but not others and the potential pressures acting on the inverted and non-inverted haplotypes will be very different. These are very interesting and important questions booth for understanding the adaptive radiations in Lake Malawi and in general, and we are actively studying crosses to understand the role of these inversions in phenotypic variation between two species. We will modify the text to make all of these points clearer.

Public Reviews:

Reviewer #1 (Public review):

Summary:

Using high-quality genomic data (long-reads, optical maps, short-reads) and advanced bioinformatic analysis, the authors aimed to document chromosomal rearrangements across a recent radiation (Lake Malawi Cichlids). Working on 11 species, they achieved a high-resolution inversion detection and then investigated how inversions are distributed within populations (using a complementary dataset of short-reads), associated with sex, and shared or fixed among lineages. The history and ancestry of the inversions is also explored.

On one hand, I am very enthusiastic about the global finding (many inversions well-characterized in a highly diverse group!) and impressed by the amount of work put into this study. On the other hand, I have struggled so much to read the manuscript that I am unsure about how much the data supports some claims. I'm afraid most readers may feel the same and really need a deep reorganisation of the text, figures, and tables. I reckon this is difficult given the complexity brought by different inversions/different species/different datasets but it is highly needed to make this study accessible.

The methods of comparing optical maps, and looking at inversions at macro-evolutionary scales can be useful for the community. For cichlids, it is a first assessment that will allow further tests about the role of inversions in speciation and ecological specialisation. However, the current version of the manuscript is hardly accessible to non-specialists and the methods are not fully reproducible.

Strengths:

(1) Evidence for the presence of inversion is well-supported by optical mapping (very nice analysis and figure!).

(2) The link between sex determination and inversion in chr 10 in one species is very clearly demonstrated by the proportion in each sex and additional crosses. This section is also the easiest to read in the manuscript and I recommend trying to rewrite other result sections in the same way.

(3) A new high-quality reference genome is provided for Metriaclima zebra (and possibly other assemblies? - unclear).

(4) The sample size is great (31 individuals with optical maps if I understand well?).

(5) Ancestry at those inversions is explored with outgroups.

(6) Polymorphism for all inversions is quantified using a complementary dataset.

Weaknesses:

(1) Lack of clarity in the paper: As it currently reads, it is very hard to follow the different species, ecotypes, samples, inversions, etc. It would be useful to provide a phylogeny explicitly positioning the samples used for assembly and the habitat preference. Then the text would benefit from being organised either by variant or by subgroups rather than by successive steps of analysis.

We have extensively rewritten the paper to improve the clarity. With respect to this point, we moved Figure 6 to Figure 1, which places the phylogeny of Lake Malawi cichlids at the beginning of the paper. We incorporated information about samples/technologies by ecogroup into this figure to help the reader gain an overview of the technologies involved. We added information about habitat for each ecogroup as well. While we considered a change to the text organization suggested here, we thought it was clearer to keep the original headings.

(2) Lack of information for reproducibility: I couldn't find clearly the filters and parameters used for the different genomic analyses for example. This is just one example and I think the methods need to be re-worked to be reproducible. Including the codes inside the methods makes it hard to follow, so why not put the scripts in an indexed repository?

We now provide a link to a github repository (https://github.com/ptmcgrat/CichlidSRSequencing/tree/Kumar_eLife) containing the scripts used for the major analysis in the paper. Because our data is behind a secure Dropbox account, readers will not be able to run the analysis, however, they can see the exact programs, filters, and parameters used for manuscript embedded within each script.

(3) Further confirmation of inversions and their breakpoints would be valuable. I don't understand why the long-reads (that were available and used for genome assembly) were not also used for SV detection and breakpoint refinement.

We did use long reads to confirm the presence of the inversions by creating five new genome assemblies from the PacBio HiFi reads: two additional Metriaclima zebra samples and three Aulonocara samples. Alignment of these five genomes to the MZ_GT3 reference is shown in Figures S2 – S7. These genome assemblies were also used to identify the breakpoints of the inversions. However, because of the extensive amount of repetitive DNA at the breakpoints (which is known to be important for the formation of large inversions), our ability to resolve the breakpoints was limited.

(4) Lack of statistical testing for the hypothesis of introgression: Although cichlids are known for high levels of hybridization, inversions can also remain balanced for a long time. what could allow us to differentiate introgression from incomplete lineage sorting?

The coalescent time between the inversions between Diplotaxodons and benthics should allow us to distinguish these two mechanisms. Our finding that the genetic distance, which is related to coalescent time, is closer within the inversions than the whole genome is supportive of introgression. However, we did not perform any simulations or statistical tests. We make it clearer in the text that incomplete lineage sorting remains a possible mechanism for the distribution of inversions within these ecogroups.

(5) The sample size is unclear: possibly 31 for Bionano, 297 for short-reads, how many for long-reads or assemblies? How is this sample size split across species? This would deserve a table.

We have included this information in the new Figure 1.

(6) Short read combines several datasets but batch effect is not tested.

We do not test for batch effect. However, we do note that all of the datasets were analyzed by the same pipeline starting from alignment so batch effects would be restricted to aspects of the reads themselves. Additionally, samples from the different data sets clustered as expected by lineage and inferred inversion, so for these purposes unlikely to have affected analysis.

(7) It is unclear how ancestry is determined because the synteny with outgroups is not shown.

Ancestry analysis was determined using the genome alignments of two outgroups from outside of Lake Malawi. This is shown in Figure S8.

(8) The level of polymorphism for the different inversions is difficult to interpret because it is unclear whether replicated are different species within an eco-group or different individuals from the same species. How could it be that homozygous references are so spread across the PCA? I guess the species-specific polymorphism is stronger than the ancestral order but in such a case, wouldn't it be worth re-doing the PCa on a subset?

The genomic PCA plots reflect the evolutionary histories that are observed in the whole genome phylogenies. Because the distribution of the inverted alleles violate the species tree, they form separate clusters on the PCA plots that can be used to genotype specific species. We have also performed this analysis on benthics (utaka/shallow benthics/deep benthics) and the distribution matches the expectation.

Reviewer #2 (Public review):

Summary:

Chromosomal inversions have been predicted to play a role in adaptive evolution and speciation because of their ability to "lock" together adaptive alleles in genomic regions of low recombination. In this study, the authors use a combination of cutting-edge genomic methods, including BioNano and PacBio HiFi sequencing, to identify six large chromosomal inversions segregating in over 100 species of Lake Malawi cichlids, a classic example of adaptive radiation and rapid speciation. By examining the frequencies of these inversions present in species from six different linages, the authors show that there is an association between the presence of specific inversions with specific lineages/habitats. Using a combination of phylogenetic analyses and sequencing data, they demonstrate that three of the inversions have been introduced to one lineage via hybridization. Finally, genotyping of wild individuals as well as laboratory crosses suggests that three inversions are associated with XY sex determination systems in a subset of species. The data add to a growing number of systems in which inversions have been associated with adaptation to divergent environments. However, like most of the other recent studies in the field, this study does not go beyond describing the presence of the inversions to demonstrate that the inversions are under sexual or natural selection or that they contribute to adaptation or speciation in this system.

Strengths:

All analyses are very well done, and the conclusions about the presence of the six inversions in Lake Malawi cichlids, the frequencies of the inversions in different species, and the presence of three inversions in the benthic lineages due to hybridization are well-supported. Genotyping of 48 individuals resulting from laboratory crosses provides strong support that the chromosome 10 inversion is associated with a sex-determination locus.

Weaknesses:

The evidence supporting a role for the chromosome 11 inversion and the chromosome 9 inversion in sex determination is based on relatively few individuals and therefore remains suggestive. The authors are mostly cautious in their interpretations of the data. However, there are a few places where they state that the inversions are favored by selection, but they provide no evidence that this is the case and there is no consideration of alternative hypotheses (i.e. that the inversions might have been fixed via drift).

We have removed mention of chromosome 9’s potential role in sex determination from the paper. While our analysis of sex association with chromosome 11 was limited compared to our analysis of chromosome 10, it was still statistically significant, and we believe it should be left in the paper. The role of 11 (and 9 and 10) in sex determination was also demonstrated using an independent dataset by Blumer et al (https://doi.org/10.1101/2024.07.28.605452)

We agree that we did not properly consider alternative hypothesis in the original submission and have rewritten the Discussion substantially to consider various alternative hypothesis.

Reviewer #3 (Public review):

This is a very interesting paper bringing truly fascinating insight into the genomic processes underlying the famous adaptive radiation seen in cichlid fishes from Lake Malawi. The authors use structural and sequence information from species belonging to distinct ecotypic categories, representing subclades of the radiation, to document structural variation across the evolutionary tree, infer introgression of inversions among branches of the clade, and even suggest that certain rearrangements constitute new sex-determining loci. The insight is intriguing and is likely to make a substantial contribution to the field and to seed new hypotheses about the ecological processes and adaptive traits involved in this radiation.

I think the paper could be clarified in its prose, and that the discussion could be more informative regarding the putative roles of the inversions in adaptation to each ecotypic niche. Identifying key, large inversions shared in various ways across the different taxa is really a great step forward. However, the population genomics analysis requires further work to describe and decipher in a more systematic way the evolutionary forces at play and their consequences on the various inversions identified.

The model of evolution involving multiple inversions putatively linking together co-adapted "cassettes" could be better spelled out since it is not entirely clear how the existing theory on the recruitment of inversions in local adaptation (e.g. Kirkpatrick and Barton) operates on multiple unlinked inversions. How such loci correspond to distinct suites of integrated traits, or not, is not very easy to envision in the current state of the manuscript.

This is a very interesting point, and we agree creates complications for a simple model of local adaptation. We imagine though that the actual evolutionary history was much more complicated than a single Rhamphochromis-type species separating from a single Diplotaxodon-type species and could have occurred sequentially involving multiple species that are now extinct. A better understanding of the role each of these inversions play in phenotypic diversity could potentially help us determine if different inversions carry variation that could be linked to distinct habit differences. We have added a line to the discussion.

The role of one inversion in sex determination is apparent and truly intriguing. However, the implication of such locus on ecological adaptation is somewhat puzzling. Also, whether sex determination loci can flow across species via introgression seems quite important as a route to chromosomal sex determination, so this could be discussed further.

Another very interesting point. If the inversions are involved in ecological adaptation (an important caveat), then potentially the inverted and non-inverted haplotypes play dual roles in the Aulonocara animals with the inverted haplotype carrying adaptive alleles to deep water and the non-inverted haplotype carrying alleles resolving sexual conflict. We have broadened our discussion about their function at the origin including non-adaptive roles.

Recommendations for the authors:

Reviewer #2 (Recommendations for the authors):

Overall, the paper is well-written and clear. I do have a few suggestions for changes that would help the reader:

(1) Figure 1: the figure legend could be expanded here to help the reader; what are the blue and yellow lines? Why are there two lines for the GT3a assembly? And, I had to somehow read the legend a few times to understand that the top line is the UMD2a reference assembly, and the next line is the new Bionano map.

Fixed in what is now Figure 2

(2) Paragraph starting on line 133: you use the word "test" to refer to the Bionano analyses; it is not clear whether anything is being tested. Perhaps "analyse the maps" or just "map" would be more clear? Or more explanation?

The text has been modified to address this point

(3) L145-146: perhaps change "a single inversion" and "a double inversion" to "single inversions" and "double inversions".

The text has been modified to address this point

(4) L157: suppression of recombination in inversion heterozygotes is "textbook" material and perhaps does not need a reference. Or, you could reference an empirical paper that demonstrates this point. Though I love the Kirkpatrick and Barton paper, it certainly is not the correct reference for this point.

The Kirkpatrick reference was incorrectly included here. The correct reference was an empirical demonstration (Conte) that there were regions of suppressed recombination that have been observed in the location of the inversions. We have also moved this reference further up in the sentence to a more appropriate position

(5) L173: how do you know this is an assembly error and not polymorphism?

The text has been modified to address this point

(6) L277(?): "currently growing in the lab" is probably unnecessary.

The text has been modified to address this point

(7) L298: "the inversion on 10 acts as an XY sex determiner": the inversion itself is not the sex determination gene; rather, it is linked. I think it would be more precise, here and throughout the paper, to say that these inversions likely harbor the sex determination locus (for example, the wording on lines 369-370 is misleading).

We agree with the larger point that the inversion might not be causal for sex determination, however, it could still be causal through positional effects. We have modified the text to make it clear that it could also carry the causal locus (or loci).

(8) Figure 6: overall, this figure is very helpful! However, it contains several problematic statements. In no case do you have evidence that these inversions are "favored by selection"; such statements should be deleted. Also, in point 3, you state that inversions 9, 11, and 20 are transferred to benthic lineages, and then that these inversions are involved in sex determination. But, your data suggests that it is chromosomes 9, 10, and 11 that are linked to sex determination.

This figure is now Figure 1. We have remove these problematic statements.

(9) L356-360: I would move the references that are currently at the end of the sentence to line 357 after the statement about the previous work on hybridization. Otherwise, it reads as if these previous papers demonstrated what you have demonstrated in your work.

The text has been modified to address this point

(10) Overall, the discussion focuses completely on adaptive explanations for your results, and I would like to see at least an acknowledgement that drift could also be involved unless you have additional data to support adaptive explanations.

We have rewritten the text to account for the possibility of drift (line 404 and 405).

Reviewer #3 (Recommendations for the authors):

The paper utilizes heterogeneous datasets coming from different sources, and it is not always clear which specimens were used to generate structural information (bionano) or sequence information. A diagram summarizing the sequence data, methodologies, and research questions would be beneficial for the reader to navigate in this paper.

Much of this information has been added to what is now Figure 1. All of this data is also found in Table S2.

The authors performed genome alignments to analyze and homologize inversion, but this process is not clearly described. For the PCA, SNP information likely involves mapping onto a common reference genome. However, it is not clear how this was achieved given the different species and varying divergence times involved.

We now include a link to the github that contains the commands that were run. Because the overall level of sequence divergence between cichlid species is quite low (2*10^-3 – Milansky et al), mapping different species onto a common reference is commonly performed in Lake Malawi cichlids.

The introgression scenario is very intriguing but its role in local adaptation of the ecogroup types is not easy to understand. I understand this is still an outstanding question, but it is unclear how the directionality of introgressions was estimated. This can be substantiated using tree topology analysis, comparative estimates of sequence divergence, and accumulation of DNA insertions. The diagram does not clearly indicate which ones are polymorphic. In some cases, polymorphic inversions could result from the coexistence of native and introgressed haplotypes.

We agree that this analysis would be interesting but is beyond the scope of this paper.

The alternative model of introgression proposed in the cited preprint is interesting and should deserve a formal analysis here. The authors consider unclear what would drive "back" introgressions of non-inverted haplotypes, but this would depend on the selection regimes acting on the inversions themselves, which can include forms of balancing selection and a role for recessive lethals (heterozygote advantage). For instance, a standard haplotype could be favored if it shelters deleterious mutations carried by an inversion. Testing the introgression history over a wider range of branches and directions would provide further insights.

We agree that this analysis would be interesting but is beyond the scope of this paper.

The prose in the paper is occasionally muddled and somewhat unclear. Referring to chromosomes solely by their numbers (e.g.. "inversion on 11") complicates readability.

This is the standard way to refer to chromosomes in cichlids and we believe while it complicates readability, any other method would be inconsistent with other papers. Changes to nomenclature might improve the readability of this paper, but would make it more difficult to compare results for these chromosomes from other papers with what we have found.

Author response:

The following is the authors’ response to the previous reviews

Reviewer #1:

This paper seeks to address the question of how quantitative trait variation and expression variation are related. scRNAseq represents an appealing approach to eQTL mapping as it is possible to simultaneously genotype individual cells and measure expression in the same cell. As eQTL mapping requires large sample sizes to identify statistical relationships, the use of scRNAseq is likely to dramatically increase the statistical power of such studies. However, there are several technical challenges associated with scRNAseq and the authors' study is focused on addressing those challenges. Most of the points raised by my review of the initial version have been addressed. However, one point remains and one additional point should be considered. In this version the authors have introduced the use of data imputation using a published algorithm, DISCERN. This has greatly increased the variation explained by their model as presented in figure 3. However, it is possible that the explained variance is now an overestimation as a result of using the imputed expression data. I think that it would be appropriate to present figure 3 using the sparse data presented in the initial version of the paper and the newly presented imputed data so that the reader can draw their own conclusions about the interpretation.

We thank the reviewer for pointing this out and decided to present the results obtained from the sparse data in the main Figure 3 to avoid any overestimation. We also performed the variance partitioning at different sample sizes and used an optimized implementation of the GREML method to be able to handle high sample sizes instead of having to use a bootstrap estimate. As for the benefits of denoising the expression data, we illustrated it in the supplementary figure S6 so that people can draw their own conclusions about this imputation method. The imputation generally increases the contribution of the expressiongenotype interaction and decreases the residuals of the model by up to 8%.

Reviewer #1:

Given that the authors overcame many technical and analytical challenges in the course of this research, the study would be greatly strengthened through analysis of at least one, and ideally several, more conditions which would expand the conclusions that could be drawn from the study and demonstrate the power of using scRNAseq to efficiently quantify expression in different environments.

Our aim was to illustrate the benefit of one-pot scRNA-seq for eQTL mapping and the association of transcriptomic variation to trait variation. We think we have reached this goal with the current study. We understand that performing another scRNA-seq experiment in a new environment would help expand/validate our conclusions, but we think this would be a better fit for a future study. 

Reviewer #2:

The authors now say the main take-home for their work is (1) they have established methods for linkage mapping with scRNA-seq and that these (2) "can help gain insights about the genotype-phenotype map at a broader scale." My opinion in this revision is much the same as it was in the first round: I agree that they have met the first goal, and the second theme has been so well explored by other literature that I'm not convinced the authors' results meet the bar for novelty and impact. To my mind, success for this manuscript would be to support the claim that the scRNA-seq approach helps "reveal hidden components of the yeast genotype-to-phenotype map." I'm not sure the authors have achieved this. I agree that the new Figure 3 is a nice addition-a result that apparently hasn't been reported elsewhere (30% of growth trait variation can't be explained by expression). The caveats are that this is a negative result that needs to be interpreted with caution; and that it would be useful for the authors to clarify whether the ability to do this calculation is a product of the scRNA-seq method per se or whether they could have used any bulk eQTL study for it. Beside this, I regret to say that I still find that the results in the revision recapitulate what the bulk eQTL literature has already found, especially for the authors' focal yeast cross: heritability, expression hotspots, the role of cis and transacting variation, etc.

We agree with the reviewer that this study does not reveal new modes of transcription regulation or phenomena that were not highlighted or hypothesized in the literature. To avoid confusion, we refrained from using the word “reveal” for such cases. However, we provide convincing evidence that one-pot scRNA-seq helps refining our understanding of genotype-phenotype map in two ways. First, the larger scalability of this approach allowed us to find a median number of eQTL per gene that is ~4 times higher than the largest bulk-eQTL mapping in the same genetic background. For 60% of these genes, i.e. the ones with higher expression heritability in our dataset, the ability to explain their transcriptomic variation from SNPs increased by ~16% on average, which is substantial. This gain in power can thus improve our understanding of the gene network by highlighting new downstream effects of mutations or transcriptome variation. Second, by performing one-pot eQTL as opposed to large-scale bulk eQTL, thousands of transcriptomes can be collected simultaneously without having to use batching strategies. This enables the association between phenotype, genotype and expression variation, which we show in figure 3 through variance partitioning. While it is possible that the growth trait variation not being fully explained by expression could be an artifact of scRNA-seq, we do not believe this is the case because most transcriptional variation is explained by genotype (~76%).

Furthermore, we show that by having to control expression for growth, by missing some hotspots of regulation and by missing multiple eQTL for each gene, previous bulk-eQTL analysis could not replicate the significant association between eQTL hotspots and QTL hotspot, which this study highlights. Thus, we agree in general that many of the insights about transcriptional regulation have been obtained through ‘brute-force’, bulk RNA-seq, which fundamentally can reach tens of thousands of transcriptomes as well, but we believe the one-pot scRNA-seq approach is much easier and expedient once genotyping the single-cells and other challenges regarding denoising and low coverage have been solved (which we believe we did). There is indeed another reviewed preprint [Boocock et al, eLife] that has used similar approaches as our study since the publication of our manuscript (in October 2023).

Likewise, when in the first round of review I recommended that the authors repeat their analyses on previous bulk RNA-seq data from Albert et al., my point was to lead the authors to a means to provide rigorous, compelling justification for the scRNA-seq approach. The response to reviewers and the text (starting on line 413) says the comparison in its current form doesn't serve this purpose because Albert et al. studied fewer segregants. Wouldn't down-sampling the current data set allow a fair comparison? Again, to my mind what the current manuscript needs is concrete evidence that the scRNA-seq method per se affords truly better insights relative to what has come before.

We agree that down-sampling the current dataset would allow for a fair comparison. Thus, we illustrate the results of the variance partitioning at different sample sizes. While the total variance explained is similar, the contribution of the genotype-expression interaction increases with sample size, highlighting the increase in the confidence of the associations between expression and genotype that contributed to trait variation. We also showed that a lot of important low-effect sizes eQTL are missing at a sample size of 1000 compared to a sample size 4000. Indeed, by increasing the scale of eQTL mapping by ~4, about 60% of genes have increased heritability and this increase is due to eQTLs that cumulatively explain more than 15% of transcript level variation.

I also recommend that the authors take care to improve the main text for readability and professionalism. It would benefit from further structural revision throughout (especially in the figure captions) to allow high-impact conclusions to be highlighted and low-impact material to be eliminated. Figure 4 and the results text sections from line 319 onward could be edited for concision or perhaps moved to supplementary if they obscure the authors' case for the scRNA-seq approach. The text could also benefit from copy editing (e.g. three clauses starting with "while" in the paragraph starting on line 456; "od ratio" on line 415). I appreciate the authors' work on the discussion, including posing big picture questions for the field (lines 426-429), but I don't see how they have anything to do with the current scRNA-seq method.

We thank the reviewer for their suggestions for improving the readability of the text. We edited some of the figure captions and result section titles to better highlight the main results. However, we do not think that the last result section obscures our findings but rather supports the fact that scRNA-seq refines our understanding of the GPM. Indeed, we discovered many new eQTLs that are related to both expression and trait variation, highlighting the potential for understanding the downstream effects of mutations on the gene network and on trait variation through multiple trans-regulation paths.

Author response:

Public Reviews:

Reviewer #1 (Public review):

This work provides a new Python toolkit for combining generative modeling of neural dynamics and inversion methods to infer likely model parameters that explain empirical neuroimaging data. The authors provided tests to show the toolkit's broad applicability and accuracy; hence, it will be very useful for people interested in using computational approaches to better understand the brain.

Strengths:

The work's primary strength is the tool's integrative nature, which seamlessly combines forward modelling with backward inference. This is important as available tools in the literature can only do one and not the other, which limits their accessibility to neuroscientists with limited computational expertise. Another strength of the paper is the demonstration of how the tool can be applied to a broad range of computational models popularly used in the field to interrogate diverse neuroimaging data, ensuring that the methodology is not optimal to only one model. Moreover, through extensive in-silico testing, the work provided evidence that the tool can accurately infer ground-truth parameters, which is important to ensure results from future hypothesis testing are meaningful.

We are happy to hear the positive feedback on our effort to provide an open-source and widely accessible tool for both fast forward simulations and flexible model inversion, applicable across popular models of large-scale brain dynamics.

Weaknesses:

Although the tool itself is the main strength of the work, the paper lacked a thorough analysis of issues concerning robustness and benchmarking relative to existing tools.

The first issue is the robustness to the choice of features to be included in the objective function. This choice significantly affects the training and changes the results, as the authors even acknowledged themselves multiple times (e.g., Page 17 last sentence of first paragraph or Page 19 first sentence of second paragraph). This brings the question of whether the accurate results found in the various demonstrations are due to the biased selection of features (possibly from priors on what worked in previous works). The robustness of the neural estimator and the inference method to noise was also not demonstrated. This is important as most neuroimaging measurements are inherently noisy to various degrees.

The second issue is on benchmarking. Because the tool developed is, in principle, only a combination of existing tools specific to modeling or Bayesian inference, the work failed to provide a more compelling demonstration of its added value. This could have been demonstrated through appropriate benchmarking relative to existing methodologies, specifically in terms of accuracy and computational efficiency.

We fully agree with the reviewer that the VBI estimation heavily depends on the choice of data features, and this is the core of the inference procedure, not its weakness. We have demonstrated different scenarios showing how the informativeness of features (commonly used in the literature) results in varying uncertainty quantification. For instance, using summary statistics of functional connectivity (FC) and functional connectivity dynamics (FCD) matrices to estimate global coupling parameter leads to fast convergence; however, it is not sufficient to accurately estimate the whole-brain heterogeneous excitability parameter, which requires features such as statistical moments of time series. VBI provides a taxonomy of data features that users can employ to test their hypotheses. It is important to note that one major advantage of VBI is its ability to make estimation using a battery of data features, rather than relying on a limited set (such as only FC or FCD) as is often the case in the literature. In the revised version, we will elaborate further by presenting additional scenarios to demonstrate the robustness of the estimation. We will also evaluate the robustness of the neural density estimators to (dynamical/additive) noise.

More importantly, relative to benchmarking, we would like to draw attention to a key point regarding existing tools and methods. The literature often uses optimization for fitting whole-brain network models, and its limitations for reliable causal hypothesis testing have been pointed out in the Introduction/Discussion. As also noted by the reviewer under strengths, and to the best of our knowledge, there are no existing tools other than VBI that can scale and generalize to operate across whole-brain models for Bayesian model inversion. Previously, we developed Hamiltonian Monte Carlo (HMC) sampling for Epileptor model in epilepsy (Hashemi et al., 2020, Jha et al., 2022). This phenomenological model is very well-behaved in terms of numerical integration, gradient calculation, and dynamical system properties (Jirsa et al., 2014). However, this does not directly generalize to other models, particularly the Montbrió model for resting-state, which exhibits bistability with noise driving transitions between states. As shown in Baldy et al., 2024, even at the level of a single neural mass model (i.e., one brain region), gradient-based HMC failed to capture such switching behaviour, particularly when only one state variable (membrane potential) was observed while the other (firing rate) was missing. Our attempts to use other methods (e.g., the second-derivative-based Laplace approximation used in Dynamic Causal Modeling) also failed, due to divergence in gradient calculation. Nevertheless, reparameterization techniques (Baldy et al., 2024) and hybrid algorithms (Gabrié et al., 2022) could offer improvements, although this remains an open problem for these classes of computational models.

In sum, for oscillatory systems, it has been shown previously that SBI approach used in VBI substantially outperforms both gradient-based and gradient-free alternative methods (Gonçalves et al., 2020, Hashemi et al., 2023, Baldy et al., 2024). Importantly, for bistable systems with switching dynamics, gradient-based methods fail to converge, while gradient-free methods do not scale to the whole-brain level (Hashemi et al., 2020). Hence, the generalizability of VBI relies on the fact that neither the model nor the data features need to be differentiable. We will clarify this point in the revised version. Moreover, we will provide better explanations for some terms mentioned by the reviewer in Recommendations.

Hashemi, M., Vattikonda, A. N., Sip, V., Guye, M., Bartolomei, F., Woodman, M. M., & Jirsa, V. K. (2020). The Bayesian Virtual Epileptic Patient: A probabilistic framework designed to infer the spatial map of epileptogenicity in a personalized large-scale brain model of epilepsy spread. NeuroImage, 217, 116839.

Jha, J., Hashemi, M., Vattikonda, A. N., Wang, H., & Jirsa, V. (2022). Fully Bayesian estimation of virtual brain parameters with self-tuning Hamiltonian Monte Carlo. Machine Learning: Science and Technology, 3(3), 035016.

Jirsa, V. K., Stacey, W. C., Quilichini, P. P., Ivanov, A. I., & Bernard, C. (2014). On the nature of seizure dynamics. Brain, 137(8), 2210-2230.

Baldy, N., Breyton, M., Woodman, M. M., Jirsa, V. K., & Hashemi, M. (2024). Inference on the macroscopic dynamics of spiking neurons. Neural Computation, 36(10), 2030-2072.

Baldy, N., Woodman, M., Jirsa, V., & Hashemi, M. (2024). Dynamic Causal Modeling in Probabilistic Programming Languages. bioRxiv, 2024-11.

Gabrié, M., Rotskoff, G. M., & Vanden-Eijnden, E. (2022). Adaptive Monte Carlo augmented with normalizing flows. Proceedings of the National Academy of Sciences, 119(10), e2109420119.

Gonçalves, P. J., Lueckmann, J. M., Deistler, M., Nonnenmacher, M., Öcal, K., Bassetto, G., ... & Macke, J. H. (2020). Training deep neural density estimators to identify mechanistic models of neural dynamics. eLife, 9, e56261.

Hashemi, M., Vattikonda, A. N., Jha, J., Sip, V., Woodman, M. M., Bartolomei, F., & Jirsa, V. K. (2023). Amortized Bayesian inference on generative dynamical network models of epilepsy using deep neural density estimators. Neural Networks, 163, 178-194.

Reviewer #2 (Public review):

Summary:

Whole-brain network modeling is a common type of dynamical systems-based method to create individualized models of brain activity incorporating subject-specific structural connectome inferred from diffusion imaging data. This type of model has often been used to infer biophysical parameters of the individual brain that cannot be directly measured using neuroimaging but may be relevant to specific cognitive functions or diseases. Here, Ziaeemehr et al introduce a new toolkit, named "Virtual Brain Inference" (VBI), offering a new computational approach for estimating these parameters using Bayesian inference powered by artificial neural networks. The basic idea is to use simulated data, given known parameters, to train artificial neural networks to solve the inverse problem, namely, to infer the posterior distribution over the parameter space given data-derived features. The authors have demonstrated the utility of the toolkit using simulated data from several commonly used whole-brain network models in case studies.

Strengths:

(1) Model inversion is an important problem in whole-brain network modeling. The toolkit presents a significant methodological step up from common practices, with the potential to broadly impact how the community infers model parameters.

(2) Notably, the method allows the estimation of the posterior distribution of parameters instead of a point estimation, which provides information about the uncertainty of the estimation, which is generally lacking in existing methods.

(3) The case studies were able to demonstrate the detection of degeneracy in the parameters, which is important. Degeneracy is quite common in this type of model. If not handled mindfully, they may lead to spurious or stable parameter estimation. Thus, the toolkit can potentially be used to improve feature selection or to simply indicate the uncertainty.

(4) In principle, the posterior distribution can be directly computed given new data without doing any additional simulation, which could improve the efficiency of parameter inference on the artificial neural network if well-trained.

We thank the reviewer for the careful consideration of important aspects of the VBI tool, such as uncertainty quantification, degeneracy detection, parallelization, and amortization strategy.

Weaknesses:

(1) While the posterior estimator was trained with a large quantity of simulated data, the testing/validation is only demonstrated with a single case study (one point in parameter space) per model. This is not sufficient to demonstrate the method's accuracy and reliability, but only its feasibility. Demonstrating the accuracy and reliability of the posterior estimation in large test sets would inspire more confidence.

(2) The authors have only demonstrated validation of the method using simulated data, but not features derived from actual EEG/MEG or fMRI data. So, it is unclear if the posterior estimator, when applied to real data, would produce results as sensible as using simulated data. Human data can often look quite different from the simulated data, which may be considered out of distribution. Thus, the authors should consider using simulated test data with out-of-distribution parameters to validate the method and using real human data to demonstrate, e.g., the reliability of the method across sessions.

(3) The z-scores used to measure prediction error are generally between 1-3, which seems quite large to me. It would give readers a better sense of the utility of the method if comparisons to simpler methods, such as k-nearest neighbor methods, are provided in terms of accuracy.

(4) A lot of simulations are required to train the posterior estimator, which seems much more than existing approaches. Inferring from Figure S1, at the required order of magnitudes of the number of simulations, the simulation time could range from days to years, depending on the hardware. Although once the estimator is well-trained, the parameter inverse given new data will be very fast, it is not clear to me how often such use cases would be encountered. Because the estimator is trained based on an individual connectome, it can only be used to do parameter inversion for the same subject. Typically, we only have one session of resting state data from each participant, while longitudinal resting state data where we can assume the structural connectome remains constant, is rare. Thus, the cost-efficiency and practical utility of training such a posterior estimator remains unclear.

We agree with the reviewer that it is necessary to show results on larger synthetic test sets, and we will elaborate further by presenting additional scenarios to demonstrate the robustness of the estimation. However, there are some points raised by the reviewer that we need to clarify.

The validation on empirical data was beyond the scope of this study, as it relates to model validation rather than the inversion algorithms. This is also because we aimed to avoid repetition, given that we have previously demonstrated model validation on empirical data using these techniques, for invasive sEEG (Hashemi et al., 2023), MEG (Sorrentino et al., 2024), EEG (Angiolelli et al., 2025) and fMRI (Lavanga et al., 2024, Rabuffo et al., 2025). Note that if the features of the observed data are not included during training, VBI ignores them, as it requires an invertible mapping function between parameters and data features.

We have used z-scores and posterior shrinkage to measure prediction performance, as these are Bayesian metrics that take into account the variance of both prior and posterior rather than only the mean value or thresholding for ranking of the prediction used in k-NN or confusion matrix methods. This helps avoid biased accuracy estimation, for instance, if the mean posterior is close to the true value but there is no posterior shrinkage. Although shrinkage is bounded between 0 and 1, we agree that z-scores have no upper bound for such diagnostics.

Finally, the number of required simulations depends on the dimensionality of the parameter space and the informativeness of the data features. For instance, estimating a single global scaling parameter requires around 100 simulations, whereas estimating whole-brain heterogeneous parameters requires substantially more simulations. Nevertheless, we have provided fast simulations, and one key advantage of VBI is that simulations can be run in parallel (unlike MCMC sampling, which is more limited in this regard). Hence, with commonly accessible CPUs/GPUs, the fast simulations and parallelization capabilities of the VBI tool allow us to run on the order of 1 million simulations within 2–3 days on desktops, or in less than half a day on supercomputers at cohort level, rather than over several years! It has been previously shown that the SBI method used in VBI provides an order-of-magnitude faster inversion than HMC for whole-brain epilepsy spread (Hashemi et al., 2023). Moreover, after training, the amortized strategy is critical for enabling hypothesis testing within seconds to minutes. We agree that longitudinal resting-state data under the assumption of a constant structural connectome is rare; however, this strategy is essential in brain diseases such as epilepsy, where experimental hypothesis testing is prohibitive.

We will clarify these points and better explain some terms mentioned by the reviewer in the revised manuscript.

Hashemi, M., Vattikonda, A. N., Jha, J., Sip, V., Woodman, M. M., Bartolomei, F., & Jirsa, V. K. (2023). Amortized Bayesian inference on generative dynamical network models of epilepsy using deep neural density estimators. Neural Networks, 163, 178-194.

Sorrentino, P., Pathak, A., Ziaeemehr, A., Lopez, E. T., Cipriano, L., Romano, A., ... & Hashemi, M. (2024). The virtual multiple sclerosis patient. Iscience, 27(7).

Angiolelli, M., Depannemaecker, D., Agouram, H., Regis, J., Carron, R., Woodman, M., ... & Sorrentino, P. (2025). The virtual parkinsonian patient. npj Systems Biology and Applications, 11(1), 40.

Lavanga, M., Stumme, J., Yalcinkaya, B. H., Fousek, J., Jockwitz, C., Sheheitli, H., ... & Jirsa, V. (2023). The virtual aging brain: Causal inference supports interhemispheric dedifferentiation in healthy aging. NeuroImage, 283, 120403.

Rabuffo, G., Lokossou, H. A., Li, Z., Ziaee-Mehr, A., Hashemi, M., Quilichini, P. P., ... & Bernard, C. (2025). Mapping global brain reconfigurations following local targeted manipulations. Proceedings of the National Academy of Sciences, 122(16), e2405706122.

Author response:

We thank all three reviewers for providing excellent suggestions that we feel will enhance the clarity and impact of our manuscript. When we submit the revised manuscript, we plan to respond to each comment and provide additional data and discussion points as requested. Below, we include an outline of the main points that we intend to address.

(1) Reviewers 1 and 2 both suggested investigating degenerative changes in Purkinje cells that are more resistant to age-related loss. We will look for hallmarks of neurodegeneration, such as shrunken dendrites and axonal swellings, in two areas: surviving Purkinje cells adjacent to stripes of cell loss, and the Purkinje cells in aged mice without Purkinje cell loss.

(2) We agree with Reviewer 2’s point that our manuscript would benefit from discussion of the differences in vulnerability between individual mice.  Therefore, we will elaborate upon possible reasons why some aged mice are more resistant to age-related Purkinje cell loss than others.

(3) We will take Reviewer 3’s suggestion to perform zebrin II co-staining in our GFP reporter mice, given our findings that calbindin staining can be unreliable in this context. 4) We appreciate Reviewer 3’s comment that quantification would support the observations made in our study. To provide quantitative evidence for our categorization of mice with striped and non-striped Purkinje cell loss, we will measure the gaps (or lack thereof) between Purkinje cell bodies in the anterior zone.

(4) We will also incorporate several minor but important changes suggested by all three reviewers.

Thank you to the reviewers and editors for taking the time and effort to review our manuscript.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public review):

Summary:

This study presents compelling evidence for a novel treatment approach in a challenging patient population with MSS/pMMR mCRC, where traditional immunotherapy has often fallen short. The combination of SBRT and tislelizumab not only yielded a high disease control rate but also indicated significant improvements in the tumor's immune landscape. The safety profile appears favorable, which is crucial for patients who have already undergone multiple lines of therapy.

Strengths:

The results underscore the potential of leveraging radiation therapy to enhance the effectiveness of immunotherapy, especially in tumor environments previously deemed hostile to immune interventions. Future research should focus on larger cohorts to validate these findings and explore the underlying mechanisms of immune modulation post-treatment.

Weaknesses:

I believe the author's work is commendable and should be considered with some minor modifications:

(1) While the author categorized patients based on the type of RAS mutation and the location of colorectal cancer metastasis, the article does not adequately address how these classifications influence treatment outcomes. Such as whether KRAS or NRAS mutations, as well as the type of metastatic lesions, affect the sensitivity to gamma-ray treatment and lead to varying responses.

Thank you very much for your question. Therefore, in the revised manuscript, we added an analysis of the impact of RAS mutation types and different metastatic sites on patient prognosis, but unfortunately, due to the limited number of samples, we were unable to obtain satisfactory results. We also placed the relevant results in the supplementary figure.

(2) In Figure 2, clarification is needed on how the author differentiated between on-target and off-target lesions. I observed that some images depicted both lesion types at the same level, which could lead to confusion.

We sincerely apologize for any oversight in our previous submission. To clarify, during the process of radiotherapy planning, we pre-select target lesions at the CT image level, and subsequently define the planning treatment volume (PTV) by marking these pre-selected areas with the 50% isodose lines. In our efficacy evaluation, we distinguish between the target lesions inside the PTV and any lesions outside the target area. In response to your valuable feedback, we have now added the isodose lines for the target lesions to the supplementary figure for greater clarity.

(3) The author performed only a basic difference analysis. A more comprehensive analysis, including calculations of markers related to treatment efficacy, could offer additional insights for clinical practice.

To identify potential markers associated with treatment efficacy, we attempted to establish a Cox proportional hazards model and conducted both univariate and multivariate Cox regression analyses. Unfortunately, due to the constraints of sample size and sequencing depth, the analyses did not yield statistically significant results, and we were unable to identify markers that could clearly predict treatment outcomes.

(4) The transcriptome sequencing analysis provides insights into how stereotactic radiotherapy sensitizes immunotherapy; however, it currently relies on a simple pre- and post-treatment group comparison. It would be beneficial to include additional subgroups to explore more nuanced findings.

We acknowledge the limitations in the depth of our analysis. In addition to performing differential analysis between the responder group (PR) and the non-responder group (Non-PR), we also conducted differential gene expression analysis on samples before and after treatment. The results revealed a consistent increase in the expression of NOS2 in both groups following Gamma Knife combined with immunotherapy, suggesting that this gene may serve as a potential prognostic factor influencing treatment outcomes. However, given the limited number of studies exploring the role of NOS2 in this context, we recognize that further research is necessary to better understand its involvement and to substantiate its potential as a predictive marker.

(5) The author briefly discusses the effects of changes in tumor fibrosis and angiogenesis on treatment outcomes. Further experiments may be necessary to validate these findings and investigate the underlying mechanisms of immune regulation following treatment.

We sincerely appreciate your thoughtful feedback on our results. In response, we conducted additional experiments, including immunohistochemical analysis of patient samples before and after combined treatment. The results demonstrated a reduction in the expression of CD31, a marker of tumor angiogenesis, following the combined treatment. This finding further supports our hypothesis that Gamma Knife treatment, in combination with immunotherapy, may effectively inhibit tumor angiogenesis, contributing to an improved therapeutic outcome.

Reviewer #2 (Public review):

Summary:

This Phase II clinical trial investigates the combination of Gamma Knife Stereotactic Body Radiation Therapy (SBRT) with Tislelizumab for the treatment of metastatic colorectal cancer (mCRC) in patients with proficient mismatch repair (pMMR). The study addresses a critical clinical challenge in the management of pMMR CRC, focusing on the selection of appropriate candidates. The results suggest that the combination of Gamma Knife SBRT and Tislelizumab provides a safe and potent treatment option for patients with pMMR/MSS/MSI-L mCRC who have become refractory to first- and second-line chemotherapy. The study design is rigorous, and the outcomes are promising.

Advantage:

The trial design was meticulously structured, and appropriate statistical methods were employed to rigorously analyze the results. Bioinformatics approaches were utilized to further elucidate alterations in the patient's tumor microenvironment and to explore the underlying factors contributing to the observed differences in treatment efficacy. The conclusions drawn from this trial offer valuable insights for managing advanced colorectal cancer in patients who have not responded to first- and second-line therapies.

Weakness:

(1) Clarity and Structure of the Abstract<br /> - Results Section: The results section should contain important data, I suggest some important sequencing data should be shown to enhance understanding.

Thank you for your insightful question. In response, we have revised the content of the article and restructured the abstract to enhance its scientific clarity and make it more accessible to readers.

(2) As the author using the NanoString assay for transcriptome analysis, more detail should be shown such as the version of R, and the bioinformatics analysis methods.

We have also addressed the missing details in our research methodology. The revised manuscript now includes a complete description of the research methods, along with the specific software and versions used.

(3) It is interesting for included patients that PD-L1 increase expression after Gamma Knife Stereotactic Body Radiation Therapy (SBRT) treatment, How to explain it?

Thank you for your thought-provoking question. PD-L1 plays a crucial role in tumor cell immune evasion, and anti-PD-1/PD-L1 inhibitors have emerged as effective immune checkpoint inhibitors, widely used in cancer therapy. In our clinical trials, we observed an increase in PD-L1 expression in some patients following combined treatment. Existing literature suggests that activation of various carcinogenic and stress response pathways, along with post-transcriptional modifications of PD-L1 (such as phosphorylation, glycosylation, acetylation, ubiquitination, and palmitoylation), can influence its expression[1]. We hypothesize that the increase in PD-L1 expression may be attributed to the activation of specific signaling pathways induced by the radiation from Gamma Knife treatment, as well as the enhanced tumor stress in response to the treatment. However, the precise mechanisms underlying this observation require further experimental investigation. A deeper understanding of these processes could potentially optimize our clinical treatment strategies.

(4) It would be helpful to include a brief discussion of the limitations of the study, such as sample size constraints and their impact on the generalizability of the results. This will give readers a more comprehensive understanding of the findings.

Thank you for highlighting the limitations of the article. In response, we have added a detailed discussion of the constraints arising from the limited number of experimental samples and insufficient sequencing depth. This addition aims to provide readers with a clearer understanding of the study's limitations and the context of our research findings.

(5) Language Accuracy: There are a few instances where wording could be more professional or precise.

Regarding the language deficiency, we are very sorry that the wording of the professional content in the article is not careful and accurate enough due to the difference in the native language environment. We have checked our article again and revised the wording and grammar in the hope that you and other readers can grasp our research content more accurately.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

The research presented in this article is commendable; however, I would like to propose several revisions for consideration:

Consideration of Concomitant Medications: It is imperative to ascertain whether enrolled patients utilized additional pharmacological agents alongside the trial regimen. Such concurrent drug use could potentially influence the final outcomes. A concise discussion of this aspect is warranted within the manuscript.

Clinical Characterization of Response Groups: An examination of the clinical characteristics distinguishing the effective and non-responsive cohorts within the trial is essential. This inquiry merits further exploration, as it may elucidate factors influencing treatment efficacy.

Tumor Microenvironment Analysis: The authors highlight the implications of tumor fibrosis and angiogenesis on therapeutic response. Identification of specific biomarkers associated with these phenotypes is crucial. I recommend undertaking straightforward testing and validation to substantiate these observations.

Thank you very much for your valuable suggestions, many of which have been incorporated into the revised manuscript. Regarding the consideration of concurrent medication, we would like to clarify that all patients included in the study were advanced CRC patients who had progressed during first- or second-line treatments. As such, targeted therapy or chemotherapy was used concurrently in the trial. Previous studies have not indicated that different targeted therapies influence the efficacy of Gamma Knife treatment, though some chemotherapy agents may vary in their side effects. However, we believe these differences do not significantly impact the final outcomes. Given that existing chemotherapy regimens do not substantially affect patient prognosis, we considered the combined drug treatment regimen to be an irrelevant variable in our analysis.

Additionally, we have carefully examined the clinical characteristics of patients across different groups. We have also included an analysis of the impact of various mutation types and metastatic sites in the revised manuscript. Furthermore, we plan to perform CD31 staining on lesions from both the responder and non-responder groups before and after Gamma Knife treatment to assess the role of angiogenesis in treatment response.

Reviewer #2 (Recommendations for the authors):

The abstract should be revised for greater clarity and include key results that substantiate the conclusions. The discussion section needs to more thoroughly address the limitations of the clinical trial, providing readers with a deeper understanding of the trial's findings and implications. Additionally, the methods section should be more rigorous and detailed, offering sufficient information to enhance the transparency and robustness of the experimental design.

Thank you for your constructive suggestions regarding the shortcomings in our manuscript. In response, we have thoroughly reviewed the article and addressed the missing content, including revisions to the abstract, results, discussion, and methods sections. Additionally, we have refined the grammar and wording throughout the manuscript to enhance its professionalism and ensure it aligns with the standards expected for publication.

(1)  YAMAGUCHI H, HSU J M, YANG W H, et al. Mechanisms regulating PD-L1 expression in cancers and associated opportunities for novel small-molecule therapeutics [J]. Nature reviews Clinical oncology, 2022, 19(5): 287-305.

Author response:

The following is the authors’ response to the previous reviews

Public Reviews:

Reviewer #1 (Public review):

Summary:

The Authors investigated the anatomical features of the excitatory synaptic boutons in layer 1 of the human temporal neocortex. They examined the size of the synapse, the macular or the perforated appearance and the size of the synaptic active zone, the number and volume of the mitochondria, the number of the synaptic and the dense core vesicles, also differentiating between the readily releasable, the recycling and the resting pool of synaptic vesicles. The coverage of the synapse by astrocytic processes was also assessed, and all the above parameters were compared to other layers of the human temporal neocortex. The Authors conclude that the subcellular morphology of the layer 1 synapses is suitable for the functions of the neocortical layer, i.e. the synaptic integration within the cortical column. The low glial coverage of the synapses might allow the glutamate spillover from the synapses enhancing synaptic crosstalk within this cortical layer.

Strengths:

The strengths of this paper are the abundant and very precious data about the fine structure of the human neocortical layer 1. Quantitative electron microscopy data (especially that derived from the human brain) are very valuable, since this is a highly time- and energy consuming work. The techniques used to obtain the data, as well as the analyses and the statistics performed by the Authors are all solid, strengthen this manuscript, and mainly support the conclusions drawn in the discussion.

Comments on latest version:

The corrected version of the article titled “Ultrastructural sublaminar specific diversity of excitatory synaptic boutons in layer 1 of the adult human temporal lobe neocortex" has been improved thanks to the comments and suggestions of the reviewers. The Authors implemented several of my comments and suggestions. However, many of them were not completed. It is understandable that the Authors did not start a whole new series of experiments investigating inhibitory synapses (as it was a misunderstanding affecting 2 reviewers from the three). But the English text is still very hard to understand and has many mistakes, although I suggested to extensively review the use of English. Furthermore, my suggestion about avoiding many abbreviations in the abstract, analyse and discuss more the perforated synapses, the figure presentation (Figure 3) and including data about the astrocytic coverage in the Results section were not implemented. My questions about the number of docked vesicles and p10 vesicles, as well as about the different categories of the vesicle pools have not been answered neither. Many other minor comments and suggestions were answered, corrected and implemented, but I think it could have been improved more if the Authors take into account all of the reviewers' suggestions, not only some of them. I still have several main and minor concerns, with a few new ones as well I did not realize earlier, but still think it is important.

We would like to thank the reviewer for the comments.

- We worked on the English again and tried to improve the language.

- We avoided to use too many abbreviations in the Abstract and reduced them to a minimum.

- We included a small paragraph about non-perforated vs. perforated active zones in both the Results and Discussion sections. However, since the majority of active zones in all cortical layers of the human TLN were of the macular type, we concluded that it is not relevant to describe their function in more detail.

- In Figure 3 A-C we added contour lines to the boutons to make their outlines more visible.

- We completed the data about the astrocytic coverage in the Results section (see also below).

- Concerning the vesicle pools please see below.

Main concerns:

(1) Epileptic patients:

As all patients were epileptic, it is not correct to state in the abstract that non-epileptic tissue was investigated. Even if the seizure onset zone was not in the region investigated, seizures usually invade the temporal lobe in TLE. If you can prove that no spiking activity occurred in the sample you investigated and the seizures did not invade that region, then you can write that it is presumably non-epileptic. I would suggest to write “L1 of the human temporal lobe neocortical biopsy tissue". See also Methods lines 608-612. Write only “non-epileptic" or “non-affected" if you verified it with EcoG. If this was the case, please write a few sentences about it in the Methods.

We rephrased Material and Methods concerning this point and added that patients were monitored with EEG, MRI and multielectrode recordings. In addition, we stated that the epileptic focus was always far away from the neocortical tissue samples. Furthermore, we added a small paragraph that functional studies using the same methodology have shown that neocortical access tissue samples taken from epilepsy surgery do not differ in electrophysiological properties and synaptic physiology when compared with acute slice preparations in experimental animals and we quoted the relevant papers.

We hope that the reviewer is now convinced that our tissue samples can be regarded as non-affected.

(2) About the inhibitory/excitatory synapses.

Since our focus was on excitatory synaptic boutons as already stated in the title we have not analyzed inhibitory SBs. Now, I do understand that only excitatory synapses were investigated. Although it was written in the title, I did not realized, since all over the manuscript the Authors were writing synapses, and were distinguishing between inhibitory and excitatory synapses in the text and showing numerous excitatory and inhibitory synapses on Figure 2 and discussing inhibitory interneurons in the Discussion as well. Maybe this was the reason why two reviewers out of the three (including myself) thought you investigated both types of synapses but did not differentiated between them. So, please, emphasize in the Abstract (line 40), Introduction (for ex. line 92-97) and the Discussion (line 369) that only excitatory synaptic boutons were investigated.

As this paper investigated only excitatory synaptic boutons, I think it is irrelevant to write such a long section in the Discussion about inhibitory interneurons and their functions in the L1 of the human temporal lobe neocortex. Same applies to the schematic drawing of the possible wiring of L1 (Figure 7). As no inhibitory interneurons were examined, neither the connection of the different excitatory cells, only the morphology of single synaptic boutons without any reference on their origin, I think this figure does not illustrate the work done in this paper. This could be a figure of a review paper about the human L1, but is inappropriate in this study.

We followed the reviewer’s suggestion and pointed out explicitly that we only investigated excitatory synaptic boutons. We also changed the Discussion and focused more on circuitry in L1 and the role of CR-cells.

(3) Perforated synapses

The findings of the Geinismann group suggesting that perforated synapses are more efficient than non-perforated ones is nowadays very controversially discussed” I did not ask the Authors to say that perforated synapses are more efficient. However, based on the literature (for ex. Harris et al, 1992; Carlin and Siekievitz, 1982; Nieto-Sampedro et al., 1982) the presence of perforated synapses is indeed a good sign of synapse division/formation - which in turn might be coupled to synaptic plasticity (Geinisman et al, 1993), increased synaptic activity (Vrensen and Cardozo, 1981), LTP (Geinisman et al, 1991, Harris et al, 2003), pathological axonal sprouting (Frotscher et al, 2006), etc. I think it is worth mentioning this at least in the Discussion.

We agree with the reviewer and added a small paragraph in the Results section about the two types of AZs in L1 of the human TLN. We pointed out that there are both types, macular non-perforated and perforated AZs, but the majority in all layers were of the non-perforated type. In the Discussion we added some paper pointing out the role of perforated synapses.

(4) Question about the vesicle pools

Results, Line 271: Still not understandable, why the RRP was defined as {less than or equal to}10 nm and {less than or equal to}20nm. Why did you use two categories? One would be sufficient (for example {less than or equal to}20nm). Or the vesicles between 10 and 20nm were considered to be part of RRP? In this case there is a typo, it should be {greater than or equal to}10 nm and {less than or equal to}20nm.

The answer of the Authors was to my question raised: We decided that also those very close within 10 and 20 nm away from the PreAZ, which is less than a SV diameter may also contribute to the RRP since it was shown that SVs are quite mobile.

This does not clarify why did you use two categories. Furthermore, I did not receive answer (such as Referee #2) for my question on how could you have 3x as many docked vesicles than vesicles {less than or equal to}10nm. The category {less than or equal to}10nm should also contain the docked vesicles. Or if this is not the case, please, clarify better what were your categories.

We thank the reviewer for pointing out that mentioning two distance criteria (p10 and p20) to define one physiological entity (RRP) is somewhat confusing and we acknowledge that the initial response to the reviewers falls short of explaining this choice. This is indeed only understandable in the context of the original paper by Sätzler et al. 2002, where these criteria were first introduced. We therefore referenced this publication more prominently in the paragraph in question.

So to explain this, we first would like to clarify the definition of the two RRP classification criteria used (p10 and p20), which has caused some confusion amongst the reviewers as to which vesicles where included or not:

- p10 criterion: p£10 nm (SVs have a minimum distance less than or equal to 10 nm from the PreAZ), including ‘docked’ vesicles which have a distance of zero or less (p0)

- p20 criterion: p£20 nm (SVs have a minimum distance less than or equal to 20 nm from the PreAZ), including vesicles of the p10 criterion.

As mentioned, these criteria were introduced first in Sätzler et al. 2002 looking at the Calyx of Held synapse. In that paper, we tried to establish a morphological correlate to existing physiological measurements, which included the RRP. As there is no known marker that would allow to discriminate between vesicles that contribute to the RRP anatomically, we looked at existing physiological experiments such as Schneggenburger et al. 1999; Wu and Borst 1999; Sun and Wu 2001 and compared their total numbers to our measurements. As the number of docked vesicles (p0, see above) was on the lower side of these physiological estimates, we also looked at vesicles close to the AZ, which we think could be recruited within a short time (£ 10 msec). Comparing with existing literature, we found that at p20 we get pool sizes comparable to midrange estimates of reported RRP sizes. In order to account for the variability of the observed physiological pool sizes, we reported all three measurements (p0, p10, p20) not only in the original Calyx of Held, but in all subsequent studies of different CNS synapses of our group since then.

As it remains uncertain if such correlate indeed exists, we therefore followed the suggestion to rephrase RRP and RP to putative RRP and putative RP (see also Rollenhagen et al. 2007). We thank both reviewers for pointing out this omission.

Concerning the difference between ‘docked’ vesicles and vesicles within the p10 perimeter criterion. First of all, the reviewer is right in saying that the category p10 ({less than or equal to}10nm) should also contain the docked vesicles (see above). The fact to have 3x as many ‘docked’ vesicles in our TEM tomography than in the p10 distance analysis could be partly explained, on the one hand, by a very high variability between patients (as expressed by the high SD, table 1) and, on the other hand, by a high intraindividual synaptic bouton variability. In both sublayers, there is a huge difference in the number of vesicles within the p10 criterion of individual synaptic boutons ranging from 0 to ~40 with a mean value of ~1 to ~4 (calculated per patient), the upper level being close to the values calculated with TEM tomography for the ‘docked’ vesicles.

(5) Astrocytic coverage

On Fig. 6 data are presented on the astrocytic coverage derived from L1 and L4. In my previous review I asked to include this in the text of the Results as well, but I still do not see it. It is also lacking from the Results how many samples from which layer were investigated in this analysis. Only percentages are given, and only for L1 (but how many patients, L1a and/or L1b and/or L4 is not provided). In contrast, Figure 6 and Supplementary Table 2 (patient table) contains the information that this analysis has been made in L4 as well. Please, include this information in the text as well (around lines 348-360).

In our previous revised version, we had included the values shown in Fig. 6 for both L1 and L4 in the Results section (L4: lines 352 – 355: ‘The findings in L1…’). However, we agree with the reviewer and have now also added the number of patients and synapses investigated (now lines 359 – 365).

About how to determine glial elements. I cannot agree with the Authors that glial elements can be determined with high certainty based only on the anatomical features of the profiles seen in the EM. “With 25 years of experience in (serial) EM work" I would say, that glial elements can be very similar to spine necks and axonal profiles.

All in all, if similar methods were used to determine the glial coverage in the different layers of the human neocortex, than it can be compared (I guess this is the case). However, I would say in the text that proper determination would need immunostaining and a new analysis. This only gives an estimation with the possibility of a certain degree of error.

We do not entirely agree with the reviewer on this point. As stated in the text, there are structural criteria to identify astrocytic elements (see citations quoted). These golden standard criteria are commonly used also by other well-known groups (DeFelipe and co-workers, Francisco Clasca and co-workers; Michael Frotscher the late and co-workers etc.). However, in a past paper about astrocytic coverage of synaptic complexes in L5 of the human TLN, immunohistochemistry against glutamine synthetase, a key enzyme in astrocytes, was carried out to describe the coverage. This experiment supports our findings in the other cortical layers of the human TLN. As the reviewer might know, immunohistochemistry always led to a reduction in ultrastructural preservation, so we decided not to use immunohistochemistry for the further publications of the other cortical layers. We added a short notice on this in the Material and Methods section.

(6) Large interindividual differences in the synapse density should be discussed in the Discussion.

As suggested by the reviewer we have included a sentence in the Discussion that interindividual differences can be either related to differences in age, gender and the use of different methodology as suggested by DeFelipe and co-workers (1999)

Reviewer #2 (Public review):

Summary:

The study of Rollenhagen et al examines the ultrastructural features of Layer 1 of human temporal cortex. The tissue was derived from drug-resistant epileptic patients undergoing surgery, and was selected as further from the epilepsy focus, and as such considered to be non-epileptic. The analyses has included 4 patients with different age, sex, medication and onset of epilepsy. The MS is a follow-on study with 3 previous publications from the same authors on different layers of the temporal cortex:

Layer 4 - Yakoubi et al 2019 eLife

Layer 5 - Yakoubi et al 2019 Cerebral Cortex,

Layer 6 - Schmuhl-Giesen et al 2022 Cerebral Cortex

They find, the L1 synaptic boutons mainly have single active zone a very large pool of synaptic vesicles and are mostly devoid of astrocytic coverage.

Strengths:

The MS is well written easy to read. Result section gives a detailed set of figures showing many morphological parameters of synaptic boutons and surrounding glial elements. The authors provide comparative data of all the layers examined by them so far in the Discussion. Given that anatomical data in human brain are still very limited, the current MS has substantial relevance.

The work appears to be generally well done, the EM and EM tomography images are of very good quality. The analyses is clear and precise.

Weaknesses:

The authors made all the corrections required, answered most of my concerns, included additional data sets, and clarified statements where needed.

My remaining points are:

Synaptic vesicle diameter (that has been established to be ~40nm independent of species) can properly be measured with EM tomography only, as it provides the possibility to find the largest diameter of every given vesicle. Measuring it in 50 nm thick sections result in underestimation (just like here the values are ~25 nm) as the measured diameter will be smaller than the true diameter if the vesicle is not cut in the middle, (which is the least probable scenario). The authors have the EM tomography data set for measuring the vesicle diameter properly.

We thank the reviewer for the helpful comments. We followed the recommendation to measure the vesicle diameter using our TEM tomography tilt series, but came to similar results concerning this synaptic parameter. As stated in our Material and Methods section, we only counted (measured) clear ring-link structures according to a paper by Abercrombie (1963). Since our results are similar for both methods, we do believe that our measurements are correct. Even random single measurements on the original 3D tilt-series yielded comparable results (Lübke and co-workers, personal observation). Furthermore, our results are within ranges, although with high variability, also described by other groups (see discussion lines 436 - 449). We therefore hope that the reviewer will now accept our measurements.

It is a bit misleading to call vesicle populations at certain arbitrary distances from the presynaptic active zone as readily releasable pool, recycling pool and resting pool, as these are functional categories, and cannot directly be translated to vesicles at certain distances. Even it is debated whether the morphologically docked vesicles are the ones, that are readily releasable, as further molecular steps, such as proper priming is also a prerequisite for release.

It would help to call these pools as "putative" correlates of the morphological categories.

We followed the suggestion by the reviewer and renamed our vesicle pools as putative RRP, putative RP and putative resting pools.

Reviewer #3 (Public review):

Summary:

Rollenhagen at al. offer a detailed description of layer 1 of the human neocortex. They use electron microscopy to assess the morphological parameters of presynaptic terminals, active zones, vesicle density/distribution, mitochondrial morphology and astrocytic coverage. The data is collected from tissue from four patients undergoing epilepsy surgery. As the epileptic focus was localized in all patients to the hippocampus, the tissue examined in this manuscript is considered non-epileptic (access) tissue.

Strengths:

The quality of the electron microscopic images is very high, and the data is analyzed carefully. Data from human tissue is always precious and the authors here provide a detailed analysis using adequate approaches, and the data is clearly presented.

Weaknesses:

The text connects functional and morphological characteristics in a very direct way. For example, connecting plasticity to any measurement the authors present would be rather difficult without any additional functional experiments. References to various vesicle pools based on the location of the vesicles is also more complex than it is suggested in the manuscript. The text should better reflect the limitations of the conclusions that can be drawn from the authors' data.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Astrocytic coverage

On Fig. 6 data are presented on the astrocytic coverage derived from L1 and L4. In my previous review I asked to include this in the text of the Results as well, but I still do not see it. It is also lacking from the Results how many samples from which layer were investigated in this analysis. Only percentages are given, and only for L1 (but how many patients, L1a and/or L1b and/or L4 is not provided). In contrast, Figure 6 and Supplementary Table 2 (patient table) contains the information that this analysis has been made in L4 as well. Please, include this information in the text as well (around lines 348-360).

See above.

About how to determine glial elements. I cannot agree with the Authors that glial elements can be determined with high certainty based only on the anatomical features of the profiles seen in the EM. “With 25 years of experience in (serial) EM work" I would say, that glial elements can be very similar to spine necks and axonal profiles. Please, see the photos below, out of the 16 circled profiles (2nd picture, very similar to each other) only 3 belong to an astroglial cell (last picture, purple profiles-purple cell), 10 are spines/spine necks/small caliber dendrites of pyramidal cells, 3 are axonal profiles (last but one picture, blue profiles, marked with arrows on the right side). If you follow in your serial sections those elements which you think are glial processes and indeed they are attached to a confidently identifiable glial cell, I agree, it is a glial process. But identifying small, almost empty profiles without any specific staining, from one single EM section, as glial process is very uncertain. Please, check the database of the Allen Institute made from the V1 visual cortex of a mouse. It is a large series of EM sections where they reconstructed thousands of neurons, astroglial and microglial cells. It is possible to double click on the EM picture on a profile and it will show the cell to which that profile belongs. https://portal.brain-map.org/connectivity/ultrastructural-connectomics Pictures included here: https://elife-rp.msubmit.net/eliferp_files/2024/11/25/00132644/02/132644_2_attach_21_29456_convrt.pdf

All in all, if similar methods were used to determine the glial coverage in the different layers of the human neocortex, than it can be compared (I guess this is the case). However, I would say in the text that proper determination would need immunostaining and a new analysis. This only gives an estimation with the possibility of a certain degree of error.

As stated above, we carried out glutamine synthetase immunohistochemistry in L5 of the human TLN and came to the same results. However, we added a sentence on this in the chapter on astrocytic coverage in the Material and Methods section. Additionally, we modified this chapter according to the reviewer’s suggestion.

Minor comments

Introduction: Last sentence is not understandable (lines 101-103), please rephrase. (contribute to understand or contribute in understanding or contribute to the understanding of..., but definitely not contribute to understanding). The authors should check and review extensively for improvements to the use of English, or use a program such as Grammarly.

Results: Grammar (line 107): L1 in the adult mammalian neocortex represents a relatively...

Line 173: “Some SBs in both sublaminae were seen to establish either two or three SBs on the same spine, spines 173 of other origin or dendritic shafts." - Some SBs established two or three SBs? I would write Some SBs established two or three synapses on...

Line 243: “The synaptic cleft size were slightly, but non-significantly different"

Line 260: “DCVs play an important role in endo- and exocytosis, the build-up of PreAZs by releasing Piccolo and Bassoon (Schoch and Gundelfinger 2006; Murkherjee et al. 2010)," - please, correct this.

We have done corrections as suggested by the reviewer.

Line 374: No point at the end of the last phrase.

Discussion:

Lines 400-404: “The majority of SBs in L1 of the human TLN had a single at most three AZs that could be of the non perforated macular or perforated type comparable with results for other layers in the human TLN but by ~1.5-fold larger than in rodent and non-human primates." - What is comparable with the other layers, but different from animals? Please rephrase this sentence, it is not understandable. I already mentioned this sentence in my previous review, but nothing happened.

Lines 435-437: “Remarkably, the total pool sizes in the human TLN were significantly larger by more than 6-fold (~550 SVs/AZ), and ~4.7-fold (~750 SVs/AZ;) than those in L4 and L5 (Yakoubi et al. 2019a, b; see also Rollenhagen et al. 2018) in rats." Please rethink what you wished to say and compare to the sentence meaning. I think you wanted to compare human TLN L1 pool size to L4 and L5 in the human TLN (Yakoubi 2019a and b) and to rat (Rollenhagen 2018). Instead, you compared all layers of the human TLN to L4 and L5 in rats (with partly wrong references). Please rephrase this. Lines 483-484: “Astrocytes serve as both a physical barrier to glutamate diffusion and as mediate neurotransmitter uptake via transporters".

This sentence is grammatically incorrect, please rephrase.

We corrected the sentences as suggested by the reviewer.

Methods:

In the text, there are only 4 patients (lines 603-604), but in the supplementary table there are 9 patients (5 new included for L4 astrocytic coverage). Please, correct it in the text.

Lines 608-609: “neocortical access tissue samples were resected to control the seizures for histological inspection by neuropathologists." - What is the meaning of this? Please, rephrase.

We thank the reviewer for the comment and included the 5 patients used for L4 to the Material and Methods section, as well as in the Results section.

The reviewer is right, and we rephrased and corrected the sentence concerning the inspection by neuropathologists.

Figures

Figures 5B: The legend says “SB (sb) synapsing on a stubby spine (sp) with a prominent spine apparatus (framed area) and a thick dendritic segment (de) in L1b" - In my opinion this is not one synaptic bouton, but two. Clearly visible membranes separate them, close to the spine.

Supplemental Table 2 (patient table). If there is no information about Hu_04 patient's epilepsy, please write N/A (=non available) instead of - (which means it does not exist).

The reviewer is right, and we corrected the figure and the legend, as well as the table accordingly.

Reviewer #2 (Recommendations for the authors):

The authors addressed almost all of my concern, only this one remained:

If there is, however, relevant literature on "methods based on EM tomography" and "stereological methods to estimate both types of error" (over- and underestimates) that we are missing out on, we would appreciate the reviewer providing us with the corresponding references so that we can include such calculations in our paper.

There is a very detailed new study on calculating correction for TEM 2D 3D, Rothman et al 2023 PLOS One. That addresses most of these issues.

We thank the reviewer for drawing our attention to the publication by Rothman et al. 2023, which is a very detailed and comprehensive study looking at accurately estimating distributions of 3D size and densities of particles from 2D measurements using – amongst others – ET and TEM images as well as synaptic vesicles for validating their method. However, we do not see how this would be relevant to the reported mean diameters and their corresponding variances. And even if we would have reported on vesicle size/diameter distributions (referred to as G(d) in Rothmann et al. 2023), the authors themselves state that “… the results from our ET and TEM image analysis highlight the difficulty in computing a complete G(d) of MFT vesicles due to their small size…

Author response:

The following is the authors’ response to the original reviews

Reviewer #1 (Public review):

Weaknesses:

It would be helpful for the authors to highlight why their technique (large scale analysis of one emm type) can yield more information than a typical GWAS analysis of invasive vs. non-invasive strains. Are SNPs easier to identify using a large-scale core genome? Is it more likely evolutionarily to find mutations in non-coding regions as opposed to the core genome and accessory genes, and this is what this technique allows? Did the analysis yield unexpected genes or new genes that had not been previously identified in other GWAS analyses? These points may need to be made more apparent in the results and deserve some thought in the discussion section.

We thank the reviewer for pointing out the importance of this study. By focusing on bacteria within a single emm type, false positives caused by confounding lineage effects can be minimized, which contributes to greater accuracy of the pan-GWAS. We have added relevant text describing the strong points of our pan-GWAS approach to the Results and Discussion sections, as shown following:

“The present pan-GWAS of bacteria within a single emm type minimized lineage effects, thus reducing false positives.” (lines 204–205)

The present study focused on emm89 S. pyogenes, known to cause increasing rates of invasive infections worldwide, and also assessed differences between emm89 strains causing invasive and non-invasive infections. By focusing on bacteria within closed phylogenies, false positives caused by confounding lineage effects were minimized, thus contributing to a higher level of accuracy of the pan-GWAS.” (lines 420–424)

In addition, we would like to comment more regarding the reviewer’s question, "Is it more likely evolutionarily to find mutations in non-coding regions as opposed to the core genome and accessory genes, and this is what this technique allows?". Mutations are generally considered to be more frequent in non-coding than coding regions. However, the actual mutation frequencies in both types of regions were not assessed in this study. Nevertheless, exploring non-coding regions using the k-mer method is of considerable importance, as variations significantly associated with infectious phenotypes may contribute to alterations in gene expression and other regulatory mechanisms.

The Alpha-fold data does not demonstrate why the mutations the authors identified could contribute to the invasive phenotype. It would be helpful to show an overlay of the predicted structures containing the different SNPs to demonstrate the potential structural differences that can occur due to the SNP. This would make the data more convincing that the SNP has a potential impact on the function of the protein. Similarly, the authors discuss modification of the hydrophobicity of the side chain in the ferrichrome transporter (lines 317-318) due to a SNP, but this is not immediately obvious in the figure (Fig. 5).

As the reviewer suggested, we have substituted Figure 5E in the previous version with a figure illustrating the molecular surface within proximity of the mutation. We speculated that the mutation may induce a small indentation on the surface, and thus attenuate the stability of the hydrophobic bound between FhuB and FhuD by invasion of solvent into the indentation. Additionally, images showing the wild-type and mutated models have been separated for better visibility instead of as an overlay of the predicted models suggested by the reviewer. Relevant text in the Results section and legend of Figure 5E have been accordingly revised, as shown following:

“The mutation was predicted to induce formation of a small indentation on the molecular surface, thus increasing the surface area accessible to the solvent, and is considered to potentially affect the stability of the hydrophobic bond between FhuB and FhuD, and thus ferrichrome transport (Figure 5E).” (lines 360–363)

“The 73rd valine in FhuB, shown in magenta, was substituted with alanine. The molecular surface is illustrated with a wireframe and that of the predicted indentation is shown with an arrowhead.” (lines 1162–1164)

Reviewer #1 (Recommendations for the author):

The figure legend for Fig. 3C needs to be explained so that it is similarly laid out as in Fig. 2C. Fig. 2C should indicate that the magenta color represents the invasive phenotype.

Based on this helpful suggestion, more detailed information about the magenta color representing the invasive phenotype has been added to the legends of Fig. 2C and 3C, with relevant text also included in the revised legends, as shown following:

“Colored bars above indicate countries and phenotypes, and magenta bars represent invasive phenotypes. Using the Roary program, gene names starting with “Group_” were automatically assigned. Position indicates the location of each SNP/indel on the core gene alignment. The full results are shown in Table S6.” (lines 1116–1120)

“Colored bars above indicate countries and phenotypes, and magenta bars represent invasive phenotypes. Using the Roary program, gene names starting with “Group_” were automatically assigned. The full results are shown in Table S8. (lines 1130–1133)

The wording and organization of results in the k-mer section started to get confusing around lines 270-271. It begins to be a list of results and would be better served by some interpretation or explanation of the significance (why it is important to find such mutations). For example, for mutations you find in non-coding regions, do you expect them to have a detrimental effects on gene expression/regulation?

As the reviewer kindly suggested, we have added interpretation or explanation of the significance of Comp_6 and Comp_24 to the Results section. We analyzed the function of the non-coding region of Comp_6 by employing web-based in silico tools, including MLDSPP and BacPP, though no promoter sequences could be identified. Next, using BLAST, a search for known promoter sequences of S. pyogenes M1 strain SF370 of the CDBProm database was attempted, because the web-based in silico promoter prediction tools are not suitable for S. pyogenes. However, neither identical nor homologous sequences were detected. Thus, the significance of this region remains unknown. In Comp_24, group_141 was also identified in the COGs-based pan-GWAS as a non-invasiveness related gene. Furthermore, group_141 showed high levels of correlation with group_139 and group_467, encoding transposase and uncharacterized protein, respectively, which suggests that the presence of an MGE is associated with a non-invasive phenotype.

Relevant text has been added to the Materials and Methods (lines 653–657) and Results (lines 308–311 and 314–319) sections, as shown following:

“Promoter sequences in intergenic regions were predicted using web-based tools, MLDSPP and BacPP[29,30]. Additionally, BLAST was employed to search the promoter sequences of S. pyogenes strain SF370 registered in the CDBProm database (https://aw.iimas.unam.mx/cdbprom/)[69]” (lines 653–657)

“We speculated that this region is related to regulation of gene expression. However, no promoter sequences were identified by utilizing MLDSPP, BacPP, and BLAST, thus the significance of this region remains to be clarified[29,30].” (lines 308–311)

“Furthermore, group_141 was also identified in the COGs-based pan-GWAS as a non-invasiveness-related gene along with group_139 and group_467, which encode transposase and uncharacterized protein, respectively (Table S8 and Figure S4). Taken together, the absence of an MGE containing group_141, and the presence of another MGE harboring group_142 and group_143 may result in an invasive phenotype.” (lines 314–319)

Additionally, new references (#29, 30, and 69) concerning bacterial promoter prediction have been included in the revised version of the manuscript.

Because there is no difference in intracellular free ferric ions in the fhuB mutant compared with the wild-type, the authors speculate that the upregulation of the fhuBCD operon can compensate for the loss of function of the fhuB gene, but there is insufficient data to support this claim.

As the reviewer indicate, the data presented in the previous version were insufficient to support our speculation. Therefore, the following sentence has been deleted from the manuscript (previous version line 367):

“Therefore, the upregulation of fhuBCD may compensate for the impaired function mediated by SNP T218C.”

The authors mention that there was no direct association between invasiveness and acquisition of genes (lines 451-455), including antibiotic resistance genes from prophages and MGEs (lines 467-469). These data should be moved to the results section to focus the results on the correlation between invasiveness and mutation of existing DNA vs acquisition of new DNA.

Accordingly, we have added relevant text to the Results section, as shown following:

“On the other hand, the present pan-GWAS found no genes encoding known virulence factors significantly associated with invasiveness, thus further analysis of the relationships of detected distribution patterns with prophages and MGEs was performed.” (lines 264–267)

Minor spelling error at line 210 ("waws" instead of "was").

As the reviewer kindly pointed out, the spelling has been corrected. (line 233)

Reviewer #2 (Recommendations for the authors):

Minor comments:

Line 55: Does this rate apply to all types of infections?

The authors appreciate this question from the reviewer. We checked what types of infections the mortality rate is applied to and confirmed that it only represents STSS. Therefore, relevant text has been revised, as shown following:

However, even with proper treatment, the mortality rate of patients with STSS remains high, ranging from 23–81%[6]”. (lines 72–73)

Line 58: Could you explain the protein encoded by the emm gene and the role of the hypervariable region in pathogenesis?

As requested, relevant text regarding the pathogenic role of the hypervariable region of M protein has been added, as shown following:

“S. pyogenes has been classified into at least 240 emm types based on a hypervariable region sequence of the emm gene, which encodes the M protein. This hypervariable region of the M protein is responsible for type-specific antigenicity and binds with high affinity to C4b-binding protein, a major fluid phase inhibitor of the classical and lectin pathways of the complement system that confers resistance to opsonophagocytosis[8].” (lines 76–81)

Line 161: Figure 1C does not show the strain with the different pattern.

The authors apologize for the lack of clarity. In Fig. 1C, the strain is shown by a pale pink color bar used to indicate the related clade. For clarity, an arrowhead pointing to the strain from outside of the tree has been added along with the following text in the legend:

“Arrowhead indicates strain belonging to the novel clade.” (lines 1102–1103)

Line 239: It could be interesting to examine the genes in the region between the mobile elements found in the global cohort, as the result profile was very different from the Japanese group, which revealed more specific genes. Consider adding this to the results section.

Based on the reviewer’s insightful suggestion, we attempted to find regions between the mobile genetic element-related genes. However, contigs generated from short reads were not adequate to identify such a genome structure. Therefore, calculations to analyze the pairwise correlation of the presence of significant COGs in the 666 strains to predict genes on prophages and MGEs were performed, and the results added to Figure S4. Eight clusters were detected as coexisting COG groups, seven of which comprised phage- or MGE-related genes. Furthermore, a cluster with antimicrobial-resistant genes was shown to be correlated with non-invasive infections. It is thus speculated that gain or loss of gene sets via phages and MGEs rather than acquisition of virulence genes may lead to changes in fitness to the environment and bacterial phenotypes. Relevant text has been added to the revised versions of the Results, Discussion, and Materials and Methods sections, as shown following:

“On the other hand, the present pan-GWAS found no genes encoding known virulence factors significantly associated with invasiveness, thus further analysis of the relationships of detected distribution patterns with prophages and MGEs was performed. For calculating the pairwise correlation of the presence of significant COGs in the 666 strains, the COGs were clustered into eight coexisting groups, seven of which contained phage- and/or MGEs-related genes (Figure S4). The largest group comprised 65 genes including phage proteins, while the second largest with 42 genes was found to be associated with non-invasive infections, and included group_2689, group_1833, and ermA1, encoding TetR/AcrR family transcriptional regulator, multidrug efflux system permease protein, and rRNA adenine N-6-methyltransferase, respectively.” (lines 264–273)

“On the other hand, a cluster comprising 49 non-invasiveness-associated genes including antibiotic-resistance genes was identified. Furthermore, among the genes showing a significant correlation with the infectious phenotype, approximately 90% (152 of 169) were associated with non-invasiveness. One possible explanation is that significantly related genes reflect the process of not only gain of factors but also loss of those affecting fitness cost.” (lines 517–522)

“The correlation of the presence of significant COGs was calculated and visualized using the R program.” (lines 643–644)

Line 548: What cutoff values were used in Fastp?

The default cutoff value for Fastp (Q>15) was used, and relevant text has been added to the Materials and Methods section in the revised version, as shown following:

“All collected sequences were subjected to quality checks using Fastp v.0.20.1, with a default cutoff value of Q>15[53].” (lines 600–601)

Line 635: Were the transcriptome experiments performed in triplicate?

We apologize for the confusion. The transcriptome experiment was performed only once with three samples for each condition. The notation “(n=3 for each condition)” has been added to the relevant text portion in the Materials and Methods section (line 696).

Discussion section: I believe the authors should place more emphasis on the fact that FhuB is associated with non-invasiveness, to provide clearer context in the discussion.

Based on this helpful suggestion, we have revised relevant text in the Discussion section, as shown following:

“Transcriptomic analysis findings suggested that the Japan-specific fhuB mutation associated with non-severe invasive infections contributes to the growth rate of S. pyogenes in human blood by adapting to the environment.” (lines 457–459)

Also, “V73A” has been removed from the relevant text in the Discussion section to provide a more clear and precise context, with the revised sentence shown following:

“Two possible roles of the FhuB mutation in the pathogenesis of severe invasive infections are thus proposed.” (lines 470–471)

Author response:

The following is the authors’ response to the previous reviews

We would like to respond to just one remaining concern from Reviewer 1 and Reviewer 2 regarding a potential overfitting in Test Set 1, which involves combinations already present in the training set. DIPx’s (and TAIJI’s) performance in Test Set 1 is better than in Test Set 2, which involves combinations not present in the training set. Let’s consider two general points to highlight why the improved performance is not the result of overfitting. 

(1) Suppose we are testing the e ect of one drug D; the training may involve, for example, selecting an optimal dose. A validated e ect of D in an independent test set is not an overfit, even though we are using the same drug in the training and the test set. Testing one drug is an extreme case, but the same idea holds for any number of drugs. What matters is the independence of the test set. 

(2) A prediction model P1 will legitimately perform better than model P2, if P1 uses better or more informative features than P2. The features could be those used directly in the model, but they could also be other observable characteristics not directly used in the model, such as optimal subregions of the feature space. DPIx or TAIJI results indicate that the identity of previously trained combinations is one such informative feature. The set of previously trained combinations corresponds to a subregion of the feature space. DIPx’s prediction performance for known combinations would be expected to follow the results from Test Set 1; we cannot expect that if there is an overfitting issue. Finally, we note that Test Set 1 was established and used in the AstraZeneca Dream Challenge for rigorously testing the prediction of known combinations.

Author response:

We appreciate the constructive and thoughtful reviews provided by the reviewers and editorial team. We thank you for the opportunity to submit a provisional response and are grateful for the detailed and critical feedback that will strengthen our work. Below, we provide a summary of our planned revisions in response to the public reviews from Reviewer #1 and Reviewer #2.

Reviewer #1 – Public Review Response Plan

(1) Sample Overlap (MR Bias):

We plan to replace several non-overlapping GWAS data sources to validate the association between aneurysms and atherosclerosis, thereby eliminating bias and Type I errors caused by sample overlap.

(2) Multivariable MR (MVMR):<br /> We will attempt to incorporate known confounding factors (e.g., hypertension, smoking, diabetes) within the multivariable MR framework to verify the robustness of our results.

(3) Clarifications and Presentation:

- We will correct eTable citations.

- Distinguish correctly between "incidence" and "prevalence".

- Reorganize results to consistently present primary analyses first (IVW), followed by sensitivity results.

- Expand the methods section to fully reflect all analyses.

Reviewer #2 – Public Review Response Plan

(1) Justification of MMP Selection:<br /> We will provide a detailed rationale for the inclusion of the 12 MMPs, based on prior literature and biological relevance.

(2) Multiple Testing Clarification:<br /> We will clarify the Bonferroni correction strategy, explicitly accounting for all tests (e.g., 72 comparisons × multiple MR methods).

(3) Instrument Selection Threshold:

- We agree with the reviewer and will revise the SNP selection strategy, starting from p < 5×10⁻⁸ and only relaxing thresholds when fewer than 3 instruments are found.

- Clarify the reasons why we do not use LD proxies.

(4) Pleiotropy and Heterogeneity Tests:

- We will add Egger's intercept results alongside MR-PRESSO.

- Specify the R packages used (e.g., TwoSampleMR).

- To prevent cluttered data presentation, we have included both heterogeneity and pleiotropy p-values in the supplementary tables.

- Supplement forest plots showing outlier exclusion effects.

(5) Clarifications in Figures and Tables:

- Fix the duplicated “simple mode” entry in Figure 2.

- Correct inconsistencies in p-values between figures and text.

- Improve figure legends (e.g., color bar labels, panel identifiers).

- Revise Table 4 title for clarity.

- Remove the term "causal" where associations are nominal (e.g., p ~ 0.05).

Author response:

We would like to express our sincere gratitude to the editor and reviewers for their thoughtful comments and suggestions on our manuscript. Below is our interim response to the reviewers’ public review:

Reviewer 1:

(1) We appreciate the reviewer’s insightful comment on the consideration of RAS mutation type and lesion metastasis site in our study. We will undertake a more comprehensive review of the literature and conduct a detailed analysis to assess how these factors influence treatment efficacy in our cohort.

(2) Regarding the radiotherapy planning process, we will provide further clarification in the revised manuscript. Specifically, we select the target lesion using CT imaging and delineate it by marking the 50% isodose line to define the planning target volume (PTV). In assessing treatment efficacy, we differentiate between target lesions (within the PTV) and off-target lesions (outside the PTV). We will update the figures to include the isodose line display for better clarity.

(3 & 4) We acknowledge the limitations of our study, particularly with respect to the sample size, which may hinder the statistical power required for a comprehensive analysis of treatment effect markers and subgroup variations. Nonetheless, we will continue to refine our analyses in the revised manuscript to provide additional insights and strengthen the conclusions where possible.

(5) During the early stages of our research, our team conducted a series of investigations into the impact of tumor fibrosis and angiogenesis on treatment outcomes. We have accumulated a substantial body of data, and we will summarize these findings in the revised manuscript to provide further context and support for our current study.

Reviewer 2:

(1, 4 & 5) We greatly appreciate the reviewer’s careful reading of the manuscript. We will revise the abstract, methods, and results sections to improve clarity and precision. Additionally, we will refine the overall wording of the manuscript to enhance its scientific rigor and professionalism.

(2) We also appreciate the reviewer’s suggestions regarding the methods and results. These will be incorporated into the revised manuscript, with additional detail in the methods section to clarify our experimental approach and strengthen the discussion of our findings.

(3) This is an intriguing point raised by the reviewer. We agree that the upregulation of PD-L1 expression following SBRT treatment could potentially enhance the efficacy of subsequent immunotherapy. To explore this further, we will conduct a detailed literature review and provide a more in-depth analysis of our data to elucidate the underlying mechanisms.

We trust that the clarifications provided above partially address the reviewers' concerns. We are committed to fully resolving the raised issues through more comprehensive revisions in the subsequent manuscript update.

Author response:

The following is the authors’ response to the original reviews

Reviewer #1 (Public Review):

Summary:

In this manuscript, Ning et al. reported that Bcas2 played an indispensable role in zebrafish primitive hematopoiesis via sequestering β-catenin in the nucleus. The authors showed that loss of Bcas2 caused primitive hematopoietic defects in zebrafish. They unraveled that Bcas2 deficiency promoted β-catenin nuclear export via a CRM1-dependent manner in vivo and in vitro. They further validated that BCAS2 directly interacted with β-catenin in the nucleus and enhanced β-catenin accumulation through its CC domains. They unveil a novel insight into Bcas2, which is critical for zebrafish primitive hematopoiesis via regulating nuclear β-catenin stabilization rather than its canonical pre-mRNA splicing functions. Overall, the study is impressive and well-performed, although there are also some issues to address.

Strengths:

The study unveils a novel function of Bcas2, which is critical for zebrafish primitive hematopoiesis by sequestering β-catenin. The authors validated the results in vivo and in vitro. Most of the figures are clear and convincing. This study nicely complements the function of Bcas2 in primitive hematopoiesis.

Weaknesses:

A portion of the figures were over-exposed.

Thank you for the time reviewing our manuscript. We agree with your suggestion and the exposure of Figure 5C and Figure 7E has been reduced. We hope that the revisions will meet your expectation.

Reviewer #2 (Public Review):

Summary:

Ning and colleagues present studies supporting a role for breast carcinoma amplified sequence 2 (Bcas2) in positively regulating primitive wave hematopoiesis through amplification of beta-catenin-dependent (canonical) Wnt signaling. The authors present compelling evidence that zebrafish bcas2 is expressed at the right time and place to be involved in primitive hematopoiesis, that there are primitive hematopoietic defects in hetero- and homozygous mutant and knockdown embryos, that Bcas2 mechanistically positively regulates canonical Wnt signaling, and that Bcas2 is required for nuclear retention of B-cat through physical interaction involving armadillo repeats 9-12 of B-cat and the coiled-coil domains of Bcas2. Overall, the data and writing are clean, clear, and compelling. This study is a first-rate analysis of a strong phenotype with highly supportive mechanistic data. The findings shed light on the controversial question of whether, when, and how canonical Wnt signaling may be involved in hematopoietic development. We detail some minor concerns and questions below, which if answered, we believe would strengthen the overall story and resolve some puzzling features of the phenotype. Notwithstanding these minor concerns, we believe this is an exceptionally well-executed and interesting manuscript that is likely suitable for publication with minor additional experimental detail and commentary.

Strengths:

(1) The study features clear and compelling phenotypes and results.

(2) The manuscript narrative exposition and writing are clear and compelling.

(3) The authors have attended to important technical nuances sometimes overlooked, for example, focusing on different pools of cytosolic or nuclear b-catenin.

(4) The study sheds light on a controversial subject: regulation of hematopoietic development by canonical Wnt signaling and presents clear evidence of a role.

(5) The authors present evidence of phylogenetic conservation of the pathway.

Weaknesses:

(1) The authors present compelling data that Bcas2 regulates nuclear retention of B-cat through physical association involving binding between the Bcas2 CC domains and B-cat arm repeats 9-12. Transcriptional activation of Wnt target genes by B-cat requires physical association between B-cat and Tcf/Lef family DNA binding factors involving key interactions in Arm repeats 2-9 (Graham et al., Cell 2000). Mutually exclusive binding by B-cat regulatory factors, such as ICAT that prevent Tcf-binding is a documented mechanism (e.g. Graham et al., Mol Cell 2002). It would appear - based on the arm repeat usage by Bcas2 (repeats 9-12)-that Bcas2 and Tcf binding might not be mutually exclusive, which would support their model that Bcas2 physical association with B-cat to retain it in the nucleus would be compatible with co-activation of genes by allowing association with Tcf. It might be nice to attempt a three-way co-IP of these factors showing that B-cat can still bind Tcf in the presence of Bcas2, or at least speculate on the plausibility of the three-way interaction.

We appreciate your assessment and generous comments for the manuscript. As you mentioned, the binding sites for TCF on β-catenin almost do not overlap with those for BCAS2. It is likely that BCAS2-mediated nuclear sequestration of β-catenin would be compatible with the initiation of gene transcription by allowing TCF to associate with β-catenin. To test this possibility, we have taken your suggestion and performed co-IP assays. The results showed that β-catenin still bound with TCF4 in the presence of BCAS2 (Supplemental Figure 12), confirming that the binding of BCAS2 to β-catenin would not interfere with the formation of β-catenin/TCF complex.

(2) A major way that canonical Wnt signaling regulates hematopoietic development is through regulation of the LPM hematopoietic competence territories by activating expression of cdx1a, cdx4, and their downstream targets hoxb5a and hoxa9a (Davidson et al., Nature 2003; Davidson et al., Dev Biol 2006; Pilon et al., Dev Biol 2006; Wang et al., PNAS 2008). Could the authors assess (in situ) the expression of cdx1a, cdx4, hoxb5a, and hoxa9a in the bcas2 mutants?

We agree with your suggestion and have examined the expression of cdx4 and hoxa9a by performing WISH. Diminished expression of cdx4 and hoxa9a was detected in the lateral plate mesoderm of bcas2^+/- embryos at the 6-somite stage (Supplemental Figure 7).

(3) The authors show compellingly that even heterozygous loss of bcas2 has strong Wnt-inhibitory effects. If Bcas2 is required for canonical Wnt signaling and bcas2 is expressed ubiquitously from the 1-cell stage through at least the beginning of gastrulation, why do bcas2 KO embryos not have morphological axis specification defects consistent with loss of early Wnt signaling, like loss of head (early), or brain anteriorization (later)? Could the authors provide some comments on this puzzle? Or if they do see any canonical Wnt signaling patterning defects in het- or homozygous embryos, could they describe and/or present them?

You have raised an interesting question. In fact, we did not observe ventralization or axis determination defects in the early embryos of bcas2^+/- mutants. Even in the very small number of homozygous mutant embryos, we did not find such morphological defects. Given that the homozygous and heterozygous mutant embryos were derived from crossing bcas2^+/- males with bcas2^+/- females, maternal Bcas2 might still remain and function in these embryos during gastrulation when axis determination and neural patterning took place. Accordingly, we have expanded our discussion to incorporate these insights (Line 565-572).

Reviewer #3 (Public Review):

Summary:

This manuscript utilized zebrafish bcas2 mutants to study the role of bcas2 in primitive hematopoiesis and further confirms that it has a similar function in mice. Moreover, they showed that bcas2 regulates the transition of hematopoietic differentiation from angioblasts via activating Wnt signaling. By performing a series of biochemical experiments, they also showed that bcas2 accomplishes this by sequestering b-catenin within the nucleus, rather than through its known function in pre-mRNA splicing.

Strengths:

The work is well-performed, and the manuscript is well-written.

Weaknesses:

Several issues need to be clarified.

(1) Is wnt signaling also required during hematopoietic differentiation from angioblasts? Can the authors test angioblast and endothelial markers in embryos with wnt inhibition? Also, can the authors add export inhibitor LMB to the mouse mutants to test if sequestering of b-catenin by bcas2 is conserved during primitive hematopoiesis in mice?

Thank you very much for your appreciation and detailed assessment. To test whether Wnt signaling is also required during hematopoietic differentiation from angioblasts, wild-type embryos were exposed to 10 µM CCT036477, a small molecule β-catenin antagonist, from 9 hpf and then collected for WISH experiments. As shown in Supplemental Figure 8, the expression of hemangioblast markers npas4l, scl, and gata2 and endothelial marker fli1a remained unchanged, but the expression of erythroid progenitor marker gata1 was significantly reduced. These results suggest that canonical Wnt pathway may not be required for the generation of hemangioblasts or their endothelial differentiation, but is pivotal for their hematopoietic differentiation.

It is quite difficult to validate the conserve role of BCAS2 during primitive hematopoiesis in mice, because the toxicity of LMB may cause severe adverse effects in mice.[1,2]

(2) Bcas2 is required for primitive myelopoiesis in ALM. Does bcas2 play a similar function in primitive myelopoiesis, or is bcas2/b-catenin interaction more important for hematopoietic differentiation in PLM?

You have raised an important question. In our study, we have demonstrated that the expression of myeloid progenitor marker pu.1 was significantly decreased in bcas2 mutants, hinting that Bcas2 is pivotal for primitive myelopoiesis. To further clarify the function of Bcas2 in primitive myelopoiesis, we injected 8 ng of bcas2 morpholino into Tg(coro1a:GFP) embryos at the 1-cell stage and examined β-catenin distribution at 17 hpf via immunostaining. We observed a significant decline of nuclear β-catenin in primitive myeloid cells (Supplemental Figure 9), indicating that Bcas2 is highly likely to play a similar role in sequestering β-catenin within the nucleus during primitive myelopoiesis.

(3) Is it possible that CC1-2 fragment sequester b-catenin? The different phenotypes between this manuscript and the previous article (Yu, 2019) may be due to different mutations in bcas2. Is it possible that the bcas2 mutation in Yu's article produces a complete CC1-2 fragment, which might sequester b-catenin?

This is an interesting perspective. To test the possibility that CC1-2 sequesters β-catenin, mRNA expressing the CC domains of BCAS2 has been co-injected with bcas2 morpholino into Tg(gata1:GFP) embryo at the one-cell stage. Increased nuclear β-catenin levels were detected in the GFP-positive hematopoietic progenitor cells at 16 hpf (Supplemental Figure 11). Our findings support that CC1-2 fragment of BCAS2 can sequester β-catenin within the nucleus.

In the previous article (Yu, 2019), a deletion 5 bases mutation in the third exon of BCAS2 was produced by TALEN, therefore the CC domains of this mutant should be affected. It is difficult to conclude that the mutant BCAS2 protein in Yu’s study still remains association with β-catenin.

(4) Can the author clarify what embryos the arrows point to in SI Figure 2D? In SI Figure 6B and B', can the author clarify how the nucleus and cytoplasm are bleached? In B, the nucleus also appears to be bleached.

Thank you for your query and suggestion. In our revisions, the corresponding clarifications have been supplemented (Line 239-242; Line 978-979).

We acknowledge that the nuclei in both the BCAS2 overexpression group and control group were slightly bleached. Given that we have performed real-time analysis for fluorescent recovery after photobleaching, and we have observed a much slower recovery of cytoplasmic fluorescence in BCAS2 overexpressed cells, the conclusion that BCAS2 inhibits the nuclear export of β-catenin but not its nuclear import, remains changed.

Reviewer #1 (Recommendations For The Authors):

Major concerns:

(1) In this study, the authors detected β-catenin distribution in erythrocytes (gata1-GFP+ cells). Estimating the β-catenin distribution in the myeloid cells is recommended.

Thank you for your assessment and we have taken your suggestion. Tg(coro1a:GFP) embryos, which is commonly used to track both macrophages and neutrophils,[3] were injected with 8 ng of bcas2 morpholino into at the 1-cell stage and collected for immunostaining to examine the β-catenin distribution at 17 hpf. We observed a significant decline of nuclear β-catenin in primitive myeloid cells (Supplemental Figure 9). This result indicates that Bcas2 is highly likely to play a similar role in sequestering β-catenin within the nucleus during primitive myelopoiesis.

(2) The reduced nuclear localization of β-catenin in Figure 3H required further evidence. It would be helpful if the authors quantified the fluorescence intensity in the cell nucleus and cytoplasm. Meanwhile, the figures (Figure 5C, Figure 7E) were over-exposed. Please validate these figures.

Thank you for your suggestions. We agree with you that the fluorescence intensity of β-catenin in the nucleus and cytoplasm should be quantified. However, as the nucleus comprises a large part of the cell, we believe it would be more appropriate to quantify the relative fluorescence intensity by dividing the fluorescence intensity of nuclear β-catenin by the fluorescence intensity of DAPI.

Such quantifications have been added for Figure 3G, 5C, 7E, S9A, and S13A. In addition, we have reduced the exposure of Figure 5C and Figure 7E. We hope that you will be satisfied with the revisions.

(3) The authors used cKO mice to validate that the erythrocytes were eliminated. It would be interesting to detect β-catenin distribution by immunofluorescent staining in primitive hematopoietic cells in cKO mice. Addressing this issue can provide further evidence to support the conservation of Bcas2.

We appreciate your suggestion. However, we found that red blood cells were almost eliminated in the yolk sac of Bcas2^F/F;Flk1-Cre mice at E12.5. It is difficult to further detect β-catenin distribution in primitive erythroid cells in these mice.

(4) The authors discovered that Bcas2 mediated β-catenin nuclear export in a CRM1-dependent manner. CRM1 is a key regulator involved in the majority of factors of nuclear export via recognizing specific nuclear export signals (NES). Validating the NES of Bcas2 is recommended. Furthermore, I wonder about the relationship between Bcas2 and CRM1 in regulating β-catenin nuclear export. One possibility is that Bcas2 covers the NES to inhibit the interaction between CRM1 and β-catenin, thus leading to β-catenin accumulation in the cell nucleus. The authors should discuss this possibility accordingly.

Thank you for providing an interesting perspective. CRM1-mediated nuclear export of β-catenin usually requires CRM1 recognition and binding with the NES sequences in chaperon proteins, such as APC, Axin and Chibby.[4-6] Moreover, CRM1 can bind directly to and function as an efficient nuclear exporter for β-catenin.[7] Since BCAS2 has not been reported to contain any recognizable NES sequences, it will be interesting to investigate whether BCAS2 competitively inhibits β-catenin from associating with CRM1, or with the chaperone proteins. We have rewritten the discussion on CRM1-dependent nuclear export of β-catenin in line with your comments (Line 572-578).

(5) It would be interesting if the authors could answer the specificity in Bcas2-mediated protein nuclear export pathway. The authors should detect other classical factors (CRM1 mediated) distribution when loss of Bcas2.

Thank you for bringing up this point. To test whether BCAS2 specifically regulates CRM1-mediated nuclear export of β-catenin, we have investigated the nucleocytoplasmic distribution of other known CRM1 cargoes, such as ATG3 and CDC37L.[8] BCAS2 overexpression in HeLa cells slightly enhanced the nuclear localization of CDC37L, and had no significant impact on that of ATG3 (Supplemental Figure 11), indicating the specificity of BCAS2 in the regulation of CRM1-dependent nuclear export of β-catenin.

Minor concerns:

(1) The name "bcas2Δ7+/- and bcas2Δ14+/-" should be changed into "bcas2+/Δ7 and bcas2+/Δ14"(+/Δ7 or +/Δ14 should be superior on the right).

Thank you for your suggestion. We have changed the names of the mutants throughout the manuscript.

(2) The scale bar position in the figures should be unified.

We agree with your suggestion and have unified the scale bar position in all figures.

(3) In Figure 4E, "Nuclear" should be changed into "Nucleus".

We apologize for the mistake and Figure 4E has been revised.

(4) There are some unaesthetic issues in the figures. The figures need to be further edited. Figure 3H "β-catenin and Merge", Figure 4D "Merge". All these words should be centered in the figures.

Thank you. We have edited all the figures to ensure that the text is centered.

Reviewer #2 (Recommendations For The Authors):

(1) It would be nice to have whole blot images for the Westerns in Supplementary Info.

Thank you for your suggestion. Whole images for immunoblotting have been supplemented as Source data.

(2) Line 292 change 5 hpf to 5 dpf.

(3) Line 301 change "primary" to "primitive"?

We apologize for the mistakes. We have incorporated these suggestions in the revised manuscript and reexamined spelling throughout the paper.

(4) Figure S2C: is "Maker" a typographical error? Change to "ladder"?

We apologize for this typographical error and we have revised it in Figure S2C.

Reference

(1) Ishizawa J, Kojima K, Hail N, Tabe Y, Andreeff M. Expression, function, and targeting of the nuclear exporter chromosome region maintenance 1 (CRM1) protein. Pharmacology & Therapeutics. 2015;153:25-35.

(2) Li X, Feng Y, Yan MF, et al. Inhibition of Autism-Related Crm1 Disrupts Mitosis and Induces Apoptosis of the Cortical Neural Progenitors. Cerebral Cortex. 2020;30(7):3960-3976.

(3) Li L, Yan B, Shi YQ, Zhang WQ, Wen ZL. Live Imaging Reveals Differing Roles of Macrophages and Neutrophils during Zebrafish Tail Fin Regeneration. Journal of Biological Chemistry. 2012;287(30):25353-25360.

(4) Neufeld KL, Nix DA, Bogerd H, et al. Adenomatous polyposis coli protein contains two nuclear export signals and shuttles between the nucleus and cytoplasm. Proceedings of the National Academy of Sciences of the United States of America. 2000;97(22):12085-12090.

(5) Li FQ, Mofunanya A, Harris K, Takemaru KI. Chibby cooperates with 14-3-3 to regulate β-catenin subcellular distribution and signaling activity. Journal of Cell Biology. 2008;181(7):1141-1154.

(6) Cong F, Varmus H. Nuclear-cytoplasmic shuttling of Axin regulates subcellular localization of β-catenin. Proceedings of the National Academy of Sciences of the United States of America. 2004;101(9):2882-2887.

(7) Ki H, Oh M, Chung SW, Kim K. β-Catenin can bind directly to CRM1 independently of adenomatous polyposis coli, which affects its nuclear localization and LEF-1/β-catenin-dependent gene expression. Cell Biology International. 2008;32(4):394-400.

(8) Kirli K, Karaca S, Dehne HJ, et al. A deep proteomics perspective on CRM1-mediated nuclear export and nucleocytoplasmic partitioning. Elife. 2015;4.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

Using a cross-modal sensory selection task in head-fixed mice, the authors attempted to characterize how different rules reconfigured representations of sensory stimuli and behavioral reports in sensory (S1, S2) and premotor cortical areas (medial motor cortex or MM, and ALM). They used silicon probe recordings during behavior, a combination of single-cell and population-level analyses of neural data, and optogenetic inhibition during the task.

Strengths:

A major strength of the manuscript was the clarity of the writing and motivation for experiments and analyses. The behavioral paradigm is somewhat simple but well-designed and wellcontrolled. The neural analyses were sophisticated, clearly presented, and generally supported the authors' interpretations. The statistics are clearly reported and easy to interpret. In general, my view is that the authors achieved their aims. They found that different rules affected preparatory activity in premotor areas, but not sensory areas, consistent with dynamical systems perspectives in the field that hold that initial conditions are important for determining trial-based dynamics.

Weaknesses:

The manuscript was generally strong. The main weakness in my view was in interpreting the optogenetic results. While the simplicity of the task was helpful for analyzing the neural data, I think it limited the informativeness of the perturbation experiments. The behavioral read-out was low dimensional -a change in hit rate or false alarm rate- but it was unclear what perceptual or cognitive process was disrupted that led to changes in these read-outs. This is a challenge for the field, and not just this paper, but was the main weakness in my view. I have some minor technical comments in the recommendations for authors that might address other minor weaknesses.

I think this is a well-performed, well-written, and interesting study that shows differences in rule representations in sensory and premotor areas and finds that rules reconfigure preparatory activity in the motor cortex to support flexible behavior.

Reviewer #2 (Public Review):

Summary:

Chang et al. investigate neuronal activity firing patterns across various cortical regions in an interesting context-dependent tactile vs visual detection task, developed previously by the authors (Chevee et al., 2021; doi: 10.1016/j.neuron.2021.11.013). The authors report the important involvement of a medial frontal cortical region (MM, probably a similar location to wM2 as described in Esmaeili et al., 2021 & 2022; doi: 10.1016/j.neuron.2021.05.005; doi: 10.1371/journal.pbio.3001667) in mice for determining task rules.

Strengths:

The experiments appear to have been well carried out and the data well analysed. The manuscript clearly describes the motivation for the analyses and reaches clear and well-justified conclusions. I find the manuscript interesting and exciting!

Weaknesses:

I did not find any major weaknesses.

Reviewer #3 (Public Review):

This study examines context-dependent stimulus selection by recording neural activity from several sensory and motor cortical areas along a sensorimotor pathway, including S1, S2, MM, and ALM. Mice are trained to either withhold licking or perform directional licking in response to visual or tactile stimulus. Depending on the task rule, the mice have to respond to one stimulus modality while ignoring the other. Neural activity to the same tactile stimulus is modulated by task in all the areas recorded, with significant activity changes in a subset of neurons and population activity occupying distinct activity subspaces. Recordings further reveal a contextual signal in the pre-stimulus baseline activity that differentiates task context. This signal is correlated with subsequent task modulation of stimulus activity. Comparison across brain areas shows that this contextual signal is stronger in frontal cortical regions than in sensory regions. Analyses link this signal to behavior by showing that it tracks the behavioral performance switch during task rule transitions. Silencing activity in frontal cortical regions during the baseline period impairs behavioral performance.

Overall, this is a superb study with solid results and thorough controls. The results are relevant for context-specific neural computation and provide a neural substrate that will surely inspire follow-up mechanistic investigations. We only have a couple of suggestions to help the authors further improve the paper.

(1) We have a comment regarding the calculation of the choice CD in Fig S3. The text on page 7 concludes that "Choice coding dimensions change with task rule". However, the motor choice response is different across blocks, i.e. lick right vs. no lick for one task and lick left vs. no lick for the other task. Therefore, the differences in the choice CD may be simply due to the motor response being different across the tasks and not due to the task rule per se. The authors may consider adding this caveat in their interpretation. This should not affect their main conclusion.

We thank the Reviewer for the suggestion. We have discussed this caveat and performed a new analysis to calculate the choice coding dimensions using right-lick and left-lick trials (Fig. S3h) on page 8. 

“Choice coding dimensions were obtained from left-lick and no-lick trials in respond-to-touch blocks and right-lick and no-lick trials in respond-to-light blocks. Because the required lick directions differed between the block types, the difference in choice CDs across task rules (Fig. S4f) could have been affected by the different motor responses. To rule out this possibility, we did a new version of this analysis using right-lick and left-lick trials to calculate the choice coding dimensions for both task rules. We found that the orientation of the choice coding dimension in a respond-to-touch block was still not aligned well with that in a respond-to-light block (Fig. S4h;  magnitude of dot product between the respond-to-touch choice CD and the respond-to-light choice CD, mean ± 95% CI for true vs shuffled data: S1: 0.39 ± [0.23, 0.55] vs 0.2 ± [0.1, 0.31], 10 sessions; S2: 0.32 ± [0.18, 0.46] vs 0.2 ± [0.11, 0.3], 8 sessions; MM: 0.35 ± [0.21, 0.48] vs 0.18 ± [0.11, 0.26], 9 sessions; ALM: 0.28 ± [0.17, 0.39] vs 0.21 ± [0.12, 0.31], 13 sessions).”

We also have included the caveats for using right-lick and left-lick trials to calculate choice coding dimensions on page 13.

“However, we also calculated choice coding dimensions using only right- and left-lick trials. In S1, S2, MM and ALM, the choice CDs calculated this way were also not aligned well across task rules (Fig. S4h), consistent with the results calculated from lick and no-lick trials (Fig. S4f). Data were limited for this analysis, however, because mice rarely licked to the unrewarded water port (# of licksunrewarded port  / # of lickstotal , respond-to-touch: 0.13, respond-to-light: 0.11). These trials usually came from rule transitions (Fig. 5a) and, in some cases, were potentially caused by exploratory behaviors. These factors could affect choice CDs.”

(2) We have a couple of questions about the effect size on single neurons vs. population dynamics. From Fig 1, about 20% of neurons in frontal cortical regions show task rule modulation in their stimulus activity. This seems like a small effect in terms of population dynamics. There is somewhat of a disconnect from Figs 4 and S3 (for stimulus CD), which show remarkably low subspace overlap in population activity across tasks. Can the authors help bridge this disconnect? Is this because the neurons showing a difference in Fig 1 are disproportionally stimulus selective neurons?

We thank the Reviewer for the insightful comment and agree that it is important to link the single-unit and population results. We have addressed these questions by (1) improving our analysis of task modulation of single neurons  (tHit-tCR selectivity) and (2) examining the relationship between tHit-tCR selective neurons and tHit-tCR subspace overlaps.  

Previously, we averaged the AUC values of time bins within the stimulus window (0-150 ms, 10 ms bins). If the 95% CI on this averaged AUC value did not include 0.5, this unit was considered to show significant selectivity. This approach was highly conservative and may underestimate the percentage of units showing significant selectivity, particularly any units showing transient selectivity. In the revised manuscript, we now define a unit as showing significant tHit-tCR selectivity when three consecutive time bins (>30 ms, 10ms bins) of AUC values were significant. Using this new criterion, the percentage of tHittCR selective neurons increased compared with the previous analysis. We have updated Figure 1h and the results on page 4:

“We found that 18-33% of neurons in these cortical areas had area under the receiver-operating curve (AUC) values significantly different from 0.5, and therefore discriminated between tHit and tCR trials (Fig. 1h; S1: 28.8%, 177 neurons; S2: 17.9%, 162 neurons; MM: 32.9%, 140 neurons; ALM: 23.4%, 256 neurons; criterion to be considered significant: Bonferroni corrected 95% CI on AUC did not include 0.5 for at least 3 consecutive 10-ms time bins).”

Next, we have checked how tHit-tCR selective neurons were distributed across sessions. We found that the percentage of tHit-tCR selective neurons in each session varied (S1: 9-46%, S2: 0-36%, MM:25-55%, ALM:0-50%). We examined the relationship between the numbers of tHit-tCR selective neurons and tHit-tCR subspace overlaps. Sessions with more neurons showing task rule modulation tended to show lower subspace overlap, but this correlation was modest and only marginally significant (r= -0.32, p= 0.08, Pearson correlation, n= 31 sessions). While we report the percentage of neurons showing significant selectivity as a simple way to summarize single-neuron effects, this does neglect the magnitude of task rule modulation of individual neurons, which may also be relevant. 

In summary, the apparent disconnect between the effect sizes of task modulation of single neurons and of population dynamics could be explained by (1) the percentages of tHit-tCR selective neurons were underestimated in our old analysis, (2) tHit-tCR selective neurons were not uniformly distributed among sessions, and (3) the percentages of tHit-tCR selective neurons were weakly correlated with tHit-tCR subspace overlaps. 

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

For the analysis of choice coding dimensions, it seems that the authors are somewhat data limited in that they cannot compare lick-right/lick-left within a block. So instead, they compare lick/no lick trials. But given that the mice are unable to initiate trials, the interpretation of the no lick trials is a bit complicated. It is not clear that the no lick trials reflect a perceptual judgment about the stimulus (i.e., a choice), or that the mice are just zoning out and not paying attention. If it's the latter case, what the authors are calling choice coding is more of an attentional or task engagement signal, which may still be interesting, but has a somewhat different interpretation than a choice coding dimension. It might be worth clarifying this point somewhere, or if I'm totally off-base, then being more clear about why lick/no lick is more consistent with choice than task engagement.

We thank the Reviewer for raising this point. We have added a new paragraph on page 13 to clarify why we used lick/no-lick trials to calculate choice coding dimensions, and we now discuss the caveat regarding task engagement.  

“No-lick trials included misses, which could be caused by mice not being engaged in the task. While the majority of no-lick trials were correct rejections (respond-to-touch: 75%; respond-to-light: 76%), we treated no-licks as one of the available choices in our task and included them to calculate choice coding dimensions (Fig. S4c,d,f). To ensure stable and balanced task engagement across task rules, we removed the last 20 trials of each session and used stimulus parameters that achieved similar behavioral performance for both task rules (Fig. 1d; ~75% correct for both rules).”

In addition, to address a point made by Reviewer 3 as well as this point, we performed a new analysis to calculate choice coding dimensions using right-lick vs left-lick trials. We report this new analysis on page 8:

“Choice coding dimensions were obtained from left-lick and no-lick trials in respond-to-touch blocks and right-lick and no-lick trials in respond-to-light blocks. Because the required lick directions differed between the block types, the difference in choice CDs across task rules (Fig. S4f) could have been affected by the different motor responses. To rule out this possibility, we did a new version of this analysis using right-lick and left-lick trials to calculate the choice coding dimensions for both task rules. We found that the orientation of the choice coding dimension in a respond-to-touch block was still not aligned well with that in a respond-to-light block (Fig. S4h;  magnitude of dot product between the respond-to-touch choice CD and the respond-to-light choice CD, mean ± 95% CI for true vs shuffled data: S1: 0.39 ± [0.23, 0.55] vs 0.2 ± [0.1, 0.31], 10 sessions; S2: 0.32 ± [0.18, 0.46] vs 0.2 ± [0.11, 0.3], 8 sessions; MM: 0.35 ± [0.21, 0.48] vs 0.18 ± [0.11, 0.26], 9 sessions; ALM: 0.28 ± [0.17, 0.39] vs 0.21 ± [0.12, 0.31], 13 sessions).” 

We added discussion of the limitations of this new analysis on page 13:

“However, we also calculated choice coding dimensions using only right- and left-lick trials. In S1, S2, MM and ALM, the choice CDs calculated this way were also not aligned well across task rules (Fig. S4h), consistent with the results calculated from lick and no-lick trials (Fig. S4f). Data were limited for this analysis, however, because mice rarely licked to the unrewarded water port (# of licksunrewarded port  / # of lickstotal , respond-to-touch: 0.13, respond-to-light: 0.11). These trials usually came from rule transitions (Fig. 5a) and, in some cases, were potentially caused by exploratory behaviors. These factors could affect choice CDs.”

The authors find that the stimulus coding direction in most areas (S1, S2, and MM) was significantly aligned between the block types. How do the authors interpret that finding? That there is no major change in stimulus coding dimension, despite the change in subspace? I think I'm missing the big picture interpretation of this result.

That there is no significant change in stimulus coding dimensions but a change in subspace suggests that the subspace change largely reflects a change in the choice coding dimensions.

As I mentioned in the public review, I thought there was a weakness with interpretation of the optogenetic experiments, which the authors generally interpret as reflecting rule sensitivity. However, given that they are inhibiting premotor areas including ALM, one might imagine that there might also be an effect on lick production or kinematics. To rule this out, the authors compare the change in lick rate relative to licks during the ITI. What is the ITI lick rate? I assume pretty low, once the animal is welltrained, in which case there may be a floor effect that could obscure meaningful effects on lick production. In addition, based on the reported CI on delta p(lick), it looks like MM and AM did suppress lick rate. I think in the future, a task with richer behavioral read-outs (or including other measurements of behavior like video), or perhaps something like a psychological process model with parameters that reflect different perceptual or cognitive processes could help resolve the effects of perturbations more precisely.

Eighteen and ten percent of trials had at least one lick in the ITI in respond-to-touch and  respond-tolight blocks, respectively. These relatively low rates of ITI licking could indeed make an effect of optogenetics on lick production harder to observe. We agree that future work would benefit from more complex tasks and measurements, and have added the following to make this point (page 14):

“To more precisely dissect the effects of perturbations on different cognitive processes in rule-dependent sensory detection, more complex behavioral tasks and richer behavioral measurements are needed in the future.”

Reviewer #2 (Recommendations For The Authors):

I have the following minor suggestions that the authors might consider in revising this already excellent manuscript :

(1) In addition to showing normalised z-score firing rates (e.g. Fig 1g), I think it is important to show the grand-average mean firing rates in Hz.

We thank the Reviewer for the suggestion and have added the grand-average mean firing rates as a new supplementary figure (Fig. S2a). To provide more details about the firing rates of individual neurons, we have also added to this new figure the distribution of peak responses during the tactile stimulus period (Fig. S2b).

(2) I think the authors could report more quantitative data in the main text. As a very basic example, I could not easily find how many neurons, sessions, and mice were used in various analyses.

We have added relevant numbers at various points throughout the Results, including within the following examples:

Page 3: “To examine how the task rules influenced the sensorimotor transformation occurring in the tactile processing stream, we performed single-unit recordings from sensory and motor cortical areas including S1, S2, MM and ALM (Fig. 1e-g, Fig. S1a-h, and Fig. S2a; S1: 6 mice, 10 sessions, 177 neurons, S2: 5 mice, 8 sessions, 162 neurons, MM: 7 mice, 9 sessions, 140 neurons, ALM: 8 mice, 13 sessions, 256 neurons).”

Page 5: “As expected, single-unit activity before stimulus onset did not discriminate between tactile and visual trials (Fig. 2d; S1: 0%, 177 neurons; S2: 0%, 162 neurons; MM: 0%, 140 neurons; ALM: 0.8%, 256 neurons). After stimulus onset, more than 35% of neurons in the sensory cortical areas and approximately 15% of neurons in the motor cortical areas showed significant stimulus discriminability (Fig. 2e; S1: 37.3%, 177 neurons; S2: 35.2%, 162 neurons; MM: 15%, 140 neurons; ALM: 14.1%, 256 neurons).”

Page 6: “Support vector machine (SVM) and Random Forest classifiers showed similar decoding abilities

(Fig. S3a,b; medians of classification accuracy [true vs shuffled]; SVM: S1 [0.6 vs 0.53], 10 sessions, S2

[0.61 vs 0.51], 8 sessions, MM [0.71 vs 0.51], 9 sessions, ALM [0.65 vs 0.52], 13 sessions; Random

Forests: S1 [0.59 vs 0.52], 10 sessions, S2 [0.6 vs 0.52], 8 sessions, MM [0.65 vs 0.49], 9 sessions, ALM [0.7 vs 0.5], 13 sessions).”

Page 6: “To assess this for the four cortical areas, we quantified how the tHit and tCR trajectories diverged from each other by calculating the Euclidean distance between matching time points for all possible pairs of tHit and tCR trajectories for a given session and then averaging these for the session (Fig. 4a,b; S1: 10 sessions, S2: 8 sessions, MM: 9 sessions, ALM: 13 sessions, individual sessions in gray and averages across sessions in black; window of analysis: -100 to 150 ms relative to stimulus onset; 10 ms bins; using the top 3 PCs; Methods).” 

Page 8: “In contrast, we found that S1, S2 and MM had stimulus CDs that were significantly aligned between the two block types (Fig. S4e; magnitude of dot product between the respond-to-touch stimulus CDs and the respond-to-light stimulus CDs, mean ± 95% CI for true vs shuffled data: S1: 0.5 ± [0.34, 0.66] vs 0.21 ± [0.12, 0.34], 10 sessions; S2: 0.62 ± [0.43, 0.78] vs 0.22 ± [0.13, 0.31], 8 sessions; MM: 0.48 ± [0.38, 0.59] vs 0.24 ± [0.16, 0.33], 9 sessions; ALM: 0.33 ± [0.2, 0.47] vs 0.21 ± [0.13, 0.31], 13 sessions).”  Page 9: “For respond-to-touch to respond-to-light block transitions, the fractions of trials classified as respond-to-touch for MM and ALM decreased progressively over the course of the transition (Fig. 5d; rank correlation of the fractions calculated for each of the separate periods spanning the transition, Kendall’s tau, mean ± 95% CI: MM: -0.39 ± [-0.67, -0.11], 9 sessions, ALM: -0.29 ± [-0.54, -0.04], 13 sessions; criterion to be considered significant: 95% CI on Kendall’s tau did not include 0).

Page 11: “Lick probability was unaffected during S1, S2, MM and ALM experiments for both tasks, indicating that the behavioral effects were not due to an inability to lick (Fig. 6i, j; 95% CI on Δ lick probability for cross-modal selection task: S1/S2 [-0.18, 0.24], 4 mice, 10 sessions; MM [-0.31, 0.03], 4 mice, 11 sessions; ALM [-0.24, 0.16], 4 mice, 10 sessions; Δ lick probability for simple tactile detection task: S1/S2 [-0.13, 0.31], 3 mice, 3 sessions; MM [-0.06, 0.45], 3 mice, 5 sessions; ALM [-0.18, 0.34], 3 mice, 4 sessions).”

(3) Please include a clearer description of trial timing. Perhaps a schematic timeline of when stimuli are delivered and when licking would be rewarded. I may have missed it, but I did not find explicit mention of the timing of the reward window or if there was any delay period.

We have added the following (page 3): 

“For each trial, the stimulus duration was 0.15 s and an answer period extended from 0.1 to 2 s from stimulus onset.”

(4) Please include a clear description of statistical tests in each figure legend as needed (for example please check Fig 4e legend).

We have added details about statistical tests in the figure legends:

Fig. 2f: “Relationship between block-type discriminability before stimulus onset and tHit-tCR discriminability after stimulus onset for units showing significant block-type discriminability prior to the stimulus. Pearson correlation: S1: r = 0.69, p = 0.056, 8 neurons; S2: r = 0.91, p = 0.093, 4 neurons; MM: r = 0.93, p < 0.001, 30 neurons; ALM: r = 0.83, p < 0.001, 26 neurons.” 

Fig. 4e: “Subspace overlap for control tHit (gray) and tCR (purple) trials in the somatosensory and motor cortical areas. Each circle is a subspace overlap of a session. Paired t-test, tCR – control tHit: S1: -0.23, 8 sessions, p = 0.0016; S2: -0.23, 7 sessions, p = 0.0086; MM: -0.36, 5 sessions, p = <0.001; ALM: -0.35, 11 sessions, p < 0.001; significance: ** for p<0.01, *** for p<0.001.”  

Fig. 5d,e: “Fraction of trials classified as coming from a respond-to-touch block based on the pre-stimulus population state, for trials occurring in different periods (see c) relative to respond-to-touch → respondto-light transitions. For MM (top row) and ALM (bottom row), progressively fewer trials were classified as coming from the respond-to-touch block as analysis windows shifted later relative to the rule transition. Kendall’s tau (rank correlation): MM: -0.39, 9 sessions; ALM: -0.29, 13 sessions. Left panels: individual sessions, right panels: mean ± 95% CI. Dash lines are chance levels (0.5). e, Same as d but for respond-to-light → respond-to-touch transitions. Kendall’s tau: MM: 0.37, 9 sessions; ALM: 0.27, 13 sessions.”

Fig. 6: “Error bars show bootstrap 95% CI. Criterion to be considered significant: 95% CI did not include 0.”

(5) P. 3 - "To examine how the task rules influenced the sensorimotor transformation occurring in the tactile processing stream, we performed single-unit recordings from sensory and motor cortical areas including S1, S2, MM, and ALM using 64-channel silicon probes (Fig. 1e-g and Fig. S1a-h)." Please specify if these areas were recorded simultaneously or not.

We have added “We recorded from one of these cortical areas per session, using 64-channel silicon probes.”  on page 3.  

(6) Figure 4b - Please describe what gray and black lines show.

The gray traces are the distance between tHit and tCR trajectories in individual sessions and the black traces are the averages across sessions in different cortical areas. We have added this information on page 6 and in the Figure 4b legend. 

Page 6: “To assess this for the four cortical areas, we quantified how the tHit and tCR trajectories diverged from each other by calculating the Euclidean distance between matching time points for all possible pairs of tHit and tCR trajectories for a given session and then averaging these for the session (Fig. 4a,b; S1: 10 sessions, S2: 8 sessions, MM: 9 sessions, ALM: 13 sessions, individual sessions in gray and averages across sessions in black; window of analysis: -100 to 150 ms relative to stimulus onset; 10 ms bins; using the top 3 PCs; Methods).

Fig. 4b: “Distance between tHit and tCR trajectories in S1, S2, MM and ALM. Gray traces show the time varying tHit-tCR distance in individual sessions and black traces are session-averaged tHit-tCR distance (S1:10 sessions; S2: 8 sessions; MM: 9 sessions; ALM: 13 sessions).”

(7) In addition to the analyses shown in Figure 5a, when investigating the timing of the rule switch, I think the authors should plot the left and right lick probabilities aligned to the timing of the rule switch time on a trial-by-trial basis averaged across mice.

We thank the Reviewer for suggesting this addition. We have added a new figure panel to show the probabilities of right- and left-licks during rule transitions (Fig. 5a).

Page 8: “The probabilities of right-licks and left-licks showed that the mice switched their motor responses during block transitions depending on task rules (Fig. 5a, mean ± 95% CI across 12 mice).” 

(8) P. 12 - "Moreover, in a separate study using the same task (Finkel et al., unpublished), high-speed video analysis demonstrated no significant differences in whisker motion between respond-to-touch and respond-to-light blocks in most (12 of 14) behavioral sessions.". Such behavioral data is important and ideally would be included in the current analysis. Was high-speed videography carried out during electrophysiology in the current study?

Finkel et al. has been accepted in principle for publication and will be available online shortly. Unfortunately we have not yet carried out simultaneous high-speed whisker video and electrophysiology in our cross-modal sensory selection task.

Reviewer #3 (Recommendations For The Authors):

(1) Minor point. For subspace overlap calculation of pre-stimulus activity in Fig 4e (light purple datapoints), please clarify whether the PCs for that condition were constructed in matched time windows. If the PCs are calculated from the stimulus period 0-150ms, the poor alignment could be due to mismatched time windows.

We thank the Reviewer for the comment and clarify our analysis here. We previously used timematched windows to calculate subspace overlaps. However, the pre-stimulus activity was much weaker than the activity during the stimulus period, so the subspaces of reference tHit were subject to noise and we were not able to obtain reliable PCs. This caused the subspace overlap values between the reference tHit and control tHit to be low and variable (mean ± SD, S1:  0.46± 0.26, n = 8 sessions, S2: 0.46± 0.18, n = 7 sessions, MM: 0.44± 0.16, n = 5 sessions, ALM: 0.38± 0.22, n = 11 sessions).  Therefore, we used the tHit activity during the stimulus window to obtain PCs and projected pre-stimulus and stimulus activity in tCR trials onto these PCs. We have now added a more detailed description of this analysis in the Methods (page 32). 

“To calculate the separation of subspaces prior to stimulus delivery, pre-stimulus activity in tCR trials (100 to 0 ms from stimulus onset) was projected to the PC space of the tHit reference group and the subspace overlap was calculated. In this analysis, we used tHit activity during stimulus delivery (0 to 150 ms from stimulus onset) to obtain reliable PCs.”   

We acknowledge this time alignment issue and have now removed the reported subspace overlap between tHit and tCR during the pre-stimulus period from Figure 4e (light purple). However, we think the correlation between pre- and post- stimulus-onset subspace overlaps should remain similar regardless of the time windows that we used for calculating the PCs. For the PCs calculated from the pre-stimulus period (-100 to 0 ms), the correlation coefficient was 0.55 (Pearson correlation, p <0.01, n = 31 sessions). For the PCs calculated from the stimulus period (0-150 ms), the correlation coefficient was 0.68 (Figure 4f, Pearson correlation, p <0.001, n = 31 sessions). Therefore, we keep Figure 4f.  

(2) Minor point. To help the readers follow the logic of the experiments, please explain why PPC and AMM were added in the later optogenetic experiment since these are not part of the electrophysiology experiment.

We have added the following rationale on page 9.

“We recorded from AMM in our cross-modal sensory selection task and observed visually-evoked activity (Fig. S1i-k), suggesting that AMM may play an important role in rule-dependent visual processing. PPC contributes to multisensory processing51–53 and sensory-motor integration50,54–58.  Therefore, we wanted to test the roles of these areas in our cross-modal sensory selection task.”

(3) Minor point. We are somewhat confused about the timing of some of the example neurons shown in figure S1. For example, many neurons show visually evoked signals only after stimulus offset, unlike tactile evoked signals (e.g. Fig S1b and f). In addition, the reaction time for visual stimulus is systematically slower than tactile stimuli for many example neurons (e.g. Fig S1b) but somehow not other neurons (e.g. Fig S1g). Are these observations correct?

These observations are all correct. We have a manuscript from a separate study using this same behavioral task (Finkel et al., accepted in principle) that examines and compares (1) the onsets of tactile- and visually-evoked activity and (2) the reaction times to tactile and visual stimuli. The reaction times to tactile stimuli were slightly but significantly shorter than the reaction times to visual stimuli (tactile vs visual, 397 ± 145 vs 521 ± 163 ms, median ± interquartile range [IQR], Tukey HSD test, p = 0.001, n =155 sessions). We examined how well activity of individual neurons in S1 could be used to discriminate the presence of the stimulus or the response of the mouse. For discriminability for the presence of the stimulus, S1 neurons could signal the presence of the tactile stimulus but not the visual stimulus. For discriminability for the response of the mouse, the onsets for significant discriminability occurred earlier for tactile compared with visual trials (two-sided Kolmogorov-Smirnov test, p = 1x10-16, n = 865 neurons with DP onset in tactile trials, n = 719 neurons with DP onset in visual trials).

Author Response

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

eLife assessment

This important study combines a range of advanced ultrastructural imaging approaches to define the unusual endosomal system of African trypanosomes. Compelling images show that instead of a distinct set of compartments, the endosome of these protists comprises a continuous system of membranes with functionally distinct subdomains as defined by canonical markers of early, late and recycling endosomes. The findings suggest that the endocytic system of bloodstream stages has evolved to facilitate the extraordinarily high rates of membrane turnover needed to remove immune complexes and survive in the blood, which is of interest to anyone studying infectious diseases.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

Bloodstream stages of the parasitic protist, Trypanosoma brucei, exhibit very high rates of constitutive endocytosis, which is needed to recycle the surface coat of Variant Surface Glycoproteins (VSGs) and remove surface immune complexes. While many studies have shown that the endo-lysosomal systems of T. brucei BF stages contain canonical domains, as defined by classical Rab markers, it has remained unclear whether these protists have evolved additional adaptations/mechanisms for sustaining these very high rates of membrane transport and protein sorting. The authors have addressed this question by reconstructing the 3D ultrastructure and functional domains of the T. brucei BF endosome membrane system using advanced electron tomography and super-resolution microscopy approaches. Their studies reveal that, unusually, the BF endosome network comprises a continuous system of cisternae and tubules that contain overlapping functional subdomains. It is proposed that a continuous membrane system allows higher rates of protein cargo segregation, sorting and recycling than can otherwise occur when transport between compartments is mediated by membrane vesicles or other fusion events.

Strengths:

The study is a technical tour-de-force using a combination of electron tomography, super-resolution/expansion microscopy, immune-EM of cryo-sections to define the 3D structures and connectivity of different endocytic compartments. The images are very clear and generally support the central conclusion that functionally distinct endocytic domains occur within a dynamic and continuous endosome network in BF stages.

Weaknesses:

The authors suggest that this dynamic endocytic network may also fulfil many of the functions of the Golgi TGN and that the latter may be absent in these stages. Although plausible, this comment needs further experimental support. For example, have the authors attempted to localize canonical makers of the TGN (e.g. GRIP proteins) in T. brucei BF and/or shown that exocytic carriers bud directly from the endosomes?

We agree with the criticism and have shortened the discussion accordingly and clearly marked it as speculation. However, we do not want to completely abandon our hypothesis.

The paragraph now reads:

Lines 740 – 751:

“Interestingly, we did not find any structural evidence of vesicular retrograde transport to the Golgi. Instead, the endosomal ‘highways’ extended throughout the posterior volume of the trypanosomes approaching the trans-Golgi interface. It is highly plausible that this region represents the convergence point where endocytic and biosynthetic membrane trafficking pathways merge. A comparable merging of endocytic and biosynthetic functions has been described for the TGN in plants. Different marker proteins for early and recycling endosomes were shown to be associated and/ or partially colocalized with the TGN suggesting its function in both secretory and endocytic pathways (reviewed in Minamino and Ueda, 2019). As we could not find structural evidence for the existence of a TGN we tentatively propose that trypanosomes may have shifted the central orchestrating function of the TGN as a sorting hub at the crossroads of biosynthetic and recycling pathways to the endosome. Although this is a speculative scenario, it is experimentally testable.”

Furthermore, we removed the lines 51 - 52, which included the suggestion of the TGN as a master regulator, from the abstract.

Reviewer #2 (Public Review):

The authors suggest that the African trypanosome endomembrane system has unusual organisation, in that the entire system is a single reticulated structure. It is not clear if this is thought to extend to the lysosome or MVB. There is also a suggestion that this unusual morphology serves as a trans-(post)Golgi network rather than the more canonical arrangement.

The work is based around very high-quality light and electron microscopy, as well as utilising several marker proteins, Rab5A, 11 and 7. These are deemed as markers for early endosomes, recycling endosomes and late or pre-lysosomes. The images are mostly of high quality but some inconsistencies in the interpretation, appearance of structures and some rather sweeping assumptions make this less easy to accept. Two perhaps major issues are claims to label the entire endosomal apparatus with a single marker protein, which is hard to accept as certainly this reviewer does not really even know where the limits to the endosomal network reside and where these interface with other structures. There are several additional compartments that have been defined by Rob proteins as well, and which are not even mentioned. Overall I am unconvinced that the authors have demonstrated the main things they claim.<br /> The endomembrane system in bloodstream form T. brucei is clearly delimited. Compared to mammalian cells it is tidy and confined to the posterior part of the spindleshaped cell. The endoplasmic reticulum is linked to one side of the longitudinal cell axis, marked by the attached flagellum, while the mitochondrion locates to the opposite side. Glycosomes are easily identifiable as spheres, as are acidocalcisomes, which are smaller than glycosomes and – in electron micrographs – are characterized by high electron density. All these organelles extend beyond the nucleus, which is not the case for the endosomal compartment, the lysosome and the Golgi. The vesicles found in the posterior half of the trypanosome cell are quantitatively identifiable as COP1, CCVI or CCVII vesicles, or exocytic carriers. The lysosome has a higher degree of morphological plasticity, but this is not topic of the present work. Thus, the endomembrane system in T. brucei is comparatively well structured and delimited, which is why we have chosen trypanosomes as cell biological model.

We have published EP1::GFP as marker for the endosome system and flagellar pocket back in 2004. We have defined the fluid phase volume of the trypanosome endosome in papers published between 2002 and 2007. This work was not intended to represent the entirety of RAB proteins. We were only interested in 3 canonical markers for endosome subtypes. We do not claim anything that is not experimentally tested, we have clearly labelled our hypotheses as such, and we do not make sweeping assumptions.

The approaches taken are state-of-the-art but not novel, and because of the difficulty in fully addressing the central tenet, I am not sure how much of an impact this will have beyond the trypanosome field. For certain this is limited to workers in the direct area and is not a generalisable finding.

To the best of our knowledge, there is no published research that has employed 3D Tokuyasu or expansion microscopy (ExM) to label endosomes. The key takeaway from our study, which is the concept that "endosomes are continuous in trypanosomes" certainly is novel. We are not aware of any other report that has demonstrated this aspect.

The doubts formulated by the reviewer regarding the impact of our work beyond the field of trypanosomes are not timely. Indeed, our results, and those of others, show that the conclusions drawn from work with just a few model organisms is not generalisable. We are finally on the verge of a new cell biology that considers the plethora of evolutionary solutions beyond ophistokonts. We believe that this message should be widely acknowledged and considered. And we are certainly not the only ones who are convinced that the term "general relevance" is unscientific and should no longer be used in biology.

Reviewer #3 (Public Review):

Summary:

As clearly highlighted by the authors, a key plank in the ability of trypanosomes to evade the mammalian host’s immune system is its high rate of endocytosis. This rapid turnover of its surface enables the trypanosome to ‘clean’ its surface removing antibodies and other immune effectors that are subsequently degraded. The high rate of endocytosis is likely reflected in the organisati’n and layout of the endosomal system in these parasites. Here, Link et al., sought to address this question using a range of light and three-dimensional electron microscopy approaches to define the endosomal organisation in this parasite.

Before this study, the vast majority of our information about the make-up of the trypanosome endosomal system was from thin-section electron microscopy and immunofluorescence studies, which did not provide the necessary resolution and 3D information to address this issue. Therefore, it was not known how the different structures observed by EM were related. Link et al., have taken advantage of the advances in technology and used an impressive combination of approaches at the LM and EM level to study the endosomal system in these parasites. This innovative combination has now shown the interconnected-ness of this network and demonstrated that there are no ‘classical’ compartments within the endosomal system, with instead different regions of the network enriched in different protein markers (Rab5a, Rab7, Rab11).

Strengths:

This is a generally well-written and clear manuscript, with the data well-presented supporting the majority of the conclusions of the authors. The authors use an impressive range of approaches to address the organisation of the endosomal system and the development of these methods for use in trypanosomes will be of use to the wider parasitology community.

I appreciate their inclusion of how they used a range of different light microscopy approaches even though for instance the dSTORM approach did not turn out to be as effective as hoped. The authors have clearly demonstrated that trypanosomes have a large interconnected endosomal network, without defined compartments and instead show enrichment for specific Rabs within this network.

Weaknesses:

My concerns are:

i) There is no evidence for functional compartmentalisation. The classical markers of different endosomal compartments do not fully overlap but there is no evidence to show a region enriched in one or other of these proteins has that specific function. The authors should temper their conclusions about this point.

The reviewer is right in stating that Rab-presence does not necessarily mean Rabfunction. However, this assumption is as old as the Rab literature. That is why we have focused on the 3 most prominent endosomal marker proteins. We report that for endosome function you do not necessarily need separate membrane compartments. This is backed by our experiments.

ii) The quality of the electron microscopy work is very high but there is a general lack of numbers. For example, how many tomograms were examined? How often were fenestrated sheets seen? Can the authors provide more information about how frequent these observations were?

The fenestrated sheets can be seen in the majority of the 37 tomograms recorded of the posterior volume of the parasites. Furthermore, we have randomly generated several hundred tiled (= very large) electron micrographs of bloodstream form trypanosomes for unbiased analyses of endomembranes. In these 2D-datasets the “footprint” of the fenestrated flat and circular cisternae is frequently detectable in the posterior cell area.

We now have included the corresponding numbers in all EM figure legends.

iii) The EM work always focussed on cells which had been processed before fixing. Now, I understand this was important to enable tracers to be used. However, given the dynamic nature of the system these processing steps and feeding experiments may have affected the endosomal organisation. Given their knowledge of the system now, the authors should fix some cells directly in culture to observe whether the organisation of the endosome aligns with their conclusions here.

This is a valid criticism; however, it is the cell culture that provides an artificial environment. As for a possible effect of cell harvesting by centrifugation on the integrity and functionality of the endosome system, we consider this very unlikely for one simple reason. The mechanical forces acting in and on the parasites as they circulate in the extremely crowded and confined environment of the mammalian bloodstream are obviously much higher than the centrifugal forces involved in cell preparation. This becomes particularly clear when one considers that the mass of the particle to be centrifuged determines the actual force exerted by the g-forces. Nevertheless, the proposed experiment is a good control, although much more complex than proposed, since tomography is a challenging technique. We have performed the suggested experiment and acquired tomograms of unprocessed cells. The corresponding data is now included as supplementary movie 2, 3 and 4. We refer to it in lines 202 – 206: To investigate potential impacts of processing steps (cargo uptake, centrifugation, washing) on endosomal organization, we directly fixed cells in the cell culture flask, embedded them in Epon, and conducted tomography. The resulting tomograms revealed endosomal organization consistent with that observed in cells fixed after processing (see Supplementary movie 2, 3, and 4).

We furthermore thank the reviewer for the experiment suggestion in the acknowledgments.

iv) The discussion needs to be revamped. At the moment it is just another run through of the results and does not take an overview of the results presenting an integrated view. Moreover, it contains reference to data that was not presented in the results.

We have improved the discussion accordingly.

Recommendations for the authors:

The reviewers concurred about the high calibre of the work and the importance of the findings.

They raised some issues and made some suggestions to improve the paper without additional experiments - key issues include

(1) Better referencing of the trypanosome endocytosis/ lysosomal trafficking literature.

The literature, especially the experimental and quantitative work, is very limited. We now provide a more complete set of references. However, we would like to mention that we had cited a recent review that critically references the trypanosome literature with emphasis on the extensive work done with mammalian cells and yeast.

(2) Moving the dSTORM data that detracts from otherwise strong data in a supplementary figure.

We have done this.

(3) Removal of the conclusion that the continuous endosome fulfils the functions of TGN, without further evidence.

As stated above, this was not a conclusion in our paper, but rather a speculation, which we have now more clearly marked as such. Lines 740 to 751 now read:

“Interestingly, we did not find any structural evidence of vesicular retrograde transport to the Golgi. Instead, the endosomal ‘highways’ extended throughout the posterior volume of the trypanosomes approaching the trans-Golgi interface. It is highly plausible that this region represents the convergence point where endocytic and biosynthetic membrane trafficking pathways merge. A comparable merging of endocytic and biosynthetic functions was already described for the TGN in plants. Different marker proteins for early and recycling endosomes were shown to be associated and/ or partially colocalized with the TGN suggesting its function in both secretory and endocytic pathways (reviewed in Minamino and Ueda, 2019). As we could not find structural evidence for the existence of a TGN we tentatively propose that trypanosomes may have shifted the central orchestrating function of the TGN as a sorting hub at the crossroads of biosynthetic and recycling pathways to the endosome. Although this is a speculative scenario, it is experimentally testable.”

(4) Broader discussion linking their findings to other examples of organelle maturation in eukaryotes (e.g cisternal maturation of the Golgi)

We have improved the discussion accordingly.

Reviewer #1 (Recommendations For The Authors):

What are the multi-vesicular vesicles that surround the marked endosomal compartments in Fig 1. Do they become labelled with fluid phase markers with longer incubations (e.g late endosome/ lysosomal)?

The function of MVBs in trypanosomes is still far from being clear. They are filled with fluid phase cargo, especially ferritin, but are devoid of VSG. Hence it is likely that MVBs are part of the lysosomal compartment. In fact, this part of the endomembrane system is highly dynamic. MVBs can be physically connected to the lysosome or can form elongated structures. The surprising dynamics of the trypanosome lysosome will be published elsewhere.

Figure 2. The compartments labelled with EP1::Halo are very poorly defined due to the low levels of expression of the reporter protein and/or sensitivity of detection of the Halo tag. Based on these images, it would be hard to conclude whether the endosome network is continuous or not. In this respect, it is unclear why the authors didn't use EP1-GFP for these analyses? Given the other data that provides more compelling evidence for a single continuous compartment, I would suggest removing Fig 2A.

We have used EP1::GFP to label the entire endosome system (Engstler and Boshart, 2004). Unfortunately, GFP is not suited for dSTORM imaging. By creating the EP1::Halo cell line, we were able to utilize the most prominent dSTORM fluorescent dye, Alexa 647. This was not primarily done to generate super resolution images, but rather to measure the dynamics of the GPI-anchored, luminal protein EP with single molecule precision. The results from this study will be published separately. But we agree with the reviewer and have relocated the dSTORM data to the supplementary material.

The observation that Rab5a/7 can be detected in the lumen of lysosome is interesting. Mechanistically, this presumably occurs by invagination of the limiting membrane of the lysosome. Is there any evidence that similar invagination of cytoplasmic markers occurs throughout or in subdomains of the endocytic network (possibly indicative of a 'late endosome' domain)?

So far, we have not observed this. The structure of the lysosome and the membrane influx from the endosome are currently being investigated.

The authors note that continuity of functionally distinct membrane compartments in the secretory/endocytic pathways has been reported in other protists (e.g T. cruzi). A particular example that could be noted is the endo-lysosomal system of Dictyostelium discoideum which mediates the continuous degradation and eventual expulsion of undigested material.

We tried to include this in the discussion but ultimately decided against it because the Dictyostelium system cannot be easily compared to the trypanosome endosome.

Reviewer #2 (Recommendations For The Authors):

Abstract

Not sure that 'common' is the correct term here. Frequent, near-universal..... it would be true that endocytosis is common across most eukaryotes.

We have changed the sentence to “common process observed in most eukaryotes” (line 33).

Immune evasion - the parasite does not escape the immune system, but does successfully avoid its impact, at least at the population level.

We have replaced the word “escape” with “evasion” (line 35).

The third sentence needs to follow on correctly from the second. Also, more than Igs are internalised and potentially part of immune evasion, such as C3, Factor H, ApoL1 etcetera.

We believe that there may be a misunderstanding here. The process of endocytic uptake and lysosomal degradation has so far only been demonstrated in the context of VSGbound antibodies, which is why we only refer to this. Of course, the immune system comprises a wide range of proteins and effector molecules, all of which could be involved in immune evasion.

I do not follow the logic that the high flux through the endocytic system in trypanosomes precludes distinct compartmentalisation - one could imagine a system where a lot of steps become optimised for example. This idea needs expanding on if it is correct.

Membrane transport by vesicle transfer between several separate membrane compartments would be slower than the measured rate of membrane flux.

Again I am not sure 'efficient' on line 40. It is fast, but how do you measure efficiency? Speed and efficiency are not the same thing.

We have replaced the word “efficient” with “fast” (line 42).

The basis for suggesting endosomes as a TGN is unclear. Given that there are AP complexes, retromer, exocyst and other factors that are part of the TGN or at least post-G differentiation of pathways in canonical systems, this seems a step too far. There really is no evidence in the rest of the MS that seems to support this.

Yes, we agree and have clarified the discussion accordingly. We have not completely removed the discussion on the TGN but have labelled it more clearly as speculation.

I am aware I am being pedantic here, but overall the abstract seems to provide an impression of greater novelty than may be the case and makes several very bold claims that I cannot see as fully valid.

We are not aware of any claim in the summary that we have not substantiated with experiments, or any hypothesis that we have not explained.

Moreover, the concept of fused or multifunctional endosomes (or even other endomembrane compartments) is old, and has been demonstrated in metazoan cells and yeast. The concept of rigid (in terms of composition) compartments really has been rejected by most folks with maturation, recycling and domain structures already well-established models and concepts.

We agree that the (transient) presence of multiple Rab proteins decorating endosomes has been demonstrated in various cell types. This finding formed the basis for the endosomal maturation model in mammals and yeast, which has replaced the previous rigid compartment model.

However, we do not appreciate attempts to question the originality of our study by claiming that similar observations have been made in metazoans or yeast. This is simply wrong. There are no reports of a functionally structured, continuous, single and large endosome in any other system. The only membrane system that might be similar was described in the American parasite Trypanosoma cruzi, however, without the use of endosome markers or any functional analysis. We refer to this study in the discussion.

In summary, the maturation model falls short in explaining the intricacies of the membrane system we have uncovered in trypanosomes. Therefore, one plausible interpretation of our data is that the overall architecture of the trypanosome endosomes represents an adaptation that enables the remarkable speed of plasma membrane recycling observed in these parasites. In our view, both our findings and their interpretation are novel and worth reporting. Again, modern cell biology should recognize that evolution has developed many solutions for similar processes in cells, about whose diversity we have learned almost nothing because of our reductionist view. A remarkable example of this are the Picozoa, tiny bipartite eukaryotes that pack the entire nutritional apparatus into one pouch and the main organelles with the locomotor system into the other. Another one is the “extreme” cell biology of many protozoan parasites such as Giardia, Toxpoplasma or Trypanosoma.

Higher plants have been well characterised, especially at the level of Rab/Arf proteins and adaptins.

We now mention plant endosomes in our brief discussion of the trypanosome TGN. Lines 744 – 747:

“A comparable merging of endocytic and biosynthetic functions was already described for the TGN in plants. Different marker proteins for early and recycling endosomes were shown to be associated and/ or partially colocalized with the TGN suggesting its function in both secretory and endocytic pathways (reviewed in Minamino and Ueda, 2019).”

The level of self-citing in the introduction is irritating and unscholarly. I have no qualms with crediting the authors with their own excellent contributions, but work from Dacks, Bangs, Field and others seems to be selectively ignored, with an awkward use of the authors' own publications. Diversity between organisms for example has been a mainstay of the Dacks lab output, Rab proteins and others from Field and work on exocytosis and late endosomal systems from Bangs. These efforts and contributions surely deserve some recognition?

This is an original article and not a review. For a comprehensive overview the reviewer might read our recent overview article on exo- and endocytic pathways in trypanosomes, in which we have extensively cited the work of Mark Field, Jay Bangs and Joel Dacks. In the present manuscript, we have cited all papers that touch on our results or are otherwise important for a thorough understanding of our hypotheses. We do not believe that this approach is unscientific, but rather improves the readability of the manuscript. Nevertheless, we have now cited additional work.

For the uninitiated, the posterior/anterior axis of the trypanosome cell as well as any other specific features should be defined.

In lines 102 - 110 we wrote:

“This process of antibody clearance is driven by hydrodynamic drag forces resulting from the continuous directional movement of trypanosomes (Engstler et al., 2007). The VSG-antibody complexes on the cell surface are dragged against the swimming direction of the parasite and accumulate at the posterior pole of the cell. This region harbours an invagination in the plasma membrane known as the flagellar pocket (FP) (Gull, 2003; Overath et al., 1997). The FP, which marks the origin of the single attached flagellum, is the exclusive site for endo- and exocytosis in trypanosomes (Gull, 2003; Overath et al., 1997). Consequently, the accumulation of VSG-antibody complexes occurs precisely in the area of bulk membrane uptake.”

We think this sufficiently introduces the cell body axes.

I don't understand the comment concerning microtubule association. In mammalian cells, such association is well established, but compartments still do not display precise positioning. This likely then has nothing to do with the microtubule association differences.

We have clarified this in the text (lines 192 – 199). There is no report of cytoplasmic microtubules in trypanosomes. All microtubules appear to be either subpellicular or within the flagellum. To maintain the structure and position of the endosomal apparatus, they should be associated either with subpellicular microtubules, as is the case with the endoplasmic reticulum, or with the more enigmatic actomyosin system of the parasites. We have been working on the latter possibility and intend to publish a follow-up paper to the present manuscript.

The inability to move past the nucleus is a poor explanation. These compartments are dynamic. Even the nucleus does interesting things in trypanosomes and squeezes past structures during development in the tsetse fly.

The distance between the nucleus and the microtubule cytoskeleton remains relatively constant even in parasites that squeeze through microfluidic channels. This is not unexpected as the nucleus can be highly deformed. A structure the size of the endosome will not be able to physically pass behind the nucleus without losing its integrity. In fact, the recycling apparatus is never found in the anterior part of the trypanosome, most probably because the flagellar pocket is located at the posterior cell pole.

L253 What is the evidence that EP1 labels the entire FP and endosomes? This may be extensive, but this claim requires rather more evidence. This is again suggested at l263. Again, please forgive me for being pedantic, but this is an overstatement unless supported by evidence that would be incredibly difficult to obtain. This is even sort of acknowledged on l271 in the context of non-uniform labelling. This comes again in l336.

The evidence that EP1 labels the entire FP and endosomes is presented here: Engstler and Boshart, 2004; 10.1101/gad.323404).

Perhaps I should refrain from comments on the dangers of expansion microscopy, or asking what has actually been gained here. Oddly, the conclusion on l290 is a fair statement that I am happy with.

An in-depth discussion regarding the advantages and disadvantages of expansion microscopy is beyond the manuscript's intended scope. Our approach involved utilizing various imaging techniques to confirm the validity of our findings. We appreciate that our concluding sentence is pleasing.

F2 - The data in panel A seem quite poor to me. I also do not really understand why the DAPI stain in the first and second columns fails to coincide or why the kinetoplast is so diffuse in the second row. The labelling for EP1 presents as very small puncta, and hence is not evidence for a continuum. What is the arrow in A IV top? The data in panel B are certainly more in line with prior art, albeit that there is considerable heterogeneity in the labelling and of the FP for example. Again, I cannot really see this as evidence for continuity. There are gaps.... Albeit I accept that labelling of such structures is unlikely to ever be homogenous.

We agree that the dSTORM data represents the least robust aspect of the findings we have presented, and we concur with relocating it to the supplementary material.

F3 - Rather apparent, and specifically for Rab7, that there is differential representation - for example, Cell 4 presents a single Rab7 structure while the remaining examples demonstrate more extensive labelling. Again, I am content that these are highly dynamic strictures but this needs to be addressed at some level and commented upon. If the claim is for continuity, the dynamics observed here suggest the usual; some level of obvious overlap of organellar markers, but the representation in F3 is clever but not sure what I am looking at. Moreover, the title of the figure is nothing new. What is also a bit odd is that the extent of the Rab7 signal, and to some extent the other two Rabs used, is rather variable, which makes this unclear to me as to what is being detected. Given that the Rab proteins may be defining microdomains or regions, I would also expect a region of unique straining as well as the common areas. This needs to at least be discussed.

The differences in the representation result from the dynamics of the labelled structures. Therefore, we have selected different cells to provide examples of what the labelling can look like. We now mention this in the results section.

The overlap of the different Rab signals was perhaps to be expected, but we now have demonstrated it experimentally. Importantly, we performed a rigorous quantification by calculating the volume overlaps and the Pearson correlation coefficients.

In previous studies the data were presented as maximal intensity projections, which inherently lack the complete 3D information.

We found that Rab proteins define microdomains and that there are regions of unique staining as well as common areas, as shown in Figure 3. The volumes do not completely overlap. This is now more clearly stated in lines 315 – 319:

“These objects showed areas of unique staining as well as partially overlapping regions. The pairwise colocalization of different endosomal markers is shown in Figure 3 A, XI - XIII and 3 B. The different cells in Figure 3 B were selected to represent the dynamic nature of the labelled structures. Consequently, the selected cells provide a variety of examples of how the labelling can appear.”

This had already been stated in lines 331 – 336:

“In summary, the quantitative colocalization analyses revealed that on the one hand, the endosomal system features a high degree of connectivity, with considerable overlap of endosomal marker regions, and on the other hand, TbRab5A, TbRab7, and TbRab11 also demarcate separated regions in that system. These results can be interpreted as evidence of a continuous endosomal membrane system harbouring functional subdomains, with a limited amount of potentially separated early, late or recycling endosomes.”

F4-6 - Fabulous images. But a couple of issues here; first, as the authors point out, there is distance between the gold and the antigen. So, this of course also works in the z-plane as well as the x/y-planes and some of the gold may well be associated with membraneous figures that are out of the plane, which would indicate an absence of colinearity on one specific membrane. Secondly, in several instances, we have Rab7 essentially mixed with Rab11 or Rab5 positive membrane. While data are data and should be accepted, this is difficult to reconcile when, at least to some level, Rab7 is a marker for a late-endosomal structure and where the presence of degradative activity could reside. As division of function is, I assume, the major reason for intracellular compartmentalisation, such a level of admixture is hard to rationalise. A continuum is one thing but the data here seem to be suggesting something else, i.e. almost complete admixture.

We are grateful for the positive feedback regarding the image quality. It is true that the "linkage error," representing the distance between the gold and the antigen, also functions to some extent in the z-axis. However, it's important to note that the zdimension of the section in these Figures is 55 nm. Nevertheless, it's interesting to observe that membranes, which may not be visible within the section itself but likely the corresponding Rab antigen, is discernible in Figure 4C (indicated by arrows).

We have clarified this in lines 397 – 400:

“Consequently, gold particles located further away may represent cytoplasmic TbRab proteins or, as the “linkage error” can also occur in the z-plane, correspond to membranes that are not visible within the 55 nm thickness of the cryosection (Figure 4, panel C, arrows). “

The coexistence of different Rabs is most likely concentrated in regions where transitions between different functions are likely. Our focus was primarily on imaging membranes labelled with two markers. We wanted to show that the prevailing model of separate compartments in the trypanosome literature is not correct.

F7 - Not sure what this adds beyond what was published by Grunfelder.

First, this figure is an important control that links our results to published work (Grünfelder et al. (2003)). Second, we include double staining of cargo with Rab5, Rab7, and Rab11, whereas Grünfelder focused only on Rab11. Therefore, our data is original and of such high quality that it warrants a main figure.

F8 - and l583. This is odd as the claim is 'proof' which in science is a hard thing to claim (and this is definitely not at a six sigma level of certainty, as used by the physics community). However, I am seeing structures in the tomograms which are not contiguous - there are gaps here between the individual features (Green in the figure).

We have replaced the term "proof". It is important to note that the structures in individual tomograms cannot all be completely continuous because the sections are limited to a thickness of 250 nm. Therefore, it is likely that they have more connectivity above and below the imaged section. Nevertheless, we believe that the quality of the tomograms is satisfactory, considering that 3D Tokuyasu is a very demanding technique and the production of serial Tokuyasu tomograms is not feasible in practice.

Discussion - Too long and the self-citing of four papers from the corresponding author to the exclusion of much prior work is again noted, with concerns about this as described above. Moreover, at least four additional Rab proteins are known associated with the trypanosome endosomal system, 4, 5B, 21 and 28. These have been completely ignored.

We have outlined our position on referencing in original articles above. We also explained why we focused on the key marker proteins associated with early (Rab5), late (Rab7) and recycling endosomes (Rab11). We did not ignore the other Rabs, we just did not include them in the present study.

Overall this is disappointing. I had expected a more robust analysis, with a clearer discussion and placement in context. I am not fully convinced that what we have here is as extreme as claimed, or that we have a substantial advance. There is nothing here that is mechanistic or the identification of a new set of gene products, process or function.

We do not think that this is constructive feedback.

This MS suggests that the endosomal system of African trypanosomes is a continuum of membrane structures rather than representing a set of distinct compartments. A combination of light and electron microscopy methods are used in support. The basic contention is very challenging to prove, and I'm not convinced that this has been. Furthermore, I am also unclear as to the significance of such an organisation; this seems not really addressed.

We acknowledge and respect varying viewpoints, but we hold a differing perspective in this matter. We are convinced that the data decisively supports our interpretation. May future work support or refute our hypothesis.

Reviewer #3 (Recommendations For The Authors):

Line 81 - delete 's

Done.

Generally, the introduction was very well written and clearly summarised our current understanding but the paragraph beginning line 134 felt out of place and repeated some of the work mentioned earlier.

We have removed this paragraph.

For the EM analysis throughout quantification would be useful as highlighted in the public review. How many tomograms were examined, and how often were types of structures seen? I understand the sample size is often small but this would help the reader appreciate the diversity of structures seen.

We have included the numbers.

Following on from this how were the cells chosen for tomogram analysis? For example, the dividing cell in 1D has palisades associating with the new pocket - is this commonly seen? Does this reflect something happening in dividing cells. This point about endosomal division was picked up in the discussion but there was little about in the main results.

This issue is undoubtedly inherent to the method itself, and we have made efforts to mitigate it by generating a series of tomograms recorded randomly. We have refrained from delving deeper into the intricacies of the cell cycle in this manuscript, as we believe that it warrants a separate paper.

As the authors prosecute, the co-localisation analysis highlights the variable nature of the endosome and the overlap of different markers. When looking at the LM analysis, I was struck by the variability in the size and number of labelled structures in the different cells. For example, in 3A Rab7 is 2 blobs but in 3B Cell 1 it is 4/5 blobs. Is this just a reflection of the increase in the endosome during the cell cycle?

The variability in representation is a direct consequence of the dynamic nature of the labelled structures. For this reason, we deliberately selected different cells to represent examples of how the labelling can look like. We have decided not to mention the dynamics of the endosome during the cell cycle. This will be the subject of a further report.

Moreover, Rab 11 looks to be the marker covering the greatest volume of the endosomal system - is this true? I think there's more analysis of this data that could be done to try and get more information about the relative volumes etc of the different markers that haven't been drawn out. The focus here is on the co-localisation.

Precisely because we recognize the importance of this point, we intend to turn our attention to the cell cycle in a separate publication.

I appreciate that it is an awful lot of work to perform the immuno-EM and the data is of good quality but in the text, there could be a greater effort to tie this to the LM data. For example, from the Rab11 staining in LM you would expect this marker to be the most extensive across the networks - is this reflected in the EM?

For the immuno-EM there were no numbers, the authors had measured the position of the gold but what was the proportion of gold that was in/near membranes for each marker? This would help the reader understand both the number of particles seen and the enrichment of the different regions.

Our original intent was to perform a thorough quantification (using stereology) of the immuno-EM data. However, we later realized that the necessary random imaging approach is not suitable for Tokuyasu sections of trypanosomes. In short, the cells are too far apart, and the cell sections are only occasionally cut so that the endosomal membranes are sufficiently visible. Nevertheless, we continue to strive to generate more quantitative data using conventional immuno-EM.

The innovative combination of Tokuyasu tomograms with immuno-EM was great. I noted though that there was a lack of fenestration in these models. Does this reflect the angle of the model or the processing of these samples?

We are grateful to the referee, as we have asked ourselves the same question. However, we do not attribute the apparent lack of fenestration to the viewing angle, since we did not find fenestration in any of the Tokuyasu tomograms. Our suspicion is more directed towards a methodological problem. In the Tokuyasu workflow, all structures are mainly fixed with aldehydes. As a result, lipids are only effectively fixed through their association with membrane proteins. We suggest that the fenestration may not be visible because the corresponding lipids may have been lost due to incomplete fixation.

We now clearly state this in the lines 563 – 568.

“Interestingly, these tomograms did not exhibit the fenestration pattern identified in conventional electron tomography. We suspect that this is due to methodological reasons. The Tokuyasu procedure uses only aldehydes to fix all structures. Consequently, effective fixation of lipids occurs only through their association with membrane proteins. Thus, the lack of visible fenestration is likely due to possible loss of lipids during incomplete fixation.”

The discussion needs to be reworked. Throughout it contains references to results not in the main results section such as supplementary movie 2 (line 735). The explicit references to the data and figures felt odd and more suited to the results rather than the discussion. Currently, each result is discussed individually in turn and more effort needs to be made to integrate the results from this analysis here but also with previous work and the data from other organisms, which at the moment sits in a standalone section at the end of the discussion.

We have improved the discussion and removed the previous supplementary movies 2 and 3. Supplementary movie 1 is now mentioned in the results section.

Line 693 - There was an interesting point about dividing cells describing the maintenance of endosomes next to the old pocket. Does that mean there was no endosome by the new pocket and if so where is this data in the manuscript? This point relates back to my question about how cells were chosen for analysis - how many dividing cells were examined by tomography?

The fate of endosomes during the cell cycle is not the subject of this paper. In this manuscript we only show only one dividing cell using tomography. An in-depth analysis focusing on what happens during the cell cycle will be published separately.

Line 729 - I'm unclear how this represents a polarization of function in the flagellar pocket. The pocket I presume is included within the endosomal system for this analysis but there was no specific mention of it in the results and no marker of each position to help define any specialisation. From the results, I thought the focus was on endosomal co-localisation of the different markers. If the authors are thinking about specialisation of the pocket this paper from Mark Field shows there is evidence for the exocyst to be distributed over the entire surface of the pocket, which is relevant to the discussion here. Boehm, C.M. et al. (2017) The trypanosome exocyst: a conserved structure revealing a new role in endocytosis. PLoS Pathog. 13, e1006063

We have formulated our statement more cautiously. However, we are convinced that membrane exchange cannot physically work without functional polarization of the pocket. We know that Rab11, for example, is not evenly distributed on the pocket. By the way, in Boehm et al. (2017) the exocyst is not shown to cover the entire pocket (as shown in Supplementary Video 1).

We now refer to Boehm et al. (Lines 700 – 703):

“Boehm et al (2017) report that in the flagellar pocket endocytic and exocytic sites are in close proximity but do not overlap. We further suggest that the fusion of EXCs with the flagellar pocket membrane and clathrin-mediated endocytosis take place on different sites of the pocket. This disparity explains the lower colocalization between TbRab11 and TbRab5A.”

Line 735 - link to data not previously mentioned I think. When I looked at this data I couldn't find a key to explain what all the different colours related to.

We have removed the previous supplementary movies 2 and 3. We now reference supplementary movie 1 in the results section.

Author response:

The following is the authors’ response to the previous reviews

Public Reviews:

Reviewer #1 (Public review):

Du et al. address the cell cycle-dependent clearance of misfolded protein aggregates mediated by the endoplasmic reticulum (ER) associated Hsp70 chaperone family and ER reorganisation. The observations are interesting and impactful to the field.

Strength:

The manuscript addresses the connection between the clearance of misfolded protein aggregates and the cell cycle using a proteostasis reporter targeted to ER in multiple cell lines. Through imaging and some biochemical assays, they establish the role of BiP, an

Hsp70 family chaperone, and Cdk1 inactivation in aggregate clearance upon mitotic exit.

Furthermore, the authors present an initial analysis of the role of ER reorganisation in this clearance. These are important correlations and could have implications for ageingassociated pathologies. Overall, the results are convincing and impactful to the field.

Weakness:

The manuscript still lacks a mechanistic understanding of aggregate clearance. Even though the authors have provided the role of different cellular components, such as BiP, Cdk1 and ATL2/3 through specific inhibitors, at least an outline establishing the sequence of events leading to clearance is missing. Moreover, the authors show that the levels of ERFlucDM-eGFP do not change significantly throughout the cell cycle, indicating that protein degradation is not in play. Therefore, addressing/elaborating on the mechanism of disassembly can add value to the work. Also, the physiological relevance of aggregate clearance upon mitotic exit has not been tested, nor have the cellular targets of this mode of clearance been identified or discussed.

Thank you for your suggestions. 

We have added descriptions about the sequence of events leading to clearance in the abstract (line 33) and discussion (line 316). 

We have commented on the future work that could address the molecular mechanisms behind the aggregate clearance in the discussion (line 388). 

It has been difficult to address the physiological relevance of aggregate clearance during cell division, as the inhibition of BiP or depletion of ATL2/3 that prevent aggregate clearance cause cellular consequences not specific to aggregate clearance. Future work that lead to understanding of aggregate clearance at the molecular level may allow us to address this more specifically. Furthermore, we have commented about the potential defects that could arise in cells expressing ER-FlucDM-eGFP that have a perturbed cellular health based on the proteomic analysis (line 359). 

To identify pathological targets that undergo clearance as the ER-FlucDM-eGFP, we tested three pathological mutants (CFTR-∆F508, AAT S and Z variants) that are known to mis-fold and accumulate in the ER. Unfortunately, expression of these mutants did not result in the confinement of aggregates in the nucleus. The data related to this have been added as Figure S1E and S1F (line 102) in this revised manuscript. We have also commented in the discussion that pathological targets are yet to be identified and could be a part of future work (line 392).

Reviewer #2 (Public review):

This paper describes an interesting observation that ER-targeted misfolded proteins are trapped within vesicles inside nucleus to facilitate quality control during cell division. This work supports the concept that transient sequestration of misfolded proteins is a fundamental mechanism of protein quality control. The authors satisfactorily addressed several points asked in the review of first submission. The manuscript is improved but still unable to fully address the mechanisms.

Strengths:

The observations in this manuscript are very interesting and open up many questions on proteostasis biology.

Weaknesses:

Despite inclusions of several protein-level experiments, the manuscript remained a microscopy-driven work and missed the opportunity to work out the mechanisms behind the observations.

Thank you for your suggestions. We believe that our study has provided a genetic basis for the involvement of ER reorganization and BiP during cell division in aggregate clearance, which is a new observation. We have also commented in this revised manuscript about the future work that could address the molecular mechanisms behind the aggregate clearance in the discussion (line 388).  

Reviewer #3 (Public review):

This paper describes a new mechanism for the clearance of protein aggregates associated to endoplasmic reticulum re-organization that occurs during mitosis.

Experimental data showing clearance of protein aggregates during mitosis is solid, statistically significant, and very interesting. The authors made several new experiments included in the revised version to address the concerns raised by reviewers. A new proteomic analysis, co-localization of the aggregates with the ER membrane Sec61beta protein, expression of the aggregate-prone protein in the nucleus does not result in accumulation of aggregates, detection of protein aggregates in the insoluble faction after cell disruption and mostly importantly knockdown of ATL proteins involved in the organization of ER shape and structure impaired the clearance mechanism. This last observation addresses one of the weakest points of the original version which was the lack of experimental correlation between ER structure capability to re-shape and the clearance mechanism.

In conclusion, this new mechanism of protein aggregate clearance from the ER was not completely understood in this work but the manuscript presented, particularly in the revised version, an ensemble of solid observations and mechanistic information to scaffold future studies that clarify more details of this mechanism. As stated by the authors: "How protein aggregates are targeted and assembled into the intranuclear membranous structure waits for future investigation". This new mechanism of aggregate clearance from the ER is not expected to be fully understood in a single work but this paper may constitute one step to better comprehend the cell capability to resolve protein aggregates in different cell compartments.

We thank the reviewer for the comments.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

The manuscript presents a very interesting set of observations that could have significant implications on age-related protein misfolding and aggregate clearance. There are a few places in the manuscript that still need more clarity. Some are listed below, which I think can improve the manuscript.

- The new data associated with proteomic analysis is appreciated, but the information gained has not been explored or elaborated sufficiently in the manuscript. Based on the differential expression of cell cycle proteins, how the authors interpret cellular health is unclear. Also, the physiological role of this mode of aggregate clearance remains unclear.

We have added our interpretation of perturbed cellular health in cells expressing ERFlucDM-eGFP in the discussion (line 359). 

It has been difficult to address the physiological relevance of aggregate clearance during cell division, as the inhibition of BiP or depletion of ATL2/3 that prevent aggregate clearance cause cellular consequences not specific to aggregate clearance. Future work that lead to understanding of aggregate clearance at the molecular level may allow us to address this more specifically.

- In Figure 3A, have the authors measured the total GFP intensity from interphase through early G1? Even though the number and area of the aggregates decrease significantly, the cytoplasmic GFP signal does not seem to increase. Considering new CHX chase experiments and total Fluorescence intensity calculations (Figure S7D), which indicate no difference, one would expect an increase in cytoplasmic signal upon the disassembly of aggregates. Therefore, the data from Figures 3A and 7D seem contradictory. Can the authors please explain?

We apologized for the confusion. The images in Figure 3A were derived from fixed cells. So, different cells were shown in every cell cycle phases and were not suitable for quantification. Fluorescence intensity changes could be better appreciated in Figure 3C or 4D as these were time-lapse microscopy images of live cells progressing through mitosis and cytokinesis. Data used in the quantification of fluorescence intensity in Figure S7D were derived from live cells taken from specific time points to avoid unwanted fluorescence bleaching during time-lapse microscopy. 

- Do the authors expect a similar clearance of pathological aggregates such as mutant FUS or TDP43 condensates? Showing aggregate disassembly of disease-relevant aggregates would be an excellent addition to the manuscript, but it might be beyond the scope of the current version. However, the authors can comment/speculate how their study might extend to pathological condensates.

We tested three pathological mutants (CFTR-∆F508, AAT S and Z variants) that are known to mis-fold and accumulate in the ER. Unfortunately, expression of these mutants did not result in the confinement of aggregates in the nucleus. The data related to this have been added as Figure S1E and S1F (line 102) in this revised manuscript. We have commented that pathological targets are yet to be identified and could be a part of future work (line 392).

- The presence of ER membrane around these aggregates is an interesting observation. This membrane is retained even after nuclear membrane breakdown. What could be the relevance of membrane-bound aggregates, especially since the membrane can limit the access of chaperones involved in disassembly? This observation becomes more important since the depletion of ER membrane fusion proteins also leads to the accumulation of aggregates. Are the membranes a beacon for disassembly? The authors may comment/ speculate. This could also be an important aspect of the mechanism of clearance.

We think that the ER membranes around the aggregates are disassembled when the ER networks reorganize during mitotic exit and this may allow accessibility of BiP to disaggregate the aggregates. We have added this in the discussion (line 316).

eLife Assessment

This important manuscript uses circuit mapping, chemogenetics, and optogenetics to demonstrate a novel hippocampal lateral septal circuit that regulates social novelty behaviours and shows that downstream of the hippocampal septal circuit, septal projections to the ventral tegmental area are necessary for general novelty discrimination. The strength of the evidence supporting the claims is convincing but would be strengthened by the inclusion of additional functional assays. The work will be of interest to systems and behavioural neuroscientists who are interested in the brain mechanisms of social behaviours.

We thank the reviewers for their thoughtful and constructive feedback. We are excited that both reviewers thought that the manuscript was of “interest to specialists in the field and to the broad readership of the journal”, that the paper was “well-written and logically organized” and that the “study opens an avenue to study these circuits further to uncover the plasticity and synaptic mechanisms regulating social novelty preference.” Additionally, the reviewers wrote that the experiments were “well-designed” “with clever controls and conditions to provide compelling evidence for their conclusion.” The reviewers additionally provided constructive feedback, which we address in our responses below.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The study investigated the neural circuits underlying social novelty preference in mice. Using viral circuit tracing, chemogenetics, and optogenetics in the vHPC, LS, and VTA, the authors found that vHPC to LS projections may contribute to the salience of social novelty investigations. In addition, the authors identify LS projections to the VTA involved in social novelty and familiar food responses. Finally, via viral tracing, they demonstrate that vHPC-LS neurons may establish direct monosynaptic connections with VTA dopaminergic neurons. The experiments are well-designed, and the conclusions are mostly very clear. The manuscript is well-written and logically organized, and the content will be of interest to specialists in the field and to the broad readership of the journal.

Strengths:

(1) The vHPC has been involved in social memory for novel and familiar conspecifics. Yet, how the vHPC conveys this information to drive motivation for novel social investigations remains unclear. The authors identified a pathway from the vHPC to the LS and eventually the VTA, that may be involved in this process.

(2) Mice became familiar with a novel conspecific by co-housing for 72h. This represents a familiarization session with a longer duration as compared to previous literature. Using this new protocol, the authors found robust social novelty preference when animals were given a choice between a novel and familiar conspecific.

(3) The effects of vHPC-LS inhibition are specific to novel social stimuli. The authors included novel food and novel object control experiments and those were not affected by neuronal manipulations.

(4) For optogenetic studies, the authors applied closed-loop photoinhibition only when the animals investigated either the novel conspecific or the familiar. This optogenetic approach allowed for the investigation of functional manipulations to selective novel or familiar stimuli approaches.

Weaknesses:

(1) The abstract and the overall manuscript pose that the authors identified a novel vHPC-LS-VTA pathway that is necessary for mice to preferentially investigate novel conspecifics. However, the authors assessed the functional manipulations of vHPC-LS and LS-VTA circuits independently and the sentence could be misleading. Therefore, a viral strategy specifically designed to target the vHPC-LS-VTA circuit combined with optogenetic/chemogenetic tools and behavior may be necessary for the statement of this conclusion.

The reviewer raises an important point. Although Figure 3 shows that vHPC (vCA1 and vCA3) is the source of the greatest number of monosynaptic inputs onto LS-VTA neurons, we did not perform any experiments that specifically manipulated vHPC neurons that project to LS-VTA neurons. While these experiments would be extremely interesting, they are technically challenging and beyond the scope of this study.

(2) The authors combined males and females in their analysis, as neural circuit manipulation affected novelty discrimination ratios in both sexes. However, supplementary Figure 1 demonstrates the chemogentic inhibition of vHPC-LS circuit may cause stronger effects in male mice as compared to females.

The reviewer makes an interesting point. We can confirm that we found no significant differences in the effectiveness of our vHPC-LS inhibition between the males and females (2-factor ANOVA with sex (male/female) and drug condition (saline/CNO) as factors on the discrimination scores of hM4Di expressing animals: interaction p=0.2241, sex: p=0.1233, drug condition: p=0.0166). These data suggest that there are no significant sex differences in the effectiveness of inhibition of the vHPC-LS neurons.

(3) In most experiments, the same animals were used for social novelty preference, for food or object novelty responses but washout periods between experiments are not mentioned in the methods section. In this line, the authors did not mention the time frame between the closed-loop optogenetic experiments that silenced the vHPC-LS only during familiar and then only novel social investigations. When using the same animals tested for social experiments in the same context there may be an effect of context-dependent social behaviors that could affect future outcomes.

We thank the reviewer for this important clarification. We apologize for not including these crucial details in our Methods section. For both the chemogenetic and optogenetic inhibition experiments, all conditions were separated by a minimum of 24 hours. In the chemogenetic inhibition experiments, saline and CNO conditions were counterbalanced between animals. Similarly, we counterbalanced the order of light ON vs light OFF conditions across animals during our optogenetic inhibition experiments.

(4) All the experiments were performed in a non-cell-type-specific manner. The viral strategies used targeted multiple neuronal subpopulations that could have divergent effects on social novelty preference. This constraint could be added in the discussion section.

The reviewer raises an important point. In our study, while we specifically manipulate projection populations (either vHPC-LS or LS-VTA), it is possible that these projection populations themselves are composed of heterogeneous cell types. It would be an interesting direction of study to pursue in the future.

(5) The authors' assumptions were all based on experiments of necessity. The authors could use an experiment of sufficiency by targeting for instance the LS-VTA circuit and assess if animals reduce novel social investigations with LS-VTA photostimulation.

We agree with the reviewers that it would be interesting to determine if LS-VTA neurons are sufficient, in addition to being necessary, to drive social novelty. These will be interesting experiments to pursue in the future.

Reviewer #2 (Public Review):

Summary:

Rashid and colleagues demonstrate a novel hippocampal lateral septal circuit that is important for social recognition and drives the exploration of novel conspecifics. Their study spans from neural tracing to close-loop optogenetic experiments with clever controls and conditions to provide compelling evidence for their conclusion. They demonstrate that downstream of the hippocampal septal circuit, septal projections to the ventral tegmental area are necessary for general novelty discrimination. The study opens an avenue to study these circuits further to uncover the plasticity and synaptic mechanisms regulating social novelty preference.

Strengths:

Chemogenetic and optogenetic experiments have excellent behavioral controls. The synaptic tracing provides important information that informs the narrative of experiments presented and invites future studies to investigate the effects of septal input on dopaminergic activity.

Weaknesses:

There are unclear methodological important details for circuit manipulation experiments and analyses where multiple measures are needed but missing. Based on the legends, the chemogenetic experiment is done in a within-animal design. That is the same mouse receives SAL and CNO. However, the data is not presented in a within-animal manner such that we can distinguish if the behavior of the same animal changes with drug treatment. Similarly, the methods specify that the optogenetic manipulations were done in three different conditions, but the analyses do not report within-animal changes across conditions nor account for multiple measures within subjects.

Thank you for raising this important point. We agree that a repeated measures ANOVA would be ideal, but there is sufficient behavioral variability that such analyses will be difficult without very large sample sizes.

Finally, it is unclear if the order of drug treatment and conditions were counterbalanced across subjects.

As mentioned in the above response to Reviewer 1, for both the chemogenetic and optogenetic inhibition experiments, all conditions were separated by a minimum of 24 hours and we counterbalanced the order of chemogenetic (saline/CNO) and optogenetic (light ON/light OFF) experimental manipulations across animals.

Author response:

The following is the authors’ response to the previous reviews

Public Reviews:

Reviewer #1 (Public review):

Summary:

This study investigates the potential of targeting specific regions within the RNA genome of the Porcine Epidemic Diarrhea Virus (PEDV) for antiviral drug development. The authors used SHAPE-MaP to analyze the structure of the PEDV RNA genome in infected cells. They categorized different regions of the genome based on their structural characteristics, focusing on those that might be good targets for drugs or small interfering RNAs (siRNAs).

They found that dynamic single-stranded regions can be stabilized by compounds (e.g., to form G-quadruplexes), which inhibit viral proliferation. They demonstrated this by targeting a specific G4-forming sequence with a compound called Braco-19. The authors also describe stable (structured) single-stranded regions that they used to design siRNAs showing that they effectively inhibited viral replication.

Strengths:

There are a number of strengths to highlight in this manuscript.

(1) The study uses a sophisticated technique (SHAPE-MaP) to analyze the PEDV RNA genome in situ, providing valuable insights into its structural features.

(2) The authors provide a strong rationale for targeting specific RNA structures for antiviral development.

(3) The study includes a range of experiments, including structural analysis, compound screening, siRNA design, and viral proliferation assays, to support their conclusions.

(4) Finally, the findings have potential implications for the development of new antiviral therapies against PEDV and other RNA viruses.

Overall, this interesting study highlights the importance of considering RNA structure when designing antiviral therapies and provides a compelling strategy for identifying promising RNA targets in viral genomes.

Weaknesses:

I have some concerns about the utility of the 3D analyses, the effects of their synonymous mutants on expression/proliferation, a potentially missed control for studies of mutants, and the therapeutic utility of the compound they tested vs. Gquadruplexes.

We thank the reviewer for their positive assessment and insightful comments. Below, we address each point of concern:

(1) The utility of the 3D analyses:

In the revised manuscript, we have toned down this discussion and moved Figure 3A to the supplementary materials to reduce any sense of fragmentation in the overall story. While SHAPE-MaP technology is mature and convenient to use and can indeed capture some RNA structural elements with special functions in certain case; we acknowledge that its application for 3D analyses requires further validation. We believe this approach will become more prevalent in future research.

(2) The effects of synonymous mutants on expression/proliferation:

In the PEDV genome, the PQS1 mutation site encodes lysine (AAG). Given that lysine has only two codons (AAG and AAA), the G3109A synonymous mutation represented our sole viable option. Published studies (Ding et al., 2024) confirm that neither AAG nor AAA are classified as rare or dominant codons in mammalian cells. Therefore, the observed changes in viral proliferation levels are likely to stem from alterations in RNA secondary structure rather than codon usage effects.

REFERENCES:

Ding W, Yu W, Chen Y, et al. Rare codon recoding for efficient noncanonical amino acid incorporation in mammalian cells. Science. 2024;384(6700):1134-1142. 

(3) Potentially missed control for studies of mutants:

In the revised manuscript, we have incorporated additional control experiments evaluating Braco-19's therapeutic effects on the PQS3 mutant strain (Figure 4 – figure supplement 3)：

(4) The therapeutic utility of Braco-19 vs. G-quadruplexes:

While Braco-19 is indeed a broad-spectrum G4 ligand, our data clearly show that not all PQSs in the viral genome can form G4 structures. Our findings primarily provide proof-of-concept that sequences with high G4-forming potential in viral genomes represent viable targets for antiviral therapy. Future studies could leverage SHAPEguided structural insights to design ligands with enhanced specificity for viral G4s, potentially improving therapeutic utility while minimizing off-target effects.

Reviewer #2 (Public review):

Summary:

Luo et. al. use SHAPE-MaP to find suitable RNA targets in Porcine Epidemic Diarrhoea Virus. Results show that dynamic and transient structures are good targets for small molecules, and that exposed strand regions are adequate targets for siRNA. This work is important to segment the RNA targeting.

Strengths:

This work is well done and the data supports its findings and conclusions. When possible, more than one technique was used to confirm some of the findings.

Weaknesses:

The study uses a cell line that is not porcine (not the natural target of the virus).

We thank the reviewer for their insightful comments and recognition of our study's value. The most commonly employed cell models for in vitro PEDV studies are monkey-derived Vero E6 cells and porcine PK1 cells. However, PEDV (particularly our strain) exhibits significantly lower replication efficiency in PK1 cells compared to Vero cells, and no cytopathic effects were observed in PK1 cells. In our preliminary attempts to perform SHAPE-MaP experiments using infected PK1 cells, the sequencing data showed less than 0.03% alignment to the PEDV genome, rendering subsequent analysis and downstream experiments unfeasible.

Reviewer #3 (Public review):

Summary:

This manuscript by Luo et al. applied SHAPE-Map to analyze the secondary structure of the Porcine Epidemic Diarrhoea Virus (PEDV) RNA genome in infected cells. By combining SHAPE reactivity and Shannon entropy, the study indicated that the folding of the PEDV genomic RNA was nonuniform, with the 5' and 3' untranslated regions being more compactly structured, which revealed potentially antiviral targetable RNA regions. Interestingly, the study also suggested that compounds bound to well-folded RNA structures in vitro did not necessarily exhibit antiviral activity in cells, because the binding of these compounds did not necessarily alter the functions of the well-folded RNA regions. Later in the manuscript, the authors focus on guanine-rich regions, which may form G-quadruplexes and be potential targets for small interfering RNA (siRNA). The manuscript shows the binding effect of Braco-19 (a G-quadruplex-binding ligand) to a predicted G4 region in vitro, along with the inhibition of PEDV proliferation in cells. This suggests that targeting high SHAPE-high Shannon G4 regions could be a promising approach against RNA viruses. Lastly, the manuscript identifies 73 singlestranded regions with high SHAPE and low Shannon entropy, which demonstrated high success in antiviral siRNA targeting.

Strengths:

The paper presents valuable data for the community. Additionally, the experimental design and data analysis are well documented.

Weakness:

The manuscript presents the effect of Braco-19 on PQS1, a single G4 region with high SHAPE and high Shannon entropy, to suggest that "the compound can selectively target the PQS1 of the high SHAPE-high Shannon region in cells" (lines 625-626). While the effect of Braco-19 on PQS1 is supported by strong evidence in the manuscript, the conclusion regarding the G4 region with high SHAPE and high Shannon entropy is based on a single target, PQS1.

We thank the reviewer for their positive assessment of our methodology and dataset. We propose that dynamic RNA structures in high SHAPE-high Shannon regions, when stabilized by small molecules, can serve as viable targets for antiviral therapy. Gquadruplexes represent a characteristic type of such dynamic structures that compete with local stem-loop formations in the genome. While we identified seven highly conserved PQSs in the PEDV genome, only PQS1 was located within a high SHAPEhigh Shannon region. To further validate this concept, we have supplemented the revised manuscript with Thioflavin T (ThT) fluorescence turn-on assays (Figures 3D, 3E, and Figure 3 – figure supplement 6), which provide additional evidence for the differential G4-forming capabilities of PQSs across regions with distinct structural features.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Major Comments:

(1) It could be valuable for the authors to spend some more effort comparing their approach to siRNA target discovery and design to current methods for siRNA design. It would be good to highlight which components are novel, and which might offer superior performance with respect to other existing methods.

We thank the reviewer for highlighting this important point. In response, we have rewritten the relevant section in the discussion:

“Our approach uniquely integrates in situ RNA structural data (SHAPE reactivity and Shannon entropy) to prioritize siRNA targets within stable single-stranded regions (high SHAPE reactivity, low Shannon entropy), which are experimentally validated as accessible in infected cells. This represents a significant departure from traditional siRNA design methods that rely primarily on sequence conservation, thermodynamic rules (e.g., Tuschl rules), or in vitro structural predictions (Ali Zaidi et al., 2023; Qureshi et al., 2018; Tang and Khvorova, 2024),which may not accurately reflect intracellular RNA accessibility. Bowden-Reid et al. designed 39 antiviral siRNAs against various SARS-CoV-2 variants based on sequence conservation, ultimately identifying 8 highly effective sequences (Bowden-Reid et al., 2023). Notably, five of these effective sequences targeted regions that were located in high SHAPE-high Shannon regions according to SARS-CoV-2 SHAPE datasets (Supplementary Table 8) (Manfredonia et al., 2020). This independent finding aligns perfectly with our conclusions and demonstrates that SHAPE-based siRNA design outperforms sequence/structureagnostic approaches, at least in terms of significantly improving antiviral siRNA screening efficiency. Given the growing availability of SHAPE datasets for numerous viruses, we are confident that our methodology will facilitate more precise design of antiviral siRNAs.”

(2) The section targeting their discovered G4 structure with Braco-19 is interesting, particularly showing effects on viral proliferation; however, it's not clear to me how this compound could be used therapeutically against PEDV, as it is a non-selective binder of G4 structures. Their results are good support for the presence and functionality of a G4 structure in PEDV, but I don't see any strategy outlined in the manuscript on how this could be specifically targeted with Braco-19.

While Braco-19 is indeed a broad-spectrum G4 ligand, our data demonstrate that not all PQSs in the viral genome can form G4 structures under physiological conditions. Our results specifically show that Braco-19 exerts its anti-PEDV activity by targeting PQS1, which is located in a high SHAPE-high Shannon entropy region. This target specificity was further confirmed by the complete resistance of the PQS1mut strain (lacking G4-forming ability) to Braco-19 treatment in our in vitro assays. 

Additionally, previous studies have reported that during rapid viral replication, viral RNA accumulates to levels that significantly exceed host RNA concentrations. This "concentration advantage" suggests that G4 ligands like Braco-19 would preferentially bind viral G4 structures over host targets, thereby enhancing their antiviral specificity in vivo. In summary, our data provide proof-of-concept that viral genomic regions with high G4-forming potential - particularly those in high SHAPE-high Shannon entropy regions - represent promising targets for antiviral therapy.

(3) The section where they proposed 3D RNA structures based on sequence similarity feels "tacked on" and I don't see how it adds to the overall story. The authors identify a short RNA hairpin in the PEDV genome with some sequence similarity to the CPEB3 nuclease P4 hairpin. However, they don't provide any evidence that this motif functions in a similar way or that it's important for the virus's life cycle. They also don't explain how this similarity could be exploited for antiviral drug development. It's not clear whether targeting this motif would have any effect on the virus. It's interesting that these two sequences share nucleotides, but it's unlikely that they share any homology...perhaps they convergently evolved (or were captured), but the similarity could also be coincidental.

We appreciate the reviewer's insightful observation regarding this section. While our intention was to demonstrate that flexible conformations in high SHAPE-high Shannon regions could potentially be targeted, we acknowledge that extensive discussion of these motifs' functions would exceed the scope of this study, resulting in some disconnection from the main narrative. In response to this valuable feedback, we have consequentially removed it from the manuscript.

(4) The authors should consider the optimality of the synonymous mutation (G3109A) that they introduced, as G3109A could swap a rare codon for a more optimal one. Even though the protein sequence is unaffected, the translation rate (and ability to proliferate) could be very different due to altered codon optimality. Additionally, to show the inactivity of the PQS3 mutant, the Braco-19 treatment studies performed on the PQS1 mutants could be repeated with PQS3 - using this as a control for these experiments.

We appreciate the reviewer's insightful comment regarding codon optimization. In the PEDV genome, the PQS1 mutation site encodes lysine (AAG). Since lysine has only two codons (AAG and AAA), the G3109A synonymous mutation was our only viable option. Published literature (Ding et al. 2024) confirms that neither AAG nor AAA are classified as either preferred or rare codons in mammalian cells. Therefore, this substitution should have minimal direct impact on translation efficiency. Compared to nonsynonymous mutations that would alter amino acid sequences, we believe this synonymous mutation represents the optimal approach for maintaining native protein function while introducing the desired structural modification.

REFERENCES:

Ding W, Yu W, Chen Y, et al. Rare codon recoding for efficient noncanonical amino acid incorporation in mammalian cells. Science. 2024;384(6700):1134-1142.

In the revised version, we have added control experiments showing the inhibitory activity of Braco-19 against the PQS3 mutant strain (Figure 4—figure supplement 3C) and discussed it in the results section.

“Furthermore, as a control, we observed nearly identical inhibitory activity of Braco19 against both the PQS3 mutant strain (AJ1102-PQS3mut) and wild-type virus (Figure 4—figure supplement 3C), demonstrating the specificity of Braco-19's action on PQS1.”

Minor Comments:

(5) The authors' description of the Shannon Entropy could be improved. The current description makes it seem like the Shannon Entropy only provides information on base pairing, however, the Shannon entropy quantifies the uncertainty of structural states at each position and is calculated based on the probabilities of the different states (paired or unpaired) that a nucleotide can adopt.

We have revised the description of Shannon entropy in the manuscript：

"The pairing probability of each nucleotide derived from SHAPE reactivities was subsequently used to calculate Shannon entropy. Regions with high Shannon entropy may adopt alternative conformations, while those with low Shannon entropy correspond to either well-defined RNA structures or persistently single-stranded regions (MATHEWS, 2004; Siegfried et al., 2014)."

(6) The overall writing of the manuscript is very good, but there are some minor grammatical issues throughout, e.g., here are some of the ones that I caught:

a) Lines 71-3: "various types of RNA structures such as hairpin structure, RNA singlestrand, RNA pseudoknot and RNA G-quadruplex (G4)" - the examples should be plural and, rather than "hairpins" (or in addition), perhaps add "helixes" to be more generically correct(?).

We have revised the relevant description: 

"various types of RNA structures such as stem-loop structures (with double-helical stems), RNA single-strand, RNA pseudoknot and RNA G-quadruplex (G4)"

b) Lines 74-5: "Of these, RNA G4 has shown considerable promise because of the high stability and modulation by small molecules" should be "Of these, RNA G4 has shown considerable promise because of its high stability and ability for modulation by small molecules."

We have revised the sentence:

“Of these, RNA G4 has shown considerable promise because of its high stability and ability for modulation by small molecules.”

c) Line 76: "have" should be "has".

We have revised the sentence.

d) Lines 104-5 (and elsewhere): "frameshift stimulation element (FSE)" should be "frameshift stimulatory element (FSE)".

We have revised the sentence.

e) Lines 428-9: following the Manfredonia's methods" should be "following Manfredonia's method" or "following the Manfredonia method".

We have made the appropriate edit.

These edits ensure grammatical accuracy and consistency with standard scientific terminology. We appreciate the reviewer's attention to detail, which has significantly improved the clarity of our manuscript.

Reviewer #2 (Recommendations for the authors):

(1) There are some important references missing, on shape-seq from Julius Lucks.

We have added citations to the foundational work by Lucks et al. (2011, PNAS) that pioneered in vitro RNA structure probing using SHAPE-seq.

(2) Describe the acronym "SHAPE",

We have now included the full name of SHAPE:“Selective 2’-Hydroxyl Acylation and Primer Extension”.

(3) Line 81: 2"-hydroxyl-selective - the prime is incorrect.

We thank the reviewer for catching this technical error. We have corrected "2"hydroxyl" to "2'-hydroxyl".

(4) Explaining a bit better how shape reagent works would be beneficial (one sentence should suffice).

We have revised the Introduction section:

“SHAPE reagents like NAI selectively modify flexible, unpaired 2′-OH groups in RNA, and these modifications are detected as mutations during reverse transcription, enabling precise mapping of RNA secondary structures through sequencing.”

(5) Line 128: cite the paper that introduced NAI.

We have now properly cited the original publication introducing NAI（Spitale et al., 2012）.

(6) Line 243: Can you describe what the compound is?

The compound is Braco-19. This has now been included in the methods section. 

(7) Line 272: describe what 3Dpol is and the source of it.

We have supplemented the relevant information as follows:

"3Dpol (recombinant RNA-dependent RNA polymerase; Abcam, ab277617, 0.02 mg/reaction)"

(8) Figure 1 legend: For both C and D, the explanation of the G4 structure and the RISC complex should be added, otherwise, it becomes unclear why they are there.

We have revised the captions for Figure 1 as follows:

"(A) Well-folded regions (low SHAPE reactivity and low Shannon entropy; 26.40% of genome). These regions represent stably folded RNA structures with minimal conformational flexibility, likely serving as structural scaffolds or functional elements in viral replication. (B) Dynamic structured regions (low SHAPE reactivity and high Shannon entropy; 11.70% of genome). These conformationally plastic domains likely mediate regulatory switches between alternative secondary structures during infection. (C) Dynamic unpaired regions (high SHAPE reactivity and high Shannon entropy; 26.90% of genome). These regions are prone to form non-canonical nucleic acid structures (e.g., G-quadruplexes), which can be stabilized by small-molecule ligands to inhibit viral replication. (D) Persistent unpaired regions (high SHAPE reactivity and low Shannon entropy; 9.67% of genome). These regions are more accessible for siRNA binding, facilitating recruitment of Argonaute proteins and Dicer to form the RNAinduced silencing complex (RISC) for targeted cleavage."

(9) Figure S2 panel A should be in Figure 1. This is a nice picture showing the backbone of the research.

In the revised manuscript, we have reorganized Figure 1 and Figure S2 by incorporating the SHAPE-MaP workflow diagram (previously Figure S2A) into Figure 1 as panel (A): 

(10) Please add the citation to Braco-19.

We have now added the appropriate citation for Braco-19 (Gowan et al., 2002) in the revised manuscript.

(11) Figure 5 legend: could you add in parenthesis the what ds means (and call Figure S28).

We appreciate the reviewer's attention to detail. In the revised manuscript, we have clarified the abbreviations in the Figure 5 legend: ss (single-stranded targeting siRNAs); ds (dual-stranded targeting siRNAs). 

(12) Line 107: I would argue that the "stabilization of a G4" inhibited viral proliferation. And that supports the point of the paper, that a small molecule that stabilizes the G4 can be used to reduce viral replication. I suggest emphasizing this thorough the paper.

We fully concur with the reviewer's insightful perspective. In the revised manuscript, we have comprehensively strengthened the point of 'G4 stabilization' as an antiviral mechanism through the following enhancements:

(1) In the Results section: We present Thioflavin T (ThT) fluorescence assays demonstrating the G4-forming capability of PQSs in the full-length PEDV genomic RNA context:

“These findings indicate that although most PQSs can form G4 structures in vitro, PQS1—located in the high SHAPE-high Shannon entropy region—demonstrates the most robust G4-forming capability when competing with local secondary structures in the genomic context.”

(2) In the Results section: The inclusion of Braco-19 inhibition assays using PQS3 mutant virus as control provides robust evidence that Braco-19 exerts its antiviral effects specifically through PQS1 stabilization:

“Furthermore, as a control, we observed nearly identical inhibitory activity of Braco-19 against both the PQS3 mutant strain (AJ1102-PQS3mut) and wild-type virus, demonstrating the specificity of Braco-19's action on PQS1.”

(3) In the Discussion section: We have rewritten the mechanistic interpretation to emphasize: 

"Crucially, Braco-19 showed no inhibitory activity against the PQS1-mutant strain while maintaining potent activity against the PQS3-mutant strain (Figure 4E, Figure 4—figure supplement 3C). This suggests that the compound can selectively target the PQS1 of the high SHAPE-high Shannon region in cells." 

(13) For PQS1, it's suggested that it is indeed a competing and transient conformation that forms the G4. I wonder if using an extended PQS1 (perhaps what is shown in Figure 3E) and using fluorescence, and/or K+ vs Li+, and/or in-vitro SHAPE could tell us more about this dynamic structure. Thioflavin T or any other fluorescent molecule that binds to G4s could be easily used to show how the formation of G4 may happen or not. In addition, how Braco-19 could really lock the dynamic structure in-vitro as well. I think the field would benefit from a deeper investigation of it.

To address the dynamic competition between G4 and alternative RNA conformations, we performed Thioflavin T (ThT) fluorescence turn-on assay (now in Figure 3D-E and Figure 3—figure supplement 6) under physiological K⁺ conditions (100 mM), with PRRSV-G4 RNA as a positive control. This reads as:

“To validate whether SHAPE analysis could reflect the competitive conformational folding of PQSs in the PEDV genome, we performed in vitro transcription to obtain local intact structures containing PQSs within dynamic single-stranded regions and stable double-stranded regions (Table S6). Thioflavin T (ThT) fluorescence turn-on assays were conducted under physiological K⁺ conditions (100 mM), with the G4 sequence of porcine reproductive and respiratory syndrome virus (PRRSV) serving as a positive control (Control-G4)(Fang et al., 2023). The results demonstrated that for short PQSs sequences containing only G4-forming motifs (Table S7), PQS1, PQS3, PQS4, and PQS6 all induced significant ThT fluorescence enhancement (Figure 3D-E, Figure 3—figure supplement 6), confirming their ability to form G4 structures. However, in long RNA fragments encompassing PQSs and their flanking sequences, only PQS1 and PQS4 exhibited pronounced ThT fluorescence responses (Figure 3DE), whereas PQS2, PQS3, and PQS6 showed negligible signals (Figure 3E, Figure 3— figure supplement 6). Notably, the PQS1-long chain displayed the strongest fluorescence signal, while its mutant counterpart (PQS1mut-long chain) exhibited the lowest background fluorescence (Figure 3D). These findings indicate that although most PQSs can form G4 structures in vitro, PQS1—located in the high SHAPE-high Shannon entropy region—demonstrates the most robust G4-forming capability when competing with local secondary structures in the genomic context. Therefore, PQS1 was selected for further structural and functional validation.”

(14) Figure S29 is nice and informative. Consider moving it to the main text.

We appreciate the reviewer's positive assessment of Figure S29. Now we have renamed this figure as "Figure 5—Supplement 2".

Author response:

Reviewer #1:

(1) Changes in blood volume due to brain activity are indirectly related to neuronal responses. The exact relationship is not clear, however, we do know two things for certain: (a) each measurable unit of blood volume change depends on the response of hundreds or thousands of neurons, and (b) the time course of the volume changes are slow compared to the potential time course of the underlying neuronal responses. Both of these mean that important variability in neuronal responses will be averaged out when measuring blood changes. For example, if two neighbouring neurons have opposite responses to a given stimulus, this will produce opposite changes in blood volume, which will cancel each other out in the blood volume measurement due to (a). This is important in the present study because blood volume changes are implicitly being used as a measure of coding in the underlying neuronal population. The authors need to acknowledge that this is a coarse measure of neuronal responses and that important aspects of neuronal responses may be missing from the blood volume measure.

The reviewer is correct: we do not measure neuronal firing, but use blood volume as a proxy for bulk local neuronal activity, which does not capture the richness of single neuron responses. We will highlight this point in the manuscript. This is why the paper focuses on large-scale spatial representations as well as cross-species comparison. For this latter purpose, fMRI responses are on par with our fUSI data, with both neuroimaging techniques showing the same weakness.

(2) More importantly for the present study, however, the effect of (b) is that any rapid changes in the response of a single neuron will be cancelled out by temporal averaging. Imagine a neuron whose response is transient, consisting of rapid excitation followed by rapid inhibition. Temporal averaging of these two responses will tend to cancel out both of them. As a result, blood volume measurements will tend to smooth out any fast, dynamic responses in the underlying neuronal population. In the present study, this temporal averaging is likely to be particularly important because the authors are comparing responses to dynamic (nonstationary) stimuli with responses to more constant stimuli. To a first approximation, neuronal responses to dynamic stimuli are themselves dynamic, and responses to constant stimuli are themselves constant. Therefore, the averaging will mean that the responses to dynamic stimuli are suppressed relative to the real responses in the underlying neurons, whereas the responses to constant stimuli are more veridical. On top of this, temporal following rates tend to decrease as one ascends the auditory hierarchy, meaning that the comparison between dynamic and stationary responses will be differently affected in different brain areas. As a result, the dynamic/stationary balance is expected to change as you ascend the hierarchy, and I would expect this to directly affect the results observed in this study.

It is not trivial to extrapolate from what we know about temporal following in the cortex to know exactly what the expected effect would be on the authors' results. As a first-pass control, I would strongly suggest incorporating into the authors' filterbank model a range of realistic temporal following rates (decreasing at higher levels), and spatially and temporally average these responses to get modelled cerebral blood flow measurements. I would want to know whether this model showed similar effects as in Figure 2. From my guess about what this model would show, I think it would not predict the effects shown by the authors in Figure 2. Nevertheless, this is an important issue to address and to provide control for.

We understand the reviewer’s concern about potential differences in response dynamics in stationary vs non-stationary sounds. In particular, it seems that the reviewer is concerned that responses to foregrounds may be suppressed in non-primary fields because foregrounds are not stationary, and non-primary regions could struggle to track and respond to these sounds. Nevertheless, we  observed the contrary, with non-primary regions over-representing non-stationary (dynamic) sounds, over stationary ones. For this reason, we are inclined to think that this explanation cannot falsify our findings.

Furthermore, background sounds are not completely constant: they are still dynamic sounds, but their temporal modulation rates are usually faster (see Figure 3B). Similarly, neural responses to these two types of sounds are dynamic (see for example Hamersky et al., 2025, Figure 1).  Thus, we are not sure that blood volume would transform the responses to these types of sounds non-linearly.

We understand the comment that temporal following rates might differ across regions in the auditory hierarchy and agree. In fact, we show that tuning to temporal rates differ across regions and partly explains the differences in background invariance we observe. We think the reviewer’s suggestion is already implemented by our spectrotemporal model, which incorporates the full range of realistic temporal following rates (up to 128 Hz). The temporal averaging is done as we take the output of the model (which varies continuously through time) and average it in the same window as we used for our fUSI data. When we fit this model to the ferret data, we find that voxels in non-primary regions, especially VP (tertiary auditory cortex), tend to be more tuned to low temporal rates (Figure 2F, G), and that background invariance is stronger in voxels tuned to low rates. This is, however, not true in humans, suggesting that background invariance in humans rely on different computational mechanisms.

(3) I do not agree with the equivalence that the authors draw between the statistical stationarity of sounds and their classification as foreground or background sounds. It is true that, in a common foreground/background situation - speech against a background of white noise - the foreground is non-stationary and the background is stationary. However, it is easy to come up with examples where this relationship is reversed. For example, a continuous pure tone is perfectly stationary, but will be perceived as a foreground sound if played loudly. Background music may be very non-stationary but still easily ignored as a background sound when listening to overlaid speech. Ultimately, the foreground/background distinction is a perceptual one that is not exclusively determined by physical characteristics of the sounds, and certainly not by a simple measure of stationarity. I understand that the use of foreground/background in the present study increases the likely reach of the paper, but I don't think it is appropriate to use this subjective/imprecise terminology in the results section of the paper.

We appreciate the reviewer’s comment that the classification of our sounds into foregrounds and backgrounds is not verified by any perceptual experiments. We use those terms to be consistent with the literature, including the paper we derived this definition from (Kell et al., 2019). These terms are widely used in studies where no perceptual or behavioral experiments are included, and even when animals are anesthetized. However, we will emphasize the limits of this definition when introducing it, as well as in the discussion.

(4) Related to the above, I think further caveats need to be acknowledged in the study. We do not know what sounds are perceived as foreground or background sounds by ferrets, or indeed whether they make this distinction reliably to the degree that humans do. Furthermore, the individual sounds used here have not been tested for their foreground/background-ness. Thus, the analysis relies on two logical jumps - first, that the stationarity of these sounds predicts their foreground/background perception in humans, and second, that this perceptual distinction is similar in ferrets and humans. I don't think it is known to what degree these jumps are justified. These issues do not directly affect the results, but I think it is essential to address these issues in the Discussion, because they are potentially major caveats to our understanding of the work.

We agree with the reviewer that the foreground-background distinction might be different in ferrets. In anticipation of that issue, we had enriched the sound set with more ecologically relevant sounds, such as ferret and other animal vocalizations. Nevertheless, the point remains valid and is already raised in the discussion. We will emphasize this limitation in addition to the limitation of our definition of foregrounds and backgrounds.

Reviewer #2:

(1) Interpretation of the cerebral blood volume signal: While the results are compelling, more caution should be exercised by the authors in framing their results, given that they are measuring an indirect measure of neural activity, this is the difference between stating "CBV in area MEG was less background invariant than in higher areas" vs. saying "MEG was less background invariant than other areas". Beyond framing, the basic properties of the CBV signal should be better explored:

a) Cortical vasculature is highly structured (e.g. Kirst et al.( 2020) Cell). One potential explanation for the results is simply differences in vasculature and blood flow between primary and secondary areas of auditory cortex, even if fUS is sensitive to changes in blood flow, changes in capillary beds, etc (Mace et al., 2011) Nat. Methods.. This concern could be addressed by either analyzing spontaneous fluctuations in the CBV signal during silent periods or computing a signal-to-noise ratio of voxels across areas across all sound types. This is especially important given the complex 3D geometry of gyri and sulci in the ferret brain.

We agree with the reviewers that there could be differences in vasculature across subregions of the auditory cortex. We will run analyses providing comparisons of basic signal properties across our different regions of interest. We note that this point would also be valid for the human fMRI data, for which we cannot run these controls. Nevertheless, this should not affect our analyses and results, which should be independent of local vascular density. First, we normalize the signal in each voxel before any analysis, so that the absolute strength of the signal, or blood volume in a given voxel, does not matter. Second, we do see sound-evoked responses in all regions (Figure S2) and only focus on reliable voxels in each region. Third, our analysis mostly relies on voxel-based correlation across sounds, which is independent of the mean and variance of the voxel responses. Thus, we believe that differences in vascular architecture across regions are unlikely to affect our results.

b) Figure 1 leaves the reader uncertain what exactly is being encoded by the CBV signal, as temporal responses to different stimuli look very similar in the examples shown. One possibility is that the CBV is an acoustic change signal. In that case, sounds that are farther apart in acoustic space from previous sounds would elicit larger responses, which is straightforward to test. Another possibility is that the fUS signal reflects time-varying features in the acoustic signal (e.g. the low-frequency envelope). This could be addressed by cross-correlating the stimulus envelope with fUS waveform. The third possibility, which the authors argue, is that the magnitude of the fUS signal encodes the stimulus ID. A better understanding of the justification for only looking at the fUS magnitude in a short time window (2-4.8 s re: stimulus onset) would increase my confidence in the results.

We thank the reviewer for raising that point as it highlights that the layout of Figure 1 is misleading. While Figure 1B shows an example snippet of our sound streams, Figure 1D shows the average timecourse of CBV time-locked to a change in sound (foreground or background, isolated or in a mixture). This is the average across all voxels and sounds, and the point is just to illustrate the dynamics for the three broad categories. In Figure 1E however, we show the cross-validated cross-correlation of CBV  across sounds (and different time lags). To obtain this, we compute for each voxel the response to each sound at each time lag, thus obtaining two vector of size number of sounds per lag, one per repeat. Then, we correlate all these vectors across the two repeats, obtaining one cross-correlation matrix per neuron. We finally average these matrices across all neurons. The fact that you see red squares demonstrates that the signal encodes sound identity, since CBV is more similar across two repeats of the same sound (for e.g., in the foreground only matrix, 0-5 s vs 0-5 s), than two different sounds (0-5 s vs. 7-12 s). We will modify the figure layout as well as the legend to improve clarity.

(2) Interpretation of the human data: The authors acknowledge in the discussion that there are several differences between fMRI and fUS. The results would be more compelling if they performed a control analysis where they downsampled the Ferret fUS data spatially and temporally to match the resolution of fMRI and demonstrated that their ferret results hold with lower spatiotemporal resolution.

We agree with the reviewer that the use of different techniques might come in the way of cross-species comparison. We will add additional discussion on this point. We already control for the temporal aspect by using the average of stimulus-evoked activity across time (note that due to scanner noise, sounds are presented cut into small pieces in the fMRI experiments). Regarding the spatial aspect, there are several things to consider. First, both species have brains of very different sizes, a factor that is conveniently compensated for by the higher spatial resolution of fUSI compared to fMRI (0.1 vs 2 mm). Downsampling to fMRI resolution would lead to having one voxel per region per slice, which is not feasible. We also summarize results with one value per region, which is a form of downsampling that is fairer across species. Furthermore, we believe that we already established in a previous study (Landemard et al, 2021 eLife) that fUSI and fMRI data are comparable signals. We indeed could predict human fMRI responses to most sounds from ferret fUSI responses to the same identical sounds.

Reviewer #3:

As mentioned above, interpretation of the invariance analyses using predictions from the spectrotemporal modulation encoding model hinges on the model's ability to accurately predict neural responses. Although Figure S5 suggests the encoding model was generally able to predict voxel responses accurately, the authors note in the introduction that, in human auditory cortex, this kind of tuning can explain responses in primary areas but not in non-primary areas (Norman-Haignere & McDermott, PLOS Biol. 2018). Indeed, the prediction accuracy histograms in Figure S5C suggest a slight difference in the model's ability to predict responses in primary versus non-primary voxels. Additional analyses should be done to a) determine whether the prediction accuracies are meaningfully different across regions and b) examine whether controlling for prediction accuracy across regions (i.e., sub-selecting voxels across regions with matched prediction accuracy) affects the outcomes of the invariance analyses.

The reviewer is correct: the spectrotemporal model tends to perform less well in human non-primary cortex. We believe this does not contradict our results but goes in the same direction: while there is a gradient in invariance in both ferrets and humans, this gradient is predicted by the spectrotemporal model in ferrets, but not in humans (possibly indeed because predictions are less good in human non-primary auditory cortex). Regardless of the mechanism, this result points to a difference across species. We will clarify these points by quantifying potential differences in prediction accuracy in both species and comment on those in the manuscript.

A related concern is the procedure used to train the encoding model. From the methods, it appears that the model may have been fit using responses to both isolated and mixture sounds. If so, this raises questions about the interpretability of the invariance analyses. In particular, fitting the model to all stimuli, including mixtures, may inflate the apparent ability of the model to "explain" invariance, since it is effectively trained on the phenomenon it is later evaluated on. Put another way, if a voxel exhibits invariance, and the model is trained to predict the voxel's responses to all types of stimuli (both isolated sounds and mixtures), then the model must also show invariance to the extent it can accurately predict voxel responses, making the result somewhat circular. A more informative approach would be to train the encoding model only on responses to isolated sounds (or even better, a completely independent set of sounds), as this would help clarify whether any observed invariance is emergent from the model (i.e., truly a result of low-level tuning to spectrotemporal features) or simply reflects what it was trained to reproduce.

We thank the reviewer for this suggestion and will run an additional prediction using only the sounds presented in isolation. This will be included in the next version of the manuscript.

Finally, the interpretation of the foreground invariance results remains somewhat unclear. In ferrets (Figure 2I), the authors report relatively little foreground invariance, whereas in humans (Figure 5G), most participants appear to show relatively high levels of foreground invariance in primary auditory cortex (around 0.6 or greater). However, the paper does not explicitly address these apparent cross-species differences. Moreover, the findings in ferrets seem at odds with other recent work in ferrets (Hamersky et al. 2025 J. Neurosci.), which shows that background sounds tend to dominate responses to mixtures, suggesting a prevalence of foreground invariance at the neuronal level. Although this comparison comes with the caveat that the methods differ substantially from those used in the current study, given the contrast with the findings of this paper, further discussion would nonetheless be valuable to help contextualize the current findings and clarify how they relate to prior work.

We thank the reviewer for this point. We will indeed add further discussion of the  difference between ferrets and humans in foreground invariance in primary auditory cortex. In addition, while we found a trend for higher background invariance than foreground invariance in ferret primary auditory cortex, this difference was not significant and many voxels exhibit similar levels of background and foreground invariance (for example in Figure 2D, G). Thus, we do not think our results are inconsistent with Hamersky et al., 2025, though we agree the bias towards background sounds is not as strong in our data. This might indeed reflect differences in methodology, both in the signal that is measured (blood volume vs spikes), and the sound presentation paradigm. We will add this point to our discussion.

Author response:

The following is the authors’ response to the original reviews

Reviewer #1 (Public review):

The ventral nerve cord (VNC) of organisms like Drosophila is an invaluable model for studying neural development and organisation in more complex organisms. Its well-defined structure allows researchers to investigate how neurons develop, differentiate, and organise into functional circuits. As a critical central nervous system component, the VNC plays a key role in controlling motor functions, reflexes, and sensory integration.

Particularly relevant to this work, the VNC provides a unique opportunity to explore neuronal hemilineages - groups of neurons that share molecular, genetic, and functional identities. Understanding these hemilineages is crucial for elucidating how neurons cooperate to form specialized circuits, essential for comprehending normal brain function and dysfunction.<br /> A significant challenge in the field has been the lack of developmentally stable, hemilineage-specific driver lines that enable precise tracking and measurement of individual VNC hemilineages. The authors address this need by generating and validating a comprehensive, lineage-specific split-GAL4 driver library.

Strengths and weaknesses

The authors select new marker genes for hemilineages from previously published single-cell data of the VNC. They generate and validate specific and temporally stable lines for almost all the hemilineages in the VNC. They successfully achieved their aims, and their results support their conclusions. This will be a valuable resource for investigating neural circuit formation and function.

We thank the reviewer for her/his positive comments and time reviewing our manuscript. We are pleased that the reviewer recognized the value of our work in generating a comprehensive, lineage-specific split-GAL4 driver library for VNC hemilineages. We agree that this will be a critical resource for investigating neural circuit formation and function, and we are encouraged by the positive comments regarding the novelty and potential impact of our approach.

Reviewer #1 (Recommendations for the authors):

I have no suggestions for further experiments, data, or analyses. There are some grammatical errors and referencing issues throughout, but the editors will hopefully catch them.

We appreciate the reviewer’s comments regarding the grammatical errors and referencing issues and have carefully checked the revised manuscript.

Reviewer #2 (Public review):

It is my pleasure to review this manuscript from Soffers, Lacin, and colleagues, in which they identify pairs of transcription factors unique to (almost) every ventral nerve cord hemilineage in Drosophila and use these pairs to create reagents to label and manipulate these cells. The advance is sold as largely technical-as a pipeline for identifying durably expressed transcription factor codes in postmitotic neurons from single cell RNAseq data, generating knock-in alleles in the relevant genes, using these to match transcriptional cell types to anatomic cell types, and then using the alleles as a genetic handle on the cells for downstream explication of their function. Yet I think the work is gorgeous in linking the expression of genes that are causal for neuron-type-specific characteristics to the anatomic instantiations of those neurons. It is astounding that the authors are able to use their deep collective knowledge of hemilineage anatomy and gene expression to match 33 of 34 transcriptional profiles. Together with other recent studies, this work drives a major course correction in developmental biology, away from empirically identified cell type "markers" (in Drosophila neuroscience, often genomic DNA fragments that contain enhancers found to be expressed in specific neurons at specific times), and towards methods in which the genes that generate neuronal type identity are actually used to study those neurons. Because the relationship between fate and form/function is built into the tools, I believe that this approach will be a trojan horse to integrate the fields of neural development and systems neuroscience.

We thank the reviewer for their time reviewing our manuscript, generous compliments, and appreciation of the potential of our study to drive a major shift in developmental biology, moving away from traditional marker-based methods toward utilizing the genes that mark neuronal type identity in “omics” datasets. Much like the Trojan Horse, which, though initially a concealed and subtle tool, we hope that the strategy outlined here will have continued impact, as we and others plan to leverage future high-resolution and developmental series of scRNAseq datasets to generate driver lines to target neuronal cell types with uttermost precision.

Reviewer #2 (Recommendations for the authors):

Line 126-127: I'm not sure if it is true to say "most TFs in the CNS are expressed in a hemilineage-specific manner." As the authors haven't formally interrogated how different neuronal features relate to expression patterns of all ~600 Drosophila TFs, how about replacing "most" with "many?"

The reviewer makes an excellent point. Work by Lacin and colleagues has demonstrated via genetic studies that lineage-specific transcription factors that regulate the specification and differentiation of postembryonic neurons are stably expressed during development. This was documented for 15 transcription factors in Lacin et al., 2014, and our lab has identified additional examples since. When we refer to the stable expression of transcription factors, we refer to such transcription factors, not the complete set of ~600 transcription factors described to date. We have added this citation to clarify this statement and replaced p6 line 135 ”Most”  by “Many”. We have also address this now in the introduction (p5 line 109-116). Of note, as we conducted this study, we found that is closer to be a rule than an exception that if a transcription factor acted cluster as marker, it was also stably expressed during development. Thus, a growing number of transcription factors is now documented to be stably expressed in a hemilineage-specific manner

Line 265: Typo? 334 should be 34?

We thank the reviewer for noting this type error. We have corrected this typographical error.

Line 522: Refs 56, 57 here related to chinmo, mamo, br-c don't show br-c or mamo mark temporal cohorts of postmitotic neurons. Consider adding PMID: 19883497, 18510932, and 31545163.

We thank the reviewer for pointing this out and have added these references that demonstrate that broad, Mamo and Chinmo mark temporal cohorts in the developing adult CNS (p17 line 535).

Reviewer #3 (Public review):

Soffers et al. developed a comprehensive genetic toolkit that enables researchers to access neuronal hemilineages during developmental and adult time points using scRNA-seq analysis to guide gene cassette exchange-based or CRISPR-based tool building. Currently, research groups studying neural circuit development are challenged with tying together findings in the development and mature circuit function of hemilineage-related neurons. Here, authors leverage publicly available scRNA-seq datasets to inform the development of a split-Gal4 library that targets 32 of 34 hemilineages in development and adult stages. The authors demonstrated that the split-Gal4 library, or genetic toolkit, can be used to assess the functional roles, neurotransmitter identity, and morphological changes in targeted cells. The tools presented in this study should prove to be incredibly useful to Drosophila neurobiologists seeking to link neural developmental changes to circuit assembly and mature circuit function. Additionally, some hemilineages have more than one split-Gal4 combination that will be advantageous for studies seeking to disrupt associated upstream genes.

Strengths:

Informing genetic tool development with publicly available scRNA-seq datasets is a powerful approach to creating specific driver lines. Additionally, this approach can be easily replicated by other researchers looking to generate similar driver lines for more specific subpopulations of cells, as mentioned in the Discussion.

The unification of optogenetic stimulation data of 8B neurons and connectomic analysis of the Giant-Fiber-induced take-off circuit was an excellent example of the utility of this study. The link between hemilineage-specific functional assays and circuit assembly has been limited by insufficient genetic tools. The tools and data present in this study will help better understand how collections of hemilineages develop in a genetically constrained manner to form circuits amongst each other selectively.

Weaknesses:

Although cell position, morphology (to some extent), and gene expression are good markers to track cell identity across developmental time, there are genetic tools available that could have been used to permanently label cells that expressed genes of interest from birth, ensuring that the same cells are being tracked in fixed tissue images.

Although gene activation is a good proxy for assaying neurochemical features, relying on whether neurochemical pathway genes are activated in a cell to determine its phenotype can be misleading given that the Trojan-Gal4 system commandeers the endogenous transcriptional regulation of a gene but not its post-transcriptional regulation. Therefore, neurochemical identity is best identified via protein detection. (strong language used in this section of the paper).

The authors mainly rely on the intersectional expression of transcription factors to generate split-Gal4 lines and target hemilineages specifically. However, the Introduction (Lines 97-99) makes a notable point about how driver lines in the past, which have also predominantly relied on the regulatory sequences of transcription factors, lack the temporal stability to investigate hemilineages across time. This point seems to directly conflict with the argument made in the Results (Lines 126-127) that states that most transcription factors are stably expressed in hemilineage neurons that express them. It is generally known that transcription factors can be expressed stably or transiently depending on the context. It is unclear how using the genes of transcription factors in this study circumvents the issue of creating temporally stable driver lines.<br />

We thank the reviewer for their time to thoroughly and carefully review our manuscript. We appreciate the reviewer’s comments on its strengths, and we to hope that this body of work will prove to be incredibly useful to Drosophila neurobiologists seeking to link neural developmental changes to circuit assembly and mature circuit function. Likewise, we also appreciate the reviews careful consideration of its weaknesses, as the reviewer raises valid points. We have addressed these in our revised manuscript and believe this has significantly improved our manuscript.

Weakness 1: Although cell position, morphology (to some extent), and gene expression are good markers to track cell identity across developmental time, there are genetic tools available that could have been used to permanently label cells that expressed genes of interest from birth, ensuring that the same cells are being tracked in fixed tissue images.

The reviewer is fully correct, and we are aware of techniques developed by the laboratories of U. Banerjee, T. Lee, and J. Truman that can make transient GAL4 expression permanent, such as G-TRACE and lineage filtering. A common feature of these techniques is that effector activity is permanent (FLP-mediated removal of the FRT-flanked stop codon preceding GFP in G-TRACE or LexA in lineage filtering) but not the GAL4 activity, which is needed to take advantage of the vast UAS based effector lines such as RNAi libraries. For example, the study of Harris et al., 2015 from the Truman lab beautifully showed the strength of this kind of approaches for labeling the hemilineages but their approach cannot be used for functional studies for the reasons mentioned above. Fly lines using these approaches already have several transgenes and require the addition of several more to be used for functional studies. Our approach requires only two transgenes and is compatible with all UAS lines. One additional advantage of the splitGAL4 combinations that we identify here is that they are inserted in genes that are stably expressed throughout larval and pupal development in postmitotic cells, such that they can be used for functional manipulations during development. We emphasized this point in the discussion on page 16 under the heading “Mapping and manipulating morphological outgrowth patterns of hemilineages during development”. 

Weakness 2: Although gene activation is a good proxy for assaying neurochemical features, relying on whether neurochemical pathway genes are activated in a cell to determine its phenotype can be misleading given that the Trojan-Gal4 system commandeers the endogenous transcriptional regulation of a gene but not its post-transcriptional regulation. Therefore, neurochemical identity is best identified via protein detection. (strong language used in this section of the paper).

We thank the reviewer for bringing up this important point. We agree that the Trojan-GAL4 approach will not faithfully recapitulate expression of genes that undergo posttranscriptional regulation. Our previous eLife paper (Lacin et al., 2019) showed that this is the case for Trojan driver lines for the ChAT gene. This study demonstrated that ChAT drivers unexpectedly but strongly labeled many GABAergic and Glutamatergic neurons in both the brain and VNC. With RNA in situ hybridization and immunostainings approaches, we showed that these neurons indeed express ChAT mRNA but not the protein. After our publication, another group showed a class of miRNA binds to the 3’UTR of the ChAT gene and regulates its expression post-transcriptionally (Griffith 2023). We believe that one major reason the Trojan driver lines do not faithfully recapitulate this expression pattern is due to the presence of the Hsp70 transcriptional terminator located at the 5’ end of the trojan exon which prematurely ends the transcript and affects the host gene’s 3’ UTR regulation. For this reason, we have recently generated new Trojan plasmids which allow the retention of the 3’UTR of the host gene in the transcript. We have revised the result section “Neurotransmitter use on pages 11-12 to address this point and have modified the language.

Weakness 3: The authors mainly rely on the intersectional expression of transcription factors to generate split-Gal4 lines and target hemilineages specifically. However, the Introduction (Lines 97-99) makes a notable point about how driver lines in the past, which have also predominantly relied on the regulatory sequences of transcription factors, lack the temporal stability to investigate hemilineages across time. This point seems to directly conflict with the argument made in the Results (Lines 126-127) that states that most transcription factors are stably expressed in hemilineage neurons that express them. It is generally known that transcription factors can be expressed stably or transiently depending on the context. It is unclear how using the genes of transcription factors in this study circumvents the issue of creating temporally stable driver lines.

We thank the reviewer for pointing out this apparent paradox, which we have clarified in the manuscript (p4. lines 94-102). Driver lines in the past have relied on the intersection of genes to label a defined set of neurons, which helped marking more narrow cell populations compared to enhancer traps in the adult CNS. Elegant and elaborate screening methods have been devised to identify hemidriver combinations that mark specific subset of neurons in the adult (Meissner et al, 2025 (eLife 98405.2) and citations therein). However, these hemidrivers do not leverage the expression pattern of hemilineage marker genes. Instead, their expression is controlled by random 2-3 kb genomic fragments. We and others observed that these drivers are not stably expressed during development. Hence, hemidrivers combinations that work beautifully to target adult neuronal cel populations can oftentimes not be directly used for developmental studies. Work by Lacin et al. 2014 has demonstrated that transcription factors that mark hemilineages are oftentimes stably expressed in the embryo larvae and even adult. When we made driver lines for these TF, using artificial exons, its complete endogenous enhancers elements remain intact. Consequently, we find that Trojan driver lines recapitulate the expression pattern of the transcription factor gene in which it was inserted, and the hemidrivers are stably expressed during development. Hence, leveraging scRNAseq cluster markers for hemilineages and converting them to Trojan driver lines, the approach we took in this paper, has proven a powerful method to generate stable driver lines for developmental studies.

Reviewer #3 (Recommendations for the authors):

(1) Line 14: Affiliations typo should be correct to "St. Louis".

We thank the reviewer for catching this and have corrected the typo.

(2) Line 26: "model systems have focused on only on a few".

We have replaced the words “a few regions” by “select regions” to better contrast that studies to date have been performed, but not at CNS level, due to the lack of genetic driver lines.

(3) Line 52: The use of "medium" here is ambiguous without a comparison.

We agree that the term “medium” in line 52 could be ambiguous without context, and we appreciate your suggestion to clarify this. The revised sentence now reads: “Drosophila has served as a powerful model system to investigate how neuronal circuits function due to its medium complexity compared to vertebrate models”

(4) Line 91-92: Consider shortening to "of behavioral circuit assembly".

Thank you for this suggestion, we have revised p4 lines 90-91 to: “Thus, taking a hemilineage-based approach is essential for a systematic and comprehensive understanding of behavioral circuit assembly during development in complex nervous systems.”

(5) Line 216-217: Consider establishing what the expected morphology and neurochemical phenotype for 2A neurons is before presenting findings.

This suggestion is well-taken, and agree that this paragraph did not fully get the point across we were trying to make. This purpose of this paragraph is to explain our workflow of how we assigned 16 hemilineages to orphan clusters, which is why we present the data in this order and present the morphology of hemilineage 2A last. To accommodate the reviewer’s suggestion, we have now clarified our approach before diving into the results to improve the flow of this paragraph (p8 lines 218-223). Briefly, the starting point to annotate the 16 orphan scRNAseq clusters was each time taking one orphan scRNAseq cluster, picking its top cluster marker genes that had not been established yet as marker genes for any hemilineage, and visualizing the morphology of the neurons that expressed such cluster marker using a reporter line for the cluster marker or an antibody stain for its protein. We then compared this to documented hemilineage morphologies, and to narrow down our search, we compared the observed trajectories to those of unannotated hemilineages that used the same neurotransmitter as the orphan scRNAseq. The evaluation of the documented morphologies of the hemilineages came at the last part of our method to annotate the hemilineages to orphan scRNAseq clusters, which is why we chose to present the expected morphology of a hemilineage at the end.

(6) If "neurochemical" phenotype and "neurotransmitter" identity are sometimes used interchangeably but seem to mean the same thing. Consider choosing one term throughout.

We thank the reviewer for this suggestion and have changed the terminology to “neurotransmitter use” (p11-12 lines 326-359).

(7) Line 235: MARCM technique citation needed.

We thank the reviewer for pointing this out, the citation (no. 37, p9 line 249) was present in the method section, but we had inadvertently omitted it in the main text and we have now corrected this.

(8) Line 281: typo, should be "patterns".

We thank the reviewer for noting this and have corrected this.

(9) Line 469: End of sentence needs a ".".

We have added the punctuation mark.

(10) Line 516: "driver line combinations to express...".

We have inserted the word “to” to correct it.

(11) Please make sure that the correct genotypes are matched in the figure legends and Table 1. For instance, knot-GAL4-DBD is listed as the hemi driver for 10B neurons in Figure 3 but only knot-p65.AD is listed in Table 1.

We thank the reviewer for catching this, we made a mistake and the correct hemidriver combination used in Figure 3L i: knot-GAL4-AD with hb9-GAL4-DBD. We have updated the legend and carefully checked the legends and tables.

(12) Consider making different color choices for readability when possible and be consistent with labeling CadN. For instance, in Figure 1 the magenta color has three separate meanings: CadN, Acj6, and unc-4. Either of the three genes can be mistaken for the other for a reader mainly paying attention to the magenta color. I find that one color can mean two things in a figure if organized properly but any more begs for confusion. Also, CadN can be easily labeled if used in a new figure (e.g. Figure 1-Supplment 1).

We thank the reviewer for this insightful observation and have adjusted figure 1 so that cadN is displayed in blue and reporter genes expressing Acj6, Unc-4 or their intersection in green. The legend is modified to reflect these changes.

(13) If Seurat object changes or additional quality control steps were taken from the original studies, please provide these changes. Similarly, provide any scRNA-seq code used or cite code used for readers to access. Also, provide a section in the methods briefly describing how genes were chosen (criteria) for tool development.

We thank the reviewer for nothing we had not described our scRNA analysis pipeline and criteria to select transcription factors in the methods section of the manuscript. We have added this section at p19 lines 548-558. Briefly, we used the Seurat object generated by Allen et al., 2015, and did not change quality control steps, normalizations or scaling. Candidate genes to make split-GAL4 drivers from were chosen based on their ability to mark the clusters defined by Allen et al. We did not use computer-based algorithms and made a list of the top cluster markers. Then, we made binary combinations amongst these cluster markers and with hemilineages markers we had identified before (Lacin et al, 2014; Lacin et al 2019), and used the code generated by Allen et al., 2015 (deposited on Github) with Seurat v5 to test if these combinations marked unique clusters. We then prioritized testing these combinations based on the availability of antibodies, BAC lines and CRiMIC/MiMIC constructs to validate their expression pattern prior to creating split-GAL4 lines for these candidates.

(14) In regard to the seemingly contradictory argument that most transcription factors are stably expressed when most drivers of the past used regulatory elements of transcription factors: the paper could be strengthened by either a) describing how older driver lines differ from the lines presented in the paper or b) remarking on the endogenous temporal stability of the transcription factors used in this study.

We thank the reviewer for pointing this out, and we agree that it is necessary to clarify this apparent paradox since it is essential for understanding the impact of the present work. We have revised our manuscript described in our response to weakness 1.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public review):

Summary:

This is a very well-written paper presenting interesting findings related to the recovery following the end-Permian event in continental settings, from N China. The finding is timely as the topic is actively discussed in the scientific community. The data provides additional insights into the faunal, and partly, floral global recovery following the EPE, adding to the global picture.

Strengths:

The conclusions are supported by an impressive amount of sedimentological and paleontological data (mainly trace fossils) and illustrations.

We thank Reviewer #1 for the positive assessments.

Weaknesses:

The occurrence of MISS (Microbially Induced Sedimentary Structures) could be discussed more in detail as these provide interesting information directly linked to the delayed recovery of the biota.

We appreciate the reviewer for highlighting this important point. In the Phanerozoic, increase of microbial abundances generally occurred with rapid warming when documented and those hyperthermal events had causal links to mass extinction in continental realms, including the Permian–Triassic mass extinction (Mays et al., 2021). Accumulations of cyanobacteria and other microbes was favored by low dissolved oxygen concentrations (Pacton et al., 2011) and the produced secondary metabolites may also be toxic to animals (Paerl and Otten, 2013). Therefore, repeated algal and bacterial blooms in the post-extinction interval could disrupt ecological stability and inhibit the restoration of ecosystems.

So, the sentence from Lines 127–130 “The depauperate ichnofauna of the late Smithian were monospecific, representing initial recolonization of empty niches by opportunists, but the coeval thrived microbial mats indicated harsh environments, which might have inhibited the recovery of freshwater ecosystems (Tu et al., 2016; Chu et al., 2017; Mays et al., 2021).” is rephased by:

“The depauperate ichnofauna of the late Smithian were monospecific, representing initial recolonization of empty niches by opportunists. However, recurrent occurrences of microbial induced sedimentary structures (MISS) in the Liujiagou Formation imply that depressed ecosystems persisted until the Smithian (Tu et al., 2016; Chu et al., 2017). Studies revealed that the increase in microbial abundances were generally associated with hyperthermals, which would be the principal causes for mass extinction on land (Mays et al., 2021). Accumulations of microbes were favored by low dissolved oxygen concentration condition and their secondary metabolites could also be toxic to animals (Pacton et al., 2011; Paerl and Otten, 2013). Therefore, repeated thriving of MISS during the Dienerian–Smithian disrupted ecological stability in freshwater ecosystem and delayed biotic recovery in North China.”

References:

Mays, C., et al. 2021. Lethal microbial blooms delayed freshwater ecosystem recovery following the end-Permian extinction. Nat. Commun. 12, 5511. https://doi.org/10.1038/s41467-021-25711-3

Pacton, M., et al. 2011. Amorphous organic matter—Experimental data on formation and the role of microbes. Rev. Palaeobot. Palynol. 166, 253–267. https://doi.org/10.1016/j.revpalbo.2011.05.011

Paerl, H. W. & Otten, T. G. 2013. Harmful cyanobacterial blooms: causes, consequences, and controls. Microb. Ecol. 65, 995–1010. https://doi.org/10.1007/s00248-012-0159-y

Reviewer #2 (Public review):

Summary:

A rapid recovery of the ecosystems during the late Early Triassic, in the aftermath of the end-Permian mass extinction, is discussed based on different types of fossils.

Strengths:

The combined study of invertebrate trace fossils, tetrapod bones, and plant remains together with their stratigraphic distribution in different sections provides a convincing case to support a rapid recovery as the authors hypothesize.

We thank Reviewer #2 for the positive comments on our work.

Weaknesses:

The study is based on three regions with Triassic successions from the North China block. While a first-hand study of other localities of similar age would be ideal, this is of course a difficult task. Instead, the authors provide comparisons with other worldwide regions to build their case and support the initial hypothesis.

Globally, ichnoassemblages reported from the Lower Triassic are relatively impoverished (Guo et al., 2019). We have compiled ichnoassemblages from several continental basins before, including South Africa, Antarctica, North America, European Basin and North China (Fig. 14 in Guo et al., 2019). However, most of the Early Triassic strata lack bioturbation (e.g., Guo et al., 2019, Buatois et al., 2021). On the contrary, the coeval deposits in North China contain diverse trace fossils, making it an ideal place for ichnological investigations. Hence, this study mainly focuses on the ichnological records in North China, but we hope more work will be done in other basins. 

References:

Guo, W.W, et al. 2019. Secular variations of ichnofossils from the terrestrial Late Permian–Middle Triassic succession at the Shichuanhe section in Shaanxi Province, North China. Glob. Planet. Change 181, 102978. https://doi.org/10.1016/j.gloplacha.2019.102978

Buatois, L.A., et al. 2021. Impact of Permian mass extinctions on continental invertebrate infauna. Terra Nova 33, 455–464. https://doi.org/10.1111/ter.12530

Reviewer #3 (Public review):

Summary:

This manuscript by Guo and colleagues features the documentation and interpretation of three successions of continental to marginal marine deposits spanning the P/T transition and their respective ichnofaunas. Based on these new data inferences concerning end-Permian mass extinction and Triassic recovery in the tropical realm are discussed.

Strengths:

The manuscript is well-written and organized and includes a large amount of new lithological and ichnological data that illuminate ecosystem evolution in a time of large-scale transition. The lithological documentations, facies interpretations, and ichnotaxonomic assignments look okay (with a few exceptions).

We thank Reviewer #3 for the positive assessments.

Weaknesses:

Some interpretations in Table 1 could be questioned: For facies association FA2 the interpretation as „terrestrial facies with periodical flooding" should be put into the right column and, given the fossil content, other interpretations, such as "marine facies" or "lagoonal environment" with some plant debris and (terrestrial) animal remains washed in, could also be possible. For FA3 the statement "bioturbation is absent" is in conflict with the next statement "strata are moderately reworked". For FA5 the observation of a "monospecific ichnoassemblage" contradicts the listing of several ichnotaxa.

We thank the reviewer for this feedback. The “FA2: terrestrial facies with periodical flooding” has been moved to the right column. As for the interpretation of depositional environment of FA2, this interval was basically terrestrial accordingly to the well-developed paleosols (Yu et al., 2022). Meanwhile, regional geological surveys have shown a faunal transition in this interval among a series of successions, from typical marine fauna containing Lingula, Eumorphotis, etc. in the southwest to a marine bivalve-terrestrial conchostracan mixed fauna in the northeast (Yin and Lin, 1979; Chu et al., 2019). Therefore, occurrence of episodic transgressions is suggested.

The FA3: Costal mudplain facies distributed to both the upper Sunjiagou Formation and Lower Heshang Formation (Fig S1), where the former lack bioturbation and the latter were moderately disturbed. We have stated this clearly in the table S1.

Ichnofauna in FA5 are dominated by Skolithos, Lockeia and Gordia, with only one poorly preserved specimen of Palaeophycus, which are distributed at the Shichuanhe and Liulin sections. However, there ichnotaxa were distributed separately, characterized by low diversity (single ichnogenus) and high density. We have deleted the “monospecific ichnoassemblage” for clarity.

References:

Chu, D., et al. 2019, Mixed continental-marine biotas following the Permian-Triassic mass extinction in South and North China: Palaeogeography, Palaeoclimatology, Palaeoecology, v. 519, p. 95–107, doi:10.1016/j.palaeo.2017.10.028.

Yu, Y., et al. 2021, Latest Permian–Early Triassic paleoclimatic reconstruction by sedimentary and isotopic analyses of paleosols from the Shichuanhe section in central North China Basin: Palaeogeography, Palaeoclimatology, Palaeoecology, v. 585, p. 110726, doi:10.1016/j.palaeo.2021.110726.

Yin, H.F., Lin, H.M., 1979. Marine Triassic faunas and the geologic time from Shihchienfeng Group in the northern Weihe River Basin, Shaanxi Province. Acta Stratigr. Sin. 3, 233–241 (in Chinese).

Concerning the structure of the manuscript, certain hypotheses related to the end-Permian mass extinction and the process of the P/T extinction and recovery, namely the existence of a long-persisting "tropic dead zone" are introduced as a foregone conclusion to which the new data seemingly shall be fit as corroborating evidence. Some of the data - e.g. the presence of a supposedly Smithian-age ichnofauna are interpreted as a fast recovery shortening the duration of the "tropic dead zone" episode - but these interpretations could also be interpreted as contradicting the idea of a "dead zone" sensu stricto in favour of a "normal" post-extinction environment with low diversity and occurrence of typical disaster taxa. Due to their large error bars the early Triassic radiometric ages did not put much of a constraint on the age determination of the earliest post-extinction ichnofaunas discussed here.

In the first ~5 Myr of the Triassic, there is evidence for a broad equatorial belt (30°N-40°S) where marine and terrestrial animals were nearly absent (namely “equatorial tetrapod gap”; Sun et al., 2012). However, the nature, duration and range of the “equatorial tetrapod gap” remain debated. Allen et al. (2020) show poleward migrations of terrestrial tetrapods during the Late Permian to Middle Triassic, with marine reptile diversity peak still restricted to northern low latitudes. Romano et al. (2020) argued that the Early Triassic equatorial terrestrial tetrapod gap would be narrower and restricted the “death belt” between 15° N and about 31° S, while Liu et al. (2022) consider that the exact boundaries of this gap likely varied with climate change (hot phases). Moreover, duration of the gap is also questioned, it’s long-lasting (Late Permian to Middle Triassic), during Induan (Bernardi et al., 2018), or from Induan to the early Spathian (Liu et al., 2022). Regardless of these discrepancies, all the related studies show the existence of the “low latitudinal tetrapod gap”, which is mentioned as background information. On this basis, this study aims to reveal when and how terrestrial ecosystems recovered from the “tropic dead zone” from the ecological point of view, rather than tetrapods only.

The fast recovered terrestrial ecosystems are represented by diverse traces, and concurrent tetrapods and plants found in the Heshanggou Formation. We acknowledge that the chronostratigraphy of the Lower Triassic in North China (and most of continental basins globally) are not controlled by precise ages, this formation, however, could be constrained to Spathian (or even straddle to earliest Middle Triassic), based on integrated magnetostratigraphic correlation, fossil records and geochemical data (Liu, 2018; Guo et al., 2022). The Smithian-age ichnofaunas here are not interpreted as a rapidly recovering biota, but early occurring opportunist-dominated communities that explore the empty ecospace under inhospitable environments. Our study also constrains roughly the “tropical dead zone” from Induan to late Smithian in North China (Fig. 4).

References:

Allen, B.J., et al. 2020. The latitudinal diversity gradient of tetrapods across the Permo-Triassic mass extinction and recovery interval. Proc Biol Sci 287, 20201125. https://doi.org/10.1098/rspb.2020.1125

Bernardi, M., et al. 2018. Tetrapod distribution and temperature rise during the Permian-Triassic mass extinction. Proc Biol Sci 285, 20172331. https://doi.org/10.1098/rspb.2017.2331

Guo, W., et al. 2022. Late Permian–Middle Triassic magnetostratigraphy in North China and its implications for terrestrial-marine correlations. Earth Planet. Sci. Lett. 585, 117519. https://doi.org/10.1016/j.epsl.2022.117519

Liu, J. 2018. New progress on the correlation of Chinese terrestrial Permo-Triassic strata. Vertebrata Palasiatica, 56, 327-342. 10.19615/j.cnki.1000-3118.180709

Liu, J., et al. 2021. Permo-Triassic tetrapods and their climate implications. Glob. Planet. Change 103618. https://doi.org/10.1016/j.gloplacha.2021.103618

Romano, M., et al. 2020. Early Triassic terrestrial tetrapod fauna: a review. Earth-Sci. Rev. 210, 103331. https://doi.org/10.1016/j.earscirev.2020.103331

Sun, Y., er al. 2012. Lethally hot temperatures during the early triassic greenhouse. Science 338, 366–70. https://doi.org/10.1126/science.1224126

Considering the somewhat equivocal evidence and controversial ideas about the P/T transition, the introduction could be improved by describing how the idea of a "tropic dead zone" arose against the background of earlier ideas, alternative views, and conflicting data. In the discussion section, alternative interpretations of the extensive data presented here - e.g. proximal-distal shifts in lithofacies with respect to the sediment source, sea level changes, preservation bias, the local occurrence of hostile environments instead of a regional scale, etc. should be discussed, also to avoid the impression that the author's conclusion was driven by confirmation bias.

As mentioned above, it’s still controversial about the nature, duration and range of the “equatorial tetrapod gap”, which primarily derived from the database (body fossils only vs. both skeletal and footprint data) and analytical methods. However, detailed discussions about these differences are beyond the scope of our study. This paper provides new evidence for the "tropical dead zone" from the ecological perspective (invertebrate ichnology, paleobotany and newly found tetrapods). Our results show that the "tropical dead zone" in North China terminated in the Smithian, followed by the reappearance of many animals in the Spathian, shedding light on the more rapidly recovering terrestrial ecosystems than previously thought.

We have improved the Introduction section by providing a summary of the “equatorial tetrapod gap”. Lines 33-35: “A tropical “tetrapod gap”, spanning between 15°N and ~31°S, prevailed through the Early Triassic, or at least during particular intervals of intense global warming (Bernardi et al., 2018; Allen et al., 2020; Romano et al., 2020; Liu et al., 2022).” is revised to:

“A tropical “tetrapod gap”, spanning between 15°N and ~31°S, prevailed in the Early Triassic, or at particular interval of intense global warming, even though the nature, duration and range remain debated (Bernardi et al., 2018; Allen et al., 2020; Romano et al., 2020; Liu et al., 2022).”

In the Discussion section, Lines 180-181: “Although the specimens are not yet fully prepared for taxonomic description, they clearly show the existence of tetrapod at this level” is revised to:

“Although the specimens are not yet fully prepared for taxonomic description, they clearly show the existence of tetrapods at this level, narrowing the “tetrapod gap” to the Spathian.”

we also add a new paragraph from Line 208:

“Our results also shed light on the timing of the tropical dead zone. The late Smithian-age ichnofauna, although impoverished, represents early opportunist-dominated communities that explored empty ecospace under inhospitable environments, which constrains the equatorial death belt to the late Smithian in North China.”

Contrary to the authors' claim, Figures S7 and S8 suggest that burrow size does not vary much within the studied sections. Size decreases and increases in the Shichuanhe and Liulin sections do not contemporaneously, are usually within the error-bar range, and might be driven by ichnotaxa composition, i.e. the presence or absence of larger ichnotaxa, rather than by size changes in the same ichnotaxon (and producer group). Here the measurement data would be needed as well to check the basis of the authors' interpretations.

We thank the reviewer for highlighting this important point. We have checked the accuracy of our raw data. Both the average size of all ichnogenera and single ichnogenera do not change obviously, but increase slightly upwards in the Spathian (Figures S7c and S8). This tendency is congruent with other coeval studies in North China (e.g., Shu et al., 2018; Xing et al., 2020). The presence of larger ichnotaxa will indeed improve the average sizes of fossil-bearing horizons, however, burrows of single ichnogenera in the Spathian generally show wider size distributions than in the Smithian, which might be associated with enriched producer groups or different growth stages of the same biota.

The asynchronous burrow size changes in the Shichuanhe and Liulin sections could be attributed to sedimentary facies. Late Permian deposits at Shichuanhe are finer than those at Linlin, which is located at the basin margin. As a result, tiny traces, like Helminthoidichnites, which were widely distributed at Shichuanhe, are absent at Linlin section. Those traces significantly reduce the average sizes in this interval, leading to inconsistent size variation patterns.

References:

Shu, W., et al. 2018. Limuloid trackways from Permian-Triassic continental successions of North China. Palaeogeogr. Palaeoclimatol. Palaeoecol. 508, 71–90. https://doi.org/10.1016/j.palaeo.2018.07.022

Xing, Z.F., et al. 2020. Trace fossils from the Lower Triassic of North China—a potential signature of the gradual recovery of a terrestrial ecosystem. Palaeoworld 30, 95–105. https://doi.org/10.1016/j.palwor.2020.06.002

Some arthropod tracks assigned here to Kouphichnium might not represent limulid traces but other (non-marine) arthropod taxa in accordance with their occurrence in terrestrial facies/non-marine units of the succession. More generally, the ichnotaxonomy of arthropod trackways is not yet well reserved - beyond Kouphichnium and Diplichnites various similar-looking types may occur that can have a variety of distinct insect, crustacean, millipede, etc. producers (including larval stages).

Well, individual trace-makers can produce different traces, and different organisms can make morphologically similar traces. In consideration of this, it’s hard to give a one-on-one relationship between trace fossils and their producers in most cases, especially for the invertebrates. So, Kouphichnium could be made by arthropods other than limuloidss.

However, horseshoe crabs, originating in the early Ordovician, invaded freshwater environments twice in the Paleozoic and once in the Mesozoic (Lamsdell, 2016), and their body fossils have been found from the Early Triassic of Germany (e.g., Hauschke and Wilde, 2008) and North China (which occur with their traces; unpublished data). Accordingly, we tentatively speculate Kouphichnium found in this interval could be primarily produced by limuloids.

References:

Hauschke, N., Wilde, V. 2008. Limuliden aus dem Oberen Buntsandstein von Süddeutschland. Hallesches Jahrb. Für Geowiss. 30, 21–26.

Lamsdell, J.C. 2016. Horseshoe crab phylogeny and independent colonizations of fresh water: ecological invasion as a driver for morphological innovation. Palaeontology 59, 181–194. https://doi.org/10.1111/pala.12220

Recommendations for the authors:

Reviewer #1 (Recommendations for The Authors):

(1)  Line 112 - was identified during..; please change to ...was identified in successions of late Changsian-early Smithian age.

Revised as suggested.

(2)  Line 116 - change prolong to prolonged.

Revised as suggested.

(3) Line 121 - change ichnofaunal to ichnofauna (check the entire sentence).

We checked the manuscript thoroughly and revised as suggested.

(4) Figure 1 caption - check sentence starting with - Base map...(delete 'of is')

Revised as suggested.

(5) Line 471 - tiny instead of tinny.

Revised as suggested.

(6) Figure S9 - would it be possible to include this reconstruction in the main manuscript?

We have moved the artistic illustration to the main text as Figure 5.

(7) Add the illustrators name / or indicate if it is produced by AI.

We have added the sentence “The artistic illustration is credited to J. Sun” at the end.

Reviewer #2 (Recommendations for The Authors):

(1) Line 15 – change 252 million years ago to ca. 252 million years ago.

Revised as suggested.

(2) Line 18 – change low-latitude North China to low-latitude present-day North China.

Actually, the paleolatitude of North China during the Early Triassic is about 17-18°N according to paleomagnetic results (Huang et al., 2018; Guo et al., 2022,).

References:

Guo, W., et al. 2022. Late Permian–Middle Triassic magnetostratigraphy in North China and its implications for terrestrial-marine correlations. Earth Planet. Sci. Lett. 585, 117519. https://doi.org/10.1016/j.epsl.2022.117519

Huang, B., et al. 2018. Paleomagnetic constraints on the paleogeography of the east asian blocks during Late Paleozoic and Early Mesozoic times. Earth-Sci. Rev. 186, 8–36. https://doi.org/10.1016/j.earscirev.2018.02.004

(3) Line 25 - "possible" doesn't seem the appropriate term here for the structure of the sentence. Could it be "to make possible" that it meant? Or otherwise you could write "possibly". Please revise this.

Revised “possible” to “possibly”.

(4) Line 33 – change “are” to “were”.

Revised as suggested.

(5) Line 43 – There are other, more appropriate articles that should (also) be cited here, especially because Mujal et al. (2017) doesn't deal with the Central European Basin (so you could even remove this reference). For sure this one should be cited:

Scholze, F., Wang, Z., Kirscher, U., Kraft, J., Schneider, J.W., Götz, A.E., Joachimski, M.M., Bachtadse, V., 2017. A multistratigraphic approach to pinpoint the Permian-Triassic boundary in continental deposits: the Zechstein–Lower Buntsandstein transition in Germany. Glob. Planet. Chang. 152, 129–151. http://dx.doi.org/10.1016/j.gloplacha.2017.03.004.

We have replaced Mujal’s paper with Scholze et al., (2017) in the main text.

(6) Line 46 – change “Roopnarinev et al., 2019” to “Roopnarine et al., 2019”.

Revised as suggested.

(7) Line 53 – Here Mujal et al. (2017) would be more appropriate, since it deals with a basin from the western peri-Tethys, also, this other article by Mujal et al. (2017) discussed the recovery in the western peri-Tethys based on tetrapod footprints:

Mujal, E., Fortuny, J., Bolet, A., Oms, O., López, J.Á., 2017. An archosauromorph dominated ichnoassemblage in fluvial settings from the late Early Triassic of the Catalan Pyrenees (NE Iberian Peninsula). PLoS One 12 (4), e0174693. http://dx.doi.org/10.1371/journal.pone.0174693.

Revised as suggested.

(8) Line 58 – change “relatively diversified trace fossils have been found during the late Early Triassic” to “because relatively diversified trace fossils have been found in upper Lower Triassic deposits”.

Revised as suggested.

(9) Line 58 – change “recovered” to “ecosystems recovered”.

Revised as suggested.

(10) Line 81 – These two paragraphs could be under a section named Geological setting or similar.

Yes, these two paragraphs are brief introductions of the geological background of North China, so we change the section name to “Geological Settings and Methods”.

(11) Line 99 – change “behavioural” to “behavioral”.

Revised as suggested and check spelling throughout.

(12) Line 103 – add “is” before adopted.

The sentence “Tiering, referring to the life position of an animal vertically in the sediment, is divided into surficial, semi-infaunal (0–0.5 cm), shallow (0.5–6 cm), intermediate (6–12 cm) and deep infaunal tiers (> 12 cm), adopted from Minter et al. (2017).” is changed to “…, based on Minter et al. (2017).”

(13) Line 113 –change “mainly” to “were mainly”.

Revised as suggested

(14) Line 116 - change prolong to prolonged.

Revised as suggested.

(15) Line 121 – add “preserved” before in.

Revised as suggested.

(16) Line 123 - change “were” to “are”.

Revised as suggested.

(17) Line 127 – “Kouphichnium” instead of “Kouphichnim”.

Revised as suggested.

(18) Line 135 – change to “Occupied by”.

Revised as suggested.

(19) Line 140 – change “bioturbations” to “bioturbated deposits”.

Revised as suggested.

(20) Line 145 – “Spathian” rather than “Spthian”.

Revised as suggested.

(21) Line 140 – change “displayed” to “displays”.

Revised as suggested.

(22) Line 160 – change “continental” to “terrestrial”.

Revised as suggested.

(23) Line 165 – “Marchetti” rather than “Marchettti”.

Revised as suggested.

(24) Line 168 – change “relationships” to “relation”.

Revised as suggested.

(25) Line 177 – “including” instead of “includes”.

Revised as suggested.

(26) Line 181 and Line 214– change “tetrapod” to “tetrapods”.

Revised as suggested.

(27) Line 195 and Line 218 – change “cooccurred” to “co-occurring”.

Revised as suggested.

(28) Line 540 – delete “herein”.

Revised as suggested.

(28) Line 559 – “Helminthoidichnites tenuis”, it should be in italics.

Revised as suggested.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public review):

Summary:

This paper is an elegant, mostly observational work, detailing observations that polysome accumulation appears to drive nucleoid splitting and segregation. Overall I think this is an insightful work with solid observations.

Thank you for your appreciation and positive comments. In our view, an appealing aspect of this proposed biophysical mechanism for nucleoid segregation is its self-organizing nature and its ability to intrinsically couple nucleoid segregation to biomass growth, regardless of nutrient conditions.

Strengths:

The strengths of this paper are the careful and rigorous observational work that leads to their hypothesis. They find the accumulation of polysomes correlates with nucleoid splitting, and that the nucleoid segregation occurring right after splitting correlates with polysome segregation. These correlations are also backed up by other observations:

(1) Faster polysome accumulation and DNA segregation at faster growth rates.

(2) Polysome distribution negatively correlating with DNA positioning near asymmetric nucleoids.

(3) Polysomes form in regions inaccessible to similarly sized particles.

These above points are observational, I have no comments on these observations leading to their hypothesis.

Thank you!

Weaknesses:

It is hard to state weaknesses in any of the observational findings, and furthermore, their two tests of causality, while not being completely definitive, are likely the best one could do to examine this interesting phenomenon.

It is indeed difficult to prove causality in a definitive manner when the proposed coupling mechanism between nucleoid segregation and gene expression is self-organizing, i.e., does not involve a dedicated regulatory molecule (e.g., a protein, RNA, metabolite) that we could have eliminated through genetic engineering to establish causality. We are grateful to the reviewer for recognizing that our two causality tests are the best that can be done in this context.

Points to consider / address:

Notably, demonstrating causality here is very difficult (given the coupling between transcription, growth, and many other processes) but an important part of the paper. They do two experiments toward demonstrating causality that help bolster - but not prove - their hypothesis. These experiments have minor caveats, my first two points.

(1) First, "Blocking transcription (with rifampicin) should instantly reduce the rate of polysome production to zero, causing an immediate arrest of nucleoid segregation". Here they show that adding rifampicin does indeed lead to polysome loss and an immediate halting of segregation - data that does fit their model. This is not definitive proof of causation, as rifampicin also (a) stops cell growth, and (b) stops the translation of secreted proteins. Neither of these two possibilities is ruled out fully.

That’s correct; cell growth also stops when gene expression is inhibited, which is consistent with our model in which gene expression within the nucleoid promotes nucleoid segregation and biomass growth (i.e., cell growth), inherently coupling these two processes. This said, we understand the reviewer’s point: the rifampicin experiment doesn’t exclude the possibility that protein secretion and cell growth drive nucleoid segregation. We are assuming that the reviewer is envisioning an alternative model in which sister nucleoids would move apart because they would be attached to the membrane through coupled transcription-translation-protein secretion (transertion) and the membrane would expand between the separating nucleoids, similar to the model proposed by Jacob et al in 1963 (doi:10.1101/SQB.1963.028.01.048). There are several observations arguing against cell elongation/transertion acting a predominant mechanism of nucleoid segregation.

(1) For this alternative mechanism to work, membrane growth must be localized at the middle of the splitting nucleoids (i.e., midcell position for slow growth and ¼ and ¾ cell positions for fast growth) to create a directional motion. To our knowledge, there is no evidence of such localized membrane incorporation. Furthermore, even if membrane growth was localized at the right places, the fluidity of the cytoplasmic membrane (PMID: 6996724, 20159151, 24735432, 27705775) would be problematic. To circumvent the membrane fluidity issue, one could potentially evoke an additional connection to the rigid peptidoglycan, but then again, peptidoglycan growth would have to be localized at the middle of the splitting nucleoid. However, peptidoglycan growth is dispersed early in the cell division cycle when the nucleoid splitting happens in fast growing cells and only appears to be zonal after the onset of cell constriction (PMID: 35705811, 36097171, 2656655).

(2) Even if we ignore the aforementioned caveats, Paul Wiggins’s group ruled out the cell elongation/transertion model by showing that the rate of cell elongation is slower than the rate of chromosome segregation (PMID: 23775792). In our revised manuscript, we clarify this point and provide confirmatory data showing that the cell elongation rate is indeed slower than the nucleoid segregation rate (Figure 1H and Figure 1 - figure supplement 5A), indicating that it cannot be the main driver.

(3) The asymmetries in nucleoid compaction that we described in our paper are predicted by our model. We do not see how they could be explained by cell growth or protein secretion.

(4) We also show that polysome accumulation at ectopic sites (outside the nucleoid) results in correlated nucleoid dynamics, consistent with our proposed mechanism. It is not clear to us how such nucleoid dynamics could be explained by cell growth or protein secretion (transertion).

(1a) As rifampicin also stops all translation, it also stops translational insertion of membrane proteins, which in many old models has been put forward as a possible driver of nucleoid segregation, and perhaps independent of growth. This should at last be mentioned in the discussion, or if there are past experiments that rule this out it would be great to note them.

It is not clear to us how the attachment of the DNA to the cytoplasmic membrane could alone create a directional force to move the sister nucleoids. We agree that old models have proposed a role for cell elongation (providing the force) and transertion (providing the membrane tether). Please see our response above for the evidence (from the literature and our work) against it. This was mentioned in the Introduction and Results section, but we agree that this was not well explained. We have now put emphasis on the related experimental data (Figure 1H, Figure 1 – figure supplement 5A, ) and revised the text (lines 199 - 210) to clarify these points.

(1b) They address at great length in the discussion the possibility that growth may play a role in nucleoid segregation. However, this is testable - by stopping surface growth with antibiotics. Cells should still accumulate polysomes for some time, it would be easy to see if nucleoids are still segregated, and to what extent, thereby possibly decoupling growth and polysome production. If successful, this or similar experiments would further validate their model.

We reviewed the literature and could not find a drug that stops cell growth without stopping gene expression. Any drug that affects the integrity or potential of the membrane depletes cells of ATP; without ATP, gene expression is inhibited. However, our experiment in which we drive polysome accumulation at ectopic sites decouples polysome accumulation from cell growth. In this experiment, by redirecting most of chromosome gene expression to a single plasmid-encoded gene, we reduce the rate of cell growth but still create a large accumulation of polysomes at an ectopic location. This ectopic polysome accumulation is sufficient to affect nucleoid dynamics in a correlated fashion. In the revised manuscript, we have clarified this point and added model simulations (Figure 7 – figure supplement 2) to show that our experimental observations are predicted by our model.

(2) In the second experiment, they express excess TagBFP2 to delocalize polysomes from midcell. Here they again see the anticorrelation of the nucleoid and the polysomes, and in some cells, it appears similar to normal (polysomes separating the nucleoid) whereas in others the nucleoid has not separated. The one concern about this data - and the differences between the "separated" and "non-separated" nuclei - is that the over-expression of TagBFP2 has a huge impact on growth, which may also have an indirect effect on DNA replication and termination in some of these cells. Could the authors demonstrate these cells contain 2 fully replicated DNA molecules that are able to segregate?

We have included new flow cytometry data of fluorescently labeled DNA to show that DNA replication is not impacted.

(3) What is not clearly stated and is needed in this paper is to explain how polysomes do (or could) "exert force" in this system to segregate the nucleoid: what a "compaction force" is by definition, and what mechanisms causes this to arise (what causes the "force") as the "compaction force" arises from new polysomes being added into the gaps between them caused by thermal motions.

They state, "polysomes exert an effective force", and they note their model requires "steric effects (repulsion) between DNA and polysomes" for the polysomes to segregate, which makes sense. But this makes it unclear to the reader what is giving the force. As written, it is unclear if (a) these repulsions alone are making the force, or (b) is it the accumulation of new polysomes in the center by adding more "repulsive" material, the force causes the nucleoids to move. If polysomes are concentrated more between nucleoids, and the polysome concentration does not increase, the DNA will not be driven apart (as in the first case) However, in the second case (which seems to be their model), the addition of new material (new polysomes) into a sterically crowded space is not exerting force - it is filling in the gaps between the molecules in that region, space that needs to arise somehow (like via Brownian motion). In other words, if the polysome region is crowded with polysomes, space must be made between these polysomes for new polysomes to be inserted, and this space must be made by thermal (or ATP-driven) fluctuations of the molecules. Thus, if polysome accumulation drives the DNA segregation, it is not "exerting force", but rather the addition of new polysomes is iteratively rectifying gaps being made by Brownian motion.

We apologize for the understandable confusion. In our picture, the polysomes and DNA (conceptually considered as small plectonemic segments) basically behave as dissolved particles. If these particles were noninteracting, they would simply mix. However, both polysomes and DNA segments are large enough to interact sterically. So as density increases, steric avoidance implies a reduced conformational entropy and thus a higher free energy per particle. We argue (based on Miangolarra et al. 2021 PMID: 34675077 and Xiang et al. 2021 PMID: 34186018) that the demixing of polysomes and DNA segments occurs because DNA segments pack better with each other than they do with polysomes. This raises the free energy cost associated with DNA-polysome interactions compared to DNA-DNA interactions. We model this effect by introducing a term in the free energy χ_np, which refers to as a repulsion between DNA and polysomes, though as explained above it arises from entropic effects. At realistic cellular densities of DNA and polysomes, this repulsive interaction is strong enough to cause the DNA and polysomes to phase separate.

This same density-dependent free energy that causes phase separation can also give rise to forces, just in the way that a higher pressure on one side of a wall can give rise to a net force on the wall. Indeed, the “compaction force” we refer to is fundamentally an osmotic pressure difference. At some stages during nucleoid segregation, the region of the cell between nucleoids has a higher polysome concentration, and therefore a higher osmotic pressure, than the regions near the poles. This results in a net poleward force on the sister nucleoids that drives their migration toward the poles. This migration continues until the osmotic pressure equilibrates. Therefore, both phase separation (due to the steric repulsion described above) and nonequilibrium polysome production and degradation (which creates the initial accumulation of polysomes around midcell) are essential ingredients for nucleoid segregation.

This has been clarified in the revised text, with the support of additional simulation results showing how the asymmetry in polysome distribution causes a compaction force (Figure 4A).

The authors use polysome accumulation and phase separation to describe what is driving nucleoid segregation. Both terms are accurate, but it might help the less physically inclined reader to have one term, or have what each of these means explicitly defined at the start. I say this most especially in terms of "phase separation", as the currently huge momentum toward liquid-liquid interactions in biology causes the phrase "phase separation" to often evoke a number of wider (and less defined) phenomena and ideas that may not apply here. Thus, a simple clear definition at the start might help some readers.

In our case, phase separation means that the DNA-polysome steric repulsion is strong enough to drive their demixing, which creates a compact nucleoid. As mentioned in a previous point, this effect is captured in the free energy by the χ_np term, which is an effective repulsion between DNA and polysomes, though it arises from entropic effects.

In the revised manuscript, we now illustrate this with our theoretical model by initializing a cell with a diffuse nucleoid and low polysome concentration. For the sake of simplicity, we assume that the cell does not elongate. We observe that the DNA-polysome steric repulsion is sufficient to compact the nucleoid and place it at mid-cell (new Figure 4A).

(4) Line 478. "Altogether, these results support the notion that ectopic polysome accumulation drives nucleoid dynamics". Is this right? Should it not read "results support the notion that ectopic polysome accumulation inhibits/redirects nucleoid dynamics"?

We think that the ectopic polysome accumulation drives nucleoid dynamics. In our theoretical model, we can introduce polysome production at fixed sources to mimic the experiments where ectopic polysome production is achieved by high plasmid expression. The model is able to recapitulate the two main phenotypes observed in experiments (Figure 7). These new simulation results have been added to the revised manuscript (Figure 7 – figure supplement 2).

(5) It would be helpful to clarify what happens as the RplA-GFP signal decreases at midcell in Figure 1- is the signal then increasing in the less "dense" parts of the cell? That is, (a) are the polysomes at midcell redistributing throughout the cell? (b) is the total concentration of polysomes in the entire cell increasing over time?

It is a redistribution—the RplA-GFP signal remains constant in concentration from cell birth to division (Figure 1 – Figure Supplement 1E). This is now clarified in the revised text.

(6) Line 154. "Cell constriction contributed to the apparent depletion of ribosomal signal from the mid-cell region at the end of the cell division cycle (Figure 1B-C and Movie S1)" - It would be helpful if when cell constriction began and ended was indicated in Figures 1B and C.

Good idea. We have added markers in Figure 1C to indicate the average start of cell constriction. This relative time from birth to division was estimated as described in the new Figure 1 – figure supplement 2. We have also indicated that cell birth and division correspond to the first and last images/timepoint in Figure 1B and C, respectively. The two-imensional average cell projections presented in Figure 3D also indicate the average timing of cell constriction, consistent with our analysis in Figure 1 – figure supplement 2.

(7) In Figure 7 they demonstrate that radial confinement is needed for longitudinal nucleoid segregation. It should be noted (and cited) that past experiments of Bacillus l-forms in microfluidic channels showed a clear requirement role for rod shape (and a given width) in the positing and the spacing of the nucleoids.

Wu et al, Nature Communications, 2020. "Geometric principles underlying the proliferation of a model cell system" https://dx.doi.org/10.1038/s41467-020-17988-7

Good point! We have revised the text to mention this work. Thank you.

(8) "The correlated variability in polysome and nucleoid patterning across cells suggests that the size of the polysome-depleted spaces helps determine where the chromosomal DNA is most concentrated along the cell length. This patterning is likely reinforced through the displacement of the polysomes away from the DNA dense region"

It should be noted this likely functions not just in one direction (polysomes dictating DNA location), but also in the reverse - as the footprint of compacted DNA should also exclude (and thus affect) the location of polysomes

We agree that the effects could go both ways at this early stage of the story. We have revised the text accordingly.

(9) Line 159. Rifampicin is a transcription inhibitor that causes polysome depletion over time. This indicates that all ribosomal enrichments consist of polysomes and therefore will be referred to as polysome accumulations hereafter". Here and throughout this paper they use the term polysome, but cells also have monosomes (and 2 somes, etc). Rifampicin stops the assembly of all of these, and thus the loss of localization could occur from both. Thus, is it accurate to state that all transcription events occur in polysomes? Or are they grouping all of the n-somes into one group?

In the original discussion, we noted that our term “polysomes” also includes monosomes for simplicity, but we agree that the term should have been defined much earlier. The manuscript has been revised accordingly. Furthermore, in the revised manuscript, we have included additional simulation results with three different diffusion coefficients that reflect different polysome sizes to show that different polysome species with less or more ribosomes give similar results (Figure 4 – figure supplement 4). This shows that the average polysome description in our model is sufficient.

Thank you for the valuable comments and suggestions!

Reviewer #2 (Public review):

Summary:

The authors perform a remarkably comprehensive, rigorous, and extensive investigation into the spatiotemporal dynamics between ribosomal accumulation, nucleoid segregation, and cell division. Using detailed experimental characterization and rigorous physical models, they offer a compelling argument that nucleoid segregation rates are determined at least in part by the accumulation of ribosomes in the center of the cell, exerting a steric force to drive nucleoid segregation prior to cell division. This evolutionarily ingenious mechanism means cells can rely on ribosomal biogenesis as the sole determinant for the growth rate and cell division rate, avoiding the need for two separate 'sensors,' which would require careful coupling.

Terrific summary! Thank you for your positive assessment.

Strengths:

In terms of strengths; the paper is very well written, the data are of extremely high quality, and the work is of fundamental importance to the field of cell growth and division. This is an important and innovative discovery enabled through a combination of rigorous experimental work and innovative conceptual, statistical, and physical modeling.

Thank you!

Weaknesses:

In terms of weaknesses, I have three specific thoughts.

Firstly, my biggest question (and this may or may not be a bona fide weakness) is how unambiguously the authors can be sure their ribosomal labeling is reporting on polysomes, specifically. My reading of the work is that the loss of spatial density upon rifampicin treatment is used to infer that spatial density corresponds to polysomes, yet this feels like a relatively indirect way to get at this question, given rifampicin targets RNA polymerase and not translation. It would be good if a more direct way to confirm polysome dependence were possible.

The heterogeneity of ribosome distribution inside E. coli cells has been attributed to polysomes by many labs (PMID: 25056965, 38678067, 22624875, 31150626, 34186018, 10675340). The attribution is also consistent with single-molecule tracking experiments showing that slow-moving ribosomes (polysomes) are excluded by the nucleoid whereas fast-diffusing ribosomes (free ribosomal subunits) are distributed throughout the cytoplasm (PMID: 25056965, 22624875). These points are now mentioned in the revised manuscript.

Second, the authors invoke a phase separation model to explain the data, yet it is unclear whether there is any particular evidence supporting such a model, whether they can exclude simpler models of entanglement/local diffusion (and/or perhaps this is what is meant by phase separation?) and it's not clear if claiming phase separation offers any additional insight/predictive power/utility. I am OK with this being proposed as a hypothesis/idea/working model, and I agree the model is consistent with the data, BUT I also feel other models are consistent with the data. I also very much do not think that this specific aspect of the paper has any bearing on the paper's impact and importance.

We appreciate the reviewer’s comment, but the output of our reaction-diffusion model is a bona fide phase separation (spinodal decomposition). So, we feel that we need to use the term when reporting the modeling results. Inside the cell, the situation is more complicated. As the reviewer points out, there are likely entanglements (not considered in our model) and other important factors (please see our discussion on the model limitations). This said, we have revised our text to clarify our terms and proposed mechanism.

Finally, the writing and the figures are of extremely high quality, but the sheer volume of data here is potentially overwhelming. I wonder if there is any way for the authors to consider stripping down the text/figures to streamline things a bit? I also think it would be useful to include visually consistent schematics of the question/hypothesis/idea each of the figures is addressing to help keep readers on the same page as to what is going on in each figure. Again, there was no figure or section I felt was particularly unclear, but the sheer volume of text/data made reading this quite the mental endurance sport! I am completely guilty of this myself, so I don't think I have any super strong suggestions for how to fix this, but just something to consider.

We agree that there is a lot to digest. We could not come up with great ideas for visuals others than the schematics we already provide. However, we have revised the text to clarify our points and added a simulation result (Figure 4A) to help explain biophysical concepts.

Reviewer #3 (Public review):

Summary:

Papagiannakis et al. present a detailed study exploring the relationship between DNA/polysome phase separation and nucleoid segregation in Escherichia coli. Using a combination of experiments and modelling, the authors aim to link physical principles with biological processes to better understand nucleoid organisation and segregation during cell growth.

Strengths:

The authors have conducted a large number of experiments under different growth conditions and physiological perturbations (using antibiotics) to analyse the biophysical factors underlying the spatial organisation of nucleoids within growing E. coli cells. A simple model of ribosome-nucleoid segregation has been developed to explain the observations.

Weaknesses:

While the study addresses an important topic, several aspects of the modelling, assumptions, and claims warrant further consideration.

Thank you for your feedback. Please see below for a response to each concern.

Major Concerns:

Oversimplification of Modelling Assumptions:

The model simplifies nucleoid organisation by focusing on the axial (long-axis) dimension of the cell while neglecting the radial dimension (cell width). While this approach simplifies the model, it fails to explain key experimental observations, such as:

(1) Inconsistencies with Experimental Evidence:

The simplified model presented in this study predicts that translation-inhibiting drugs like chloramphenicol would maintain separated nucleoids due to increased polysome fractions. However, experimental evidence shows the opposite-separated nucleoids condense into a single lobe post-treatment (Bakshi et al 2014), indicating limitations in the model's assumptions/predictions. For the nucleoids to coalesce into a single lobe, polysomes must cross the nucleoid zones via the radial shells around the nucleoid lobes.

We do not think that the results from chloramphenicol-treated cells are inconsistent with our model. Our proposed mechanism predicts that nucleoids will condense in the presence of chloramphenicol, consistent with experiments. It also predicts that nucleoids that were still relatively close at the time of chloramphenicol treatment could fuse if they eventually touched through diffusion (thermal fluctuation) to reduce their interaction with the polysomes and minimize their conformational energy. Fusion is, however, not expected for well-separated nucleoids since their diffusion is slow in the crowded cytoplasm. This is consistent with our experimental observations: In the presence of a growth-inhibitory concentration of chloramphenicol (70 μg/mL), nucleoids in relatively close proximity can fuse, but well-separated nucleoids condense and do not fuse. Since the growth rate inhibition is not immediate upon chloramphenicol treatment, many cells with well-separated condensed nucleoids divide during the first hour. As a result, the non-fusion phenotype is more obvious in non-dividing cells, achieved by pre-treating cells with the cell division inhibitor cephalexin (50μg/mL). In these polyploid elongated cells, well-separated nucleoids condensed but did not fuse, not even after an hour in the presence of chloramphenicol. We have revised the manuscript to add these data (illustrative images + a quantitative analysis) in Figure 4 – figure supplement 1.

(2) The peripheral localisation of nucleoids observed after A22 treatment in this study and others (e.g., Japaridze et al., 2020; Wu et al., 2019), which conflicts with the model's assumptions and predictions. The assumption of radial confinement would predict nucleoids to fill up the volume or ribosomes to go near the cell wall, not the nucleoid, as seen in the data.

The reviewer makes a good point that DNA attachment to the membrane through transertion could contribute to the nucleoid being peripherally localized in A22 cells. We have revised the text to add this point. However, we do not think that this contradicts the proposed nucleoid segregation mechanism described in our model. On the contrary, by attaching the nucleoid to the cytoplasmic membrane along the cell width, transertion might help reduce the diffusion and thus exchange of polysomes across nucleoids. We have revised the text to discuss transertion over radial confinement.

(3) The radial compaction of the nucleoid upon rifampicin or chloramphenicol treatment, as reported by Bakshi et al. (2014) and Spahn et al. (2023), also contradicts the model's predictions. This is not expected if the nucleoid is already radially confined.

We originally evoked radial confinement to explain the observation that polysome accumulations do not equilibrate between DNA-free regions. We agree that transertion is an alternative explanation. Thank you for bringing it to our attention. However, please note that this does not contradict the model. In our view, it actually supports the 1D model by providing a reasonable explanation for the slow exchange of polysomes across DNA-free regions. The attachment of the nucleoid to the membrane along the cell width may act as diffusion barrier. We have revised the text and the title of the manuscript accordingly.

(4) Radial Distribution of Nucleoid and Ribosomal Shell:

The study does not account for well-documented features such as the membrane attachment of chromosomes and the ribosomal shell surrounding the nucleoid, observed in super-resolution studies (Bakshi et al., 2012; Sanamrad et al., 2014). These features are critical for understanding nucleoid dynamics, particularly under conditions of transcription-translation coupling or drug-induced detachment. Work by Yongren et al. (2014) has also shown that the radial organisation of the nucleoid is highly sensitive to growth and the multifork nature of DNA replication in bacteria.

We have revised the manuscript to discuss the membrane attachment. Please see the previous response.

The omission of organisation in the radial dimension and the entropic effects it entails, such as ribosome localisation near the membrane and nucleoid centralisation in expanded cells, undermines the model's explanatory power and predictive ability. Some observations have been previously explained by the membrane attachment of nucleoids (a hypothesis proposed by Rabinovitch et al., 2003, and supported by experiments from Bakshi et al., 2014, and recent super-resolution measurements by Spahn et al.).

We agree—we have revised the text to discuss membrane attachment in the radial dimension. See previous responses.

Ignoring the radial dimension and membrane attachment of nucleoid (which might coordinate cell growth with nucleoid expansion and segregation) presents a simplistic but potentially misleading picture of the underlying factors.

Please see above.

This reviewer suggests that the authors consider an alternative mechanism, supported by strong experimental evidence, as a potential explanation for the observed phenomena:

Nucleoids may transiently attach to the cell membrane, possibly through transertion, allowing for coordinated increases in nucleoid volume and length alongside cell growth and DNA replication. Polysomes likely occupy cellular spaces devoid of the nucleoid, contributing to nucleoid compaction due to mutual exclusion effects. After the nucleoids separate following ter separation, axial expansion of the cell membrane could lead to their spatial separation.

This “membrane attachment/cell elongation” model is reminiscent to the hypothesis proposed by Jacob et al in 1963 (doi:10.1101/SQB.1963.028.01.048). There are several lines of evidence arguing against it as the major driver of nucleoid segregation:

(Below is a slightly modified version of our response to a comment from Reviewer 1—see page 3)

(1) For this alternative model to work, axial membrane expansion (i.e., cell elongation) would have to be localized at the middle of the splitting nucleoids (i.e., midcell position for slow growth and ¼ and ¾ cell positions for fast growth) to create a directional motion. To our knowledge, there is no evidence of such localized membrane incorporation. Furthermore, even if membrane growth was localized at the right places, the fluidity of the cytoplasmic membrane (PMID: 6996724, 20159151, 24735432, 27705775) would be problematic. To go around this fluidity issue, one could potentially evoke a potential connection to the rigid peptidoglycan, but then again, peptidoglycan growth would have to be localized at the middle of the splitting nucleoid to “push” the sister nucleoid apart from each other. However, peptidoglycan growth is dispersed prior to cell constriction (PMID: 35705811, 36097171, 2656655).

(2) Even if we ignore the aforementioned caveats, Paul Wiggins’s group ruled out the cell elongation/transertion model by showing that the rate of cell elongation is slower than the rate of chromosome segregation (PMID: 23775792). In the revised manuscript, we confirm that the cell elongation rate is indeed overall slower than the nucleoid segregation rate (see Figure 1 - figure supplement 5A where the subtraction of the cell elongation rate to the nucleoid segregation rate at the single-cell level leads to positive values).

(3) Furthermore, our correlation analysis comparing the rate of nucleoid segregation to the rate of either cell elongation or polysome accumulation argues that polysome accumulation plays a larger role than cell elongation in nucleoid segregation. These data were already shown in the original manuscript (Figure 1I and Figure 1 – figure supplement 5B) but were not highlighted in this context. We have revised the text to clarify this point.

(4) The membrane attachment/cell elongation model does not explain the nucleoid asymmetries described in our paper (Figure 3), whereas they can be recapitulated by our model.

(5) The cell elongation/transertion model cannot predict the aberrant nucleoid dynamics observed when chromosomal expression is largely redirected to plasmid expression (Figure 7). In the revised manuscript, we have added simulation results showing that these nucleoid dynamics are predicted by our model (Figure 7 – figure supplement 2).

Based on these arguments, we do not believe that a mechanism based on membrane attachment and cell elongation is the major driver of nucleoid segregations. However, we do believe that it may play a complementary role (see “Nucleoid segregation likely involves multiple factors” in the Discussion). We have revised the text to clarify our thoughts and mention the potential role of transertion.

Incorporating this perspective into the discussion or future iterations of the model may provide a more comprehensive framework that aligns with the experimental observations in this study and previous work.

As noted above, we have revised the text to mention transertion.

Simplification of Ribosome States:

Combining monomeric and translating ribosomes into a single 'polysome' category may overlook spatial variations in these states, particularly during ribosome accumulation at the mid-cell. Without validating uniform mRNA distribution or conducting experimental controls such as FRAP or single-molecule measurements to estimate the proportions of ribosome states based on diffusion, this assumption remains speculative.

Indeed, for simplicity, we adopt an average description of all polysomes with an average diffusion coefficient and interaction parameters, which is sufficient for capturing the fundamental mechanism underlying nucleoid segregation. To illustrate that considering multiple polysome species does not change the physical picture, we have considered an extension of our model, which contains three polysome species, each with a different diffusion coefficient (D_P = 0.018, 0.023, or 0.028 μm²/s), reflecting that polysomes with more ribosomes will have a lower diffusion coefficient. Simulation of this model reveals that the different polysome species have essentially the same concentration distribution, suggesting that the average description in our minimal model is sufficient for our purposes. We present these new simulation results in Figure 4 – figure supplement 4 of the revised manuscript.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) Does the polysome density correlate with the origins? If the majority of ribosomal genes are expressed near the origins,

This is indeed an interesting point that we mention in the discussion. The fact that the chromosomal origin is surrounded by highly expressed genes (PMID: 30904377) and is located near the middle of the nucleoid prior to DNA replication (PMID: 15960977, 27332118, 34385314, 37980336) can only help the model that we propose by increasing the polysome density at the mid-nucleoid position.

(2) Red lines in 3C are hard to resolve - can the authors make them darker?

Absolutely. Sorry about that.

Reviewer #2 (Recommendations for the authors):

The authors use rifampicin treatment as a mechanism to trigger polysome disassembly and show this leads to homogenous RplA distribution. This is a really important experiment as it is used to link RplA localization to polysomes, and tp argue that RplA density is reporting on polysomes. Given rifampicin inhibits RNA polymerase, and given the only reference of the three linking rifampicin to polysome disassembly is the 1971 Blundell and Wild ref), it would perhaps be useful to more conclusively show that polysome depletion (as opposed to inhibition of mRNA synthesis, which is upstream of polysome assembly) by using an alternative compound more commonly linked to polysome disassembly (e.g., puromycin) and show timelapse loss of density as a function of treatment time. This is not a required experiment, but given the idea that RplA density reports on polysomes is central to the authors' interpretation, it feels like this would be a thing worth being certain of. An alternative model is that ribosomes undergo self-assembly into local storage depots when not being used, but those depots are not translationally active/lack polysomes. I don't know if I think this is likely, but I'm not convinced the rifampicin treatment + waiting for a relatively long period of time unambiguously excludes other possible mechanisms given the large scale remodeling of the intracellular environment upon mRNA inhibition. I 100% buy the relationship between ribosomal distribution and nucleoid segregation (and the ectopic expression experiments are amazing in this regard), so my own pause for thought here is "do we know those ribosomes are in polysomes in the ribosome-dense regions". I'm not sure the answer to this question has any bearing on the impact and importance of this work (in my mind, it doesn't, but perhaps there's a reason it does?). The way to unambiguously show this would really be to do CryoET and show polysomes in the dense ribosomal regions, but I would never suggest the authors do that here (that's an entire other paper!).

We agree that mRNAs play a role, as mRNAs are major components of polysomes and most mRNAs are expected to be in the form of polysomes (i.e., in complex with ribosomes). In addition, as mentioned above, the enrichments of ribosome distribution are known to be associated with polysomes (PMID: 25056965, 38678067, 22624875, 31150626, 34186018, 10675340). The attribution is consistent with single-molecule tracking experiments showing that slow-moving ribosomes (polysomes) are excluded by the nucleoid whereas fast-diffusing ribosomes (free ribosomal subunits) are distributed throughout the cytoplasm (PMID: 25056965, 22624875). This is also consistent with cryo-ET results that we actually published (see Figure S5, PMID: 34186018). We have added this information to the revised manuscript. Thank you for alerting us of this oversight.

On line 320 the authors state "Our single-cell studies provided experimental support that phase separation between polysomes and DNA contributes to nucleoid segregation." - this comes pretty out of left field? I didn't see any discussion of this hypothesis leading up to this sentence, nor is there evidence I can see that necessitates phase separation as a mechanistic explanation unless we are simply using phase separation to mean cellular regions with distinct cellular properties (which I would advise against). If the authors really want to pursue this model I think much more support needs to be provided here, including (1) defining what the different phases are, (2) providing explicit description of what the attractive/repulsive determinants of these different phases could be/are, and (3) ruling out a model where the behavior observed is driven by a combination of DNA / polysome entanglement + steric exclusion; if this is actually the model, then being much more explicit about this being a locally arrested percolation phenomenon would be essential. Overall, however, I would probably dissuade the authors from pursuing the specific underlying physics of what drives the effects they're seeing in a Results section, solely because I think ruling in/out a model unambiguously is very difficult. Instead, this would be a useful topic for a Discussion, especially couched under a "our data are consistent with..." if they cannot exclude other models (which I think is unreasonably difficult to do).

Thank you for your advice. We have revised the text to more carefully choose our words and define our terms.

Minor comments:

The results in "Cell elongation may also contribute to sister nucleoid migration near the end of the division cycle" are really interesting, but this section is one big paragraph, and I might encourage the authors to divide this paragraph up to help the reader parse this complex (and fascinating) set of results!

We have revised this section to hopefully make it more accessible.

Reviewer #3 (Recommendations for the authors):

Technical Controls:

The authors should conduct a photobleaching control to confirm that the perceived 'higher' brightness of new ribosomes at the mid-cell position is not an artefact caused by older ribosomes being photobleached during the imaging process. Comparing results at various imaging frequencies and intensities is necessary to address this issue.

The ribosome localization data across 30 nutrient conditions (Figure 2, Figure 1 – figure supplement 6, Figure 2 – Figure supplement 1, Figure 2 – Figure supplement 3 and Figure 5) are from snapshot images, which do not have any photobleaching issue. They confirm the mid-cell accumulation seen by time-lapse microscopy. We have revised the text to clarify this point.

Novelty of Experimental Measurements:

While the scale of the study is unprecedented, claims of novelty (e.g., line 142) regarding ribosome-nucleoid segregation tracking are overstated. Similar observations have been made previously (e.g., Bakshi et al., 2012; Bakshi et al., 2014; Chai et al., 2014).

Our apologies. The text in line 142 oversimplified our rationale. This has been corrected in the revised manuscript.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

This manuscript reports experiments designed to dissect the function of N-cadherin during mammalian folliculogenesis, using the mouse as a model system. Prior studies have shown that this is the principal cadherin expressed by the follicular granulosa cells. Two main strategies are used - small-molecule inhibitors that target N-cadherin and a conditional knockout where the gene encoding N-cad is deleted in granulosa cells. The authors also take advantage of the ability to reproduce key events of folliculogenesis, such as oocyte meiotic maturation, in vitro. Four main conclusions are drawn from the studies: (i) cadherin-based cell contact is required to maintain cadherin (N-cad in the granulosa cells; E-cad in the oocyte) at the plasma membrane; (ii) N-cad is required for cumulus layer expansion; (iii) N-cad is required for meiotic maturation of the oocyte; (iv) N-cad is required for ovulation.

Strengths:

The experiments are logically conceived, clearly described and presented, and carefully interpreted. A key strength of the paper is that multiple approaches have been used (drugs, knockouts, immunofluorescence, PLA, in vitro and in vivo studies). Taken together, they clearly establish essential roles for N-cadherin during folliculogenesis.

It is intriguing that, when cadherin activity is impaired, the cadherins are lost from the plasma membrane. This suggests that, in a multicellular context, interactions with other cadherins, either in cis within the same cell or in trans with a neighboring cell, are required to maintain cadherins at the membrane. Hence, beyond their significance for understanding female reproductive biology, these experiments have broader implications for cell biology.

Weaknesses:

A few points could be considered or clarified by the authors:

The YAP experiments were confusing to the reviewer. CRS-066 increased YAP activity, as indicated by increased expression of target genes. Since CRS-066 prevents expansion, this result suggests that YAP antagonizes expansion. Therefore, blocking YAP should favor expansion. Yet, the YAP inhibitor impaired expansion. In the reviewer's eyes, these results seem to be contradictory.

The mechanism through which N-cadherin inhibitors block cumulus expansion isn’t fully elucidated but isn’t deemed to be through YAP alone. The transcriptional changes indicate crosstalk between N-cadherin, β-catenin and Hippo/YAP pathways, as well as impacting on the signalling between cumulus cells and the oocyte.

It is intriguing that the inhibitors were able to efficiently block oocyte maturation. Oocytes from which the cumulus granulosa cells have been removed (denuded) will mature in vitro in the absence of LH or EGF. Since the effect of the inhibitors is to break the contact between the cumulus cells and oocyte, one might have predicted that this would not impair the ability of the oocytes to mature. Perhaps the authors could comment on this.

Indeed, removal of cumulus cells permits oocyte meiotic maturation by reducing oocyte cAMP, leading to activation of meiosis promoting factor (MPF). A hypothesis would be that cyclic nucleotides and MPF arrest in the oocyte are maintained when N-cadherin contacts are blocked but this was not determined.

Regarding the experiments where the inhibitors were administered intra-peritoneally, the authors might comment on the rationale for choosing the doses that were used. An additional point to consider is that, since N-cadherin is expressed in a variety of tissues, an effect of interfering with N-cadherin at these non-ovarian sites could indirectly influence ovarian function.

Doses were chosen based on previous reported use of these inhibitors in vivo (Mrozik et al. 2020). Possible effects of the N-cadherin antagonists in other tissues was a carefully considered in this and the previous Mrozik et al study. While we saw no evidence of effects in gross morphological observations, or closer examination of vasculature or blood in these studies, this potential is not excluded.

Reviewer #2 (Public Review):

Summary:

The manuscript entitled "N-cadherin mechanosensing in ovarian follicles controls oocyte maturation and ovulation" aimed to investigate the role of N-cadherin in different ovarian physiological processes, including cumulus oocyte expansion, oocyte maturation, and ovulation. The authors performed several in vitro and in vivo mice experiments, using diverse techniques to reinforce their results.

First, they identified two compounds (N-cadherin antagonists) that block the adhesion of periovulatory COCs to fibronectin through screening a small molecule library, using the xCELLigenceTM system, performing proper and complementary controls. Second, the authors showed the presence of N-cadherin adherens junctions between granulosa cells and cumulus cells and at the interface of cumulus cell transzonal projections and the oocyte throughout folliculogenesis. And that these adherens complexes between cumulus cells and oocytes were disrupted when inhibited N-cadherin, as observed by nice representative confocal images. Then, the authors assessed COC expansion and oocyte meiotic maturation to determine whether the loss of oocyte membrane β-catenin and E-cadherin upon N-cadherin inhibitor treatment disrupts the bi-directional communication between cumulus cells and the oocyte. Indeed, N-cadherin antagonists disrupted both processes (cumulus expansion and oocyte meiotic). However, the expression of known mediators of COC expansion (E.g., Areg and Ptgs2) were either increased or unaffected. Nevertheless, RNA-Seq showed consistent effects on cell signaling mRNA genes by the antagonist CRS-066.

In vivo studies using mice were also achieved using stimulated protocols (together with one of the antagonists or vehicle) or granulosa-specific Cdh2 Knockouts to further analyze the role of N-cadherin. N-cadherin antagonist CRS-066 (but not LCRF-0006) significantly reduced mouse ovulation compared to controls. RNA-sequencing data analysis identified distinct gene expression profiles in CRS-066 treated compared to control ovaries. Ovulation in CdhFl/FL; Amhr2Cre mice after stimulation were also significantly reduced; multiple large unruptured follicles were observed in these granulosa-specific Cdh2 mutant ovaries, and the mRNA expression of Areg and Ptgs2 were reduced.

The authors conclude that their study identified N-cadherin as a mechanosensory regulator important in ovarian granulosa cell differentiation able to respond to hormone stimuli both in vivo and in vitro, demonstrating a critical role for N-cadherin in ovarian follicular development and ovulation. They highlighted the potential to inhibit ovulation by targeting this signaling mechanism.

Strengths:

This remarkable manuscript is very well designed, performed, and discussed. The authors analyzed different aspects, and their data supports their conclusions.

Weaknesses:

This study was performed using the mouse as a research model; further studies in larger animals and humans would be interesting and warranted.

Indeed, this would be interesting. Ongoing research into therapeutic applications of N-cadherin targeting is reviewed in Blaschuk OW. Front Cell Dev Biol. 2022 Mar 3;10:866200

Minor comments:

Some results are intriguing. While the AREG y PTGS2 mRNA increased within the COC in vitro by the N-cadherin antagonists, in vivo, the treatment induced a significant increase in both genes when analyzing the whole ovary. What are the authors' ideas that could explain these discrepancies in outcomes?

Comparing the responses in IVM COCs to in vivo whole ovaries carries multiple caveats, though as noted, the observations are consistent with altered mechanotransduction in each case. It is important to note the change in pre-ovulatory follicle gene expression in vivo, which likely affects the response of follicles to ovulatory stimulus.

The authors stated that the ovaries from mice treated in the same manner and collected either before hCG treatment (eCG 44 h) or 11 h after hCG showed equivalent numbers of follicles at each stage of development from primary to antral. However, in Panel l from Figure 5, there is a significant increase in the number of antral follicles in the CRS-066 group (hCG 11 h) compared to the vehicle. Could the author discuss it in the manuscript?

A small change in these follicle types was significant in hCG 11h treated mice and is consistent with the altered response to the ovulatory stimulus and reduced ovulation resulting in persistent antral follicles.

Recommendations For The Authors:

Reviewer #1 (Recommendations For The Authors):

Is the mechanism by which the small molecules block N-cad's adhesive activity known? And is the stable residence of cadherins in the plasma membrane known to depend on their engagement with other cadherins either in cis or in trans?

Adhesion interactions between N-cadherin in Cis or Trans results in their clustering and enrichment at the membrane. Molecular docking models of the small molecule N-cadherin inhibitors are not available. However, these inhibitors were designed as peptidomimetics of the N-cadherin amino-terminus that is shown to interacts in Trans with N-cadherin on neighbouring cells (Blaschuk OW. Front Cell Dev Biol. 2022 Mar 3;10:866200).

Since the inhibitors are blocking cadherin activity, one might have expected the cumulus cell mass to disaggregate into individual cells. Yet, Figures 3a and 3c show that this does not happen. Could the authors speculate how the cells are being held together?

Author Response

We appreciate the insightful feedback provided by the editors and reviewers who have recognized the novelty of our study. We have mapped the spatial distribution of six endogenous somatic histone H1 variants within the nuclei of several human cell lines using specific antibodies, which strongly suggest functional differences between variants. We will submit a reviewed version of the manuscript to accommodate the reviewers comments.

To answer the reviewers comments at this stage:

1. We do have investigated co-localization of H1 variants with HP1 proteins and we are eager to add some of this data in a revised version of this manuscript.

2. Respect to the functional significance of the results presented here, we want to stress that as a consequence of the differential distribution and abundance of H1 variants among cell types, depletion of different variants has different consequences. For example, H1.2 depletion but not others has a great impact on chromatin compaction. Besides, cell lines lacking H1.3/H1.5 expression present a basal up-regulation of some Interferon stimulated genes (ISGs) and particular repetive elements, as it was previously described upon induced depletion of H1.2/H1.4 in a breast cancer cell line or in pancreatic adenocarcinomas with lower levels of replication-dependent H1 variants (Izquierdo et al. 2017 NAR 45:11622). So, our results reinforce the existing link between H1 content and immune signature. We are eager to add this data in a revised version of this manuscript. Moreover, we also analyzed the chromatin structural changes upon combined depletion of H1.2 and H1.4. Combined H1.2/H1.4 depletion triggers a global chromatin decompaction, which supports previous observations from ATAC-Seq and Hi-C experiments in these cells (Izquierdo et al. 2017 NAR 45:11622; Serna-Pujol et al. 2022 NAR 50:3892). Although H1 content is more compromised in these cells (30% total H1 reduction) compared to single H1 KDs, the phenotype observed could not be recapitulated when other H1 KD combinations, in which total H1 content was reduced similarly, were investigated (Izquierdo et al. 2017 NAR 45:11622), supporting that the deleterious defects were due to the non-redundant role of H1.2 and H1.4 proteins. Indeed, this manuscript supports this notion, as H1.2 and H1.4 show a different genome-wide and nuclear distribution.

3. We totally agree with the reviewers that the use of commercially available antibodies does not guarantee their quality and specificity. As this issue was crucial for our studies, we extensively assayed performance and specificity of the antibodies, using different approaches. The validations were shown in our previous publications where these antibodies where successfully used for ChIP-seq (Serna et al. 2022 NAR 50:3892; Salinas-Pena et al, under revision). In summary, performance of H1.0 (05-629l, Millipore), H1.2 (ab4086, abcam), H1.4 (702876; Invitrogen), H1.5 (711912, Invitrogen) and H1X (ab31972; abcam) antibodies was tested by Western-Blot, ChIP and proteomic analyses (all the results are included in Supplementary Figure 1 in Serna et al. 2022 NAR 50:3892). Concretely, we tested specificity using inducible KDs for the depletion of each of the somatic H1 variants in T47D. We also checked that the antibodies did not recognize additional H1 variants using recombinant proteins or cell lines naturally lacking some of the variants. All the experiments confirmed that antibodies were variant-specific. In addition, when the corresponding epitope was absent, the antibodies did not gain new cross-reactivity with other variants. More recently, validation of the specificicity of the H1.3 antibody (ab203948) was performed following the same experimental approaches described for the rest of antibodies (Salinas-Pena et al, under revision).

4. Our immunofluorescence data, together with ChIP-seq data, do not discard binding of H1 variants to a great variety of chromatin, but show enrichment or preferential binding to certain regions or chromatin types. Our data on the interphase nuclei does not suggest at all any type of quenching or saturation. Obviously, detection with antibodies depends on epitope accessibility, just like all immunofluorescence data ever published, and we have acknowledged that post-translational modifications of H1 may occlude antibody accessibility as some phospho-H1 antibodies give distribution patterns different than total/unmodified H1 antibodies. Thus, we cannot exclude that specific modified-H1s exhibit particular distribution patterns that are not being recapitulated in our data. This represents another layer of complexity in H1 diversity and we agree that exploration of the repertoire of H1 PTMs and their functional roles are an interesting matter of study that needs to be addressed. Still, our data is highly relevant as it demonstrates for the first time the unique distribution patterns of H1 variants among multiple cell lines and it does not use overexpression of tagged H1 variants that in our experience produces mislocalization of H1s.

5. We will further explain how the relative quantification of H1 variants in different cell lines was performed if not clear enough. We agree that more sophisticated mass spectrometry-based quantification is desirable and we are collaborating to do this using internal H1 peptide controls, but this is out of the scope of this manuscript as the observed patterns of distribution of H1 variants do not depend on mild differences in variants abundance. Only the absence of H1.3 and H1.5 in some cell lines alters the distribution of other variants.

6. We have also studied the spatial distribution of H1 variants in non-tumorogenic cell lines and we are eager to add this in a revised version of the manuscript.

Author Response

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

Reviewer 1:

Public review:

In this study, Porter et al report on outcomes from a small, open-label, pilot randomized clinical trial comparing dornase-alfa to the best available care in patients hospitalized with COVID-19 pneumonia. As the number of randomized participants is small, investigators describe also a contemporary cohort of controls and the study concludes about a decrease of inflammation (reflected by CRP levels) aJer 7 days of treatment but no other statistically significant clinical benefit.

Suggestions to the authors:

• The RCT does not follow CONSORT statement and reporting guidelines

We thank you for this suggestion and have now amended the order and content of the manuscript to follow the CONSORT statement as closely as possible.

• The authors have chosen a primary outcome that cannot be at least considered as clinically relevant or interesting. AJer 3 years of the pandemic with so much research, why investigate if a drug reduces CRP levels as we already have marketed drugs that provide beneficial clinical outcomes such as dexamethasone, anakinra, tocilizumab and baricitinib.

We thank the reviewer for bringing up this central topic. The answer to this question has both a historical and practical component. This trial was initiated in June of 2020 and was completed in June of 2021. At that time there were no known treatments for the severe immune pathology of COVID19 pneumonia. In June 2020, dexamethasone data came out and we incorporated dexamethasone into the study design. It took much longer for all other anti-inflammatories to be tested. Hence, our decision to trial an approved endonuclease was based purely on basic science work on the pathogenic role of cell-free chromatin and NETs in murine sepsis and flu models and the ability of DNase I to clear them and reduce pathology in these animal models. In addition, evidence for the presence of cell-free chromatin components in COVID-19 patient plasma had already been communicated in a pre-print. Finally, several studies had reported the anti-inflammatory effects of dornase treatment in CF patients. Hence there was a strong case for a cheap, safe, pulmonary noninvasive treatment that could be self-administered outside the clinical se]ng.

The Identification of novel/repurposed treatments effective for COVID-19 were hampered by patient recruitment to competing studies during a pandemic. This resulted in small studies with inconclusive or contrary findings. In general, effective treatments were only picked up in very large RCTs. For example, demonstrating dexamethasone as effective in COVID-19 required recruitment of 6,425 patients into the RECOVERY study. Multiple trials with anti-IL-6 gave conflicting evidence until RECOVERY recruited 4116 adults with COVID-19 (n=2022, tocilizumab and 2094, control) similar for Baracitinib (4,148 randomised to treatment and 4,008 to standard care). Anakinra is approved for patients with elevated suPAR, based on data from one randomized clinical trial of 594 patients, of whom 405 had active treatment (PMID: 34625750). However, a systematic review analysing over 1,627 patients (anakinra 888, control 739) with COVID-19 showed no benefit (PMID: 36841793). Regarding the choice of the primary endpoint, there is a wealth of clinical evidence to support the relevance of CRP as a prognostic marker for COVID-19 pneumonia patients and it is a standard diagnostic and prognostic clinical parameter in infectious disease wards. This choice in March 2020 was supported by evidence of the prognostic value of IL-6; CRP is a surrogate of IL-6. We also provide our own data from a large study of severe COVID-19 pneumonia in figure 1, showing how well CRP correlates with survival.

In summary, our data suggest that Dornase yields an anti-inflammatory effect that is comparable or potentially superior to cytokine-blocking monotherapies at a fraction of the cost and potentially without the additional adverse effects such as the increase for co-infections.

We now provide additional justification on these points in the introduction on pg.4 as follows:

“The trial was ini.ated in June 2020 and was completed in September of 2021. At the start of the trial only dexamethasone had been proven to benefit hospitalized COVID-19 pneumonia pa.ents and was thus included in both arms of the trial. To increase the chance of reaching significance under challenging constraints in pa.ent access, we opted to increase our sample size by using a combina.on of randomized individuals and available CRP data from matched contemporary controls (CC) hospitalized at UCL but not recruited to a trial. These approaches demonstrated that when combined with dexamethasone, nebulized DNase treatment was an effec.ve an.-inflammatory treatment in randomized individuals with or without the implementa.on of CC data.”

We also added the following explanation in the discussion on pg. 16:

“Our study design offered a solution to the early screening of compounds for inclusion in larger platform trials. The study took advantage of frequent repeated measures of quantifiable CRP in each patient, to allow a smaller sample size to determine efficacy/futility than if powered on clinical outcomes. We applied a CRP-based approach that was similar to the CATALYST and ATTRACT studies. CATALYST showed in much smaller groups (usual care, 54, namilumab, 57 and infliximab, 35) that namilumab that is an antibody that blocks the cytokine GM-CSF reduced CRP even in participants treated with dexamethasone whereas infliximab that targets TNF-α had no significant effect on CRP. This led to a suggestion that namilumab should be considered as an agent to be prioritised for further investigation in the RECOVERY trial. A direct comparison of our results with CATALYST is difficult due to the different nature of the modelling employed in the two studies. However, in general Dornase alfa exhibited comparable significance in the reduction in CRP compared to standard of care as described for namilumab at a fraction of the cost. Furthermore, endonuclease therapies may prove superior to cytokine blocking monotherapies, as they are unlikely to increase the risk for microbial co-infections that have been reported for antibody therapies that neutralize cytokines that are critical for immune defence such as IL-1β, IL-6 or GM-CSF. “

• Please provide in Methods the timeframe for the investigation of the primary endpoint

This information is provided in the analysis on pg. 8:

“The primary outcome was the least square (LS) mean CRP up to 7 days or at hospital discharge whichever was sooner.”

• Why day 35 was chosen for the read-out of the endpointt?

We now state on pg. 8 that “Day 35 was chosen as being likely to include most early mortality due to COVID-19 being 4 weeks after completion of a week of treatment. ( i.e. d7 of treatment +28 (4 x 7 days))”

• The authors performed an RCT but in parallel chose to compare also controls. They should explain their rationale as this is not usual. I am not very enthusiastic to see mixed results like Figures 2c and 2d.

We initially aimed at a fully randomized trial. However, the swiJ implementation of trial prioritization strategies towards large and pre-established trial plamorms in the UK made the recruitment COVID19 patients to small studies extremely challenging. Thus, we struggled to gain access to patients. Our power calculations suggested that a mixed trial with randomized and contemporary controls was the best way forward under these restrictions in patient access that could provide sufficient power.

That being said, we also provide the primary endpoint (CRP) results in Fig. 3B as well as the results for the length of hospitalization (Fig. S3D) for the randomized subjects only.

• Analysis is performed in mITT; this is a major limitation. The authors should provide at least ITT results. And they should describe in the main manuscript why they chose mITT analysis.

We apologize if this point was confusing. We performed the analysis on the ITT as defined in our SAP: “The primary analysis population will be all evaluable patients randomised to BAC + dornase alfa or BAC only who have at least one post-baseline CRP measurement, as well as matched historical comparators.”

We understand that the reason this might be mistaken as an mITT is because the N in the ITT (39) doesn’t match the number randomised and because we had stated on pg. 8 that “ Efficacy assessments of primary and secondary outcomes in the modified inten.on-to-treat popula.on were performed.”

However, we did randomise 41 participants, but:

One participant in the DA arm never received treatment. The individual withdrew consent and was replaced. We also have no CRP data for this participant in the database, so they were unevaluable, and we couldn’t include them in the baseline table even if we wanted to. In addition, 1 participant in BAC only had a baseline CRP measurement available. Hence not evaluable as we only have a baseline CRP measurement for this participant.

We have corrected the confusing statement on pg. 8 and added an additional explanation.

“Efficacy assessments of primary and secondary outcomes in the inten.on-to-treat (ITT) popula.on were performed on all randomised par.cipants who had received at least one dose of dornase alfa if randomized to treatment. For full details see Sta.s.cal Analysis Plan. The ITT was adjusted to mi.gate the following protocol viola.ons where one par.cipant in the BAC arm and one in the DA arm withdrew before they received treatment and provided only a baseline CRP measurement available. The par.cipant in the DA arm was replaced with an addi.onal recruited pa.ent. Exploratory endpoints were only available in randomised par.cipants and not in the CC. In this case, a post hoc within group analysis was conducted to compare baseline and post-baseline measurements.”

• It is also not usual to exclude patients from analysis because investigators just do not have serial measurements. This is lost to follow up and investigators should have pre-decided what to do with lost-to-follow-up.

Our protocol pre-specified that the primary analysis population should have at least one postbaseline CRP measurement (pg. 13 of protocol). The patient that was excluded was one that initially joined the trial but withdrew consent after the first treatment but before the first post-treatment blood sample could be drawn. Hence, the pre-treatment CRP of this patient alone provided no useful information.

• In Table 1 I would like to see all randomized patients (n=39), which is missing. There are also baseline characteristics that are missing, like which other treatments as BAT received by those patients except for dexamethasone.

Table 1 includes all 39 patients plus 60 CCs.<br /> Table 2 shows additional treatments given for COVID-19 as part of BAC.

• In the first paragraph of clinical outcomes, the authors refer to a cohort that is not previously introduced in the manuscript. This is confusing. And I do not understand why this analysis is performed in the context of this RCT although I understand its pilot nature.

One of the main criticisms we have encountered in this study has been the choice of the primary endpoint. The best way respond to these questions was to provide data to support the prognostic relevance of CRP in COVID-19 pneumonia from a separate independent study where no other treatments such as dexamethasone, anakinra or anti-IL6 therapies were administered. We think this is very useful analysis and provides essential context for the trial and the choice of the primary endpoint, indicating that CRP has good enough resolution to predict clinical outcomes.

• Propensity-score selected contemporary controls may introduce bias in favor of the primary study analysis, since controls are already adjusted for age, sex and comorbidities.

The contemporary controls were selected to best match the characteristics of the randomized patients including that the first CRP measurement upon admission surpassed the trial threshold, so we do not see how this selection process introduces biases, as it was blinded with regards to the course of the CRP measurements. Given that this was a small trial, matching for baseline characteristics is necessary to minimize confounding effects.

• The authors do not clearly present numerically survivors and non-survivors at day 34, even though this is one of the main secondary outcomes.

We now provide the mortality numbers in the following paragraph on pg. 13.

“Over 35 days follow up, 1 person in the BAC + dornase-alfa group died, compared to 8 in the BAC group. The hazard ra.o observed in the Cox propor.onal hazards model (95% CI) was 0.47 (0.06, 3.86), which es.mates that throughout 35 days follow-up, there was a 53% reduced chance of death at any given .mepoint in the BAC + dornase-alfa group compared to the BAC group, though the confidence intervals are wide due to a small number of events. The p-value from a log-rank test was 0.460, which does not reach sta.s.cal significance at an alpha of 0.05.”

• It is unclear why another cohort (Berlin) was used to associate CRP with mortality. CRP association with mortality should (also) be performed within the current study.

As we explained above, the Berlin cohort CRP data serve to substantiate the relevance of CRP as a primary endpoint in a cohort that experienced sufficient mortality as this cohort did not receive any approved anti-inflammatory therapy. Mortality in our COVASE trial was minimal, since all patients were on dexamethasone and did not reach the highest severity grade, since we opted to treat patients before they deteriorated further. The overall mortality was 8% across all arms of our study, which does not provide enough events for mortality measurements. In contrast the Berlin cohort did not receive dexamethasone and all patients had reached a WHO severity grade 7 category with mortality at 30%.

My other concerns are:

• This report is about an RCT and the authors should follow the CONSORT reporting guidelines. Please amend the manuscript and Figure 1b accordingly and provide a CONSORT checklist.

We now provide a CONSORT checklist and have amended the CONSORT diagram accordingly.

• Please provide in brief the exclusion criteria in the main manuscript

We have now included the exclusion criteria in the manuscript on pg. 6.

“1.1.1 Exclusion criteria

1. Females who are pregnant, planning pregnancy or breasmeeding

2. Concurrent and/or recent involvement in other research or use of another experimental inves.ga.onal medicinal product that is likely to interfere with the study medica.on within (specify .me period e.g. last 3 months) of study enrolment 3. Serious condi.on mee.ng one of the following:

a. Respiratory distress with respiratory rate >=40 breaths/min

b. oxygen satura.on<=93% on high-flow oxygen

1. Require mechanical invasive or non-invasive ven.la.on at screening

2. Concurrent severe respiratory disease such as asthma, COPD and/or ILD

3. Any major disorder that in the opinion of the Inves.gator would interfere with the evalua.on of the results or cons.tute a health risk for the trial par.cipant

4. Terminal disease and life expectancy <12 months without COVID-19

5. Known allergies to dornase alfa and excipients

6. Par.cipants who are unable to inhale or exhale orally throughout the en.re nebulisa.on period So briefly Patients were excluded if they were:

7. pregnant, planning pregnancy or breasmeeding

8. Serious condition meeting one of the following:

a. Respiratory distress with respiratory rate >=40 breaths/min

b. oxygen satura.on<=93% on high-flow oxygen

1. Require ven.la.on at screening

2. Concurrent severe respiratory disease such as asthma, COPD and/or ILD

3. Terminal disease and life expectancy <12 months without COVID-19

4. Known allergies to dornase alfa and excipients

5. Participants who are unable to inhale or exhale orally throughout the en.re nebulisa.on period”

• "The final trial visit occurred at day 35." "Analysis included mortality at day 35". I am not sure I understand why. In clinicaltrials.gov all endpoints are meant to be studies at day 7 except for mortality rate day 28. Why day 35 was chosen? Please be consistent.

Thank you for identifying this inconsistency. We have amended the record on clinicaltrials.gov to read ‘’the time to event data was censored at 28 days post last dose (up to d35) for the randomised participants and at the date of the last electronic record for the CC.”

• Please provide in Methods the timeframe for the investigation of the primary endpoint

• The authors performed an RCT but in parallel chose to compare also controls. They should explain their rationale as this is not usual. I am not very enthusiastic to see mixed results like Figures 2c and 2d.

• Analysis is performed in mITT; this is a major limitation. The authors should provide at least ITT results. And they should describe in the main manuscript why they chose mITT analysis.

• It is also not usual to exclude patients from analysis because investigators just do not have serial measurements. This is lost to follow up and investigators should have pre-decided what to do with lost-to-follow-up.

• Figure 1b as in CONSORT statement, please provide reasons why screened patients were not enrolled.

• In Table 1 I would like to see all randomized patients (n=39), which is missing. There are also baseline characteristics that are missing, like which other treatment as BAT received those patients except for dexamethasone.

• In the first paragraph of clinical outcomes, the authors refer to a cohort that is not previously introduced in the manuscript. This is confusing. And I do not understand why this analysis is performed in the context of this RCT although I understand its pilot nature.

• In Figure 2 the authors draw results about ITT although in methods describe that they performed an mITT analysis. Please be consistent.

Please see answers provided to these queries above.

Reviewer #2 (Recommendations For The Authors):

1) Suppl Figure 2B would be more informative if presented as a Table with N of patients with per day sampling

We now provide the primary end point daily sampling table in Table 3.

2) The numbers at risk should figure under the KM curves

The numbers at risk for figures 1E, 2C, 2D have been added as graphs either in the main figures or in the supplement.

3) HD in Supplementary figure 3 should be explained

We apologize for this omission. We now provide a description for the healthy donor samples that we used in the cell-free DNA measurements in figure S3B on pg. 14:

“Compared to the plasma of anonymized healthy donors volunteers at the Francis Crick ins.tute (HD), plasma cf-DNA levels were elevated in both BAC and DA-treated COVASE par.cipants.

4) Presentation is inappropriate for Table S4

We thank the reviewer for pointing this issue. We have now formaxed Table S4 to be consistent with all other tables.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public review):

Summary:

This manuscript is a focused investigation of the phosphor-regulation of a C. elegans kinesin-2 motor protein, OSM-3. In C-elegans sensory ciliary, kinesin-2 motor proteins Kinesin-II complex and OSM-3 homodimer transport IFT trains anterogradely to the ciliary tip. Kinesin-II carries OSM-3 as an inactive passenger from the ciliary base to the middle segment, where kinesin-II dissociates from IFT trains and OSM-3 gets activated and transports IFT trains to the distal segment. Therefore, activation/inactivation of OSM-3 plays an essential role in its ciliary function.

Strengths:

In this study, using mass spectrometry, the authors have shown that the NEKL-3 kinase phosphorylates a serine/threonine patch at the hinge region between coiled coils 1 and 2 of an OSM-3 dimer, referred to as the elbow region in ubiquitous kinesin-1. Phosphomimic mutants of these sites inhibit OSM-3 motility both in vitro and in vivo, suggesting that this phosphorylation is critical for the autoinhibition of the motor. Conversely, phospho-dead mutants of these sites hyperactivate OSM-3 motility in vitro and affect the localization of OSM3 in C. elegans. The authors also showed that Alanine to Tyrosine mutation of one of the phosphorylation rescues OS-3 function in live worms.

Weaknesses:

Collectively, this study presents evidence for the physiological role of OSM-3 elbow phosphorylation in its autoregulation, which affects ciliary localization and function of this motor. Overall, the work is well performed, and the results mostly support the conclusions of this manuscript. However, the work will benefit from additional experiments to further support conclusions and rule out alternative explanations, filling some logical gaps with new experimental evidence and in-text clarifications, and improving writing before I can recommend publication.

We appreciate Reviewer #1’s comments and suggestions. We have now provided additional evidences and discussions to further support our conclusions and fill the logical gaps. We have also provided alternative explanations to our data and improved writing.

Reviewer #2 (Public review):

Summary:

The regulation of kinesin is fundamental to cellular morphogenesis. Previously, it has been shown that OSM-3, a kinesin required for intraflagellar transport (IFT), is regulated by autoinhibition. However, it remains totally elusive how the autoinhibition of OSM-3 is released. In this study, the authors have shown that NEKL-3 phosphorylates OSM-3 and releases its autoinhibition.

The authors found NEKL-3 directly phosphorylates OSM-3 (although the method is not described clearly) (Figure 1). The phophorylated residue is the "elbow" of OSM-3. The authors introduced phospho-dead (PD) and phospho-mimic (PM) mutations by genome editing and found that the OSM-3(PD) protein does not form cilia, and instead, accumulates to the axonal tips. The phenotype is similar to another constitutive active mutant of OSM-3, OSM-3(G444A) (Imanishi et al., 2006; Xie et al., 2024). osm-3(PM) has shorter cilia, which resembles with loss of function mutants of osm-3 (Figure 3). The authors did structural prediction and showed that G444E and PD mutations change the conformation of OSM-3 protein (Figure 3). In the single-molecule assays G444E and PD mutations exhibited increased landing rate (Figure 4). By unbiased genetic screening, the authors identified a suppressor mutant of osm-3(PD), in which A489T occurs. The result confirms the importance of this residue. Based on these results, the authors suggest that NEKL-3 induces phosphorylation of the elbow domain and inactivates OSM-3 motor when the motor is synthesized in the cell body. This regulation is essential for proper cilia formation.

Strengths:

The finding is interesting and gives new insight into how the IFT motor is regulated.

Weaknesses:

The methods section has not presented sufficient information to reproduce this study.

We appreciate that Reviewer #2 is also positive to our study. We have now provided sufficient information in the revised Methods section.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Major Concerns

(1) Why do the authors think that NEKL-3 phosphorylates OSM-3 in the first place? This seems to come out of nowhere and prior evidence indicating that NEKL-3 may be phosphorylating OSM-3 is not even mentioned in the Introduction.