Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

---

Referee #3

Evidence, reproducibility and clarity

The work is interesting. Cumulin is a heterodimer hormone formed of GDF9 and BMP15. It is the main oocyte secreted factor. Being an heterodimer, gene knockout provides very little information about its mechanism of action. The team has a unique form of cumulin that is stable. This is why I think this work is important. However, I found two technical issues: one regarding mitochondrial count using MitoTracker and the other about comparing gene lists between the two cell types when protein input submitted to mass spectrometry differ between the two cell types. It is expected to find more with more input material. The text would need to be adjusted accordingly. Also, there is a lot of free statements and a lack of precision that is annoying. In my opinion, there are many overstatements that are not supported by the data because the work was not designed to test what is stated. The Discussion is very circular as the same statements come back on the next pages.

Detailed review:

The manuscript entitled "Oocyte and cumulus cell cooperativity and metabolic plasticity under the direction of oocyte paracrine factors" reports an in depth analysis of the exposure of cumulus oocyte complexes to either BMP15 or cumulin, the GDF9-BMP15 heterodimer. Impact assessment was done by determining developmental competence of the exposed oocytes, comparative profiling of the proteomes and spectral emissions as well as testing a potential impact at the ultrastructure level by electron microscopy imagery. Mitochondrial respiration as well as abundance of related metabolites was contrasted between the two treatments.

Overall, the work is interesting. It is very difficult to study hormonal heterodimers because they originate from two different genes and they can naturally be found in a monomeric as well as a dimeric state. Such functional analysis cannot be done using gene knockout mouse lines. Genetic disruption provided the background that GDF9 and BMP15 are key oocyte secreted factors however only functional work as the one presented in this manuscript can find the mechanisms of action of these hormones.

Comments:

I really appreciated the reference to auto-symbiosis. We often see the reference to a cellular syncytium but this one is interesting.

Although I appreciated the work, two important technical issues (between cell types comparisons and mitochondrial count) have been raised and there is a bit of unnecessary overselling throughout the manuscript. Sticking to the results would keep the value of the work high and wouldn't give that impression of overstatement.

Technical issues:

While the gene/protein enrichment analysis can be influenced by the input material submitted to mass spectrometry, the gene network analysis is influenced by the number of gene/proteins available for the enrichment analysis. It is thus difficult to compare both cell types.

Also, when performing GO terms analysis, the level of "branching" can explain the results. In other words, GO terms are organized in a tree like structure where general elements (e.g. nucleus) are delineated in finer elements (e.g. nuclear function) leading to finer ones (e.g. DNA binding)... to finer ones (e.g. DNA repair)... etc. The number of genes/proteins available in the initial list directly dictates to which level of precision the analysis can reach. In the present work, the number of identified network may simply reflect the number of elements available in the initial lists. With more info on the cumulus cells side, it is logical to be able to reach finer branches that contain only a few genes. I have looked in the supplemental data files but could not find more info about the background used. Was it all known proteins? Was it all identified proteins where the differentially expressed proteins are compared to the detected proteins? Using the list of detected proteins as background for the analysis could help. Proteome Discoverer generated much less differentially expressed proteins between treatments than Mascot/Scaffold (2-17 vs. 74-390). Maybe use the Mascot/Scaffold data using the same number of top genes (e.g. 87) between both cell types. Then it would be much more comparable.

Line 226 and 324-328 and line 350: I have never seen the use of MitoTracker Orange to count mitochondria. According to the manufacturer: "MitoTracker{trade mark, serif} Orange CMTMRos is an orange-fluorescent dye that stains mitochondria in live cells and its accumulation is dependent upon membrane potential. The dye is well-retained after aldehyde fixation." It is indicative of mitochondrial potential but it is not a method to count the number of mitochondria within a cell. I do not agree that more fluorescence means more mitochondria.

I understand that the MitoTracker data is counterintuitive to the oxygen consumption rate and stable levels of energetic metabolites. However, as the authors mention, mitochondria are known to be capable of switching from aerobic to anaerobic energy production. In some cases, heterogeneity in the mitochondrial population (such as the one in the oocyte) could mean that a mosaic respiratory potential exists where some mitochondria are more aerobic than others... To change the number of mitochondria, either fission or mitophagy must occur. Although mitochondrial DNA replication is done in approximatively 2 h and fission/division can occur over 1 h, and protein ubiquitination is done over 12 h-18 h during mitophagy, TEM micrographs (figure 5) do not show elongated mitochondria in the process of division. To detect active mitophagy, protein markers and association with lysosome would be needed. A shift in mitochondrial number may not be the suitable interpretation of the data.

For the spectral data analysis (Figure 3D), how did the three replicates perform? The figure does not show the replication variance relative to the treatment variance.

Wording/interpretation issues

Lines 114-116: "This intercellular cooperativity facilitates oocyte maturation while simultaneously protecting germ-line genomic integrity, in a manner which could not be achieved by a single cell." This is an overstatement because genomic integrity was not assessed. Why consider that the nuclear function found in the proteome contrast is necessarily associated with genomic integrity. Miosis requires in dept chromatin handling. What evidence provided from the results is associated with cellular numbers. The presence of cumulus cells is known to support meiosis but it doesn't mean that some of the cellular processes have been imparted to the surrounding somatic cells. The work done for this manuscript does not test any of this claim.

On numerous occasions, the statements are imprecise. For example: Line 274: "More than double the number..." Since doubling a minute value does not mean the same thing as doubling a large value, values, measurements with units and ideally with SEM should be added.

Line 287: "... and almost a third more significant networks..." Please add values.

On the same statement, since sample input material to the mass spectrometry is vastly different between cumulus cells and oocytes, is it truly comparable? Could these differences between the two cell types be associated with the amounts of proteins in the extracted samples? Typically, more variable results are obtained with the low input. It sometimes lead to apparently more difference between treatments simply because of low count numbers. On line 292, authors mentioned that protein loading was considered. How was that done? Low input cannot be compensated or normalized. The following statement on line 293 indicate that more proteins were identified in cumulus cells. This is probably due to more input material submitted to mass spectrometry. It is not necessarily a difference in protein diversity between cumulus cells and oocytes.

Line 293: "... resulted in the identification of about double the number..." Please add values.

Line 294: "However, there were 4-5 times as many differentially expressed proteins..." Please add values.

Line 298: "...difference was quite marked..." More factual info should be added.

Line 305: Again, the whole comparison between cell types could be argued from the standpoint of input material subjected to the analysis. Given the point is to state that cumulin has a profound impact on cumulus cells, maybe it is not necessary to compare with the oocyte data. It is logical that an oocyte secreted factor targets the neighbouring cells. The point can be made without raising the question about the potential issue of input material.

Line 317-317: "... exhibited more rounded and swollen mitochondria..." How was that determined? In the periphery of the oolemma, mitochondria aggregates in clusters which can be quite different from one another. Maybe proportions of different shapes of mitochondria could be provided if enough mitochondria are counted from the EM micrographs.

Line 169: What do you mean by "The results were merged based on consistency..."? This seems to be a trivial way to analyse the data.

Line 170: "A further requirement was that at least one, if not both methods..." Again, when did you decide to use one method or to use both? Why not use the common ground from both methods?

Line 384: Is the paracrine signaling remodeling COC metabolism or is it enhancing the rate at which it is done? I believe this switch in metabolism occurs in untreated COCs.

The Discussion is somewhat circular. Section will need to be adjusted if the Mitotracker-based mitochondrial count and between cell types gene/protein lists comparisons are removed.

Accounts for mitochondrial counts: (lines 387-393) (lines 424-427) (line 463).

Accounts for comparisons of gene lists length between cell types: Lines 389-391 and 475-477 and 496-499).

Line 395: "... a substantial number of oocyte upregulated proteins... Please provide number.

Line 397: The data was not designed to test the potential of cumulin to preserve meiotic fidelity. This is an overstatement since DNA binding is part of the normal course of even during meiosis. Again, cumulin could accelerate the kinetic of meiosis.

Line 402-405: the work was not designed to determine if cumulin would shift work allocation between the oocyte and the cumulus cells. Showing that cumulin drives meosis is interesting by itself.

Line 453-455: the link with the epigenome is an overstatement. RNA and DNA processing pathways are general cellular processes.

Minor details

Line 36: I suggest to be more precise on the "nuclear" function that is affected in the oocyte. Given that oocytes are transcriptionaly quiescent at this stage, some might argue that it is a vague statement.

In vitro should be in italic because it is Latin.

Lines 125-126: are the batch numbers relevant to anything?

Line 168: Mascor = Mascot

Line 168: a reference for the software?

Line 178: need a reference for the software?

Line 187: Need a complete source for "Procure, 812"

Line 188: Need a complete source for "Diatome"

Line 197: Need a complete source for "Cell-Tak"

Line 232: though = through

Line 243: define OCR

Line 268: If I am not mistaking, it is not a multispectral analysis. The multispectral values were analysed through a principal component analysis.

Line 363: What is the "behaviour" of an oocyte and cumulus cells?

Line 512-513: Maybe add more on the fact that most clinics use ovulated eggs and do not perform IVM. However, IVM is needed is specific contexts such as PCOS.

Significance

Cumulin is the most potent oocyte secreted factor. Its mecanism of action is still unknown.

I have been working on the mammalian oocyte for the past 25 years.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

---

Referee #2

Evidence, reproducibility and clarity

Summary:

The report by Richani et al, presents a research carried out in mice, in which they treated cumulus-oocyte complexes with either BMP15 and cumulin. Upon treatment they evaluated a series of biologically relevant parameters in oocytes and cumulus cells. Their findings indicate that the treatment with these molecules alter the molecular composition of oocytes and cumulus cells (proteome and metabolome), mitochondrial morphology in cumulus cells and overall oxygen consumption in COCs.

Major comments:

• Are the key conclusions convincing?
• Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?
  • part of the discussion related to metabolic pathways being up regulated due to the treatments need to the revised because For instance, It is hard for me to grasp how a pathway with 2 proteins achieved FDR significance below 0.01, as I see in figure 4c
  • In the discussion the authors use the term "oocyte secreted factors" a lot (one example lanes 490, 515, 516, 517), but they should specify BMP15 and cumulin, because these were their treatments.
  • Including in the title, you did not evaluate all oocyte paracrine factors, just BMP15 and cumulin
• Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation.

NA - Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated cost and time investment for substantial experiments.

NA - Are the data and the methods presented in such a way that they can be reproduced? - no, in some instances, the methods are not described, see my comment below about enrichment analysis. - Are the experiments adequately replicated and statistical analysis adequate? - I was not able to access enrichment analysis. - lines 241-242: "MitoTracker staining and data from metabolite analysis by mass spectrometry were analysed by one-way ANOVA with Tukey's (parametric data) or Kruskal-Wallis (non- parametric data) post-hoc tests. " Specify which test was used for which data

Minor comments:

• Specific experimental issues that are easily addressable.

NA - Are prior studies referenced appropriately?

Yes - Are the text and figures clear and accurate? - lines 178-180: "expressed proteins list was further analyzed using STRING software to explore clustering and enrichment of specific molecular functions, and biological pathways. Detailed methodology and rationale for this approach is provided in the supplementary methods." I did not read text in the supplementary materials indicating how enrichment analysis was carried out. - What was the concentration of treatment for the samples used for proteome and mascot/scaffold experiments? - lanes 263 and 264: "Cell types and treatment conditions can be clearly distinguished based on these orthogonal global approaches." I did not see what is the basis for this statement - I did not understand the discrepancy between the numbers observed in Figure 3A and Figure 3B. - I could not make sense of the shades of green or red that were used in 4C and 4D. Is the reader only supposed to make those comparisons within column? - Do you have suggestions that would help the authors improve the presentation of their data and conclusions? - Figure 4 is really hard to process. At least in my pdf it spanned 4 pages. - I did not understand why put networks that are not significant as up-regulated or down-regulated. Besides, as mentioned above, I do not know how significance was assessed..

Significance

• Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field. - Place the work in the context of the existing literature (provide references, where appropriate).

This paper is significant because it provided a variety of measurements following the treatment of cumulus cells with BMP15 and cumulin. The authors show that these two oocyte factors can impact the molecular structure, physiology and structure of organelles in cumulus cells. The work is well contextualized with the current literature. - State what audience might be interested in and influenced by the reported findings.

Researchers in the field of developmental biology would be most interested in this report. - Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate.

I do not have expertise in hyperspectral analysis. I have been working with cumulus-oocyte complexes for over a decade, mixing technologies in cell biology, microscopy, high-throughput genome, and proteome analysis. We do all our bioinformatics work in-house.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

---

Referee #1

Evidence, reproducibility and clarity

Summary:

Cells within multicellular organisms are mutually dependent on each other - cells of one type or in one location provide signals that can regulate the health and differentiation of the target cells that receive those signals. Such signalling can operate bi-directionally, emphasizing the co-dependence of cells upon each other. The ovarian follicle provides an excellent model system to study intercellular signaling and its consequences, in this case between the oocyte and the somatic granulosa cells that surround it. Oocytes secrete members of the TGFbeta growth factor family that are required for normal differentiation of the granulosa cells, which in turn is necessary for normal development of the oocyte. Here the autohors show that adding TGFB-type growth factors (cumulin or BMP15) to the cuture medium during in vitro maturation increases the fraction of oocytes that can reach the blastocyst stage (improved developmental competence) and alters the pattern of protein landscape in both the (cumulus) granulosa cells and the oocyte. Changes in the mitochondria and parameters relevant to energy metabolism are also altered. They conclude that these changes underpin the acquisition of developmental competence by the oocytes.

Major issues:

The authors are world leaders in this field and therefore exceptionally well-qualified to carry out the proposed work. There are a number of issues, however, that limit the confidence with which conclusions may be drawn.

First, the experimental strategy makes drawing inferences about the role of cumulin and BMP15 challenging. Maturing oocytes express GDF9 and BMP15 (the components of cumulin). Thus, the experiments are not comparing presence vs absence of cumulin and BMP15, but rather comparing oocytes and cumulus cells exposed to supra-physiological levels of these factors to controls that are exposed to physiological levels. In other words, the experimental setup detects changes that occur in response to higher than normal levels of the factors. Ideally, one would have complementary experiments where GDF9 and BMP15 were deleted from the system, to illustrate the effects of their absence. This would be a massive additional undertaking, however. Yet, without such experiments, relying on the results of the overexpression approach to understand the functions of cumulin and BMP15 at physiological levels is risky.

Second, the granulosa cells and oocytes interact throughout the prolonged period of growth, and this is the time when the beneficial effects of the granulosa cells on the oocyte have been most clearly documented. Yet the experiments focus on the much shorter period of meiotic maturation. This is when oocyte-granulosa cell interaction is being down-regulated, even if not entirely disrupted.

Third, the data reported illustrate associations or correlations, but no experiments test the function of the changes in the proteome or of the changes in the morphology of the mitochondria or ER. Which if any of these is linked to the improved development of the oocytes after fertilization is unknown. Moreover, no experiments address how the growth factors cause the observed changes, which occur over a period of a few hours.

Taken together, these issues unfortunately limit the potential impact of the work. But the amount of work required to address them would be substantial and not really feasible for this manuscript. The best route may be to present the work as an overexpression study that has identified associations, with a discussion that acknowledges the limitations of this approach.

Minor issues:

The text of the manuscript should be revised in a number of places. 32: We characterized the molecular mechanisms by which two model OSFs, cumulin and BMP15, regulate oocyte maturation and cumulus-oocyte cooperativity.

--Mechanistic studies were not performed.

40: Collectively, these data demonstrate that OSFs remodel cumulus cell metabolism during oocyte maturation in preparation for ensuing fertilization and embryonic development.

--No mechanistic studies demonstrate this.

46: Oocyte-secreted factors downregulate protein catabolic processes, and upregulate DNA binding, translation, and ribosome assembly in oocytes.

--No direct evidence is provided.

48: Oocyte-secreted factors alter mitochondrial number...

--Need to establish that the MitoTracker is a suitable tool to measure the number of mitochondria.

79: ...for maintaining genomic stability and integrity of the oocyte...

83: ...minimizing secondary production of potentially DNA damaging free radicals.

--Please provide supporting references from the literature.

373: This study provides a detailed exploration of the mechanisms by which oocyte-secreted factors...

--No mechanistic studies were performed.

383: Collectively, these data demonstrate that oocyte paracrine signaling remodels COC metabolism in preparation for ensuing fertilization and embryonic development.

--Studies do not show that the differences observed between control and treatment groups are related to fertilizability or embryonic development.

396: suggesting that cumulin affects meiosis in the oocyte and may increase meiotic fidelity...

--This statement is highly speculative.

409: ...lacks the machinery for amino acid uptake...

--Is the oocyte unable to take up any amino acids or only certain amino acids?

In general, the manuscript is written clearly. However, in several places, technical terms or jargon will make tough going for readers who are not already familiar with the techniques being used. These should be explained using language that will be understood by journal readers who are unfamiliar with the details of the techniques.

Examples include:

51: define metabolic workload using scientific terms.

67: metabolically 'inept' requires more precision.

262: explain 'multispectral analysis'

268: how is 'limited' overlap defined.

318: define higher workload

324: provide documentation or citations to support the assertion that the intensity of MitoTracker staining is an accurate proxy for the number of mitochondria.

358: Multispectral discrimination modelling utilised cellular image features from the autofluorescent profiles of oocytes and cumulus cells.

--Please clarify this merthodology and provide support for its utility.

360: intersection of union of 5-22%

Comments on Figures.

Fig. 3A, B. The total number of proteins and the number of differentially expressed proteins among the treatment groups don't match between A and B. For example, A (Mascot-Sheffield) indicates that 17 proteins were differentially expressed between untreated and cumulin-treated oocytes. B shows (138 + 74) expressed un the untreated but not cumulin-treated and (156 + 87) expressed in the cumulin-treated but not untreated. Please account for this difference.

Fig. 3D. What do the circles represent and how were their parameters (size, position) established?

Significance

These studies identify changes in cumulus cells and oocytes that occur in response to addition of cumulin or BMP15 to the culture medium during in vitro maturation. While the data are new, the significance of the advance is limited by (i) the fact that the control group were exposed to physiological levels of GDF9 and BMP15, so this is essentially an over-epxression study and (ii) no mechanistic studies experimentally tested how the observed changes (eg, in quantity of a specific protein) affect the developmental potential of the oocytes or cumulus cells.

Reviewer expertise: growth and meiotic maturation of the mammalian oocyte

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

Learn more at Review Commons

---

Reply to the reviewers

Reviewer #1 (Evidence, reproducibility and clarity (Required)):

This manuscript reports an investigation into the metabolic alterations induced by Zika virus (ZIKV) infection in human neuronal progenitor cells. The authors differentiated human iPSCs to derive neuronal progenitor cells (NPCs) at different days of incubation to represent the different stages of foetal CNS development. They found differences in the levels of ZIKV NS1 proteins as well as marginal differences in ZIKV titres in infected early and late hi-NPCs. Correspondingly, they also showed differences in glucose consumption, lipid metabolism and mitochondrial stress in ZIKV-infected early and late hi-NPCs. They concluded that differences in energy metabolism in neuronal progenitors both before and upon infection may contribute to the brain damage observed in congenital Zika syndrome.

The evidence supporting a role for dysregulated metabolism in mediating the pathogenesis of congenital Zika syndrome is gaining traction and findings from this study could add to this body of knowledge. However, in its present form, this study has several gaps that limit the extent to which it informs on the clinical pathogenesis of congenital Zika syndrome.

Major concerns: 1. The most important concern in this study is the strain of ZIKV used in all of the studies. ZIKV MP1751 was isolated from a mosquito and belongs to the African lineage of ZIKV. Unlike the Asian lineage ZIKV isolated from Latin America and French Polynesia, gestational infection with ZIKV of the African lineage has not been clinically associated with increased risk of foetal abnormality. It is thus uncertain how the changes observed in this study relates to the observed neonatal pathology. Perhaps a way to address this issue is to argue that a difference in these lineages it the ability of the virus to evade systemic and endothelial innate immune responses to cross the placental and blood brain barriers (several papers on attenuated ZIKV have shown this data). Once these barriers are breached, strain differences should not materially affect the similar pathogenic processes in neuronal cells, as also been shown by others using the MR766 strain of ZIKV. Such a discussion would be helpful to contextualise the clinical relevance of this study.

The authors agree with the reviewer and have now included a discussion paragraph to address this relevant point. In addition, the authors acquired a strain from the Asian lineage (PRVACB59) and performed a single round side-by-side infection in three patient-derived lines with the two different strains of ZIKV. From the infected samples, the authors measured glucose consumption, lactate release and viral output to demonstrate, within the same research, that in in vitro assays, whereas number of ZIKV particles available to infect pools of brain progenitors is not mediated by tissue tropism/advantage. This data has been included in the extended figure 5.

While the metabolic changes upon ZIKV infection are all interesting, how these changes affect CNS development is unclear. Figure 2F shows marginal impact on productive ZIKV infection and comparable extent of cell death in early and late hi-NPCs. What specific CNS pathology is dependent on the reported metabolic changes?

The authors acknowledge that a correlation of findings from in vitro research and Zika congenital syndrome would be e.g., observing greater differences in cell survival and/or viral replication kinetics. After revisions of the current data, the authors have reanalysed the datasets corresponding to glucose and lactate metabolism, cell survival and viral output and corrected the results and discussion, accordingly. This correction was done due by calculating cell survival/death using lactate dehydrogenase (LDH) readouts. Thus, the data from CCK-8 was replaced due to the conversion of tetrazolium-based salts are metabolically depending on NAD+ and mitochondrial function– which may well have skewed the results as Zika virus potentially alters mitochondrial homeostasis. The preliminary graphs presented in the previous version of this manuscript are presented in the extended data figure 8 for comparison purposes. Datasets corrected for LDH mirror the in-vitro observations in which Zika-infected early hi-NPCs exhibit greater cell death than late hi-NPCs – this may potentially translate to the fetal pathogenesis observed over different trimesters. The corrected data also shows a significantly greater ZIKV release from late hi-NPCs at 48 and 56 h.p.i. suggesting a window of efficient replication in these cells. Lastly, the authors have expanded the conclusion paragraph highlighting intrauterine pathogenesis correlated with impaired brain metabolism to link how metabolic changes induced by ZIKV may correlate with pathophysiological phenotypes aggravated during early trimesters.

Figure 2D: The most remarkable virological difference observed is the significant difference in cytoplasmic NS1 levels between early and late hi-NPCs at 56 hpi. Although the data in Fig 2D in general could have been compromised by the quality of the anti-NS1 mAb (the anti-E assay in Fig 2E used polyclonal antibody), it would have been useful to test for NS1 expression using western blot on a denaturing gel (and appropriate anti-NS1 antibody). The mAb used in this study binds a conformational epitope on NS1. The difference in data in Figure 2D and 2E could thus have been misfolding of NS1. Misfolded NS1 could contribute to ER stress that could be important for dysregulated CNS development. A more detailed investigation of the finding in Figure 2D could be highly informative.

The authors appreciate the comments and hypothesis that NS1 detection may have been compromised due to potential misfolding. This hypothesis was tested by the authors showing no detection of NS1 by denaturing western blot with the referred antibody. However, before using a different NS1 antibody to investigate this potentially relevant phenomenon, the authors attempted to detect NS1 by flow cytometry using fewer markers than in the previous experiments. The authors decided to take this approach due to, although to low levels, NS1 was detected by imaging flow cytometry at early timepoints. When using a combination of markers that did not compromise the signal of NS1 by light compensations and low signal secondary fluorophore, the authors successfully detected NS1 and to similar levels of the Envelope protein. Thus, the authors discarded the possibility that the lowered levels presented in the previous version were due to misfolded NS1. The graphs within Figure 2 have now been corrected with this newly generated dataset.

Figure 3A and related text: The fold-change in GLUT1, HK-1 and GAPDH expression are shown in log10 scale. In this scale, 1 would indicate 10-fold increase in expression. The data in Figure 3A are entirely inconsistent with the description in the related text. Which is correct?

The authors thank the reviewer and would like to highlight that both the Figure 3A and its description were correct in the previous version of this manuscript. The description of the data, however, may have been misleading and unclear thus, the authors have amended the text for clarity. Figure 3 displays the fold-change of key glycolytic genes in early and late Zika infected hi-NPCs, each normalised to their respective controls. The in-text description, besides highlighting this important feature of the ZIKV infection in hi-NPCs, it highlight a more important finding correlated to the significances computed when compare the ratio of fold change between infected early and late hi-NPCs.

Minor concerns:

1. Figure 5: The effects of ZIKV infection on the mitochondria of hi-NPCs are interesting and the comparison between ZIKV-infected and uninfected cells in the same culture is a strength of this study. It would be helpful to readers if the authors could include a discussion on the kinetics of ZIKV infection; diminished differences at 48 and 72 hours could be due to the mixture of cells infected at inoculation and hence observed at 24 hours and newly infected cells that were negative for ZIKV E protein at 24 hours. Emphasis should thus be on the 24 h data in Figures 5 C-E.

The authors thank the reviewer for highlighting the relevance of our experimental approach. The authors also thank for the interpretation of the data focused at early timepoints during the infection kinetics of ZIKV when the metabolic alterations are likely to be exclusive from cells infected at inoculation. The discussion of the data in this new revision of the manuscript emphasises the results based on the kinetics of infection and also clarify and strengthen the findings on Env+ and Env- cells within the pool of infected cells.

1. Hi-NPCs likely have a diploid genome and thus a finite lifespan. Using the term "cell line" to describe these cells is technically incorrect. Please consider using other terms, such as cell strain.

The authors appreciate the comment and have amended the text accordingly. The authors decided to use the terminology patient line.

Discussion section, 3rd paragraph, lines 6-7. The authors suggest thermal decay as an explanation for their observation yet Figure 2B argues against this explanation. Moreover, Kostyuchenko et al (Nature 2016; 533:425-8) have also shown that ZIKV is relatively thermostable. This explanation offered by the authors lack supporting evidence.

The authors thank the reviewer for the observation. Regarding the discussion on thermal decay, the authors aimed to highlight that circulating virions without available hosts to continue replication (due to cell death) may suffer from thermal decay as the difference in collection timepoints exceeds the tested 3 h reported in this research (Fig 2B). The results from Kostyuchenko et al (Nature 2016; 533:425-8) show thermal stability of ZIKV at different ranges of temperature yet, similar to Fig 2B, only under 2 h. Unpublished data from our group using an FFU assay shows that infectivity of ZIKV virions decrease after 10 h at 37C, knowledge that was used for the discussion. Moreover, the authors have strengthened the discussion by including in the discussion the native immunity of hi-NPCs at different stages of differentiation.

Discussion section, 3rd paragraph, line 16 and Supplementary Figure 2. I believe the authors are referring to Supplementary Figure 3 and not 2. The indentations observed could be due to ZIKV replication although the data, as presented is not convincing. Co-staining for ZIKV E protein would be useful.

The authors have corrected the issue and confirm that the reference to potential replication sites of ZIKV was made to the Extended Figure 3 rather than 2. To strengthen these findings, the authors have now acquired nuclear data by confocal imaging of infected late hi-NPCs co-stained for ZIKV E protein and DAPI. Representative images are included in the Extended Figure 6.

CROSS-CONSULTATION COMMENTS

• I fully agree with both Reviewers #2 and #3 on the quality of the immunofluorescence images and that these images alone are not sufficiently convincing to support the inferences the authors are making.

The authors would like to clarify that the confocal imaging displayed in the current manuscript was not used for the interpretation of the data but rather validation of the immunostaining used in fluorescence flow cytometry. The nuclear screening comprised the only result generated from microscopy analysis. The bulk of data presented on this manuscript was generated by imaging flow cytometry (Imagestream) due to the higher degree of unbiased screening and the larger sample size. The authors acknowledge that Imagestream produces low quality immunofluorescence imaging compared to confocal imaging but believe this is justified by the greater data and unbiased analysis offered by imaging flow cytometry. The power of analysis displayed in this research is unlikely to be achieved by confocal microscopy in which no more than 1.000 cells are screened whilst the authors screened 10.000 cells from each patient line to generate each dataset. Lastly, the authors acknowledge the lack of robustness in the images from nuclei and ZIKV replication thus, for late hi-NPCs in which the perinuclear replication sites where evident, data was acquired by confocal imaging; samples co-stained for ZIKV E protein and nuclei DAPI (Extended Figure 6).

• I also appreciate the first major comment from Reviewer #3. That is important insight and the authors should test their assumption that they have monocultures of human progenitor cells.

The authors have paraphrased the document for better clarity and accuracy as consider the text was confusing causing misinterpretation of the data. This research is intended to show the impact of ZIKV in two pools of cortical progenitor cells (less and more differentiated/mature) clustered by their distinct metabolic profiles rather than single cell types present at different stages of brain development. Both early and late hi-NPCs comprise pools of cells generated during hiPSC differentiation of cortical progenitors. The authors showed in Fig.1 that both pools have brain identity and express several brain markers to similar levels. When gating these cells by populations present in the developing brain small differences were observed exclusively in one out of three subgroups. Nevertheless, the main distinction of these two populations was due to significant differences in their metabolic profile. Thus, the results obtained in this research are likely to obey to the metabolic maturation of early and late hi-NPCs rather than the percentages of different brain cell types present within these pools.

Reviewer #1 (Significance (Required)):

The focus on the metabolism and mitochondrial stress in ZIKV infected neuronal progenitors is interesting and could fill an important gap in knowledge on Zika pathogenesis. The study uses human iPSC derived NPCs instead of animal cells, which is also more clinically relevant than animal models. The findings would thus be of interest to all who are interested in Zika pathogenesis as well as therapeutic/vaccine development. If the above concerns could be addressed, the findings in this study could form the missing links in our current understanding of congenital Zika syndrome.

Expertise: Flavivirology and immunology. Flavivirus-host interactions.

The authors thank the reviewer for the comments provided to improve several aspects of the current research. The authors also thank the reviewer for the positive feedback and for highlighting the relevance of our research.

Reviewer #2 (Evidence, reproducibility and clarity (Required)):

In the manuscript entitled "Zika virus-induces metabolic alterations in fetal neuronal progenitors that could influence in neurodevelopment during early pregnancy", Javier G.-J. and colleagues investigated the role of cellular metabolism during ZIKA virus infection in hiPSC-derived neural progenitor cells (NPC) at different stage of differentiation. Indeed, the authors use a modified protocol of 2D cultures to obtain early-hiNPC and by continuing the cultures for two additional passages, they obtain late-hiNPCs. These two cell populations are characterized by cell morphology and marker expression. Then they test their susceptibility to ZIKV infection and show that late-hiNPCs are more efficient than early- hiNPCs to support viral replication. Moreover, authors demonstrate that the two cell populations are characterized by different cellular metabolism as glucose consumption is higher in early-hiNPCs than in late-hiNPCs although the overall glycolytic capacity is not different between the two subtypes. However, during ZIKV productive infection, late-hNPCs increased the glucose consumption (Fig. 3). The authors examined the mitochondrial alterations during infection showing different kinetics in early vs. late hiNPCs. Then, they show alterations of expression in genes of the lipid metabolism and content of lipid droplets that follow different kinetics of expression during infection in early vs. late hiNPCs. Overall, no significant differences were observed in the lipid droplet homeostasis between the two subtypes. This is a potentially interesting manuscript as they analyze the susceptibility of subtypes of neural progenitors to ZIKV infection and their metabolic alterations before and during infection. However, there are some concerns listed below.

Major issues:

1. It is well established that the NPC maturation during neurodevelopment is complex and cells at different stage of maturation play an important role. The authors propose a model that may recapitulate distinct populations of neural progenitors present during neurodevelopment. They use a modified protocol that it is well described. However, the characterization of these NPC subtypes needs to be improved. The pictures selected to be shown in Fig 1B and C do not highlight the morphology of these cells as described in the text.

The authors thank the reviewer for the comment and have improved the description of the morphology of the cells constituting the two pools of cells at different stages of differentiation. In addition, the authors have included a new figure (Extended Fig. 1) with different magnifications of the acquired brightfield images for better representation of the morphological differences observed between early and late hi-NPCS.

Late-hiNPC are more efficient than early-hiNPC in supporting ZIKV replication, however the differences are present exclusively at 56 h post-infection and they are modest (ca. 2-fold). Nevertheless, ZIKV cytopathic effects are similar between the two subtypes at 72 h post-infection. Authors should try to lower the MOI and extend the timing of analysis to up 10 days post-infection. They used MOI of 1, but it would be informative to know whether the different efficiency of viral replication is dependent on the MOI. Furthermore, are the differences between early and late-hiNPCs dependent on expression of entry receptor, or a different interferon response to virus infection or state of cell proliferation?

The authors thank the reviewer for the insights provided on this matter and appreciate the overall positive response towards the research. After several revisions, the authors have corrected the data using a normalisation method that is expected to rely less on cellular metabolism which may well be disturbed during ZIKV infection. Although still modest, differences in virion release between early and late hi-NPCs are observed at 48 and 56 h.p.i. This data matches the increased accumulation of transcripts. Moreover, in the new version of this manuscript, the cytopathic effects are distinct between hi-NPCs. Regarding the reviewer’ comment on MOI. The authors selected the MOI of 1 due to metabolic dysregulations due to viral infection require a good proportion of cells infected with the inoculum. To support this, the authors highlight the comments from reviewer 1 whom encouraged that the main interpretation should be done at the 24 h timepoint as the population is reflecting alterations due to the initial round of infection rather than differences potentially being masked by cells at different stages of ZIKV replication. In addition, MOI of 1 has been reported elsewhere for ZIKV and other flaviviruses. Although it sounds interesting to the authors the lower MOI exposure for longer period of times, this approach is not feasible to conduct in our models as early hi-NPCs have a length of 3-4 days in culture due to exacerbated cell proliferation. After this time, cells need to be passaged. Similar technical complications will be faced if extending the infection times in late hi-NPCs as these cells require passaging/freezing after day 5. Lastly, the authors consider studying the expression of entry receptors in early and late hi-NPCs to be important in explaining potential differences in viral kinetics yet, the lack of differences in virion release at early timepoints of infection suggest the entry and length of the ZIKV replication cycle is conserved between early and late hi-NPCs. Thus, differences during the ZIKV kinetics potentially due to other mechanisms. For this reason, the authors have included a discussion paragraph in which they highlight potential differences may be due to the development of the native immunity of hi-NPCs during differentiation. It is still controversial whether IFN responses are significant in hi-NPCs, with research suggesting that greater IFN responses are observed upon maturation of NPCs.

The authors state that that this is the first report showing differential changes in nuclear morphology between neural progenitor cells. They show a main finding that is the perinuclear centers only visible in late but not in early-hiNPC in a supplemental figure. These results are not convincing, and an effort should be made in order to support these claims.

The authors have now addressed this issue by using confocal microscopy and co-stained DAPI (nuclei) and ZIKV envelope protein to better show the perinuclear centres in late hi-NPCs (Extended Fig. 6). Confocal images of early hi-NPCs with non-perinuclear replication centres than late hi-NPCs was not possible to acquire as early hi-NPCs did not adhere to glass coverslips for more than 24 h.

Gene expression should be supported by data of protein expression (western blot) of some of the enzymes reported in Fig. 5.

The authors have detected some of the proteins (western blot) involved in lipid metabolism in both early and late hi-NPCs. The authors screened for FASN, PDK2 and ACACA to validate the findings at the mRNA level. The authors selected 56 h.p.i. as a timepoint to measure proteins mainly due to high ZIKV infection levels but not abundant cell death that facilitates obtaining sufficient material for WB. The image of the WB is included in the Extended Fig. 4 of the new version of this manuscript.

Minor issues: 1. Since this work is based on in vitro data, I would suggest using the term infection rather than challenge when referring to infection experiments.

The authors have edited the document and replaced the terminology for what was suggested by the reviewer.

Improve quality of the graphs. Enlarge symbols as in Fig. 6. Try to use linear scales as the differences are not dramatic and a linear scale would highlight them better.

The authors thank the reviewer for the observation on the graphs. The authors have enlarged the symbols where possible – in some cases this could not be conducted as otherwise the error bars were not visible for example when displaying the viral output. The authors appreciate the comment on plotting the data on a linear scale to reflect subtle differences to a larger magnitude yet, this may well fit into misleading the interpretation of some results due to the nature of the analysis (e.g., fold-change); where possible, these changes have been applied.

CROSS-CONSULTATION COMMENTS

I agree with all comments of Rev#1 and 3. Some/many of the claims are made without the supporting evidence.

Reviewer #2 (Significance (Required)):

Many papers have reported the efficient ZIKV infection of neural progenitor cells that have been derived from the reprogramming of human pluripotent stem cells (PSC). Most of this literature has not been cited. In fact, ZIKV virus infects human PSC-derived brain neural progenitors causing heightened cell toxicity, dysregulation of cell-cycle and reduced cell growth as reported in many papers. In this manuscript, the advancement consists in having used NPC subtypes that are at different stage of differentiation and having studied their susceptibility to ZIKV infection. Then, the author analyze the fluctations of the glucose and lipid metabolism during infection.

The audiance that is interested in this manuscript are virologists and , in particular, experts in arboviruses that are for the most part neurotropic viruses. In addition, this is a topic for experts of neurodevelopment.

My expertise is virology. Key words: Zika virus, neural progenitors, antivirals.

The authors thank the reviewer for the productive constructivism provided to this research. We would like to highlight that most of the literature in Zika infection and hi-NPCs was not included as this does not directly focuses on metabolism. However, as the document has been amended, some of this literature has now been included to contextualise the current knowledge of ZIKV infectivity in relevant in vitro systems.

Reviewer #3 (Evidence, reproducibility and clarity (Required)):

Summary The authors present the analysis of the cellular metabolism in Zika virus-infected human neural cell cultures differentiated from fibroblast-derived hiPSCs. Neural cell cultures from days 12-15 after hiPSC induction to neuronal lineages were designated as 'early neuronal progenitors' (early hi-NPCs), while cultures from days 18-21 post induction were designated as 'late neuronal progenitors' (late hi-NPCs). The outcomes of ZIKV infection in both types of neural cell cultures were analyzed. The authors first characterized several viral parameters of infection including accumulation of viral RNA and proteins and ZIKV replication, as well as nuclear morphology of infected cells. The major body of evidence in the presented study encompasses the comparative analysis of several parameters of glucose and lipid metabolism as well as mitochondrial function and cellular lipid accumulation and storage. The authors postulate that ZIKV replicates differentially in early and late hi-NPCs, inducing some common metabolic responses like upregulated glycolytic capacity, as well as responses unique to either early or late differentiation stage of the neural cultures like the lipid metabolism, lipid droplet homeostasis or mitochondrial function. The authors propose that the differential metabolic responses to ZIKV infection in early and late neural progenitors might help to explain the differences in the fetal brain damage in early or late pregnancy.

Major comments 1. The authors refer to the induced neural cell cultures as monocultures of human neural progenitors. This assumption is incorrect and it undermines the proper interpretation of the presented data. The neural lineage induction of iPSC produces neural cell cultures, which depending on the differentiation stage consist of neural stem cells of neuroepithelial-like morphology (Nestin and Sox-2 positive) which differentiate further to more elongated early progenitors with radial glial cell morphology (Nestin, sox2 and PAX-6 positive). Radial glial cells differentiate further into several neural lineages including oligodendrocyte precursors, astrocytes (S100B - positive) and intermediate progenitors (TBR2 - positive). The intermediate progenitors then divide to produce one progenitor and one post mitotic immature neuron (still TBR-positive and also beta II tubulin, Tuj1/TuB3-positive). The neurons then mature further and become NeuN and MAP2-positive. All the above mentioned differentiation markers were used by the authors to characterize the early and late cell cultures. According to the data presented in Fig 1E, both cultures were positive for all the markers indicating that they are not monocultures. The immunofluorescence data provided in Extended Fig.1 in support of the analysis presented in Fig. 1E clearly shows that both cultures stain similarly for Tuj1 (also known as TuB3), a marker of post-mitotic neurons, which are clearly present in both cultures. Abundant MAP2 positive cells (marker of mature neurons) in "early hi-NPCs" presented in extended figure 1B is quite surprising and confusing and is not presented for 1A panel - "late hi-NPCs", which suggests that perhaps figure 1A and 1B were mislabeled. The immunostaining for PAX6 presented in the same figure 1B presents strong cytoplasmic staining while PAX6 is expected to be detected in the cell nucleus, suggesting that the red staining comes most probably from the overexposed background. On a closer look the PAX6 staining presented on panel 1A shows weak and underexposed but most probably positive nuclear staining. Of note, the authors argue that the only significant difference in the staining for differentiation markers was observed for PAX6 (early neural progenitor marker) which was higher in late cultures than the early ones. In the same figure all the pictures in red are generally underexposed except from PAX6, while the DAPI staining is overexposed. This makes the interpretation of the data difficult especially when looking at the merged images. Despite the overall confusion with this part of results it is clear that the early and late cultures consist of different cell types including early and intermediate progenitors as well as astrocytes (S100B - positive), probably glial cells (not tested for) and post-mitotic neurons. The relative ratio of these populations might be different in the two cultures, however the cultures are not monocultures of early or late neural progenitors. They might contain different ratios of both and thus respond differently to the infection. Therefore, the metabolic and virological analysis performed globally on these cell cultures might just as well reflect the cell type ratio related effects rather than the differential responses of the early or late progenitors. This has never been addressed or explained by the authors.

The authors thank the reviewer for the comment and observation regarding the nomenclature used in the preliminary version of this document. The authors used the terminology “subtypes” not to refer as a monoculture of a particular cell type within the developing brain but to the group of cells that share a metabolic profile. This was due to the abundance of markers to characterize cellular lineages within each population reflected to be similar at the two stages of differentiation. The authors showed cell type differences only in the ratio of glial Pax6 +ve cells (Fig. 1D) whilst greater significant differences were observed in the metabolism between both cultures. Thus, differences to viral responses are most likely to obey to the maturation stage (longer times under culture) rather than different ratio of brain cells. Nevertheless, the authors acknowledge the confusion that the terminology “neuronal subtypes” could have caused and have changed it to “cortical progenitors at different stages of differentiation” or “hi-NPCs”. The authors would like to address the reviewer’ comment on the data presented in Extended Fig. 2 (MAP2 staining in early hi-NPCs). This is not a mislabel between the figure but rather a demonstration that the staining was observed in early hi-NPCs. This staining was not performed in late hi-NPCs thus not showed. Moreover, the data used to quantify the presence of brain markers in early and late hi-NPCs was generated by flow cytometry and not by confocal imaging (ICC). The ICC included in the Extended Fig. 2 was used to demonstrate the antibody staining and to discard potential unspecific antibody binding that may generate false positive detection by flow cytometry. The authors agree with the reviewer that the staining for Pax6 was not clear in the previous version of this manuscript and have redone the figure.

The data presented is often based on the analysis of the immunofluorescence images, however the quality of the images presented (resolution, magnification, over or under saturation) is often insufficient to support the findings and claims. The most striking example are images supporting the analysis of nuclear morphology in ZIKV-infected cells presented in the Extended figure 2.

The authors thank the reviewer and would like to clarify that, although displaying some confocal images within the figure, the graphs were generated from data collected by imaging flow cytometry (Imagestream). In response to the comments from reviewer 1 and 2, the authors explained the advantages and disadvantages of using Imagestream over confocal imaging. The main rationale behind this is the greater sample size and unbiased acquisition of data yet compromising the immunostaining resolution. Regarding the displayed confocal images within the text, the authors have redone the figures and/or acquired new images to correct the issues of oversaturation/overexposure. The authors acknowledge that the data interpretation from the nuclear imaging needs to be done with caution due to its low quality yet, as early hi-NPCs do not adhere to glass as efficiently as late, any confocal acquisition will be limited to plastic-based materials ending in lower resolution. Thus, thanking the reviewers for the observation, the authors have now conducted confocal microscopy co-stained for ZIKV envelope protein and nuclei (DAPI) in late hi-NPCs to better display the nuclear morphology upon infection and the replication centres.

Some of the claims are made without the supporting evidence. For example in the discussion the authors claim that "Our main finding was that viral perinuclear replication centers (26) (white arrows, Supplementary Figure 2) were only visible in late hi-NPCs and not in early hi-NPCs". This conclusion is made based presumably on Extended Figure 3 (Figure 2 does not have arrows) based on the nuclear morphology of infected cells without staining for any of the viral proteins localizing to the replication centers. Despite low image quality similar crescent-shaped nuclei to the ones indicated by the arrows in "late hi-NPCs" and many more of them are visible in "early hi-NPCs" (Extended Fig.2), however the authors seem ignore them.

The authors thank the reviewer for the comment and notify that the respective amendments have been done. The data presented in the new version of this manuscript related to the nuclear morphology is from a new dataset of co-stained ZIKV envelope and DAPI (Extended Fig. 6).

Based on the arguments presented above the conclusions are not convincing, lacking the supporting evidence or ignoring some of the essential facts of the chosen experimental system.

The authors thank the reviewer for the criticism on the research and notify that several changes throughout the document have been made to support the claims and conclusions of this manuscript.

The study in presented form, where all the analysis is performed in globally is not informative and would require the characterization of the metabolic and virological responses in different cell populations as characterized by the expression of neural differentiation markers. Alternatively, the population sizes of different types of cells should be determined and accounted for when analyzing the experimental data. It should be determined which types of cells are targeted by the Zika virus and replicate the virus. It could be done by, for example, co-staining for viral and neural differentiation markers. This would however require the entirely different experimental approach from the one presented in this manuscript.

The authors thank the reviewer for the comments and inputs provided to our research. We would like to highlight that, although it would be highly relevant to distinguish ZIKV infection and metabolic dysregulations in different cell populations; all the published literature in neuronal progenitors and ZIKV infection do not contain insights on the infection per cell populations. This may be due to the difficulties in isolating/sorting populations of immature cells within the pool of in vitro cortical differentiation whilst achieving significant cell survival. The authors would like to address this comment by highlighting that the population sizes of different types of cells within the pools of early and late hi-NPCs was accounted as a starting point of this research (Fig 1D). This characterization was done using a commercially available kit aimed for the sorting of human neural stem cells. These results showed small differences in the ratio of cell types present in early and late hi-NPC cultures. ZIKV has been reported to target all brain cells with lower impact on mature neurons, which arguably will be present in either of the cortical progenitor pools used for this research. Thus, the authors focused on interpreting the results as an impact of ZIKV infection in the overall metabolism of each pool of hi-NPCs used in this study. Metabolism that is likely influenced by most of the cell types present within each hi-NPC pool.

Some methods are not explained clearly. For the metabolic analysis like Oleic acid oxidation and others, it is not clear at which step of the protocol ZIKV infection was performed. In "Extracellular lactate measurement" freshly made running buffer is mentioned but no composition of the buffer is provided.

The authors thank the observation of the reviewer and have now amended the method section to clearly state several methods that could have been difficult to understand in the previous version of this manuscript.

Minor comments 1. Figure 2G shows the survival of ZIKV-infected hi-NPC subtypes. Clearly for "late hi-NPCs" there is 50% cell death at 56 hours post infection and about 70% death at 72 hours. Subsequent analysis of many metabolic parameters is measured at 56 and sometimes even at 72 hours when the significant differences in responses are observed for example Fig. 3 B and C. The role of cell death in the critical analysis of these parameters is not provided.

The authors appreciate the reviewers’ comment and would like to emphasise that all the analysis at every timepoint during ZIKV infection was normalised to the cell number present at the time. The use of different timepoints for different measurements (e.g., 56 and 72 h.p.i.) were selected by the authors to adjust the used methodology. In short, 56 h.p.i. was used as the last timepoint to study mitochondrial and lipid dysregulations by imagestream as we observed the highest viral output with sufficient cell survival in early hi-NPCs; 72 h.p.i. will not give sufficient cell number for counting 10.000 cells by Imagestream. However, 72 h.p.i. was used to assess the genetic expression of metabolically relevant genes as the material obtained was sufficient and will signify the interpretation of the latest point during the ZIKV kinetics in our models.

There are multiple spelling mistakes throughout. The professional terminology of the virology part of the study is often missing. Example the levels of ZIKV RNA measured in the infected cells are designated as "transcriptional levels of ZIKV" which seems incorrect as the level of the genomes is the effect of viral replication and not transcription.

The authors thank the reviewer for the observation made and have corrected the terminology throughout the document. The spelling has been checked.

CROSS-CONSULTATION COMMENTS

Agree with Rev #1 comment on Fig 2D and the levels of NS1. It is striking that the levels of expression drop below the level detected at 48h while the ZIKV E protein continues to accumulate (Fig. 2E) at the same time. Both proteins are translated from the same polyprotein and are processed similarly. It is also confusing that at 48h only about 5% live cells express NS1 while at the same time 15% of live cells express E protein. In my experience both proteins are expressed in all infected cells. The reason why 10% of infected and still alive cells would express only E and not NS1 is difficult to conceive.

The authors have addressed this issue and highlighted that the limitations of the Imagestream technique may have caused this oddity due to loss of signal detection by compensation. The experiments conducted to correct this manuscript include a new detection by flow cytometry using a smaller panel of markers and labelled-secondary antibodies that will provide greater signal. This approach has demonstrated that detection levels of NS1 mirror those of Envelope.

I stand with the Rev #1 in asking what specific CNS pathology is dependent on the reported metabolic changes? There is no attempt to link the findings to ZIKV-induced CNS pathologies.

The authors have included discussion paragraphs to link the observed phenotypes during ZIKV infection to relevant CNS pathologies.

In relation to major comment 2 from the Rev #2, I disagree that Late-hiNPC are more efficient than early-hiNPC in supporting ZIKV replication. 2-fold difference in a viral plaquing assay falls within the error of the assay which is usually quite substantial for the plaquing assay. Lack of error bars for late hi-NPCs 56 h raise my suspicion as to how real is this effect in viral replication.

The authors would like to clarify that the error bars were present in the graph but the size of the symbol difficulted the visualisation. After correcting all the datasets to a less dependent metabolic assay (LDH based survival), the differences in virion release are observed at 48 and 56 h.p.i., like that of ZIKV replication measured by qPCR. The authors also clarify that, although a 2-fold difference may fall within the technical error of plaque assay, minor differences observed in our research are potentially greater if displayed in a different form as our analysis comprises three independent ZIKV infection conducted in three patients’ lines and further normalisation to cell number.

I stand behind Rev #2 major comment 2 there there is no evidence to support the claims about the nuclear morphology or the replication centers

This issue has now been addressed (Extended Fig. 6).

Reviewer #3 (Significance (Required)):

Despite global research efforts the course of Zika virus infection of the fetal brain during pregnancy is not clear. Among many knowledge gaps, the molecular determinants of differential outcomes of Zika virus infection during early or late pregnancy are unknown. The study aims to address this highly significant issue by focusing on the metabolic responses of cells to infection. In the presented form the study however, fails to deliver significant progress in our understanding of Zika virus infection of developing fetal brain. The experimental design and the quality of presented data does not allow to make unbiased conclusions and to support the claims. My expertise is in iPSC-derived neural cell cultures, molecular virology, in particular that of Flaviviruses like Zika virus and hepatitis c virus and confocal microscopy. I am not familiar with metabolic techniques and I find the description of methods for this part of the study insufficient to fully understand the experimental approach.

The authors thank the reviewer for the valuable inputs provided to the research. We would like to highlight that, whereas possible, new sets of experiments have been conducted to better support some of the conclusions and claims. Lastly, the authors would like to mention that the method section has been corrected for a better understanding of the metabolic assays and data normalisation. Within the text, paragraphs have been added to clarify the nature of the results and data acquisition.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

---

Referee #3

Evidence, reproducibility and clarity

Summary

The authors present the analysis of the cellular metabolism in Zika virus-infected human neural cell cultures differentiated from fibroblast-derived hiPSCs. Neural cell cultures from days 12-15 after hiPSC induction to neuronal lineages were designated as 'early neuronal progenitors' (early hi-NPCs), while cultures from days 18-21 post induction were designated as 'late neuronal progenitors' (late hi-NPCs). The outcomes of ZIKV infection in both types of neural cell cultures were analyzed. The authors first characterized several viral parameters of infection including accumulation of viral RNA and proteins and ZIKV replication, as well as nuclear morphology of infected cells. The major body of evidence in the presented study encompasses the comparative analysis of several parameters of glucose and lipid metabolism as well as mitochondrial function and cellular lipid accumulation and storage. The authors postulate that ZIKV replicates differentially in early and late hi-NPCs, inducing some common metabolic responses like upregulated glycolytic capacity, as well as responses unique to either early or late differentiation stage of the neural cultures like the lipid metabolism, lipid droplet homeostasis or mitochondrial function. The authors propose that the differential metabolic responses to ZIKV infection in early and late neural progenitors might help to explain the differences in the fetal brain damage in early or late pregnancy.

Major comments

1. The authors refer to the induced neural cell cultures as monocultures of human neural progenitors. This assumption is incorrect and it undermines the proper interpretation of the presented data. The neural lineage induction of iPSC produces neural cell cultures, which depending on the differentiation stage consist of neural stem cells of neuroepithelial-like morphology (Nestin and Sox-2 positive) which differentiate further to more elongated early progenitors with radial glial cell morphology (Nestin, sox2 and PAX-6 positive). Radial glial cells differentiate further into several neural lineages including oligodendrocyte precursors, astrocytes (S100B - positive) and intermediate progenitors (TBR2 - positive). The intermediate progenitors then divide to produce one progenitor and one post mitotic immature neuron (still TBR-positive and also beta II tubulin, Tuj1/TuB3-positive). The neurons then mature further and become NeuN and MAP2-positive. All the above mentioned differentiation markers were used by the authors to characterize the early and late cell cultures. According to the data presented in Fig 1E, both cultures were positive for all the markers indicating that they are not monocultures. The immunofluorescence data provided in Extended Fig.1 in support of the analysis presented in Fig. 1E clearly shows that both cultures stain similarly for Tuj1 (also known as TuB3), a marker of post-mitotic neurons, which are clearly present in both cultures. Abundant MAP2 positive cells (marker of mature neurons) in "early hi-NPCs" presented in extended figure 1B is quite surprising and confusing and is not presented for 1A panel - "late hi-NPCs", which suggests that perhaps figure 1A and 1B were mislabeled. The immunostaining for PAX6 presented in the same figure 1B presents strong cytoplasmic staining while PAX6 is expected to be detected in the cell nucleus, suggesting that the red staining comes most probably from the overexposed background. On a closer look the PAX6 staining presented on panel 1A shows weak and underexposed but most probably positive nuclear staining. Of note, the authors argue that the only significant difference in the staining for differentiation markers was observed for PAX6 (early neural progenitor marker) which was higher in late cultures than the early ones. In the same figure all the pictures in red are generally underexposed except from PAX6, while the DAPI staining is overexposed. This makes the interpretation of the data difficult especially when looking at the merged images. Despite the overall confusion with this part of results it is clear that the early and late cultures consist of different cell types including early and intermediate progenitors as well as astrocytes (S100B - positive), probably glial cells (not tested for) and post-mitotic neurons. The relative ratio of these populations might be different in the two cultures, however the cultures are not monocultures of early or late neural progenitors. They might contain different ratios of both and thus respond differently to the infection. Therefore, the metabolic and virological analysis performed globally on these cell cultures might just as well reflect the cell type ratio related effects rather than the differential responses of the early or late progenitors. This has never been addressed or explained by the authors.
2. The data presented is often based on the analysis of the immunofluorescence images, however the quality of the images presented (resolution, magnification, over or under saturation) is often insufficient to support the findings and claims. The most striking example are images supporting the analysis of nuclear morphology in ZIKV-infected cells presented in the Extended figure 2.
3. Some of the claims are made without the supporting evidence. For example in the discussion the authors claim that "Our main finding was that viral perinuclear replication centers (26) (white arrows, Supplementary Figure 2) were only visible in late hi-NPCs and not in early hi-NPCs". This conclusion is made based presumably on Extended Figure 3 (Figure 2 does not have arrows) based on the nuclear morphology of infected cells without staining for any of the viral proteins localizing to the replication centers. Despite low image quality similar crescent-shaped nuclei to the ones indicated by the arrows in "late hi-NPCs" and many more of them are visible in "early hi-NPCs" (Extended Fig.2), however the authors seem ignore them.
4. Based on the arguments presented above the conclusions are not convincing, lacking the supporting evidence or ignoring some of the essential facts of the chosen experimental system.
5. The study in presented form, where all the analysis is performed in globally is not informative and would require the characterization of the metabolic and virological responses in different cell populations as characterized by the expression of neural differentiation markers. Alternatively, the population sizes of different types of cells should be determined and accounted for when analyzing the experimental data. It should be determined which types of cells are targeted by the Zika virus and replicate the virus. It could be done by, for example, co-staining for viral and neural differentiation markers. This would however require the entirely different experimental approach from the one presented in this manuscript.
6. Some methods are not explained clearly. For the metabolic analysis like Oleic acid oxidation and others, it is not clear at which step of the protocol ZIKV infection was performed. In "Extracellular lactate measurement" freshly made running buffer is mentioned but no composition of the buffer is provided.

Minor comments

1. Figure 2G shows the survival of ZIKV-infected hi-NPC subtypes. Clearly for "late hi-NPCs" there is 50% cell death at 56 hours post infection and about 70% death at 72 hours. Subsequent analysis of many metabolic parameters is measured at 56 and sometimes even at 72 hours when the significant differences in responses are observed for example Fig. 3 B and C. The role of cell death in the critical analysis of these parameters is not provided.
2. There are multiple spelling mistakes throughout. The professional terminology of the virology part of the study is often missing. Example the levels of ZIKV RNA measured in the infected cells are designated as "transcriptional levels of ZIKV" which seems incorrect as the level of the genomes is the effect of viral replication and not transcription.

Referees cross-commenting

Agree with Rev #1 comment on Fig 2D and the levels of NS1. It is striking that the levels of expression drop below the level detected at 48h while the ZIKV E protein continues to accumulate (Fig. 2E) at the same time. Both proteins are translated from the same polyprotein and are processed similarly. It is also confusing that at 48h only about 5% live cells express NS1 while at the same time 15% of live cells express E protein. In my experience both proteins are expressed in all infected cells. The reason why 10% of infected and still alive cells would express only E and not NS1 is difficult to conceive.

I stand with the Rev #1 in asking what specific CNS pathology is dependent on the reported metabolic changes? There is no attempt to link the findings to ZIKV-induced CNS pathologies.

In relation to major comment 2 from the Rev #2, I disagree that Late-hiNPC are more efficient than early-hiNPC in supporting ZIKV replication. 2-fold difference in a viral plaquing assay falls within the error of the assay which is usually quite substantial for the plaquing assay. Lack of error bars for late hi-NPCs 56 h raise my suspicion as to how real is this effect in viral replication.

I stand behind Rev #2 major comment 2 there there is no evidence to support the claims about the nuclear morphology or the replication centers

Significance

Despite global research efforts the course of Zika virus infection of the fetal brain during pregnancy is not clear. Among many knowledge gaps, the molecular determinants of differential outcomes of Zika virus infection during early or late pregnancy are unknown. The study aims to address this highly significant issue by focusing on the metabolic responses of cells to infection. In the presented form the study however, fails to deliver significant progress in our understanding of Zika virus infection of developing fetal brain. The experimental design and the quality of presented data does not allow to make unbiased conclusions and to support the claims.

My expertise is in iPSC-derived neural cell cultures, molecular virology, in particular that of Flaviviruses like Zika virus and hepatitis c virus and confocal microscopy. I am not familiar with metabolic techniques and I find the description of methods for this part of the study insufficient to fully understand the experimental approach.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

---

Referee #2

Evidence, reproducibility and clarity

In the manuscript entitled "Zika virus-induces metabolic alterations in fetal neuronal progenitors that could influence in neurodevelopment during early pregnancy", Javier G.-J. and colleagues investigated the role of cellular metabolism during ZIKA virus infection in hiPSC-derived neural progenitor cells (NPC) at different stage of differentiation. Indeed, the authors use a modified protocol of 2D cultures to obtain early-hiNPC and by continuing the cultures for two additional passages, they obtain late-hiNPCs. These two cell populations are characterized by cell morphology and marker expression. Then they test their susceptibility to ZIKV infection and show that late-hiNPCs are more efficient than early- hiNPCs to support viral replication. Moreover, authors demonstrate that the two cell populations are characterized by different cellular metabolism as glucose consumption is higher in early-hiNPCs than in late-hiNPCs although the overall glycolytic capacity is not different between the two subtypes. However, during ZIKV productive infection, late-hNPCs increased the glucose consumption (Fig. 3). The authors examined the mitochondrial alterations during infection showing different kinetics in early vs. late hiNPCs. Then, they show alterations of expression in genes of the lipid metabolism and content of lipid droplets that follow different kinetics of expression during infection in early vs. late hiNPCs. Overall, no significant differences were observed in the lipid droplet homeostasis between the two subtypes.<br /> This is a potentially interesting manuscript as they analyze the susceptibility of subtypes of neural progenitors to ZIKV infection and their metabolic alterations before and during infection. However, there are some concerns listed below.

Major issues:

1. It is well established that the NPC maturation during neurodevelopment is complex and cells at different stage of maturation play an important role. The authors propose a model that may recapitulate distinct populations of neural progenitors present during neurodevelopment. They use a modified protocol that it is well described. However, the characterization of these NPC subtypes needs to be improved. The pictures selected to be shown in Fig 1B and C do not highlight the morphology of these cells as described in the text.
2. Late-hiNPC are more efficient than early-hiNPC in supporting ZIKV replication, however the differences are present exclusively at 56 h post-infection and they are modest (ca. 2-fold). Nevertheless, ZIKV cytopathic effects are similar between the two subtypes at 72 h post-infection. Authors should try to lower the MOI and extend the timing of analysis to up 10 days post-infection. They used MOI of 1, but it would be informative to know whether the different efficiency of viral replication is dependent on the MOI. Furthermore, are the differences between early and late-hiNPCs dependent on expression of entry receptor, or a different interferon response to virus infection or state of cell proliferation?
3. The authors state that that this is the first report showing differential changes in nuclear morphology between neural progenitor cells. They show a main finding that is the perinuclear centers only visible in late but not in early-hiNPC in a supplemental figure. These results are not convincing, and an effort should be made in order to support these claims.
4. Gene expression should be supported by data of protein expression (western blot) of some of the enzymes reported in Fig. 5.

Minor issues:

1. Since this work is based on in vitro data, I would suggest using the term infection rather than challenge when referring to infection experiments.
2. Improve quality of the graphs. Enlarge symbols as in Fig. 6. Try to use linear scales as the differences are not dramatic and a linear scale would highlight them better.

Referees cross-commenting

I agree with all comments of Rev#1 and 3. Some/many of the claims are made without the supporting evidence.

Significance

Many papers have reported the efficient ZIKV infection of neural progenitor cells that have been derived from the reprogramming of human pluripotent stem cells (PSC). Most of this literature has not been cited. In fact, ZIKV virus infects human PSC-derived brain neural progenitors causing heightened cell toxicity, dysregulation of cell-cycle and reduced cell growth as reported in many papers. In this manuscript, the advancement consists in having used NPC subtypes that are at different stage of differentiation and having studied their susceptibility to ZIKV infection. Then, the author analyze the fluctations of the glucose and lipid metabolism during infection.

The audiance that is interested in this manuscript are virologists and , in particular, experts in arboviruses that are for the most part neurotropic viruses. In addition, this is a topic for experts of neurodevelopment.

My expertise is virology. Key words: Zika virus, neural progenitors, antivirals.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

---

Referee #1

Evidence, reproducibility and clarity

This manuscript reports an investigation into the metabolic alterations induced by Zika virus (ZIKV) infection in human neuronal progenitor cells. The authors differentiated human iPSCs to derive neuronal progenitor cells (NPCs) at different days of incubation to represent the different stages of foetal CNS development. They found differences in the levels of ZIKV NS1 proteins as well as marginal differences in ZIKV titres in infected early and late hi-NPCs. Correspondingly, they also showed differences in glucose consumption, lipid metabolism and mitochondrial stress in ZIKV-infected early and late hi-NPCs. They concluded that differences in energy metabolism in neuronal progenitors both before and upon infection may contribute to the brain damage observed in congenital Zika syndrome.

The evidence supporting a role for dysregulated metabolism in mediating the pathogenesis of congenital Zika syndrome is gaining traction and findings from this study could add to this body of knowledge. However, in its present form, this study has several gaps that limit the extent to which it informs on the clinical pathogenesis of congenital Zika syndrome.

Major concerns:

1. The most important concern in this study is the strain of ZIKV used in all of the studies. ZIKV MP1751 was isolated from a mosquito and belongs to the African lineage of ZIKV. Unlike the Asian lineage ZIKV isolated from Latin America and French Polynesia, gestational infection with ZIKV of the African lineage has not been clinically associated with increased risk of foetal abnormality. It is thus uncertain how the changes observed in this study relates to the observed neonatal pathology. Perhaps a way to address this issue is to argue that a difference in these lineages it the ability of the virus to evade systemic and endothelial innate immune responses to cross the placental and blood brain barriers (several papers on attenuated ZIKV have shown this data). Once these barriers are breached, strain differences should not materially affect the similar pathogenic processes in neuronal cells, as also been shown by others using the MR766 strain of ZIKV. Such a discussion would be helpful to contextualise the clinical relevance of this study.
2. While the metabolic changes upon ZIKV infection are all interesting, how these changes affect CNS development is unclear. Figure 2F shows marginal impact on productive ZIKV infection and comparable extent of cell death in early and late hi-NPCs. What specific CNS pathology is dependent on the reported metabolic changes?
3. Figure 2D: The most remarkable virological difference observed is the significant difference in cytoplasmic NS1 levels between early and late hi-NPCs at 56 hpi. Although the data in Fig 2D in general could have been compromised by the quality of the anti-NS1 mAb (the anti-E assay in Fig 2E used polyclonal antibody), it would have been useful to test for NS1 expression using western blot on a denaturing gel (and appropriate anti-NS1 antibody). The mAb used in this study binds a conformational epitope on NS1. The difference in data in Figure 2D and 2E could thus have been misfolding of NS1. Misfolded NS1 could contribute to ER stress that could be important for dysregulated CNS development. A more detailed investigation of the finding in Figure 2D could be highly informative.
4. Figure 3A and related text: The fold-change in GLUT1, HK-1 and GAPDH expression are shown in log10 scale. In this scale, 1 would indicate 10-fold increase in expression. The data in Figure 3A are entirely inconsistent with the description in the related text. Which is correct?

Minor concerns:

1. Figure 5: The effects of ZIKV infection on the mitochondria of hi-NPCs are interesting and the comparison between ZIKV-infected and uninfected cells in the same culture is a strength of this study. It would be helpful to readers if the authors could include a discussion on the kinetics of ZIKV infection; diminished differences at 48 and 72 hours could be due to the mixture of cells infected at inoculation and hence observed at 24 hours and newly infected cells that were negative for ZIKV E protein at 24 hours. Emphasis should thus be on the 24 h data in Figures 5 C-E.
2. Hi-NPCs likely have a diploid genome and thus a finite lifespan. Using the term "cell line" to describe these cells is technically incorrect. Please consider using other terms, such as cell strain.
3. Discussion section, 3rd paragraph, lines 6-7. The authors suggest thermal decay as an explanation for their observation yet Figure 2B argues against this explanation. Moreover, Kostyuchenko et al (Nature 2016; 533:425-8) have also shown that ZIKV is relatively thermostable. This explanation offered by the authors lack supporting evidence.
4. Discussion section, 3rd paragraph, line 16 and Supplementary Figure 2. I believe the authors are referring to Supplementary Figure 3 and not 2. The indentations observed could be due to ZIKV replication although the data, as presented is not convincing. Co-staining for ZIKV E protein would be useful.

Referees cross-commenting

• I fully agree with both Reviewers #2 and #3 on the quality of the immunofluorescence images and that these images alone are not sufficiently convincing to support the inferences the authors are making.
• I also appreciate the first major comment from Reviewer #3. That is important insight and the authors should test their assumption that they have monocultures of human progenitor cells.

Significance

The focus on the metabolism and mitochondrial stress in ZIKV infected neuronal progenitors is interesting and could fill an important gap in knowledge on Zika pathogenesis. The study uses human iPSC derived NPCs instead of animal cells, which is also more clinically relevant than animal models. The findings would thus be of interest to all who are interested in Zika pathogenesis as well as therapeutic/vaccine development. If the above concerns could be addressed, the findings in this study could form the missing links in our current understanding of congenital Zika syndrome.

Expertise: Flavivirology and immunology. Flavivirus-host interactions.

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

Learn more at Review Commons

---

Reply to the reviewers

Reviewer #1 (Evidence, reproducibility and clarity (Required)):

In this manuscript the authors examine PAR-binding properties of PARP1 and identify ZnF3, BRCT and WGR domains as PAR-binding domains, which show cooperative effects on PAR binding. Their affinity for PARylated PARP2 was slightly higher compared to naked PAR. PAR binding competes with the binding to DNA strand breaks (SSB or DSB) and promotes DNA dissociation. PAR also reduces DNA-dependent activation of PARP1 catalytic activity. The findings are based on biochemical and biophysical experiments and the cellular significance of these findings has not been investigated.

Major comments:

1) The conclusion that the binding affinity of the three domains for PAR is high should be adjusted as the Kd is in the low micromolar range (3-5 uM). The PAR-binding affinity of individual domains compared to the full-length protein (Kd=39 nM) is thus rather low.

Response: We agree with the reviewer that the Kd for PARP1 (measured using BLI) is low compared to that reported for individual PAR-binding domains (measured using ITC). But we have also measured the combined KD for three high-affinity PAR binding domains (ZnF3-BRCT-WGR) which is in the nanomolar range (~140 nM) which infers that the domains show cooperativity or synergy for PAR binding, while the affinity for PARP1 is ~39nM. The difference in KD can be attributed to the absence of ZnF1 and CAT domains in construct ZnF3-BRCT-WGR which could contribute to higher affinity in the case of PARP1.

2) What is the affinity of PARP1 lacking ZnF3, BRCT and WGR for PAR? What is the DNA binding affinity of this mutant and its catalytic activity? If PAR competes with DNA binding, then this mutant is expected to show stronger DNA binding and stronger catalytic activation.

Response: We thank the reviewer for raising the concern, but we differ from the reviewer’s assumption “If PAR competes with DNA binding, then this mutant is expected to show stronger DNA binding and stronger catalytic activation” since DNA recognition and DNA-dependent stimulation of PARP1 is independent of PAR binding.

To address the concern, we cloned, expressed, and purified the ZnF1-2-CAT construct which lacks ZnF3, BRCT, and WGR domains (Figure S2j), and performed FP binding studies. Our results show that ZnF(1-2)-CAT binds to SSB with almost similar affinity as PARP1 while the affinity for DSB has reduced ~9 times due to lack of ZnF3 and WGR domain which contribute to DSB recognition (Figure S8a-d).

We also assessed the catalytic activity of ZnF(1-2)-CAT using PNC1-OPT assay. Our results show that the construct could not be stimulated by SSB DNA but show a little more than basal-level activity, which is expected because the interdomain contacts required for communication of DNA-dependent stimulation signal from the N-terminus to the catalytic domain are lost due to the absence of ZnF3, BRCT, and WGR domains (Fig 6B). In addition, automodification (PARylation) domain, which is located between BRCT and WGR is also lost. We only performed the assay with SSB because it showed almost the same affinity as PARP1.

3) The role of the WGR domain in DNA and PAR binding is unclear from the experiments in Figs. 4 and 5. The lower PAR concentration required to dissociate DNA from PARP1 in the case of full-length PARP1 vs ZnF-BRCT construct cannot be interpreted as being due to the WGR domain present in the full-length protein. To clearly show that this effect is due to the WGR domain, two experiments can be conducted: (i) compare full-length PARP1 and PARP1 mutant lacking WGR; (ii) compare ZnF-BRCT and ZnF-BRCT-WGR.

Response: We thank the reviewer for suggesting experiments to further validate the role of the WGR domain in PAR-dependent DNA dissociation from PARP1. To perform the experiments, we cloned expressed and tried to purify ZnF(1-2-3)-BRCT-CAT (PARP1ΔWGR) and ZnF(1-2-3)-BRCT-WGR (PARP1ΔCAT) variants of PARP1 which lack the WGR domain and CAT domains (Figure S2l), respectively. We were unable to purify PARP1ΔWGR since it ended up in inclusion bodies.

We conducted the FP binding experiments of ZnF(1-2-3)-BRCT-WGR with DNA breaks and it showed an almost similar binding affinity for DSB and SSB as that of PARP1 since all the domains involved in both the DNA breaks recognition are present in the construct (compare Figure 5c-d to Figure S8a-b). Furthermore, Ki values of PAR required to dissociate DSB and SSB from ZnF(1-2-3)-BRCT-WGR are ~ 1.8 and ~1.4 times, respectively, (Figure 5g-h) lesser than required for DNA dissociation from ZnF(1-2-3)-BRCT (Figure 5e-f), which again indicates that the WGR domain plays important role in PAR-induced DNA-break dissociation from PARP1.

4) What is missing to make this study of higher impact are cellular assays to show, for example, how the absence of ZnF3, BRCT and WGR affects PARP1 recruitment to and retention at DNA damage sites.

Response: We strongly agree with the reviewer that having cell-based experiments in the paper will give more insights into the PAR-dependent regulation of PARP1, but our data using several truncated and deleted variants of purified PARP1, binding, and competition binding studies, competition enzyme assays with multiple complementary techniques clearly shows that PAR plays major roles in modulating DNA dependent activities of PARP1. Certainly, this is in future plans with collaboration.

Minor comments:

The manuscript should be edited to improve readability and the presentation of the data.

Response: We have edited the manuscript to improve the readability and presentation of diata.

Reviewer #1 (Significance (Required)):

Auto-PARylation of PARP1 was previously shown to cause its dissociation from DNA. Here the authors show that PAR binding through ZnF3, BRCT and WGR domains also causes dissociation from DNA and reduces PARP1 catalytic activity. These findings contribute to our understanding of how PARP1 DNA binding and activity can be regulated and will be of interest to researchers in the field of PARylation.

I have expertise in biochemical analysis of PARylation.

Reviewer #2 (Evidence, reproducibility and clarity (Required)):

The authors investigate the impact of poly adenosine repeats on the parylation activity of Poly(ADP-ribose) polymerase 1 (PARP1). They show that PAR can bind to PARP1 through specific domains and that binding occurs in some cases to be as strong as binding to DNA. This work indicates that PAR binds to PARP1 sufficiently well to allosterically alter the biological consequences of PARP1 through parylation. For example, DNA appears to bind PARP1 and negatively regulate Parylation, therefore the work is potentially highly significant.

**Referees cross-commenting**

I agree with comments from Reviewer 1. Especially with the descriptions of binding affinity. low micromolar binding is relatively low affinity. The authors should revise this description. Other comments from Reviewer 1 are also appropriate.

Response: We have addressed all the comments from reviewer 1.

Reviewer #2 (Significance (Required)):

General assessment: the authors use many purified domains from PARP1 that are purified and are used for quantitative binding experiments. The binding experiments appear to be done thoroughly with appropriate instrumentation.

Advance: This work fills in a gap in understanding PARP1 and its key role in recruiting proteins to damaged DNA; that being the role of PAR in direct binding to PARP1.

Audience: PARP1 is a major target for inhibition in treating cancer. The audience will include those interested in targeting PARP1 in a different way. As an enzymologist with interests in DNA repair, this paper was interesting and the results were properly analyzed.

There are a few instances in which the text needs to be checked for grammar. Overall, the manuscript is clearly written. The data appear to be well presented except for items listed below.

The equation used to fit fluorescence polarization data should be listed in the methods section. The competition binding studies were performed with 40 nM protein and 20 nM probe DNA. Under these conditions, Ki values below 20 nM should represent saturation binding rather than equilibrium binding. It would be useful to know whether the Ki values are reproduced with lower probe concentrations (below the Ki values). How is this taken into account in the data analysis?

Response: Thanks for the reviewer suggestion to include equation to fit competition-binding data. As the reviewer suggested, we included the equation in the materials and methods section (Fluorescence Polarization (FP) studies).

We completely agree with the reviewer that at a probe concentration of 20 nM, Ki values below 20 nM would represent saturation binding rather than equilibrium binding, but none of our Ki values are D * and Ki values differed marginally from corresponding values at 20 nM probe (DNA) concentration (Figure 4 and Figure S8a-b and e-h).

Fig.4. The concentration of PARP1 and concentration of the DSB or SSB DNA should be stated. Also, the equation for fitting the data should be shown.

Response: We have mentioned the concentrations of proteins and DNAs in the figure legends. The equation used for fitting competition-binding data has been included in the materials and methods (Fluorescence Polarization (FP) studies).

Fig 5. list the concentration of enzyme and the 5-FAM DNA in the legend.

Response: We have mentioned the concentrations of proteins and DNAs in the figure legends.

Fig 6. In panel A, what form of DNA is shown in the gel image?

Response: In Fig. 6, panel A, SSB has been used to show the DNA-dependent PARP1 stimulation. We have mentioned the name of DNA in figure, figure legend and corresponding text.

Supplemental fig. 8 also need to list the concentration of the DNA.

Response: We have mentioned the concentrations DNAs in the figure legends

Reference is made to Figure S9, but there is no Figure S9.

Response: Removed.

A table that summarizes binding activity and catalytic activity would be helpful.

__Response: __A table summarizing the binding affinity and catalytic activity of the constructs has been included in Supplementary File (Table S2).

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

---

Referee #2

Evidence, reproducibility and clarity

The authors investigate the impact of poly adenosine repeats on the parylation activity of Poly(ADP-ribose) polymerase 1 (PARP1). They show that PAR can bind to PARP1 through specific domains and that binding occurs in some cases to be as strong as binding to DNA. This work indicates that PAR binds to PARP1 sufficiently well to allosterically alter the biological consequences of PARP1 through parylation. For example, DNA appears to bind PARP1 and negatively regulate Parylation, therefore the work is potentially highly significant.

Referees cross-commenting

I agree with comments from Reviewer 1. Especially with the descriptions of binding affinity. low micromolar binding is relatively low affinity. The authors should revise this description. Other comments from Reviewer 1 are also appropriate.

Significance

General assessment: the authors use many purified domains from PARP1 that are purified and are used for quantitative binding experiments. The binding experiments appear to be done thoroughly with appropriate instrumentation.

Advance: This work fills in a gap in understanding PARP1 and its key role in recruiting proteins to damaged DNA; that being the role of PAR in direct binding to PARP1.

Audience: PARP1 is a major target for inhibition in treating cancer. The audience will include those interested in targeting PARP1 in a different way. As an enzymologist with interests in DNA repair, this paper was interesting and the results were properly analyzed.

There are a few instances in which the text needs to be checked for grammar. Overall, the manuscript is clearly written. The data appear to be well presented except for items listed below.

The equation used to fit fluorescence polarization data should be listed in the methods section. The competition binding studies were performed with 40 nM protein and 20 nM probe DNA. Under these conditions, Ki values below 20 nM should represent saturation binding rather than equilibrium binding. It would be useful to know whether the Ki values are reproduced with lower probe concentrations (below the Ki values). How is this taken into account in the data analysis?

Fig.4. The concentration of PARP1 and concentration of the DSB or SSB DNA should be stated. Also, the equation for fitting the data should be shown.

Fig 5. list the concentration of enzyme and the 5-FAM DNA in the legend.

Fig 6. In panel A, what form of DNA is shown in the gel image?

Supplemental fig. 8 also need to list the concentration of the DNA.

Reference is made to Figure S9, but there is no Figure S9.

A table that summarizes binding activity and catalytic activity would be helpful.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

---

Referee #1

Evidence, reproducibility and clarity

In this manuscript the authors examine PAR-binding properties of PARP1 and identify ZnF3, BRCT and WGR domains as PAR-binding domains, which show cooperative effects on PAR binding. Their affinity for PARylated PARP2 was slightly higher compared to naked PAR. PAR binding competes with the binding to DNA strand breaks (SSB or DSB) and promotes DNA dissociation. PAR also reduces DNA-dependent activation of PARP1 catalytic activity. The findings are based on biochemical and biophysical experiments and the cellular significance of these findings has not been investigated.

Major comments:

1. The conclusion that the binding affinity of the three domains for PAR is high should be adjusted as the Kd is in the low micromolar range (3-5 uM). The PAR-binding affinity of individual domains compared to the full-length protein (Kd=39 nM) is thus rather low.
2. What is the affinity of PARP1 lacking ZnF3, BRCT and WGR for PAR? What is the DNA binding affinity of this mutant and its catalytic activity? If PAR competes with DNA binding, then this mutant is expected to show stronger DNA binding and stronger catalytic activation.
3. The role of the WGR domain in DNA and PAR binding is unclear from the experiments in Figs. 4 and 5. The lower PAR concentration required to dissociate DNA from PARP1 in the case of full-length PARP1 vs ZnF-BRCT construct cannot be interpreted as being due to the WGR domain present in the full-length protein. To clearly show that this effect is due to the WGR domain, two experiments can be conducted: (i) compare full-length PARP1 and PARP1 mutant lacking WGR; (ii) compare ZnF-BRCT and ZnF-BRCT-WGR.
4. What is missing to make this study of higher impact are cellular assays to show, for example, how the absence of ZnF3, BRCT and WGR affects PARP1 recruitment to and retention at DNA damage sites.

Minor comments:

The manuscript should be edited to improve readability and the presentation of the data.

Significance

Auto-PARylation of PARP1 was previously shown to cause its dissociation from DNA. Here the authors show that PAR binding through ZnF3, BRCT and WGR domains also causes dissociation from DNA and reduces PARP1 catalytic activity. These findings contribute to our understanding of how PARP1 DNA binding and activity can be regulated and will be of interest to researchers in the field of PARylation.

I have expertise in biochemical analysis of PARylation.

Transparent Peer Review

---

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

</br>

Transparent Peer Review

---

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

</br>

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

Learn more at Review Commons

---

Reply to the reviewers

Manuscript number: RC-2021-01111

Corresponding author(s): Esther Stoeckli

[Please use this template only if the submitted manuscript should be considered by the affiliate journal as a full revision in response to the points raised by the reviewers.

If you wish to submit a preliminary revision with a revision plan, please use our "Revision Plan" template. It is important to use the appropriate template to clearly inform the editors of your intentions.]

1. General Statements [optional]

Dear editors at Review Commons

Thanks for your patience. We have finally carried out a full revision of our originally submitted manuscript summarizing our findings on the role of Cables1 in axon guidance.

In our study, we provide in vitro and in vivo evidence for a role of Cables1 as a linker between axon guidance signaling pathways. Commissural axons in the developing spinal cord leave their intermediate target, the floor plate, due to a switch from attraction to repulsion mediated by the specific trafficking of Robo1 receptors to the growth cone surface. The presence of Robo1 on growth cones after contact with the floor plate allows them to respond to Slit, the negative guidance cue associated with the floor plate. After leaving the floor plate on the contralateral side, growth cones respond to a Wnt gradient along the antero-posterior axis. The responsiveness to Wnt of post- but not pre-crossing axons is regulated by the trafficking of Fzd3 receptors to the growth cone membrane of post-crossing axons (Alther et al., 2016), but also by the specific phosphorylation of β-Catenin at tyrosine Y489 by Abl kinase. Cables1 mediates this phosphorylation by transferring Abl kinase from the C-terminus of Robo1 to β-Catenin (this study).

The revised version of the manuscript contains additional experiments in vitro, in vivo and ex vivo combined with live imaging to further support our conclusion about the role of Cables1 as a linker between Robo/Slit and Wnt signaling.

It took as longer than expected to carry out these new experiments, as Nikole Zuñiga, the first author of the paper, left the lab after her PhD defense to take up a job in industry. Unfortunately for the study, but fortunately for Giuseppe Vaccaro, he also got a job soon after taking over the project. Therefore, the revision was delayed again. We hope that the additional experiments will solve the issues that were raised by the reviewers. We thank them for their contributions and suggestions.

Best regards

Esther Stoeckli

2. Point-by-point description of the revisions

This section is mandatory. *Please insert a point-by-point reply describing the revisions that were already carried out and included in the transferred manuscript. *

Point to point response to reviewers’ comments

Reviewer #1 (Evidence, reproducibility and clarity (Required)): In this work by Zuñiga et al. the authors study the role of the adaptor protein Cables1 on the guidance of post-comissural spinal cord neurons. They hypothesize that commissural axons need Cables1 to leave the floor plate and turn to ascend to the brain. They propose that during this process, Cables1 acts as a linker of two key axon guidance pathways, Slit and Wnt. Cables1 would localize β-catenin phosphorylated at tyrosine 489 to the distal axon and this would be necessary for the correct turning and navigation of post-crossing commissural axons. Although the work may be potentially interesting, there are major issues that authors need to address in order to state their claims:

-Fig. 2. To visualize the axonal phenotype after downregulation of Cables1 the authors use DiI labelling. This difficults the interpretation of the results as both electroporated and non- electroporated axons are labelled. Since the authors have a Math1::tdTomatoF reporter construct (as in Fig. 3), it would be desirable to use this construct Math1::tdTomatoF in combination with the dsCables1 plasmid to better visualize the phenotype. Alternatively and less preferred, GFP signal should be also shown in Fig.2B experiments.

We respectfully disagree. Most likely, the reviewer thinks about a defined nerve that has a particular trajectory and then when labelled with a fluorescent marker, deviations from this pathway, or defasciculated growth can be easily visualized. However, in the spinal cord, the dI1 axons run ventrally more like a ‘curtain’. Therefore, the aberrant behavior of axons is difficult to see. We therefore, opted for the alternative suggestion and added the GFP images to visualize clearly that the axons labelled with DiI are from the injected area. We also would like to add that we are extremely careful in injecting DiI only to the dI1 population of commissural axons to avoid mixing populations with different trajectories. As the analysis is done by a person blind to the experimental condition, we are convinced that our way of analyzing the phenotype is valid. An approach that has been successfully used by many groups for decades now. Please also keep in mind that we are always comparing groups of embryos with each other. Furthermore, having axons traced by DiI which were not targeted by dsRNA electroporation would not increase but rather decrease the likelihood of aberrant behavior. Therefore, we are convinced that our method of quantification is valid.

However, we have added new experiments using live-imaging which also demonstrate that many axons in embryos electroporated with dsCables1 fail to turn properly at the floor-plate exit site (see Movie 2). These experiments provide additional evidence for the validity of our results.

-Fig. 2B and Supp.Fig.3. Comparable DiI labellings should be shown in the different conditions. The three examples shown in this panel despite different amount of DiI-labeled axons making it difficult to compare them.

We have exchanged the image of the control-treated embryo in Figure 2 to have more comparable DiI injection sites. However, as we detail in our Material & Method section, the quantification was done in such a way that the number of axons does not matter. We rephrased this paragraph to make this point more clear (lines 630ff). Please also refer to the GFP-expressing control sample shown in Figure 6A.

We counted a DiI injection site as showing floor-plate stalling when at least 50% of the fibers entering the floor plate failed to reach the exit site. Similarly, ‘No turn’ means that at least 50% of the axons at the exit site failed to turn rostrally. Because, these two phenotypes are not independent of each other (100% stalling prevents the analysis of the turning phenotype), we only did a statistical analysis for the DiI injection sites with correctly turning axons. We also would like to point out that we hardly had injection sites where it was difficult to decide whether the 50% threshold was reached or not.

-Fig. 2D. An scheme depicting the different phenotypes: "normal", "FP stalling" and "no turn" would help to understand the results. They can use schemes similar to those shown in Fig. 2K Parra et al. 2010.

We have added a scheme outlining the different phenotypes, as suggested to Figure 2A.

-Fig. 3A. The open-book drawing is confusing. It seems that they are analyzing open-book preparations in this experiment when this is not the case.

Now Figure 4: We have changed the schematic explaining our experimental design. We wanted to illustrate that we only took the dorsal-most part of the spinal cord, dissected from open-book preparations of the spinal cord, as explants to avoid the inclusion of other cell types.

-Fig. 3B. Authors claim that Cables1 is not required in pre-crossing axons as dsCables electroporation does not affect axonal growth of DiI neurons taken at HH22. However, to be sure that Cables1 mRNA levels are downregulated in pre-crossing axons, relative levels of Cables1 mRNA and/or protein should be also determined at HH22 not only at HH25.

We have clarified the quantification of downregulation efficiency. The qPCR data are taken from HH23, that is one day after electroporation. The Western blot data show differences in protein levels at HH25, that is 2 days after electroporation. In both cases, the downregulation efficiency is about 50%. This means that we got rid of all Cables1 mRNA, as we successfully transfected 50% of the cells in the targeted area (52.5% in n=4 embryos). The cell numbers were determined by counting the ratio of GFP-positive cells from transfected spinal cords in a single cell suspension.

-Fig. 4. The incapacity of Slit to induce axonal retraction in dsCables1 neurons is used to conclude that Cables1 is required to respond to Slit. However, downregulation of Cables1 by itself is even more effective inhibiting axonal growth than Slit treatment. Upon this strong effect as a background, it is difficult to assay slit response. Authors should point this observation in the manuscript.

We disagree. There is no significant difference between the neurite lengths between the control neurons in the presence of Slit and the neurons lacking Cables1 (dsCables1), p=023, or the neurons lacking Cables1 in the presence of Slit (dsCables1 and Slit), p>0.9999. As seen in the images and also from the measured neurite lengths, axons still show growth and further reduction would have been possible. We would also like to point out that the conclusion from this experiment is that Cables1 is required for the response of axons to either Slit or Wnt.

To support our claims, we have added another experiment addressing the need for Cables1 for post-crossing axons’ responsiveness to Slit by downregulation of Robo receptors (Figure 10). These experiments confirmed that Slit/Robo signaling is required for the effect of Cables1 on post-crossing axons, in line with our final conclusion that Slit binding to Robo triggers internalization and Cables then transfers Abl from the C-term of Robo to β-Catenin. This results in phosphorylation of β-Catenin at tyrosine489 (β-Catenin pY489) and responsiveness to Wnt5a.

-Fig. 5B. In this Figure they do not differentiate between FP stalling or no turn phenotypes. A quantification taking into account the different phenotypes as shown in Fig.2D should be included.

Done, as suggested. This is Figure 6C in the revised manuscript.

-Fig. 6D,E. As postulated in the manuscript and based on the Rhee, et al. paper, the β-catenin phosphorylation is triggered by Abl quinase upon Slit-Robo signaling. How the authors explain then that isolated cells with axons growing on a plate recapitulate specific distal phosphorilation of β-catenin at Y489 in the absence of Slit signaling? This experiment shows that postcrossing axons contain more phosphorylated β-catenin as an intrinsic characteristic rather than as a consecuence of contact with floor plate signals. Authors should try a similar experiment but exposing the neurons (or explants) to Slit. Also, why β-catenin phosphorylation was not measured at the growth cone?

In Figure 6D and E (now Figure 7D,E), we compare pre- and post-crossing axons. Post-crossing axons do have ‘a memory’ of their contact with the floor plate, as this contact has changed the localization of Robo receptors to the surface (Philipp et al., 2012; Alther et al., 2016). Floor-plate contact also initiates differences in gene expression (e.g. Hhip expression in a Shh-and Glypican-dependent manner; Wilson and Stoeckli, 2013). The difference in Robo localization has also been described by others (Pignata et al., Cell Rep 29(2019)347).

In fact, the distal localization of pY-489 β-Catenin is in perfect agreement with our results: The localization of Robo1 on the distal portion of the axon is in line with published data from our own lab but also from the Castellani and the Tessier-Lavigne lab. Our results suggest that Cables is recruited to Abl bound to the C-term of Robo. Cables transfers Abl then to β-Catenin which is phosphorylated by Abl. Thus pY-489 β-Catenin would be localized predominantly where Robo is localized, i.e. the distal axon. In support of these results, experiments added to the revised version of the manuscript indicate that the response to Slit is required for the increase in β-Catenin pY489 (Figure 10B).

-Fig. 7. CAG::hrGFP electroporation is not specific for dl1 neurons. This experiment should be performed with Math1::tdTomatoF in order to analyze β-cat pY489 with or without dsCables1 specifically in dl1 neurons. Also, why GFP staining at the growth cones in Fig.7B is not visible in the axon?

As indicated in our schematic drawing (Figure 7A) we only cultured explants from the dorsal-most part of open-book preparations of spinal cords, making sure that our cultures are not mixtures with more ventral populations of neurons. We opted for CAG::hrGFP because Math1 is a weak promoter and the expression of GFP was very difficult to see after dissociating cells and culturing them in vitro. We used a GFP version that is not farnesylated to avoid interference with axonal staining of pY-489 β-Catenin. Therefore, GFP is not visible in axons with the imaging conditions used.

-Fig. 8. This experiment does not distinguish whether phosphorylated β-Cat is necessary for the correct navigation of post-crossing commissural axons (as it is claimed in the abstract) or it is also required for midline crossing. As it has been previously shown, correct navigation of post-crossing commisusal axons is a Wnt5 dependent process. As dsCables1 abrogates Wnt5a responsiveness (Fig.4B,C), does the phosphomimetic β-catenin Y489E construc rescue the Wnt5a response in dsCables1 electroporated neurons? Moreover, can the phosphomimetic β-catenin Y489E construc rescue the Slit response in dsCables1 electroporated neurons? Testing these effects on explants as in Fig. 4B,C but including phosphomimetic β-catenin, will help to understand to what extend phosphorylation of β-catenin is important for crossing, turning or both processes.

Yes, the phosphomimetic Y489E version of β-Catenin reduces the percentage of DiI injections sites with aberrant axonal navigation to control levels (Figure 9 in the revised manuscript). In contrast, a mutant version of β-Catenin that cannot be phosphorylated, β-CateninY489F, cannot rescue the axon guidance phenotype seen in the absence of Cables1.

-How do the authors envision the mechanism of Cables1/β-catenin mediated crossing and turning? A working model summarizing their hypothesis would help the reader to understand the results.

**Minor points:** -Homogeneize the term "scale bars" or "bars" in the Figure Legends.

done

-Scale bar of insets in Fig.1C is missing.

The scale bar is now added, we apologize for the mistake.

-The antisense control for Cables probe should be shown at HH-22/24. Otherwise is not possible to distinguish whether they do not detect signal because is a negative control or because Cables1 is not expressed at HH25.

We have added the image of an adjacent section hybridized with the sense probe for HH25, in addition to HH22 to clarify that Cables expression is higher during floorplate crossing, exiting and turning rostrally but then levels decrease when post-crossing axons have initiated their growth along the rostro-caudal axis.

-Figure legend for Fig. 2D is missing

corrected

-Fig. 8B right panel is contaminated with growthing axons coming from the below DiI injection. Please replace the picture.

We have changed the outline of this figure.

-The quantification of the different phenotypes "FP stalling", "no turn" should be better explained in the Mat and Met section. The sentence " more than 50% of the axons...." is not clear. Was this measured by eye? Otherwise, please indicate the soIware used to measure.

Yes, as mentioned above, it was hardly ever a close call. It is very easy for a person blind to the experimental condition to go through the DiI injection sites of an open-book preparation and to assess whether 50% or more of the axons that enter the floorplate reach the exit site, or not. Similarly, it is very easy to do the same for the turning behavior. We have changed the text describing this method of quantification to be more explicit (lines 630ff).

-Provide the quantification of the WB in Supplementary Fig. 2B normalising to Gapdh.

Added as Supplementary Figure 2C.

Reviewer #1 (Significance (Required)): Previous results have demonstrated that Slit-induced modulation of adhesion is mediated by cables that links Robo-bound Abl kinase to N-cadherin-bound betacat (Rhee et al., 2007). Here the authors propose that a similar mechanism is operating in commissural neurons leave the midline after crossing and turn immediately after. The role of Cables in the process has not been previously addressed. Thus, after proper addressing of my main concerns, I consider this paper may advance in our knowlege of how growing axons navigate intermediate targets.

We appreciate this positive evaluation of our study and hope that the additional experiments and more detailed explanations have helped clarify open questions of the reviewer.

Reviewer #2 (Evidence, reproducibility and clarity (Required)): In this paper entitled "Cables1 links Slit/Robo and Wnt/Frizzled signaling in commissural axon guidance", authors aim to the find the mechanisms the coordinate the floor plate exit and the rostral turning of commissural axons. During development thousands of axons have to navigate long distances to reach their targets and build functional circuits. To facilitate their journey, their paths is divided into small portions by intermediated targets. The most studied intermediated target is the floorplate (FP) at the midline of the ventral spinal cord. Glia cells forming the FP express plethora of guidance cues. Commissural neurons, which have their cells bodies located in the dorsal part of the spinal cord, send their axons towards the FP. These axons are first attracted by the FP which facilitate their entry within the FP. However, they switch this attractive response into a repulsive one in order to exit the FP and turn rostrally to connect their brain targets. In order to ensure that this process will go smoothly, commissural axons have to adapt the composition of their receptors and the signaling pathways to switch from attractiveness to repulsion. So far, many processes have been involved such as the alternative splicing of receptors (Robo3; Chen et al Neuron 2008), protease regulation of receptor expression (Nawabi et al Genes & Dev 2010), trafficking of receptors, or their interaction profiles (Delloye et al Nat Neuro 2015). However, it is still not clear how 2 events (here exit from the FP and rostral turning) are linked. Authors propose an original mechanism that involved the adaptor protein Cables 1. This protein has been shown to link the Robo/Slit1 signaling to Cadherins. Cables regulates the repulsive response to Slit and adhesion by the phosphoryla4on of b-Catenin by the kinase Abelson (Rhee et al Nat Cell Biol 2007). The story developed here is very original and interesting: Cables would link the exit of FP (mediated by Robo/Slit signaling) and the rostral turning of the commissural axons (controlled by the Wnt/Fzd pathway. Below I'm proposing some experiments as many questions raised upon reading this beautiful work. The experiments are sound and could be reproducible. The statistic analysis looks fine.

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

I would suggest some experiments to strengthen the whole work: •Authors might want to consider to perform some biochemistry experiments to show that Cables is able to interact with Robo1 and Fzd3: are these proteins in the same molecular complex? They could do 2 experiments: one in vitro by transfecting a cell line (such as HEK293 or cos cells) with plasmids coding for Robo1, Cables and Fzd3 or at least Cables and Fzd3 (as for Robo1/Cables they could refers to Rhee et al 2007). Another one would be in vivo: extracting proteins from the pre-crossing stage, the FP and post crossing stage; immunoprecipitation of Cables1 and see whether Robo1 and/or Fzd are pull down with Cables 1.

We decided not to do these experiments, as we felt that this would go beyond the current study. In fact, for our effects it is not necessary that Cables interacts physically with Robo or Fzd3. The important aspect is that Abl bound to Robo is transferred by Cables to β-Catenin. A direct interaction with Fzd3 is not necessary.

• From the pictures it seems that most of the axons are stalling in the FP when embryos are electroporated with dsCables1. It would be nice to show more examples of axons that are able to exit the FP but have turning problems. Given the data, as it is presented, it seems that Cables regulates more the FP exit (and therefore, as it was shown in Rhee et al, the responsiveness to Robo/Slit signaling).

The major phenotype is ‘no turn’. However, as we describe in response to reviewer 1 and in the manuscript, the ‘floorplate stalling’ and the ‘no turn’ phenotypes are not independent of each other. At DiI injection sites, where almost all axons stall in the floorplate, the turning cannot be assessed. Thus, the ‘no turn’ phenotype tends to be underestimated in conditions where floorplate crossing is also affected, as is the case after silencing Cables1.

In the same line, in Fig 4, Authors need to add a condition using dsCables and ds Fzd in order to see the effect of Cables on axon turning (response to Wnt). As it is this figure supports the role of Cables on FP exit but it's hard to make the link with commissural axon responsiveness to Wnt.

We belief that experiment 4 clearly demonstrates the absence of the Wnt responsiveness, as axons fail to grow in response to Wnt when they extend from neurons transfected with dsCables1 (Figure 4C). Because dsCables1 alone already abolishes all responsiveness to Wnt, the removal of Fzd at the same time would not change anything.

• Authors aim to show that Cables is a linker between 2 events: maybe it should be nice to try to disconnect these events. One way would be (if technically possible) to modulated expression of Cables at different stages. What would happen if Cables was down regulated upon FP crossing? Would axons still be able to respond to Wnt? The question I'm wondering about is whether the responsiveness to Slit and Wnt is acquired at the same time or whether axons should become sensitive to Slit and this event will prime them to respond to Slit. In order to address the following experiment could be performed: explants from HH22-HH23 embryos, could be treated with medium containing Slit first and then Wnt or vice et versa and perform some collapse assay.

Unfortunately, the experiment as proposed by the reviewer is not possible. The axons take on average 5.5 hours to cross the floorplate (entry – exit; Dumoulin et al., 2021). Most importantly, the protein that is already made before axons are at the exit site, could not be removed. Therefore, it is not possible to prevent the production of Cables only after axons have crossed the midline. As shown in Figure 1, Cables1 mRNA is present at HH22, that is when axons have reached and are about to enter the floorplate. We also do not belief that the in vitro experiment suggested by the reviewer would work. We would have to wash cell intensively to remove Slit added to the medium. This would interefere with their potential to grow in response to Wnt immediately after addition. However, we added experiments where we looked at the effect of Wnt after removal of Robo (Figure 10). These experiments demonstrate that responsiveness to Wnt can only be established when axons can respond to Slit, i.e. when Robo is activated.

• In Fig3 I was wondering whether post crossing axons were growing less because of the change in the regulation of adhesion: Rhee et al shows that Cables is able to modulate adhesion through N-cadherin. It would be interesting to perform immunostaining on these explant cultures to assess any change in adhesion molecules.

We have not found any changes in the expression levels of Contactin-2 (Axonin-1), NrCAM, or most importantly β1-Integrin, as our cultures grow on laminin.

• It is not clear whether Robo1 and/or Fzd induces the phosphorylation of b-catenin: is the Robo1/Slit binding induce the phosphorylation of b-cat and this event will prime the axons to respond to Wnt/Fzd? Or Wnt/Fzd is also able to control b-cat phosphorylation?

We have added an experiment, where we remove Robo1 from commissural neurons and compare pY489 β-Catenin levels (Figure 10). Furthermore, we demonstrate that in the absence of Robo1, Wnt has no stimulatory effect on axons (Figure 10C,D). These experiments supports our conclusion that Cables1 transfers Abl kinase from the C-terminal part of Robo to β-Catenin, which gets phosphorylated and thus is ready to act in the Wnt signaling pathway.

• The staining with the antibody needs to be detailed: as it is reported this antibody recognizes "a domain of Cables1 that is 90% identical to the corresponding region of Cables2": it seems that the Cables protein enrichment in the floor plate (around the central canal) is Cables 2 as its mRNA expression matches this profile of expression. The one expressed in the crossing axons might be Cables 1: one way to verify this, is to perform the staining on sections from embryos electroporated with dsCables 1. This is a very important control of the antibody to reinforce this point of the paper.

We belief that the staining of the cells around the central canal could be due to endfeet of precursors spanning the neural tube from the apical to the basal side. All cells seem to express some Cables1 (Figure 1B,C). As we did not find any effect of Cables2 on commissural axon navigation and we do not use antibodies to functionally interfere with Cables1 function, we did not do this experiment, as the antibody is not able to distinguish the two proteins. Most likely, there is little, if any, Cables2 expressed in the spinal cord during this time window. We still did some functional analyses but found no effect on axon guidance (Supplementary Figure 3).

• In Figures 3-4: why not performing some co culture of spinal cord explants with COS or HEK 293 cells expressing Slit1 or Wnt? This experiment will provide a clear-cut response to see the role of Cables in axon guidance. As there it is, Fig3 shows a role of Cables in axon growth but not guidance.

We respectfully disagree that in vitro experiment would help to show guidance versus growth. Guidance can only be shown in vivo. This is what we do. Our in vitro results are only included to address specific responsiveness of axons or expression changes in total β-Catenin or pY489 β-Catenin. But all our conclusions about the role of Cables in axon guidance are demonstrated in vivo. Experiments using co-cultures of axons with COS or HEK cells would be impossible to control for timing and amount of Slit or Wnt release.

• In Figure 6: my understanding of axon guidance is that every guidance decision happens at the level of the growth cone. However, it seems that in post crossing stage, there is a strong decrease of b-cat and phosphor- b cat within the growth cone compared to the precrossing stage. If beta cat is the effector of Cables to link Robo/Slit and Wnt/Fzd signaling I would expect it to be localized at the growth cone. I think authors should discuss this point. Regarding the normalization, it would be better to counterstaing the neurons with actin and use the measure of its fluorescence to normalize phopho-beta cat.

There must be a misunderstanding. We do not demonstrate or claim that there is a decrease in β-Catenin or pY489 β-Catenin between pre- and post-crossing axons. We only demonstrate that the distribution of pY489 β-Catenin is clustered in distal post- but not pre-crossing axons. This change in localization of pY489 β-Catenin is supporting our model that Cables1 transfers Abl kinase to β-Catenin and phosphorylates it and prepare it for signaling in the Wnt pathway. And, as demonstrated pY489 β-Catenin and β-Catenin are in the growth cone. However, for quantification we concentrated on the axon, as the difference in growth cone morphology would have complicated the quantification.

**Minor points:** •In figure 2: it seems that there are few axons labelled with DiI in the dsCables1 condition (Fig2B): it would be the choice of the picture or maybe the downregulation of Cables 1 interfere with the survival of dl1 neurons (even though in supp 1C it is shown that most of the populations are still there with no difference with the control side) or maybe some axons are delayed to reach to FP on time: the picture is focused on the FP: are there any axons still growing in the side of the open book preparation? Again, the picture that could be misleading.

We have exchanged the images for alternatives with a better matched number of DiI-labelled axons. There is indeed no evidence for cell death, as axons are still there at normal numbers when we analyze open-book preparations a day later than usually. The difference in the number of axons labelled by DiI is only due to the variability in the amount of DiI injected per injection site.

• In Fig1 legends, maybe Authors wanted to write "At HH18 dl1 commissural neurons start to extend their axons in the ventral spinal cord"?

No, what we mean is, as shown in Figure 1A, that axons emerge from the cell body at this time. They reach the ventral spinal cord by HH21 and the floor plate by HH22.

• I would also remove the yellow shadow on the Fig1A: it could be misleading as at first glance the reader might wonder whether there are 2 populations of dl1 neurons.

We have done as suggested to make the image clearer.

Reviewer #2 (Significance (Required)): It is still not clear how axons cross the midline. So far, many processes have been involved such as the alternative splicing of receptors (Robo3; Chen et al Neuron 2008), protease regulation of receptor expression (Nawabi et al Genes & Dev 2010), trafficking of receptors, or their interaction profiles (Delloye et al Nat Neuro 2015). However, it is still not clear how 2 events (here exit from the FP and rostral turning) are linked. Authors propose an original mechanism that involved the adaptor protein Cables 1. This protein has been shown to link the Robo/Slit1 signaling to Cadherins. Cables regulates the repulsive response to Slit and adhesion by the phosphorylation of b-Catenin by the kinase Abelson (Rhee et al Nat Cell Biol 2007). The audience that will be interested in this work is the neurodevelopment filed, axon regeneration field and overall people interested in neuronal circuit formation and function. My field of expertise is molecular and cellular neuroscience applied to axon guidance (crossing the FP) in mice models, axon regeneration and circuit formation.

We are happy to learn about the positive assessment of our work by a specialist.

Reviewer #3 (Evidence, reproducibility and clarity (Required)): In their manuscript, Zuniga and Stoeckli characterize the role of Cables in commissural axon guidance in the developing chick spinal cord. Based on a combination of in vitro outgrowth assays and in vivo dye-tracing experiments, the authors propose that Cables participates in both normal repulsive responses to Slit and attractive responses to Wnt5. Using combinations of low-does knock down of cables/robo and or B-catenin, the author suggest an in vivo link between these pathways. Using IF with phospho-specific antibodies to B-catenin, the authors suggest that there is elevated P-Bcatenin in the post-crossing segments of distal axons. While potentially interesting, the present study falls short of adequately supporting the major claims. In addition, there are several instances where experiments lack appropriate controls.

**Specific Comments** The conclusions reached by the authors are over-stated given the experiments performed. For example, the authors describe 'silencing' cables throughout the paper; however, the knock down that they achieve is approximately 50%. Indeed, it is quite surprising that such strong effects on growth/guidance can be achieved with a two-fold depletion of the gene product. Nevertheless, the rescue experiments provide nice evidence that dsRNA for Cables is causing a phenotype. This partial knockdown precludes strong conclusions, like for Figure 3, where they state that 'Cables is not required for pre-crossing.' The language needs to be tempered.

We rephrased the paragraph where we describe the effect of Cables 1 and the efficiency of downregulation to stress that the parameters that we use for electroporation result in around 50% of the cells successfully transfected (lines 154 – 162, and legend of Supplementary Figure 2). Therefore, to find mRNA levels and protein levels reduced to about half indicates that our method is extremely efficient and removes the targeted mRNA and the protein almost completely. We need to point out here that we always analyze the temporal expression pattern to electroporated embryos before the protein of interest has accumulated, as in ovo RNAi obviously does not remove protein but only prevents translation and therefore the synthesis of new protein. As proteins can be extremely stable compared to the time line of embryonic development, we inject and electroporate dsRNA before we find expression of mRNA.

Figure 4: the authors use bath application of Slit and Wnt to test effects of cables on Slit and Wnt responses. The observed effect sizes are very small and a single assay of this type does not allow such strong conclusions like 'loss of Cables prevents responsiveness.' Again, it is difficult to imagine that 50% reduction would completely prevent responses, raising questions about the suitability of this assay for measuring responsiveness- perhaps growth cone collapse would give more convincing results.

As mentioned above, we are almost completely eliminating the targeted protein in the transfected neurons. For the explants, we only looked at the neurons expressing td-Tomato driven by the Math1 promoter. Thus, these neurons were transfected. Obviously, we cannot be sure that 100% of our cells took up the plasmid and the dsRNA, but the chances are very high that this is the case based on the ration between plasmid and dsRNA.

Figure 5: The authors should more clearly document the effects they are seeing in these manipulations. As written, all we know is that there are 'significant effects on axon guidance.' What are these effects? Do they see the predicted differences between robo/cables and Bcatenin/cables phenotypes? e.g re-crossing defects in the case of robo and anterior turning defects in the case of B-catenin?

We have added the analysis of the detailed axon guidance problems seen in the absence of Robo1, Cables1, βCatenin, or combinations, now Figure 6C. Indeed, we find that the phenotype ‘no turn’ is more prevalent in the condition with loss of both Cables and βCatenin. However, as mentioned above in response to a question raised by Reviewer 2, the two phenotypes are not independent of each other. Stalling in the floor plate of the majority of axons prevents the analysis of the turning phenotype. That is why we only use the ‘normal’ DiI injection sites for the statistical analysis.

Also related to Figure 5: The authors do not validate the dsRNA knockdown of either Robo or B catenin. It is unclear what the interpretation or expectation of the triple knock down condition is.

We have used the same ESTs to produce dsRNA derived from Robo and βCatenin in our previous publications (Alther et al., Development 143(2016)994; Avilés and Stoeckli Dev Neurobiol 76(2016)190). Therefore, we only repeated the functional experiments to verify reproducibility of the effect but we did not quantify the efficiency of downregulation in detail again.

Figure 6: For this reviewer images showing enhanced P-Catenin in post-crossing distal axons is not convincing. The differences are not obvious by eye and the quantification suggests an ~30% increase. In contrast a nearly 4-fold increase is reported in Figure 7 for this same measurement. This raises concerns about the reproducibility of this 'phenotype.'

Staining intensities are subject to batch-to-batch variability. Therefore, the experiments shown in Figure 7 (Figure 6 in the original manuscript) cannot be directly compared to the levels in Figure 8 (previously Figure 7). However, within the experiments, we carefully normalized data. We do not make any claims about absolute staining intensities.

Also related to Figure 6: No validation of antibody specificity is provided or described.

Again, please keep in mind that we do not make any claims about absolute values. All are results are based on stainings with the same antibody and comparison between different areas of the same axons. Therefore, the specificity of the antibody is important but not a fundamental aspect of our results.

Figure 8: As for figure 5, phenotypic documentation is incomplete. In addition, no controls are shown to assure that the different mutant forms of B-catenin are comparably expressed, nor is there an unmutated wild-type control. The authors state that expression of these constructs alone has no effect on normal guidance; however, the supplemental data 6B would seem to indicate that both forms lead to increases abnormal phenotypes.

There is an increase in the number of injection sites with aberrant axon guidance, however, this was not significant. We cannot exclude the possibility that premature expression, or overexpression of βCatenin pY489E or βCatenin pY489F does interfere with the endogenous βCatenin pY489. We still decided to keep these experiments in the revised version of the manuscript as they support our conclusion that Cables1 is required for axonal responsiveness to Slit and Wnts, and that this effect is mediated by phosphorylation of βCatenin at Y489. We are aware that this experiment in isolation is not sufficient.

Reviewer #3 (Significance (Required)): The work builds on in vitro observa4ons from Rhee, 2007 about links between Robo signaling and Cables func4on. If adequately demonstrated, integra4on and coordina4on of Robo and Wnt axon guidance pathways is quite significant.

We thank the reviewer for this positive assessment.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

---

Referee #3

Evidence, reproducibility and clarity

In their manuscript, Zuniga and Stoeckli characterize the role of Cables in commissural axon guidance in the developing chick spinal cord. Based on a combination of in vitro outgrowth assays and in vivo dye-tracing experiments, the authors propose that Cables participates in both normal repulsive responses to Slit and attractive responses to Wnt5. Using combinations of low-does knock down of cables/robo and or B-catenin, the author suggest an in vivo link between these pathways. Using IF with phospho-specific antibodies to B-catenin, the authors suggest that there is elevated P-Bcatenin in the post-crossing segments of distal axons. While potentially interesting, the present study falls short of adequately supporting the major claims. In addition, there are several instances where experiments lack appropriate controls.

Specific Comments

The conclusions reached by the authors are over-stated given the experiments performed. For example, the authors describe 'silencing' cables throughout the paper; however, the knock down that they achieve is approximately 50%. Indeed, it is quite surprising that such strong effects on growth/guidance can be achieved with a two-fold depletion of the gene product. Nevertheless, the rescue experiments provide nice evidence that dsRNA for Cables is causing a phenotype.

This partial knockdown precludes strong conclusions, like for Figure 3, where they state that 'Cables is not required for pre-crossing.' The language needs to be tempered.

Figure 4: the authors use bath application of Slit and Wnt to test effects of cables on Slit and Wnt responses. The observed effect sizes are very small and a single assay of this type does not allow such strong conclusions like 'loss of Cables prevents responsiveness.' Again, it is difficult to imagine that 50% reduction would completely prevent responses, raising questions about the suitability of this assay for measuring responsiveness- perhaps growth cone collapse would give more convincing results.

Figure 5: The authors should more clearly document the effects they are seeing in these manipulations. As written, all we know is that there are 'significant effects on axon guidance.' What are these effects? Do they see the predicted differences between robo/cables and Bcatenin/cables phenotypes? e.g re-crossing defects in the case of robo and anterior turning defects in the case of B-catenin?

Also related to Figure 5:

The authors do not validate the dsRNA knockdown of either Robo or B catenin. It is unclear what the interpretation or expectation of the triple knock down condition is.

Figure 6: For this reviewer images showing enhanced P-Catenin in post-crossing distal axons is not convincing. The differences are not obvious by eye and the quantification suggests an ~30% increase. In contrast a nearly 4-fold increase is reported in Figure 7 for this same measurement. This raises concerns about the reproducibility of this 'phenotype.' Also related to Figure 6:

No validation of antibody specificity is provided or described.

Figure 8: As for figure 5, phenotypic documentation is incomplete. In addition, no controls are shown to assure that the different mutant forms of B-catenin are comparably expressed, nor is there an unmutated wild-type control. The authors state that expression of these constructs alone has no effect on normal guidance; however, the supplemental data 6B would seem to indicate that both forms lead to increases abnormal phenotypes.

Significance

The work builds on in vitro observations from Rhee, 2007 about links between Robo signaling and Cables function. If adequately demonstrated, integration and coordination of Robo and Wnt axon guidance pathways is quite significant.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

---

Referee #2

Evidence, reproducibility and clarity

In this paper entitled "Cables1 links Slit/Robo and Wnt/Frizzled signaling in commissural axon guidance", authors aim to the find the mechanisms the coordinate the floor plate exit and the rostral turning of commissural axons. During development thousands of axons have to navigate long distances to reach their targets and build functional circuits. To facilitate their journey, their paths is divided into small portions by intermediated targets. The most studied intermediated target is the floorplate (FP) at the midline of the ventral spinal cord. Glia cells forming the FP express plethora of guidance cues. Commissural neurons, which have their cells bodies located in the dorsal part of the spinal cord, send their axons towards the FP. These axons are first attracted by the FP which facilitate their entry within the FP. However, they switch this attractive response into a repulsive one in order to exit the FP and turn rostrally to connect their brain targets.

In order to ensure that this process will go smoothly, commissural axons have to adapt the composition of their receptors and the signaling pathways to switch from attractiveness to repulsion. So far, many processes have been involved such as the alternative splicing of receptors (Robo3; Chen et al Neuron 2008), protease regulation of receptor expression (Nawabi et al Genes & Dev 2010), trafficking of receptors, or their interaction profiles (Delloye et al Nat Neuro 2015). However, it is still not clear how 2 events (here exit from the FP and rostral turning) are linked.

Authors propose an original mechanism that involved the adaptor protein Cables 1. This protein has been shown to link the Robo/Slit1 signaling to Cadherins. Cables regulates the repulsive response to Slit and adhesion by the phosphorylation of b-Catenin by the kinase Abelson (Rhee et al Nat Cell Biol 2007). The story developed here is very original and interesting: Cables would link the exit of FP (mediated by Robo/Slit signaling) and the rostral turning of the commissural axons (controlled by the Wnt/Fzd pathway. Below I'm proposing some experiments as many questions raised upon reading this beautiful work. The experiments are sound and could be reproducible. The statistic analysis looks fine.

I would suggest some experiments to strengthen the whole work:

•Authors might want to consider to perform some biochemistry experiments to show that Cables is able to interact with Robo1 and Fzd3: are these proteins in the same molecular complex? They could do 2 experiments: one in vitro by transfecting a cell line (such as HEK293 or cos cells) with plasmids coding for Robo1, Cables and Fzd3 or at least Cables and Fzd3 (as for Robo1/Cables they could refers to Rhee et al 2007). Another one would be in vivo: extracting proteins from the pre-crossing stage, the FP and post crossing stage; immunoprecipitation of Cables1 and see whether Robo1 and/or Fzd are pull down with Cables 1.

•From the pictures it seems that most of the axons are stalling in the FP when embryos are electroporated with dsCables1. It would be nice to show more examples of axons that are able to exit the FP but have turning problems. Given the data, as it is presented, it seems that Cables regulates more the FP exit (and therefore, as it was shown in Rhee et al, the responsiveness to Robo/Slit signaling). In the same line, in Fig 4, Authors need to add a condition using dsCables and ds Fzd in order to see the effect of Cables on axon turning (response to Wnt). As it is this figure supports the role of Cables on FP exit but it's hard to make the link with commissural axon responsiveness to Wnt.

•Authors aim to show that Cables is a linker between 2 events: maybe it should be nice to try to disconnect these events. One way would be (if technically possible) to modulated expression of Cables at different stages. What would happen if Cables was down regulated upon FP crossing? Would axons still be able to respond to Wnt? The question I'm wondering about is whether the responsiveness to Slit and Wnt is acquired at the same time or whether axons should become sensitive to Slit and this event will prime them to respond to Slit. In order to address the following experiment could be performed: explants from HH22-HH23 embryos, could be treated with medium containing Slit first and then Wnt or vice et versa and perform some collapse assay.

•In Fig3 I was wondering whether post crossing axons were growing less because of the change in the regulation of adhesion: Rhee et al shows that Cables is able to modulate adhesion through N-cadherin. It would be interesting to perform immunostaining on these explant cultures to assess any change in adhesion molecules.

•It is not clear whether Robo1 and/or Fzd induces the phosphorylation of b-catenin: is the Robo1/Slit binding induce the phosphorylation of b-cat and this event will prime the axons to respond to Wnt/Fzd? Or Wnt/Fzd is also able to control b-cat phosphorylation?

•The staining with the antibody needs to be detailed: as it is reported this antibody recognizes "a domain of Cables1 that is 90% identical to the corresponding region of Cables2": it seems that the Cables protein enrichment in the floor plate (around the central canal) is Cables 2 as its mRNA expression matches this profile of expression. The one expressed in the crossing axons might be Cables 1: one way to verify this, is to perform the staining on sections from embryos electroporated with dsCables 1. This is a very important control of the antibody to reinforce this point of the paper.

•In Figures 3-4: why not performing some co culture of spinal cord explants with COS or HEK 293 cells expressing Slit1 or Wnt? This experiment will provide a clear-cut response to see the role of Cables in axon guidance. As there it is, Fig3 shows a role of Cables in axon growth but not guidance.

•In Figure 6: my understanding of axon guidance is that every guidance decision happens at the level of the growth cone. However, it seems that in post crossing stage, there is a strong decrease of b-cat and phosphor- b cat within the growth cone compared to the precrossing stage. If beta cat is the effector of Cables to link Robo/Slit and Wnt/Fzd signaling I would expect it to be localized at the growth cone. I think authors should discuss this point. Regarding the normalization, it would be better to counterstaing the neurons with actin and use the measure of its fluorescence to normalize phopho-beta cat.

Minor points:

•In figure 2: it seems that there are few axons labelled with DiI in the dsCables1 condition (Fig2B): it would be the choice of the picture or maybe the downregulation of Cables 1 interfere with the survival of dl1 neurons (even though in supp 1C it is shown that most of the populations are still there with no difference with the control side) or maybe some axons are delayed to reach to FP on time: the picture is focused on the FP: are there any axons still growing in the side of the open book preparation? Again, the picture that could be misleading.

•In Fig1 legends, maybe Authors wanted to write "At HH18 dl1 commissural neurons start to extend their axons in the ventral spinal cord"?

•I would also remove the yellow shadow on the Fig1A: it could be misleading as at first glance the reader might wonder whether there are 2 populations of dl1 neurons.

Significance

It is still not clear how axons cross the midline. So far, many processes have been involved such as the alternative splicing of receptors (Robo3; Chen et al Neuron 2008), protease regulation of receptor expression (Nawabi et al Genes & Dev 2010), trafficking of receptors, or their interaction profiles (Delloye et al Nat Neuro 2015). However, it is still not clear how 2 events (here exit from the FP and rostral turning) are linked.

Authors propose an original mechanism that involved the adaptor protein Cables 1. This protein has been shown to link the Robo/Slit1 signaling to Cadherins. Cables regulates the repulsive response to Slit and adhesion by the phosphorylation of b-Catenin by the kinase Abelson (Rhee et al Nat Cell Biol 2007). The audience that will be interested in this work is the neurodevelopment filed, axon regeneration field and overall people interested in neuronal circuit formation and function.

My field of expertise is molecular and cellular neuroscience applied to axon guidance (crossing the FP) in mice models, axon regeneration and circuit formation.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

---

Referee #1

Evidence, reproducibility and clarity

In this work by Zuñiga et al. the authors study the role of the adaptor protein Cables1 on the guidance of post-comissural spinal cord neurons. They hypothetize that commissural axons need Cables1 to leave the floor plate and turn to ascend to the brain. They propose that during this process, Cables1 acts as a linker of two key axon guidance pathways, Slit and Wnt. Cables1 would localize β-catenin phosphorylated at tyrosine 489 to the distal axon and this would be necessary for the correct turning and navigation of post-crossing commissural axons. Although the work may be potentially interesting, there are major issues that authors need to address in order to state their claims:

-Fig. 2. To visualize the axonal phenotype after downregulation of Cables1 the authors use DiI labelling. This difficults the interpretation of the results as both electroporated and non-electroporated axons are labelled. Since the authors have a Math1::tdTomatoF reporter construct (as in Fig. 3), it would be desirable to use this construct Math1::tdTomatoF in combination with the dsCables1 plasmid to better visualize the phenotype. Alternatively and less preferred, GFP signal should be also shown in Fig.2B experiments.

-Fig. 2B and Supp.Fig.3. Comparable DiI labellings should be shown in the different conditions. The three examples shown in this panel despite different amount of DiI-labeled axons making it difficult to compare them.

-Fig. 2D. An scheme depicting the different phenotypes: "normal", "FP stalling" and "no turn" would help to understand the results. They can use schemes similar to those shown in Fig. 2K Parra et al. 2010.

-Fig. 3A. The open-book drawing is confusing. It seems that they are analyzing open-book preparations in this experiment when this is not the case.

-Fig. 3B. Authors claim that Cables1 is not required in pre-crossing axons as dsCables electroporation does not affect axonal growth of DiI neurons taken at HH22. However, to be sure that Cables1 mRNA levels are downregulated in pre-crossing axons, relative levels of Cables1 mRNA and/or protein should be also determined at HH22 not only at HH25.

-Fig. 4. The incapacity of Slit to induce axonal retraction in dsCables1 neurons is used to conclude that Cables1 is required to respond to Slit. However, downregulation of Cables1 by itself is even more effective inhibiting axonal growth than Slit treatment. Upon this strong effect as a background, it is difficult to assay slit response. Authors should point this observation in the manuscript.

-Fig. 5B. In this Figure they do not differentiate between FP stalling or no turn phenotypes. A quantification taking into account the different phenotypes as shown in Fig.2D should be included.

-Fig. 6D,E. As postulated in the manuscript and based on the Rhee, et al. paper, the β-catenin phosphorylation is triggered by Abl quinase upon Slit-Robo signaling. How the authors explain then that isolated cells with axons growing on a plate recapitulate specific distal phosphorilation of β-catenin at Y489 in the absence of Slit signaling? This experiment shows that postcrossing axons contain more phosphorylated β-catenin as an intrinsic characteristic rather than as a consecuence of contact with floor plate signals. Authors should try a similar experiment but exposing the neurons (or explants) to Slit. Also, why β-catenin phosphorylation was not measured at the growth cone?

-Fig. 7. CAG::hrGFP electroporation is not specific for dl1 neurons. This experiment should be performed with Math1::tdTomatoF in order to analyze β-cat pY489 with or without dsCables1 specifically in dl1 neurons. Also, why GFP staining at the growth cones in Fig.7B is not visible in the axon?

-Fig. 8. This experiment does not distinguish whether phosphorylated β-Cat is necessary for the correct navigation of post-crossing commissural axons (as it is claimed in the abstract) or it is also required for midline crossing. As it has been previously shown, correct navigation of post-crossing commisusal axons is a Wnt5 dependent process. As dsCables1 abrogates Wnt5a responsiveness (Fig. 4B,C), does the phosphomimetic β-catenin Y489E construc rescue the Wnt5a response in dsCables1 electroporated neurons? Moreover, can the phosphomimetic β-catenin Y489E construc rescue the Slit response in dsCables1 electroporated neurons? Testing these effects on explants as in Fig. 4B,C but including phosphomimetic β-catenin, will help to understand to what extend phosphorylation of β-catenin is important for crossing, turning or both processes.

-How do the authors envision the mechanism of Cables1/β-catenin mediated crossing and turning? A working model summarizing their hypothesis would help the reader to understand the results.

Minor points:

-Homogeneize the term "scale bars" or "bars" in the Figure Legends.

-Scale bar of insets in Fig.1C is missing.

-The antisense control for Cables probe should be shown at HH-22/24. Otherwise is not possible to distinguish whether they do not detect signal because is a negative control or because Cables1 is not expressed at HH25.

-Figure legend for Fig. 2D is missing

-Fig. 8B right panel is contaminated with growthing axons coming from the below DiI injection. Please replace the picture.

-The quantification of the different phenotypes "FP stalling", "no turn" should be better explained in the Mat and Met section. The sentence " more than 50% of the axons...." is not clear. Was this measured by eye? Otherwise, please indicate the software used to measure.

-Provide the quantification of the WB in Supplementary Fig. 2B normalising to Gapdh.

Significance

Previous results have demonstrated that Slit-induced modulation of adhesion is mediated by cables that links Robo-bound Abl kinase to N-cadherin-bound betacat (Rhee et al., 2007). Here the authors propose that a similar mechanism is operating in commissural neurons leave the midline after crossing and turn immediately after. The role of Cables in the process has not been previously addressed. Thus, after proper addressing of my main concerns, I consider this paper may advance in our knowleged of how growing axons navigate intermediate targets.

Transparent Peer Review

---

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

</br>

Transparent Peer Review

---

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

</br>

Transparent Peer Review

---

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

</br>

Transparent Peer Review

---

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

</br>

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

Learn more at Review Commons

---

Reply to the reviewers

Manuscript number: RC-2022-01758

Corresponding author(s): Harbison, Susan and Souto-Maior, Caetano

[Please use this template only if the submitted manuscript should be considered by the affiliate journal as a full revision in response to the points raised by the reviewers.

If you wish to submit a preliminary revision with a revision plan, please use our "Revision Plan" template. It is important to use the appropriate template to clearly inform the editors of your intentions.]

1. General Statements [optional]

This section is optional. Insert here any general statements you wish to make about the goal of the study or about the reviews.

We thank the reviewers for their time and care in evaluating our manuscript. They raise several important points, which we have addressed, resulting in a greatly improved manuscript. Please note that we numbered the comments from both reviewers for ease of reference, as we cross-referenced comments in some cases. Reviewer comments are in italics; our responses are provided in plain text.

2. Point-by-point description of the revisions

This section is mandatory. *Please insert a point-by-point reply describing the revisions that were already carried out and included in the transferred manuscript. *

Reviewer #1 (Evidence, reproducibility and clarity (Required)):

*Summary*:

*The authors of this work generated a Sleep Advanced Intercross Population from 10 extreme sleeper Drosophila Genetics Reference Panel. This new out-bred population was subjected to a artificial selection with the aim of understanding the genes underlying the sleep duration differences between three populations: short-sleep, unselected, and long-sleep. Using analysis of variance the authors identified up to nearly 400 of genes that were significant selected over the various generations and showed opposite trends for long and short sleep, thus potentially relevant for the regulation of sleep duration. 85 of these genes were consistent between male and females sub-populations, suggesting a small number of genetic divergences may underlie sex-independent mechanisms of sleep.

Given the time-course nature of the generational data obtained, the authors studied potential correlations and interactions between these 85 identified candidate genes. Initially, the authors used pairwise Spearman correlation, noticing how this method could not filter most of pairwise interaction (around 40% of all possibilities were significant). To overcome the linear limitations of the previous approach, the authors implemented a more complex, non-linear Gaussian process model able to account for pairwise interactions. This new approach was able to identify a smaller number of different, and potentially more informative, correlations between the candidate genes previously identified.

Lastly, with genetic manipulations, the authors show in vivo that a subset of the candidate genes is causally related with the sleep duration as well as partially validating some of the correlation identified by their new model.

The authors conclude that, given the non-linear and complex nature of biological systems, simplistic linear approaches may not suffice to fully capture underlying mechanisms of complex traits such as sleep.

*Major comments*

1. Most of the the work presented focus on the computational and statistical analysis of different populations submitted (or not) to a process of artificial selection for short or long sleep duration. As such, the amount of potentially relevant biological conclusions to be tested is mostly unfeasible. The authors already present additional experiments to partially support some, though not all, of their findings. Given the manuscript is written as a method innovation, these additional experiments illustrate the potential uses of the method described. *

Our response: The reviewer raises a very important point, one that is at the very impetus of our work. We agree that it is not possible to test all combinations of genes in all contexts to determine whether they influence sleep or not. In contrast to the situation for circadian rhythms, where the core clock is controlled by just four genes, recent work has concluded that sleep is a set of complex traits influenced by large numbers of genes. Robust computational methods are needed to identify the complex interactions among genes. The current manuscript is a first step towards achieving this goal.

*(OPTIONAL) However, since the one of the focuses of this work in identifying potential gene interactions, it would be interesting if the authors could test a "double knockout" and perhaps demonstrate evidence for epistasis between two of the identified genes. Having access to single mutants, this experiment should be realistic. However, I have no hands-on experience working with Drosophila and I am unable to accurately estimate the amount of resources and time such and experiment could take. My initial guess would be 3-6 months work should suffice. *

Our response: The reviewer makes an interesting proposal. While such an experiment would provide some additional information, our method does not make any prediction about what a double knockout would do, either to the sleep phenotypes or to gene expression.

2. In regards to the gene CG1304, it seems to be an important example used throughout the manuscript. It should be carefully re-analyzed as was considered for interaction analyses without showing opposite trends for short- and long-sleep populations (see minor comments on figure 2).

Our response: We are not entirely certain that we understand the reviewer’s point. We note that significant genotype-by-selection-scheme interactions may not manifest as opposite trends and this is not what is being tested for significance. The likelihood ratio is a test for a significant effect of including sel x gen coefficients for both short and long schemes; therefore, GLM significance may mean that either one or the two selection schemes are significantly different from controls, not from each other. We could, for instance, apply three different tests: one (i) comparing between long and short flies; the second (ii) __comparing short flies to controls; and the third (iii) __comparing long to controls and find that the first test is significant — i.e. short is different from long — and that the two others are not — i.e. neither scheme is found to be different from controls. The opposite could also happen: short and long flies may not be different from each other, but with both being different from controls.

Since we are interested in identifying differences of either to controls, our choice of statistical test is equivalent to performing tests (ii) __and (iii)__ without the need to perform and correct for multiple tests. While there are caveats to this choice (like all choices), linear model-based differential expression analysis has its own caveats, and has limited ability to pick up arbitrary trends, so it serves as a coarse-grained filter for large shifts since it’s too costly (computationally) to run the Gaussian process on 50 million pairwise combinations.

*3. One major comment would be that the claim that the Gaussian process method is more sensitive and specific than simpler approaches, though intuitively understandable, does not seem to be fully correct from a strict statistical point of view, given the lack of a gold standard reference to compare if the new method is indeed picking more true positives/negatives. I would reconsider re-rephrasing such statement in the absence of a biologically relevant validation set. *

Our response: We agree with the reviewer that there is no ‘gold standard’ reference data set with which to compare our findings. We have softened this language a bit in response, where it occurs in both the Abstract and the Results.

Under Abstract, we changed “Our method not only is considerably more specific than standard correlation metrics but also more sensitive, finding correlations not significant by other methods” to “Our method appears to be not only more specific than standard correlation metrics but also more sensitive, finding correlations not significant by other methods.”

Under Results, we changed “Therefore, computing correlations between genes using covariance estimates from the Gaussian Processes greatly increases specificity over direct correlations. Furthermore, the Gaussian processes are not only more specific but more sensitive…” to “Therefore, computing correlations between genes using covariance estimates from the Gaussian Processes appears to increase specificity over direct correlations. Furthermore, the Gaussian Processes appear to be more sensitive…”

*4. Finally, the study appears to be well powered and it is clear that the authors were careful in their explanation of the statistical methods. However, I could not find the copy of the code/script used for the model. Without it, it would be very difficult to fully reproduce the results as both the language used (Stan) and the method itself are not common in the sleep research field. *

Our response: We thank the reviewer for noticing this, and apologize for this oversight. The code used for analysis has been deposited in GitHub under: https://github.com/caesoma/Multiple-shifts-in-gene-network-interactions-shape-phenotypes-of-Drosophila-melanogaster.

We have noted the script location in the Data Availability statement. We added a statement to read “All scripts used for the model have been deposited in Git Hub https://github.com/caesoma/Multiple-shifts-in-gene-network-interactions-shape-phenotypes-of-Drosophila-melanogaster.”

* * *Minor comments* * 5. The statistical cut-off used for gene expression hierarchical GLMM after BH correction was of 0.001, which is 50 times more strict than the common 0.05. Could the authors comment on how this choice may impact the results compared to those available in the literature and on the rational for choosing such a value.*

Our response: A FDR of 0.05 would increase the number of genes identified (3,544 for females; 1,136 for males, with 462 overlapping). The FDR of 0.001 is consistent with the lowest threshold typically used for gene expression data collected during other artificial selection experiments (Mackay et al., 2005; Morozova et al., 2007; Edwards et al., 2006), though thresholds as high as 0.20 have been used (Sorensen et al., 2007). We have added to the last statement to the Methods and Materials section under “Generalized Linear Model analysis of expression data” to read “Model p-values were corrected for multiple testing using the Benjamini-Hochberg method (Benjamini and Hochberg, 1995), with significance defined at the 0.001 level, consistent with the lower threshold applied in other artificial selection studies (Mackay et al., 2005; Morozova et al., 2007; Edwards et al., 2006).”

*6. Heritability calculations are not mentioned in the methods. Could it be useful to include a small paragraph? Could a small comment be done on the differences in h2 for the short sleep replicates which show ~10x difference? *

Our response: We thank the reviewer for noticing this omission and apologize for the oversight. We have added the following statements to the Methods and Materials under “Quantitative genetic analyses of selected and correlated phenotypic responses.”

“We estimated realized heritability h2 using the breeder’s equation:

h2 = ΣR/ΣS

where ΣR and ΣS are the cumulative selection response and differential, respectively (Falconer and Mackay, 1996). The selection response is computed as the difference between the offspring mean night sleep and the mean night sleep of the parental generation. The selection differential is the difference between the mean night sleep of the selected parents and the mean night sleep of the parental generation.”

Additionally, we thank the reviewer for noticing the large difference in the realized heritability between the short sleeping population replicates; the heritability for replicate 1 is a typo and should be 0.169, not 0.0169. Hence, the heritabilities of both replicate populations are quite similar, i.e., 0.169 for replicate 1 and 0.183 for replicate 2. We have corrected this error in the Results.

7. In regards to the model implementation, what would be the implications of not enforcing positive semi-definiteness on the co-variance matrix, given than that these are strictly positive semi-defined?

Our response: All covariance matrices are by definition positive semi-definite (PSD), since they cannot yield negative values for the probabilities associated to them, so it would not be possible to relax that assumption generally. The only choice we could make would be on the number of genes included (M) in each multi-channel gaussian process model, and this in turn would by design enforce positive semi-definiteness on an matrix of size MN, (N being the number of generations). As noted in the appendix, “enforcing” positive semi-definiteness on smaller blocks of a larger 2D-array of covariances (which is not itself a covariance matrix) does not imply the latter is PSD and therefore seems like a softer constraint. In practice scaling up to a model where M >> 40 is not trivial from a computational and inference point of view, so the choice of smaller M is in a way imposed on us, and fortunately it is the less limiting one. We provide the appendix as a general clarification on the subtleties of Gaussian Processes, but a comprehensive assessment is beyond the multidisciplinary scope of this article and would require a narrower mathematical/statistical description in a standalone methodological article or technical note.

1. *The methods mention that PCA projection were performed on the first 3 components, however only the first two are showed. *

Our response: PCA was performed on 10 components, although the algorithms will commonly compute all components and return only the selected number. The variance of the third component is smaller than ~5% (that of the second PC). In practice PC1 is by itself enough to show the clear separation of expression per sex with ~65% of the variance; PC2 is in fact only shown to improve visualization. Plots of the remaining components will not show clear separation among samples as the variance explained is so small. We have corrected the Methods to indicate that PCA was performed on 10 components rather than 3.

*9. Figure 1 refers to the mean night sleep time of the population. Could some measurement of variability (se or sd) be represented to provide a general idea of the distribution of the values? Additionally, the standard deviation of associated with the CVe estimates are mentioned but not showed explicitly. Could they maybe be added to the text as to illustrate how much such deviations were reduced? *

Our response: We thank the reviewer for this comment. Including either the standard errors or standard deviations on the plot of the response to selection (Figure 1A) makes visualization unwieldy; thus we have added an additional supplemental table, Supplementary Table S15, that contains the mean night sleep, standard deviation, and number of flies measured for each generation in each replicate population. We also added a plot of the standard deviation in night sleep per generation to Supplemental Figure S2 (letter “Q” in the figure) so that the reduction over time in each population can be seen.

Under “Data Availability,” We added the following: “Night sleep phenotypes per selection scheme/sex/generation/population replicate are listed in Table S15.”

*10. Figure 2 shows the linear model fits for gene CG1304. I find this gene on the list of significant genes for both sexes (tables S5/6), but it does not seem to be one that shows opposite trend for short- and long-sleep (tables S7/8). Surprisingly, it shows up again on table S10! However, the text introducing the figure reads like this should be one of the 85 sex-independent genes. Would it be best to provide an example of what a significant gene looks like? *

Our response: As mentioned in our response to comment #2 above, significance in the likelihood-ratio test does not imply opposite trends between long and short selection schemes, but between a model that includes specific slope coefficients for selection scheme by generation (both long and short) compared to a reduced model where the only slope is one associated to generation and therefore independent of selection scheme.

11. *Figure 3 would be interesting to have both the GP correlations and the Spearman correlations to illustrate the methodological differences. I would be curious to see at least one pairwise expression scatter-plot as well just to see how they correlate in one plot. *

__Our response: __Table S11 contains all (significant and nonsignificant) GP and Spearman values side-by-side for comparison. High correlations are likely to conform to the Spearman assumptions of a monotonic relationship; nevertheless, this will not be so for the majority of genes since the difference in the number of Spearman and GP-significant genes is tenfold or more, so it would be misleading to focus on individual-gene relationships without taking into consideration the transcriptome wide results for any method employed.

We would like to stress that there is nothing particularly special about CG1304 in and of itself; furthermore, there are no “representative” genes or figures in this manuscript. Instead, CG1304 is chosen because its GLM and GP fits are illustrative of the limitations and capabilities of each model to pick up certain kinds of trends, and especially because it is especially instructive of how correlations arise from the GP model, which may not be intuitively clear to all readers.

12. Figures 3S3/4 are described as showing single- and multi-channel models don't change substantially. Would this be expected and why?

Our response: This is not necessarily expected, as scaling up from a single to a multi-channel model will add additional parameters as well as constraints, like positive the semi-definiteness mentioned in comment #7 above. If that seemed to have considerable impact on the fits it could challenge our assumption that the signal variance parameters estimated from the single-channel are good priors for the same parameters in the two-channel model (although this is not a hard constraint, so in the worst case the result could still only be a slight bias).

*13. Having build different networks of pairwise associations of genes (projecting on a unified network as illustrated on figure 5), it could in interesting to compare the network topologies at a basic level such as node degrees, overlapping sub-networks, are they potentially scale free as previously described for biological systems, etc. *

__Our response: __The reviewer makes an interesting point. Indeed summaries of the network could be useful information about the system level parameters, which are the main results of this paper. We now include the number of connections (i.e., the degree) to each gene in each of the four networks presented in Figure 5 in a new supplemental Table (Table S13). We also plot the distribution of node connectivity below. The distributions do not appear random (i.e., a normal distribution), and appear closer to a power-law or scale-free distribution. However, the small size and low average degree of these networks make a formal test unfeasible, and a recent study suggests that a log-normal distribution is in general more likely than a power-law distribution (Broido et al., Nat Comm, 2019), so we lack the evidence to claim that these networks are scale-free.

We have added to the Results under “Gaussian Process model analysis uncovers nonlinear trends and specifically identifies covariance in expression between genes”: “Table S13 lists the number of connections (degrees) that each gene has with others in the network. The average number of connections for long-sleeper males was 2.6; the other three networks had average degrees of 2.0 or less (2.0 for long-sleeper females and short-sleeper males; 1.75 for short-sleeper females).”

*14. On table S6 I noticed some gene symbols were loaded as dates (1-Dec) *

Our response: We thank the reviewer for noticing this, the gene symbol is supposed to be dec. We have corrected this in Table S6 (now Table S7).

1. *In results, the phenotypical response to artificial selection is sometimes described in minutes, other times in hours. Though this is an hurdle, it could make the values easier to compere if they were consistently formatted as minutes (hours). *

Our response: We are unsure what the reviewer is referring to. We only see one sentence in which we used hours, and that was the concluding sentence under Results, “Phenotypic response to artificial selection.” The remainder of the manuscript refers to sleep times in minutes, phenotypes in all of the figures are plotted as minutes, and all of the supplemental material refers to times in minutes.

16. *Over 99% of chains converged after three runs. Even though the reasons for the lack of convergence of these chains was not investigated, could this be a relevant effect? 1% of 3570 interactions is still 35 potential interactions. Do the non convergent chains relate with specific genes? *

Our response: Bayesian MCMC inference is a stochastic algorithm, so there is a finite chance that any given run doesn’t converge, and that means that all eight parallel chains must converge and mix as measured by the stringent choice of R-hat metric being within 0.05 of unity. Relaxing the interval to 0.1 or 0.2 could still be acceptable, but we made the choice of a stringent threshold to avoid making interpretations on less-than-ideal runs. There is no evidence that there is any gene-specific problem, usually it would be one out of eight chains that would not mix well and throw off the diagnostic metrics (like relaxing the metrics, an acceptable approach could be accepting a run with 6-7 chains converging properly, but we decided to rerun all chains and only accept 100% convergence but accept a possible loss). Non-converging/nonmixing runs are likely to eventually do so, but since were are running tens of thousands of runs (3570 pairwise combinations × 3 schemes × 8 chains) a massively parallel implementation in a HPC cluster is required. Finally, seeing that 145 is ~4% of the total number of interactions, a naïve expectation would be that no more than one interaction would come out significant — while there is a chance that an interesting interaction was identified, the same can be said for potential false negatives computed using the GLM, which is a consequence of working at a high-throughput scale.

17. The GO terms identified as significantly enriched after pvalue correction point to a clear association of the 85 genes identified with Serine proteases. Could this be discussed further to highlight biological findings of the work in the context of neuronal function or sleep regulation?

Our response: The reviewer is correct, nine putative Serine proteases are significantly enriched among the 85 genes. All nine exhibit some expression in neurons and in epithelial cells, and all are expressed at the adult stage. The appearance of these enzymes is interesting given their role in proteolysis.

We have updated the Discussion to read, “Interestingly, our Gene Ontology analysis identified nine genes from the 85-gene network with predicted Serine endopeptidase/peptidase/hydrolase activity: CG1304, CG10472, CG14990, CG32523, CG9676, grass, Jon65Ai, Jon65Aii, and Jon99Fii. All of these genes are expressed in neurons and epithelial cells, and all genes are expressed at the adult stage (Li et al., 2022). Serine proteases are a large group of proteins (257 in Drosophila) that perform a variety of functions (Cao and Jiang, 2018). Their predicted enzymatic activity suggests a putative role in proteolysis. This is an intriguing observation given pioneering work in mammals which suggested a role for sleep in exchanging interstitial fluid and metabolites between the brain and cerebral spinal fluid (Xie et al., 2013). Recent work demonstrated that a similar function is conserved in flies via vesicular trafficking through the fly blood-brain barrier (Artiushin et al., 2018). It would be interesting to determine whether these genes function in this process.”

*18. Could the authors discuss the little overlap between males/females and shot/long sleep for 145 gene pairs identified after the MCMC runs. Similarly, how can the network differences be explained from a biological/evolutionary perspective? *

Our response: The reviewer asks an interesting question. We did not detect sex-specific responses to artificial selection for long or short sleep in the present experiment. Yet differences in gene expression network pairs between males and females exist, and as the reviewer mentions, we also observed differences in network pairs between long sleepers and short sleepers. These differences reflect an inescapable conclusion: a given sleep duration phenotype can originate from more than one gene expression network configuration.

19. *In the mutational analyses it is pointed out that CG12560 and Jon65Aii only affect females significantly. However, in the following sentence, the authors claim these two genes had the greatest effect on both sexes, which seems contradictory, at least in the way it is described. *

Our response: Our wording may have been confusing, given that it came after a comment about Jon65Aii. Our exact statement was “Effects of the Minos insertions on night sleep duration were stronger in females than in males; when sexes were examined separately, only mutations in CG12560 and Jon65Aii affected male night sleep duration.” This was meant to convey that the effects of all Minos insertions were the same directionally for both males and females, but that only CG12560 and Jon65Aii insertions had statistically significant effects on each sex separately. We have re-worded this sentence to read “All Minos insertions had the same directional effect on night sleep for both males and females, but only the CG12560 and Jon65Aii insertions had statistically significant effects on night sleep on each sex separately.”

20. *Maybe a small comment on how unchanged expression could lead to the observed phenotypical variation could help understanding how Minos mutations effects are biological mediated for those not familiar with the method. This seems to be the authors expectation so, could it be non-functional proteins or something else? *

Our response: The reviewer raises an interesting point. We did not observe changes in gene expression for CG13793, Cyp6a16, or hiw compared to w1118 controls. Thus far, we have examined gene expression relative to the control for a single timepoint, and only in pooled whole flies. Differential gene expression between the Minos mutants and controls might occur at a different timepoint, or in a small set of key neurons that would be undetectable when comparing whole flies.

We expand on this in Results, under “Mutational analyses confirms the role of candidate genes and interacting expression networks in sleep”: “Potential reasons for the lack of a significant change in gene expression in the remaining lines include: the position of the insertion within the targeted gene, which has variable effects on its expression; the relatively low statistical power of the experiment; confining our observation to a single timepoint during the day; or pooling whole flies, which might obscure gene expression changes occurring at a single-tissue level.”

*21. The assumption that interacting genes would have their expression ratio changed by the Minos insertion would hold on situation where the affected gene causally interferes with the candidates expression. As far as I understand, causality cannot be inferred by the proposed method. Thus in a situation where both genes are co-regulated by a third factor, no change in expression ratio is to expected. How would the authors re-interpret their final result when considering this direct vs indirect interaction distinction? *

Our response: Our method only gives us the hypothesis that two genes interact based on their correlation, and that is what we test using the Minos insertions. We do not as yet have a way to identify a third gene or factor that might be regulating the two. Given the number of genes affecting sleep, it is quite likely that there are such factors, but we can only report and test what we’ve observed. Any interpretation based on an arbitrary third factor would be purely speculative.

**Referees cross-commenting**

22. *I agree with Reviewer #2 comments which, to me, reads as generally pointing out the lack of biological interpretation of the results (and thus connecting this study with previous literature). Adding this component would make the manuscript well-rounded and attractive to a wider audience. *

Our response: We agree with both reviewers that additional biological interpretation of the results would make the manuscript more attractive to a wider audience. Accordingly, we have added the following paragraph to the Discussion: “The genes we identify herein overlap and extend previous work. Of the 1,140 genes implicated in the generalized linear model, 151 (13.2 percent) overlapped with previous candidate gene, random mutagenesis, gene expression, and genome-wide association studies of sleep and circadian behavior in flies (Pegoraro e t al., 2022; Dissel et al., 2015; Seugnet et al., 2017; Shalaby et al., 2018; Thimgan et al., 2010, Thimgan et al., 2018, He et al., 2013; Mallon et al., 2014; Roessingh et al., 2019, Feng et al., 2018; Lee et al., 2021; Khoury et al., 2020; Wu et al., 2018; Harbison et al., 2013; Harbison et al., 2009; Harbison et al., 2017; Harbison et al., 2019). Notably, previous studies identified the genes CG17574, cry, dro, mip120, Mtk, NPFR1, pdgy, PGRP-LC, Shal, and vari as affecting sleep duration (Feng e t al., 2018, Dissel et al., 2015; Pegoraro et al., 2022; Thimgan et al., 2018; Mallon et al., 2014; He et al., 2013; Khoury et al., 2020; Harbison et al., 2013). Two genes, ringer and mip120, overlapped with our previous study of DNA sequence variation in flies selected for long and short sleep (Harbison et al., 2017). In that study we identified a polymorphism in an intron of ringer that changed in allele frequency with selection, with increases in the population frequency of the ‘G’ allele with increasing sleep, while the frequency of the ‘A’ allele increased with decreasing sleep. When the selective breeding procedure was relaxed, the frequency of the ‘G’ allele increased in short-sleeping populations, paralleling an increase in sleep (Souto-Maior et al., 2020). One possibility is that this polymorphism contributes to the changes in gene expression in ringer that we observed in the present study. Of the 85 genes common to both sexes that we used in the gene interaction networks, 11 (13 percent) appear in other studies of sleep: CG10444, CG2003, CG5142, CG6785, CG9114, CG9676, CR42646, hiw, NPFR1, Tie, and wb (He et al., 2013; Seugnet et al., 2017; Wu et al., 2018; Harbison et al., 2013). Thus, our study corroborates genes known to affect sleep, and identifies new candidate genes for sleep as well.”

Reviewer #1 (Significance (Required)):

*This study proposes the application of advanced non-linear methods to study complex traits such as sleep. As implemented, Gaussian Processes are able to identify non-linear correlations between two biological features (e.g. transcripts) over time (e.g. generations), representing an attempt to push the analytical methods available beyond the single gene paradigm. As such, more than the relevance of the biological results themselves, the authors focus on the explaining and illustrating the application of methodological advances obtained, and its relevance to obtain a better understanding of biological systems.

However the mathematical principles required to understand the implemented method are not trivial and require advanced knowledge of machine learning and statistics. This is a potential barrier, though not an impediment, to its quick and wide adoption by the community. In addition, even if demonstrated to be a valid method when working with Drosophila, the resolution required to perform such a study may be difficult to obtain with other model systems, which would likely require further refinement of the statistical approach.

The main audience interested in this work would be basic sleep researchers. However, this work is also related to the understanding gene selection over an artificial evolutionary process, thus evolutionary and developmental biologist may be also be interested. The methodology itself, already used in other fields of study, is a general statistical tool that could be adopted by a broad range of researchers for a diversity of topics. As such, I believe with this work, the authors will be able to stimulate the development and/or rethinking of the available analytical methods to study complex biological systems, though this would likely be done either in collaboration with the authors themselves or by a specific subset of researchers who regularly work with advanced mathematical, statistical and computational principles.

(disclaimer) My mathematical formation does not reach the PhD level expertise that may be required to fully understand the methodology described. I have never personally worked with D. melonogaster or used Gaussian Processes in a professional setting. As such, I may not be able to fully evaluate/appreciate the more detailed technical aspects of this work.

Reviewer #2 (Evidence, reproducibility and clarity (Required)):

Souto-Mairo et al. reports phenotypic and genotypic effects of artificially selecting for short and long sleep in flies. They generated an impressive time-series dataset where one could examine genetic and phenotypic changes across time (generations, total 13 generations) in response to the selection pressure. The authors explored the relationships between pairs of genes in addition to just identifying potential candidate genes involved in the regulation of the amount of sleep.

Major points:

1. Harbison et al 2017: This study seems to be a continuation of Harbison et al 2017. There needs to be a clearer approach in the text (introduction?) in elucidating how this study is really advancing the findings of Harbison et al., 2017. Do the two studies use the same selection lines? If not, how are they different? If they are not different, what might cause the phenotypes evolving differently? For example, day sleep, day bout number do not respond to the selection pressure similarly in both studies etc. *

Our response: We would like to emphasize that this study is not a continuation of the Harbison et al., PLoS Genetics, 2017 paper, where we examined the changes in DNA sequence during artificial selection, and it does not use the same selection lines. The fact that the two studies are different can be seen from an examination of Figure 1A of the current study and Figure 1A of the Harbison et al 2017 study. The trajectories of each population across generation are very different. Out of convenience, we used the same nomenclature to refer to the populations in both studies (L1, L2, S1, S2, etc.), and apologize if this is the source of the confusion. Both studies do originate from the same outbred population, however, and to get to the broader question that the reviewer is asking, should one expect to see the same correlated responses to selection for night sleep among selection lines originating from the same outbred population? The answer is no, not unless the selected trait and the responding trait have a genetic correlation of 1.0. We previously estimated the correlation between day sleep and night sleep to be between 0.29 - 0.38 and between day bout number and night sleep to be -0.05 (Harbison et al., 2013; Harbison et al. 2009). In the Harbison et al. 2017 study we noted that day sleep and day bout number had correlated responses to selection for night sleep, but neither have correlated responses in the current study. The relatively low genetic correlations between these two measures and night sleep explain why we do not see a consistent correlated response among studies.

We didn’t really elaborate on these observations in the manuscript, and so have added to the Results under “Correlated response of other sleep traits to selection for night sleep” the following: “These correlated responses concur with previous observations we made in selected populations originating from the same outbred population for night sleep and night average bout length, and night sleep and sleep latency (Harbison et al., 2017). However, unlike the previous study, we did not see a correlated response between night sleep and day sleep, and night sleep and day bout number (Harbison et al., 2017). The lack of correlated response reflects the relatively low genetic correlations these two traits have with night sleep (Harbison et al., 2013; Harbison et al., 2009).”

2. Zeitgeber Time (ZT) for RNA collection: It is puzzling that the study reports that the RNA was collected at 12 PM. I do not understand what this information means; especially in a project where one is working with sleep. The authors might want to report ZT. Also, why a particular ZT was chosen should be discussed. These genes are potential sleep-relevant genes - hence it is not too esoteric to think that the ZT of data collection matters a lot as some of them might be cycling. To get a more appropriate picture, multiple time points of data collection might be even better. The authors seem to have ignored this crucial aspect of a clock/sleep study - time of data collection and how time of data collection might shape your findings.

Our response: We agree with the reviewer that it would be better to have multiple timepoints for collection, but this is difficult to implement in practice as it would require an additional 5,280 flies per generation (4 pools of 10 flies per sex per population) for 12 timepoints as recommended by Hughes et al., JBR, 2017. We mention collection time in the Methods and Materials because we are aware of the changes in gene expression over the circadian day. 12PM is the midpoint between the start of the lights-on and lights-off period (i.e., ZT6), and was chosen arbitrarily. We have added the ZT notation to the Methods and Materials for clarity.

3. Short sleeping flies: Are there reports of flies sleeping this less? "We found 2,830 interactions; 8 of these were one of the 3,570 between the 85 genes, but none of them overlapped with the 145 gene pairs found to be different from controls. The gene interactions we observed may therefore be unique to extreme sleep." What is extreme sleep? How does this study then claim to have identified evolution of potential sleep-relevant gene expression for normal, physiologically relevant sleep?

Our response: Our statement was not very well worded, and we thank the reviewer for noticing this. What we intended to say was that the lack of overlap between our data and a known protein-protein interaction database may due to the interactions being unique to sleep as opposed to some other complex trait. We have re-worded this statement to say “The gene interactions we observed may therefore be unique to sleep.”

*Minor points:

4. The article uses an unnecessarily defensive tone to establish their approach to understand underlying mechanisms of sleep 'better' than that of the others (in both introduction and discussion): "In spite the large amount of studies and data generated for many systems, identifying underlying processes is still very rare; this is clear indication that better methods are needed to obtain understanding of biological processes from data." The 'still very rare' part is just factually incorrect and misleading as far as sleep is concerned. Even if we just see Drosophila studies on sleep, there is a huge progress that's being made in terms of genes, neurons and circuits relevant for sleep: both in terms of baseline sleep as an output of the circadian clock and the rebound/homeostatic sleep. Most, if not all, of these elegant and pioneering studies from multiple, independent groups took approaches that did not require artificial selection regimes. As a substitution for their defense, the authors might attempt to present their findings in the context of the existing knowledge of sleep in flies. For example, what about genes already implicated in sleep? Do they show up in their analysis? For example, Sleepless, DATfmn, Sandman, AstA, AstA-receptor, Wide-awake etc. This could really help the manuscript.*

Our response: We certainly did not intend for this statement to suggest that no progress had been made in the identification of genes and circuits for sleep, and we agree that elegant and pioneering approaches have made significant progress in our understanding of the phenomenon. Rather, we were thinking more in terms of fully described biochemical networks. To avoid this interpretation by other readers, we have altered the “still very rare” sentence in the Introduction to read: “Despite the large amount of studies and data generated for many systems, a full understanding of underlying processes has not yet been achieved…’

We also agree with the reviewer that it would be helpful to put our work in the context of what is already known in flies. We have added the following paragraph to the Discussion to relate the work with previous work on sleep in flies: “The genes we identify herein overlap and extend previous work. Of the 1,140 genes implicated in the generalized linear model, 151 (13.2 percent) overlapped with previous candidate gene, random mutagenesis, gene expression, and genome-wide association studies of sleep and circadian behavior in flies (Pegoraro e t al., 2022; Dissel et al., 2015; Seugnet et al., 2017; Shalaby et al., 2018; Thimgan et al., 2010, Thimgan et al., 2018, He et al., 2013; Mallon et al., 2014; Roessingh et al., 2019, Feng et al., 2018; Lee et al., 2021; Khoury et al., 2020; Wu et al., 2018; Harbison et al., 2013; Harbison et al., 2009; Harbison et al., 2017; Harbison et al., 2019). Notably, previous studies identified the genes CG17574, cry, dro, mip120, Mtk, NPFR1, pdgy, PGRP-LC, Shal, and vari as affecting sleep duration (Feng e t al., 2018, Dissel et al., 2015; Pegoraro et al., 2022; Thimgan et al., 2018; Mallon et al., 2014; He et al., 2013; Khoury et al., 2020; Harbison et al., 2013). Two genes, ringer and mip120, overlapped with our previous study of DNA sequence variation in flies selected for long and short sleep (Harbison et al., 2017). In that study we identified a polymorphism in an intron of ringer that changed in allele frequency with selection, with increases in the population frequency of the ‘G’ allele with increasing sleep, while the frequency of the ‘A’ allele increased with decreasing sleep. When the selective breeding procedure was relaxed, the frequency of the ‘G’ allele increased in short-sleeping populations, paralleling an increase in sleep (Souto-Maior et al., 2020). One possibility is that this polymorphism contributes to the changes in gene expression in ringer that we observed in the present study. Of the 85 genes common to both sexes that we used in the gene interaction networks, 11 (13 percent) appear in other studies of sleep: CG10444, CG2003, CG5142, CG6785, CG9114, CG9676, CR42646, hiw, NPFR1, Tie, and wb (He et al., 2013; Seugnet et al., 2017; Wu et al., 2018; Harbison et al., 2013). Thus, our study corroborates genes known to affect sleep, and identifies new candidate genes for sleep as well.”

Reviewer #2 (Significance (Required)):

5. I believe that the authors should attempt to put this study in the context of what is already known in sleep in flies and how this study advances the knowledge. And how the knowledge generated by this study would help other sleep researchers, who, for obvious reasons, would like to employ techniques other than artificial selection and big data. The data is elegant. The work seems to be extremely laborious. Nonetheless, as it stands now, this manuscript is only very specific for an audience who work with artificial selection to understand underlying genetics of behavior. In fact, even within the fly sleep field, most people might not find this manuscript very useful.

Our response: The reviewer may not have considered the wider application of this work. This framework is applicable to any data set of gene expression sampled across time, whether sampled across generation, as we did, or across the 24-hour circadian day, or sampled at other time intervals. We have added a statement to the Discussion to stress this fact: “The Gaussian Processes we apply herein have broad applications to other experimental designs, such as gene expression measured at varying time intervals over the circadian day, or time-based sampling of gene expression responses to drug administration.”

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

---

Referee #2

Evidence, reproducibility and clarity

Souto-Mairo et al. reports phenotypic and genotypic effects of artificially selecting for short and long sleep in flies. They generated an impressive time-series dataset where one could examine genetic and phenotypic changes across time (generations, total 13 generations) in response to the selection pressure. The authors explored the relationships between pairs of genes in addition to just identifying potential candidate genes involved in the regulation of the amount of sleep.

Major points:

1. Harbison et al 2017: This study seems to be a continuation of Harbison et al 2017. There needs to be a clearer approach in the text (introduction?) in elucidating how this study is really advancing the findings of Harbison et al., 2017. Do the two studies use the same selection lines? If not, how are they different? If they are not different, what might cause the phenotypes evolving differently? For example, day sleep, day bout number do not respond to the selection pressure similarly in both studies etc.
2. Zeitgeber Time (ZT) for RNA collection: It is puzzling that the study reports that the RNA was collected at 12 PM. I do not understand what this information means; especially in a project where one is working with sleep. The authors might want to report ZT. Also, why a particular ZT was chosen should be discussed. These genes are potential sleep-relevant genes - hence it is not too esoteric to think that the ZT of data collection matters a lot as some of them might be cycling. To get a more appropriate picture, multiple time points of data collection might be even better. The authors seem to have ignored this crucial aspect of a clock/sleep study - time of data collection and how time of data collection might shape your findings.
3. Short sleeping flies: Are there reports of flies sleeping this less? "We found 2,830 interactions; 8 of these were one of the 3,570 between the 85 genes, but none of them overlapped with the 145 gene pairs found to be different from controls. The gene interactions we observed may therefore be unique to extreme sleep." What is extreme sleep? How does this study then claim to have identified evolution of potential sleep-relevant gene expression for normal, physiologically relevant sleep?

Minor points:

The article uses an unnecessarily defensive tone to establish their approach to understand underlying mechanisms of sleep 'better' than that of the others (in both introduction and discussion): "In spite the large amount of studies and data generated for many systems, identifying underlying processes is still very rare; this is clear indication that better methods are needed to obtain understanding of biological processes from data." The 'still very rare' part is just factually incorrect and misleading as far as sleep is concerned. Even if we just see Drosophila studies on sleep, there is a huge progress that's being made in terms of genes, neurons and circuits relevant for sleep: both in terms of baseline sleep as an output of the circadian clock and the rebound/homeostatic sleep. Most, if not all, of these elegant and pioneering studies from multiple, independent groups took approaches that did not require artificial selection regimes. As a substitution for their defense, the authors might attempt to present their findings in the context of the existing knowledge of sleep in flies. For example, what about genes already implicated in sleep? Do they show up in their analysis? For example, Sleepless, DATfmn, Sandman, AstA, AstA-receptor, Wide-awake etc. This could really help the manuscript.

Significance

I believe that the authors should attempt to put this study in the context of what is already known in sleep in flies and how this study advances the knowledge. And how the knowledge generated by this study would help other sleep researchers, who, for obvious reasons, would like to employ techniques other than artificial selection and big data.

The data is elegant. The work seems to be extremely laborious. Nonetheless, as it stands now, this manuscript is only very specific for an audience who work with artificial selection to understand underlying genetics of behavior. In fact, even within the fly sleep field, most people might not find this manuscript very useful.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

---

Referee #1

Evidence, reproducibility and clarity

Summary:

The authors of this work generated a Sleep Advanced Intercross Population from 10 extreme sleeper Drosophila Genetics Reference Panel. This new out-bred population was subjected to a artificial selection with the aim of understanding the genes underlying the sleep duration differences between three populations: short-sleep, unselected, and long-sleep. Using analysis of variance the authors identified up to nearly 400 of genes that were significant selected over the various generations and showed opposite trends for long and short sleep, thus potentially relevant for the regulation of sleep duration. 85 of these genes were consistent between male and females sub-populations, suggesting a small number of genetic divergences may underlie sex-independent mechanisms of sleep.

Given the time-course nature of the generational data obtained, the authors studied potential correlations and interactions between these 85 identified candidate genes. Initially, the authors used pairwise Spearman correlation, noticing how this method could not filter most of pairwise interaction (around 40% of all possibilities were significant). To overcome the linear limitations of the previous approach, the authors implemented a more complex, non-linear Gaussian process model able to account for pairwise interactions. This new approach was able to identify a smaller number of different, and potentially more informative, correlations between the candidate genes previously identified.

Lastly, with genetic manipulations, the authors show in vivo that a subset of the candidate genes is causally related with the sleep duration as well as partially validating some of the correlation identified by their new model.

The authors conclude that, given the non-linear and complex nature of biological systems, simplistic linear approaches may not suffice to fully capture underlying mechanisms of complex traits such as sleep.

Major comments

Most of the the work presented focus on the computational and statistical analysis of different populations submitted (or not) to a process of artificial selection for short or long sleep duration. As such, the amount of potentially relevant biological conclusions to be tested is mostly unfeasible. The authors already present additional experiments to partially support some, though not all, of their findings. Given the manuscript is written as a method innovation, these additional experiments illustrate the potential uses of the method described.

(OPTIONAL) However, since the one of the focuses of this work in identifying potential gene interactions, it would be interesting if the authors could test a "double knockout" and perhaps demonstrate evidence for epistasis between two of the identified genes. Having access to single mutants, this experiment should be realistic. However, I have no hands-on experience working with Drosophila and I am unable to accurately estimate the amount of resources and time such and experiment could take. My initial guess would be 3-6 months work should suffice.

In regards to the gene CG1304, it seems to be an important example used throughout the manuscript. It should be carefully re-analyzed as was considered for interaction analyses without showing opposite trends for short- and long-sleep populations (see minor comments on figure 2)

One major comment would be that the claim that the Gaussian process method is more sensitive and specific than simpler approaches, though intuitively understandable, does not seem to be fully correct from a strict statistical point of view, given the lack of a gold standard reference to compare if the new method is indeed picking more true positives/negatives. I would reconsider re-rephrasing such statement in the absence of a biologically relevant validation set.

Finally, the study appears to be well powered and it is clear that the authors were careful in their explanation of the statistical methods. However, I could not find the copy of the code/script used for the model. Without it, it would be very difficult to fully reproduce the results as both the language used (Stan) and the method itself are not common in the sleep research field.

Minor comments

The statistical cut-off used for gene expression hierarchical GLMM after BH correction was of 0.001, which is 50 times more strict than the common 0.05. Could the authors comment on how this choice may impact the results compared to those available in the literature and on the rational for choosing such a value.

Heritability calculations are not mentioned in the methods. Could it be useful to include a small paragraph? Could a small comment be done on the differences in h2 for the short sleep replicates which show ~10x difference?

In regards to the model implementation, what would be the implications of not enforcing positive semi-definiteness on the co-variance matrix, given than that these are strictly positive semi-defined?

The methods mention that PCA projection were performed on the first 3 components, however only the first two are showed.

Figure 1 refers to the mean night sleep time of the population. Could some measurement of variability (se or sd) be represented to provide a general idea of the distribution of the values? Additionally, the standard deviation of associated with the CVe estimates are mentioned but not showed explicitly. Could they maybe be added to the text as to illustrate how much such deviations were reduced?

Figure 2 shows the linear model fits for gene CG1304. I find this gene on the list of significant genes for both sexes (tables S5/6), but it does not seem to be one that shows opposite trend for short- and long-sleep (tables S7/8). Surprisingly, it shows up again on table S10! However, the text introducing the figure reads like this should be one of the 85 sex-independent genes. Would it be best to provide an example of what a significant gene looks like?

Figure 3 would be interesting to have both the GP correlations and the Spearman correlations to illustrate the methodological differences. I would be curious to see at least one pairwise expression scatter-plot as well just to see how they correlate in one plot.

Figures 3S3/4 are described as showing single- and multi-channel models don't change substantially. Would this be expected and why?

Having build different networks of pairwise associations of genes (projecting on a unified network as illustrated on figure 5), it could in interesting to compare the network topologies at a basic level such as node degrees, overlapping sub-networks, are they potentially scale free as previously described for biological systems, etc.

On table S6 I noticed some gene symbols were loaded as dates (1-Dec)

In results, the phenotypical response to artificial selection is sometimes described in minutes, other times in hours. Though this is an hurdle, it could make the values easier to compere if they were consistently formatted as minutes (hours).

Over 99% of chains converged after three runs. Even though the reasons for the lack of convergence of these chains was not investigated, could this be a relevant effect? 1% of 3570 interactions is still 35 potential interactions. Do the non convergent chains relate with specific genes?

The GO terms identified as significantly enriched after pvalue correction point to a clear association of the 85 genes identified with Serine proteases. Could this be discussed further to highlight biological findings of the work in the context of neuronal function or sleep regulation?

Could the authors discuss the little overlap between males/females and shot/long sleep for 145 gene pairs identified after the MCMC runs. Similarly, how can the network differences be explained from a biological/evolutionary perspective?

In the mutational analyses it is pointed out that CG12560 and Jon65Aii only affect females significantly. However, in the following sentence, the authors claim these two genes had the greatest effect on both sexes, which seems contradictory, at least in the way it is described.

Maybe a small comment on how unchanged expression could lead to the observed phenotypical variation could help understanding how Minos mutations effects are biological mediated for those not familiar with the method. This seems to be the authors expectation so, could it be non-functional proteins or something else?

The assumption that interacting genes would have their expression ratio changed by the Minos insertion would hold on situation where the affected gene causally interferes with the candidates expression. As far as I understand, causality cannot be inferred by the proposed method. Thus in a situation where both genes are co-regulated by a third factor, no change in expression ratio is to expected. How would the authors re-interpret their final result when considering this direct vs indirect interaction distinction?

Referees cross-commenting

I agree with Reviewer #2 comments which, to me, reads as generally pointing out the lack of biological interpretation of the results (and thus connecting this study with previous literature). Adding this component would make the manuscript well-rounded and attractive to a wider audience.

Significance

This study proposes the application of advanced non-linear methods to study complex traits such as sleep. As implemented, Gaussian Processes are able to identify non-linear correlations between two biological features (e.g. transcripts) over time (e.g. generations), representing an attempt to push the analytical methods available beyond the single gene paradigm. As such, more than the relevance of the biological results themselves, the authors focus on the explaining and illustrating the application of methodological advances obtained, and its relevance to obtain a better understanding of biological systems.

However the mathematical principles required to understand the implemented method are not trivial and require advanced knowledge of machine learning and statistics. This is a potential barrier, though not an impediment, to its quick and wide adoption by the community. In addition, even if demonstrated to be a valid method when working with Drosophila, the resolution required to perform such a study may be difficult to obtain with other model systems, which would likely require further refinement of the statistical approach.

The main audience interested in this work would be basic sleep researchers. However, this work is also related to the understanding gene selection over an artificial evolutionary process, thus evolutionary and developmental biologist may be also be interested. The methodology itself, already used in other fields of study, is a general statistical tool that could be adopted by a broad range of researchers for a diversity of topics. As such, I believe with this work, the authors will be able to stimulate the development and/or rethinking of the available analytical methods to study complex biological systems, though this would likely be done either in collaboration with the authors themselves or by a specific subset of researchers who regularly work with advanced mathematical, statistical and computational principles.

(disclaimer) My mathematical formation does not reach the PhD level expertise that may be required to fully understand the methodology described. I have never personally worked with D. melonogaster or used Gaussian Processes in a professional setting. As such, I may not be able to fully evaluate/appreciate the more detailed technical aspects of this work.

Transparent Peer Review

---

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

</br>

Transparent Peer Review

---

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

</br>

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

Learn more at Review Commons

---

Reply to the reviewers

We are very grateful to the reviewers for their constructive comments. In response to their critiques, we have made extensive modifications to the manuscript, including documenting new experiments and analyses, and improving data presentation. Here we provide a point-by-point response to the reviewers’ comments.

Reviewer #1 (Evidence, reproducibility and clarity (Required)):

Summary:

It is well established that localization of oskar (osk) RNA in the Drosophila ovary proceeds in multiple steps. The first step depends upon dynein and results in delivery of osk into the oocyte. The second step involves kinesin-driven transport of osk to the oocyte posterior pole. The manuscript by Gáspár et al brings together several lines of evidence that support an tantagonistic relationship with respect to motor binding between two osk-interacting proteins, Egalitarian (Egl) and Staufen (Stau). As staufen RNA and protein accumulate in the oocyte, Egl dissociates from osk, down-regulating dynein and enabling the second stage of osk transport to begin.

Major comments:

In general the experimental results support the conclusions drawn, and the paper includes a strong mix of in vitro and in vivo approaches. Nevertheless I have a few concerns.

(1)In Fig 1D it is apparent that stau KD increases the speed of both plus-end and minus-end runs to a highly significant degree, not just minus-end runs. The stimulating effect of loss of Stau on speed of plus-end runs is not mentioned in the text, and it perhaps muddies the argument that Stau is simply a negative regulator of dynein-dependent minus-end directed transport. This result needs to be explicitly discussed in the text.

We thank the reviewer for this important comment. Indeed, our previous analysis of the overall population of oskar RNPs showed that plus-end-directed runs had increased velocity in the absence of Staufen (although the magnitude of the effect was considerably smaller than observed for minus-end-directed runs). The reviewer’s comment prompted us to analyze the effects on motility in more detail. In particular, we have now stratified the data based on the RNA content of the RNPs to control for effects of Staufen depletion on RNA copy number of the motile oskar RNPs. These analyses, which are documented in Fig 1B-F of the revised manuscript and discussed between lines 96-143, indicate that the previous velocity and run length data was somewhat confounded by the Staufen-depleted condition having a lower fraction of moving complexes with a large RNA content, which generally move more slowly. Accounting for this effect shows that impairing Staufen has no significant effect on plus-end-directed run lengths, whereas minus-end-directed run lengths are substantially increased. The velocity of runs is also specifically increased in the minus-end direction in the Staufen-depleted background for RNPs that have a relative RNA content of 1 or 2 units, which represent the majority of the RNP population in that genotype. Whilst RNPs with larger RNA content (2 relative units) do have significantly higher plus-end-directed velocity compared to the same category in the control, the effect is of much smaller magnitude than observed for minus-end-directed movements by this population. To help clarify these results, magnitudes of the effects are now shown in the new Fig. 1 E and F.

These data strengthen the case that Staufen predominantly affects minus-end-directed motion. Given many documented examples of the interdependence of dynein and kinesin on bidirectional cargoes (Hancock et al. 2014), it is conceivable that the modest effects on plus-end-directed velocity for a subset of RNPs arise indirectly from the influence of Staufen on dynein activity. However, we agree with the reviewer that we should not rule out the alternative possibility that Staufen has additional roles in regulating oskar transport, including potentially modulating kinesin-1 directly. We have therefore added a section to the Discussion that covers this issue (lines 496-514).

(2) I recognize the importance of quantitative imaging to rigorously measure small differences in localization patterns. Nevertheless I find the data in Fig 3 extremely difficult to interpret. Presumably there is standard deviation everywhere there is green signal, but the magenta signal that corresponds to SD is not visible in most places that are green. I suggest adding to Fig 3 a single representative image for each genotype to illustrate each localization pattern, as well as a much clearer explanation of the quantitative imaging data. Perhaps the quantitative images could be moved to a supplemental figure.

Reviewer 2 also suggested that we include representative images in addition to the quantitative readout. We have now replaced the old Figure 3 with a new one showing representative examples of oskar distribution in the different genotypes and moved the quantitative images to the supplement (Figure S4). We have also improved the legends and labeling of this supplementary figure to add clarity.

**Minor comments:**

(1)Color/density scales should be added to Figs 1A and S1A, otherwise the yellow/white signal at the posterior could be interpreted as something other than high abundance.

We thank the reviewer for spotting this. We have now added a color scale to the relevant figures.

(2)In Fig 4A and 4C, I find it odd to have different halves of images photographed under different intensity settings and would prefer duplicate whole images.

We used this layout to illustrate in the most compact way possible the (co)localization of the two RBPs and oskar RNA in the nurse cell and oocyte compartments, where signal intensities can differ dramatically. Following the reviewer’s comment, we now show whole images with different intensity settings (Figure 4 A, A’, C, C’).

(3)The references to Fig 3G on page 13 should be corrected to Fig 4G.

We thank the reviewer for spotting this error, which has now been corrected.

Reviewer #1 (Significance (Required)):

The paper represents a substantial advance over existing knowledge and it extends our understanding about how RNAs can shuttle between different motor proteins to achieve a localized pattern. However, the Mohr et al 2021 PLoS Genetics paper covers some of the same ground. As that paper has now been published for several months, I believe a revised version of this paper should discuss that other work more prominently, making it apparent where the two studies concur and where this study extends the conclusions of the other one. If there are any contradictions between the two, those should be made explicit as well.

We had discussed the Mohr et al. study in our manuscript, which came out when our work was in preparation. Following the reviewer’s comment, we now address explicitly how our study differs from theirs and how our work extends their findings. The relevant paragraphs in the Discussion begin on lines 437 and 496. Briefly, a key point of difference is that Mohr et al. focused on the Transport and Anchoring Sequence (TAS) (including its ability to associate with Egl) and other Staufen recognition sites (SRSs) in oskar mRNA. Their study also includes an experiment examining the effect of Egl overexpression on oskar localization (as described in our original submission). In contrast, our study directly examines the interplay between the RBPs Staufen and Egl on oskar RNPs. We are the first to show that Staufen directly antagonizes dynein-based transport and that this is associated, at least in part, with an ability to impair Egl association with RNPs. Moreover, we provide insights into the in vivo role of Egl/BicD in recruitment vs activation of dynein on RNPs and how the activity of Staufen is coordinated in space and time via Egl-mediated delivery of stau mRNA, which constitutes a novel type of feed-forward mechanism. We do not believe there are any contradictions between the two studies.

Reviewer #2 (Evidence, reproducibility and clarity (Required)):

In this manuscript, Gáspár et al. investigated the molecular mechanisms underlying the switching of motors for osk mRNA transport in the Drosophila ovary: from dynein in the nurse cells to kinesin-1 in the oocyte. They demonstrated that it requires two RNA-binding proteins, Egalitarian (Egl) and Staufen (Stau) to achieve the posterior localization of osk mRNA in the oocyte. Their data show that Egl is responsible for the stau mRNA transport into the oocyte, while Stau protein inhibits Egl-dependent dynein transport in the oocyte. Thus, they proposed a feed-forward mechanism in which Egl transports mRNA encoding its own antagonist Stau into the oocyte and thus achieves the switch of the osk mRNA transport from dynein to kinesin-1.

The antagonistic interaction between Egl and Staufen is well documented both in vitro and in vivo. All the results are carefully analyzed, but the data presentation is not reader-friendly. Overall, our main concern is about the role of Staufen in osk mRNA transport.

**Here are specific points:**

(1)According to the model, lack of Stau should result in failure of displacing Egl from the RNP complex and thus more dynein-driven transport in the oocyte. However, the increase of minus-end run length in stau-RNAi is very small (Figure 1E). It makes us wonder whether Stau is not a dominant inhibitor of Egl/dynein transport of osk RNPs. On the other hand, the speed increase of minus-end run in stau-RNAi is more dramatic than the run length (Figure 1D-1E). Does it mean that in stau-RNAi dynein-driven osk transport has a shorter duration of run? Additionally, in Figure 1D, there is a statistically-significant increase of plus-end-directed transport velocity in stau-RNAi. While the author did mention that in the results "analysis of the speed and length of oskar RNP runs in ooplasmic extracts indicated that Khc activity was not compromised upon staufen knock-down", it does not explain the increased velocity towards the plus-end.

We thank the reviewer for these insightful comments.

We and others (Zimyanin et al. 2008; Gaspar et al., 2014) have shown that there is only a small posterior-directed bias in oskar RNP transport in the wild-type ooplasm at mid-oogenesis. Thus, small increases in minus-end-directed transport parameters are expected to be sufficient for anterior mislocalization of a subset of RNPs, as is seen in stau mutants (note that we would not expect a dramatic increase in minus-end-directed motile properties in the stau RNAi condition, as a significant fraction of oskar RNA is targeted posteriorly). To allow the readers to better judge the magnitude of the effects, we now include the percentage change in mean velocity and run length values on the graphs (new Figure 1E and F).

Regarding the reviewer’s question about the run duration, indeed it is shorter for the minus-end directed runs in the absence of Staufen. In the motor field, it is typical to present velocity and run length only because duration is dependent on these two parameters.

Reviewer 1 also made a similar comment about plus-end directed velocity of RNPs. As we wrote in response to their comment, we have now stratified the data based on the RNA content of the RNPs to control for effects of Staufen depletion on RNA copy number of the motile oskar RNPs. These analyses, which are documented in Fig 1 B-F of the revised manuscript and discussed between lines 96-143, indicate that the previous velocity and run length data were somewhat confounded by the Staufen-depleted condition having a lower fraction of moving complexes with a large RNA content, which generally move more slowly. Accounting for this effect shows that impairing Staufen has no significant effect on plus-end-directed run lengths, whereas minus-end-directed run lengths are substantially increased. The velocity of runs is also increased only in the minus-end direction in the Staufen-depleted background for RNPs that have a RNA content of 1 or 2 relative units, which represent the majority of the RNP population in that genotype. Whilst RNPs with larger RNA content (2 relative units) do have significantly higher plus-end-directed velocity compared to the same category in the control, the effect is of much smaller magnitude than observed for minus-end-directed movement for this population.

These data strengthen the case that Staufen predominantly affects minus-end-directed motion. Given many documented examples of the interdependence of dynein and kinesin on cargoes (Hancock et al., 2014), it is conceivable that the modest effects on plus-end-directed velocity arise indirectly due to the influence of Staufen on dynein activity. However, we agree with the reviewer that we should not rule out the alternative possibility that Staufen has additional roles in regulating oskar transport, including potentially modulating kinesin-1 activity directly. We have therefore added a section to the Discussion that covers this issue (lines 496-514).

(2) What happened to osk mRNP transport in nurse cells with Staufen overexpression? The authors briefly mentioned that "GFP-Staufen overexpression has no major effect on the localization of oskar (Fig S1F-I)" on page 10. This is quite puzzling, as the authors propose that Staufen antagonized the Egl/dynein-driven transport. If the model holds true, we would expect to see that overexpression of Staufen causes less osk transport in nurse cells and thus less osk accumulated in the oocyte. Can the authors examine the osk mRNP transport in nurse cells in control and in GFP-Staufen overexpressing mutant and quantify the total amount of osk mRNA in the oocyte in control and after GFP-Staufen overexpression?

We showed in the initial submission that strong overexpression of GFP-Staufen in early oogenesis (e.g. with osk-Gal4) disrupts oskar localization, including causing ectopic accumulation in the nurse cells (Fig S7F and G, now marked with arrowheads). Fig S1F-I, to which the reviewer refers, documents an experiment in which the expression of GFP-Staufen was directly driven by the maternal tubulin promoter (i.e. not through the UAS-Gal4 system; now indicated in Fig. S1F). We had assumed that the difference in behavior of the different GFP-Staufen transgenes was caused by the timing and the amount of overexpression – maternal Gal4 drivers are capable of very strong and, in the case of osk-Gal4, early expression of UAS transgenes. Prompted by the reviewer, we have now examined GFP-Staufen expression in these lines in more detail. This confirmed our previous assumptions about timing and levels of ectopic expression. We now included a new panel Fig S7I to document the expression of maternal tubulin promoter-driven GFP-Staufen and have updated the manuscript to include details about the mode of Staufen overexpression used in different experiments (lines 205, 408-417).

(3)Is osk mRNP transport in the nurse cells affected by stau-RNAi? The authors showed the Khc association with oskar mRNPs in the nurse cells in Figure 1C. We hope they could quantify the velocity and run length of the osk mRNP particles in nurse cells and compare control with stau-RNAi.

We have never succeeded in making squashes of nurse cells that maintain oskMS2 RNA transport. Therefore, we are unable to evaluate directional transport of oskar in these cells. However, Staufen does not accumulate to appreciable levels in the nurse cells, as shown by Little et al., 2015 and also Figure 4A and A’ (left panels). Moreover, we did not detect significant colocalization between Staufen and oskar in the nurse cells (Fig. 4B). Therefore, depletion of Staufen with RNAi is not expected to influence motility of oskar in this part of the egg chamber.

(4)The kymograms of in vitro motility assays (Figure 2A and Figure S2) clearly showed two different moving populations, fast and slow. Did the authors include both types of events in their quantifications? What are the N numbers for each quantification? What do the dots mean in Figure 2B-2G? Does each dot represent a single track in the kymograph? If so, we believe that the sample sizes are too small for in vitro motility assay.

For completeness, we did not exclude particles from our analysis based on their speed of movement. We have now made this point clear in an updated section of the Methods (lines 799-802), which provides additional information on particle inclusion criteria.

We did document in the legends what the dots represent (values for single microtubules). We have now also included information on the number of complexes analyzed, which is 586-1341 single RNA particles or 1247-2207 single dynein particles per condition. These sample sizes are considerably larger than those used in most in vitro motility studies.

(5)The in vitro motility assay showed that Staufen impairs dynein-driven transport of osk 5'-UTR (Figure 2). Based on these data, it is unclear whether the effect of Staufen is osk mRNA-dependent or Egl-dependent. We suggest performing the motility assay in the absence of osk 5'-UTR and Egl. Dynein, dynactin, and BicD should be sufficient to constitute the processive dynein complex in vitro. The addition of Staufen to the dynein complex will help to understand whether Staufen could directly affect dynein activity. We bring up this point because we noticed that the Staufen displacement of Egl in osk RNPs does not alter the amount of dynein complex associated (Figure 6), implying that Staufen inactivates dynein activity on the RNP complex, independently of Egl-driven dynein recruitment.

We cannot look at transport of dynein in the presence of only dynactin and full-length BicD as BicD is not activated (and thus unable to effectively bind dynein and dynactin) without Egl and RNA (McClintock et al. 2018, Sladewski et al. 2018). However, the reviewer’s comment prompted us to investigate the effect of Staufen on dynein-dynactin motility that is stimulated by the constitutively active truncated mammalian BicD2, so called BicD2N (Schlager et al. 2014, McKenney et al. 2014). We find that Staufen partially inhibits DDB motility but not to the extent seen with the full-length BicD in the presence of Egl and RNA (new main figure panels 2H and I, and Figure S3). As stated between lines 187-188, these data suggest that Staufen inhibits both the activation of dynein-dynactin motility by BicD proteins, as well as stimulation of this event by Egl and RNA. This finding is also incorporated in a new section of the Discussion that covers possible roles of Staufen in addition to competing for Egl’s binding to RNA (between lines 496-514). We are very grateful to the reviewer for suggesting this approach, as it has provided significant new insight into Staufen’s function.

(6)In Figure 4, it is hard to see any colocalization between GFP and osk mRNA. And the authors compared overexpressed Egl-GFP (driven by mat atub-Gal4 in mid-oogenesis) with Staufen-GFP under its endogenous promoter. An endogenous promoter-driven Egl-GFP would be much more appropriate for the comparison.

Colocalization between GFP and oskar signals is seen as white in Fig. 4A and C. We have now added arrows to highlight a few examples of colocalization. The degree of colocalization was quantified in an unbiased fashion (shown in panels Fig 4B and D).

Regarding the expression of Egl-GFP: it was driven directly by the aTub84B promoter and not by matTub-Gal4. Western blot analysis performed in response to the reviewer’s comment shows that Egl-GFP is expressed at similar levels to endogenous Egl in this line (new Fig. S5I).

(7)In a recent publication (Mohr et al., 2021), a different model was proposed, in which Egl mediates transport, and Staufen facilitates the dissociation from the transport machinery for posterior anchoring. Although the authors referred to their paper in the discussion, they should acknowledge the differences and try to reconcile it (at least in the discussion).

We now further discuss our work in the light of the findings by Mohr et al. (a request also made by Reviewer 1) (in paragraphs starting on lines 436 and 496). In our opinion, the data of Mohr et al. in fixed material cannot discriminate between effects of Staufen (or the TAS) on transport vs anchorage. In contrast, our dynamic imaging in vitro and ex vivo shows unambiguously that Staufen can modulate transport processes. As accumulation of RNA at the cortex is dependent on directional transport, we do not think it necessary to invoke a separate anchorage role of Staufen. We have now raised the possibility that transport and cortical localization are two facets of the same underlying process in the hope that this will stimulate further investigation (lines 455-459).

(8)In the feed-forward model, Egl is required for the staufen mRNA transport from the nurse cells to the oocyte. Are Egl-GFP dots colocalized with staufen mRNAs in the nurse cells?

We showed in Fig 7I of the original submission that Egl-GFP puncta are colocalized with stau mRNAs in nurse cells. Indeed, this is a key piece of evidence for our model. These data are now in Figure 7F.

Furthermore, to our understanding, in this model, the translation of the staufen mRNA would be critical for the switching motors between dynein and kinesin-1. In this sense, staufen mRNA translation is either suppressed in the nurse cells or only activated in the oocytes. I think the authors should at least address this point in the discussion.

This is another excellent suggestion. We have now included in the Discussion (from line 525) the point that Staufen translation may be suppressed during transit to the oocyte or that the protein may be translated en route but only build up to meaningful levels where the RNA is concentrated in the oocyte.

**Minor points:**

1)I hope the authors would show the osk mRNA localization in egl mutant in in individual stage 9 egg chambers. I can only find the osk mRNA in egl-RNAi early stage egg chambers (Figure 7E), in which osk mRNA still shows an accumulation in the oocyte, although to a much lesser extent compared to control. In another publication (Sanghavi et al., 2016), it seems that the knockdown of Egl by RNAi causes some retention of osk mRNA in the nurse cells; but there are still noticeable amount of osk mRNA in the oocyte (Figure 3A-B). We wonder whether the authors could quantify the amount of osk mRNA both in the nurse cells and in the oocyte of control and egl-RNAi. Also I wonder whether the authors could comment on fact that some osk mRNA transported into the oocyte. Could it be due to an egl-independent transport mechanism?

egl null mutants do not reach stage 9 due to a defect in retention of oocyte fate, hence the use of egl RNAi in our study and the one by Sanghavi et al. Whilst we can’t rule out a (minor) Egl-independent mechanism for localizing oskar RNA in the oocyte, to date no other pathway has been implicated in the delivery of this or any other mRNA from the nurse cells. We favor a scenario in which residual oskar accumulation in the oocyte in egl RNAi egg chambers is due to incomplete depletion of Egl protein in the knockdown condition. We have noted this in the relevant figure legend and also clarify that the RNAi is a tool for knockdown in line 383 of the Results section.

The below plot shows a quantification of oskar mRNA localization in egl and control RNAi egg chambers, which the reviewer was wondering about.

In the egl RNAi egg-chambers, there is a significant increase in the mean signal intensity of oskar mRNA in the nurse cells, while oskar mRNA levels are substantially reduced in the oocyte, in line with the findings of Sanghavi et al., 2016.

2)It is always nice to how the average distribution of osk mRNA (e.g., Figure 3, Figure S1, and Figure S3). But we recommend having a representative image of each genotype (a single egg) next to the average distribution. It will help the readers to better appreciate the differences among these genotypes.

This suggestion was also made by Reviewer 1. We have added representative images to Figure 3 and moved the images depicting average distributions to the supplement (Fig S4). We have also improved the legend and labeling for Fig S4.

3)The figure legends are overall hard to read and sometimes impossible to get information about the experiments (for example, Figure 4 legend). Can the authors improve their figure legends making them reader-friendly?

We have edited the legends to make them clearer, including an extensive reworking of those for Figure 4. We thank the reviewer for encouraging us to do this.

4)For moderate overexpression, the authors used P{matα4-GAL-VP16} (FBtp0009293). However, there are two different transgenic lines associated with FBtp0009293 (V2H and V37), which have slightly different expression levels. The authors should specify which line they used in the experiments.

The matTub-Gal4 transgene we used in our study is inserted in the 2nd chromosome. We now mention this in the Methods section (line 567). We received this line from another lab many years ago, with no additional information provided.

5) On page 13 "PCR on egg-chambers co-expressing Egl-GFP and either staufen RNAi or a control RNAi (white) in the germline (Fig 3G)", it should be Figure 4G.

We apologize for this mistake, which has now been fixed.

Reviewer #2 (Significance (Required)):

see above

Reviewer #3 (Evidence, reproducibility and clarity (Required)):

Some additional experimental evidence is needed to solidify the conclusions and provide definitive support for this model, as discussed below.

Biochemical experiments using UV crosslinking and GFP immunoprecipitation followed by quantitative PCR were performed to show that Staufen antagonizes the association of Egl with oskar mRNA in vivo. -The authors need to show the quantitative analysis, which was not present in the figure, specifically the effects of Staufen RNAi compared to control.

These quantitative data, which are key for our model, were shown in the original submission (Fig 4G in the original and revised manuscript). We mistakenly called out the panel as 3G in the original submission. We apologize for this error, which has now been dealt with.

Is the ability of Staufen to antagonize and displace Egl dependent on Staufen binding to Oscar RNA? Will a Staufen mutant that can't bind to RNA also displace Egl? Alternatively, the mechanism may be independent of RNA binding and perhaps due to protein-protein interactions.

While the details of how Staufen displaces Egl are certainly an interesting topic for future research, we consider that addressing this goes well beyond the scope of this study, which already covers a lot of ground. Staufen contains four double stranded RNA-binding domains, and deleting or mutating all of these domains is likely to interfere with overall folding of Staufen, thus confounding the interpretation of the results.

As an alternative approach to elucidating RNA-dependent vs RNA-independent roles of Staufen, we have now assessed the effect of the protein on in vitro motility of dynein-dynactin complexes formed in the presence of a constitutively active truncation of mammalian BicD2 (BicD2N). We find that Staufen partially inhibits motility of these ‘DDB’ complexes but not to the extent seen with the full length BicD in the presence of Egl and RNA (new Fig 2H, I and S3). As stated in the manuscript (lines 187-188) these data suggest that Staufen inhibits both the activation of dynein-dynactin motility by BicD proteins, as well as stimulation of this event by Egl and RNA. We believe these experiments provide significant new insight into Staufen’s function. This finding is also incorporated into a new section of the Discussion dealing with potential roles of Staufen in addition to displacing Egl from RNPs.

A key question addressed is how does Staufen play a role in directing Oscar RNA localization to the posterior pole. The spatiotemporal control of Staufen at stage 9 seems to be a critical step. A number of experiments are performed to show that Staufen RNA enters the oocyte and accumulates to anterior pole through a process dependent on Egl (Fig. 7).

-Definitive evidence is needed to show the role of 3'UTR of Stau and Egl binding. As it stands now, no evidence is presented to prove that delivery of staufen RNA via Egl, rather than dumping of Staufen protein into oocytes is the necessary trigger for the switch. It is well known that Staufen protein is also transported through ring canals to deliver Staufen into oocytes. There is no need to invoke an additional mechanism of Egl mediated staufen mRNA delivery. A key experiment is to perturb the Egl interaction with staufen 3'UTR and show this is a necessary component to impact oscar. Related to this comment, they should first perform biochemistry IP and PCR to demonstrate association of Egl with staufen RNA, and then somehow perturb this interaction to assess effects on oscar RNA localization. For example, is the 3'UTR of staufen RNA necessary for this mechanism? What if staufen RNA was ectopically localized in some inappropriate manner, for example localized to posterior pole? Would this prevent the switch of oscar RNA to move to posterior pole? The key question is: is it necessary that translation of Stau be coupled to Egl in order to drive the switch.

Mapping of the Egl-binding site in stau mRNA is a major undertaking requiring the production and evaluation of multiple new transgenic fly lines. We feel that this would constitute an entirely new study. Moreover, multiple lines of evidence already support a functional interaction between Egl and stau mRNA, notably the presence of Egl on stau RNPs (previously Fig. 7I, now Fig. 7F), the strongly impaired accumulation of stau mRNA in the oocyte of egl RNAi egg chambers, and the ability of Egl overexpression to reposition a subset of the stau mRNA population at the anterior cortex.

We have now performed new experiments and analyses to test the alternative hypothesis that Staufen protein is transported into the oocyte in the absence of stau mRNA transport. We find that disrupting Egl function with RNAi impairs localisation of both stau mRNA and protein in the proto-oocyte (new Figure 7A-D). As Egl has no known function in protein transport, these data argue against an RNA-independent mechanism for Staufen protein delivery. Moreover, we showed that both stau mRNA and Staufen are enriched in early oocytes lacking oskar mRNA, the main target of Staufen protein in the female germline. This result shows that Staufen protein is not appreciably transported from the nurse cells to the oocyte by hitchhiking on its RNA targets.

Whilst Mhlanga et al. 2009 did report transport of large GFP-Staufen particles through ring canals, the line used (matTub4>GFP-Staufen from the St Johnston lab, which was also used for our rescue experiments) is known to make protein aggregates which is not the case for the endogenous protein (Zimyanin et al., 2008 and our new Figures 7B and S7E-I) and are therefore likely to be artefactual. Neither we, nor previous studies (Little et al., NCB, 2015), detected endogenous Staufen protein in nurse cells.

Finally, the reviewer asks if coupling Staufen translation to Egl-mediated enrichment of stau mRNA in the oocyte is important: we showed in the original submission that strong overexpression of GFP-Staufen by Gal4 drivers leads to mislocalization of Staufen in the nurse cells of early egg-chambers, presumably due to saturation of the Egl-based transport machinery. In these egg-chambers, we observed defects in RNA enrichment in the primordial oocyte and defects in oogenesis, consistent with the need to exclude Staufen protein from the nurse cells.

These findings are now presented in new panels of the updated Figures 7 and S7, with the corresponding section of the manuscript revised accordingly (lines 408-417). We think that altogether these lines of evidence strongly support our model that Egl transports stau mRNA into the developing oocyte and that this process is pivotal for oskar RNA localization.

**Minor comments**

"Substantially more oskar mRNA was co-immunoprecipitated with Egl-GFP from extracts of egg-chambers expressing staufen RNAi compared to the control (Fig 3G). -This data is not shown in 3G, but rather only in Fig. S4H which needs quantitative analysis shown.

This point stems from us calling out the wrong panel in the first submission; this has now been addressed, as described above. We apologize for the error.

"Addition of recombinant Staufen to the Egl, BicD, dynein and dynactin assembly mix significantly reduced the number of oskar mRNA transport events (Fig. 2A and B)."

-In Fig. 2A, the Y axis shows velocity not number of transport events

Fig 2A is a kymograph that is representative of the overall effect, where the Y-axis represents time. The reviewer may be referring to Fig 2B but this shows the frequency of processive oskar RNA movements (expressed as ‘number / micron / minute’), not velocity (micron/minute).

Fig. 3. - This is very unclear figure as to what is being shown. More details are needed to explain the figure, and add arrows to help reader note what is being described.

We have changed this figure to show representative images of individual egg chambers, as requested by the other two reviewers. The original Fig 3 is now moved to the Supplement as Fig S4. We have added arrows to the figure to indicate the anterior mislocalization of oskar mRNA and edited the legend for clarity.

Staufen may also be required for the efficient release of the mRNA from the anterior cortex. This may reflect a role of Staufen in the coupling of the mRNA to the kinesin-dependent posterior transport pathway. This could be discussed as another aspect of the inhibition of dynein and handoff to kinesin.

This is an interesting idea but it does not fit with our observation that Staufen depletion does not alter the association of oskar RNPs with kinesin-1 (originally Fig. 1C, now Fig. 1D). We do, however, now include in the Discussion a section on other ways, in addition to promoting Egl disassociation, that Staufen might orchestrate oskar mRNA transport.

Reviewer #3 (Significance (Required)):

This elegant manuscript by Gaspar et al provides new insight into the spatiotemporal regulation of Staufen mediated localization of oscar mRNA to the posterior pole in Drosophila oocytes. Here the authors demonstrate the competitive displacement of the RNA binding protein Egalitarian, which antagonizes dynein dependent localization at the anterior pole. This work done in this well characterized model of mRNA localization in Drosophila oocytes has broader implications for how the bidirectional transport of mRNAs is regulated in other polarized and highly differentiated cells, where very little is know about how mRNA transport direction might be regulated by opposing activities of kinesin and dynein motors. The strengths of this study are the integration of microscopy, biochemisty and genetic mutants to provide very nice experimental support for the two major aspects to the proposed model: 1) the competition between Staufen and Egl on oscar RNA which affects localization, 2) evidence for Egl mediated localization of staufen RNA into the oocyte as a key trigger for competitive displacement to bias localization of oscar RNA via kinesin. However, some additional experimental evidence is needed to solidify the conclusions and provide definitive support for this model, as discussed in other section.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

---

Referee #3

Evidence, reproducibility and clarity

Some additional experimental evidence is needed to solidify the conclusions and provide definitive support for this model, as discussed below.

Biochemical experiments using UV crosslinking and GFP immunoprecipitation followed by quantitative PCR were performed to show that Staufen antagonizes the association of Egl with oskar mRNA in vivo. -The authors need to show the quantitative analysis, which was not present in the figure, specifically the effects of Staufen RNAi compared to control.

Is the ability of Staufen to antagonize and displace Egl dependent on Staufen binding to Oscar RNA? Will a Staufen mutant that can't bind to RNA also displace Egl? Alternatively, the mechanism may be independent of RNA binding and perhaps due to protein-protein interactions.

A key question addressed is how does Staufen play a role in directing Oscar RNA localization to the posterior pole. The spatiotemporal control of Staufen at stage 9 seems to be a critical step. A number of experiments are performed to show that Staufen RNA enters the oocyte and accumulates to anterior pole through a process dependent on Egl (Fig. 7). -Definitive evidence is needed to show the role of 3'UTR of Stau and Egl binding. As it stands now, no evidence is presented to prove that delivery of staufen RNA via Egl, rather than dumping of Staufen protein into oocytes is the necessary trigger for the switch. It is well known that Staufen protein is also transported through ring canals to deliver Staufen into oocytes. There is no need to invoke an additional mechanism of Egl mediated staufen mRNA delivery. A key experiment is to perturb the Egl interaction with staufen 3'UTR and show this is a necessary component to impact oscar. Related to this comment, they should first perform biochemistry IP and PCR to demonstrate association of Egl with staufen RNA, and then somehow perturb this interaction to assess effects on oscar RNA localization. For example, is the 3'UTR of staufen RNA necessary for this mechanism? What if staufen RNA was ectopically localized in some inappropriate manner, for example localized to posterior pole? Would this prevent the switch of oscar RNA to move to posterior pole? The key question is: is it necessary that translation of Stau be coupled to Egl in order to drive the switch.

Minor comments

"Substantially more oskar mRNA was co-immunoprecipitated with Egl-GFP f rom extracts of egg-chambers expressing staufen RNAi compared t o t he control (Fig 3G). -This data is not shown in 3G, but rather only in Fig. S4H which needs quantitative analysis shown.

"Addition of recombinant Staufen to the Egl, BicD, dynein and dynactin assembly mix significantly reduced the number of oskar mRNA transport events (Fig. 2A and B)."

-In Fig. 2A, the Y axis shows velocity not number of transport events

Fig. 3. - This is very unclear figure as to what is being shown. More details are needed to explain the figure, and add arrows to help reader note what is being described.

Staufen may also be required for the efficient release of the mRNA from the anterior cortex. This may reflect a role of Staufen in the coupling of the mRNA to the kinesin-dependent posterior transport pathway. This could be discussed as another aspect of the inhibition of dynein and handoff to kinesin.

Significance

This elegant manuscript by Gaspar et al provides new insight into the spatiotemporal regulation of Staufen mediated localization of oscar mRNA to the posterior pole in Drosophila oocytes. Here the authors demonstrate the competitive displacement of the RNA binding protein Egalitarian, which antagonizes dynein dependent localization at the anterior pole. This work done in this well characterized model of mRNA localization in Drosophila oocytes has broader implications for how the bidirectional transport of mRNAs is regulated in other polarized and highly differentiated cells, where very little is know about how mRNA transport direction might be regulated by opposing activities of kinesin and dynein motors. The strengths of this study are the integration of microscopy, biochemisty and genetic mutants to provide very nice experimental support for the two major aspects to the proposed model: 1) the competition between Staufen and Egl on oscar RNA which affects localization, 2) evidence for Egl mediated localization of staufen RNA into the oocyte as a key trigger for competitive displacement to bias localization of oscar RNA via kinesin. However, some additional experimental evidence is needed to solidify the conclusions and provide definitive support for this model, as discussed in other section.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

---

Referee #2

Evidence, reproducibility and clarity

In this manuscript, Gáspár et al. investigated the molecular mechanisms underlying the switching of motors for osk mRNA transport in the Drosophila ovary: from dynein in the nurse cells to kinesin-1 in the oocyte. They demonstrated that it requires two RNA-binding proteins, Egalitarian (Egl) and Staufen (Stau) to achieve the posterior localization of osk mRNA in the oocyte. Their data show that Egl is responsible for the stau mRNA transport into the oocyte, while Stau protein inhibits Egl-dependent dynein transport in the oocyte. Thus, they proposed a feed-forward mechanism in which Egl transports mRNA encoding its own antagonist Stau into the oocyte and thus achieves the switch of the osk mRNA transport from dynein to kinesin-1.

The antagonistic interaction between Egl and Staufen is well documented both in vitro and in vivo. All the results are carefully analyzed, but the data presentation is not reader-friendly. Overall, our main concern is about the role of Staufen in osk mRNA transport.

Here are specific points:

(1)According to the model, lack of Stau should result in failure of displacing Egl from the RNP complex and thus more dynein-driven transport in the oocyte. However, the increase of minus-end run length in stau-RNAi is very small (Figure 1E). It makes us wonder whether Stau is not a dominant inhibitor of Egl/dynein transport of osk RNPs. On the other hand, the speed increase of minus-end run in stau-RNAi is more dramatic than the run length (Figure 1D-1E). Does it mean that in stau-RNAi dynein-driven osk transport has a shorter duration of run? Additionally, in Figure 1D, there is a statistically-significant increase of plus-end-directed transport velocity in stau-RNAi. While the author did mention that in the results "analysis of the speed and length of oskar RNP runs in ooplasmic extracts indicated that Khc activity was not compromised upon staufen knock-down", it does not explain the increased velocity towards the plus-end.

(2)What happened to osk mRNP transport in nurse cells with Staufen overexpression? The authors briefly mentioned that "GFP-Staufen overexpression has no major effect on the localization of oskar (Fig S1F-I)" on page 10. This is quite puzzling, as the authors propose that Staufen antagonized the Egl/dynein-driven transport. If the model holds true, we would expect to see that overexpression of Staufen causes less osk transport in nurse cells and thus less osk accumulated in the oocyte. Can the authors examine the osk mRNP transport in nurse cells in control and in GFP-Staufen overexpressing mutant and quantify the total amount of osk mRNA in the oocyte in control and after GFP-Staufen overexpression?

(3)Is osk mRNP transport in the nurse cells affected by stau-RNAi? The authors showed the Khc association with oskar mRNPs in the nurse cells in Figure 1C. We hope they could quantify the velocity and run length of the osk mRNP particles in nurse cells and compare control with stau-RNAi.

(4)The kymograms of in vitro motility assays (Figure 2A and Figure S2) clearly showed two different moving populations, fast and slow. Did the authors include both types of events in their quantifications? What are the N numbers for each quantification? What do the dots mean in Figure 2B-2G? Does each dot represent a single track in the kymograph? If so, we believe that the sample sizes are too small for in vitro motility assay.

(5)The in vitro motility assay showed that Staufen impairs dynein-driven transport of osk 5'-UTR (Figure 2). Based on these data, it is unclear whether the effect of Staufen is osk mRNA-dependent or Egl-dependent. We suggest performing the motility assay in the absence of osk 5'-UTR and Egl. Dynein, dynactin, and BicD should be sufficient to constitute the processive dynein complex in vitro. The addition of Staufen to the dynein complex will help to understand whether Staufen could directly affect dynein activity. We bring up this point because we noticed that the Staufen displacement of Egl in osk RNPs does not alter the amount of dynein complex associated (Figure 6), implying that Staufen inactivates dynein activity on the RNP complex, independently of Egl-driven dynein recruitment.

(6)In Figure 4, it is hard to see any colocalization between GFP and osk mRNA. And the authors compared overexpressed Egl-GFP (driven by mat atub-Gal4 in mid-oogenesis) with Staufen-GFP under its endogenous promoter. An endogenous promoter-driven Egl-GFP would be much more appropriate for the comparison.

(7)In a recent publication (Mohr et al., 2021), a different model was proposed, in which Egl mediates transport, and Staufen facilitates the dissociation from the transport machinery for posterior anchoring. Although the authors referred to their paper in the discussion, they should acknowledge the differences and try to reconcile it (at least in the discussion).

(8)In the feed-forward model, Egl is required for the staufen mRNA transport from the nurse cells to the oocyte. Are Egl-GFP dots colocalized with staufen mRNAs in the nurse cells? Furthermore, to our understanding, in this model, the translation of the staufen mRNA would be critical for the switching motors between dynein and kinesin-1. In this sense, staufen mRNA translation is either suppressed in the nurse cells or only activated in the oocytes. I think the authors should at least address this point in the discussion.

Minor points:

1)I hope the authors would show the osk mRNA localization in egl mutant in in individual stage 9 egg chambers. I can only find the osk mRNA in egl-RNAi early stage egg chambers (Figure 7E), in which osk mRNA still shows an accumulation in the oocyte, although to a much lesser extent compared to control. In another publication (Sanghavi et al., 2016), it seems that the knockdown of Egl by RNAi causes some retention of osk mRNA in the nurse cells; but there are still noticeable amount of osk mRNA in the oocyte (Figure 3A-B). We wonder whether the authors could quantify the amount of osk mRNA both in the nurse cells and in the oocyte of control and egl-RNAi. Also I wonder whether the authors could comment on fact that some osk mRNA transported into the oocyte. Could it be due to an egl-independent transport mechanism?

2)It is always nice to how the average distribution of osk mRNA (e.g., Figure 3, Figure S1, and Figure S3). But we recommend having a representative image of each genotype (a single egg) next to the average distribution. It will help the readers to better appreciate the differences among these genotypes.

3)The figure legends are overall hard to read and sometimes impossible to get information about the experiments (for example, Figure 4 legend). Can the authors improve their figure legends making them reader-friendly?

4)For moderate overexpression, the authors used P{matα4-GAL-VP16} (FBtp0009293). However, there are two different transgenic lines associated with FBtp0009293 (V2H and V37), which have slightly different expression levels. The authors should specify which line they used in the experiments.

5)On page 13 "PCR on egg-chambers co-expressing Egl-GFP and either staufen RNAi or a control RNAi (white) in the germline (Fig 3G)", it should be Figure 4G.

Significance

see above

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

---

Referee #1

Evidence, reproducibility and clarity

Summary:

It is well established that localization of oskar (osk) RNA in the Drosophila ovary proceeds in multiple steps. The first step depends upon dynein and results in delivery of osk into the oocyte. The second step involves kinesin-driven transport of osk to the oocyte posterior pole. The manuscript by Gáspár et al brings together several lines of evidence that support an antagonistic relationship with respect to motor binding between two osk-interacting proteins, Egalitarian (Egl) and Staufen (Stau). As staufen RNA and protein accumulate in the oocyte, Egl dissociates from osk, down-regulating dynein and enabling the second stage of osk transport to begin.

Major comments:

In general the experimental results support the conclusions drawn, and the paper includes a strong mix of in vitro and in vivo approaches. Nevertheless I have a few concerns.

(1)In Fig 1D it is apparent that stau KD increases the speed of both plus-end and minus-end runs to a highly significant degree, not just minus-end runs. The stimulating effect of loss of Stau on speed of plus-end runs is not mentioned in the text, and it perhaps muddies the argument that Stau is simply a negative regulator of dynein-dependent minus-end directed transport. This result needs to be explicitly discussed in the text.

(2)I recognize the importance of quantitative imaging to rigorously measure small differences in localization patterns. Nevertheless I find the data in Fig 3 extremely difficult to interpret. Presumably there is standard deviation everywhere there is green signal, but the magenta signal that corresponds to SD is not visible in most places that are green. I suggest adding to Fig 3 a single representative image for each genotype to illustrate each localization pattern, as well as a much clearer explanation of the quantitative imaging data. Perhaps the quantitative images could be moved to a supplemental figure.

Minor comments:

(1)Color/density scales should be added to Figs 1A and S1A, otherwise the yellow/white signal at the posterior could be interpreted as something other than high abundance.

(2)In Fig 4A and 4C, I find it odd to have different halves of images photographed under different intensity settings and would prefer duplicate whole images.

(3)The references to Fig 3G on page 13 should be corrected to Fig 4G.

Significance

The paper represents a substantial advance over existing knowledge and it extends our understanding about how RNAs can shuttle between different motor proteins to achieve a localized pattern. However, the Mohr et al 2021 PLoS Genetics paper covers some of the same ground. As that paper has now been published for several months, I believe a revised version of this paper should discuss that other work more prominently, making it apparent where the two studies concur and where this study extends the conclusions of the other one. If there are any contradictions between the two, those should be made explicit as well.

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

Learn more at Review Commons

---

Reply to the reviewers

Response to Reviewer Comments

Reviewer #1 (Evidence, reproducibility and clarity (Required)):

Summary:

In developing systems, morphogens gradients pattern tissues such that cells along the patterning length sense varying levels of the morphogen. This process has a low positional error even in the presence of biological noise in numerous tissues including the early embryo of the Drosophila melanogaster. The authors of this manuscript developed a mathematical model to test the effect of noise and mean cell diameter on gradient variability and the positional error they convey.

They solved the 1D reaction-diffusion equation for N cells with diameters and kinetic parameters sampled from a physiologically relevant mean and coefficient of variation (CV). They fit the resulting morphogen gradients to a hyperbolic cosine profile and determined the decay length (DL) and amplitude (A) for a thousand independent runs and reported the CV in DL and A.

The authors found that CV in DL and A increases with increase in mean cell diameter. They propose a mathematical relationship between CV in DL scales as an inverse-square-root of N. Whereas the CV in DL and A is a weak function of CV of cell surface area (CVa) if CVa __They further looked at the shift in readout boundaries and compared four different readout metrics: spatial averaging, centroid readout, random readout and readout along the length of the cilium. Their results show that spatial averaging and centroid have a high readout precision.

They finally showed that the positional error (PE) increases along the patterning length of the tissue and increases with increasing mean cell diameter.

The authors also supported their theoretical and simulated results by looking at mean cell areas reported for in patterning tissues in literature which also have a higher readout precision with smaller cell diameters.

Major comments:

Most of the key conclusions are convincing. However, there are four major points that should be addressed. First, the authors conclude the section titled, "The positional error scales with the square root of the average cell diameter," by saying that morphogen systems with small cells can have high precision in absolute length scales, but not on the scale of one cell diameter. They state this would result in salt and pepper patterns in the transition zones. The authors should either support this with biological examples or explain why this is not observed experimentally.

We thank the referee for pointing out this imprecise comment, which we have removed. The exact nature of transition zones between patterning domains is a subject of ongoing research in our group, and goes beyond the scope of the present work. We will be sharing our results on this aspect in a separate forthcoming publication.

Second, perhaps the main conclusion of the paper is that morphogen gradients pattern best when the average cell diameter is small. The authors support this by reviewing the apical cell area of epithelial systems that are known to be patterned by morphogens and those that are not (presumably taking apical cell area as a proxy for cell diameter). However, the key parameter is not absolute cell diameter, but the cell diameter relative to the morphogen length scale. The authors should report the ratio of these two quantities in their literature analysis.

Since cell areas and cell diameters are monotonically increasing functions of one another for reasonably regular cell shapes, we indeed consider apical cell areas as proxies for the cell diameter, as the referee correctly noted. Cell areas are more frequently reported in the literature than cell diameters, which is why we compiled these in our analysis.We have now revised our analysis of the effect of the cell diameter on patterning precision to further length scales relevant in the patterning process. We show by example of the Drosophila wing disc how the parallel changes in cell diameter and morphogen source size compensate for the increase in gradient length and domain size, which would otherwise reduce patterning precision over time as the readout positions shift away from the source to maintain the same relative position in the growing wing disc.

Lamentably, accurate measurements of morphogen gradients in epithelial tissues are still rare. In fact, among the listed tissues that are patterned by gradients, we are only aware of measurements of the SHH and BMP gradients in the mouse NT (lambda = 20 µm) and of the Dpp gradients in the Drosophila wing and eye discs [Wartlick, et al., Science, 2011 & Wartlick et al., Development, 2014]. We agree that it would be great if experimental groups would measure this in more tissues. In this revised and extended analysis, we show that the positional error increases with the cell diameter in absolute terms, not only relative to any reference length, be it the gradient length or cell diameter.

Third, as part of their literature analysis, the authors state that in the Drosophila syncytium, there are morphogen gradients, but they imply that because these gradients operate prior to cellularization, one cannot use the large distances between nuclei as counter evidence to their main conclusion. Rather than simply dismissing the case of the Drosophila syncytium, the authors should explain why this case does not apply, using reasoning based on their model assumptions.

Our paper is concerned with patterning of epithelia (which we now make clearer in the manuscript), and we would not want to stretch our paper to other tissue types, as the reaction-diffusion process in them differs. But we do not share the referee’s sentiment that the syncytium would present a counter-example. Since our model explicitly represents kinetic variability between spatial regions bounded by cell membranes, which are absent in the syncytium, our model is not directly applicable to it. We now provide this argument in the discussion, as requested by the referee.

At 100 µm [Gregor et al., Cell, 2007], the Bicoid gradient is 5 times longer than the SHH/BMP gradients in the mouse neural tube and more than 10 times the reported length of the WNT gradient in the Drosophila wing disc [Kicheva et al., Science, 2007]. The nuclei become smaller as they divide because the anterior-posterior length of the Drosophila embryo remains about 500 µm [Gregor et al., Cell, 2007], but even at the earliest patterning stage their diameter will not be larger than 10 µm at midinterphase 12 [Gregor et al., Cell, 2007, Fig. 3A].

Fourth, related to the above: the authors then state that there are no morphogen gradients known during cellularization. Unless I am misunderstanding their point, this is untrue. The Dpp gradient acts during the process of cellularization and specifies at least three distinct spatial domains of gene expression. Furthermore, not long after gastrulation, EGFR signaling patterns the ventral ectoderm into at least two distinct domains of gene expression. What are the cell areas in that case?

Unfortunately, the referee does not provide literature references, and we were not able to find anything in the literature ourselves. We have now rephrased the statement to “we are not aware of morphogen gradient readout during cellularisation”.

Minor comments:

Figs 1cd:

The way the system is set-up: (DL = 20 micron, Patterning Length (LP) = 250 micron, Nominal cell diameter (D) = 5 micron) the DL/L ~ 0.08 which makes the exponential profile far to a small value around 100 micron. This means in all these simulations, the LP was only around 100 micron, cells beyond that saw nearly zero concentration.

Because of this, when diameters were varied from 0.2 - 40 micron, there could be as few as 2.5 cells in the "patterning region" which could be responsible for higher variability in DL and A.

Patterning in the neural tube works across several 100 µm. At x=100µm, there is still exp(-5)=0.0067 of the signal left, which likely well translates into appreciable numbers of the morphogen molecule (see [Vetter & Iber, 2022] for a discussion of concentration ranges cells might sense). Unfortunately, very little is known about absolute morphogen numbers in the different patterning systems — experimental data is available only on relative scales, not in absolute nu mbers. While more quantitative experiments are still outstanding, modeling work needs to be based on reasonable assumptions. The seemingly quick decay of exponential profiles (when plotted on a linear scale) can be deceiving. In fact, exponential profiles describe the same fold-change over repeated equal distances, which makes them biologically very useful for different readout mechanisms operating on different levels of morphogen abundance. Our simulations are not limited to a patterning length of 100µm. Our work merely shows that variable exponential gradients stay precise over a long distance. We draw no conclusion on whether cells are able to interpret the low morphogen concentrations that arise far in the patterning domain - this aspect certainly deserves further research.

The referee’s observation is correct in that for a cell diameter of up to 40 µm, there are only few cells in the patterning domain (namely down to about six, for a length of 250µm, as used in the simulations). It is also correct that this is the reason why gradients in such a tissue have greater variability in lambda and C0. This is precisely the main point we are making in this study: The narrower the cells in a tissue of given size, the less variable the morphogen gradients, and the more accurate the positional information they carry. Conversely, the wider the cells in x direction, the more variable the gradients.

Would any of the results change if DL/L was higher, around 0.2?

As we consider steady state gradients, nothing changes if we fix the (mean) gradient decay length and only shorten the patterning domain, except for a small boundary effect at the far end of the tissue due to zero-flux conditions applied there. At a fixed gradient length, the steady-state gradients just extend further if DL/L is increased (for example to 0.2), reaching lower concentrations, but the shape remains unchanged, and so does the morphogen concentration at a given absolute readout position.

To demonstrate what happens at DL/L = 0.2, as requested by the referee, we repeated simulations with an increased gradient decay length of DL=50 micrometers; the length of the patterning domain remained unchanged at L=250 micrometers. As it is not possible to include image files in this response, we have made the plots available at https://git.bsse.ethz.ch/iber/Publications/2022_adelmann_vetter_cell_size/-/blob/main/revision_increased_dl.pdf for the time of the reviewing process. The plots show the resulting gradient variability, which is analogous to Fig 1c,d in the original manuscript. For both gradient parameters, we still recover the identical scaling laws.

The source region is 25 microns in length and all cell diameters above 25 micron get defaulted back to 25 micron which explains the flatness lines in the region beyond mu_delta/mu_DL> 1

Thanks for pointing this out. We now mention this in the manuscript. Note that it’s the ratio mu_delta/L_s that matters, not mu_delta/mu_lambda. It just so happens in this case, that both are nearly equal, because L_s=5*mu_lambda/4 in our simulations.

Results:

Pg 2 (bottom left): In the git repository code, the morphogen gradients are fit to a hyperbolic cosines function (described in reference 19) which is not described in the main text. Having this in the main text would help readers understand why fig 1c has variation in d only, D only and all k parameters whereas fig 1d has variation with all individual parameters p, d and D and all k.

The reason why the impact of CV_p alone on CV_lambda is not plotted in Fig 1c is that it is minuscule. We now mention this in the figure legend. This follows from the fact that the gradient length lambda is determined in the patterning domain, whereas the production rate p sets the morphogen concentration in the source domain, and thus, the gradient amplitude, but not its characteristic length. This is unrelated to the functional form used to fit the shape of the gradients, be it exponential or a hyperbolic cosine. We mention that we fit hyperbolic cosines to the numerical gradients in section Gradient parameter extraction in the Methods section, and we refer the interested reader to the original reference [Vetter & Iber, 2022], which contains all mathematical details, should they be needed.

Figure 3b:

In figures where markers are overlapping perhaps the authors can use a "dot" to identify one set of simulations and a "o" to identify the ones under it. The way the plots are set up currently makes it hard for the reader to understand where certain points on the plot are.

We use a color code to represent the readout strategy and different symbols to represent the cell diameter in Fig 3b. We agree that for the smallest of the cell diameters, the diamond-shaped data points lie so close that they are not easy to tell apart at first sight. For this reason, we chose different symbol sizes. We would like to keep the symbols as they are to maintain visual consistency with the other figures, which we think is an important feature of our presentation that facilitates the interpretation. Note that all our figures are vector graphics, which allow the reader to zoom in arbitrarily deep, and to easily distinguish the data points. Note also that in this particular case, telling the data points apart is not necessary; recognizing that they are nearly identical is sufficient for the interpretation of our results.

Methods:

The Methods can be more descriptive to include certain aspects of the simulations such as adjusted lambda which is only described in the code and not the main text or supplementary.

We apologize for this omitted detail. As shown in Fig. 8g in [Vetter & Iber, 2022], the mean fitted value of lambda drifts away from the prescribed value, depending on which of the kinetic parameters are varied, and by how much. To report the true observed mean gradient length in our results, we corrected for this drift in our implementation, as the referee correctly noticed. We now describe this in the methods section, and we have extended the methods also on other aspects.

Git code:

The git code function handles do not represent figure numbers and should be updated to make it easier for readers to find the right code

Thank you for pointing this out — it was an oversight from an earlier preprint version. The function names now correspond to the figure numbers.

Reviewer #1 (Significance (Required)):

This manuscript contributes certain key aspects to the patterning domain. The three most important contributions of this work to the current literature are: (1) the scaling relationships developed here are important, (2) the idea that PE increases at the tail-end of the morphogen profile is nicely shown and (3) Comparison of various readout strategies.

Thank you for the positive assessment.

Reviewer #2 (Evidence, reproducibility and clarity (Required)):

Summary:

How morphogen gradients yield to precise patterning outputs is an important problem in developmental biology. In this manuscript, Adelmann et al. study the impact of cell size in the precision of morphogen gradients and use a theoretical framework to show that positional error is proportional to the square root of cell diameter, suggesting that the smaller the cells in a patterning field, the more precise patterns can be established against morphogen gradient variability. This result remains true even when cells average the morphogen signal across their surface or spatial correlations between cells are introduced. Thus, the authors suggest that epithelial tissues patterned by morphogen gradients buffer morphogen variability by reducing apical cell areas and support their hypothesis by examining several experimental examples of gradient-based vs. non-gradient-based patterning systems.

Major comments:

While the idea that smaller cells yield to more precise morphogen gradient outputs is attractive, it is unclear whether patterning systems use this strategy to make patterns more precise, as there are several mechanisms that could achieve precision. Do actual developmental systems use it as a mechanism to increase precision? Or precision is achieved through other mechanisms (for example, cell sorting as in the zebrafish neural tube; Xiong et al. Cell, 2013). Indeed, classical patterning work on Drosophila embryo suggest that segmentation patterns are of an absolute size rather by an absolute number of cells (Sullivan, Nature, 1987). According to the authors, the patterning stripes should be more precise when embryos have higher cell densities than in the wild-type, but stripes are remarkably precise in wild-type embryos. This is likely due to other precision-ensuring mechanisms (such as downstream transcriptional repressors, in this case).

We want to emphasize that our predictions concern the precision of the gradients, not the precision of their readout, which can be strongly affected by readout noise, as we will show in a forthcoming paper. Cell sorting can sharpen boundaries in the transition zone, but this would not address errors in target domain sizes and is thus different from gradient precision as we discuss it here. Also, cell sorting as observed in the zebrafish neural tube requires higher cell motility than what is observed in most epithelial tissues. The work by Sullivan, Nature, 1987, is concerned with patterning of the early Drosophila embryo, and the stripes are defined already before cellularisation. We are unfortunately not aware of any work that quantified gradient precision at different cell densities in epithelia. This would, of course, be highly interesting data and would indeed put our predictions to a test. We are, to the best of our knowledge, the first to propose this principle with the present work. We have now made these points and distinctions clearer in the revised manuscript. Thank you for bringing this up.

Their modeling approach is based on exponential gradients formed by diffusion and linear degradation, but in reality, actual morphogen gradients are affected by receptor and proteoglycan binding and are likely not simply exponential and/or interpreted at the steady state. Do the main results of the manuscript hold even for non-exponential gradients or before they reach a steady state?

We can confirm that our results also hold for non-exponential gradients, as they emerge for example when morphogen degradation is self-enhanced (i.e., non-linear). This result will be published in a follow-up study [BioRxiv: 10.1101/2022.11.04.514993], which we now cite in the concluding remarks in the revised manuscript.

The analysis of pre-steady-state gradients lies outside of the scope of the present work, and so the question as to whether our results are applicable to them as well, remains to be answered in future research. We have added a comment on this to the discussion.

In their Discussion section, the authors note that several patterning systems, such as the Drosophila wing and eye discs, show smaller cells near the morphogen source relative to other regions in the tissue. This observation suggests a prediction of the authors' hypothesis that can be tested experimentally. In the Drosophila wing and eye discs genetic mosaics of ectopic morphogen sources (such as Dpp) can (and have) been made. Therefore, one could predict that the patterning outputs in a region of larger cross-sectional areas will be more imprecise than in the endogenous source. Since this is a theoretical paper, it is understandable that authors are not going to make this experiment themselves, but I wonder if they can use published data to test this prediction or at least mention it in the manuscript to offer the experimental biology reader an idea of how their hypothesis can be tested experimentally.

We appreciate that the referee would like to help us inspire the experimental community. Unfortunately, the problem with the proposal is that Dpp has been shown to result in a lengthening of the cells (and thus a smaller cell width) [Widmann & Dahman, J Cell Sci, 2009]. The Dpp gradient thus ensures a small cell width close to its source, which makes it virtually impossible to test this proposal experimentally in the suggested way. Nevertheless, we have added brief comments on potential experimental testing of our predictions to the discussion.

Other comments:

The Methods section should be expanded and should include more details about how authors consider cell size in their simulations. As presented, I believe that experimental biologists will not be able to grasp how the analysis was done.

We have expanded on the technical details of our model in the methods section, in particular in relation to the cell size, as requested. To avoid being overly redundant with existing published descriptions of the modeling details [Vetter & Iber, 2022], we focus here on a description of what has not been covered already, and refer the interested reader to our previous publication. It is inevitable for any kind of work, be it theoretical or experimental, to be less accessible to experts in other disciplines, but we believe that the presentation of our results is independent enough of modeling aspects to be accessible to experimental biologists, too.

Authors use adjectives such as 'little' as 'small' without a comparative reference. For example in the abstract, the authors say that apical areas "are indeed small in developmental tissues." What does "small" mean? This should be avoided throughout the text.

We thank the referee for raising this point. Where appropriate, we changed the phrasing accordingly to clarify what the comparative reference is. We leave all sentences unchanged where the statement holds in absolute terms. Note that in the substantially revised analysis on the impact of the different length scales involved in the patterning process, we now explicitly show with simulation data and theory that the absolute positional error increases with increasing absolute cell diameter.

Reviewer #2 (Significance (Required)):

Overall, I believe that the manuscript is well written and deserves consideration for publication. However, authors should consider the points outlined above in order to make their manuscript more accessible and relevant to the developmental biology community.

Thank you for the positive assessment.

Reviewer #3 (Evidence, reproducibility and clarity (Required)):

In their mansucript "Impact of cell size on morphogen gradient precision" the authors Adelmann, Vetter and Iber numerically analyse a one-dimensional PDE-based model of morphogen gradient formation in tissues in which the cell sizes and cell-specific parameters locally affecting the gradient properties are varied according to predefined distributions. They find that the average cell size has the largest impact on the variance of the gradient shape and the read-out precision downstream, while other factors such as details of the readout mechanism have markedly less influence on these properties. In addition they demonstrate that averaging gradient concentrations over typical cell areas induces a shift of the readout position, which however appears to be insignificant (~1% of the cell diameter) for typical parameters.

Overall this manuscript is in very good shape already and tackles an interesting topic. I still would like the authors to address the comments below before I would recommend any publication. My main criticism pertains to some of the authors' derivations which, as I find, partly do deserve more detail, and to their conclusions about gradient readout precision.

Thank you for the positive assessment.

MAJOR COMMENTS

p. 1, left column: The positional error of the readout position does not only depend on the variation of the gradient parameters, as suggested by the first part of the introduction. A very important factor is also the fluctuations due to random arrival of molecules to the promoters that perform the readout due to the limited (and typically low) molecule number. In fact, for positions very distant to the source of the gradient, this noise source is expected to be dominant over gradient shape fluctuations. Importantly, these fluctuations also arise for non-fluctuating, "perfect" gradient inputs if copy numbers of the morphogen molecules are limited (which they always are). This important contribution to the noise is neglected in the work of the authors. This is OK if the purpose is focusing on the origin and influence of the gradient shape fluctuations, but that focus should be clearly highlighted in the introduction, saying explicitly that noise due to diffusive arrival of transcription factors is not taken into account in the given work (see, e.g., Tkacik, Gregor, Bialek, PLoS ONE 3, 2008)

In the present work, only precision of the gradients, but not the readout itself is studied. We have now mentioned this more explicitly in the introduction. We also acknowledge the fact that the readout itself introduces additional noise into the system. We are currently finishing up work that addresses exactly this subject, which is outside of the scope of the present paper.

What may have led to misinterpretation of the scope of our work is that we called x_theta the readout position. x_theta defines the location where cells sense (i.e., read out) a certain concentration threshold, and is not meant to be interpreted as the location of a certain readout (a downstream transcription factor) of the morphogen. We have made this distinction clearer in the revised manuscript.

p.1, right column: Why exactly are the parameters p, d, D assumed to follow a log-normal distribution? Such a distribution has been verified for cell size, but the rationale behind choosing it also for the named parameters should be explained, in particular for D. Why would D depend on local properties of the cell? Which diffusion / transport mechanism precisely is assumed here?

The motivations for the used log-normal distributions for the kinetic parameters are the following:

The morphogen production rates, degradation rates and diffusivities must be strictly positive. This rules out a normal distribution. The probability density of near-zero kinetic parameters must vanish quickly, as otherwise no successful patterning can occur. For example, a tiny diffusion coefficient would not enable morphogen transport over biologically useful distances within useful timeframes. This rules out a normal distribution truncated at zero, because very low diffusivities would occur rather frequently for such a distribution. Given the absence of reports on distributions for p, d, D from the literature, we chose a plausible probability distribution that fulfills the above two criteria and possesses just two parameters, such that they are fully defined by a mean value and coefficient of variation. This is given by a lognormal distribution. Our results are largely independent of the exact choice of probability distribution assumed for the kinetic parameters, under the constraints mentioned above. To demonstrate this, we have repeated a set of simulations with a gamma distribution with equal mean and variance as used for the lognormal distribution. Below are some simulation results for a gamma distribution with shape parameters a = 1/CV^2 and inverse scale parameter b = mu*CV^2 with CV = 0.3 as used in the results shown in the paper. As can be appreciated from these plots, the results do not change substantially, and our conclusions still hold. As we believe this information is potentially relevant for the readership of our paper, we have added this result and discussion to the supplement and to the conclusion in the main text.

We assume extracellular, Fickean morphogen diffusion with effective diffusivity D along the epithelial cells, as specified by Eq. 2. We now state this more explicitly just below Eq. 2 in the revised manuscript. Cell-to-cell variability in the effective diffusivity may arise from effects that alter the effective diffusion path and dynamics along the surface of cells, which we do not model explicitly, but lump into the effective values of D. Such effects may include different diffusion paths (different tortuosities) or transient binding, among others.

Moreover, is there any relationship between A_i and p_i, d_i and D_i, or are these parameters varied completely independently? If yes, is there a justification for that?

The parameters are all varied independently, as written in the paragraph below Eq. 2 on the first page (“drawn for each cell independently”). To our knowledge there is no reported evidence for correlations between cell areas, morphogen production rates, degradation rates, or transport rates across epithelia, that we could base our model on. The choice of independent cell parameters therefore represents a plausible model of least assumptions made. Note that we explore the effect of potential spatial correlations in the kinetic parameters between neighboring cells in the section “The effect of spatial correlation”, finding that such correlations, if at all present, are unlikely to significantly alter our results.

p. 2, right column, section on "Spatial averaging": First of all, how is "averaging" exactly defined here? Do the authors assume that the cells can perfectly integrate over their surface in the dimensions perpendicular to their height? If yes, then this should be briefly mentioned here. Secondly, the shift \Delta x calculated by the authors ultimately seems to trace back to the fact that the cells average over an exponential gradient, whose derivative also is exponential, such that levels further to the anterior from the cell center are higher (on average) than levels to the posterior of it. I suppose, therefore, that a similar calculation for linear gradients would not lead to any shift. If these things are true they deserve being mentioned in this part of the manuscript because they provide an intuitive explanation for the shift. Thirdly, in Fig. 2A the cell sizes seem exaggerated with respect to the gradient length. This seems fine for illustrative purposes, but if it is the case it should be mentioned. Also, I believe that this figure panel would benefit from showing another readout case where the average concentration e.g. in cell 1 maps to its corresponding readout position, in order to show that this process repeats in every cell. Moreover, it could be indicated that in the shown case C_\theta matches the average concentration in cell 2 at the indicated position.

Spatial averaging is defined as perfect integration along the spatial coordinate over a length of 2r (which can generally be equal to, or smaller than, or larger than one cell diameter) as detailed in the supplementary material. In simulations, we use the trapezoid method for numerical integration to get the average concentration a cell experiences along its surface area perpendicular to their height.

The reviewer is correct, that the shift is a consequence of averaging over an exponential gradient. The average of an exponential gradient is higher compared to the concentration at the centroid of the cell, thus the small shift. This is mentioned e.g. in the caption of Fig. S1, but also in the main text (“spatial averaging of an exponential gradient results in a higher average concentration than centroid readout”). We have now added this information also to the caption of Fig. 2. As pointed out correctly by the referee, linear gradients would not result in such a shift. A brief comment on this has been added to the revised manuscript.

We now mention that the cell size is exaggerated in comparison to the gradient decay length for illustration purposes in the schematic of Fig. 2a, as requested.

Unfortunately, we had a hard time following the reviewer’s final point. We show a specific readout threshold concentration, C_theta, in Fig. 2a. A cell determines its fate based on whether its sensed (possibly averaged) concentration is greater or smaller than C_theta. In the illustration, cells 1 and 2 sense a concentration greater than C_theta, and all further cells sense a concentration smaller than C_theta. Cell fate boundaries necessarily develop at cell boundaries (here; between cells 2 and 3, red). Additionally, the readout position for a continuous domain, where morphogen sensing can occur at an arbitrary point along the patterning axis, is shown (blue). This position can be different from the one restricted to cell borders. Thus, different readout positions in the patterning domain result from the two scenarios, which is what the schematic illustrates. Given that our illustration seems to go well with the other referees, we are unsure in what way it could be improved.

As for the significance of the magnitude of the shift for typical parameters as calculated by the authors: I believe that it could be said more explicitly and clearly that under biological conditions the calculated shift overall seems insignificant, as it amounts to a small fraction of the cell diameter.

We have made this more explicit in the text.

Finally, and most importantly: The term "spatial averaging" can have a different meaning in developmental biology than the one employed by the authors. While the authors mean by it that individual cells average the gradient concentration over their area, in other works "spatial averaging" typically means that individual cells sense "their" gradient value (by whatever mechanism) and then exchange molecules activated by it, which encode the read-out gradient value downstream, between neighboring cells, in order to average out the gradient values "measured" under noisy conditions. The noise reduction effect of such spatial averaging can be very significant, as evidenced by this (incomplete) list of works which the authors can refer to:

- Erdmann, Howard, ten Wolde, PRL 103, 2009

- Sokolowski & Tkacik, PRE 91, 2015

- Ellison et al., PNAS 113, 2016

- Mugler, Levchenko, Nemenman, PNAS 113, 2016

The main point, however, is that this is a different mechanism as the one described by the authors, and this should be clearly mentioned in order to distinguished them. I would therefore also advise the authors to make the section title more precise here, by changing "Spatial averaging barely affects ..." to "Spatial averaging across the cell area barely affects ..." for clarity.

Most theory development has previously indeed been done with the syncitium of the early Drosophila embryo in mind. However, most patterning in development happens in epithelial (or mesenchymal) tissues, where spatial averaging via translated proteins is not as straightforward and natural as in a syncitium. In fact, a bucket transport of a produced protein from cell to cell would be difficult to arrange (as upon internalization, degradation would have to be prevented), be subject to much molecular noise, and be rather slow. Our paper is concerned with patterning in epithelia, which we have now stated more clearly in the manuscript.

Regarding the section title: Our analysis does not only cover spatial morphogen averaging over the cell area, but it also includes averaging radii below (in the theory) and far above (in the theory and in the new Fig. 4c, previously 3c) half a cell diameter. With cilia of sufficient length r, epithelial cells could potentially average over spatial regions extending further than their own cell area, without need for inter-cellular molecular exchange between neighboring cells. This is the kind of spatial averaging we explored here. Restricting the section title to the cell area only would therefore be misleading. However, we agree with the referee that the distinction between different meanings of “spatial averaging” is important, and we now emphasize our interpretation and the scope of our work more in the revised text.

p. 3, Figure 3: It would be good to highlight the fact that the colours in panel A correspond to the bullet colors in the other panels also in the main text.

We now added this also in the main text.

As to the comparison of different readout strategies: How exactly were the different readout mechanisms compared on the mathematical side? More precisely: How was the readout by the whole area matched (in terms of fluxes) to the readout at a single point, be it in the center of the cell or a randomly chosen point? How was it ensured that the comparison is done at equal footing?

Our model considers that a cell can sense a single concentration even if it is exposed to a gradient of concentrations. Assuming the French flag model is correct, a cell must make a binary decision based on a sensed concentration in order to determine its fate. The different readout strategies are hypothetical and simplified mechanisms for how a cell could, in principle, detect a local morphogen signal. It is unclear to us what the referee is referring to when mentioning “matching in terms of fluxes”, as there are no fluxes involved in the modeled readout strategies. We make no assumption on the underlying biochemical mechanism that would allow cells to implement one of the strategies. The main goal of this analysis was to determine whether various different sensing strategies had a significant effect on the precision of morphogen gradients experienced by cells. To assure that we can compare the different mechanisms at equal footing, we simulated gradients and then calculated from each gradient the readout concentration in each cell and for each of the methods.

p. 3, right column: "... similar gradient variabilities, and thus readout precision": Linking to comment 1 above, this is strictly speaking only the case when the only source of fluctuations in the readout is the gradient fluctuations. I would therefore leave this statement out.

To avoid confusion, we have removed parts of the sentence. Thank you for pointing this out.

p. 3, section on positional error (right column): In this part I had most troubles following the thoughts of the authors.

First of all, the measure that the authors use for the positional error is sigma_x / mu_lambda, i.e. the standard deviation of the readout position relative to the gradient length. The question is whether this is the correct measure. It should be specified what the motivation for normalizing by mu_lambda is. In the end, one could argue, what the cells really do care about would be that the developmental process can assign cell fates with single cell precision, for which the other measure shown in Eq. (6) is the representative one. Now in contrast to the former measure, the latter actually increases with decreasing cell diameter.

We thank the referee for raising this point, and acknowledge that we have not presented this aspect well enough. We have rewritten the entire section and the discussion about biological implications. Instead of normalizing with a constant mean gradient length in the formulas and figures, which has left room for misinterpretation, we now instead varied all relevant length scales in the patterning system, to determine the impact of each of them independently on the positional error. We now show that the positional error increases (to leading order) proportionally to the mean gradient length, the square root of the cell diameter, the square root of the location in the patterned tissue, and inversely proportional to the length of the source domain. We support these new aspects with new simulation data (Fig. 2E-2H, Fig. 3D-G, Fig. S5, Fig. S6). As the positional error is now reported in absolute terms, rather than relative to a particular length scale, the question of the relevant scale is addressed. We now show that the absolute positional error increases with increasing absolute cell diameter.

We believe that this extension provides additional important insight into what affects the patterning precision. We thank the referee very much for motivating us to expand our analysis.

Secondly, even when the former measure (sigma_x / mu_lambda) is employed, Fig. 3(D) shows that while it decreases with decreasing cell diameters, in the regime of small diameters the std. dev. of the readout position becomes larger than the average cell diameter, which actually would mean that cell fates cannot be assigned with single-cell precision. While the authors later report both quantities for specific gradients, it should be clarified beforehand which of the measures is the relevant one.

This has now been addressed by considering absolute length scales as discussed at length in our answer to the previous point.

Moreover, in the following derivations, mu_x is not properly introduced. What exactly is the definition of that quantity? Is it the mean readout position? If yes, it is not clear why exactly it would be interesting and relevant to the cell. This should be properly explained in a way that does not require the reader to look up further details in another publication.

The referee is correct in that mu_x is the mean readout position. We apologize for not being clear enough on this, and have now defined this in the introduction together with the definition of sigma_x.

At the end of this section the authors come back to the sigma_x / mu_delta measure again and indeed point out that it increases with decreasing mu_delta, which causes a bit of confusion because the initial part of the section only talks about the increase of the pos. error with mu_delta. Overall I find that this section should be rewritten more clearly. Right now it leaves the reader with the "take home message" that small cells are good because they lead to smaller pos. error, but when the--in my opinion--relevant measure (sigma_x/mu_delta) is employed the opposite is the case. This is confusing and unclear about the authors' intentions in that part.

See the answer above. The “take-home message” is now reformulated in absolute terms regarding the effect of cell diameter, rather than relative to a certain choice of reference scale. Our new analysis revealed a new relative ratio that determines the positional error, mu_lambda/L_s. We now discuss this relative measure also regarding its biological significance. Once again, we thank the referee for pointing us at this source of confusion, the elimination of which allowed us to improve our analysis.

__Finally, the authors could also supplement the numbers that they name for the FGF8 and SHH gradients by the known numbers for the Bcd gradient in Drosophila, which has been studied excessively and constitutes a paradigm of developmental biology. Here mu_delta ~= 6.5 um, while mu_lambda ~= 100 um, such that mu_delta/mu_lambda While we appreciate that most theoretical work has been done for syncytia, this paper is concerned with patterning of epithelia, which have different patterning constraints, as also explained in a reply further above. We now make the scope of our work clearer in the revised manuscript. But as the referee points out, the diameter of the nucleus relative to the gradient length is such that gradients can be expected to be sufficiently precise.

p. 4, section on the effect of spatial correlation: Here the authors chose to order the kinetic parameters in ascending or descending order. Is there any biological motivation for that particular choice? Other types of correlations seem possible, e.g. imposing the rule that successive parameter values are sampled starting from the previous value, p_i+1 = o_i +- delta_i+1 where delta_i+1 are random numbers with a defined variance.

In the simulations we go from zero correlation (every cell has independent kinetic parameters) to maximal correlation (every cell has the same parameters, resulting effectively in a patterning domain that consists of a single effective “cell”), see Fig. S3. Biologically plausible correlations in between these extremes should retain the same kinetic variability levels (same CVs) which we took from the measured range reported in the literature. We accomplish this by ordering the parameters after independently sampling the parameters for each cell from probability distributions with the desired CV. The motivation for this approach is that this produces a type of maximal correlation that still reflects the measured biological cell-to-cell variability, to demonstrate in Fig. S3, that even such a maximal degree of spatial correlation does not qualitatively alter our results. The kind of correlation that the referee suggests introduces a spatial correlation length that lies in between the extremes that we simulated. Since even for maximal correlation using the ordering approach, we find our conclusions to still apply, we have no reason to expect that intermediate levels of correlation would behave any differently.

The idea brought forward by the referee effectively introduces a correlation length scale. We discuss this case in the paper, noting that the positional error will scale as x~N , where N is the number of cells sharing the same kinetic parameters. A correlation length scale will be proportional to N and will therefore simply uniformly scale the positional error accordingly, but will likely not reveal any new insight beyond that.

Moreover, using the idea of the referee as an additional way to introduce correlation is difficult to realise in practice, as we need to recover the mean and variance of the kinetic parameters, while ensuring strict positivity for each of them. A simple random walk, as proposed, would not lend itself easily to achieve this without introducing a bias in the distribution, because negative values need to be prevented. As explained in a reply further above, an important feature of the kinetic parameters is that they are not too small to prevent the formation of a meaningful gradient, which is not straightforward to ensure with the proposed method.

We acknowledge that there are different types of correlations conceivable, but we expect these correlations to lie between the two extremes that we present in the paper, which show no qualitative difference in the results.

p.5, Discussion: "..., but with nuclei much wider than the average cell diameter". To be honest, I could not completely imagine what is meant with this sentence. Intuitively, it seems that the nuclei cannot be larger than the cells, but I suppose that some kind of special anisotropy is considered here? In any case, this should be made precise.

The main tissues that are patterned by gradients are epithelia. Our paper focuses on such tissues. It is a well-known feature of pseudostratified epithelia that nuclei are on average wider than the cell width averaged over the apical-basis axis. Nature solves this problem by stacking nuclei above each other along the apical-basal axis, resulting in a single-layered tissue that appears to be a multi-layered stratified tissue when only looking at nuclei. For a schematic illustration of this, see Fig. 1 in [DOI: 10.1016/j.gde.2022.101916]. An image search for “pseudostratified epithelia” on Google yields a plethora of microscopy images. Right at the end of the quote recited by the referee, we also cite our own study [Gomez et al, 2021], which quantifies this in Fig. 5.

Moreover, I find that the conclusion that morphogen gradients "provide precise positional information even far away from the morphogen source" goes to far based on the authors' work, precisely for the fact input fluctuations due to limited morphogen copy number, which can become detrimentally low far away from the source, are not considered, neither the timescales needed to both establish and sample such low concentrations far away from the source. While thus, according to the work of the authors, the fluctuations in the morphogen signal may be favorably small, these other factors are supposed to exert a strong limit on positional information. This conclusion therefore seems unjustified and should be toned down, or even better taken out and replaced by a more accurate one, which only focuses on the gradient shape fluctuations, not on the conveyed positional information.

There is no evidence so far that morphogen gradient concentrations become too low to be sensed by epithelial cells, to the best of our knowledge. What we show is that the gradient variability between embryos remains low enough that precise patterning remains possible. Whether the morphogen concentration remains high enough to be read out reliably by cells is a subject that requires future research. Genetic evidence from the mouse neural tube demonstrates that the SHH gradient is still sensed at a distance beyond 15 lambda (SHH signalling represses PAX7 expression at the dorsal end of the neural tube) [Dessaud et al., Nature, 2007], where an exponential concentration has dropped more than 3-million-fold.

As the referee correctly recites, we state that “morphogen gradients remain highly accurate over very long distances, providing precise positional information even far away from the morphogen source”. This statement is restricted to the positional information that the gradients convey, and does not touch potentially precision-enhancing or -deteriorating readout effects, nor does it concern the absolute number of morphogen molecules.

Positional information goes through several steps. The gradients themselves convey a first level of positional information, by being variable in patterning direction, as quantified by the positional error. This is what we draw our conclusion about. This positional information from the gradients can then be translated into positional information further downstream, by specific readout mechanisms, inter-cellular processes, temporal averaging, etc. About these further levels of positional information, we make no statement.

We therefore disagree that our conclusion is unjustified. In fact, we have phrased it exactly having the limited scope of our study in mind, making sure that we restrict the conclusion to the gradients themselves.

MINOR COMMENTS

- p. 1: "and find that positional accuracy is the higher, the narrower the cells".

(This sentence, however, should be anyhow revised in view of major comment 5 above.)

We have added “the”.

- p. 4: "... with an even slightly smaller prefactor."

We have removed “even”.

Reviewer #3 (Significance (Required)):

I believe that this work is significant to the community working on the theoretical foundations of morphogen gradient precision in developmental systems. The main interesting findings are that small cell diameters lead to smaller positional error (although the relevant measure should be clarified according to my comment no. 5), and that the gradient shape fluctuations are surprisingly robust with respect to the readout mechanism.

Its limitations consist of the fact that the impact of small copy numbers on the readout and associated timescales are neglected, such that the findings of the authors on gradient robustness cannot be simply transferred by simple conversion formulas to readout robustness / positional information. Comment 5 goes hand in hand with this, as a different conclusion may emerge depending on how the relevant positional error measure is defined. This should be fixed by the authors as indicated in the main part of the report.

Thank you for your assessment.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

---

Referee #3

Evidence, reproducibility and clarity

In their mansucript "Impact of cell size on morphogen gradient precision" the authors Adelmann, Vetter and Iber numerically analyse a one-dimensional PDE-based model of morphogen gradient formation in tissues in which the cell sizes and cell-specific parameters locally affecting the gradient properties are varied according to predefined distributions. They find that the average cell size has the largest impact on the variance of the gradient shape and the read-out precision downstream, while other factors such as details of the readout mechanism have markedly less influence on these properties. In addition they demonstrate that averaging gradient concentrations over typical cell areas induces a shift of the readout position, which however appears to be insignificant (~1% of the cell diameter) for typical parameters.

Overall this manuscript is in very good shape already and tackles an interesting topic. I still would like the authors to address the comments below before I would recommend any publication. My main criticism pertains to some of the authors' derivations which, as I find, partly do deserve more detail, and to their conclusions about gradient readout precision.

MAJOR COMMENTS

1 - p. 1, left column: The positional error of the readout position does not only depend on the variation of the gradient parameters, as suggested by the first part of the introduction. A very important factor is also the fluctuations due to random arrival of molecules to the promoters that perform the readout due to the limited (and typically low) molecule number. In fact, for positions very distant to the source of the gradient, this noise source is expected to be dominant over gradient shape fluctuations. Importantly, these fluctuations also arise for non-fluctuating, "perfect" gradient inputs if copy numbers of the morphogen molecules are limited (which they always are). This important contribution to the noise is neglected in the work of the authors. This is OK if the purpose is focusing on the origin and influence of the gradient shape fluctuations, but that focus should be clearly highlighted in the introduction, saying explicitly that noise due to diffusive arrival of transcription factors is not taken into account in the given work (see, e.g., Tkacik, Gregor, Bialek, PLoS ONE 3, 2008)

2 - p.1, right column: Why exactly are the parameters p, d, D assumed to follow a log-normal distribution? Such distribution has been verified for cell size, but the rationale behind choosing it also for the named parameters should be explained, in particular for D. Why would D depend on local properties of the cell? Which diffusion / transport mechanism precisely is assumed here?

Moreover, is there any relationship between A_i and p_i, d_i and D_i, or are these parameters varied completely independently? If yes, is there a justification for that?

3 - p. 2, right column, section on "Spatial averaging": First of all, how is "averaging" exactly defined here? Do the authors assume that the cells can perfectly integrate over their surface in the dimensions perpendicular to their height? If yes, then this should be briefly mentioned here. Secondly, the shift \Delta x calculated by the authors ultimately seems to trace back to the fact that the cells average over an exponential gradient, whose derivative also is exponential, such that levels further to the anterior from the cell center are higher (on average) than levels to the posterior of it. I suppose, therefore, that a similar calculation for linear gradients would not lead to any shift. If these things are true they deserve being mentioned in this part of the manuscript because they provide an intuitive explanation for the shift. Thirdly, in Fig. 2A the cell sizes seem exaggerated with respect to the gradient length. This seems fine for illustrative purposes, but if it is the case it should be mentioned. Also, I believe that this figure panel would benefit from showing another readout case where the average concentration e.g. in cell 1 maps to its corresponding readout position, in order to show that this process repeats in every cell. Moreover, it could be indicated that in the shown case C_\theta matches the average concentration in cell 2 at the indicated position.

As for the significance of the magnitude of the shift for typical parameters as calculated by the authors: I believe that it could be said more explicitly and clearly that under biological conditions the calculated shift overall seems insignificant, as it amounts to a small fraction of the cell diameter.

Finally, and most importantly: The term "spatial averaging" can have a different meaning in developmental biology than the one employed by the authors. While the authors mean by it that individual cells average the gradient concentration over their area, in other works "spatial averaging" typically means that individual cells sense "their" gradient value (by whatever mechanism) and then exchange molecules activated by it, which encode the read-out gradient value downstream, between neighboring cells, in order to average out the gradient values "measured" under noisy conditions. The noise reduction effect of such spatial averaging can be very significant, as evidenced by this (incomplete) list of works which the authors can refer to:

• Erdmann, Howard, ten Wolde, PRL 103, 2009
• Sokolowski & Tkacik, PRE 91, 2015
• Ellison et al., PNAS 113, 2016
• Mugler, Levchenko, Nemenman, PNAS 113, 2016

The main point, however, is that this is a different mechanism as the one described by the authors, and this should be clearly mentioned in order to distinguished them. I would therefore also advise the authors to make the section title more precise here, by changing "Spatial averaging barely affects ..." to "Spatial averaging across the cell area barely affects ..." for clarity.

4 - p. 3, Figure 3: It would be good to highlight the fact that the colours in panel A correspond to the bullet colors in the other panels also in the main text.

As to the comparison of different readout strategies: How exactly were the different readout mechanisms compared on the mathematical side? More precisely: How was the readout by the whole area matched (in terms of fluxes) to the readout at a single point, be it in the center of the cell or a ranomly chosen point? How was it ensured that the comparison is done at equal footing?

p. 3, right column: "... similar gradient variabilities, and thus readout precision": Linking to comment 1 above, this is strictly speaking only the case when the only source of fluctuations in the readout is the gradient fluctuations. I would therefore leave this statement out.

5 - p. 3, section on positional error (right column): In this part I had most troubles following the thoughts of the authors.

First of all, the measure that the authors use for the positional error is sigma_x / mu_lambda, i.e. the standard deviation of the readout position relative to the gradient length. The question is whether this is the correct measure. It should be specified what the motivation for normalizing by mu_lambda is. In the end, one could argue, what the cells really do care about would be that the developmental process can assign cell fates with single cell precision, for which the other measure shown in Eq. (6) is the representative one. Now in contrast to the former measure, the latter actually increases with decreasing cell diameter.

Secondly, even when the former measure (sigma_x / mu_lambda) is employed, Fig. 3(D) shows that while it decreases with decreasing cell diameters, in the regime of small diameters the std. dev. of the readout position becomes larger than the average cell diameter, which actually would mean that cell fates cannot be assigned with single-cell precision. While the authors later report both quantities for specific gradients, it should be clarified beforehand which of the measures is the relevant one.

Moreover, in the following derivations, mu_x is not properly introduced. What exactly is the definition of that quantity? Is it the mean readout position? If yes, it is not clear why exactly it would be interesting and relevant to the cell. This should be properly explained in a way that does not require the reader to look up further details in another publication.

At the end of this section the authors come back to the sigma_x / mu_delta measure again and indeed point out that it increases with decreasing mu_delta, which causes a bit of confusion because the initial part of the section only talks about the increase of the pos. error with mu_delta. Overall I find that this section should be rewritten more clearly. Right now it leaves the reader with the "take home message" that small cells are good because they lead to smaller pos. error, but when the--in my opinion--relevant measure (sigma_x/mu_delta) is employed the opposite is the case. This is confusing and unclear about the authors' intentions in that part.

Finally, the authors could also supplement the numbers that they name for the FGF8 and SHH gradients by the known numbers for the Bcd gradient in Drosophila, which has been studied excessively and constitutes a paradigm of developmental biology. Here mu_delta ~= 6.5 um, while mu_lambda ~= 100 um, such that mu_delta/mu_lambda < 1/15, which defines yet another regime than the other two gradients. It would be interesting to compare their respective numbers altogether, and also discuss the ones for Drosophila in view of the fact that in experiments sigma_x ~= mu_delta for this species.

6 - p. 4, section on the effect of spatial correlation: Here the authors chose to order the kinetic parameters in ascending or descending order. Is there any biological motivation for that particular choice? Other types of correlations seem possible, e.g. imposing the rule that successive parameter values are sampled starting from the previous value, p_i+1 = o_i +- delta_i+1 where delta_i+1 are random numbers with a defined variance.

7 - p.5, Discussion: "..., but with nuclei much wider than the average cell diameter". To be honest, I could not completely imagine what is meant with this sentence. Intuitively, it seems that the nuclei cannot be larger than the cells, but I suppose that some kind of special anisotropy is considered here? In any case, this should be made precise.

Moreover, I find that the conclusion that morphogen gradients "provide precise positional information even far away from the morphogen source" goes to far based on the authors' work, precisely for the fact input fluctuations due to limited morphogen copy number, which can become detrimentally low far away from the source, are not considered, neither the timescales needed to both establish and sample such low concentrations far away from the source. While thus, according to the work of the authors, the fluctuations in the morphogen signal may be favorably small, these other factors are supposed to exert a strong limit on positional information. This conclusion therefore seems unjustified and should be toned down, or even better taken out and replaced by a more accurate one, which only focuses on the gradient shape fluctuations, not on the conveyed positional information.

MINOR COMMENTS

• p. 1: "and find that positional accuracy is the higher, the narrower the cells".

(This sentence, however, should be anyhow revised in view of major comment 5 above.)

• p. 4: "... with an even slightly smaller prefactor."

Significance

I believe that this work is significant to the community working on the theoretical foundations of morphogen gradient precision in developmental systems. The main interesting findings are that small cell diameters lead to smaller positional error (although the relevant measure should be clarified according to my comment no. 5), and that the gradient shape fluctuations are surprisingly robust with respect to the readout mechanism.

Its limitations consist of the fact that the impact of small copy numbers on the readout and associated timescales are neglected, such that the findings of the authors on gradient robustness cannot be simply transferred by simple conversion formulas to readout robustness / positional information. Comment 5 goes hand in hand with this, as a different conclusion may emerge depending on how the relevant positional error measure is defined. This should be fixed by the authors as indicated in the main part of the report.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

---

Referee #2

Evidence, reproducibility and clarity

Summary:

How morphogen gradients yield to precise patterning outputs is an important problem in developmental biology. In this manuscript, Adelmann et al. study the impact of cell size in the precision of morphogen gradients and use a theoretical framework to show that positional error is proportional to the square root of cell diameter, suggesting that the smaller the cells in a patterning field, the more precise patterns can be established against morphogen gradient variability. This result remains true even when cells average the morphogen signal across their surface or spatial correlations between cells are introduced. Thus, the authors suggest that epithelial tissues patterned by morphogen gradients buffer morphogen variability by reducing apical cell areas and support their hypothesis by examining several experimental examples of gradient-based vs. non-gradient-based patterning systems.

Major comments:

1. While the idea that smaller cells yield to more precise morphogen gradient outputs is attractive, it is unclear whether patterning systems use this strategy to make patterns more precise, as there are several mechanisms that could achieve precision. Do actual developmental systems use it as a mechanism to increase precision? Or precision is achieved through other mechanisms (for example, cell sorting as in the zebrafish neural tube; Xiong et al. Cell, 2013). Indeed, classical patterning work on Drosophila embryo suggest that segmentation patterns are of an absolute size rather by an absolute number of cells (Sullivan, Nature, 1987). According to the authors, the patterning stripes should be more precise when embryos have higher cell densities than in the wild-type, but stripes are remarkably precise in wild-type embryos. This is likely due to other precision-ensuring mechanisms (such as downstream transcriptional repressors, in this case).

2. Their modeling approach is based on exponential gradients formed by diffusion and linear degradation, but in reality, actual morphogen gradients are affected by receptor and proteoglycan binding and are likely not simply exponential and/or interpreted at the steady state. Do the main results of the manuscript hold even for non-exponential gradients or before they reach a steady state?

3. In their Discussion section, the authors note that several patterning systems, such as the Drosophila wing and eye discs, show smaller cells near the morphogen source relative to other regions in the tissue. This observation suggests a prediction of the authors' hypothesis that can be tested experimentally. In the Drosophila wing and eye discs genetic mosaics of ectopic morphogen sources (such as Dpp) can (and have) been made. Therefore, one could predict that the patterning outputs in a region of larger cross-sectional areas will be more imprecise than in the endogenous source. Since this is a theoretical paper, it is understandable that authors are not going to make this experiment themselves, but I wonder if they can use published data to test this prediction or at least mention it in the manuscript to offer the experimental biology reader an idea of how their hypothesis can be tested experimentally.

Other comments:

• The Methods section should be expanded and should include more details about how authors consider cell size in their simulations. As presented, I believe that experimental biologists will not be able to grasp how the analysis was done.

• Authors use adjectives such as 'little' as 'small' without a comparative reference. For example in the abstract, the authors say that apical areas "are indeed small in developmental tissues." What does "small" mean? This should be avoided throughout the text.

Significance

Overall, I believe that the manuscript is well written and deserves consideration for publication. However, authors should consider the points outlined above in order to make their manuscript more accessible and relevant to the developmental biology community.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

---

Referee #1

Evidence, reproducibility and clarity

Summary:

• In developing systems, morphogens gradients pattern tissues such that cells along the patterning length sense varying levels of the morphogen. This process has a low positional error even in the presence of biological noise in numerous tissues including the early embryo of the Drosophila melanogaster. The authors of this manuscript developed a mathematical model to test the effect of noise and mean cell diameter on gradient variability and the positional error they convey.

• They solved the 1D reaction-diffusion equation for N cells with diameters and kinetic parameters sampled from a physiologically relevant mean and coefficient of variation (CV). They fit the resulting morphogen gradients to a hyperbolic cosine profile and determined the decay length (DL) and amplitude (A) for a thousand independent runs and reported the CV in DL and A.

• The authors found that CV in DL and A increases with increase in mean cell diameter. They propose a mathematical relationship between CV in DL scales as an inverse-square-root of N. Whereas the CV in DL and A is a weak function of CV of cell surface area (CVa) if CVa < 1.

• They further looked at the shift in readout boundaries and compared four different readout metrics: spatial averaging, centroid readout, random readout and readout along the length of the cilium. Their results show that spatial averaging and centroid have a high readout precision.

• They finally showed that the positional error (PE) increases along the patterning length of the tissue and increases with increasing mean cell diameter.

• The authors also supported their theoretical and simulated results by looking at mean cell areas reported for in patterning tissues in literature which also have a higher readout precision with smaller cell diameters.

Major comments:

• Most of the key conclusions are convincing. However, there are four major points that should be addressed. First, the authors conclude the section titled, "The positional error scales with the square root of the average cell diameter," by saying that morphogen systems with small cells can have high precision in absolute length scales, but not on the scale of one cell diameter. They state this would result in salt and pepper patterns in the transition zones. The authors should either support this with biological examples or explain why this is not observed experimentally.

• Second, perhaps the main conclusion of the paper is that morphogen gradients pattern best when the average cell diameter is small. The authors support this by reviewing the apical cell area of epithelial systems that are known to be patterned by morphogens and those that are not (presumably taking apical cell area as a proxy for cell diameter). However, the key parameter is not absolute cell diameter, but the cell diameter relative to the morphogen length scale. The authors should report the ratio of these two quantities in their literature analysis.

• Third, as part of their literature analysis, the authors state that in the Drosophila syncytium, there are morphogen gradients, but they imply that because these gradients operate prior to cellularization, one cannot use the large distances between nuclei as counter evidence to their main conclusion. Rather than simply dismissing the case of the Drosophila syncytium, the authors should explain why this case does not apply, using reasoning based on their model assumptions.

• Fourth, related to the above: the authors then state that there are no morphogen gradients known during cellularization. Unless I am misunderstanding their point, this is untrue. The Dpp gradient acts during the process of cellularization and specifies at least three distinct spatial domains of gene expression. Furthermore, not long after gastrulation, EGFR signaling patterns the ventral ectoderm into at least two distinct domains of gene expression. What are the cell areas in that case?

Minor comments:

• Figs 1cd:

The way the system is set-up: (DL = 20 micron, Patterning Length (LP) = 250 micron, Nominal cell diameter (D) = 5 micron) the DL/L ~ 0.08 which makes the exponential profile far to a small value around 100 micron. This means in all these simulations, the LP was only around 100 micron, cells beyond that saw nearly zero concentration. Because of this, when diameters were varied from 0.2 - 40 micron, there could be as few as 2.5 cells in the "patterning region" which could be responsible for higher variability in DL and A.

Would any of the results change if DL/L was higher, around 0.2?

The source region is 25 microns in length and all cell diameters above 25 micron get defaulted back to 25 micron which explains the flatness lines in the region beyond mu_delta/mu_DL> 1

Results:

Pg 2 (bottom left): In the git repository code, the morphogen gradients are fit to a hyperbolic cosines function (described in reference 19) which is not described in the main text. Having this in the main text would help readers understand why fig 1c has variation in d only, D only and all k parameters whereas fig 1d has variation with all individual parameters p, d and D and all k.

• Figure 3b:

In figures where markers are overlapping perhaps the authors can use a "dot" to identify one set of simulations and a "o" to identify the ones under it. The way the plots are set up currently makes it hard for the reader to understand where certain points on the plot are.

Methods:

The Methods can be more descriptive to include certain aspects of the simulations such as adjusted lambda which is only described in the code and not the main text or supplementary.

Git code:

The git code function handles do not represent figure numbers and should be updated to make it easier for readers to find the right code

Significance

This manuscript contributes certain key aspects to the patterning domain. The three most important contributions of this work to the current literature are: (1) the scaling relationships developed here are important, (2) the idea that PE increases at the tail-end of the morphogen profile is nicely shown and (3) Comparison of various readout strategies.

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

Learn more at Review Commons

---

Reply to the reviewers

Reviewer #1 (Evidence, reproducibility and clarity):

Summary:<br /> The Authors report on the synthesis and characterization of a class of small molecules, the tanshinone mimics (TMs), which interfere with binding of the RNA binding protein (RBP) HuR to its mRNA targets. HuR is an important regulator of mRNA stability and translation of genes involved in key homeostatic (cell cycle, stress response) and pathologic process (inflammation, carcinogenesis). In particular, the first part of the study describes the compounds' chemical synthesis and some pharmacokinetic parameters (i.e., definition of molecular binding, solubility, bioavailability, prodrug generation etc). The second part undertakes, in in vitro and ex-vivo model of LPS-induced mouse macrophage activation, the identification of HuR-bound mRNA targets, which is then evaluated within the global LPS-induced transcriptome; finally, the study evaluates the ability of TMs to inhibit HuR-mediated proinflammatory gene regulation, indicating their use and potential value as therapeutic anti-inflammatory strategy.<br /> Major comments:<br /> The manuscript contains a wealth of data generated from different experimental systems, spanning from synthetic chemistry to preclinical models of gene regulation, requiring cultural backgrounds in chemistry and biology as well. The key conclusions are well supported by the data, but it takes a great effort to get to the core results and thus critically read and evaluate their interpretation. Although the complexity and sheer size of data sets generated lends itself to a hard read, this is further complicated by data presentation, which especially in the second part needs to be significantly improved to gain clarity and focus. For ease of referral, specific comments will be addressed related to Figures whenever possible.<br /> 1.1 • Page 15: To measure TM7nox disrupting ability of HuR:mRNA complex for the HTRF assay (Figure 2G) and for biotin pull down assay (Figure 5C), it was chosen a biotinylated probe containing the AU rich elements of the TNFα, as known HuR target. Please comment on the rationale, and whether could it be relevant reevaluate these parameters post-hoc, based on the sequences identified in HuR targets more susceptible of modulation by TM compound (listed in table 1, Figure 5 A/B) and based on the absence of regulation of TNFa (Figures 3D, 4D, 7A) found in the tested systems.

R1.1 - We thank the reviewer for this observation. We have been using the biotinylated probe containing the AU-rich elements of TNFα as a representative probe for HuR for biochemical assays in several articles (PMID: 29313684, PMID: 26553968, PMID: 23951323). As the reviewer suggests, a posteriori, it is worth reevaluating the representative probe to be used for evaluating the disrupting ability of TMs based on the data we present here. Indeed, we will tackle this problem in our following efforts, as it is a meaningful although time-consuming task which is outside of the scope of this manuscript.

1.2 • Page 16-18: Description of the RNAseq data shown in Figure 3 should be more centered around the main findings regarding the effect of TMnox that are further pursued in the study: that is, (Figure 3B), the 249 downregulated DEGs found modulated by TM7nox in presence of LPS, where was observed a strong enrichment of categories related to the inflammatory response: cytokines (Il1b, Cxcl10, Il10, Il19, Il33), immune cell chemotaxis (Ccl12, Ccl22, Ccl17, Ccl6) and innate immune response.

The description of the GO for the remaining data should be shortened to main points, perhaps reporting what described in the results with each section of the Venn in a table, while referring to the whole list in the supplements as already done. This could replace Figures 3D, E which do not add substantially to what provided in the supplementary table 2 and to which they can be added as further visualization.

R1.2 - We thank the reviewer for this suggestion, accordingly, we simplified the text keeping only the description of the genes modulated by TM7nox during LPS treatment. The other information originally there was moved to Supplementary table 2. Revised figures 3E and 3F now focus only on the 249 downregulated genes of this group.

1.3 • Page 18-19: Description of the results of the RIP-seq shown in Figure 4 set is very confusing: onward from the line "477 HuR-bound transcripts (log2 FC > 3) were also upregulated by LPS at the transcriptional level..." the numbers do not match or reconcile with those shown in the Venn diagram (Fig. 4B) nor with those listed in the figure legend of Figure S8.

R1.3 - We agree with the reviewer, we apologize for having reported the wrong numbers, and we clarified this point in general by deeply revising the text. A more precise explanation of the selection procedure for the genes of interest is now reported and better explained also by adding a scheme (Fig 4D in the revised manuscript).

1.4 Moreover, as previously remarked for Figure 3 (and even more for this dataset in which initial description of Venn in 4B is unclear), panel 4E does not add as much to the info provided in Table 1/supplementary Table 1, where they can eventually be added as further data visualization; Instead, Figure S8 displays very informative data merging together the results obtained in RNAseq (Fig. 3) and RIP-Seq (Fig.4) and should be displayed in Figure 4, as in the result section they are indeed presented together.

R1.4 - We agree with this remark, thus we have removed the old panels 3E in S8C and 4E in S9B, and we now provide the information previously contained in old S8 in the main figure 4E of the revised manuscript.

1.5 • Page 19-20: Description of the modulation by TM7nox of HuR binding to specific consensus sequences is summarized at the end of the relative paragraph as follows: "TM7nox reshapes HuR binding to target genes in presence of LPS by disrupting the binding of HuR towards target genes containing a lower number of HuR consensus sequences than the average observed in the HuR-bound transcripts". Understanding of these data through the provided text and the Supplementary Figure 9 is very laborious and referring of an entire dataset to a supplementary figure makes it even harder. It would be best to show this as main figure, not supplemental, either adding a Venn diagram as in 3B/4B showing to which dataset each part of the analysis refers, or even more efficaciously, extrapolate a representative gene set for the main analyses showing TM7nox activity in target genes with higher vs lower consensus sequences; same approach for the analysis in Figure 9B, where the effect on a gene with sequence #1 or #10 could be compared with one bearing sequence #3 for example.

R1.5 - We agree with the reviewer, thus we moved the information of old S9 in figure 4C of the revised manuscript. We deeply revised the information provided also by taking into account the request to compare this experiment to the one in Lal et al. NAR 2017 (please see also R2.4). We made an effort to identify a subset of genes that follow a coherent modulation, identifying 82 genes highlighted in Supplementary Table 1. All such genes show increased expression during LPS or LPS/TMnox vs DMSO conditions, and decreased association to HuR during LPS/TMnox vs LPS. As 47 of these, i.e. more that 50%, contain less AU rich sequences than the average (highlighted in Supplementary Table 1), we can consider them as a representative gene ensemble modulated in accordance with the presence of AU rich sequences.

1.6 • Page 21: Description of the effect of three TMs (TM6, TM7nox and TM7nred) on LPS response in macrophages at the single gene level (Figure 5 and Figure 6): TM6 and TM7nox were used in exps in Fig. 5 A and E, while only TM7nred was used for CXCL10 secretion analysis (fig.5 D and F): please describe the compound choices' rationale (as done for experiments in Figure 6).

R1.6 – Following the reviewer suggestion, we now explain our rationale in choosing the small molecules, that is the use of the ones bearing the active quinone species. We have performed additional experiments, and now we report TM6n, TM7nox, and the control DHTS activity in decreasing the secretion of Cxcl10 (figure 5E in the revised manuscript). All compounds behave similarly in this experiment. TM7nred is now used to show its equivalence to TM7nox in figure 5E and in figure 6 of the revised manuscript.

1.7 • Page 21-22: The effect on HuR expression of siRNA silencing and, more importantly, of TMs shown in Figure 6A, first panel, should be visualized at protein level by western blot. This is an important point as for CXCL10 and iL1b there seems to be an additive effect between decreased HuR levels and pharmacological blocking.

R1.7 - Following the reviewer suggestion, we now show the protein level as measured by intracellular Elisa; as we were not able to detect the proteins by western blot. The protein level is in general agreement with the gene expression level. We do not observe an additive effect by pharmacological inhibition during HuR silencing, but we rather observe a slight increase in the protein level during HuR silencing. We do not have an explanation for this effect, which may depend on several reasons - for example, an aspecific effect of the TMs when their molecular target HuR is absent.

1.8 • Page 24: please rephrase the statement 'These observations suggest the utilization of TMs in autoinflammatory and autoimmune diseases' as 'These observations suggest the evaluation of TMs in specific preclinical models for autoinflammatory and autoimmune diseases'.

R1.8 - We fully agree with the reviewer, and we changed the text in the revised manuscript accordingly.

1.9 • In the discussion, please include a paragraph with study limitation and possible biases (for example, the choice of RNP-IP without crosslinking has pros and cons).

R1.9 – Thank you for the good suggestion, we added a paragraph in the discussion which describes study limitations due to the utilization of RNP-IP vs crosslinking.

The data and the methods are correctly presented for reproducibility, replicates and statistical analysis are adequate. Minor comments: 1.10 • At least in the single gene validation experiments (Fig.5), a negative control (such as recombinant HuR with mutated RRMs in trans-, or ARE-less/non-HuR targetable sequence in cis, or inactive TM) would be advisable.

R1.10- We thank the reviewer for the suggestion. Accordingly, we used an ARE-less/non-HuR targetable gene as RPLP0 for validation.

1.11 • Figure 6B/C: for immunofluorescence panels, zooming on a smaller number of cells will render more visible HuR and NFkB nucleocytoplasmic shuttling, given that quantification and statistics are provided by imaging software. Negative control stainings (secondary Abs only) should be included.

R1.11 – In accordance with this suggestion, we now report a higher magnification of the immunofluorescence images. We also report the standard DHTS effect, showing a difference vs TMnox activity which may suggest its impact on NFkB shuttling.

1.12 • Figure 7A: in the X axis LPS+8n is indicated: is it a typo for LPD+6n or was compound TM8n indeed used?

R1.12 – Thanks for your spotting our mistake, the prodrug 8 described in figure 1 was used.

1.13 • In the Methods section please include protocols and materials for immunofluorescence (results shown in Fig. 6B/C).

R1.13 – As for your suggestion, protocols and materials for immunofluorescence were added to the methods.

1.14 • There are some typos and repetition in figure legends (legend Figure S9).

R1.14- Thank you for this, we revised all the figure legends.

Prior studies are referenced appropriately. Review Cross-commenting I fully agree with the Reviewer's remarks. I would add that a general concern expressed is that this manuscript in its present form has a readership issue: the first part is for chemistry/pharmacology audience, the second is biology-based. Splitting the work has been suggested; or, the Authors may decide which part is more impactful and present the other in a streamlined version.

Reviewer #1 (Significance):

This is a large study reporting progress in the development of synthetic antagonists of HuR function, which is the Authors' well-established line of research. The TM compounds are small molecules with anti-inflammatory effects with strong potential for therapeutic use due to selected inhibition of HuR-mediated upregulation of proinflammatory molecules. The physicochemical and early biological characterization done in this study will allow further testing of their efficacy and of the overall role of HuR-mediated regulation as targetable mechanism in several preclinical human disease models. Targeting of the RNA-binding protein HuR has been tackled as therapeutic approach in cancer, less in chronic immune and inflammatory diseases despite many common mechanisms and mediators. This study could be well received by researchers involved in basic science and drug development (chemistry, biochemistry/biophysics, pharmacology, computational modeling) and biologists/physician scientists interested in testing these compounds in translational research settings where HuR-driven functions can be relevant (cancer, chronic inflammation), though the chemical part would be less accessible to the latter audience. Reviewer's background is in preclinical human models of chronic inflammation with interest in posttranscriptional gene regulation with familiarity with RNAseq and RIPseq dataset and analysis. For the part of the manuscript regarding the synthesis and physicochemical characterization of the TN compound I requested assistance to a faculty from the chemistry department with expertise in that field, who did not request any specific clarification or addendum.

Reviewer #2 (Evidence, reproducibility and clarity):

In the manuscript entitled "HuR modulation with tanshinone mimics impairs LPS response in murine macrophages" the authors have described the synthesis and application of small molecule mimics of the naturally occurring compound tanshinone, which is known to inhibit the binding of the RBP HuR to a class of its mRNA targets. The authors have shown that the tanshinone mimics (TMs) used by them block the binding of RRM1-2 of HuR to ARE-containing RNA in vitro, and reduce the interaction of HuR with a set of ARE-containing mRNAs in LPS-treated mouse macrophage cells. This reduction of interaction of HuR with some of these mRNAs correlates with the reduction in their level in the cells treated with the TMs, and in the secreted level of their proteins in the serum of animals with LPS-induced peritonitis. Together, the study demonstrates the role of these TMs as modulators of the LPS-induced inflammatory response by blocking the binding of HuR to a subset of LPS-induced inflammatory mRNAs and thereby downregulating their mRNA and protein levels in inflammatory cells. The manuscript describes a comprehensive study, ranging from chemical synthesis of TMs, MD simulations to demonstrate the binding site of the TMs to the cleft formed by the RRM1-linker-RRM2 domains of HuR, which has been shown in crystal structure to be the main binding site of A/U-rich RNA molecules, in vitro studies showing the ability of the TMs to hinder ARE-containing RNA binding to HuR RRM1-2, whole transcriptome analysis to show the effect of the TMs on LPS-induced differential gene expression in murine macrophages, and on HuR binding to target mRNAs, and animal studies to show the effect of the TMs on the level of some inflammatory mediators in the serum of mice with LPS-induced peritonitis. The results are quite convincing and is in line with what is generally known about the effect of HuR on the expression of a large number of genes encoding pro-inflammatory proteins, and the ability of tanshinone derivatives/mimics in inhibiting HuR binding to target mRNAs. The authors put these two information together in this study and the results are on expected lines. While the results are convincing and quite comprehensive, I would suggest the following in order to substantiate and strengthen the results: 2.1. The experiments do not have any "positive control", such that the performance of the TMs can be compared with that of a molecule with known HuR binding inhibition activity, such as DHTS. It would be good to have such a comparison, to understand whether the TMs work similar to DHTS or differently, both qualitatively in terms of the mRNA targets which they affect and the extent of their anti-inflammatory activity.

R2.1- We added DHTS as a comparison to TMs, following the reviewer’s comment. In this model, the net effect of DHTS is partially overlapping with TMs, at least for the parameters that we checked (see Figure 5, 6 and 7), showing some differences in the modulation of NF-kB shuttling upon LPS stimulation. Therefore, we suggest that DHTS and TMs show partially different effects on mRNA targets and in terms of anti-inflammatory activities.

2.2. It is not clear to me whether the mRNAs which show differential expression in the RNAseq analysis of cells treated with LPS and TMs are exactly the ones which show difference in binding with HuR in the RIPseq analysis in presence of the TMs. This analysis is important for a number of reasons: all the mRNA binding targets of HuR are not affected by HuR at the level of mRNA stability, many are affected at the level of translation, without change in mRNA level. These mRNAs should therefore show change in binding of HuR in the RIPseq assay in presence of TM, but not show change in expression. Secondly, there may be mRNAs which show a change in expression in presence of TMs, but do not show binding of HuR, suggesting pleiotropic roles of the TMs. Therefore, instead of an overall correlation between differential expression and change in HuR binding of mRNAs, a table comparing the RIPseq status of individual mRNAs with that of their differential expression status, in presence and absence of LPS/TMs would be useful, further designating the different groups of mRNAs based on these differential status (change in HuR binding/change in expression, change in HuR binding/no change in expression etc.).

R2.2 – We tried to rationalize the data following the reviewer’ suggestion, however, we could not fully adopt this strategy due to the complexity of the experiment design. Indeed, we have focused our attention on the effect of TMs during LPS stimulus, which induces a strong transcriptional response, rather than in steady state conditions. This is why we reported the overall correlation of LPS vs DMSO and TM7nox/LPS vs DMSO. Then, we evaluated whether the observed difference in the correlation may be reflected on a change of HuR binding, and we checked the RIPseq status during co-treatment vs LPS. This was the case for a subset of genes that are reported in Supplementary Table 1. Nevertheless, to be fully compliant with the reviewer’s request we now report a Supplementary Table 1 containing the entire gene list, so that the reader can immediately filter out the subsets according only to the comparison TM7nox/LPS vs LPS.

2.3. Nuclear/cytoplasmic localization of HuR and NFkb is impossible to discern at the magnification of the immunofluorescence images in Fig 6 B and C. Higher magnification images are required to understand changes in localization.

R2.3 – In accordance with this suggestion, we now report higher magnification, please see also R1.11. We do not observe any change in nuclear/cytoplasmic localization of HuR and NFkb due to TMs treatment. We rather observe LPS-induced NFkB nuclear accumulation, ActD-induced HuR cytoplasmic shuttling and inhibition of NFkB translocation, during LPS and DHTS treatment.

2.4. It has been shown that DHTS-I increases the binding of HuR to the mRNAs with longer 3'UTR and with higher density of U/AU-rich elements, whereas it reduces the interaction of HuR with the mRNAs having shorter 3'UTR and with low density of U/AU-rich elements (Lal et al., NAR, 2017). It is not clear if the same is observed in case of the TMs or not, and such a comparative analysis would be useful to address this point.

R2.4 – We re-analysed the data, checking the density of U/AU rich elements and the length of the 3’UTR of the displaced mRNA as in Lal et al. NAR 2017. Although we could not compare DHTS and TMs within the same biological system, it appears that the rules dictating their mechanism of action are similar.

I think that the above suggested points are feasible as most of them really involve re-analysis of existing data. Only the suggestion to add DHTS or tanshinone as a positive/comparison control will require experimentation and addition of new data.

Review Cross-commenting

I think most of the reviewers' comments coincide in the evaluation of the manuscript. I would especially like to draw attention to the fact that all three reviewers found that the content and form of data presented in the paper is very dense and bogs down the reader and distracts from the overall focus of the manuscript.

Reviewer #2 (Significance):

The work described in the manuscript is comprehensive as it ranges from chemical synthesis and in vitro evaluation of the TMs to the characterization of their effects in vivo. Although the effect of tanshinone derivatives on HuR mRNA target binding is known, and the effect of HuR on inflammatory gene expression is also known, the manuscript is significant as it brings these two information together and tests the effect of these TMs on HuR-mediated regulation of inflammatory gene expression.<br /> However the extensiveness of the work also makes it quite dense, and I feel that the focus of the paper is often lost in the details. Also, the text of the manuscript is dense and verbose and uses many irregular grammatical and phraseological usages, for eg "their<br /> modulation or mis-localization lead to the insurgence of complex phenotypes and diseases". It appears to me that it would be ideal if the chemical synthesis, MD simulation studies and in vitro studies are presented in an independent manuscript. Also, that would allow a more exhaustive referencing of the known studies in literature where tanshinone derivatives, and other small molecules, have been used to modulate HuR binding to mRNA targets.<br /> This work would be of interest to molecular cell biologists in general and RNA biologists in particular, especially those who are studying RNA-protein interactions, and scientists who are interested in drug development using RNA-protein interactions as drug targets.<br /> My interest in the work lies in my expertise in studying RNA-protein interactions, especially of RNA-binding proteins such as HuR involved in regulating the translation of mRNAs encoded by inflammatory genes. I do not have expertise in chemical synthesis and am therefore not qualified to evaluate the first set of results describing the chemical synthesis of TMs.

Reviewer #3 (Evidence, reproducibility and clarity):

In this study, the authors investigated the modulation of HuR by tanshinone mimics and how it mitigates LPS response in murine macrophages. This represents a nice integration of synthetic chemistry, molecular simulations, and in vitro as well as in vivo experimental validations. Overall, this is an interesting study, and will add to the growing interest in HuR in inflammatory-mediated disease. The paper contains a lot of data (actually several papers in one) which may bog down the reader and distract from the overall message. it is suggested that they condense the data and simplify the figures and use more supplemental figures.<br /> Major Comments:<br /> 3.1. The authors have shown the dose response and cytotoxicity effect of tanshinone mimics; The authors show that TMs affect the overall HuR mRNA but they don't show protein levels.

R3.1 – In accordance with the reviewer’s comment, we now show also protein levels, as we performed intracellular ELISA (Figure 6 in the revised manuscript); please see also R1.7.

3.2. It is unclear the timing of certain experiments for LPS vs TMs (whether macrophages were pre-treated with TMs before LPS)-e.g fig 5. The authors should clarify for all experiments as the long-term clinical paradigm would be treatment after inflammation has been established.

R3.2 – In most experiments TMs are co-administered with LPS. Only in one of the two protocols used for Actinomycin D chase experiment TMs are added after LPS with Act D, as we wanted to discriminate between transcriptional and post-transcriptional effects of TMs (see also R3.3).

3.3. They have also identified differentially expressed genes which are RNA binding ligands of HuR by RIP-Seq. However, it would be necessary to check whether TM7nox affects the stability of those targets before conclusions can be made that TMs don't inhibit the primary transcriptional response (as mentioned in the Discussion section). Transcriptional effects of HUR chemical inhibition or genetic silencing has been reported previously in other cell systems.

R3.3 – The reviewer is entirely correct, and we accordingly amended our conclusions. Indeed, TMs have an impact on gene transcription during co-administration with LPS as now suggested by Actinomycin D chase experiments reported in Figure 6C in the revised data and discussion in the manuscript.

3.4. HuR competes with many RBPs (e.g. TTP and KSRP) as well as microRNAs (including miR-21 and miR-122) to regulate the stability/translational efficiency of several AU-rich transcripts. Does TM binding to HuR lead to increase access of these RBPs/microRNA to the transcripts? This could be addressed by RNA IP with antibodies to TTP or KSRP.

R3.4 – The reviewer is suggesting an important experiment that requires multiple controls and significant efforts. Indeed, we are planning to study the specificity of TMs, and we prefer to tackle and report this point in a later publication.

3.5. Another aspect of HuR functioning is the dimerization of HuR. HuR dimerization has been linked with many pathophysiologic conditions. The authors should show the effect of TM7nox on HuR dimerization. In figure 2, for example, there is a suggestion of this in the representative EMSAs where an intermediate shifted band is seen with the addition of TMs. Also, the legend should make clear which ligand is being tested in the modeling (purple structure) versus the RNA probe in the EMSAs. It would help the reader to identify the RNA probe used-e.g. "5′-DY681-labeled ARE RNA probe.

R3.5 – We agree with the reviewer’s suggestion, and we investigated whether TM7nox influences HuR dimerization in the absence of RNA as performed in PMID 17632515 (Meisner et al 2007). We used MS-444 as a positive control, and we did not observe inhibition of dimerization by TMs at least at the used dosages. Data are reported in Supplementary Figure S6B of the revised manuscript.

3.6. HuR does alter M2-associated targets like IL-10 and this should be addressed more directly. Fig. 3 suggests that IL-10 is reduced by TM7nox but the variance is so high that the statistics show NS. HuR regulates IL-10 in other cellular contexts and this would be important to determine for TM7 in the long run.

R3.6 – Although we acknowledge its relevance, however, we did not investigate this gene directly. The variance becomes significant in the RIP-seq experiment (Supplementary Figure 9D). Therefore, we confirm that Il10 is among the 47/82 genes that show the same behavior as Cxcl10, Il1b and many other cytokines as Ccl12, Ccl7, Fas, Il1a, Il33; in conclusion, it is among the restricted list of genes modulated by TM7nox according to the presence of less AU rich sequences than average.

3.7. Fig. 5-10 um of the TM used here produces significant toxicity to BMDM according to fig. S7. This may distort the ELISA/qPCR results as the RNA levels may be lower due to toxicity. The authors should address this or use a lower dose that is not toxic.

R3.7 – The viability curves mentioned by the reviewer are run at 24-48 hours, and no toxic effects have been observed using TMs after 6 hours of treatment.

3.8. In Fig 6 the immunocytochemistry is difficult to interpret as the magnification is too small to appreciate the N/C ratio. The investigators should provide higher magnification. A nuclear/cytoplasmic western blot is recommended as well to confirm that TM does not impair HuR shuttling (or NFkb shifts). This is an important area as there is a suggestion that TM blocks dimerization (Fig. 2) which does impair shuttling. Also, the modeling data suggest that TMs appear to sit in a similar groove between RRM1 and 2 as other HuR inhbitors that block shuttling.

R3.8 – This point has also been raised by other reviewers, and we replied in R2.3 and R1.11. We understand the reviewer’s points, and we agree with the observation. However, we do not observe a change in HuR nuclear/cytoplasmic shuttling by immunofluorescence, neither we see an effect on HuR dimerization.

3.9. IL-6 does not appear to be affected by TM treatment after LPS stimulation in vivo or in vitro -either mRNA or protein. However, DHTS did suppress this cytokine. The authors should address this discrepancy. Likewise, TNFa data here show no change and possibly a trend upward (Fig 3,4 and 7). This is in contrast to the effect of DHTS on TNF-a reported by the authors in a prior publication (D'Agnistino et al). The authors should address this discrepancy. There are reports suggesting that HuR is a translational inhibitor of TNFa in macrophages--Katsanou V, Papadaki O, Milatos S, Blackshear PJ, Anderson P, Kollias G, Kontoyiannis DL. HuR as a negative posttranscriptional modulator in inflammation (PMID 16168373)

R3.9 – The reviewer’s comments are correct, but we do not have an explanation for this. In theory, there could be several possibilities such as 1) a DHTS effect on NFkB, 2) the fact that previously mentioned experiments with DHTS are not run with the same cells-at the same doses and timing as our current TM experiments, and 3) that HuR silencing is only partially overlapping with TMs treatment also in our recent experiments. Irrespective of specific transcripts, we think we have shown that TMs’ mechanism of action involves the modulation of HuR binding at the transcriptional level in our experimental condition.

Review Cross-commenting

I think the other reviewers' comments are pertinent and well thought out. I have no further suggestions.

Reviewer #3 (Significance):

The characterization of novel HuR inhibitors derived from tanshinones is an important advancement to the field which is rapidly growing. This complements other work with small molecule inhibitors and will allow the field to better understand the role of HuR in different disease contexts (cancer, neuroinflammatory etc) and cell types (e.g. macrophages, microglia, astrocytes). The ultimate significance is the clinical application of the inhibitors and the more options the better, particularly if there are toxic effects of some. My expertise is in post-trasnscriptional regulation of cytokines and we have already characterized some potent effects in cancer.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

---

Referee #3

Evidence, reproducibility and clarity

In this study, the authors investigated the modulation of HuR by tanshinone mimics and how it mitigates LPS response in murine macrophages. This represents a nice integration of synthetic chemistry, molecular simulations, and in vitro as well as in vivo experimental validations. Overall, this is an interesting study, and will add to the growing interest in HuR in inflammatory-mediated disease. The paper contains a lot of data (actually several papers in one) which may bog down the reader and distract from the overall message. it is suggested that they condense the data and simplify the figures and use more supplemental figures.

Major Comments:

1. The authors have shown the dose response and cytotoxicity effect of tanshinone mimics; The authors show that TMs affect the overall HuR mRNA but they don't show protein levels.
2. It is unclear the timing of certain experiments for LPS vs TMs (whether macrophages were pre-treated with TMs before LPS)-e.g fig 5. The authors should clarify for all experiments as the long-term clinical paradigm would be treatment after inflammation has been established.
3. They have also identified differentially expressed genes which are RNA binding ligands of HuR by RIP-Seq. However, it would be necessary to check whether TM7nox affects the stability of those targets before conclusions can be made that TMs don't inhibit the primary transcriptional response (as mentioned in the Discussion section). Transcriptional effects of HUR chemical inhbiition or genetic silencing has been reported previously inother cell systems.
4. HuR competes with many RBPs (e.g. TTP and KSRP) as well as microRNAs (including miR-21 and miR-122) to regulate the stability/translational efficiency of several AU-rich transcripts. Does TM binding to HuR lead to increase access of these RBPs/microRNA to the transcripts? This could be addressed by RNA IP with antibodies to TTP or KSRP.
5. Another aspect of HuR functioning is the dimerization of HuR. HuR dimerization has been linked with many pathophysiologic conditions. The authors should show the effect of TM7nox on HuR dimerization. In figure 2, for example, there is a suggestion of this in the representative EMSAs where an intermediate shifted band is seen with the addition of TMs. Also, the legend should make clear which ligand is being tested in the modeling (purple structure) versus the RNA probe in the EMSAs. It would help the reader to identify the RNA probe used-e.g. "5′-DY681-labeled ARE RNA probe.
6. HuR does alter M2-associated targets like IL-10 and this should be addressed more directly. Fig. 3 suggests that IL-10 is reduced by TM7nox but the variance is so high that the statistics show NS. HuR regulates IL-10 in other cellular contexts and this would be important to determine for TM7 in the long run.
7. Fig. 5-10 um of the TM used here produces significant toxicity to BMDM according to fig. S7. This may distort the ELISA/qPCR results as the RNA levels may be lower due to toxicity.The authors should address this or use a lower dose that is not toxic.
8. In Fig 6 the immunocytochemistry is difficult to interpret as the magnification is too small to appreciate the N/C ratio. The investigators should provide higher magnification and provide examples of ActD, LPS and LPS + drug. A nuclear/cytoplasmic western blot is recommended as well to confirm that TM does not impair HuR shuttling (or NFkb shifts). This is an important area as there is a suggestion that TM blocks dimerization (Fig. 2) which does impair shuttling. Also, the modeling data suggest that TMs appear to sit in a similar groove between RRM1 and 2 as other HuR inhbitors that block shuttling.
9. IL-6 does not appear to be affected by TM treatment after LPS stimulation in vivo or in vitro -either mRNA or protein. However, DHTS did suppress this cytokine. The authors should address this discrepancy. Llikewise, TNFa data here show no change and possibly a trend upward (Fig 3,4 and 7). This is in contrast to the effect of DHTS on TNF-a reported by the authors in a prior publication (D'Agnistino et al). The authors should address this discrepancy. There are reports suggesting that HuR is a translational inhbitor of TNFa in macrophages--Katsanou V, Papadaki O, Milatos S, Blackshear PJ, Anderson P, Kollias G, Kontoyiannis DL. HuR as a negative posttranscriptional modulator in inflammation (PMID 16168373)

Review Cross-commenting

I think the other reviewers' comments are pertinent and well thought out. I have no further suggestions.

Significance

The characterization of novel HuR inhibitors derived from tanshinones is an important advancement to the field which is rapidly growing. This complements other work with small molecule inhibitors and will allow the field to better understand the role of HuR in different disease contexts (cancer, neuroinflammatory etc) and cell types (e.g. macrophages, microglia, astrocytes). The ultimate significance is the clinical application of the inhibitors and the more options the better, particularly if there are toxic effects of some. My expertise is in post-trasnscriptional regulation of cytokines and we have already characterized some potent effects in cancer.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

---

Referee #2

Evidence, reproducibility and clarity

In the manuscript entitled "HuR modulation with tanshinone mimics impairs LPS response in murine<br /> macrophages" the authors have described the synthesis and application of small molecule mimics of the naturally occurring compound tanshinone, which is known to inhibit the binding of the RBP HuR to a class of its mRNA targets. The authors have shown that the tanshinone mimics (TMs) used by them block the binding of RRM1-2 of HuR to ARE-containing RNA in vitro, and reduce the interaction of HuR with a set of ARE-containing mRNAs in LPS-treated mouse macrophage cells. This reduction of interaction of HuR with some of these mRNAs correlates with the reduction in their level in the cells treated with the TMs, and in the secreted level of their proteins in the serum of animals with LPS-induced peritonitis. Together, the study demonstrates the role of these TMs as modulators of the LPS-induced inflammatory response by blocking the binding of HuR to a subset of LPS-induced inflammatory mRNAs and thereby downregulating their mRNA and protein levels in inflammatory cells.

The manuscript describes a comprehensive study, ranging from chemical synthesis of TMs, MD simulations to demonstrate the binding site of the TMs to the cleft formed by the RRM1-linker-RRM2 domains of HuR, which has been shown in crystal structure to be the main binding site of A/U-rich RNA molecules, in vitro studies showing the ability of the TMs to hinder ARE-containing RNA binding to HuR RRM1-2, whole transcriptome analysis to show the effect of the TMs on LPS-induced differential gene expression in murine macrophages, and on HuR binding to target mRNAs, and animal studies to show the effect of the TMs on the level of some inflammatory mediators in the serum of mice with LPS-induced peritonitis. The results are quite convincing and is in line with what is generally known about the effect of HuR on the expression of a large number of genes encoding pro-inflammatory proteins, and the ability of tanshinone derivatives/mimics in inhibiting HuR binding to target mRNAs. The authors put these two information together in this study and the results are on expected lines. While the results are convincing and quite comprehensive, I would suggest the following in order to substantiate and strengthen the results:

1. The experiments do not have any "positive control", such that the performance of the TMs can be compared with that of a molecule with known HuR binding inhibition activity, such as DHTS. It would be good to have such a comparison, to understand whether the TMs work similar to DHTS or differently, both qualitatively in terms of the mRNA targets which they affect and the extent of their anti-inflammatory activity.
2. It is not clear to me whether the mRNAs which show differential expression in the RNAseq analysis of cells treated with LPS and TMs are exactly the ones which show difference in binding with HuR in the RIPseq analysis in presence of the TMs. This analysis is important for a number of reasons: all the mRNA binding targets of HuR are not affected by HuR at the level of mRNA stability, many are affected at the level of translation, without change in mRNA level. These mRNAs should therefore show change in binding of HuR in the RIPseq assay in presence of TM, but not show change in expression. Secondly, there may be mRNAs which show a change in expression in presence of TMs, but do not show binding of HuR, suggesting pleiotropic roles of the TMs. Therefore, instead of an overall correlation between differential expression and change in HuR binding of mRNAs, a table comparing the RIPseq status of individual mRNAs with that of their differential expression status, in presence and absence of LPS/TMs would be useful, further designating the different groups of mRNAs based on these differential status (change in HuR binding/change in expression, change in HuR binding/no change in expression etc.).
3. Nuclear/cytoplasmic localization of HuR and NFkb is impossible to discern at the magnification of the immunofluorescence images in Fig 6 B and C. Higher magnification images are required to understand changes in localization.
4. It has been shown that DHTS-I increases the binding of HuR to the mRNAs with longer 3'UTR and with higher density of U/AU-rich elements, whereas it reduces the interaction of HuR with the mRNAs having shorter 3'UTR and with low density of U/AU-rich elements (Lal et al., NAR, 2017). It is not clear if the same is observed in case of the TMs or not, and such a comparative analysis would be useful to address this point.

I think that the above suggested points are feasible as most of them really involve re-analysis of existing data. Only the suggestion to add DHTS or tanshinone as a positive/comparison control will require experimentation and addition of new data.

Review Cross-commenting

I think most of the reviewers' comments coincide in the evaluation of the manuscript. I would especially like to draw attention to the fact that all three reviewers found that the content and form of data presented in the paper is very dense and bogs down the reader and distracts from the overall focus of the manuscript.

Significance

The work described in the manuscript is comprehensive as it ranges from chemical synthesis and in vitro evaluation of the TMs to the characterization of their effects in vivo. Although the effect of tanshinone derivatives on HuR mRNA target binding is known, and the effect of HuR on inflammatory gene expression is also known, the manuscript is significant as it brings these two information together and tests the effect of these TMs on HuR-mediated regulation of inflammatory gene expression.<br /> However the extensiveness of the work also makes it quite dense, and I feel that the focus of the paper is often lost in the details. Also, the text of the manuscript is dense and verbose and uses many irregular grammatical and phraseological usages, for eg "their<br /> modulation or mis-localization lead to the insurgence of complex phenotypes and diseases". It appears to me that it would be ideal if the chemical synthesis, MD simulation studies and in vitro studies are presented in an independent manuscript. Also, that would allow a more exhaustive referencing of the known studies in literature where tanshinone derivatives, and other small molecules, have been used to modulate HuR binding to mRNA targets.<br /> This work would be of interest to molecular cell biologists in general and RNA biologists in particular, especially those who are studying RNA-protein interactions, and scientists who are interested in drug development using RNA-protein interactions as drug targets.<br /> My interest in the work lies in my expertise in studying RNA-protein interactions, especially of RNA-binding proteins such as HuR involved in regulating the translation of mRNAs encoded by inflammatory genes. I do not have expertise in chemical synthesis and am therefore not qualified to evaluate the first set of results describing the chemical synthesis of TMs.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

---

Referee #1

Evidence, reproducibility and clarity

Summary:

The Authors report on the synthesis and characterization of a class of small molecules, the tanshinone mimics (TMs), which interfere with binding of the RNA binding protein (RBP) HuR to its mRNA targets. HuR is an important regulator of mRNA stability and translation of genes involved in key homeostatic (cell cycle, stress response) and pathologic process (inflammation, carcinogenesis). In particular, the first part of the study describes the compounds' chemical synthesis and some pharmacokinetic parameters (i.e., definition of molecular binding, solubility, bioavailability, prodrug generation etc). The second part undertakes, in in vitro and ex-vivo model of LPS-induced mouse macrophage activation, the identification of HuR-bound mRNA targets, which is then evaluated within the global LPS-induced transcriptome; finally, the study evaluates the ability of TMs to inhibit HuR-mediated proinflammatory gene regulation, indicating their use and potential value as therapeutic anti-inflammatory strategy.

Major comments:

The manuscript contains a wealth of data generated from different experimental systems, spanning from synthetic chemistry to preclinical models of gene regulation, requiring cultural backgrounds in chemistry and biology as well. The key conclusions are well supported by the data, but it takes a great effort to get to the core results and thus critically read and evaluate their interpretation. Although the complexity and sheer size of data sets generated lends itself to a hard read, this is further complicated by data presentation, which especially in the second part needs to be significantly improved to gain clarity and focus. For ease of referral, specific comments will be addressed related to Figures whenever possible.

• Page 15: To measure TM7nox disrupting ability of HuR:mRNA complex for the HTRF assay (Figure 2G) and for biotin pull down assay (Figure 5C), it was chosen a biotinylated probe containing the AU rich elements of the TNFα, as known HuR target. Please comment on the rationale, and whether could it be relevant reevaluate these parameters post-hoc, based on the sequences identified in HuR targets more susceptible of modulation by TM compound (listed in table 1, Figure 5 A/B) and based on the absence of regulation of TNF (Figures 3D, 4D, 7A) found in the tested systems.
• Page 16-18: Description of the RNAseq data shown in Figure 3 should be more centered around the main findings regarding the effect of TMnox that are further pursued in the study: that is, (Figure 3B), the 249 downregulated DEGs found modulated by TM7nox in presence of LPS, where was observed a strong enrichment of categories related to the inflammatory response: cytokines (Il1b, Cxcl10, Il10, Il19, Il33), immune cell chemotaxis (Ccl12, Ccl22, Ccl17, Ccl6) and innate immune response. The description of the GO for the remaining data should be shortened to main points, perhaps reporting what described in the results with each section of the Venn in a table, while referring to the whole list in the supplements as already done. This could replace Figures 3D, E which do not add substantially to what provided in the supplementary table 2 and to which they can be added as further visualization.
• Page 18-19: Description of the results of the RIP-seq shown in Figure 4 set is very confusing: onward from the line "477 HuR-bound transcripts (log2 FC > 3) were also upregulated by LPS at the transcriptional level..." the numbers do not match or reconcile with those shown in the Venn diagram (Fig. 4B) nor with those listed in the figure legend of Figure S8. Moreover, as previously remarked for Figure 3 (and even more for this dataset in which initial description of Venn in 4B is unclear), panel 4E does not add as much to the info provided in Table 1/supplementary Table 1, where they can eventually be added as further data visualization; Instead, Figure S8 displays very informative data merging together the results obtained in RNAseq (Fig. 3) and RIP-Seq (Fig.4) and should be displayed in Figure 4, as in the result section they are indeed presented together.
• Page 19-20: Description of the modulation by TM7nox of HuR binding to specific consensus sequences is summarized at the end of the relative paragraph as follows: "TM7nox reshapes HuR binding to target genes in presence of LPS by disrupting the binding of HuR towards target genes containing a lower number of HuR consensus sequences than the average observed in the HuR-bound transcripts". Understanding of these data through the provided text and the Supplementary Figure 9 is very laborious and referring of an entire dataset to a supplementary figure makes it even harder. It would be best to show this as main figure, not supplemental, either adding a Venn diagram as in 3B/4B showing to which dataset each part of the analysis refers, or even more efficaciously, extrapolate a representative gene set for the main analyses showing TM7nox activity in target genes with higher vs lower consensus sequences; same approach for the analysis in Figure 9B, where the effect on a gene with sequence #1 or #10 could be compared with one bearing sequence #3 for example.
• Page 21: Description of the effect of three TMs (TM6, TM7nox and TM7nred) on LPS response in macrophages at the single gene level (Figure 5 and Figure 6): TM6 and TM7nox were used in exps in Fig. 5 A and E, while only TM7nred was used for CXCL10 secretion analysis (fig.5 D and F): please describe the compound choices' rationale (as done for experiments in Figure 6).
• Page 21-22: The effect on HuR expression of siRNA silencing and, more importantly, of TMs shown in Figure 6A, first panel, should be visualized at protein level by western blot. This is an important point as for CXCL10 and iL1there seems to be an additive effect between decreased HuR levels and pharmacological blocking.
• Page 24: please rephrase the statement 'These observations suggest the utilization of TMs in autoinflammatory and autoimmune diseases' as 'These observations suggest the evaluation of TMs in specific preclinical models for autoinflammatory and autoimmune diseases'.
• In the discussion, please include a paragraph with study limitation and possible biases (for example, the choice of RNP-IP without crosslinking has pros and cons).
• The data and the methods are correctly presented for reproducibility, replicates and statistical analysis are adequate.

Minor comments:

• At least in the single gene validation experiments (Fig.5), a negative control (such as recombinant HuR with mutated RRMs in trans-, or ARE-less/non-HuR targetable sequence in cis, or inactive TM) would be advisable.
• Figure 6B/C: for immunofluorescence panels, zooming on a smaller number of cells will render more visible HuR and NFB nucleocytoplasmic shuttling, given that quantification and statistics are provided by imaging software. Negative control stainings (secondary Abs only) should be included.
• Figure 7A: in the X axis LPS+8n is indicated: is it a typo for LPD+6n or was compound TM8n indeed used?
• In the Methods section please include protocols and materials for immunofluorescence (results shown in Fig. 6B/C).
• There are some typos and repetition in figure legends (legend Figure S9).
• Prior studies are referenced appropriately.

Review Cross-commenting

I fully agree with the Reviewer's remarks. I would add that a general concern expressed is that this manuscript in its present form has a readership issue: the first part is for chemistry/pharmacology audience, the second is biology-based. Splitting the work has been suggested; or, the Authors may decide which part is more impactful and present the other in a streamlined version.

Significance

This is a large study reporting progress in the development of synthetic antagonists of HuR function, which is the Authors' well-established line of research. The TM compounds are small molecules with anti-inflammatory effects with strong potential for therapeutic use due to selected inhibition of HuR-mediated upregulation of proinflammatory molecules. The physicochemical and early biological characterization done in this study will allow further testing of their efficacy and of the overall role of HuR-mediated regulation as targetable mechanism in several preclinical human disease models.

Targeting of the RNA-binding protein HuR has been tackled as therapeutic approach in cancer, less in chronic immune and inflammatory diseases despite many common mechanisms and mediators.

This study could be well received by researchers involved in basic science and drug development (chemistry, biochemistry/biophysics, pharmacology, computational modeling) and biologists/physician scientists interested in testing these compounds in translational research settings where HuR-driven functions can be relevant (cancer, chronic inflammation), though the chemical part would be less accessible to the latter audience.

Reviewer's background is in preclinical human models of chronic inflammation with interest in posttranscriptional gene regulation with familiarity with RNAseq and RIPseq dataset and analysis. For the part of the manuscript regarding the synthesis and physicochemical characterization of the TN compound I requested assistance to a faculty from the chemistry department with expertise in that field, who did not request any specific clarification or addendum.

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

Learn more at Review Commons

---

Reply to the reviewers

1. General Statements

Imbalance in gut-derived AhR ligands has been shown to be involved in inflammatory bowel disease and in neuro-inflammation. The aim of our study was to address the role of dietary AhR ligands in a context that had not been previously explored. We decided to focus on allergy because AhR has broad functions in barrier tissues homeostasis, which is directly relevant to allergy.

2. Description of the planned revisions

Additional experiments in response to Reviewer 2

"The authors make a strong claim that the epidermal barrier function is not affected by AhR poor diet conditions (claim made in abstract and last paragraph of the discussion). This should be experimentally validated."

We already performed footpad histology and observed that the stratum corneum is not affected by the diet (fig1A and figS1E). We will provide a quantitative analysis by measuring stratum corneum thickness on the images, and add this data to figure 1. To strengthen this point, we will also perform ultra-structural analysis of the epidermis in the two diet groups using electron microscopy of the skin. This will provide a deeper characterization of the epidermal structure, including cornified layers and intercellular tight junctions.

"Injection into the footpad as a route of administration is also physiologically distinct from epicutaneous sensitization given the natural barriers are artificially breached via needle puncture. Did the authors consider epicutaneous sensitization via the skin without additional barrier disruption? Does this yield the same response?"

We will perform skin sensitization without barrier disruption by applying papain (or vehicle) on shaved flank skin. To minimize skin abrasion, mice will be shaved the day before the application. We will analyze dendritic cells migration to the draining lymph nodes after 48h by flow cytometry, and helper T cell responses in the draining lymph nodes after 6 days by measuring cytokine secretion.

Text edits

Comments from Reviewer 1

• We will add appropriate references in response to comments from reviewer 1: " in several places they cited review articles instead of original articles for key findings. Ex. For the expression of Mucin 5 and CLCA1 a review is cited." and " the role of AHR in ILC2 (PMID: 30446384) and alveolar epithelial cells (PMID: 35935956) has been documented. The authors should add these references."
• We will modify the figures legends according to reviewer 1's suggestions: " Although the authors mentioned treatment schedule and stimulants used in the method, a short description in the figure legend will be helpful for the readers".
• We will address other comments from reviewer 1 by modifying the text where appropriate:

"1. In the introduction section, the authors should explain adequately why they thought that AHR signaling is important for allergy.<br /> 2. Since IL-5, IL-13 production by skin draining lymph nodes and pulmonary lymph nodes was different, is this difference due to difference in AHR expression?<br /> 3. In Fig.3, the authors showed that intra-nasal stimulation does not induce eosinophil migration or IL-5, IL-13 induction in I3C diet group. These data and the data shown in figure-2 are in contrast. The authors should discuss this discrepancy."

Comments from Reviewer 2

• "How to explain the difference between IL4 (no effect between the two diets/or absence/presence LCs in Fig. 4D) and IL5/IL13 (small effect in Fig 1D and 4D). "

This is an interesting point. It has been shown that IL4 is produced in lymph nodes by T cells distinct from those producing IL5 and IL13 (https://doi.org/10.1038/ni.2182). In addition, IL4 expression is regulated at the transcriptional level by distinct mechanisms from IL5 and IL13 expression (https://doi.org/10.1016/S1074-7613(00)80073-4, https://doi.org/10.1038/ni.1966).<br /> We speculate that IL4-producing T cells are not affected by Langerhans cells presence in the lymph nodes. We will add a point in the discussion section to discuss this. - We will tune down our conclusion regarding the different effects of diet-derived and microbiota-derived AhR ligands according to the comments of the reviewer: "This part seemed far-fetched. There are many more differences between germ free and specific pathogen free mice than only the presence/absence of AhR ligands. Hence, it seemed like a very big step to compare both conditions and draw the conclusion that microbiota-derived AhR ligands activate different sets of genes. It would also make more sense if Fig. 5 would be immediately followed by Fig. 7". We also propose to move Fig6 to the supplementary data.

3. Description of the revisions that have already been incorporated in the transferred manuscript

4. Description of analyses that authors prefer not to carry out

Comments from Reviewer 1

"In Fig.4, the authors show there is no difference in total number, but difference in migration, was there a difference in expression of migratory markers?"

We assume the reviewer is referring to the number of Langerhans cells in the epidermis in steady-state, which is not different between diets (fig4A). We actually already show in supplementary figure S3E classical cell surface markers that are upregulated upon dendritic cells migration (MHC class II and CD40). We found no difference in the expression of these markers between diet groups.

Comments from Reviewer 2

"Fig. 1D Cytokine production<br /> In AhR poor diet the spread between the individual data points is much larger and the difference between presence/absence of dietary ligands in IL5 and IL13 seems to be based merely on a few outliers (which especially in the case of IL13 appear to be completely out of range). Most other datapoints do not seem to be highly different from the ones in the AhR rich diet.<br /> Where does this high variation come from in AhR poor diet (and what is the reason for these high outliers)? Would the data have been significantly different without the outliers? "

Throughout the manuscript, we have represented raw data and individual data points for transparency. We observed some variability between biological replicates, not just for cytokine secretion (fig1D) but in most assays (for instance cell counts in lymph nodes in fig1C or inflammatory cell counts in fig2A and fig3A or antibody production in fig2E), yet the reviewer focuses their comments on fig1D. In the case of fig1D, we have performed Kruskal-Wallis statistical tests to account for this variation, and the difference between diet groups was statistically significant. We do not understand how we could remove the so-called ‘outliers’ without data manipulation to perform an alternative statistical test. We also disagree with the reviewer that 4 out of 11 points can be considered ‘outliers’.

"In general, increases of all canonical T-helper cytokine responses (except for IL4) can be noted in the LN and the difference in IL10, IL17 or IFNg production between AhR poor and rich diet appears even more pronounced than the difference in IL5/IL13 (Fig. S1F). Still the authors decide to focus the entire story on the allergic response after stating that a 'lack of dietary AhR ligands amplifies allergic responses'. Why was this choice made?"

Imbalance in gut-derived AhR ligands has been shown to be involved in inflammatory bowel disease and in neuro-inflammation. The aim of the project was to address the role of dietary AhR ligands in a context that had not been previously explored. We decided to focus on allergy because AhR has broad functions in barrier tissues homeostasis, which is directly relevant to allergy. We will better explain this point in the introduction. In the course of the study, we analyzed IL10, IL17 and IFNg production by lymph node T cells to get a complete view of helper responses, and we provided this data in supplementary information for transparency. We believe this information might be useful for other groups studying other types of skin inflammation.

"Would the authors expect other inflammatory models via the skin (e.g. bacterial, viral infection) to confer better/worse outcomes under an AhR poor diet?"

This is an interesting question. Unfortunately, we do not have the means to analyze bacterial or viral skin infections for lack of adequate facilities (i.e. BSL2 animal facility) or ethics approval for this kind of experiments. We believe that our work may prompt in the future other groups to analyze the impact of dietary AhR ligands in other inflammatory skin contexts.

"At a mechanistic level, how do LC suppress the activation of T cells in the LN, and how would this impact secretion of certain cytokines but not others?"

"it remains a bit speculative how migration of LCs to the dLNs of the skin contributes to suppressing Th2 immunity in the airways. Several hypotheses have been put forward in the discussion. What is their thought about this and how to validate experimentally?"

This is an important question. A regulatory role for Langerhans cells has been evidenced by other studies, but the molecular mechanisms involved remain elusive. This point is discussed in the discussion part of the manuscript. We believe that deciphering the mechanism of action of Langerhans cells is beyond the scope of the present study (and is unrelated to the direct effect of the diet), and would represent an entire project in itself.

“Fig. 3 - Why would the alteration of diet pose a confounding factor to the model? Did the authors determine that such diet-associated changes are only important at the sensitization phase? The footpad (Fig. 1) and pulmonary (Fig 2) models were performed with the altered diets throughout the entire length of the experiment. If anything, wouldn't changing the diet after sensitization also provide an additional variable here? Is it known what happens (e.g. inflammatory state, genetic changes) when a normal diet is resumed after a period of adaptation? This reviewer does not understand the reason for all-of-a-sudden changing the diet after the sensitization phase.”

Our goal with this experiment was to address the effect of the dietary AhR ligands during the skin sensitization phase only. This is why diets are different only in this phase of the protocol. We want to emphasize that the IC3 diet and the AhR-poor diet only differ in the presence of one molecule, which is I3C. The composition of the food is otherwise exactly the same, therefore we do not believe that a change between AhR-poor and I3C would represent a confounding factor. This is different to the adaptation period when we place the mice on I3C or AhR-poor diets instead of normal chow diet (which has a completely different formulation). We will make this point clearer in the text.

"Fig. 7 Role of TGFb<br /> At first site, it seems counterintuitive that TGFb, which is a molecule generally associated with homeostasis and dampening of inflammation, is associated here with more profound inflammation. How to reconcile? At this point the data on TGFb are merely correlative. Did the authors directly test the contribution of TGFb to LC migration? In addition, did they check whether they could restore defects in LC migration in absence of AhR ligands by blocking the formation of active TGFb? "

We agree with the reviewer that the role of TGFb seems counter-intuitive. However, multiple studies have shown that TGFb produced by keratinocytes retains Langerhans cells in the epidermis, using a variety of experimental approaches including genetic tools (https://doi.org/10.1073/pnas.1119178109, https://doi.org/10.1038/ni.3396,

https://doi.org/10.4049/jimmunol.1000981, https://doi.org/10.1016/j.xjidi.2021.100028). We do not have any reason to doubt the validity of these studies. Therefore, we believe that demonstrating again the role of TGFb in Langerhans cells migration is not necessary.

Using blocking antibodies against TGFb or its receptor, as suggested by the reviewer, would most probably not allow us to address whether it restores the defect in Langerhans cells migration. Indeed, results from the literature (cited above) indicate that such blocking would increase Langerhans cells migration in both diet groups, therefore it will most likely be impossible to conclude.

In addition, we have provided several lines of evidence that AhR activation regulates the expression of Integrin-beta8 in keratinocytes and the release of bioactive TGFb, at transcriptomic and protein levels, in both mouse and human keratinocytes (fig7). Therefore, we believe that additional experiments to support the link between AhR ligands and TGFb are not necessary within the scope of the revision.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

---

Referee #2

Evidence, reproducibility and clarity

In this paper Cros et al describe how the absence of dietary ligands of AhR exacerbate cutaneous papain-induced allergy. This was only observed when papain was applied topically, but not intranasally. However, lack of dietary AhR ligands also worsened allergic airway inflammation after cutaneous sensitization. At a mechanistic level, the authors found that the absence of dietary AhR ligands hampered migration of Langerhans cells (LC) to the lymph nodes, where they are claimed to be needed to suppress T cell activation. Complementary models that lead to loss of LCs gave a similar phenotype. The authors performed RNA-sequencing on epidermal cells derived from mice that were either fed an AhR ligand rich or poor diet to define differences in transcriptome signature. They uncovered increased expression of the integrin Itgb8 in absence of AhR ligands, which is needed for production of active TGF, a factor known from literature to contribute to LC retention in the skin.

In general, the study is well done, and the different experimental conditions are well controlled for. The experiments are built up in a logical fashion, and most of the times, the interpretation is appropriate (except for a few claims, see further). The paper reads very fluently, and the key points are interesting.

Major comments:

• Fig. 1D Cytokine production
• In AhR poor diet the spread between the individual data points is much larger and the difference between presence/absence of dietary ligands in IL5 and IL13 seems to be based merely on a few outliers (which especially in the case of IL13 appear to be completely out of range). Most other datapoints do not seem to be highly different from the ones in the AhR rich diet.<br /> Where does this high variation come from in AhR poor diet (and what is the reason for these high outliers)? Would the data have been significantly different without the outliers?<br /> How to explain the difference between IL4 (no effect between the two diets/or absence/presence LCs in Fig. 4D) and IL5/IL13 (small effect in Fig 1D and 4D).
• In general, increases of all canonical T-helper cytokine responses (except for IL4) can be noted in the LN and the difference in IL10, IL17 or IFNg production between AhR poor and rich diet appears even more pronounced than the difference in IL5/IL13 (Fig. S1F). Still the authors decide to focus the entire story on the allergic response after stating that a 'lack of dietary AhR ligands amplifies allergic responses'. Why was this choice made?<br /> Would the authors expect other inflammatory models via the skin (e.g. bacterial, viral infection) to confer better/worse outcomes under an AhR poor diet?<br /> At a mechanistic level, how do LC suppress the activation of T cells in the LN, and how would this impact secretion of certain cytokines but not others?

Fig. 3 - Why would the alteration of diet pose a confounding factor to the model? Did the authors determine that such diet-associated changes are only important at the sensitization phase? The footpad (Fig. 1) and pulmonary (Fig 2) models were performed with the altered diets throughout the entire length of the experiment. If anything, wouldn't changing the diet after sensitization also provide an additional variable here? Is it known what happens (e.g. inflammatory state, genetic changes) when a normal diet is resumed after a period of adaptation? This reviewer does not understand the reason for all-of-a-sudden changing the diet after the sensitization phase.<br /> - Fig. 6: Microbiota-derived and diet-derived AhR ligands modulate different sets of epidermal genes.<br /> This part seemed far-fetched. There are many more differences between germ free and specific pathogen free mice than only the presence/absence of AhR ligands. Hence, it seemed like a very big step to compare both conditions and draw the conclusion that microbiota-derived AhR ligands activate different sets of genes.<br /> It would also make more sense if Fig. 5 would be immediately followed by Fig. 7<br /> - The authors make a strong claim that the epidermal barrier function is not affected by AhR poor diet conditions (claim made in abstract and last paragraph of the discussion). This should be experimentally validated. Injection into the footpad as a route of administration is also physiologically distinct from epicutaneous sensitization given the natural barriers are artificially breached via needle puncture. Did the authors consider epicutaneous sensitization via the skin without additional barrier disruption? Does this yield the same response?

Fig. 7 Role of TGFb<br /> - At first site, it seems counterintuitive that TGFb, which is a molecule generally associated with homeostasis and dampening of inflammation, is associated here with more profound inflammation. How to reconcile? At this point the data on TGFb are merely correlative. Did the authors directly test the contribution of TGFb to LC migration? In addition, did they check whether they could restore defects in LC migration in absence of AhR ligands by blocking the formation of active TGFb?

Finally, also other steps of the proposed model by the authors are based on literature rather than direct experiments. In this regard, it remains a bit speculative how migration of LCs to the dLNs of the skin contributes to suppressing Th2 immunity in the airways. Several hypotheses have been put forward in the discussion. What is their thought about this and how to validate experimentally?

Significance

The major strength of the paper (and the most interesting finding) is the explanation of why the effect of the diet is only detectable after cutaneous but not intranasal sensitisation and the causal link to the LCs present in the skin.

The major limitations of the paper is that many parts of the proposed model are not experimentally validated but based on literature suggestions (eg the claim that TGFb would prevent LC migration to LN, that LC would suppress T cell responses in the LN, that the suppression of T cell cytokines (with very limited effects on IL5 and IL13 but no effect on IL4) would be sufficient to explain improved allergy symptoms in the lung...). It is also unclear why the authors studied allergic symptoms while effects on other T cell cytokines appeared more prominent. There are a few questions on the change in model from figure 1-2 to figure 3.

The key findings are interesting and the paper is nice to read.<br /> The findings will be interesting to specialised audience: LC biology, allergy and Th2 immunity people<br /> Own research field, dendritic cell biology and papain-induced models of allergy

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

---

Referee #1

Evidence, reproducibility and clarity

The manuscript is well written. The authors mostly cited appropriate papers but in several places they cited review articles instead of original articles for key findings. Ex. For the expression of Mucin 5 and CLCA1 a review is cited.

General comments

1. the role of AHR in ILC2 (PMID: 30446384) and alveolar epithelial cells (PMID: 35935956) has been documented. The authors should add these references.
2. Although the authors mentioned treatment schedule and stimulants used in the method, a short description in the figure legend will be helpful for the readers.

Specific comments

1. In the introduction section, the authors should explain adequately why they thought that AHR signaling is important for allergy.
2. Since IL-5, IL-13 production by skin draining lymph nodes and pulmonary lymph nodes was different, is this difference due to difference in AHR expression?
3. In Fig.3, the authors showed that intra-nasal stimulation does not induce eosinophil migration or IL-5, IL-13 induction in I3C diet group. These data and the data shown in figure-2 are in contrast. The authors should discuss this discrepancy.
4. In Fig.4, the authors show there is no difference in total number, but difference in migration, was there a difference in expression of migratory markers?

Minor points

1. TGF-β, TCR-β and cytokine names should be written consistently across the manuscript.
2. The authors should use "β" instead of beta

Significance

The work is significant and will impact the field

Transparent Peer Review

---

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

</br>

Transparent Peer Review

---

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

</br>

Transparent Peer Review

---

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

</br>

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

Learn more at Review Commons

---

Reply to the reviewers

Reviewer #1 (Evidence, reproducibility and clarity (Required)):

Summary:

The submitted manuscript is comparing the effect of individual chaperones and heat-resistant obscure (Hero) proteins on the overall folding of the TDP-43 LCD-domain and its relation to aggregation propensity. Therefore, the authors apply smFRET in order to deduce eventual morphological changes of the LCD-domain from FRET efficiencies. The authors observe that the LCD domain extends its structure upon binding of chaperone/Hero proteins whereas it is collapsed in the absence of those. Furthermore, immunoblotting of filter trap assays indicate that overexpression of chaperones and Hero proteins reduce aggregation of TDP-43 in vivo. Both, the morphological effects on the LCD-domain and the aggregation propensity are significantly enhanced for the TDP-43 A315T mutant. Moreover, the authors tested a charge depleted Hero protein version with reduced "chaperone-like" behaviour. Therefore, the authors conclude that the binding or chaperone activity of the Hero protein is based on its residue specific charges. Finally, the authors conclude that Hero proteins can act similar to chaperones in order to keep protein homeostasis under stress conditions.

We thank the Reviewer for their insightful evaluation of our study.

Major comments:

The similar effect of chaperones and Hero proteins on the morphology of TDP-43 found by the authors is intriguing and the applied experimental procedures seem well described and conducted.

However, the assumption of the authors that a change in morphology of the LCD-domain by the chaperones and Hero proteins is directly connected to the reduction of TDP-43 aggregation is not entirely clear. Whether an overexpression of individual chaperones and Hero proteins has a direct effect on TDP-43 aggregation cannot be tested in vivo, only. It cannot be excluded that inside the cell the here tested chaperones and Hero proteins control intermediate processes or work as co-factors for other proteins involved in protein homeostasis rather directly influencing the aggregation of TDP-43. Therefore, I recommend in vitro aggregation experiments, using ThT signal as a readout. By adding chaperones, Hero proteins and a negative (BSA or others) control individually, a direct effect on TDP-43 aggregation could be concluded. Those experiments have been extensively used in the field and are quick and straightforward to handle.

As the Reviewer explains, indirect effects on TDP-43 aggregation in cells may be accounted for by conducting aggregation experiments in vitro, with recombinant proteins. We are currently designing such experiments based on a previously described full-length recombinant TDP-43 with a TEV-cleavable MBP tag (Wang 2018 EMBO J). This can be incubated with Hero/DNAJA2/Control, and aggregation induced by cleavage of the tag, after which aggregation can be measured via filter trap similar to the method described in our work. We will include these results in our revised manuscript.

We thank the Reviewer for their advice. While we note that it is controversial whether ThT binds to aggregates formed from full-length TDP-43 (used in all our assays in the current manuscript), it is reasonable to apply this assay to the LCD fragment as in the paper referenced by the Reviewer below (Lu 2022 Nat Cell Biol). Such an assay is also a reasonable method for confirming effects of Hero protein and DNAJA2 in vitro, and we can conduct this assay as a back-up if the above does not work.

In addition, focusing on the LCD-domain as a main driver for TDP-43 aggregation is limiting this study. In particular, recent studies [1] indicate that the RRM1 and RRM2 sites of TDP-43 have a major impact on TDP-43 gelation and maturation to solid aggregates. Unfortunately, those sites have not been studied in this manuscript.

We thank the Reviewer for their insight. While we are keen to investigate the impact of other regions on the aggregation of TDP-43 in the future, we chose to focus on the LCD in our current study because our smFRET assay is particularly suitable to monitor the dynamic conformational nature of this flexible, unfolded region.

However, we agree with the Reviewer that it is possible the RRMs have an effect on the activities of Hero11 and DNAJA2. We will create constructs for the RRM-depleted variant, TDP43ΔRRM1&2, and RNA-binding deficient variant, TDP435FL for use in our cell-based assay. This will allow us to investigate how this domain influences the effects of Hero and DNAJA2, and we will include this in our revised manuscript.

As an optional alternative for using Hero11KR->G could be the alteration of buffer conditions and using higher number of salts to promote charge screening. It would be of interest whether the results with the Hero11KR->G could be reproduced with wild type Hero11.

We will perform our assays with Hero11 in high salt conditions for charge screening. While we agree that it may be a great alternative experiment, we note that changing the salt concentration may directly affect the LCD conformation, possibly complicating interpretation of results.

[1] Lu et al. Nat Cell Biol;24(9):1378-1393 (2022)

Minor comments:

Overall, the text is clearly written, and the figures are appropriate.

Whether the activity of individual chaperones or Hero proteins on TDP-43 aggregation "may result in the overall fitness of the cell" or "reinforcing the conformational health of the proteome" is disputable without knowing how the overexpression of certain chaperones or Hero proteins alter the formation of toxic TDP-43 oligomers.

We thank the Reviewer for their balanced critique. We will remove or weaken this point regarding how Hero proteins "may result in the overall fitness of the cell" or may be "reinforcing the conformational health of the proteome" from the discussion.

Reviewer #1 (Significance (Required)):

Studying the mechanistic effects of chaperones on aggregating proteins is of major interest for the field in order to understand aging related disbalance of protein homeostasis and the progression of neurological decline, such as seen for amyotrophic lateral sclerosis (ALS). Furthermore, finding homolog proteins, also being able to inhibit protein aggregation, can help to understand overall mechanisms of protein aggregation and processes preventing such fatal behaviour. However, the technique used in this manuscript are not very novel and have been used numerously times before. smFRET is a common technique to look at protein folding/unfolding and is used frequently as a molecular ruler. The manuscript is of interest for the field of protein aggregation and folding, smFRET and neurodegeneration.

My expertise lies in the field of protein aggregation and inhibition due to chaperones, measuring molecular interactions and neurodegenerative diseases.

We greatly appreciate the Reviewer’s expert opinion on our work. As the Reviewer explains, we believe our work will contribute to the fields of protein aggregation and folding, smFRET and neurodegeneration. While the smFRET method may not be novel on its own, to our knowledge this is the first observation of the TDP-43 LCD, with the effect of a pathogenic mutation, at the single-molecule level. In fact, the production, dye-labeling and isolation of individual molecules is extremely challenging for TDP-43. This was made possible by our technical advances using genetic code expansion to site-specifically introduce an unnatural amino acid in TDP-43, purifying and labeling the TDP-43 from HEK cells, and isolation on glass slides.

Reviewer #2 (Evidence, reproducibility and clarity (Required)):

In this manuscript, the authors build on their findings (Tsuboyama 2020) that electrostatically charged IDPs (Heros) can protect proteins from denaturation and aggregation. In their previous work, they demonstrate that these Hero proteins could decrease the fraction of insoluble GFP-TDP43∆NLS in mammalian cell lines and that this mode of action was related to the electrostatic charge of the proteins and not sequence dependent. Although this protective mode of action appears to be similar to that of canonical chaperones, it is unclear how the Hero proteins compare. In this study, the authors compare Hero11 to a panel of canonical chaperones in their cell-based assays and show that it prevents aggregation in a comparable way to DNJA2. It appears that Hero11 decreases the GFP-TDP43∆NLS aggregates better than some other chaperones. They then utilise their expertise in smFRET analysis (Tsuboyama, 2018) to compare what effect DNJA2 and Hero11 (along with Hero11KR-->G (non-charged control)) have on the dynamic structures of the GFP-TDP43∆NLS (labelled with complementary fluorophores in the LCD domain). Based on analysis of the WT GFP-TDP43∆NLS and the A315T GFP-TDP43∆NLS, the authors suggest that the presence of Hero11 and DNJA2 maintain the LCD-domain of TDP43 in an extended conformation and that by doing so, aggregation can be prevented (as assessed in the cell-based assay).

Despite finding the results very interesting, I feel that the study is preliminary and the conclusions drawn are not fully substantiated by the presented experimental work. Many questions need addressing to validate these findings and conclusions (please see more in the "Significance" section). I have tried to list the main concerns below.

We thank the Reviewer for their detailed and critical assessment of our current study.

Questions/concerns:

Authors used double transient transfections but have not shown quantification of protein levels of the chaperones versus TDP43 - western blots to confirm proper expression (and levels) of the chaperones/Hero protein is crucial without it, we cannot assume that the differences in TDP-43 aggregation are a result of effective chaperoning or due to a lack of expression of any of the chaperone proteins, or high expression of others.

We agree with the Reviewer that this is an important and straightforward validation experiment. We will perform the Western Blotting to confirm the proper and comparable expression of the chaperones/Hero proteins.

Authors used quite a high BSA concentration in the smFRET work; it would be useful to see what the TDP43 smFRET trace looks like without BSA incubation (to ensure it is not causing some effect). Also, is there a concentration dependence? The Authors mention they are unable to identify a Hero/TDP43 complex; but if the amount of Hero protein is high (given that it is single molecules tethered), the change in compaction may not relate to the levels/ratios found in the cells (where changes to aggregation are occurring). have the authors considered whether positively charged polymers (poly-Lys) have any impact on the TDP-43 smFRET distribution? Given that the smFRET trace is so heterogeneous, to understand what is happening here would require the comparison of more than 2 variants.

As the Reviewer suggests, we will include additional smFRET experiments in our revision.

First, we will perform the smFRET experiment of the TDP-43 alone in the PBS buffer. However, we would like to clarify the reason we used BSA incubation for comparison in the current experiment is to account for the possibility of non-specific macromolecular crowding effects on the conformation of the LCD (an effect reported for IDPs in general, for example in Banks 2018 Biophys. J.); we expected that it would be fair to compare Hero11 against another protein, rather than buffer alone. As the Reviewer suggested, we can also perform the same experiments at lower concentrations of Hero11 and DNAJA2, including equimolar concentrations (as suggested below). Moreover, we can also test poly-K peptides for comparison.

Although the A315T variant has a very distinct smFRET profile, it is clear that the effects of Hero11KR-->G (that is proposed to have no effect on aggregation or on the smFRET of WT TDP43) has a clear impact on A315T. Why is this?

We thank the Reviewer for raising this interesting point. We envision that the observed effect is due to weak interactions between the LCD domain of TDP-43 and Hero11KR->G; even without K and R, there many other functional amino acids that are fully accessible due to the extremely disordered nature of the protein. The effect is easier to be observed with the A315T mutant, compared to the WT TDP-43, presumably because the mutant tends to take more compact conformations on its own. Nonetheless, unlike WT Hero11, Hero11KR->G fails to accumulate the very extended form of the LCD (FRET signal of ~0; please see below for the explanation of this value), which appears to be associated with suppression of aggregation. We will include these in our discussion.

The LCD region is prone to PMTs - as the tethered protein is taken from expression in mammalian cells, how can the authors be sure that it has no PMTs? Although a clear difference is observed between WT and A315T in terms of "compactness" of the LCD domain, we cannot assume that the effect of DNAJ2 and Hero11 are the same - in fact, the Hero11 KR-->G control for the A315T is clearly different from the negative control (BSA) and the effect that was seen in WT. As the LCD domain is well-known to be the site of post-translational modifications (ie. Phosphorylation - this would have an effect on an electrostatic Hero11), could the effects be related to changes in PMTs as well?

We thank the Reviewer for their insight. We would like to clarify that we make no assumption that our dye-labeled TDP-43 is free of post-translational modifications. Indeed, the fact that it is derived from HEK293 cells suggests it should have post-translational modifications relevant to humans and may be even considered an advantage of our method. (Most structural methods require purification of a large amount of protein, often only possible through recombinant expression in E. coli, thus lacking human-relevant PTMs.) As the Reviewer points out, the LCD is known to have many phosphorylation sites, which may help explain how the positively charged Hero11 interacts with it. Thus, we will perform mass spectrometry of TDP-43 and the A315T variant expressed in HEK cells to identify what post-translational modifications are present.

The authors mention other studies on DNJA proteins on TDP-43; is the mechanism by which they suppress aggregation known? If the authors want to compare the unknown effects of Hero11, it would be useful to know what DNJ2A is doing, otherwise, the results are still not conclusive, only that "change is similar" in two experiments. What is known about DNJ2A interactions with TDP-43? Did the authors do any pulldown assays to detect a complex in cellulo?

While previous studies have identified various DNAJ (specifically J-domain protein B-subfamily) proteins that suppress aggregation of overexpressed TDP-43, not much is known of this specific interaction (Udan-Johns 2014 Hum Mol Genet, Chen 2016 Brain, Park 2017 PLOS Genet). To address the Reviewer’s questions, we will include experiments characterizing the effects of DNAJA2 on TDP-43. We will perform colocalization experiments, explaining effects of DNAJA2 and Hero11 on TDP-43 in the cell. As explained below, we will also perform Pulse Shape Analysis (PulSA), a flow cytometry-based method that can be used to study protein localization patterns in cell, which will also provide insight into the effects on the distribution of TDP-43 in cells. We can also perform co-IP of TDP-43 to detect if there is a detectable, stable complex with DNAJA2 and/or Hero11. Together, these will clarify the similarities and differences between DNAJA2 and Hero11.

It is unclear how the findings of the smFRET relate to structural understanding of the LCD-domain of TDP43 (i.e. NMR studies?); is it known whether PTMs are more prominent with the A315T variant as this may explain it's more compact nature? As well, putative helical structure in the LCD domain may lend to the changes in compaction.

The Reviewer brings up an interesting and careful discussion. Currently, it is unknown if PTMs actually cause more compaction, or if they are more prominent in the A315T variant, but we will perform mass spectrometry to detect PTMs.

As the Reviewer mentions, it would be very interesting to compare our smFRET results to other studies of specific LCD structures. However, it is not trivial to deduce lengths (and structure) from smFRET data as various other factors, for example, dye orientation and local chemical environment, may affect FRET efficiency. Nonetheless, we can still cautiously provide a discussion of how our FRET results compare with previous studies.

For the dye pair used in our study, Cy3 and ATTO647N, the low/no FRET signals promoted by DNAJA2 and Hero11 correspond to a range of end-to-end distances of 6.9 nm to 10.2 nm (FRET signals of 0.1 to 0.01, respectively). Assuming that the LCD behaves like a ~140 amino acid worm-like chain (WLC) with persistence length (Lp) = 0.8 nm, we expect a mean end-to-end distance of 7.35 nm. Thus, the low FRET peak can be well explained by promotion of an extended WLC behavior of the LCD by DNAJA2 and Hero11. On the other hand, the FRET peaks of WT LCD and the A315T mutant (in the absence of Hero11 or DNAJA2) correspond to ~4 and ~3.3 nm, respectively. We will include a careful discussion of how our results relate to known structural understanding of the LCD in the revised discussion.

It is unclear how there can be such a prominent FRET ~0 peak and in fact negative values.

We regret that we did not clearly explain this in the manuscript. Negative values arise when applying correction factors from the alternating laser scheme (ALEX) to FRET signals. FRET efficiency, E, is the ratio of acceptor signal intensity, IA, over the total signal intensity, ID+IA, (with the application of a correction factor, γ, but this doesn’t affect the negative values and won’t be discussed further here) and is given by the equation: E=IA/(γ×ID+IA). However, due to leakage of the donor signal into the acceptor channel and direct excitation of the acceptor dye by the donor laser, raw IA values, IA,raw, are erroneously higher than in reality. For example, the ~0 FRET peaks in question appear to be around 0.1–0.2 before correction. These are accounted for by applying the respective correction factors, Dleakage and Adirect, through the equation: IA=IA,raw–Dleakage×ID–Adirect×IAA. (IAA is the acceptor signal during excitation of the acceptor dye.) These two correction factors are determined by observing the traces and choosing the mean values using iSMS software (2015 Preus Nat Methods) and applied uniformly to all traces in an experiment. When IA is especially low, such as when FRET is almost 0, the magnitude of the correction factor terms may be larger than IA,raw, resulting in negative values. This does not mean that values less than 0 are invalid, but merely that they have been overcompensated in the error application. For the dye pair in our study, FRET efficiencies less than 0.1 correspond to distances greater than 6.9 nm, meaning peaks around zero represent LCD behaviors with end-to-end distances greater than around 7 nm. Please also note that kernel density estimation often gives distributions with values beyond the (0,1) range just because of how these plots are constructed. This will be added to the methods in the revised manuscript.

Conclusion is that Hero11 and DNJA2 maintain the TDP43 LCD-domain (soluble protein) in an extended form and that this is linked with the decrease in aggregates found in the cell; however, with the cell-based assay, no analysis to quantify the expression levels of the TDP43 and the chaperones/Hero are presented, and more importantly, no analysis on the complementary soluble fraction (to the filter assay) has been done to show that indeed, these biomolecules maintain the proteins in a soluble form. It is possible that the TDP-43 is being degraded?

As described above, we plan to perform Western Blotting to examine the expression levels of these proteins. To address the concerns about solubility, we will perform Pulse Shape Analysis (PulSA) to quantitatively measure the expression and soluble/aggregated distribution GFP-tagged TDP43 in HEK293T cells. Measuring the soluble diffuse signals and the punctate aggregate signals will also tell us if there are differences in how GFP-TDP43 is aggregated between Hero11, DNAJA2 and controls. In addition, to support results from the FTA, we will provide sedimentation assays, where the soluble and aggregate fraction from cells is separated by centrifugation and analyzed (Krobitsch 2000 PNAS). These will provide information on TDP-43 in the soluble fraction.

Reviewer #2 (Significance (Required)):

Contextually, this study has novelty and potential value for basic research. Firstly, understanding the underlying mechanisms by which Hero protein prevent aggregation would be valuable towards understanding the players in protein homeostasis which can be imbalanced with respect to disease. Secondly, the use of smFRET as a tool in understanding the dynamics of TDP-43 and mutational variants can be powerful in defining structural attributes with pathological consequences in ALS. Although this work shows comparisons between the effect of a canonical chaperone (DNJA2) and Hero11 on the dynamics of monomeric protein and the effect on cellular aggregation, proposing a general mechanism on the data from two TDP-43 variants and a cell-based aggregation assay is premature and more experimental evidence is needed to define the critical link that prevents aggregation of TDP-43 within the cell. Mechanistically, the study does not give a lot of additional insight into the mode of action of Hero11 in the process of preventing aggregation (nor does it explain what DNJA2 is doing and therefore how Hero11 compares and contrasts). The proposed "extended versus collapsed" switch is simplistic and doesn't account for the complexity of TDP-43 structural dynamics. To support their proposed mechanism of action, the authors needs to examine TDP-43 mutational variants (specifically disease-related ones) using their smFRET to understand exactly what the "collapsed" and "extended" data is defining before making the leap that this effect is what is preventing aggregation. There are some structural studies about residual structure in this region (via NMR) that should be considered (https://doi.org/10.1016/j.str.2016.07.007). Although the A315T variant has a very distinct smFRET profile, it is clear that the effects of Hero11KR-->G (that is proposed to have no effect on aggregation or on the smFRET of WT TDP43) has a clear impact on A315T. Why is this? Have the authors considered that the LCD domain of TDP43 is prone to post-translational modifications? Is this variant more phosphorylated - a PMT like phosphorylation is surely to have an impact on interactions with Hero proteins as they are positively charged. Given that the protein is expressed in mammalian cells, it is likely that PMTs have occurred (but the authors should analyse for this).

With regards to the cell-based aggregation assays, the authors again present a simplified relationship - however, a number of control experiments and additional questions arise. It appears that there is less aggregation with co-expression of some chaperones and the Hero11, but what about the soluble fraction? What is the impact of these biomolecules? Is this that it is maintaining soluble protein, enhancing degradation, propagating soluble oligomers? Equally, how do we know that the levels of the chaperones/Heros and the TDP-43 is the same in each cell - these are transient transfections, and no western blots are shown to confirm the levels of the proteins. In fact, the authors state that "co-transfection of HSP70 (HSPA8), HSP90 (HSP90AB1) or HOP all failed to suppress TDP-43 aggregation compared to GST" and mention that this is in contrast to other studies, but could this be a failure to express these in the cell models? Some western blot/lysate analysis is needed. Chaperones often form complexes with their client proteins, is there any evidence of complexes in these cell models (i.e. using immunoprecipitation)?

We thank the Reviewer for their detailed evaluation and interest in our work. As the Reviewer describes, smFRET is a powerful tool for studying the conformational dynamics of TDP-43, and we hope that this study will contribute to our understanding of how Hero proteins and chaperones prevent aggregation.

We are also grateful to the Reviewer for their constructive criticism of our current model, and we will revise it accordingly. We completely agree with the Reviewer that there are complex structural dynamics within the LCD that determine aggregation and phase separation behaviors. Our simple model was intended to explain how external factors that suppress aggregation, DNAJA2 and Hero11, could affect the conformation of LCD at the single-molecule level. As discussed above, we were cautious to over-interpret how our FRET observations correlate to specific conformations, leading to this simplistic model. We do not intend for our explanation of “extended versus collapsed” in the model to explain all structural dynamics of the LCD; rather, we wanted to highlight the characteristic low FRET state promoted by DNAJA2 and Hero11. We believe that the experiment plan explained above will address the Reviewer’s concerns in full, and we thank the Reviewer again for helping us to significantly improve our manuscript.

Reviewer #3 (Evidence, reproducibility and clarity (Required)):

- In a recent study (PLosBiol, 2020) the same authors described an interesting class of proteins they call 'Hero'. Based on their analyses in cultured cells and transgenic Drosophila models the authors concluded that 'Hero' proteins protect against protein instability and aggregation. So far, this class of proteins has not been analyzed by independent groups.

In the current manuscript, they mainly confirm their own previous finding that Hero 11 prevents There are several concerns about the presented data:

We thank the Reviewer for their critical comments on our current manuscript.

- Based on the filter trap assays shown in figure 1 and 3 the authors conclude that DNAJB8 and Hero11 specifically interfere with the aggregation of TDP-43. However, they do not show that the expression levels of TDP-43 are not altered by the co-expression of the different proteins and are comparable in the different samples. In order to make a relevant statement about the anti-aggregation activity of the analyzed proteins, the ratio between soluble and aggregated TDP-43 has to be analyzed.

We would like to clarify that the Reviewer means DNAJA2, not DNAJB8. Following the Reviewer’s advice, we will perform Western Blotting combined with sedimentation assays, where the soluble and aggregate fraction from cells is separated by centrifugation and analyzed to examine the expression levels. We will also perform colocalization experiments and Pulse Shape Analysis (PulSA), a flow cytometry-based method that can be used to study protein localization patterns in cell, which will provide insight into the anti-aggregation activities.

- The FRET assays shown in figures 2 and 4 indicate a slightly higher FRET efficiency in the presence of Hero11 and DNAJA2 and Hero11. The authors postulate that is phenomenon is causally linked to the activity of Hero11 to prevent aggregation of TDP-43. First, it remains unclear whether the slight increase is really significant. Second, I could not find any experimental evidence to support the assumption that a more collapse conformation of the TDP-43 LCD measured in single molecule FRET assays, correlates with an increased aggregation tendency of TDP-43.

We apologize that we are not sure what the Reviewer refers to by “a slightly higher FRET efficiency in the presence of Hero11 and DNAJA2 (and Hero11).” We would like to clarify that, in the presence of Hero11 and DNAJA2, what we observed was a very low (not slightly higher) FRET efficiency of ~0 (Figure 2g and h), suggesting an extended conformation. In contrast, the aggregation-prone A315T variant of TDP-43 shows a very high FRET efficiency of ~0.9 (Figure 4a), which indicates a collapsed conformation.

A minor comment, if the authors would like to compare the specific activity of different proteins, they should use equal molarities of the different proteins and not equal amounts.

As the Reviewer suggests, we will include experiments at equal molarities in the revision.

- For a one-way ANOVA, the response variable residuals have to be normally distributed. With an n = 3 this cannot be tested. Thus, the quantifications of the results shown in figure 1 and 3 are not reliable.

We thank the Reviewer for their critical comment on the statistical analysis. We would like to clarify that statistically significant differences in aggregation between conditions compared to a control are based on Dunnett’s test. While ANOVA is typically first performed to test for any significant difference in means before performing a post-hoc test, Dunnett’s test is independent and can be performed without ANOVA.

Following the Reviewer’s advice, we carefully re-examined our assumption of normality for this data. It is reasonable to perform Dunnett’s test on a sample size of n = 3, and it is generally safe to assume that data from three independent experiments will be reasonably normally distributed. In support of this, performing Kolmogorov-Smirnov test on our data in Figure 1 showed none of the groups differ significantly from normal distributions with the respective mean and standard deviation (p-values greater than 0.05). Thus, we believe it is reasonable to assume the data are normally distributed, the residuals normally distributed, and our statistical analyses reliable. This analysis will be included in the revision to support the normality assumption.

However, even if we did not assume a normal distribution of our data in Figure 1, we still would have obtained statistically significant differences; If we had relied on a Kruskal-Wallis test as a non-parametric equivalent of ANOVA, thus making no assumption of normality, we would have seen p = 0.005176, a value much lower than our significance level of α = 0.05, indicating sufficient evidence that there is a difference in aggregation among these groups.

- The title is imprecise and overstate the presented data:

'canonical chaperone' suggest that their results are valid for chaperones in general. However, they only tested DNAJA2 in the single -molecule FRET assay. Moreover, HAPA8, another canonical chaperone, obviously had an opposite effect on TDP-43 aggregation (Fig.1). Similarly, they only tested Hero11. Thus, 'canonical chaperone' has to be replaced by 'DNAJA2' and 'a heat-resistant obscure (Hero) protein' by 'Hero11'. Similarly, the term 'conformational modulation' is not as concise one would one expect for the title of a research paper.

We would like to clarify that the Reviewer means HSPA8 (not HAPA8). According to the Reviewer’s suggestion, we will change the title to “DNAJA2 and Hero11 mediate similar conformational extension and aggregation suppression of TDP-43”.

Reviewer #3 (Significance (Required)):

In a recent study (PLosBiol, 2020) the same authors described an interesting class of proteins they call 'Hero'. Based on their analyses in cultured cells and transgenic Drosophila models the authors concluded that 'Hero' proteins protect against protein instability and aggregation. So far, this class of proteins has not been analyzed by independent groups.

In the current manuscript, they mainly confirm their own previous finding that Hero 11 prevents aggregation of TDP-43 and present very few new data that would provide new insights. Specifically, only the FRET assays shown in figure 2 and 4 are really new, which, by the way, could easily be shown in one figure.

We thank the Reviewer for their critical evaluation of our current study. As the Reviewer suggests, we believe our smFRET results provide new insights into how Hero11 and DNAJA2 function. We would like to emphasize that, rather than confirming our previous findings, our current manuscript mainly addresses a critical point that remained unknown in our previous study by investigating the mechanism of how Hero proteins prevent aggregation. Moreover, to our knowledge, this is the first observation of the TDP-43 LCD, with the effect of a pathogenic mutation, at the single-molecule level.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

---

Referee #3

Evidence, reproducibility and clarity

• In a recent study (PLosBiol, 2020) the same authors described an interesting class of proteins they call 'Hero'. Based on their analyses in cultured cells and transgenic Drosophila models the authors concluded that 'Hero' proteins protect against protein instability and aggregation. So far, this class of proteins has not been analyzed by independent groups.

In the current manuscript, they mainly confirm their own previous finding that Hero 11 prevents There are several concerns about the presented data: - Based on the filter trap assays shown in figure 1 and 3 the authors conclude that DNAJB8 and Hero11 specifically interfere with the aggregation of TDP-43. However, they do not show that the expression levels of TDP-43 are not altered by the co-expression of the different proteins and are comparable in the different samples. In order to make a relevant statement about the anti-aggregation activity of the analyzed proteins, the ratio between soluble and aggregated TDP-43 has to be analyzed. - The FRET assays shown in figures 2 and 4 indicate a slightly higher FRET efficiency in the presence of Hero11 and DNAJA2 and Hero11. The authors postulate that is phenomenon is causally linked to the activity of Hero11 to prevent aggregation of TDP-43. First, it remains unclear whether the slight increase is really significant. Second, I could not find any experimental evidence to support the assumption that a more collapse conformation of the TDP-43 LCD measured in single molecule FRET assays, correlates with an increased aggregation tendency of TDP-43. A minor comment, if the authors would like to compare the specific activity of different proteins, they should use equal molarities of the different proteins and not equal amounts. - For a one-way ANOVA, the response variable residuals have to be normally distributed. With an n = 3 this cannot be tested. Thus, the quantifications of the results shown in figure 1 and 3 are not reliable. - The title is imprecise and overstate the presented data: 'canonical chaperone' suggest that their results are valid for chaperones in general. However, they only tested DNAJA2 in the single -molecule FRET assay. Moreover, HAPA8, another canonical chaperone, obviously had an opposite effect on TDP-43 aggregation (Fig.1). Similarly, they only tested Hero11. Thus, 'canonical chaperone' has to be replaced by 'DNAJA2' and 'a heat-resistant obscure (Hero) protein' by 'Hero11'. Similarly, the term 'conformational modulation' is not as concise one would one expect for the title of a research paper.

Significance

In a recent study (PLosBiol, 2020) the same authors described an interesting class of proteins they call 'Hero'. Based on their analyses in cultured cells and transgenic Drosophila models the authors concluded that 'Hero' proteins protect against protein instability and aggregation. So far, this class of proteins has not been analyzed by independent groups.

In the current manuscript, they mainly confirm their own previous finding that Hero 11 prevents aggregation of TDP-43 and present very few new data that would provide new insights. Specifically, only the FRET assays shown in figure 2 and 4 are really new, which, by the way, could easily be shown in one figure.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

---

Referee #2

Evidence, reproducibility and clarity

In this manuscript, the authors build on their findings (Tsuboyama 2020) that electrostatically charged IDPs (Heros) can protect proteins from denaturation and aggregation. In their previous work, they demonstrate that these Hero proteins could decrease the fraction of insoluble GFP-TDP43∆NLS in mammalian cell lines and that this mode of action was related to the electrostatic charge of the proteins and not sequence dependent. Although this protective mode of action appears to be similar to that of canonical chaperones, it is unclear how the Hero proteins compare. In this study, the authors compare Hero11 to a panel of canonical chaperones in their cell-based assays and show that it prevents aggregation in a comparable way to DNJA2. It appears that Hero11 decreases the GFP-TDP43∆NLS aggregates better than some other chaperones. They then utilise their expertise in smFRET analysis (Tsuboyama, 2018) to compare what effect DNJA2 and Hero11 (along with Hero11KR-->G (non-charged control)) have on the dynamic structures of the GFP-TDP43∆NLS (labelled with complementary fluorophores in the LCD domain). Based on analysis of the WT GFP-TDP43∆NLS and the A315T GFP-TDP43∆NLS, the authors suggest that the presence of Hero11 and DNJA2 maintain the LCD-domain of TDP43 in an extended conformation and that by doing so, aggregation can be prevented (as assessed in the cell-based assay).

Despite finding the results very interesting, I feel that the study is preliminary and the conclusions drawn are not fully substantiated by the presented experimental work. Many questions need addressing to validate these findings and conclusions (please see more in the "Significance" section). I have tried to list the main concerns below.

Questions/concerns:

Authors used double transient transfections but have not shown quantification of protein levels of the chaperones versus TDP43 - western blots to confirm proper expression (and levels) of the chaperones/Hero protein is crucial without it, we cannot assume that the differences in TDP-43 aggregation are a result of effective chaperoning or due to a lack of expression of any of the chaperone proteins, or high expression of others.

Authors used quite a high BSA concentration in the smFRET work; it would be useful to see what the TDP43 smFRET trace looks like without BSA incubation (to ensure it is not causing some effect). Also, is there a concentration dependence? The Authors mention they are unable to identify a Hero/TDP43 complex; but if the amount of Hero protein is high (given that it is single molecules tethered), the change in compaction may not relate to the levels/ratios found in the cells (where changes to aggregation are occurring). have the authors considered whether positively charged polymers (poly-Lys) have any impact on the TDP-43 smFRET distribution? Given that the smFRET trace is so heterogeneous, to understand what is happening here would require the comparison of more than 2 variants.

Although the A315T variant has a very distinct smFRET profile, it is clear that the effects of Hero11KR-->G (that is proposed to have no effect on aggregation or on the smFRET of WT TDP43) has a clear impact on A315T. Why is this?

The LCD region is prone to PMTs - as the tethered protein is taken from expression in mammalian cells, how can the authors be sure that it has no PMTs? Although a clear difference is observed between WT and A315T in terms of "compactness" of the LCD domain, we cannot assume that the effect of DNAJ2 and Hero11 are the same - in fact, the Hero11 KR-->G control for the A315T is clearly different from the negative control (BSA) and the effect that was seen in WT. As the LCD domain is well-known to be the site of post-translational modifications (ie. Phosphorylation - this would have an effect on an electrostatic Hero11), could the effects be related to changes in PMTs as well?

The authors mention other studies on DNJA proteins on TDP-43; is the mechanism by which they suppress aggregation known? If the authors want to compare the unknown effects of Hero11, it would be useful to know what DNJ2A is doing, otherwise, the results are still not conclusive, only that "change is similar" in two experiments. What is known about DNJ2A interactions with TDP-43? Did the authors do any pulldown assays to detect a complex in cellulo?

It is unclear how the findings of the smFRET relate to structural understanding of the LCD-domain of TDP43 (i.e. NMR studies?); is it known whether PTMs are more prominent with the A315T variant as this may explain it's more compact nature? As well, putative helical structure in the LCD domain may lend to the changes in compaction.

It is unclear how there can be such a prominent FRET ~0 peak and in fact negative values.

Conclusion is that Hero11 and DNJA2 maintain the TDP43 LCD-domain (soluble protein) in an extended form and that this is linked with the decrease in aggregates found in the cell; however, with the cell-based assay, no analysis to quantify the expression levels of the TDP43 and the chaperones/Hero are presented, and more importantly, no analysis on the complementary soluble fraction (to the filter assay) has been done to show that indeed, these biomolecules maintain the proteins in a soluble form. It is possible that the TDP-43 is being degraded?

Significance

Contextually, this study has novelty and potential value for basic research. Firstly, understanding the underlying mechanisms by which Hero protein prevent aggregation would be valuable towards understanding the players in protein homeostasis which can be imbalanced with respect to disease. Secondly, the use of smFRET as a tool in understanding the dynamics of TDP-43 and mutational variants can be powerful in defining structural attributes with pathological consequences in ALS. Although this work shows comparisons between the effect of a canonical chaperone (DNJA2) and Hero11 on the dynamics of monomeric protein and the effect on cellular aggregation, proposing a general mechanism on the data from two TDP-43 variants and a cell-based aggregation assay is premature and more experimental evidence is needed to define the critical link that prevents aggregation of TDP-43 within the cell. Mechanistically, the study does not give a lot of additional insight into the mode of action of Hero11 in the process of preventing aggregation (nor does it explain what DNJA2 is doing and therefore how Hero11 compares and contrasts). The proposed "extended versus collapsed" switch is simplistic and doesn't account for the complexity of TDP-43 structural dynamics. To support their proposed mechanism of action, the authors needs to examine TDP-43 mutational variants (specifically disease-related ones) using their smFRET to understand exactly what the "collapsed" and "extended" data is defining before making the leap that this effect is what is preventing aggregation. There are some structural studies about residual structure in this region (via NMR) that should be considered (https://doi.org/10.1016/j.str.2016.07.007). Although the A315T variant has a very distinct smFRET profile, it is clear that the effects of Hero11KR-->G (that is proposed to have no effect on aggregation or on the smFRET of WT TDP43) has a clear impact on A315T. Why is this? Have the authors considered that the LCD domain of TDP43 is prone to post-translational modifications? Is this variant more phosphorylated - a PMT like phosphorylation is surely to have an impact on interactions with Hero proteins as they are positively charged. Given that the protein is expressed in mammalian cells, it is likely that PMTs have occurred (but the authors should analyse for this).

With regards to the cell-based aggregation assays, the authors again present a simplified relationship - however, a number of control experiments and additional questions arise. It appears that there is less aggregation with co-expression of some chaperones and the Hero11, but what about the soluble fraction? What is the impact of these biomolecules? Is this that it is maintaining soluble protein, enhancing degradation, propagating soluble oligomers? Equally, how do we know that the levels of the chaperones/Heros and the TDP-43 is the same in each cell - these are transient transfections, and no western blots are shown to confirm the levels of the proteins. In fact, the authors state that "co-transfection of HSP70 (HSPA8), HSP90 (HSP90AB1) or HOP all failed to suppress TDP-43 aggregation compared to GST" and mention that this is in contrast to other studies, but could this be a failure to express these in the cell models? Some western blot/lysate analysis is needed. Chaperones often form complexes with their client proteins, is there any evidence of complexes in these cell models (i.e. using immunoprecipitation)?

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

---

Referee #1

Evidence, reproducibility and clarity

Summary:

The submitted manuscript is comparing the effect of individual chaperones and heat-resistant obscure (Hero) proteins on the overall folding of the TDP-43 LCD-domain and its relation to aggregation propensity. Therefore, the authors apply smFRET in order to deduce eventual morphological changes of the LCD-domain from FRET efficiencies. The authors observe that the LCD domain extends its structure upon binding of chaperone/Hero proteins whereas it is collapsed in the absence of those. Furthermore, immunoblotting of filter trap assays indicate that overexpression of chaperones and Hero proteins reduce aggregation of TDP-43 in vivo. Both, the morphological effects on the LCD-domain and the aggregation propensity are significantly enhanced for the TDP-43 A315T mutant. Moreover, the authors tested a charge depleted Hero protein version with reduced "chaperone-like" behaviour. Therefore, the authors conclude that the binding or chaperone activity of the Hero protein is based on its residue specific charges. Finally, the authors conclude that Hero proteins can act similar to chaperones in order to keep protein homeostasis under stress conditions.

Major comments:

The similar effect of chaperones and Hero proteins on the morphology of TDP-43 found by the authors is intriguing and the applied experimental procedures seem well described and conducted.

However, the assumption of the authors that a change in morphology of the LCD-domain by the chaperones and Hero proteins is directly connected to the reduction of TDP-43 aggregation is not entirely clear. Whether an overexpression of individual chaperones and Hero proteins has a direct effect on TDP-43 aggregation cannot be tested in vivo, only. It cannot be excluded that inside the cell the here tested chaperones and Hero proteins control intermediate processes or work as co-factors for other proteins involved in protein homeostasis rather directly influencing the aggregation of TDP-43. Therefore, I recommend in vitro aggregation experiments, using ThT signal as a readout. By adding chaperones, Hero proteins and a negative (BSA or others) control individually, a direct effect on TDP-43 aggregation could be concluded. Those experiments have been extensively used in the field and are quick and straightforward to handle.

In addition, focusing on the LCD-domain as a main driver for TDP-43 aggregation is limiting this study. In particular, recent studies [1] indicate that the RRM1 and RRM2 sites of TDP-43 have a major impact on TDP-43 gelation and maturation to solid aggregates. Unfortunately, those sites have not been studied in this manuscript.

As an optional alternative for using Hero11KR->G could be the alteration of buffer conditions and using higher number of salts to promote charge screening. It would be of interest whether the results with the Hero11KR->G could be reproduced with wild type Hero11.

[1] Lu et al. Nat Cell Biol;24(9):1378-1393 (2022)

Minor comments:

Overall, the text is clearly written, and the figures are appropriate.<br /> Whether the activity of individual chaperones or Hero proteins on TDP-43 aggregation "may result in the overall fitness of the cell" or "reinforcing the conformational health of the proteome" is disputable without knowing how the overexpression of certain chaperones or Hero proteins alter the formation of toxic TDP-43 oligomers.

Significance

Studying the mechanistic effects of chaperones on aggregating proteins is of major interest for the field in order to understand aging related disbalance of protein homeostasis and the progression of neurological decline, such as seen for amyotrophic lateral sclerosis (ALS). Furthermore, finding homolog proteins, also being able to inhibit protein aggregation, can help to understand overall mechanisms of protein aggregation and processes preventing such fatal behaviour. However, the technique used in this manuscript are not very novel and have been used numerously times before. smFRET is a common technique to look at protein folding/unfolding and is used frequently as a molecular ruler. The manuscript is of interest for the field of protein aggregation and folding, smFRET and neurodegeneration.

My expertise lies in the field of protein aggregation and inhibition due to chaperones, measuring molecular interactions and neurodegenerative diseases.

Transparent Peer Review

---

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

</br>

Transparent Peer Review

---

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

</br>

Transparent Peer Review

---

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

</br>

Transparent Peer Review

---

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

</br>

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

Learn more at Review Commons

---

Reply to the reviewers

We are obviously very pleased with the general support expressed by the referees, and appreciate their critical comments. We detail below how we propose to respond to their suggestions and queries.

In view of the fact that my lab is no longer in existence, I will have to rely on the kind generosity of my colleagues at EMBL to host former team members (the two first authors) for a limited period to come back to Heidelberg to carry out any further experimental work that may be needed. This means we will have to limit the work we can do to those experiments with the highest priority. However, we are optimistic that we will be able to obtain indicative results.

We will also follow most of the referees’ other suggestions and requests for additional data and quantifications, as outlined (or already included) below.

Reviewer #1 (Evidence, reproducibility and clarity (Required)):

Summary: ASC is the Pyrin/CARD-containing adapter protein that functions as a core component of inflammasome signaling complexes. ASC functions downstream of various NLR- and ALR-inflammasome initiator proteins and upstream of the inflammatory caspases that function as inflammasome effector enzymes. This study uses a novel chimeric construct (Opto-ASC) comprising the Arabidopsis photo-oligomerizable cryptochrome 2 (Cry2-olig) protein with zebrafish ASC to generate transgenic zebrafish larvae wherein ASC oligomerization can be rapidly, dynamically and spatially induced by blue light illumination of either the entire larva or single cells within discrete tissues of an intact larva. Induction of these "opto-inflammasome" complexes is coupled with state-of-the-art, live-cell optical imaging of multiple single cell and integrative tissue parameters to assay various modes of regulated cell death within the peridermal and basal cellular layers of the larval skin. This experimental model was further combined with genetic manipulation of the expression of various zebrafish inflammatory or apoptotic caspases, as well as the two zebrafish members of the gasdermin family of pore-forming proteins which can mediate disruption of plasma membrane permeability without (pre-lytic) or with (pyroptosis) progression to lytic cell death.

The main results of the study are: 1) introduction of a novel experimental system for dynamic and spatially resolved ASC oligomerization and speck formation within the cells of intact epithelial tissues of a living organism; 2) the ability of these optically induced ASC oligomers/specks to drive multiple modes of regulated cell death which exhibit some (but not all) features of lytic pyroptosis or non-lytic apoptosis depending on cell type and tissue location; 3) the ability of the dying epithelial cells containing optically-induced ASC specks to coordinate rapid adaptive responses in adjacent non-dying cells to maintain integrity/ continuity of skin epithelial barrier; and 4) unexpectedly, no obvious role for either of the two zebrafish gasdermins in the regulated cell death responses.

Major Comments:

1. Are the claims and the conclusions supported by the data or do they require additional experiments or analyses to support them? The major goal of this MS is to present a new experimental model (optogenetic activation of ASC oligomerization in transgenic zebrafish) that has the potential to provide new insights regarding the multiple mechanisms by which ASC can regulate inflammasome/ cell death signaling responses in the context of an intact organism. As noted above, some of the observed results are unexpected (e.g., lytic cell death independent of the zebrafish gasdermins in particular epithelial cells) and may reflect mechanisms unique to zebrafish as a non-mammalian vertebrate model versus the mammalian experimental systems (murine and human) that have informed most of our current understanding of how ASC coordinates inflammasome and cell death responses. However, the authors have used rigorous genetic approaches to rule out trivial explanations for the unexpected observations. Thus, no major additional experiments are required to support the claims and conclusions presented in the MS.

2. Are the suggested experiments realistic in terms of time and resources? Yes. It would help if you could add an estimated time investment for substantial experiments: A few weeks.

3. Are the data and the methods presented in such a way that they can be reproduced? Are the experiments adequately replicated and statistical analysis adequate? Yes.

4. Are the experiments adequately replicated and statistical analysis adequate? Yes.

Minor comments

1. Specific experimental issues that are easily addressable:

There's a significant concern with the use of LDC7559 (line 387) as a putative small molecule inhibitor of gasdermin D function to test roles (or lack thereof) of the zebrafish gasdermins in the ASC-triggered lytic cell death responses. A recent study (Amara et al. 2021. Cell. PMID34320407) has reported that LDC7559 does not inhibit gasdermin D (and possibly other gasdermins) but rather acts as an allosteric activator of PFKL (phosphofructosekinase-1 liver type) in neutrophils and thereby suppress generation of the NADPH required for the phagocytic oxidative burst and consequent NETosis. Thus, use of LDC7559 as a presumed gasdermin inhibitor in the current MS is problematic and should be deleted. As an alternative pharmacological approach to suppress gasdermin function, the authors might consider the use of either disulfiram (Hu et al. 2020. Nature Immunology. PMID32367036) and/or dimethylfumarate (Humphries et al. Science. 2020. PMID32820063). These would be straightforward additional experiments.

We have ordered the reagents to do these experiments. We are optimistic that we will obtain data that will strengthen this part of the ms and be a pointer for future studies by others.

We propose to keep the information on LDC7559 included, but to discuss the reservations the referee lists above - otherwise, others might ask why we did not even try this inhibitor. .

Are prior studies referenced appropriately? there are some problems; see below. 2a. One paper is cited twice in lines 724-726 and 727-729. 2b. Another paper is cited twice in lines 790-792 and 793-795. 2c. No journal is included for the referenced study by Shkarina et al in lines 827-828. 2d. No journal is included for the referenced study by Stein et al in lines 831-832. 2e. No journal is included for the referenced study by Masumoto et al in lines 793-795. 2f. No journal is included for the referenced study by Kuri et al in lines 774-775.

We are embarrassed about these omissions and mistakes and have corrected them..

Are the text and figures clear and accurate? Generally, yes but with a few exceptions noted below: 3a. line 28: "morphological distinct" should read "morphologically distinct" 3b. line 161: this sentence contains in parentheses "for how long?" I think this was a comment by one author that wasn't removed from the final submitted MS 3c. line 945: spelling "balck" > "black" 3d. line 268: "whereas showed a delayed speck formation": the authors need to specify what model/ condition showed a delayed speck formation 3e. line 262: spelling "egnerated" > "generated"

Thank you, all corrected.

CROSS-CONSULTATION COMMENTS I also agree with the comments of the other 2 reviewers. Between the 3 sets of comments and suggestions, the aggregate review will provide the authors with a suitable range of feasible recommendations that will improve an already strong MS.

Reviewer #1 (Significance (Required)):

1. General assessment: As noted above, this the major goal of this MS is to present a new experimental model (optogenetic activation of ASC oligomerization in transgenic zebrafish) that has the potential to provide new insights regarding the multiple mechanisms by which ASC can regulate inflammasome/ cell death signaling responses in the context of an intact organism. The authors have used rigorous genetic approaches to rule out trivial explanations for the unexpected observations. In general, the MS describes an elegant model system that will provide a platform for identifying new mechanisms of ASC-dependent inflammasome signaling and regulated cell death.

2. Advance: This MS describes a highly novel experimental model. Zebrafish are increasingly being used as a genetically tractable model for inflammasome signaling within integrated tissues of intact organism. As noted above, the advances are technical but also conceptual. Future application of this novel model is likely to yield identification of new mechanisms for ASC function in innate immunity and regulated cell death within the context of tissue homeostasis and host defense.

3. Audience: Basic research and discovery.

4. Please define your field of expertise with a few keywords to help the authors contextualize your point of view: My group investigates multiple aspects of inflammasome signaling biology at the cellular level with an emphasis on cell-type specific roles for gasdermins in coordinating downstream innate immune responses to inflammasome activation in various myeloid leukocytes (macrophages, dendritic cells, neutrophils, eosinophils, mast cells).

Reviewer #2 (Evidence, reproducibility and clarity (Required)):

Programmed cell death is critical for host defense and tissue homeostasis. How dead cells initiate cellular responses in the microenvironment with neighbouring cells in vivo is still largely unknown. The authors have chosen a Zebrafish model to tackle this question, given that this model shows advantages for imaging and addresses these pathways in a complex in vivo setting. Their recent development of light-induced activation of caspases (published in JEM) enabled them to investigate cellular responses to a specific type of cell death in vivo at a single cell resolution. In this study, the author further developed a light-induced activation of ASC to specifically look at inflammasome activation-mediated cell death in vivo. The authors have successfully established this system in zebrafish and also observed that Opto-Asc-induced cell death showed different phenotypes as compared to Opto-caspase-a/b-induced cell death. However, it is not really clear why. I have a few specific comments to be addressed or discussed.

1. In Fig.3 and Fig.4, the majority of Opto-Asc localizes to the plasma membrane but not endogenous Asc. It seems that tagging affects its localization, which could potentially explain its slow kinetics in oligomerization.

That is an interesting suggestion. The membrane enrichment is indeed reproducible and we have no full explanation for it. However, ASC itself seems to have some affinity for the cell cortex as seen by its association with the apical actin ridges in keratinocytes in the resting state (see e.g. figure 3A). Affinity of ASC for actin is also documented in the literature:(F-actin dampens NLRP3 inflammasome activity via flightless-1 and LRRFIP2 OPEN; https://doi.org/10.1038/srep29834).

Perhaps the fusion to the optogenetic module somehow enhances the affinity through the initial dimerization. But we can only speculate and have no further evidence that would allow reliable conclusions.

In Fig.7, the authors showed that deletion of Caspb, but not Caspa, affected the apical extrusion, without affecting cell death. This may indicate that other caspases, like Caspase-8 or/and caspase-3 were involved. This could be addressed through deletion of Caspase-8 or/and caspase-3.

These are experiments we had in fact done. Unfortunately, they did not allow us to address the question, because the deletions resulted in embryonic lethality. We have added this information to the text.

It is very surprising that Opto-Asc-mediated cell death is not dependent on Gasdermins, at least in Caspb-dependent apically extruded dead cells.

Indeed – but see comment by and our response to reviewer 1. We hope to be able to provide additional data.

CROSS-CONSULTATION COMMENTS I agree with the other two reviewers and don't have further comments.

Reviewer #2 (Significance (Required)):

The Opto-Asc zebrafish model developed in this study will enable us to specifically look at inflammasome-mediated cell death in vivo. This model is more physiologically relevant compared to Opto-caspase1 model.

Audience interested in physiological function of inflammasome activation, but it is questionable whether such a tool will address mechanisms in mammalian cells. Eventually, more evidence for the latter could be provided.

Reviewer #3 (Evidence, reproducibility and clarity (Required)):

In this article, de Carvahlo and colleagues describe a novel optogenetic tool allowing single cell and temporally controlled induction of ASC clusters in vivo (in zebrafish), a central adaptator protein of the inflammasome complex which is involved in the induction of pyroptosis. This alternative mode of programmed cell death is involved in pathogen response and promote cell swelling and the release of pro-inflammatory factors. Previous works have shown that the inflammasome activation is associated with the formation of a large cluster of ASC protein (called speck) which promotes then the recruitment and the activation of caspase 1. Specks were previously characterised by the same group in vivo (in zebrafish larvae) and could be induced by the overexpression of ASC protein. This however was not compatible with fine spatio-temporal control of speck formation, thus preventing very refined characterisation of the dynamics and the distinction of the cell autonomous and non-cell autonomous effects.

By fusing ASC to the blue-light sensitive oligomerising protein Cry2-olig under the control of a heat shock promoter, they could induce time controlled induction of speck at the single cell level, which is then followed by cell extrusion and cell death both in the periderm and the basal cell of the skin of zebrafish larvae. Doing so, they could characterise the dynamics of speck formation as well as key paramters affecting its dynamics and the subsequent extrusion. While ASC activation led to apical or basal extrusion in the periderm layer followed by non-apoptotic cell death, it triggers basal extrusion and apoptosis in the basal layer. Importantly, periderm cell elimination does not seem to strictly follow all the features of pyroptosis as it does not require GSDM, and relies on Caspb (not Caspa). It is also associated with strong Calcium release both in the dying and neighbouring cells.

The authors performed a very careful characterisation of the tools and the optimisation of the condition to form speck and eliminate cells. The experiments are very well performed with all the necessary controls. The results, while to some extend still hard to fully interpret for some aspects, illustrate the plasticity of cell death and cell extrusion, which include several very interesting observations on the direction of extrusion, putative compensatory modes of cell death upon Caspase1 perturbation and the difference of response to ASC clustering depending on the tissue layer. While it is not the main point of this study, the observation that the direction of extrusion can vary very significantly in different genetic backgrounds is also extremely interesting.

The atypical cell elimination revealed in the system may require further characterisation in the future and suggest that the tools may not be the best to study bona fide pyroptosis. However, I don't believe there is always such strict separation between the modes of cell death and I am sure that it could lead to very interesting insights on inflammasome formation, extrusion and charcaterisation of downstream signalling in vivo, so overall this will be a very interesting resource for the community working on inflammasome, cell death and extrusion.

I have some suggestions that could help to better characterise the mode of elimination as well as the mechanism of speck formation. I have also some suggestions for comparison with other published results as well as some text editing.

Main points :

1. So far, it remains a bit unclear how the authors define precisely speck versus any aggregate and the light induced clusters of Cry2 olig. Is it related to the timescale of formation and/or the lifetime of the aggregates? Is it related to their size?

There Is no ‘formal’ definition of an inflammatory speck apart from it being the unusually large aggregates that ASC forms once it is activated. Light-induced clusters of Cry2Olig alone, or of Cry2olig fusions with proteins that do not normally oligomerize are much smaller (extensive documentation in the literature).

A speck is thus a stable aggregate of ASC which is usually around 1 µm in size and is able to activate downstream caspases. But neither of these aspects alone are unique to ASC: prion-like structures can also be large aggregates (indeed ASC-specks have been compared to prions), and much smaller molecular assemblies can activate caspases. Thus ‘speck’ is more an operational definition, and ‘natural’ specks do have both of these properties, but as our experiments show, the properties can actually be separated. I would rather not try to narrow or change the definition, but leave any further discussion to the experts in the field.

Figure 4E shows a number of variants of ‘speck’-like and other multimers: ASC-mKate and Opto-ASC form large single specks in the presence of endogenous ASC. Opto-ASC specks are only slightly smaller than those formed by endogenously tagged ASC-GFP (see also Supplementary Figure 2E.. Opto-PYD recruits endogenous ASC and becomes incorporated into a speck of approximately the same size, while Opto-CARD does so less efficiently. All of these kill cells. In the absence of endogenous ASC, Opto-ASC forms much smaller specks, and very many in each cell, but these are still functional as seen by the fact that they still kill cells (not the large spot at t = 60 min in the right half of Fig. 4E is not a speck, but the contracted dying cell). Both Opto-PYD and Opto-CARD also form only the small aggregates (quantification will be included), with Opto-PYD still killing the cell by virtue of its ability to recruit caspases via their PYD, whereas Opto-CARD does not.

Since the authors use most of the time constant blue light illumination, could they also assess how long the speck remains after stopping blue light exposure and quantify their lifetime (relative to the CRY2olig cluster lifetime)?

Briefly, any speck that contains a functional ASC moiety remains stable and does not disassemble once the blue light is turned off. In skin cells it is not possible to make quantitative measurements because they are killed by the speck. Opto-ASC specks remain stable until they are taken up by macrophages, as originally reported for ASC-GFP specks in Kuri et al. 2017.

Stability can best be assessed in muscle cells, which do not die upon speck formation. The figure below shows that specks begin to form within minutes of a short pulse of illumination and remain stable (and indeed grow further) for at least 60 min.

Here is an example:

Revisions Figure A:

__Stability of __Opto-ASC specks in muscle cells after exposure to a single pulse of blue light

Specks in muscle cells expressing Opto-AscTg(mCherry-Cry2olig-asc) are induced by a single illumination with blue light (488nm) at t = 0 for 32 seconds. Multiple oligomers begin to form within 6 minutes, continue to gradually increase in number and, and remain until the end of the movie (60 mins).

Cell outlines in the overlying epithelium labeled by AKT-PH-GFP are faintly visible in the first frame. Scale bar is 20 mm.

Similarly could they provide some comparison of the size and localisation of CRY2 olig clusters compared to the speck.

For size, see above. In addition, the size of the Cry2 oligomers as well as of Opto-ASC specks can vary with expression levels.

For location, Cry2olig clusters are usually distributed throughout the cell, as seen in most of the right panels in Fig 4E, and in earlier work in cultured cells (e.g. Taslimi et al 2014). ASC specks can form anywhere in the cell, while Cry2olig-ASC has a preference for the cell cortex, but this is not absolute. In keratinocytes, but not in basal cells, the speck usually forms close to the lateral membrane. In the absence of endogenous ASC no real speck is formed but Opto-ASC in this case shows no clear localisation of Opto-ASC to the membrane.

In view of the variation we see, a strict quantification is difficult: what would be the ‘correct’ definition of classes to look at? To make statistically significant statements, we would need an enormous number of examples in which we could control for all of the variation of expression levels, cell size, day to day variation etc, and we currently don’t have these. We hope the qualitative evidence in the micrographs we show represents the differences well, and we will be happy to provide a larger number of images, if the referees feel this would be helpful.

With the non functional CRY2olig Asc fusion (Cter fusion), do they still see transient olig2 clustering which then reverse when blue light illumination is gone? I think it might be useful to clarify these points in the main text since most of the quantifications are based on speck localisation/numbering, so their characteristics have to be very well defined.

That would be interesting to work out, but after our initial experiments with this construct, we did not pursue this further, since it was not a pressing issue at the time. If we can fit this into our planned experimental time table, we will re-assess it. However, while of interest, we feel these data would not add substantially to what we know at this point.

1. In all the snapshots of speck formation, there seems to be a relative enrichment of the ASC signal at the cytoplasmic membrane (relative to the cytoplasm) prior to strong speck formation. This seems specific of optoASC as it does not seem to happen for the endogeneous ASC or upon overexpression of ASC-mKate (both in this study and in the previous study published by the same group). Is this apparent membrane enrichment something reproducible? (I see that on pretty much every example of this manuscript). If so what could be the explanation? Is there an actual recruitment at the membrane or is it because the membrane/cortical pool takes longer to be recruited in the speck (hence looking relatively more enriched at intermediate time points).

See our speculations in response to point 1 of the first referee.

We too would really like to understand this, but see no easy and efficient way of testing it at this point.

1. There is also a very distinctive ring accumulation that seems to match with apical constriction and/or a putative actomyosin ring (since this is perfectly round, it could match with a structure with high line tension) (see Figure 1E, Figure 3B, Figure 4D...). Is it something already known? Could the authors comment a bit more on this? This could suggest that ASC accumulates in actomyosin cortex, which would be a very interesting property.

We see that we had failed to be clear about this.

There are two types of actin-labelled rings that appear around dying cells. One is formed by the epithelial cells that surround the dying cell. This structure becomes visible as soon as the cell begins to shrink. That it is formed by the surrounding cells is clear from mosaics where the dying cell does not express the actin marker (e.g. suppl. Figure 4A) and the parts of the ring are seen only in the subset of surrounding cells that do express the marker. This ring is also not circular, but follows the polygonal shape of the shrinking cell. We believe that this is the contractile structure that closes the wound, as observed in many other cases of wound healing.

The other is the one the referee describes here. It is formed within the dying cell, as shown by the fact that it is visible in labelled cells when all the surrounding cells are negative for the marker. The other difference is that it appears only once the dying cell has already contracted considerably and begins to round up and be extruded (most clearly seen in Fig. 1E). The third referee had raised a similar point in relation to the same structure seen in Fig. 6C, and we provide below the requested analysis. It relies on resolution in the y-axis, which is unsatisfactory, but nevertheless, it is clear that this ring is in a plane above the apical surface of the epithelium (marked by the red membrane marker, i.e is present in the detaching cell. It may well simply be actin appearing in the entire cortex of the cell as it rounds up and looking like a ring when seen from above. A completely different method for imaging would have to be set up to document this reliably, but we hope that these explanations help to clarify the confusion we may have created.

We do not see this accumulation in cells that leave the epithelium towards the interior (see figure in the response to ‘minor points’ below).

In the end, since cell death can also occur without visible speck formation, I am wondering if they are eventually the most relevant structure to be quantified. Is it because speck can be dissolved upon caspase activation and could it relates to the speed at which caspase are activated (which may not leave enough time for strong aggregation and visible speck formation)? I believe it would help to get more explanation/discussion on this point.

As already mentioned above, it is indeed not obvious what the significance of the large speck is (and it is extremely puzzling why it is that normally one a single one forms in each cell). We agree that it is not necessarily functionally relevant for the signalling outcome to quantify this property – but nor was this the purpose of this work. Regardless of what kind of aggregate is formed, the optogenetic tool allows the induction of ASC-dependent cell death, and therefore the study of the ensuing cellular events.

The compensatory mechanisms that lead to cell death/extrusion despite depletion of caspb is very interesting. Could the authors use some pan caspase inhibitor (like zvad FMK) to confirm that this block opto-ASC cell death also in this context? Alternatively could they check the status of effector caspase activation using live probe (nucview) or immunostaining in the context of caspb depletion?

Those would be interesting avenues to pursue. However, for the reason stated above (Leptin lab closing down, members of fish group no longer at EMBL), we are forced to restrict ourselves to the most important experiments, and think we should prioritize the ones mentioned above.

1. If I understand well, Figure 7C on the right side suggest that the double KO cells don't extrude (if indeed "no change" mean no extrusion, by the way this nomenclature may deserve some clarification in the legend). I don't think these results are mentioned at any point in the main text, but it would be important to include them (since this is an important control).

This interpretation is in fact correct, and we have changed the labelling in the figure to ‘no immediate death’

1. Waves of calcium following cell death and cell extrusion have been previously characterised (Takeushi et al. Curr Biol 2020, Y Fujita group). Interestingly, in this previous article they observed waves of calcium near Caspase8 induced death (in MDCK) as well as near laser induced death in zebrafish, while apparently the authors don't see such Calcium waves upon Caspase8 activation in the zebrafish here. I think it would be important to include a comparison of the authors results with this previous paper in the discussion

We have included this in our discussion.

There is also a previous study which characterised the impact of caspase1 on cell extrusion (Bonfim Melo et al. Cell Report 2022, A. Yap lab) which promotes apical extrusion in Caco2 cells. I think it would also be important to include this work in the discussion and to compare with the results obtain here in vivo.

We have included this in our discussion.

Other minor points:

1. Line 439: are the numbers given in percentage? if these are absolute numbers, it is out of how many cells ? Same remark line 445: what are the number of cases representing? (percentage?)

We have rephrased this to make it unambiguous.

Figure 5: could the authors show periderm and basal cell extrusion with the same type of markers? (membrane or actin or ZO1)? This would help to really compare accurately the morphology and the remodellings associated.

We used Utr-mNeonGreen to lable actin both in periderm and basal cells. Actin labeling of extruded periderm cells is shown in figure 6C, actin labeling of a dying basal cells and the overlying periderm cells is shown in supplementary figure 5A.

Is there any obvious differences in cell size or characteristic cell shape between the classic lab strains (golden, AB, AB2B2) and the WIK and experiment strain used here? I do acknowledge that this is clearly not the focus of this study, but given this striking difference (which is related to an important question in the field of extrusion), it would interesting to mention this if there is anything obvious.

We will make these measurements and include the data.

1. Figure 6C: what is exactly the localisation in Z of this strong actin accumulation observed during apical extrusion? Is it apical or is it rather on the basal side of the cell? A lateral view of actin could be useful in this figure for all the different conditions described.

See response to ‘main point 3’ above.

The images that show this are below. However, even from these images it is hard to appreciate the locations. They are in fact much easier to see by following the movies over time, and through the z-sections at any given time point. We will of course submit the movies with the manuscript.

Revisions figure B:

Localization of actin in the yz and xz planes in Opto-Asc-induced cell death and Opto-caspase-8-induced apoptosis

Orthogonal projections of images of apically (A) and basally (B, C) extruded cells at four time points from time lapse recordings. Each time point shows the x-z plane and the orthogonal yz and and xz planes, in which the apical sides of the epithelium faces the x-z image.

Actin is labeled with mNeonGreen-UtrCH (cyan), plasma membranes and internal membranes by lyn-tagRFP (magenta). Actin is initially concentrated in the apical cortical ridges of periderm cells.

1. Apically extruded cell after death is induced by Opto-Asc. As the cell dies actin is lost from the apical ridges and accumulates in the cell cortex in a plane above the original apical surface of the epithelium
2. Basally extruded cell after death is induced by Opto-Asc. Actin is retained in the apical ridges as the cell shrinks and moves below the epithelium within the dying cell.
3. Basally extruded cell after death is induced by Opto-Caspase 8. The apical surfaces forms a transient dome in which the actin ridges remain intact before the dying cell is internalized. .

Figure S3B: could the authors show the utrophin-neonGreen channel separatly? Is there a ring of actin in the dying cell? Also are the membrane protrusion formed more basally? (I suspect this is a z projection, but this would need to be specified in the legend).

1. Figure 4A legend: I guess the authors meant red arrowheads rather than frame ? This has been corrected

2. I list below a number of typos I could find in the main text

Thanks for noticing these, we have corrected all of these, as well as further typos we found.

Line 29: in Line 30: but Line 151 : from the ...[...] (tissue ?) Line 161: there is most likely a text commenting that was not removed (for how long?) Line 262: generated (egnrtd) Line 268: whereas showed a delay (the subject is missing) Line 269: a point is missing Line 362: which the lack Line 368: a point is missing Line 400: a space is lacking "cellsdepending" Line 438: shrinkwe (space) Line 459 : or I infections Line 525: there is a point missing.

CROSS-CONSULTATION COMMENTS I generally agree with all the comments raised by the other reviewers which partially overlap with comments I had (see for instance referee two for the role of other caspases and the membrane localisation of the probe).

Reviewer #3 (Significance (Required)):

In this article, de Carvahlo and colleagues describe a novel optogenetic tool allowing single cell and temporally controlled induction of ASC clusters in vivo (in zebrafish), a central adaptator protein of the inflammasome complex which is involved in the induction of pyroptosis. This alternative mode of programmed cell death is involved in pathogen response and promote cell swelling and the release of pro-inflammatory factors. Previous works have shown that the inflammasome activation is associated with the formation of a large cluster of ASC protein (called speck) which promotes then the recruitment and the activation of caspase 1. Specks were previously characterised by the same group in vivo (in zebrafish larvae) and could be induced by the overexpression of ASC protein. This however was not compatible with fine spatio-temporal control of speck formation, thus preventing very refined characterisation of the dynamics and the distinction of the cell autonomous and non-cell autonomous effects.

By fusing ASC to the blue-light sensitive oligomerising protein Cry2-olig under the control of a heat shock promoter, they could induce time controlled induction of speck at the single cell level, which is then followed by cell extrusion and cell death both in the periderm and the basal cell of the skin of zebrafish larvae. Doing so, they could characterise the dynamics of speck formation as well as key paramters affecting its dynamics and the subsequent extrusion. While ASC activation led to apical or basal extrusion in the periderm layer followed by non-apoptotic cell death, it triggers basal extrusion and apoptosis in the basal layer. Importantly, periderm cell elimination does not seem to strictly follow all the features of pyroptosis as it does not require GSDM, and relies on Caspb (not Caspa). It is also associated with strong Calcium release both in the dying and neighbouring cells.

The authors performed a very careful characterisation of the tools and the optimisation of the condition to form speck and eliminate cells. The experiments are very well performed with all the necessary controls. The results, while to some extend still hard to fully interpret for some aspect, illustrate the plasticity of cell death and cell extrusion, which include several very interesting observations on the direction of extrusion, putative compensatory modes of cell death upon Caspase1 perturbation and the difference of response to ASC clustering depending on the tissue layer. While it is not the main point of this study, the observation that the direction of extrusion can vary very significantly in different genetic backgrounds is also extremely interesting.

The atypical cell elimination revealed in the system may require further characterisation in the future and suggest that the tools may not be the best to study bona fide pyroptosis. However, I don't believe there is always such strict separation between the modes of cell death and I am sure that it could lead to very interesting insights on inflammasome formation, extrusion and charcaterisation of downstream signalling in vivo, so overall this will be a very interesting resource for the community working on inflammasome, cell death and extrusion.

My expertise are in cell extrusion, optogenetics, apoptosis and epithelial mechanics. I am not a specialist of the inflammasome and pyroptosis.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

---

Referee #3

Evidence, reproducibility and clarity

In this article, de Carvahlo and colleagues describe a novel optogenetic tool allowing single cell and temporally controlled induction of ASC clusters in vivo (in zebrafish), a central adaptator protein of the inflammasome complex which is involved in the induction of pyroptosis. This alternative mode of programmed cell death is involved in pathogen response and promote cell swelling and the release of pro-inflammatory factors. Previous works have shown that the inflammasome activation is associated with the formation of a large cluster of ASC protein (called speck) which promotes then the recruitment and the activation of caspase 1. Specks were previously characterised by the same group in vivo (in zebrafish larvae) and could be induced by the overexpression of ASC protein. This however was not compatible with fine spatio-temporal control of speck formation, thus preventing very refined characterisation of the dynamics and the distinction of the cell autonomous and non-cell autonomous effects.

• By fusing ASC to the blue-light sensitive oligomerising protein Cry2-olig under the control of a heat shock promoter, they could induce time controlled induction of speck at the single cell level, which is then followed by cell extrusion and cell death both in the periderm and the basal cell of the skin of zebrafish larvae. Doing so, they could characterise the dynamics of speck formation as well as key paramters affecting its dynamics and the subsequent extrusion. While ASC activation led to apical or basal extrusion in the periderm layer followed by non-apoptotic cell death, it triggers basal extrusion and apoptosis in the basal layer. Importantly, periderm cell elimination does not seem to strictly follow all the features of pyroptosis as it does not require GSDM, and relies on Caspb (not Caspa). It is also associated with strong Calcium release both in the dying and neighbouring cells.

• The authors performed a very careful characterisation of the tools and the optimisation of the condition to form speck and eliminate cells. The experiments are very well performed with all the necessary controls. The results, while to some extend still hard to fully interpret for some aspects, illustrate the plasticity of cell death and cell extrusion, which include several very interesting observations on the direction of extrusion, putative compensatory modes of cell death upon Caspase1 perturbation and the difference of response to ASC clustering depending on the tissue layer. While it is not the main point of this study, the observation that the direction of extrusion can vary very significantly in different genetic backgrounds is also extremely interesting.

• The atypical cell elimination revealed in the system may require further characterisation in the future and suggest that the tools may not be the best to study bona fide pyroptosis. However, I don't believe there is always such strict separation between the modes of cell death and I am sure that it could lead to very interesting insights on inflammasome formation, extrusion and charcaterisation of downstream signalling in vivo, so overall this will be a very interesting resource for the community working on inflammasome, cell death and extrusion.

I have some suggestions that could help to better characterise the mode of elimination as well as the mechanism of speck formation. I have also some suggestions for comparison with other published results as well as some text editing.

Main points:

1. So far, it remains a bit unclear how the authors define precisely speck versus any aggregate and the light induced clusters of Cry2 olig. Is it related to the timescale of formation and/or the lifetime of the aggregates ? Is it related to their size ? Since the authors use most of the time constant blue light illumination, could they also assess how long the speck remains after stoping blue light exposure and quantify their lifetime (relative to the CRY2olig cluster lifetime) ? Similarly could they provide some comparison of the size and localisation of CRY2 olig clusters compared to the speck. With the non functional CRY2olig Asc fusion (Cter fusion), do they still see transient olig2 clustering which then reverse when blue light illumination is gone ? I think it might be useful to clarify these points in the main text since most of the quantifications are based on speck localisation/numbering, so their characteristics have to be very well defined.

2. In all the snapshots of speck formation, there seems to be a relative enrichment of the ASC signal at the cytoplasmic membrane (relative to the cytoplasm) prior to strong speck formation. This seems specific of optoASC as it does not seem to happen for the endogeneous ASC or upon overexpression of ASC-mKate (both in this study and in the previous study published by the same group). Is this apparent membrane enrichment something reproducible ? (I see that on pretty much every example of this manuscript). If so what could be the explanation ? Is there an actual recruitment at the membrane or is it because the membrane/cortical pool takes longer to be recruited in the speck (hence looking relatively more enriched at intermediate time points).

3. There is also a very distinctive ring accumulation that seems to match with apical constriction and/or a putative actomyosin ring (since this is perfectly round, it could match with a structure with high line tension) (see Figure 1E, Figure 3B, Figure 4D...). Is it something already known ? Could the authors comment a bit more on this ? This could suggest that ASC accumulates in actomyosin cortex, which would be a very interesting property.

4. In the end, since cell death can also occur without visible speck formation, I am wondering if they are eventually the most relevant structure to be quantified. Is it because speck can be dissolved upon caspase activation and could it relates to the speed at which caspase are activated (which may not leave enough time for strong aggregation and visible speck formation) ? I believe it would help to get more explanation/discussion on this point.

5. The compensatory mechanisms that lead to cell death/extrusion despite depletion of caspb is very interesting. Could the authors use some pan caspase inhibitor ( like zvad FMK) to confirm that this block opto-ASC cell death also in this context ? Alternatively could they check the status of effector caspase activation using live probe (nucview) or immunostaining in the context of caspb depletion ?

6. If I understand well, Figure 7C on the right side suggest that the double KO cells don't extrude (if indeed "no change" mean no extrusion, by the way this nomenclature may deserve some clarification in the legend). I don't think these results are mentioned at any point in the main text, but it would be important to include them (since this is an important control).

7. Waves of calcium following cell death and cell extrusion have been previously characterised (Takeushui et al. Curr Biol 2020, Y Fujita group). Interestingly, in this previous article they observed waves of calcium near Caspase8 induced death (in MDCK) as well as near laser induced death in zebrafish, while apparently the authors don't see such Calcium waves upon Caspase8 activation in the zebrafish here. I think it would be important to include a comparison of the authors results with this previous paper in the discussion

8. There is also a previous study which characterised the impact of caspase1 on cell extrusion (Bonfim Melo et al. Cell Report 2022, A. Yap lab) which promotes apical extrusion in Caco2 cells. I think it would also be important to include this work in the discussion and to compare with the results obtain here in vivo.

Other minor points:

1. Line 439: are the numbers given in percentage ? if these are absolute numbers, it is out of how many cells ? Same remark line 445 : what are the number of cases representing ? (percentage ?)

2. Figure 5: could the authors show periderm and basal cell extrusion with the same type of markers ? (membrane or actin or ZO1) ? This would help to really compare accurately the morphology and the remodellings associated.

3. Is there any obvious differences in cell size or characteristic cell shape between the classic lab strains (golden, AB, AB2B2) and the WIK and experiment strain used here ? I do acknowledge that this is clearly not the focus of this study, but given this striking difference (which is related to an important question in the field of extrusion), it would interesting to mention this if there is anything obvious.

4. Figure 6C: what is exactly the localisation in Z of this strong actin accumulation observed during apical extrusion ? Is it apical or is it rather on the basal side of the cell ? A lateral view of actin could be useful in this figure for all the different conditions described.

5. Figure S3B: could the authors show the utrophin-neonGreen channel separatly ? Is there a ring of actin in the dying cell ? Also are the membrane protrusion formed more basally ? (I suspect this is a z projection, but this would need to be specified in the legend).

6. Figure 4A legend : I guess the authors meant red arrowheads rather than frame ?

7. I list below a number of typos I could find in the main text

Line 29: in

Line 30: but

Line 151 : from the ...[...] (tissue ?)

Line 161: there is most likely a text commenting that was not removed (for how long?)

Line 262: generated (egnrtd)

Line 268: whereas showed a delay (the subject is missing)

Line 269: a point is missing

Line 362: which the lack

Line 368: a point is missing

Line 400: a space is lacking "cellsdepending"

Line 438: shrinkwe (space)

Line 459 : or I infections

Line 525: there is a point missing.

CROSS-CONSULTATION COMMENTS

I generally agree with all the comments raised by the other reviewers which partially overlap with comments I had (see for instance referee two for the role of other caspases and the membrane localisation of the probe).

Significance

In this article, de Carvahlo and colleagues describe a novel optogenetic tool allowing single cell and temporally controlled induction of ASC clusters in vivo (in zebrafish), a central adaptator protein of the inflammasome complex which is involved in the induction of pyroptosis. This alternative mode of programmed cell death is involved in pathogen response and promote cell swelling and the release of pro-inflammatory factors. Previous works have shown that the inflammasome activation is associated with the formation of a large cluster of ASC protein (called speck) which promotes then the recruitment and the activation of caspase 1. Specks were previously characterised by the same group in vivo (in zebrafish larvae) and could be induced by the overexpression of ASC protein. This however was not compatible with fine spatio-temporal control of speck formation, thus preventing very refined characterisation of the dynamics and the distinction of the cell autonomous and non-cell autonomous effects.

• By fusing ASC to the blue-light sensitive oligomerising protein Cry2-olig under the control of a heat shock promoter, they could induce time controlled induction of speck at the single cell level, which is then followed by cell extrusion and cell death both in the periderm and the basal cell of the skin of zebrafish larvae. Doing so, they could characterise the dynamics of speck formation as well as key paramters affecting its dynamics and the subsequent extrusion. While ASC activation led to apical or basal extrusion in the periderm layer followed by non-apoptotic cell death, it triggers basal extrusion and apoptosis in the basal layer. Importantly, periderm cell elimination does not seem to strictly follow all the features of pyroptosis as it does not require GSDM, and relies on Caspb (not Caspa). It is also associated with strong Calcium release both in the dying and neighbouring cells.

• The authors performed a very careful characterisation of the tools and the optimisation of the condition to form speck and eliminate cells. The experiments are very well performed with all the necessary controls. The results, while to some extend still hard to fully interpret for some aspect, illustrate the plasticity of cell death and cell extrusion, which include several very interesting observations on the direction of extrusion, putative compensatory modes of cell death upon Caspase1 perturbation and the difference of response to ASC clustering depending on the tissue layer. While it is not the main point of this study, the observation that the direction of extrusion can vary very significantly in different genetic backgrounds is also extremely interesting.

• The atypical cell elimination revealed in the system may require further characterisation in the future and suggest that the tools may not be the best to study bona fide pyroptosis. However, I don't believe there is always such strict separation between the modes of cell death and I am sure that it could lead to very interesting insights on inflammasome formation, extrusion and charcaterisation of downstream signalling in vivo, so overall this will be a very interesting resource for the community working on inflammasome, cell death and extrusion.

• My expertise are in cell extrusion, optogenetics, apoptosis and epithelial mechanics. I am not a specialist of the inflammasome and pyroptosis.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

---

Referee #2

Evidence, reproducibility and clarity

Programmed cell death is critical for host defense and tissue homeostasis. How dead cells initiate cellular responses in the microenvironment with neighbouring cells in vivo is still largely unknown. The authors have chosen a Zebrafish model to tackle this question, given that this model shows advantages for imaging and addresses these pathways in a complex in vivo setting. Their recent development of light-induced activation of caspases (published in JEM) enabled them to investigate cellular responses to a specific type of cell death in vivo at a single cell resolution. In this study, the author further developed a light-induced activation of ASC to specifically look at inflammasome activation-mediated cell death in vivo. The authors have successfully established this system in zebrafish and also observed that Opto-Asc-induced cell death showed different phenotypes as compared to Opto-caspase-a/b-induced cell death. However, it is not really clear why. I have a few specific comments to be addressed or discussed.

1. In Fig.3 and Fig.4, the majority of Opto-Asc localizes to the plasma membrane but not endogenous Asc. It seems that tagging affects its localization, which could potentially explain its slow kinetics in oligomerization.

2. In Fig.7, the authors showed that deletion of Caspb, but not Caspa, affected the apical extrusion, without affecting cell death. This may indicate that other caspases, like Caspase-8 or/and caspase-3 were involved. This could be addressed through deletion of Caspase-8 or/and caspase-3.

3. It is very surprising that Opto-Asc-mediated cell death is not dependent on Gasdermins, at least in Caspb-dependent apically extruded dead cells.

CROSS-CONSULTATION COMMENTS

I agree with the other two reviewers and don't have further comments.

Significance

The Opto-Asc zebrafish model developed in this study will enable us to specifically look at inflammasome-mediated cell death in vivo. This model is more physiologically relevant compared to Opto-caspase1 model.

Audience interested in physiological function of inflammasome activation, but it is questionable whether such a tool will address mechanisms in mammalian cells. Eventually, more evidence for the latter could be provided.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

---

Referee #1

Evidence, reproducibility and clarity

Summary:

ASC is the Pyrin/CARD-containing adapter protein that functions as a core component of inflammasome signaling complexes. ASC functions downstream of various NLR- and ALR-inflammasome initiator proteins and upstream of the inflammatory caspases that function as inflammasome effector enzymes. This study uses a novel chimeric construct (Opto-ASC) comprising the Arabidopsis photo-oligomerizable cryptochrome 2 (Cry2-olig) protein with zebrafish ASC to generate transgenic zebrafish larvae wherein ASC oligomerization can be rapidly, dynamically and spatially induced by blue light illumination of either the entire larva or single cells within discrete tissues of an intact larva. Induction of these "opto-inflammasome" complexes is coupled with state-of-the-art, live-cell optical imaging of multiple single cell and integrative tissue parameters to assay various modes of regulated cell death within the peridermal and basal cellular layers of the larval skin. This experimental model was further combined with genetic manipulation of the expression of various zebrafish inflammatory or apoptotic caspases, as well as the two zebrafish members of the gasdermin family of pore-forming proteins which can mediate disruption of plasma membrane permeability without (pre-lytic) or with (pyroptosis) progression to lytic cell death.

The main results of the study are:

1) introduction of a novel experimental system for dynamic and spatially resolved ASC oligomerization and speck formation within the cells of intact epithelial tissues of a living organism;

2) the ability of these optically induced ASC oligomers/specks to drive multiple modes of regulated cell death which exhibit some (but not all) features of lytic pyroptosis or non-lytic apoptosis depending on cell type and tissue location;

3) the ability of the dying epithelial cells containing optically-induced ASC specks to coordinate rapid adaptive responses in adjacent non-dying cells to maintain integrity/ continuity of skin epithelial barrier; and

4) unexpectedly, no obvious role for either of the two zebrafish gasdermins in the regulated cell death responses.

Major Comments:

1. Are the claims and the conclusions supported by the data or do they require additional experiments or analyses to support them? The major goal of this MS is to present a new experimental model (optogenetic activation of ASC oligomerization in transgenic zebrafish) that has the potential to provide new insights regarding the multiple mechanisms by which ASC can regulate inflammasome/ cell death signaling responses in the context of an intact organism. As noted above, some of the observed results are unexpected (e.g., lytic cell death independent of the zebrafish gasdermins in particular epithelial cells) and may reflect mechanisms unique to zebrafish as a non-mammalian vertebrate model versus the mammalian experimental systems (murine and human) that have informed most of our current understanding of how ASC coordinates inflammasome and cell death responses. However, the authors have used rigorous genetic approaches to rule out trivial explanations for the unexpected observations. Thus, no major additional experiments are required to support the claims and conclusions presented in the MS.

2. Are the suggested experiments realistic in terms of time and resources? Yes. It would help if you could add an estimated time investment for substantial experiments: A few weeks.

3. Are the data and the methods presented in such a way that they can be reproduced? Are the experiments adequately replicated and statistical analysis adequate? Yes.

4. Are the experiments adequately replicated and statistical analysis adequate? Yes.

Minor comments:

1. Specific experimental issues that are easily addressable:

There's a significant concern with the use of LDC7559 (line 387) as a putative small molecule inhibitor of gasdermin D function to test roles (or lack thereof) of the zebrafish gasdermins in the ASC-triggered lytic cell death responses. A recent study (Amara et al. 2021. Cell. PMID34320407) has reported that LDC7559 does not inhibit gasdermin D (and possibly other gasdermins) but rather acts as an allosteric activator of PFKL (phosphofructosekinase-1 liver type) in neutrophils and thereby suppress generation of the NADPH required for the phagocytic oxidative burst and consequent NETosis. Thus, use of LDC7559 as a presumed gasdermin inhibitor in the current MS is problematic and should be deleted. As an alternative pharmacological approach to suppress gasdermin function, the authors might consider the use of either disulfiram (Hu et al. 2020. Nature Immunology. PMID32367036) and/or dimethylfumarate (Humphries et al. Science. 2020. PMID32820063). These would be straightforward additional experiments.

1. Are prior studies referenced appropriately? there are some problems; see below.

2a. One paper is cited twice in lines 724-726 and 727-729.

2b. Another paper is cited twice in lines 790-792 and 793-795.

2c. No journal is included for the referenced study by Shkarina et al in lines 827-828.

2d. No journal is included for the referenced study by Stein et al in lines 831-832.

2e. No journal is included for the referenced study by Masumoto et al in lines 793-795.

2f. No journal is included for the referenced study by Kuri et al in lines 774-775.

1. Are the text and figures clear and accurate? Generally, yes but with a few exceptions noted below:

3a. line 28: "morphological distinct" should read "morphologically distinct"

3b. line 161: this sentence contains in parentheses "for how long?" I think this was a comment by one author that wasn't removed from the final submitted MS

3c. line 945: spelling "balck" > "black"

3d. line 268: "whereas showed a delayed speck formation": the authors need to specify what model/ condition showed a delayed speck formation

3e. line 262: spelling "egnerated" > "generated"

CROSS-CONSULTATION COMMENTS

I also agree with the comments of the other 2 reviewers. Between the 3 sets of comments and suggestions, the aggregate review will provide the authors with a suitable range of feasible recommendations that will improve an already strong MS.

Significance

1. General assessment:

As noted above, this the major goal of this MS is to present a new experimental model (optogenetic activation of ASC oligomerization in transgenic zebrafish) that has the potential to provide new insights regarding the multiple mechanisms by which ASC can regulate inflammasome/ cell death signaling responses in the context of an intact organism. The authors have used rigorous genetic approaches to rule out trivial explanations for the unexpected observations. In general, the MS describes an elegant model system that will provide a platform for identifying new mechanisms of ASC-dependent inflammasome signaling and regulated cell death.

1. Advance:

This MS describes a highly novel experimental model. Zebrafish are increasingly being used as a genetically tractable model for inflammasome signaling within integrated tissues of intact organism. As noted above, the advances are technical but also conceptual. Future application of this novel model is likely to yield identification of new mechanisms for ASC function in innate immunity and regulated cell death within the context of tissue homeostasis and host defense.

1. Audience:

Basic research and discovery.

1. Please define your field of expertise with a few keywords to help the authors contextualize your point of view:

My group investigates multiple aspects of inflammasome signaling biology at the cellular level with an emphasis on cell-type specific roles for gasdermins in coordinating downstream innate immune responses to inflammasome activation in various myeloid leukocytes (macrophages, dendritic cells, neutrophils, eosinophils, mast cells).

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

Learn more at Review Commons

---

Reply to the reviewers

Response to reviewers

Reviewer #1

Reviewer #1 (evidence, reproducibility and clarity (required)):

Winter et al. present a study of Ebola virus fusion in the acidic environment of the late endosome. Based on cryo-ET of Ebola virions undergoing entry into cells, they note that the VP40 matrix is disassembled and dissociated from the viral membrane in virions seen in the endosome. Subsequent in vitro and computational analyses suggest that protons diffuse across the viral membrane and neutralize anionic lipids on the inner leaflet. They argue that this loss of negative charge reduces the affinity of VP40 for the viral membrane. They further suggest that VP40 dissociation from the viral membrane precedes GP-mediated membrane fusion and contributes to reduction in the energy barrier for membrane stalk formation. Whereas most studies have focused on the importance of acidic pH in triggering GP conformational changes during fusion, the present work contributes new appreciation for VP40-membrane interactions.

We would like to thank the reviewer for all the insightful comments and appreciation of the novelty.

In the cryo-ET experiments aimed at visualizing Ebola entry, do the authors see evidence of viral membrane fusion? There is no mention of this in the text. Knowing that the virions that show disassembly of the VP40 matrix are in fact the virions that productively enter cells would support the conclusions of the study. As is stands, one is forced to wonder whether the virions that show VP40 disassembly prior to fusion ultimately fuse.

*We first note that the EBOV virions shown in Figure 1 entering host cells were captured by cryo-ET at 48 hours post infection and resulted from 2-3 rounds of infection, thus the virions can productively enter the cells by micropinocytosis. Virions that are not able to undergo membrane fusion would be processed in the lysosomes and would not be detectable by cryo-ET at 48 hours post infection. In addition, the virions captured in late endosomes contain nucleocapsids, hence these virions are likely infectious. Together, this is good evidence that we really see events after successful membrane fusion. *

*We fully agree with the reviewer that capturing a fusion event would provide further proof that fusion depends on prior disassembly of the VP40 matrix layer. To address this, we acquired additional data on cells infected at different time-points post-infection (15 cells imaged); regrettably, we have not been successful in capturing a membrane fusion event, presumably due its fast kinetics. In this study we are technically limited with the amount of the virus we can use for infection in BSL4. The current dataset was generated at an MOI of 0.1 and this makes capturing entry events difficult as we would need an MOI of at least 100-1000 to increase the chances of capturing such a rare event. *

*Considering the technical difficulties to perform the experiment under BSL4 conditions, we have in addition performed a similar experiment using EBOV VLPs at high concentration (estimated MOI > 100) composed of VP40 and GP (Fig. S5). Despite the high VLP concentration, we could only find 2 tomograms out of 18 tomograms showing VLP entry events. These clearly show that the VP40 matrix is disassembled in VLPs residing in endosomes. The same lamellae displayed sites of viral fusion as evident from enlarged endosomal membrane surfaces studded with GPs facing endosomal lumina. Hence, this new data supports our results that VLPs that undergo VP40 disassembly are able to fuse. We have included the new supplementary figure S5 and added the following sentence to the main text: *

Lines 96-102: “We were not able to capture virions residing in endosomes in the process of fusing with the endosomal membrane, presumably because virus membrane fusion is a rapid event. However, in a similar experiment using EBOV VLPs composed of VP40 and GP, we could confirm the absence of ordered VP40 matrix layers in VLPs inside endosomal compartments. Moreover, we were able to capture one fusion event and several intracellular membranes studded with luminal GPs, indicating that fusion had taken place (Fig. S5).”

In the cryo-ET experiments that evaluate VP40 disassembly in vitro, why do the authors leave out NP from their VLP preparations? There is some evidence in the literature (Li et al., JVI 2016) that NP is necessary to form particles with native morphology. If the authors feel that NP is not necessary for their experiment, perhaps this could be noted.

*Thank you very much for this important comment. Throughout this study, we mainly focused on the fate of the VP40 matrix during entry and thus reduced the complexity of the VLPs used to the minimum – VP40 and GP, so indeed NP was left out before. To address the role of the nucleocapsid in Ebola VLPs uncoating, we have now also included data on VLPs prepared by expression of nucleocapsid components (NP, VP24 and VP35) in addition to GP and VP40. Cryo-ET analysis of these VLPs showed that VLPs mainly contain loosely coiled nucleocapsid. This is consistent with a study by Bharat et al 2012, which shows that compared to virions, VLPs displayed heterogeneous nucleocapsid assembly states and reduced incorporation of nucleocapsids. It is important to note that VLPs containing nucleocapsid also displayed disassembled VP40 matrices at low pH (Fig. S7). Hence, nucleocapsid proteins do not influence the VP40 disassembly driven by low pH and GP-VP40 VLPs can be used as model to study VP40 uncoating. *

*We included a statement shown on lines 150-153: “We further repeated the experiment using VLPs composed of VP40, GP and the nucleocapsid proteins NP, VP24 and VP35, and observed the same low pH-phenotype described above. These results show that nucleocapsid proteins do not influence the VP40 disassembly driven by low pH.” *

The authors argue that acidic pH neutralizes the charge of PS phospholipids, thereby removing the electrostatic interactions of basic residues in VP40 and PS. They also note in the Methods section that 7 amino acids in VP40 are predicted by PROPKA to be protonated at pH 4.5. If the authors feel that protonation of these 7 amino acids is not involved in the loss of affinity for PS, this could be stated explicitly and justified. Could the protonation of these 7 amino acids contribute to disassembly of the VP40 lattice, rather than dissociation from the membrane?

Thank you for this interesting comment. We note that the amino acids predicted to be protonated (*E76, E325, H61, H124, H210, H269, H315, see below) are far away from the interaction interface with the membrane and also away from the intra-dimerization domain. Hence, they do not likely contribute to the loss of affinity for PS but may contribute to conformational changes that facilitate the disassembly of the VP40 matrix. For clarification, we have added the following statement to the methods section: *

Lines 541-544: “Importantly, these residues are located away from the interaction interface of VP40 with the membrane and their protonation accordingly does not influence membrane-binding. However, protonation of these residues may contribute to conformational changes that facilitate the VP40 matrix disassembly. ”

Minor: Figure S5C is difficult to interpret. The red frame on the bars that indicates data acquired at low pH is nearly invisible. Better might be to indicate explicitly (ie, with words) the pH at which data were acquired.

Thank you very much for this comment. We have changed the design of the graph accordingly. Please note that the figure numbering has changed and that Figure S5C is now Figure S6C.* * Reviewer #1 (significance (required)): The significance of the study stems from the idea that the VP40 lattice and its interaction with the viral membrane plays a direct role in facilitating viral fusion. To my knowledge, this has not been previously addressed. The significance would be considerably increased if the authors were able to demonstrate by cryo-ET that the virions with disassembled VP40 were in fact the virions that productively fused. Nonetheless, this work should be of broad interest to researchers studying viral fusion as it may represent a phenomenon relevant to numerous viruses that enter cells via the endocytic route.

Reviewer #2 Reviewer #2 (evidence, reproducibility and clarity (required)):

The manuscript by Winter et al., entitled "The Ebola virus VP40 matrix undergoes endosomal disassembly essential for membrane fusion" describes the structural aspects of the events that precede Ebola virus (EBOV) membrane fusion in late endosome and virion uncoating in the cytosol. By combining state-of-the-art cryo-electron tomography (cryo-ET) with biophysical and computational techniques, they have elucidated the pivotal role of the ebolaviral matrix virion protein 40 (VP40) in modulating the fusion process, in particular discovering that disassembly of the VP40 ordered lattice is low pH-driven, occurs despite the absence of a viral ion channel within the filovirus envelope and takes place through the weakening of VP40 interactions with lipids at the interface between the ebolaviral envelope and matrix. Overall, the manuscript is well written and the research work is very well conceived, with solid orthogonal experimental approaches that mutually validate their respective results. It is opinion of this reviewer that the paper contributes to the elucidation of a key step in the EBOV infection cycle and that it will be of great interest for the readership of Review Commons and for the community of structural biologists. Therefore, I recommend the publication of this paper, however after some minor revision to the text, the figures and the figure legends, which show inconsistencies in the terminology used, the acronyms and could be easily improved by some little graphical editing.

Thank you very much for your positive feedback and your comments.

Comments:

• By starting their abstract and introduction sessions with the term "Ebola viruses" the authors are (on purpose?) preparing the reader to the implicit statement that their findings could be a paradigm model for the other members of the Ebolavirus genus. This is an exciting picture, especially in perspective of VP40-targeting drugs development. Therefore, although conclusions in this sense would probably require further studies, I encourage the authors to implement their figure 3 (or related supplementary figure) with a multiple-sequence alignment, and the relative text in the manuscript, by showing if and how much the basic patch at the C-terminus of VP40 is conserved within the Ebolavirus genus, especially the residues Lys224, Lys225, Lys274 and Lys275.

Thank you very much for this comment. We have added a corresponding sequence alignment highlighting the high conservation of the basic patch of amino acids across all Ebola virus species (Suppl. Fig. S6). In the text, we refer to the sequence conservation as follows:

Lines 213-215: “These interactions are driven by basic patches of amino acids which are highly conserved across all EBOV species (Fig. S8 H), further emphasizing their importance in adaptable membrane binding.”

• It is a bit inconvenient for the reader to follow how a story unfolds while jumping back and forth between figures, and this is why I would recommend to move the period of the sentence at lines 88-91 to the session where figure 5 is discussed.

*We refer in fact to Figure 1 and fixed the reference accordingly (line 95). *

• Please, avoid the use of the slang "Ebola" without the apposition "virus", and make the text consistent throughout the manuscript by only using the acronym of each term after it was introduced for the first time.

Thank you for this comment. We have thoroughly revised the use of technical terms.

Minor revisions: Line 1: "matrix protein undergoes" We refer here to the entire VP40 matrix layer composed of many VP40 proteins and not to single VP40 proteins (as the individual proteins do not disassemble, but their macromolecular assembly does). For clarification, we changed the title to “matrix layer undergoes”.

Line 19: "the matrix viral protein 40 (VP40)" We have corrected the statement.

Line 18: considering that a virus "exists" in the form of a virion while temporarily located outside the cell, and as a "molecular entity" consisting of viral proteins and nucleic acids organised in macromolecular complexes during its life cycle inside the infected cell, this reviewer encourages the authors to rephrase as follows: " Ebola viruses (EBOVs) virions are filamentous particles, ..." Thank you for your suggestion. We have rephrased it to: „Ebola viruses (EBOVs) assemble into filamentous virions“ (line 18).

Lines 35-36 and line 40: "that is determined by the matrix made up by the viral protein 40 (VP40), which drives ..." And then, directly use the acronym VP24 at line 40

We have corrected the statement.

Line 40: as VP24 and VP35 interact with NP but do not interact with the ssRNA genome, please rephrase as follows "the nucleoprotein (NP) which encapsidates the ssRNA genome, and the viral proteins VP24 and VP35 which, together with NP, form the nucleocapsid"

We have corrected the statement.

Lines 47-48: "...fusion glycoprotein (GP)...[...] the ebolaviral envelope"

We have corrected the statement.

Line 51: "...remarkably long virion of EBOVs undergoes..."

We have rephrased the statement: line 55: “…remarkably long EBOV virions undergo…”

Line 63: "... in vitro, and in endo-lysosomal compartments in situ, by cryo-electron..."

We have corrected the statement.

Lines 70-71: " to shed light on EBOVs ... [...] with EBOV (Zaire ebolavirus species, Mayinga strain) in biosafety level 4 (BSL4) containment"

We have corrected the statement.

Line 72: chemically fixed by? (PFA and GA acronyms have been annotated in figure 1, but should be first mentioned in their explicit form in the text)

We have now mentioned annotations for GA and PFA both in the main text and in the figure legend in their explicit forms.

Line 73 (cryo-FIB)

We have corrected the acronym.

Line 80: EBOV virions

We have corrected the statement.

Figure 1A and line 97: for consistency with the terminology used in the main text, should be perhaps in the second step preferred the term "vitrification" instead of cryofixation? Readers not familiar with the field could be confused by the use of the two synonyms

We have replaced the term as suggested.

Lines 92-93: "...these data indicate [...] and suggest..."

We have corrected the statement.

Figure 1C and line 100: in the color legend EBOV is annotated as dark teal, however in the segmentation of the reconstructed tomogram there are three objects, one of which in dark teal is evidently a portion of EBOV virion inside the endosome, and other two are in different shades of green. What are those? Please, could author specify their identity in the figure legend with their corresponding color code? The same applies to supplementary figure S2 (see comment below).

Thank you very much for this comment. All three green objects are EBOV virions. For clarification, we have added numbers 1-3 to the figure and legend and adjusted the text in the legend accordingly (lines 109-110).

Line 95: "...tomography of EBOV virions..."

We have corrected the statement.

Line 98: "...showing EBOV virions..." (This reviewer refers to the use of the term 'EBOVs' as for different species within the genus rather than for different EBOV particles within a dataset)

We have corrected the statement.

Line 105: "... a purified EBOV before..." *We realized a mistake in our phrasing: the virion shown in Fig. 1 H is not purified, but a virion found adjacent to the plasma membrane of an infected cell. We have changed the phrasing accordingly (lines 117-118). *

Line 110 and 113: "...EBOV matrix..." And "EBOV virus-like particles (VLP)"

We have corrected the statement.

Line 140, 141, 145 and 147: "EBOV VLPs" and "EBOV VLP"; idem at lines 188-189, 209 and anywhere else in the manuscript (including figure 4A) We have corrected the use of “EBOV VLP(s)” as suggested.

Line 235: "influenza virus ion channel..."

We have corrected the statement.

Line 249: please, use directly the above-introduced acronym for the detergent

We have revised the use of acronyms.

Figure 5F: in plot's X axis label: thermolysin (T)?

Yes, this is correct and stated in the figure legend.* * Line 342: "EBOV have remarkably long..."

We have corrected the statement.

Line 420 "...matrix-specific"

We have corrected the spelling error.

Line 464: "grids"

We have corrected the spelling error.

Line 465: "for cryo-FIB milling"

We have corrected the statement.

Line 611: "influenza virus M2 ..." (Please, from which influenza virus strain does the gene come from? Alternatively, which is the NCBI Protein and/or UniProt database code?)

We have added the information to the Methods (line 648): “….A/Udorn/307/1972 (subtype H3N2))…”

Line 623: please, use the above-designated acronym for the detergent

*We have used the acronym as suggested. *

Line 716: "...based on cryo-ET..." We have corrected the statement.

Line 718: "influenza virus" We have corrected the term.

Line 734: "cryo-ET data" We have corrected the term.

Fig. S8: for consistency with the main text, "thermolysin" We have corrected the spelling of thermolysin throughout the manuscript.

Fig. S2, C and F: are these EBOV virions (as mentioned in the figure title) or EBOV VLPs (as the legends in the two panels of this figure seem to suggest)? Please, the authors should clarify

Thank you very much for spotting this mistake! These are indeed EBOV virions and we have changed the legends within the figure accordingly.

Line 1046: "malleable lipid envelope of the EBOV"; this adjective sounds confusing; the reviewer encourages the authors to rephrase for more clarity.

We have removed the adjective „malleable”.

Reviewer #2 (significance (required)): see above.

__Reviewer #3__Reviewer #3 (evidence, reproducibility and clarity (required)):

Winter and colleagues describe the molecular architecture of Ebola virus during entry into host cells. The main claims of the paper are that VP40 is disassembled prior to fusion. Disassembly is driven by the low pH environment in the endosomes. PH-induced uncoating works via "passive equilibration" because the Ebola virus envelope does not contain an ion channel. The authors conclude that structural remodeling of VP40 acts as a molecular switch coupling uncoating to fusion. The main novel results of the manuscript are: In situ cryo-ET of endosomal compartments shows EBOV particles with intact condensed nucleocapsids and disordered protein densities that may relate to detached VP40. Five EBOV particles were imaged in the endosome and all had detached VP40 layers. Controls, budding virions and extracellular virions showed intact VP40 layers. Incubation of VP40-Gp VLPs with a pH 4.5 buffer leads to the disorder of the VP40 matrix in vitro, which is independent of Gp presence in the VLPs. MD simulation showed VP40 dimer binding to model membranes containing 30 % PS at pH7 and reduced binding at pH 4.5. Lipidomics revealed the lipid composition of VP40-Gp VLPs demonstrating 9% PS.

VP40-PHluorin fusions were used to determine acidification of VLPs in vitro and to calculate a permeability coefficient of 1.2 Å sec-1, which is quite low compared to the permeability of the plasma membrane (345 Å sec-1). Next they modeled membrane fusion showing that fusion is more favorable after VP40 disassembly, especially favoring stalk formation. The authors propose further that fusion pore opening is more favorable in the presence of VP40. The authors claim that strong interactions of lipids with VP40 stabilizes the hemifusion intermediate. VP40 Gp VLPs can enter host cells independent of pH once Gp has been activated by thermolysin.

We thank the reviewer for these interesting comments and valuable suggestions.

Some of the results are over interpreted and require appropriate modifications.

Main points that need to be addressed: Imperfections of the membrane could be induced by proteins. Does acidification of the virion depend on GP and its transmembrane region? This can be tested with chimeric GP replacing its TM by unrelated trimeric TMs.

We agree that this is important to consider. We have addressed this question in Fig. 2 K using VLPs composed of VP40 alone. These VLPs lack GP and still display luminal acidification as evident from the disassembled VP40 matrix when incubated at low pH. Therefore, acidification does not depend on GP. For clarification, we have adjusted the following sentence in the discussion:

Lines 410-413: “Using VLPs of minimal protein composition (VP40 and GP, and VP40 alone), we show that VP40‑disassembly, i.e. the detachment of the matrix from the viral envelope is triggered by low endosomal pH (Fig. 2). This indicates that VP40 disassembly does not depend on structural changes of other viral proteins, including GP, and is driven solely by the acidic environment.*” *

Virus entry assays, line 292. The low pH is not only used for Gp cleavage, but induces the conformational changes leading to the post fusion conformation of Gp2. The authors need to check what happens to Gp once it is cleaved by thermolysin. Is this sufficient to induce the conformational changes in Gp? And if so how does entry of such VLPs work, because once the conformational change is triggered, GP2 will adopt the post fusion conformation which is inactive in fusion. This requires further clarification.

To our knowledge, there is only one study showing that EBOV GP2 changes conformation at low pH in the form of a re-arrangement of the fusion peptide from an extended loop to a kinked conformation (Gregory et al 2011). Importantly, low pH alone is not sufficient to trigger GP mediated membrane fusion and NPC1 is needed as a trigger for membrane fusion process (Das et al, 2020). Hence proteolytically processed GP requires NPC1 binding to change its conformation to post-fusion state. We addressed this question by using pre-cleaved (= GP2) and low pH- treated VLPs in our entry assay (Fig. 5 F). Since low pH-treated VLPs enter host cells as efficiently as VLPs incubated at neutral pH, and low pH-treated and additionally pre-cleaved VLPs enter even more efficiently, it is highly unlikely that low pH triggers the post-fusion conformation as this should inhibit virus entry (as the reviewer pointed out). In conclusion, low pH does not induce the post-conformation in GP2 and we have included a respective sentence for clarification:

Lines 339-343: * “Since thermolysin-treated EBOV VLPs efficiently enter untreated host cells at neutral and low pH, we further conclude that low pH alone does not induce the GP2 post-fusion conformation, which would inhibit virus entry. Together, this suggests a role of low endosomal pH beyond proteolytic processing of EBOV GP, likely for the disassembly of the VP40 matrix.”*

In the fusion model, the authors claim that VP40 disassembly is more favorable for stalk formation, which is likely true. However, they also claim that strong VP40 interaction, which I would interpret as VP40 filaments interacting with the membrane, favor fusion pore opening. The tomograms and the in vitro experiments with VLPs indicate that the complete VP40 matrix is detached from the membrane under low pH conditions.

We would like to stress that the modelling results for hemifusion formation and pore opening are independently calculated but have to be interpreted together because they occur sequentially. Hemifusion precedes formation of the pore and hence even though the model shows that the fusion pore opening is favored in the presence of VP40 interaction, membrane fusion cannot proceed to this stage because hemifusion is blocked until the VP40 matrix layer disassembles from the membrane. We apologize for lack of clarity, and we have added the sentences:

Lines 315-318: “However, it is important to note that hemifusion precedes pore formation in the membrane fusion pathway. Since the disassembly of the VP40 matrix is required for hemifusion and hence for the initiation of membrane fusion, it determines the outcome of the membrane fusion pathway.*” *

VLPs are purified. Can the authors exclude the possibility that the purification protocol does not damage the VLP membrane leading to in vitro acidification in a low pH environment? Can some of the assays be repeated with non-purified VLPs?

*Thank you very much for this important comment. To address this question, we had performed the cryo-ET experiments using purified and unpurified VLPs and found that they are virtually indistinguishable. Importantly, unpurified VLPs also undergo VP40 disassembly. We now show images from unpurified VLPs in a supplementary figure (Fig. S7). Thereby, the manuscript contains data of purified VLPs while we also provide proof that the purification protocol does not influence the disassembly of the VP40 matrix. We added the following explanatory sentence to the main text: *

Lines 151-156: “*We further repeated the experiment using VLPs composed of VP40, GP and the nucleocapsid proteins NP, VP24 and VP35, and observed the same low pH-phenotype described above (Fig. S5 C). Performing the experiments on unpurified VLPs harvested from the supernatant of transfected cells confirmed that the purification protocol applied did not influence the disassembly of the VP40 matrix (Fig. S7). “ *

Does acidification only work at pH 4.5?

*We also attempted to verify the acidification of VLPs at higher pH (~5.5. and ~6.0) by cryo-ET, however, subtle structural differences were difficult to quantify. Considering the lower permeability of the VLP membrane compared to the plasma membrane, we think that acidification occurs indeed also at higher pH (as shown for cells), albeit at slower kinetics. *

Minor points Line 37: Ruigrok et al. 2000 J Mol Biol showed first that Ebola VP40 requires negatively charged lipids for interaction.

*Thank you for pointing out this reference. We have included it in the text. *

Fig. 1f: Is VP40 detaching as a filament?

We have not observed that VP40 detaches as a filament or a linear segment of multiple VP40 dimers. *Since the VP40 dimer is inherently flexible (Fig. 3, Fig. S8) and can rotate along the N- and C-terminal intra- and inter-dimer interfaces, we believe disassembly occurs in a non-ordered fashion (not as filaments, see also Figure 2 G-K). *

References 8 and 28 are the same. We have corrected the reference duplication.

Lipidomics: The authors find only 9% PS in the VLPs. How do these results compare to the composition of other enevloped viruses that have been reported to assemble on negatively charged lipids.

*We compared the lipid composition of the EBOV VLPs to the lipid composition of influenza viruses and HIV, which both bud from the plasma membrane and require negatively charged lipids. When grown in eggs, the envelope of influenza viruses contains 22-25 % PS (Ivanova et al 2015, Li et al 2011), and approximately 12% when produced from MDCK cells (Gerl et al 2012). The envelope of HIV virions produced from HeLa or MT4 cells contains 10-15% PS. These numbers suggest that the producing cell line strongly influences the lipid composition of the virus particles. Besides differences in the producing cell line, the lower amount of PS found in EBOV VLPs could have multiple implications: first, apart from PS, PIP2 has also been shown to interact specifically with VP40 at budding sites in the plasma membrane (Jeevan et al 2017, Johnson et al 2018) and thus also contributes to virion assembly (potentially allowing for a lower PS concentration); second, as recently shown for paramyxoviruses (Norris et al 2022), binding of PS to viral proteins is not based on charge alone but may include specific binding – in which case a high affinity of viral proteins to PS may allow for a lower PS concentration in the target membrane. Overall, the rather low PS content in Ebola VLPs might be important for VP40 interaction and low pH-driven disassembly. *

EBO virus was suggested to assemble at lipid rafts. Is this reflected by the lipid composition?

*Yes, that is correct. A hallmark of lipid rafts is the enrichment of cholesterol and sphingomyelin (~32 mol% cholesterol, ~ 14 mol% sphingomyelin) in the microdomains (Pike et al 2002). The lipid composition of the EBOV VLPs determined in our study (~ 39% cholesterol and ~10 mol% sphingomyelin) is consistent with the assembly at lipid rafts. Minor differences stem from the different cell lines and lipidomic approaches used to determine the lipid species. *

Reviewer #3 (significance (required)): In summary, the manuscript is of high technical quality and the observation that VP40 detaches from the viral membrane prior to membrane fusion is novel and interesting to the field of virus fusion. How acidification occurs in the absence of an ion channel remains to be determined. The authors provide little insight how this might work. The strong part of the manuscript is the EM part, which shows convincing detachement of the VP40 matrix. I cannot comment too much on the modelling part, which, however, sounds solid.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

---

Referee #3

Evidence, reproducibility and clarity

Winter and colleagues describe the molecular architecture of Ebola virus during entry into host cells. The main claims of the paper are that VP40 is disassembled prior to fusion. Disassembly is driven by the low pH environment in the endosomes. PH-induced uncoating works via "passive equilibration" because the Ebola virus envelope does not contain an ion channel. The authors conclude that structural remodeling of VP40 acts as a molecular switch coupling uncoating to fusion.

The main novel results of the manuscript are:

• In situ cryo-ET of endosomal compartments shows EBOV particles with intact condensed nucleocapsids and disordered protein densities that may relate to detached VP40.

• Five EBOV particles were imaged in the endosome and all had detached VP40 layers. Controls, budding virions and extracellular virions showed intact VP40 layers.

• Incubation of VP40-Gp VLPs with a pH 4.5 buffer leads to the disorder of the VP40 matrix in vitro, which is independent of Gp presence in the VLPs.

• MD simulation showed VP40 dimer binding to model membranes containing 30 % PS at pH7 and reduced binding at pH 4.5.

• Lipidomics revealed the lipid composition of VP40-Gp VLPs demonstrating 9% PS.

• VP40-PHluorin fusions were used to determine acidification of VLPs in vitro and to calculate a permeability coefficient of 1.2 Å sec-1, which is quite low compared to the permeability of the plasma membrane (345 Å sec-1).

• Next they modeled membrane fusion showing that fusion is more favorable after VP40 disassembly, especially favoring stalk formation.

• The authors propose further that fusion pore opening is more favorable in the presence of VP40.

• The authors claim that strong interactions of lipids with VP40 stabilizes the hemifusion intermediate.

• VP40 Gp VLPs can enter host cells independent of pH once Gp has been activated by thermolysin.

• Some of the results are over interpreted and require appropriate modifications.

Main points that need to be addressed:

• Imperfections of the membrane could be induced by proteins. Does acidification of the virion depend on GP and its transmembrane region? This can be tested with chimeric GP replacing its TM by unrelated trimeric TMs.

• Virus entry assays, line 292. The low pH is not only used for Gp cleavage, but induces the conformational changes leading to the post fusion conformation of Gp2. The authors need to check what happens to Gp once it is cleaved by thermolysin. Is this sufficient to induce the conformational changes in Gp? And if so how does entry of such VLPs work, because once the conformational change is triggered, GP2 will adopt the post fusion conformation which is inactive in fusion. This requires further clarification.

• In the fusion model, the authors claim that VP40 disassembly is more favorable for stalk formation, which is likely true. However, they also claim that strong VP40 interaction, which I would interpret as VP40 filaments interacting with the membrane, favor fusion pore opening. The tomograms and the in vitro experiments with VLPs indicate that the complete VP40 matrix is detached from the membrane under low pH conditions. VLPs are purified. Can the authors exclude the possibility that the purification protocol does not damage the VLP membrane leading to in vitro acidification in a low pH environment?

• Can some of the assays be repeated with non-purified VLPs?

• Does acidification only work at pH 4.5?

Minor points

• Line 37: Ruigrok et al. 2000 J Mol Biol showed first that Ebola VP40 requires negatively charged lipids for interaction.

• Fig. 1f: Is VP40 detaching as a filament?

• References 8 and 28 are the same.

• Lipidomics: The authors find only 9% PS in the VLPs. How do these results compare to the composition of other enevloped viruses that have been reported to assemble on negatively charged lipids.

• EBO virus was suggested to assemble at lipid rafts. Is this reflected by the lipid composition?

Significance

In summary, the manuscript is of high technical quality and the observation that VP40 detaches from the viral membrane prior to membrane fusion is novel and interesting to the field of virus fusion. How acidification occurs in the absence of an ion channel remains to be determined. The authors provide little insight how this might work.

The strong part of the manuscript is the EM part, which shows convincing detachement of the VP40 matrix. I cannot comment too much on the modelling part, which, however, sounds solid.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

---

Referee #2

Evidence, reproducibility and clarity

The manuscript by Winter et al., entitled "The Ebola virus VP40 matrix undergoes endosomal disassembly essential for membrane fusion" describes the structural aspects of the events that precede Ebola virus (EBOV) membrane fusion in late endosome and virion uncoating in the cytosol. By combining state-of-the-art cryo-electron tomography (cryo-ET) with biophysical and computational techniques, they have elucidated the pivotal role of the ebolaviral matrix virion protein 40 (VP40) in modulating the fusion process, in particular discovering that disassembly of the VP40 ordered lattice is low pH-driven, occurs despite the absence of a viral ion channel within the filovirus envelope and takes place through the weakening of VP40 interactions with lipids at the interface between the ebolaviral envelope and matrix. Overall, the manuscript is well written and the research work is very well conceived, with solid orthogonal experimental approaches that mutually validate their respective results. It is opinion of this reviewer that the paper contributes to the elucidation of a key step in the EBOV infection cycle and that it will be of great interest for the readership of Review Commons and for the community of structural biologists. Therefore, I recommend the publication of this paper, however after some minor revision to the text, the figures and the figure legends, which show inconsistencies in the terminology used, the acronyms and could be easily improved by some little graphical editing.

Comments:

• By starting their abstract and introduction sessions with the term "Ebola viruses" the authors are (on purpose?) preparing the reader to the implicit statement that their findings could be a paradigm model for the other members of the Ebolavirus genus. This is an exciting picture, especially in perspective of VP40-targeting drugs development. Therefore, although conclusions in this sense would probably require further studies, I encourage the authors to implement their figure 3 (or related supplementary figure) with a multiple-sequence alignment, and the relative text in the manuscript, by showing if and how much the basic patch at the C-terminus of VP40 is conserved within the Ebolavirus genus, especially the residues Lys224, Lys225, Lys274 and Lys275.

• It is a bit inconvenient for the reader to follow how a story unfolds while jumping back and forth between figures, and this is why I would recommend to move the period of the sentence at lines 88-91 to the session where figure 5 is discussed.

• Please, avoid the use of the slang "Ebola" without the apposition "virus", and make the text consistent throughout the manuscript by only using the acronym of each term after it was introduced for the first time.

Minor revisions:

Line 1: "matrix protein undergoes"

Line 19: "the matrix viral protein 40 (VP40)"

Line 18: considering that a virus "exists" in the form of a virion while temporarily located outside the cell, and as a "molecular entity" consisting of viral proteins and nucleic acids organised in macromolecular complexes during its life cycle inside the infected cell, this reviewer encourages the authors to rephrase as follows: " Ebola viruses (EBOVs) virions are filamentous particles, ..."

Lines 35-36 and line 40: "that is determined by the matrix made up by the viral protein 40 (VP40), which drives ..." And then, directly use the acronym VP24 at line 40

Line 40: as VP24 and VP35 interact with NP but do not interact with the ssRNA genome, please rephrase as follows "the nucleoprotein (NP) which encapsidates the ssRNA genome, and the viral proteins VP24 and VP35 which, together with NP, form the nucleocapsid"

Lines 47-48: "...fusion glycoprotein (GP)...[...] the ebolaviral envelope"

Line 51: "...remarkably long virion of EBOVs undergoes..."

Line 63: "... in vitro, and in endo-lysosomal compartments in situ, by cryo-electron..."

Lines 70-71: " to shed light on EBOVs ... [...] with EBOV (Zaire ebolavirus species, Mayinga strain) in biosafety level 4 (BSL4) containment"

Line 72: chemically fixed by? (PFA and GA acronyms have been annotated in figure 1, but should be first mentioned in their explicit form in the text)

Line 73 (cryo-FIB)

Line 80: EBOV virions

Figure 1A and line 97: for consistency with the terminology used in the main text, should be perhaps in the second step preferred the term "vitrification" instead of cryofixation? Readers not familiar with the field could be confused by the use of the two synonyms

Lines 92-93: "...these data indicate [...] and suggest..."

Figure 1C and line 100: in the color legend EBOV is annotated as dark teal, however in the segmentation of the reconstructed tomogram there are three objects, one of which in dark teal is evidently a portion of EBOV virion inside the endosome, and other two are in different shades of green. What are those? Please, could author specify their identity in the figure legend with their corresponding color code? The same applies to supplementary figure S2 (see comment below).

Line 95: "...tomography of EBOV virions..."

Line 98: "...showing EBOV virions..." (This reviewer refers to the use of the term 'EBOVs' as for different species within the genus rather than for different EBOV particles within a dataset)

Line 105: "... a purified EBOV before..."

Line 110 and 113: "...EBOV matrix..." And "EBOV virus-like particles (VLP)"

Line 140, 141, 145 and 147: "EBOV VLPs" and "EBOV VLP"; idem at lines 188-189, 209 and anywhere else in the manuscript (including figure 4A)

Line 235: "influenza virus ion channel..."

Line 249: please, use directly the above-introduced acronym for the detergent

Figure 5F: in plot's X axis label: thermolysin (T)?

Line 342: "EBOV have remarkably long..."

Line 420 "...matrix-specific"

Line 464: "grids"

Line 465: "for cryo-FIB milling"

Line 611: "influenza virus M2 ..." (Please, from which influenza virus strain does the gene come from? Alternatively, which is the NCBI Protein and/or UniProt database code?)

Line 623: please, use the above-designated acronym for the detergent

Line 716: "...based on cryo-ET..."

Line 718: "influenza virus"

Line 734: "cryo-ET data"

Fig. S8: for consistency with the main text, "thermolysin"

Fig. S2, C and F: are these EBOV virions (as mentioned in the figure title) or EBOV VLPs (as the legends in the two panels of this figure seem to suggest)? Please, the authors should clarify

Line 1046: "malleable lipid envelope of the EBOV"; this adjective sounds confusing; the reviewer encourages the authors to rephrase for more clarity.

Significance

see above.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

---

Referee #1

Evidence, reproducibility and clarity

Winter et al. present a study of Ebola virus fusion in the acidic environment of the late endosome. Based on cryo-ET of Ebola virions undergoing entry into cells, they note that the VP40 matrix is disassembled and dissociated from the viral membrane in virions seen in the endosome. Subsequent in vitro and computational analyses suggest that protons diffuse across the viral membrane and neutralize anionic lipids on the inner leaflet. They argue that this loss of negative charge reduces the affinity of VP40 for the viral membrane. They further suggest that VP40 dissociation from the viral membrane precedes GP-mediated membrane fusion and contributes to reduction in the energy barrier for membrane stalk formation. Whereas most studies have focused on the importance of acidic pH in triggering GP conformational changes during fusion, the present work contributes new appreciation for VP40-membrane interactions.

• In the cryo-ET experiments aimed at visualizing Ebola entry, do the authors see evidence of viral membrane fusion? There is no mention of this in the text. Knowing that the virions that show disassembly of the VP40 matrix are in fact the virions that productively enter cells would support the conclusions of the study. As is stands, one is forced to wonder whether the virions that show VP40 disassembly prior to fusion ultimately fuse.

• In the cryo-ET experiments that evaluate VP40 disassembly in vitro, why do the authors leave out NP from their VLP preparations? There is some evidence in the literature (Li et al., JVI 2016) that NP is necessary to form particles with native morphology. If the authors feel that NP is not necessary for their experiment, perhaps this could be noted.

• The authors argue that acidic pH neutralizes the charge of PS phospholipids, thereby removing the electrostatic interactions of basic residues in VP40 and PS. They also note in the Methods section that 7 amino acids in VP40 are predicted by PROPKA to be protonated at pH 4.5. If the authors feel that protonation of these 7 amino acids is not involved in the loss of affinity for PS, this could be stated explicitly and justified. Could the protonation of these 7 amino acids contribute to disassembly of the VP40 lattice, rather than dissociation from the membrane?

• Minor: Figure S5C is difficult to interpret. The red frame on the bars that indicates data acquired at low pH is nearly invisible. Better might be to indicate explicitly (ie, with words) the pH at which data were acquired.

Significance

The significance of the study stems from the idea that the VP40 lattice and its interaction with the viral membrane plays a direct role in facilitating viral fusion. To my knowledge, this has not been previously addressed. The significance would be considerably increased if the authors were able to demonstrate by cryo-ET that the virions with disassembled VP40 were in fact the virions that productively fused. Nonetheless, this work should be of broad interest to researchers studying viral fusion as it may represent a phenomenon relevant to numerous viruses that enter cells via the endocytic route.

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

Learn more at Review Commons

---

Reply to the reviewers

Manuscript number: RC-2022-01756

Corresponding author(s): Wenya, Hou

1. General Responses

Dear Editors and Reviewers,

We deeply appreciate all critical comments and constructive suggestion from all Reviewers, which have inspired us to conceive at least 8 new important experiments and mathematic analysis/modeling (shown in dark red). In addition, we will include more repeats with quantification for spot assays (with more HU doses) and biochemical experiments as well as language revision (shown in orange).

Below we only list the general response to the Major Concerns raised by at least two Reviewers:

• To perform mathematic analysis of the single-cell quantitative data (Fig 4, Fig 5 and Fig S4) (Analysis #1).

50% Sic1 degradation time from Sic1peak

WT SC

7.62 min

whi7 whi5 SC

7.91 min

WT HU

36 min

whi7 whi5 del HU

7.49 min

50% nuclear exit time of Whi5

WT SC

4.69 min

rad53Δsml1Δ SC

7.60 min

WT HU

22.33 min

rad53Δsml1ΔHU

13.41 min

Table R1. 50% Sic1 degradation time calculated from Sic1peak and 50% nuclear exit time of Whi5 based on the experimental data shown in Fig 5 and Fig 4, respectively.

(2) To reinterpret the HU-induced extension of G1/S transition with an updated model (Analysis #2).

(3) predict that like WHI7/5 overexpression, CKS1 deletion (PMID: 7958905) or sic1 mutants with longer destruction timing (T2,5S-VLLPP or T2,5S-RXL reported in Fig. 6C, PMID: 32296067), can suppress the HU sensitivity of rad53 mutants according to our model. Moreover, their suppression effects should be epistatic to WHI7/5 overexpression. Alternatively, the dosage suppression of WHI7/5 might be reversed by CKS1 overexpression or sic1 mutants with shorter destruction timing (unfortunately no such mutant has been reported yet). We will perform this set of genetic experiment to test these predictions and thereby functionally reinforce the Whi7/5-Cks1-Sic1 axis (Experiment #1).

(4) do DNA replication profiling to examine the number of origin firing or replication capacity (Experiment #2).

(5) To address the suppression effect of phosphorylation in Fig 2E. We agree that the phenotypes of the A-mutants of Whi7 have a weak difference compared with WT, but become much stronger (5-fold difference between two dilutions) compared with the D-mutants. As shown lately in Fig 3, phosphorylation solely facilitates protein stabilization/total levels, which can be masked by ectopic overexpression from an extra plasmid. Moreover, phosphorylation does NOT enhance Whi7’s interaction with Cks1. We should tune down the contribution of phosphorylation and focus more on the stability/protein level. Furthermore, we will do competition assays using A-/D- mutants with GFP and RFP labels (Experiment #3), and add back whi7 13A or 13D in its endogenous locus in the whi7Δwhi5Δ double mutant to test the effect on Sic1 turnover (Experiment #4).

(6) To add more repeats with quantification for spot assays (with more HU doses) and biochemical experiments (shown in orange).

Besides reinforcing the current model, these experiments, analysis and re-interpretation may help to clarify two concepts which remain elusive in current version:

• S-CDK activation can switch from an abrupt/all-or-none pattern under normal condition to a gradually flattened one under replication stress.
• Consequently, the Whi7/5-Cks1-S-CDKs axis may determine replication capacity and/or number of origin firing. Thus, we did not include a preliminary revision this time due to significant changes. We plan to request at least 6 months for an extensive full revision (e.g., from a short letter to a regular article) to improve this study to a higher level with more general significance. Therefore, we request a revision opportunity from The EMBO Journal.

2. Point-to-point responses

Reviewer #1 (Evidence, reproducibility and clarity (Required)):

SUMMARY

This work begins with a heterologous screen, introducing human genes in double mec1,sml1 yeast deletants, which are alive, but sensitive to hydroxyurea. The readout was mec1,sml1 proliferation in the presence of hydroxyurea. They found that mec1,sml1 yeast mutants carrying the human RB1 gene (a G1/S transcriptional repressor) proliferated on hydroxyurea. Then, they test if known yeast G1/S transcriptional repressors (Whi5 and Whi7) could have similar effects if provided at higher than normal levels (they did). With this initial result, followed up by a variety of experiments, the authors then go on to propose that replication stress, which activates Mec1 and Rad53, triggers the phosphorylation of Whi7 (by Mec1) and Whi5 (by both Rad53 and Mec1) blocking their eviction from the nucleus, allowing them instead to bind and inhibit Cks1, a Cdk processivity factor, needed for the complete phosphorylation and degradation of a Cdk inhibitor, Sic1. This is different from published work a decade earlier in mammalian cells (ref. 37; Bertoli et al.), which showed that upon replication stress, Chk1 phosphorylates G1/S transcriptional repressors to maintain G1/S transcription, which could help genome stability. Here, the authors propose that replication stress could block the G1/S transition. While the model and some of the experiments are interesting, the rationale for some experiments was shaky, and the data do not fully support the conclusions.

MAJOR POINTS

1. • Any cell that undergoes DNA replication must have already destroyed Sic1. It has been known for 25+ years that targeting Sic1 is the only necessary function of G1/Cdk to enable DNA replication (PMID: 8755551). Sic1 does not reappear until the M/G1 transition. Hence, in the authors' model, where cells are already in the S phase, how can multisite phosphorylation and degradation of Sic1 be the critical and final output of the pathway they propose when there shouldn't be any Sic1 around, to begin with? Why would a cell that has already completed Start and the G1/S transition, is in the S phase and experiencing replication stress, care about going through the G1/S? A: Yes, S-CDK activity is regarded as an abrupt or so-called “all-or-none transition” due to a relative short half-life of Sic1 controlled by a robust double-negative feedback loop (PMID: 24130459; 23230424). Sic1 degradation requires multi-phosphorylation events including prime phosphorylation by G1-CDKs, two opposing multi-phosphorylation by S-CDK complex (Clb5–Cdk1–Cks1), one to trigger phosphodegrons and the other to terminate the degron route (PMID: 32296067). The timing and speed (or “sharpness”) of Sic1 degradation is determined by G1-CDKs and S-CDKs, respectively (PMID: 24130459 and PMID: 32296067). Sic1 degradation is not an instantaneous “all-or-none” event even under the optimal growth conditions. The Sic1 destruction timing calculated from Start (defined as 50% nuclear exit of Whi5) is about 14.2 min, whereas the time between Start and Sic1peak is about 5 min from independent studies (Fig 4G, PMID: 24130459; Fig. 6C, PMID: 32296067; Fig. 7B, 32976810). Similarly, the 50% Sic1 degradation time calculated from Sic1peak (50% of Sic1peak) is about 8 min for WT and whi7, in agreement with the results in Figure 2E, PMID: 24130459. However, in the presence of HU, the 50% of Sic1peak time remains constant (7.49 min) in whi7Δwhi5Δ cells but becomes greater than 36 min in WT. Meanwhile, the 50% nuclear exit time of Whi5 (Start) is about 22 min in WT compared to 13 min in rad53Δsml1*Δ upon HU treatment.

50% Sic1 degradation time from Sic1peak

WT SC

7.62 min

whi7 whi5 SC

7.91 min

WT HU

36 min

whi7 whi5 del HU

7.49 min

50% nuclear exit time of Whi5

WT SC

4.69 min

rad53Δsml1Δ SC

7.60 min

WT HU

22.33 min

rad53Δsml1ΔHU

13.41 min

Table R1. 50% Sic1 degradation time calculated from Sic1peak and 50% nuclear exit time of Whi5 based on the experimental data shown in Fig 5 and Fig 4, respectively.

Therefore, G1/S transition is a “transition zone” (from Start to 50% of Sic1peak) rather than a single borderline. The key finding of this study is that in the presence of HU, Sic1 degradation speed/sharpness is significantly reduced (Figure 5), mechanistically due to the inhibition of S-CDK-Cks1 by Whi7/5. This eventually reflects a flattened S-CDK activity curve, no longer an “all-or-none activation” any more upon replication stress. S-CDKs phosphorylate the two essential targets (Sld2 and Sld3) to enable DNA replication. Therefore, the Sic1 levels determine the S-CDK activities, which in turn determine the DNA replication capacity (the maximal amount of DNA a cell can synthesize per unit time). In sum, under optimal conditions, the S-CDK activity appears an abrupt/sharp transition and cells replicate DNA in its maximum capacity (i.e., minimal S phase length). When cells encounter replication stress (HU), S-CDK is activated very slowly (very low Sic1 destruction speed) and replicate DNA with a low capacity (slow fork speed and/or few origin firing) to meet the limited resource. Recently, the de Bruin group demonstrates that replication capacity can be tuned by E2F-dependent transcription (includes S-Cyclin genes) in mammalian cells (PMID: 32665547).

Inspired by these questions, we plan to

(1) perform mathematic analysis of the single-cell quantitative data (Fig. 5 and S4) (Analysis #1).

(2) reinterpret the HU-induced extension of G1/S transition with an updated model (Analysis #2).

(3) predict that like WHI7/5 overexpression, CKS1 deletion (PMID: 7958905) or sic1 mutants with longer destruction timing (T2,5S-VLLPP or T2,5S-RXL reported in Fig. 6C, PMID: 32296067), can suppress the HU sensitivity of rad53 mutants according to our model. Moreover, their suppression effects should be epistatic to WHI7/5 overexpression. Alternatively, the dosage suppression of WHI7/5 might be reversed by CKS1 overexpression or sic1 mutants with shorter destruction timing (unfortunately no such mutant has been reported yet). We will perform this set of genetic experiment to test these predictions and thereby functionally reinforce the Whi7/5-Cks1-Sic1 axis (Experiment #1).

(4) do DNA replication profiling to examine the number of origin firing or replication capacity (Experiment #2).

• The results in Figure 2C are confusing and difficult to interpret. For example, comparing lane 8 (WT without hydroxyurea) to lane 7 (WT with hydroxyurea), it appears that there is more phosphorylated Whi7 in lane 7 (hydroxyurea treatment) than in lane 8 (no treatment). But, the ratio of phosphorylated/unphosphorylated Whi7 is not that different (there is very little unphosphorylated Whi7 in lane 8). Same problem when comparing lanes 3 and 4. I understand that they later show that Whi7 is stabilized by hydroxyurea, but from the data in this figure, what exactly can they conclude here?*

A: Yes, phosphorylation is a bit confusing according to the current statement. Without HU, Whi7 is phosphorylated by G1-CDKs with a much less total protein level as well. With HU, whi7 is phosphorylated by Mec1 and Rad53, because Whi7-P largely disappeared in rad53 mutant (lane 1) and 13A (with all putative Mec1-Rad53 sites mutated, lane 5). Lanes 3 and 4 are biological repeats of Lanes 7-8 with less loading. We will clarify our statement.

• Their data in Figure 2E show that phosphorylation of Whi7 is not required for suppressing the lethality of rad53,sml1 cells treated with hydroxyurea. Cells carrying Whi7-41A (lacking all possible phosphorylations) suppressed nearly as well as wild-type Whi7 did. The purported differences in the suppression are minuscule at best and not evident at the dilutions tested. It is not clear at all how they can conclude that phosphorylation of Whi7 has anything to do with the ability of Whi7 overexpression to suppress the lethality of rad53,sml1 cells.*

A: Yes, we agree that the phenotypes of the A-mutants of Whi7 have a weak difference compared with WT, but become much stronger (5-fold difference between two dilutions) compared with the D-mutants. As shown lately in Fig 3, phosphorylation solely facilitates protein stabilization/total levels, which can be masked by ectopic overexpression from an extra plasmid. Moreover, phosphorylation does NOT enhance Whi7’s interaction with Cks1.

Anyway, we should tune down the contribution of phosphorylation and focus more on the stability/protein level. Furthermore, we will do competition assays using A-/D- mutants with GFP and RFP labels __(Experiment #3) __and add back whi7 13A or 13D in its endogenous locus in the whi7

• For all the arguments they make about this new role of Whi5 and Whi7 at Start, they do not examine size homeostasis or the kinetics of cell cycle progression in any of their experiments and their mutants, with or without hydroxyurea treatment.*

A: Good suggestion. We will examine size homeostasis, budding index or the cell cycle progression in the related experiments (Experiment #5). In Fig. S5, we only showed the cell cycle progression profiles in wild-type cells carrying an extra copy of Whi7 WIQ or Whi7 WIQ ΔC. WIQ mutant (without Swi6 binding activity) significantly slowed the cell cycle progression under normal conditions.

• The Sic1 stability experiments they show in Figure 5 are nice. They would need to be extended to their various mutants, including their Whi7 phosphomutants, to make a case for phosphorylation by Rad53 and Mec1 in this process.*

A: Thanks, very good suggestion, we will add back whi7 13A or 13D in its endogenous locus in the whi7Δwhi5Δ double mutant (Experiment #4), to avoid the effects of overexpression.

MINOR POINTS

1. • The language is awkward. Editing for style will be necessary.* A: We will request language editing.

2. They use different hydroxyurea doses in the experiments they show, making it difficult to conclude much when comparing different figures. Why aren't they consistent from experiment to experiment?*

A: Sorry for the confusing. We used at least three HU concentration gradients in each experiment, but only showed one of them to save the space for a short article. Notably, S. cerevisiae has a much broader range of HU doses (up to 300 mM) than other species (less than 10 mM). We’ll add other Figures during revision.

**Referees cross-commenting**

Overall, all reviews are well-aligned. The points raised by the other reviewers are valid, and the reviews are thorough and detailed. I don't know whether the authors will be able to respond since the list is quite long. Even if they do, the manuscript will look very different. I do not have anything else to add.

Reviewer #1 (Significance (Required)):

The manuscript presents some interesting data, most notably the role of Whi7 and Whi5 in the stability of Sic1 in vivo and the various in vitro experiments the authors present. The advance is conceptual and mechanistic, offering a different and unanticipated model for the role of these proteins at Start, under replication stress. Unfortunately, the significance of the manuscript is limited. A convincing case for their model and its importance has not been made. For example, their data in Figure 2E, measuring the ability of phosphomutants to suppress the lethality of rad53,sml1 cells upon replication stress, is underwhelming and undermines the importance of the study, particularly to a wider audience.

A: Thanks for the suggestion, we will improve the model as discussed above.

Reviewer #2 (Evidence, reproducibility and clarity (Required)):

Summary:

Jin et al demonstrate a novel type of regulation of the G1/S transition in response to hydroxyurea stress. They approach this by first screening a library of human proteins (cDNA on yeast plasmids) for repressors of the mec1 or rad53 HU sensitivity. HU inhibits ribonucleotide reductase and thus lowers dNTP pools needed for S-phase. This slows replication and leads to stalled replication forks, triggering a "replication stress" response, which is executed by the kinases Mec1 and Rad53. Deletions of mec1 or rad53 are viable in unstressed conditions (with additional sml1 deletion), but are lethal on even low doses of HU. One main hit that rescued this lethality was the human G1/S inhibitor RB. They then went on to confirm that also the yeast analogs Whi5 and Whi7 can rescue mec1 or rad53 lethality when overexpressed. To track down the mechanism, the authors do a variety of genetic and biochemical assays. The resulting model is that Mec1 and Rad53 phosphorylate and stabilize Whi7, which binds to and inhibits the S-phase-CDK complex via the processivity factor Cks1. So on top of acting as a transcriptional repressor, Whi7 (and probably also Whi5) is also a direct interactor and inhibitor of CDK. The binding of Whi7 to Cks1-Clb5/6-CDK prevents the hyperphosphorylation and degradation of the inhibitor Sic1, and thus slows the G1/S transition in response to HU.

Major comments:

- Are the key conclusions convincing?

->Overall I think the sum of the evidence supports the suggested model, individual claims though are on somewhat shaky grounds based often on single replicates, see below.

My main conceptual issue may be somewhat just a "semantic" problem. In my understanding "replication stress" refers to stalled replications forks and/or large stretches of single-strand DNA which then triggers a checkpoint response. So how would slowing the G1/S transition help to deal with "replication stress", if replication is not yet happening in these cells? I am assuming Mec1 senses dNTP depletion also in the absence of replication and that is how Mec1 and Rad53 become active in G1. But then maybe the model and the arguments can be phrased differently? What exactly is slowing down Sic1 degradation doing for the cell? Replenishing dNTP pools before the first origins fire? Or is maybe Sic1 not the most important target of this regulation? Maybe also during S-phase, partially inhibiting CDK is beneficial, maybe to stretch out origin firing... or?

A: Thank you, very good suggestion. This also helps to address the Major Point 1 raised by Reviewer #1. This also reminds us about the work from Pasero’s group demonstrating that Mec1 is activated at the onset of normal S phase by low dNTPs (PMID: 32169162). We will revise the text, and do DNA replication profiling __(Experiment #2) __to examine the number of origin firing or replication speed. Also see response to Point 1 of Reviewer #1.

- Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?

->Most of the work is done on Whi7 and then some Whi5 in the end, I would tone down on the Whi5 claims a bit.

A: Very good suggestion. We have to include Whi5 in the story because it plays a redundant role with Whi7.

- Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation.

-> Since the authors are clearly able to do quantitative live cell imaging, I do not understand why they do not quantify Whi7 concentrations and localization in response to HU instead of using Western blots of synchronized cells. This would make the whole thing much more credible, especially given the current lack of replicates (see below). This would also allow correlating the timing and amount of the Whi7 response with the stabilizing of Sic1 in single cells.

A: Yes, we tried but did not see Whi7-GFP clearly because of its very low protein abundance, which is also not shown in literature as far as we know. Only overexpressed Whi7 fluorescence detection(PMID: 33443080).

->The causality of phosphorylation being required for stabilization seems plausible from the genetics, but is far from clear in the western blots. Here, concentration increase seems to precede phosphorylation. Could this due to induced Whi7 transcription?

A: Good suggestion. We will detect Whi7 mRNA levels through qPCR (Experiment #6).

->Many if not most claims are based on single replicates. See below.

- Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated cost and time investment for substantial experiments.

-I am not suggesting any different types of experiments or new methods, so it should be doable within a few weeks.

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

-I would suggest the authors spell out all of their experimental procedures instead of referring to "as described previously". I think everyone knows the pains of going on a wild goose chase of following references to the original method description.

A: Good suggestion. I will described all experimental procedures to replace "as described previously".

- Are the experiments adequately replicated and statistical analysis adequate?

-The key weakness of this entire paper is imho that many claims are based on single experiments, that are neither replicated nor quantified. For example, all the co-IPs (such as 1E or 3F) should be replicated and the ratio of bait to target quantified and averaged.

A: Good suggestion. We will show the biological repeats and quantification.

-If a claim is made regarding increased phosphorylation in vivo, then again this should be replicated and the ratio of phosphorylated to unphosphorylated bands quantified. In many Whi7 gels it looks like it is mainly the total amount of the protein that is changing rather than the phosphorylation state. But again, by eye and from a single replicate, this is hard to tell.

A: Good suggestion. We will add more repeats.

-A similar thing holds true for the spot assays. Spot assays are great to show lethality and rescue as in the first figure. But making semi-quantitative claims of different degrees of "partial rescue" from a single spot assay is a bit speculative. This seems especially true since the authors are using different and seemingly random HU concentrations for every spot assay, which suggests that the effect is not very robust and can only be seen in very specific concentration ranges. If e.g. the degree of rescue between WT, A and D mutants or truncations matters for the model/the storyline, then more quantitative growth or competition assays should be added.

A: Good suggestion. sorry for the confusing. We used at least three HU concentration gradients in each experiment, but only showed one of them to save the space for a short article. Notably, S. cerevisiae has a much broader range of HU doses (up to 300 mM) than other species (less than 10 mM). We’ll add other Figures during revision, and do competition assays using A-/D- mutants with GFP and RFP labels

Minor comments:

- Specific experimental issues that are easily addressable.

->At least some of the alpha-factor release experiments should contain infos on budding index and/or DNA content to understand see the delay in timing by HU addition.

A: Good suggestion. We will examine size homeostasis, budding index or the cell cycle progression in the related experiments (Experiment #5).

- Are prior studies referenced appropriately?

->Seems fine from the G1/S side, but I don't know the Mec1/Rad53 literature well enough to judge.

- Are the text and figures clear and accurate?

->The authors could do another round of proofing figures and legends. For example, Fig 5C contains scale bars that are not defined, blot 3E has an asterix labeling that is not defined, the model in 5E has misspelled "degradation"...

A: We will proofread and revise the full text again.

- Do you have suggestions that would help the authors improve the presentation of their data and conclusions?

-> The authors use a lot of different mutants (especially for Whi7). Even for someone who knows the proteins fairly well, it is hard to remember throughout the text which abbreviation is relating to which mutations and which function that is addressing. Maybe occasionally remind the reader of what the mutant is or use terms like Whi7non-binding rather than WIQ.

A: Thank you for your suggestion. We will add (TF non-binding) after WIQ.

->The text could also use another round of proof-reading. The overall flow of the storyline is easily comprehensible, but sometimes there is a sudden switch of topics or new proteins come out of nowhere. Some expressions are used in a way that is not common English.

A: We will request language editing.

Reviewer #2 (Significance (Required)):

- Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field.

->This study is a major conceptual contribution to understanding G1/S regulation in perturbed conditions (assuming the results can be replicated and quantified as detailed above). That Whi7 (and maybe Whi5) directly inhibit Clb5/Clb6-CDK through Cks1 binding is an important addition/modification to the current model and may well be important beyond genotoxic stress.

A: Thanks and we’ll reinforce it with more repeats and quantification.

- Place the work in the context of the existing literature (provide references, where appropriate).

->The authors do this reasonably well.

- State what audience might be interested in and influenced by the reported findings.

-> Anyone in the yeast cell cycle/replication field should find this interesting. It should also have important implications for the mammalian cell cycle/replication/DNA damage field.

- Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate.

->I am well familiar with G1/S control and all the methods used in the study. I am not an expert on replication stress/DNA damage/ checkpoint signaling.

Reviewer #3 (Evidence, reproducibility and clarity (Required)):

Summary

In their manuscript "Transcription-independent hold of the G1/S transition is exploited to cope with DNA replication stress", Jin et al. intend to show that Retinoblastoma-like G1/S transcriptional repressors can also work as S-CDK-Cks1 inhibitors in response to DNA replication stress, hence prolongating the G1/S transition to enable cells to deal with replication stress. In particular, they aim to identify the mechanism by which Whi7/Srl3 (Suppressor of Rad53 Lethality) rescues the lethality of rad53 yeast mutants. Even though their very first experiment is performed using human RB1, the remainder or the work is performed in the yeast model organism. Experimental methods used include mostly immunoprecipitation experiments (Western blots), spot assays, and some single cell microscopy (not specified if widefield or confocal).

Major comments

1) the authors refer to a cross-species screen where they aim to detect human proteins that rescue, upon overexpression, the yeast mec1Dsml1D and rad53Dsml1D lethality (of note, not mec1D/rad53D: why?). They identify hRB1 this way. But the entire screen data is missing, either is the analysis pipeline and "hit selection thresholds" (if applicable). Then no more experiments are performed on human cells or using human proteins. In my opinion this cross-specie approach is not necessary, or not developed enough.

A: Yes, we have only performed a pilot screen based on the growing on 4 mM HU. We consider removing it. The reason to use mec1Dsml1D for genetic screen is that mec1D/rad53D cells are dead even without HU, whereas dissection assays do not fit for large-scale screening.

2) Moreover, the interpretation of the data provided as a whole is strongly complicated by the variability in the HU doses used to trigger the Mec1/Rad53 response. While most immunoprecipitation experiments are performed with 200mM, spot assays are performed at various HU concentrations ranging from 3 to 21mM (and exploring the entire range). Sometimes HU concentrations differ on the same Figure panels. Downstream effects of such diverse HU concentrations might also be very diverse and due to this it is difficult to get an understanding of how the different experiments fit together.

A: Sorry for the confusing. We used at least three HU concentration gradients in each experiment, but only showed one of them to save the space for a short article. Notably, S. cerevisiae has a much broader range of HU doses (up to 300 mM) than other species (less than 10 mM). Spot assays (HU are persistent) are mostly done in the mec1Dsml1D and rad53Dsml1D background (sensitive to 4 mM HU), whereas the IP experiments (only 2-3 h treatment and then removal) are mainly performed in WT or at least in comparison with WT background (resistant up to 250 mM HU). We’ll add other Figures during revision.

3) Likewise, some experiments are performed only on rad53D backgrounds, or only on mec1D backgrounds (e.g. Fig1B and Fig1F, respectively), while results are claimed valid for the two gene deletion backgrounds.

A: Thank you. We will add some “not shown data” and remove the invalid claims without data.

4) Finally, the experiments performed in this study and/or their quantitative analysis are insufficient to support several of the claims, and results are often "over-interpreted". Below I have listed some of such insufficient experiments/analyses, in regard of the interpretation that the authors make of each piece of data.

- Fig1B could indeed show that Whi7 could rescue rad53D lethality but it is hard to judge from just one tetrad. Many tetrads should be shown to exclude "random sampling" effects.

A: Thank you. We will add more repeats and remove over-statements. Fig 1B was carried out for at least 12 tetrads but the original picture has been unintentionally lost. We can repeat it if necessary, but the result was validated by the plasmid shuffling experiment (Fig 1C).

- Fig1F indeed shows that the rescue effect of Whi7 overexpression on mec1Dsml1D lethality in HU does not require its G1/S transcription factor-binding motif (GTB); however, it does not prove that it is independent on any putative effects that Whi7 could have on transcription (it could affect other transcription factors, or even the same ones via other domains).

A: Good suggestion. As far as we know, there are no reports proving that Whi7 binds to other transcription factors. To rule out this possibility, we will detect whether overexpression of WHI7 affects the transcription of representative G1/S genes (Experiment #7).

- FigS2A does not really support the authors' claim that Whi7 is hyperphosphorylated upon HU-treatment: the first lane before HU treatment already show the same hyperphosphorylated bands than the second lane (see "darker exposure"); however, the signal intensity is clearly lower so the overall levels of Whi7 are clearly increased by HU, rather than the relative fractions of phosphorylated species.

A: Yes, we will modify the statement as suggested.

- Fig2B shows that HU-dependent increase in Whi7 levels is partially abrogated in rad53Dsml1D and mec1Dsml1D mutant backgrounds, which demonstrates that Whi7 upregulation requires either Rad53 or Sml1, and Mec1 or Sml1, but not Rad53/Mec1 as claimed by the authors.

A: Thank you, we will revise the statement. The only known function of Sml1 is a small unstructured protein inhibitor of Rnr1.

- Likewise, Fig2B does not show any significant Whi7 phosphorylation following HU-treatment in the whi7-13AP mutant with all CDK consensus sites mutated to alanine. There is indeed a slightly slower migrating band appearing as acknowledge by the authors, which also appears in the mec1Dsml1D and rad53Dsml1D backgrounds. Again here, higher Whi7 levels in the WT background make the comparison with mec1Dsml1D and rad53Dsml1D backgrounds almost impossible. Quantification of the blots, including normalization of the signals of each phosphorylated band to the total signal, could help. But overall this figure does not demonstrate any Mec1/Rad53-dependent Whi7 phosphorylation following HU treatment. The phostag gel Fig2C might show the same result, as the differences in phosTag signals between different conditions might just simply reflect the differences in total amount of Whi7 between those same conditions. However, I acknowledge that Figs 2D and S2C shows Rad53- and Mec1-triggered Whi7 phosphorylation in vitro, but the conditions of this experiments likely differ a lot from in vivo context (kinase levels, competing substrates, presence of co-factors...).

A: Thank you, we will quantify the blotting as suggested.

- Along the same lines, Fig3E seems to show that truncation of Whi7 C terminus slightly reduces its efficiency in pulling down Cks1 (indicating reduced interaction). However, the total amount of WT Whi7 in the pull down seems to exceed the total amount of Whi7-DeltaC protein, which could in part explain the difference in Cks1 signal. Here again, quantification of the WB signals and adequate normalization would maybe make this figure more convincing.

A: Good suggestion. We will show the biological repeats and quantification.

- Fig4A-B (Whi5 GFP data): the cell representing the absence of HU shows Whi5 nuclear export and therefore likely passes through G1/S; the HU-treated cell shown as example does not export Whi5 from the nucleus, certainly because it does not pass G1/S. IMHO this demonstrates that the G1/S transition is delayed in HU-treated cells (as shown previously), irrespective of any role of Whi5 or Whi7 in this delay.

- Likewise, Fig4C shows the absence of HU-induced delay in Whi5 nuclear export in rad53Dsml1D cells; however, while the authors claim this indicates "Rad53-dependent nuclear detention of Whi5", it is equally plausible that it indicates that rad53Dsml1D cells do not delay the G1/S transition under HU treatment.

A: good comments. We should claim both possibilities at this stage. Previous studies mainly show delays in the Start stage (e.g., down-regulate SBF transcription). CLN1/2 deletion is known to delay DNA replication in a Sic1-dependent manner albeit with unknown mechanism in the S-CDK activation stage.

- The same ambiguity holds for Fig5A,B (Sic1-GFP quantification in whi5Dwhi7D double deletion strain following release from alpha factor block): indeed Sic1 is degraded fast after release from alpha factor block both in presence of HU, while in WT cells Sic1 is not immediately degraded in presence of HU. While authors claim that "Whi7 and Whi5 significantly slow down the Sic1 degradation", this result could also likely reflect that whi5Dwhi7D cells pass G1/S even in this context, and therefore that whi5 or whi7 or both have a role in maintaining cells in G1, not showing any direct implication of Whi5/Whi7 in Sic1 degradation.

A: good comments. It only provides some indirect hints. For instance, whi5Dwhi7D cells pass G1/S in a same timing as WT in the absence of HU (Fig. S4), indicating that the role of Whi5/7 in the G1/S delay is related to additional checkpoint function, not normal G1 maintaining function. Moreover, it should be combined with other results, for example, dosage suppression effects in the presence of HU and inhibitory effects in the absence of HU. Direct evidence of Whi5/Whi7 in Sic1 degradation and Cks1 inhibition comes only from the biochemical experiments shown in Fig 3E-3H.

- FigS5: the authors show here that overexpression of Whi7-WIQ (that does not bind SBF) slows down the G1/S transition following release from alpha factor blockade, but this data does not demonstrate anything related to the role of Whi7 in the DNA replication stress response. Indeed, since Whi7 sequesters Cln3 in the ER (independent of any putative role on transcription regulation), its overexpression could simply reflect an increased sequestering of Cln3 pool. What does this result become in a cln3D background?

A: Very good suggestion. We will check whether cln3Δ affects the suppression effect of Whi7 (Experiment #8).

Due to the fundamental concerns raised above in the interpretation of the data, it is difficult to predict the outcome of more controlled experiments that would aim to prove the same statements. This makes the estimation of the time and resources required to complete the study almost impossible.

Minor comments

Owing to the major comments above, an important re-structuration of the study is required, and minor comments I may have on this version are likely to be irrelevant to the revised manuscript.

Reviewer #3 (Significance (Required)):

The study aims to establish a molecular link between the progression through the G1/S transition and the DNA damage and DNA replication stress responses. Establishing molecular links between different phases of the cell cycle is an important question in basic research, and might be of interest for a broad range of cell biologists, even though the study is conducted in a model organism (budding yeast). The link proposed involves G1/S inhibitors Whi5 and Whi7, that would bind and inhibit the Cks1 subunit of S-CDK complexes, downstream of Rad53 and Mec1 signaling. The authors confirm some known results (e.g., Whi7 overexpression bypasses rad53 lethality in presence of HU) and gather new pieces of data using well-established methods (immunoprecipitation, spot assays, fluorescence microscopy). However, many experiments reported in this study are not sufficient to support the authors' claims, and therefore the novel mechanistic insight that this study ambitions to provide is not established.

My scientific background being more in bio-imaging than in biochemistry, it is possible that I missed some hands-on experience to correctly interpret artefacts on western blots, however I do not feel like I missed sufficient expertise to evaluate any section of the manuscript.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

---

Referee #3

Evidence, reproducibility and clarity

Summary

In their manuscript "Transcription-independent hold of the G1/S transition is exploited to cope with DNA replication stress", Jin et al. intend to show that Retinoblastoma-like G1/S transcriptional repressors can also work as S-CDK-Cks1 inhibitors in response to DNA replication stress, hence prolongating the G1/S transition to enable cells to deal with replication stress. In particular, they aim to identify the mechanism by which Whi7/Srl3 (Suppressor of Rad53 Lethality) rescues the lethality of rad53 yeast mutants. Even though their very first experiment is performed using human RB1, the remainder or the work is performed in the yeast model organism. Experimental methods used include mostly immunoprecipitation experiments (Western blots), spot assays, and some single cell microscopy (not specified if widefield or confocal).

Major comments

1. the authors refer to a cross-species screen where they aim to detect human proteins that rescue, upon overexpression, the yeast mec1Dsml1D and rad53Dsml1D lethality (of note, not mec1D/rad53D: why?). They identify hRB1 this way. But the entire screen data is missing, either is the analysis pipeline and "hit selection thresholds" (if applicable). Then no more experiments are performed on human cells or using human proteins. In my opinion this cross-specie approach is not necessary, or not developed enough.
2. Moreover, the interpretation of the data provided as a whole is strongly complicated by the variability in the HU doses used to trigger the Mec1/Rad53 response. While most immunoprecipitation experiments are performed with 200mM, spot assays are performed at various HU concentrations ranging from 3 to 21mM (and exploring the entire range). Sometimes HU concentrations differ on the same Figure panels. Downstream effects of such diverse HU concentrations might also be very diverse and due to this it is difficult to get an understanding of how the different experiments fit together.
3. Likewise, some experiments are performed only on rad53D backgrounds, or only on mec1D backgrounds (e.g. Fig1B and Fig1F, respectively), while results are claimed valid for the two gene deletion backgrounds.

4. Finally, the experiments performed in this study and/or their quantitative analysis are insufficient to support several of the claims, and results are often "over-interpreted". Below I have listed some of such insufficient experiments/analyses, in regard of the interpretation that the authors make of each piece of data.

5. Fig1B could indeed show that Whi7 could rescue rad53D lethality but it is hard to judge from just one tetrad. Many tetrads should be shown to exclude "random sampling" effects.

6. Fig1F indeed shows that the rescue effect of Whi7 overexpression on mec1Dsml1D lethality in HU does not require its G1/S transcription factor-binding motif (GTB); however, it does not prove that it is independent on any putative effects that Whi7 could have on transcription (it could affect other transcription factors, or even the same ones via other domains).
7. FigS2A does not really support the authors' claim that Whi7 is hyperphosphorylated upon HU-treatment: the first lane before HU treatment already show the same hyperphosphorylated bands than the second lane (see "darker exposure"); however, the signal intensity is clearly lower so the overall levels of Whi7 are clearly increased by HU, rather than the relative fractions of phosphorylated species.
8. Fig2B shows that HU-dependent increase in Whi7 levels is partially abrogated in rad53Dsml1D and mec1Dsml1D mutant backgrounds, which demonstrates that Whi7 upregulation requires either Rad53 or Sml1, and Mec1 or Sml1, but not Rad53/Mec1 as claimed by the authors.
9. Likewise, Fig2B does not show any significant Whi7 phosphorylation following HU-treatment in the whi7-13AP mutant with all CDK consensus sites mutated to alanine. There is indeed a slightly slower migrating band appearing as acknowledge by the authors, which also appears in the mec1Dsml1D and rad53Dsml1D backgrounds. Again here, higher Whi7 levels in the WT background make the comparison with mec1Dsml1D and rad53Dsml1D backgrounds almost impossible. Quantification of the blots, including normalization of the signals of each phosphorylated band to the total signal, could help. But overall this figure does not demonstrate any Mec1/Rad53-dependent Whi7 phosphorylation following HU treatment. The phostag gel Fig2C might show the same result, as the differences in phosTag signals between different conditions might just simply reflect the differences in total amount of Whi7 between those same conditions. However, I acknowledge that Figs 2D and S2C shows Rad53- and Mec1-triggered Whi7 phosphorylation in vitro, but the conditions of this experiments likely differ a lot from in vivo context (kinase levels, competing substrates, presence of co-factors...).
10. Along the same lines, Fig3E seems to show that truncation of Whi7 C terminus slightly reduces its efficiency in pulling down Cks1 (indicating reduced interaction). However, the total amount of WT Whi7 in the pull down seems to exceed the total amount of Whi7-DeltaC protein, which could in part explain the difference in Cks1 signal. Here again, quantification of the WB signals and adequate normalization would maybe make this figure more convincing.
11. Fig4A-B (Whi5 GFP data): the cell representing the absence of HU shows Whi5 nuclear export and therefore likely passes through G1/S; the HU-treated cell shown as example does not export Whi5 from the nucleus, certainly because it does not pass G1/S. IMHO this demonstrates that the G1/S transition is delayed in HU-treated cells (as shown previously), irrespective of any role of Whi5 or Whi7 in this delay.
12. Likewise, Fig4C shows the absence of HU-induced delay in Whi5 nuclear export in rad53Dsml1D cells; however, while the authors claim this indicates "Rad53-dependent nuclear detention of Whi5", it is equally plausible that it indicates that rad53Dsml1D cells do not delay the G1/S transition under HU treatment.
13. The same ambiguity holds for Fig5A,B (Sic1-GFP quantification in whi5Dwhi7D double deletion strain following release from alpha factor block): indeed Sic1 is degraded fast after release from alpha factor block both in presence of HU, while in WT cells Sic1 is not immediately degraded in presence of HU. While authors claim that "Whi7 and Whi5 significantly slow down the Sic1 degradation", this result could also likely reflect that whi5Dwhi7D cells pass G1/S even in this context, and therefore that whi5 or whi7 or both have a role in maintaining cells in G1, not showing any direct implication of Whi5/Whi7 in Sic1 degradation.
14. FigS5: the authors show here that overexpression of Whi7-WIQ (that does not bind SBF) slows down the G1/S transition following release from alpha factor blockade, but this data does not demonstrate anything related to the role of Whi7 in the DNA replication stress response. Indeed, since Whi7 sequesters Cln3 in the ER (independent of any putative role on transcription regulation), its overexpression could simply reflect an increased sequestering of Cln3 pool. What does this result become in a cln3D background? Due to the fundamental concerns raised above in the interpretation of the data, it is difficult to predict the outcome of more controlled experiments that would aim to prove the same statements. This makes the estimation of the time and resources required to complete the study almost impossible.

Minor comments

Owing to the major comments above, an important re-structuration of the study is required, and minor comments I may have on this version are likely to be irrelevant to the revised manuscript.

Significance

The study aims to establish a molecular link between the progression through the G1/S transition and the DNA damage and DNA replication stress responses. Establishing molecular links between different phases of the cell cycle is an important question in basic research, and might be of interest for a broad range of cell biologists, even though the study is conducted in a model organism (budding yeast). The link proposed involves G1/S inhibitors Whi5 and Whi7, that would bind and inhibit the Cks1 subunit of S-CDK complexes, downstream of Rad53 and Mec1 signaling. The authors confirm some known results (e.g., Whi7 overexpression bypasses rad53 lethality in presence of HU) and gather new pieces of data using well-established methods (immunoprecipitation, spot assays, fluorescence microscopy). However, many experiments reported in this study are not sufficient to support the authors' claims, and therefore the novel mechanistic insight that this study ambitions to provide is not established.

My scientific background being more in bio-imaging than in biochemistry, it is possible that I missed some hands-on experience to correctly interpret artefacts on western blots, however I do not feel like I missed sufficient expertise to evaluate any section of the manuscript.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

---

Referee #2

Evidence, reproducibility and clarity

Summary:

Jin et al demonstrate a novel type of regulation of the G1/S transition in response to hydroxyurea stress. They approach this by first screening a library of human proteins (cDNA on yeast plasmids) for repressors of the mec1 or rad53 HU sensitivity. HU inhibits ribonucleotide reductase and thus lowers dNTP pools needed for S-phase. This slows replication and leads to stalled replication forks, triggering a "replication stress" response, which is executed by the kinases Mec1 and Rad53. Deletions of mec1 or rad53 are viable in unstressed conditions (with additional sml1 deletion), but are lethal on even low doses of HU. One main hit that rescued this lethality was the human G1/S inhibitor RB. They then went on to confirm that also the yeast analogs Whi5 and Whi7 can rescue mec1 or rad53 lethality when overexpressed. To track down the mechanism, the authors do a variety of genetic and biochemical assays. The resulting model is that Mec1 and Rad53 phosphorylate and stabilize Whi7, which binds to and inhibits the S-phase-CDK complex via the processivity factor Cks1. So on top of acting as a transcriptional repressor, Whi7 (and probably also Whi5) is also a direct interactor and inhibitor of CDK. The binding of Whi7 to Cks1-Clb5/6-CDK prevents the hyperphosphorylation and degradation of the inhibitor Sic1, and thus slows the G1/S transition in response to HU.

Major comments:

• Are the key conclusions convincing?

Overall I think the sum of the evidence supports the suggested model, individual claims though are on somewhat shaky grounds based often on single replicates, see below.

My main conceptual issue may be somewhat just a "semantic" problem. In my understanding "replication stress" refers to stalled replications forks and/or large stretches of single-strand DNA which then triggers a checkpoint response. So how would slowing the G1/S transition help to deal with "replication stress", if replication is not yet happening in these cells? I am assuming Mec1 senses dNTP depletion also in the absence of replication and that is how Mec1 and Rad53 become active in G1. But then maybe the model and the arguments can be phrased differently? What exactly is slowing down Sic1 degradation doing for the cell? Replenishing dNTP pools before the first origins fire? Or is maybe Sic1 not the most important target of this regulation? Maybe also during S-phase, partially inhibiting CDK is beneficial, maybe to stretch out origin firing... or? - Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?

Most of the work is done on Whi7 and then some Whi5 in the end, I would tone down on the Whi5 claims a bit. - Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation.

Since the authors are clearly able to do quantitative live cell imaging, I do not understand why they do not quantify Whi7 concentrations and localization in response to HU instead of using Western blots of synchronized cells. This would make the whole thing much more credible, especially given the current lack of replicates (see below). This would also allow correlating the timing and amount of the Whi7 response with the stabilizing of Sic1 in single cells.

The causality of phosphorylation being required for stabilization seems plausible from the genetics, but is far from clear in the western blots. Here, concentration increase seems to precede phosphorylation. Could this due to induced Whi7 transcription?

Many if not most claims are based on single replicates. See below. - Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated cost and time investment for substantial experiments.

I am not suggesting any different types of experiments or new methods, so it should be doable within a few weeks. - Are the data and the methods presented in such a way that they can be reproduced?

I would suggest the authors spell out all of their experimental procedures instead of referring to "as described previously". I think everyone knows the pains of going on a wild goose chase of following references to the original method description. - Are the experiments adequately replicated and statistical analysis adequate?

The key weakness of this entire paper is imho that many claims are based on single experiments, that are neither replicated nor quantified. For example, all the co-IPs (such as 1E or 3F) should be replicated and the ratio of bait to target quantified and averaged.

If a claim is made regarding increased phosphorylation in vivo, then again this should be replicated and the ratio of phosphorylated to unphosphorylated bands quantified. In many Whi7 gels it looks like it is mainly the total amount of the protein that is changing rather than the phosphorylation state. But again, by eye and from a single replicate, this is hard to tell.

A similar thing holds true for the spot assays. Spot assays are great to show lethality and rescue as in the first figure. But making semi-quantitative claims of different degrees of "partial rescue" from a single spot assay is a bit speculative. This seems especially true since the authors are using different and seemingly random HU concentrations for every spot assay, which suggests that the effect is not very robust and can only be seen in very specific concentration ranges. If e.g. the degree of rescue between WT, A and D mutants or truncations matters for the model/the storyline, then more quantitative growth or competition assays should be added.

Minor comments:

• Specific experimental issues that are easily addressable.

At least some of the alpha-factor release experiments should contain infos on budding index and/or DNA content to understand see the delay in timing by HU addition. - Are prior studies referenced appropriately?

Seems fine from the G1/S side, but I don't know the Mec1/Rad53 literature well enough to judge. - Are the text and figures clear and accurate?

The authors could do another round of proofing figures and legends. For example, Fig 5C contains scale bars that are not defined, blot 3E has an asterix labeling that is not defined, the model in 5E has misspelled "degradation"... - Do you have suggestions that would help the authors improve the presentation of their data and conclusions?

The authors use a lot of different mutants (especially for Whi7). Even for someone who knows the proteins fairly well, it is hard to remember throughout the text which abbreviation is relating to which mutations and which function that is addressing. Maybe occasionally remind the reader of what the mutant is or use terms like Whi7non-binding rather than WIQ.

The text could also use another round of proof-reading. The overall flow of the storyline is easily comprehensible, but sometimes there is a sudden switch of topics or new proteins come out of nowhere. Some expressions are used in a way that is not common English.

Significance

• Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field.

This study is a major conceptual contribution to understanding G1/S regulation in perturbed conditions (assuming the results can be replicated and quantified as detailed above). That Whi7 (and maybe Whi5) directly inhibit Clb5/Clb6-CDK through Cks1 binding is an important addition/modification to the current model and may well be important beyond genotoxic stress. - Place the work in the context of the existing literature (provide references, where appropriate).

The authors do this reasonably well. - State what audience might be interested in and influenced by the reported findings.

Anyone in the yeast cell cycle/replication field should find this interesting. It should also have important implications for the mammalian cell cycle/replication/DNA damage field. - Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate.

I am well familiar with G1/S control and all the methods used in the study. I am not an expert on replication stress/DNA damage/ checkpoint signaling.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

---

Referee #1

Evidence, reproducibility and clarity

Summary

This work begins with a heterologous screen, introducing human genes in double mec1,sml1 yeast deletants, which are alive, but sensitive to hydroxyurea. The readout was mec1,sml1 proliferation in the presence of hydroxyurea. They found that mec1,sml1 yeast mutants carrying the human RB1 gene (a G1/S transcriptional repressor) proliferated on hydroxyurea. Then, they test if known yeast G1/S transcriptional repressors (Whi5 and Whi7) could have similar effects if provided at higher than normal levels (they did). With this initial result, followed up by a variety of experiments, the authors then go on to propose that replication stress, which activates Mec1 and Rad53, triggers the phosphorylation of Whi7 (by Mec1) and Whi5 (by both Rad53 and Mec1) blocking their eviction from the nucleus, allowing them instead to bind and inhibit Cks1, a Cdk processivity factor, needed for the complete phosphorylation and degradation of a Cdk inhibitor, Sic1. This is different from published work a decade earlier in mammalian cells (ref. 37; Bertoli et al.), which showed that upon replication stress, Chk1 phosphorylates G1/S transcriptional repressors to maintain G1/S transcription, which could help genome stability. Here, the authors propose that replication stress could block the G1/S transition. While the model and some of the experiments are interesting, the rationale for some experiments was shaky, and the data do not fully support the conclusions.

Major points

1. Any cell that undergoes DNA replication must have already destroyed Sic1. It has been known for 25+ years that targeting Sic1 is the only necessary function of G1/Cdk to enable DNA replication (PMID: 8755551). Sic1 does not reappear until the M/G1 transition. Hence, in the authors' model, where cells are already in the S phase, how can multisite phosphorylation and degradation of Sic1 be the critical and final output of the pathway they propose when there shouldn't be any Sic1 around, to begin with? Why would a cell that has already completed Start and the G1/S transition, is in the S phase and experiencing replication stress, care about going through the G1/S?
2. The results in Figure 2C are confusing and difficult to interpret. For example, comparing lane 8 (WT without hydroxyurea) to lane 7 (WT with hydroxyurea), it appears that there is more phosphorylated Whi7 in lane 7 (hydroxyurea treatment) than in lane 8 (no treatment). But, the ratio of phosphorylated/unphosphorylated Whi7 is not that different (there is very little unphosphorylated Whi7 in lane 8). Same problem when comparing lanes 3 and 3. I understand that they later show that Whi7 is stabilized by hydroxyurea, but from the data in this figure, what exactly can they conclude here?
3. Their data in Figure 2E show that phosphorylation of Whi7 is not required for suppressing the lethality of rad53,sml1 cells treated with hydroxyurea. Cells carrying Whi7-41A (lacking all possible phosphorylations) suppressed nearly as well as wild-type Whi7 did. The purported differences in the suppression are minuscule at best and not evident at the dilutions tested. It is not clear at all how they can conclude that phosphorylation of Whi7 has anything to do with the ability of Whi7 overexpression to suppress the lethality of rad53,sml1 cells.
4. For all the arguments they make about this new role of Whi5 and Whi7 at Start, they do not examine size homeostasis or the kinetics of cell cycle progression in any of their experiments and their mutants, with or without hydroxyurea treatment.
5. The Sic1 stability experiments they show in Figure 5 are nice. They would need to be extended to their various mutants, including their Whi7 phosphomutants, to make a case for phosphorylation by Rad53 and Mec1 in this process.

Minor points

1. The language is awkward. Editing for style will be necessary.
2. They use different hydroxyurea doses in the experiments they show, making it difficult to conclude much when comparing different figures. Why aren't they consistent from experiment to experiment?

Referees cross-commenting

Overall, all reviews are well-aligned. The points raised by the other reviewers are valid, and the reviews are thorough and detailed. I don't know whether the authors will be able to respond since the list is quite long. Even if they do, the manuscript will look very different. I do not have anything else to add.

Significance

The manuscript presents some interesting data, most notably the role of Whi7 and Whi5 in the stability of Sic1 in vivo and the various in vitro experiments the authors present. The advance is conceptual and mechanistic, offering a different and unanticipated model for the role of these proteins at Start, under replication stress. Unfortunately, the significance of the manuscript is limited. A convincing case for their model and its importance has not been made. For example, their data in Figure 2E, measuring the ability of phosphomutants to suppress the lethality of rad53,sml1 cells upon replication stress, is underwhelming and undermines the importance of the study, particularly to a wider audience.

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

Learn more at Review Commons

---

Reply to the reviewers

*Reviewer #1 (Evidence, reproducibility and clarity (Required)):

In this manuscript, the authors report dorsomedial hypothalamus-specific PR-domain containing protein 13-knockout (DMH-Prdm13-KO) mice recapitulated age-associated sleep alterations such as sleep fragmentation and increased sleep attempts during sleep deprivation (SD). These phenotypes were further exacerbated during aging, with increased adiposity and decreased physical activity, resulting in shortened lifespan. Moreover, overexpression of Prdm13 in the DMH ameliorated sleep fragmentation and excessive sleepiness during SD in old mice. They identified maintaining Prdm13 signaling in the DMH might play an important role to control sleep-wake patterns during aging. These findings are interesting and novel and the evidence they provided looks solid.*

We deeply appreciate that this reviewer found our findings are interesting and the evidence solid.

*Major comments 1. The author spent a lot of words on Sirt1 in the introduction. Since Sirt1 regulates Prdm13, is there a link between the two in age-related sleep changes? If so, you can add some results and discussion. *

Thank you very much for raising this important issue. Our previous study demonstrated that a mouse model with high hypothalamic Sirt1 activity displays reduced number of transitions between wakefulness and NREM sleep (reference # 15), revealing that hypothalamic Sirt1, as well as Prdm13, is involved in the regulation of sleep fragmentation.However, sleep propensity was not altered in Sirt1-overexpressing transgenic mice (reference #13) and DMH-Prdm13-KO mice (Fig. 1). Based on these findings, we added the following sentence in the Results.

On page 11, line 267-274

"...... Similarly, a mouse model with high hypothalamic Sirt1 activity displays reduced number of transitions between wakefulness and NREM sleep15, revealing that hypothalamic Sirt1, as well as Prdm13, is involved in the regulation of sleep fragmentation. Sleep propensity was not altered in Sirt1-overexpressing transgenic mice13. Given that the level of hypothalamic Prdm13 and its function decline with age, age-associated sleep fragmentation could be promoted through the reduction of Prdm13/Sirt1 signaling in the DMH, but sleep propensity might be increased by other mechanisms. "

• In Figure 2e, the author describes n=7-8 in the figure legend, but why do both groups on the column show eight data? Is there something wrong with the statistics? Please check the statistics in the article carefully. *

We corrected n=7-8 to n=8 in the figure legend of Fig. 2e.

• DMH is known as one of the major outputs of hypothalamus circadian system and is involved in the circadian regulation of sleep-wakefulness (J.Neurosci. 23, 10691-10702 ; Nat Neurosci 4:732-738). Does Prdm13 correlate with circadian rhythms? The author can add relevant content to the discussion *

As per this reviewer's suggestion, we added the following sentence in the Discussion on page 20, line 500-508,

"For instance, it would be of great interest to elucidate whether Prdm13 signaling in the DMH contributes to regulate the circadian system, since the DMH is known to be involved in the regulation of several circadian behaviors32,33. Although DMH-Prdm13-KO mice did not display abnormal period length compared with controls, further studies are needed to address this possibility."

*Minor comments 1. The immunohistochemical diagram in the paper is not representative enough, as shown in FIG. 2b and c. *

We apologize that our presentation in Figs. 2a-c was confusing. Although Fig. 2b shows the numbers of cFos cells in the entire region of the DMH (summed up from three DMH regions), the images in Fig. 2c are from one of DMH regions for each condition. To avoid confusion, we revised the legend of Figs. 2a-c and the manuscript in the Results as follows:

-In the figure legend of Figs. 2a-c

"a, Total numbers of cFos+ cells ......... b,c, Images of DMH sections at bregma -1.67 mm ......."

-In the Results on page 7, line 180

"...... the hypothalamus, the DMH (summed up from bregma -1.67 to -1.91mm) showed a greater number of cFos+ cells during SD compared to SD-Cont (Fig. 2a-c, Supplementary Fig. 2a)..... "

• In FIG. 5h, the authors showed that the effect of overexpression of Prdm13 was very obvious, but the expression range of the virus after injection was lacking. Is there a fluorescent gene such as GFP on the virus to directly see the expression of the virus in the brain? *

Unfortunately, we do not hold extra samples to check the distribution of the virus after injection. However, we have established sufficient injection technique to target the DMH using the lentivirus system that we used in this study (Satoh et al Cell Metab 2013).

• Were mice singly housed or housed in groups? *

Most of the mice were housed in groups, except for the DR study. We added this information in the section Animal models of the Methods on page 41, line 935

".....RIKEN BRC. Most of the mice were housed in groups, except for the DR study. For the DR study ,..... "

• The part of sleep analysis needs to be further refined. How can REM and NREM in mice be distinguished and according to what criteria? *

We added the criteria to define NREM and REM in the section Sleep analysis of the Methods on page 42, line 995-998.

".......with visual examination. EEG periods dominated by higher amplitude delta wave activity with nuchal muscle atonia were scored as NREM sleep epochs. REM sleep consisted of periods of semi-uniform theta activity EEG with muscle atonia and/or muscle atonia with brief myoclonic twitches. Score was blinded ......"

• The authors may consider adding more recent literature related to DMH and sleep, such as DOI: 10.1093/cercor/bhac258 * We incorporated this reference to the following sentence in the section Results on page 8, line 194.

"........ Although DMH neurons are linked to sleep21, aging and longevity .... "

*Reviewer #2 (Evidence, reproducibility and clarity (Required)):

Summary: In this study, Tsuji et al. demonstrate that Prdm13 signaling is involved in the regulation of sleep-wake pattern. They also identified Prdm13 as a transcription factor in the DMH neurons. Major comments: 1. The evidence presented in Fig. 1 of age-related sleep fragmentation is potentially problematic. Although many previous studies have demonstrated fragmented sleep, especially fragmentation of NREM sleep, in aged mice compared to young mice, the data here do not suggest NREM fragmentation, because no change in the NREM bout duration was found. REM, on the other hand, may indeed have fragmentation during the dark phase, but REM only takes a small portion of the total sleep. Therefore, the conclusion that sleep is fragmented in old mice is not fully supported by Fig. 1. I noticed that the authors used 4-6 months old mice as the young group. Mice of this age can hardly be called "young". The females even start to have lowered fertility. This might be one of the reasons for the discrepancy between this and other studies. Repeating these experiments (and others involving the young group) with mice of more appropriate age (usually 2-3 months old) is recommended. Nonetheless, aging-caused sleep change is not new knowledge and has been reported repeatedly. This part of the results should be in the supplementary figures. *

We deeply appreciate this reviewer's comment. In accordance with this reviewer's suggestion, we carefully reconsidered the age of young mice. Most of published studies used mice at 2 to 4 months of age as the young group [2 to 4-month-old (7 studies), 4.6-month-old (1 study), 6-month-old (1 study), 2 to 6-month-old (1 study)]. Thus, to strictly use mice at 3-4 months of age as the young group, we excluded data of one cohort using mice at 6 months of age (2 mice each age group). Consistent with many previous studies, our revised data demonstrated that sleep fragmentation during NREM sleep is predominantly observed in old mice compared with young mice, particularly during the dark period. Based on these new results, we revised Fig.1, Suppl Fig.1, and all description related to Fig. 1 (manuscript on page 5-7, line 103-171). We would like to keep Fig. 1 as it is. Since most of the previous studies used males but not females, data from females are still lacking in the field (Campos-Beltran and Marshall, Pflugers. Arch., 473:841-851, 2021).

• The sleep phenotypes in aged mice and in Prdm13-KO mice are clearly distinct from each other. In the old mice (Fig. 1), REM sleep is fragmented but the total amount remains unchanged, and NREM sleep is increased (both bout number and total amount), indicating there may be more REM-to-NREM transitions, which the authors should quantify. However, Fig. 3 shows in Prdm13-KO mice, there is no REM fragmentation. In fact, it even seems to stabilize REM. But NREM duration is shorted, and no change in the total NREM or REM sleep time. These results suggest that the sleep alterations caused by aging and Prdm13-KO might have some overlap but are mostly in parallel and likely through different mechanisms. Therefore, the rationale of connecting Prdm13 signaling to aging-caused sleep changes is questionable. Is there a developmental change of Prdm13 expression in DMH between young and old mice? The authors also showed that Prdm13-KO in old mice caused decrease in NREM duration but has no effect on REM sleep, but in normal old mice, it is REM, but not NREM that has a defect. Prdm13 overexpression also only mildly decreased NREM bout number without affecting the episode duration of either NREM or REM, which can hardly be interpreted as "ameliorating sleep fragmentation". To me, all these results just suggest parallel actions of Prdm13 and aging on sleep, with Prdm13 mostly affecting NREM sleep but aging mostly impairing REM sleep. *

We deeply appreciate this reviewer's keen eyes. We carefully reassessed REM sleep data in Fig. 3. The revised data showed that whereas the duration of NREM episodes in DMH-Prdm13-KO mice during the dark period were significantly shorter compared to control group, the duration of REM episodes in the KO mice was not significantly altered. Therefore, after revising Fig. 1 and 3, our results showed that both aging and Prdm13-KO similarly affect the duration of NREM sleep episodes. These results suggest that sleep fragmentation, in particular, during NREM sleep, is commonly observed in old mice and DMH-Prdm13-KO mice. In addition to sleep fragmentation during NREM sleep, excessive sleepiness during SD was also commonly observed in old mice and DMH-Prdm13-KO mice. On the other hand, the effect of aging and Prdm13-KO on sleep propensity was distinct from each other. We think that age-associated sleep fragmentation could be promoted through Prdm13 signaling in the DMH, but sleep propensity might be increased by other mechanisms. We described these results and possibilities in the Results, and revised the Abstract as follows:

On page 11, line 264-274

"activity in DMH-Prdm13-KO mice (Fig. 3h, Supplementary Fig. 3f-h). Together, sleep fragmentation during NREM sleep and excessive sleepiness during SD are commonly observed in old mice and DMH-Prdm13-KO mice, but the effects of aging and Prdm13-KO on sleep propensity were distinct from each other.............. Given that the level of hypothalamic Prdm13 and its function decline with age16, age-associated sleep fragmentation could be promoted through the reduction of Prdm13/Sirt1 signaling in the DMH, but sleep propensity might be increased by other mechanisms."

On page 2, line 45-46

"Dietary restriction (DR), a well-known anti-aging intervention in diverse organisms, ameliorated age-associated sleep fragmentation and increased sleep attempts during SD, whereas these effects of DR were abrogated in DMH-Prdm13-KO mice."

As this reviewer pointed out, the effect of Prdm13 overexpression on NREM sleep fragmentation seems to be moderate, but we still observed effects on excessive sleepiness during SD. Thus, we revised the manuscript related to Prdm13-overexpression study in the Abstract and Results as follows:

On page 2, line 47-48

"Moreover, overexpression of Prdm13 in the DMH ameliorated sleep fragmentation and excessive sleepiness during SD in old mice."

On page 16, line 387-401

"Overexpression of Prdm13 in the DMH partially affects age-associated sleep alterations

...... (Fig. 5h). The number of wakefulness and NREM sleep episodes in old Prdm13-OE mice were significantly lower, whereas duration of wakefulness in old Prdm13-OE mice tended to be longer than old control mice during the dark period with no change in the duration of NREM episodes (Fig. 5i,j). Intriguingly, .... Thus, the restoration of Prdm13 signaling in the DMH partially rescue age-associated sleep alterations, but its effect on sleep fragmentation is moderate."

• What is the control manipulation for sleep deprivation? The authors need to clarify this in the Methods. Also, sleep deprivation has confounding effects including but not limited to stress, food deprivation (since food was removed during SD), human experimenter (since a gentle-touch method was used). Without proper controls for these variables, the authors should avoid concluding that the changes they saw at cellular level are due to sleep loss. *

Thank you very much for this suggestion. We added detailed description for AL-SD (the control manipulation for SD) in the section SD study of the Materials as follows:

On page 42-43, line 1014-1020

"Mice for control manipulation (AL-SD) were also individually housed prior to the experiment without SD and food removal. We checked the level of blood glucose in the SD study, and found that the level of blood glucose was indistinguishable between SD and AL-SD groups (126±6 and 131±4 mg/dL, respectively), revealing that nutritional status is equal between these two groups."

Identification of Prdm13+ cells using neuronal markers should be performed in addition to electrophysiological characterizations.

We performed immunofluorescence using anti-MAP2 antibody and confirmed that most Prdm13+ cells are neurons. We added this new result in Suppl Fig. 2g.

• Figs. 6 and 7 seem very disconnected from the main story. Identification of Prdm13 as a transcription factor is potentially interesting, but how does it account for its role in affecting sleep? The criteria of picking Cck, Grp and Pmch out of other candidate genes potentially regulated by Prdm13 and the rationale to investigate these genes seem unclear. More importantly, no evidence was shown regarding how Cck/Grp *

Base on RNA-sequencing using DMH samples from DMH-Prdm13-KO and control mice, we got several candidate genes as downstream genes of Prdm13. After validating the candidate genes by qRT-PCR, Cck, Grp and Pmch were detected as top-hit genes. We thus further assessed these three genes in this study. Our result showed that Cckexpression in the hypothalamus significantly declines with age. Based on other literature, hypothalamic Cck seems to be involved in sleep control. Therefore, it is conceivable that Prdm13 controls age-associated sleep alterations via modulating Cck expression. However, as this reviewer pointed out, we are still lacking the evidence showing the role of Prdm13/Cck axis in age-associated sleep alterations. We now clearly described the limitation of our study in the Discussion on page 23, line 560-562.

"However, the detailed molecular mechanisms by which Prdm13 in the DMH regulates age-associated sleep fragmentation and excessive sleepiness during SD still need to be elucidated in future study. "

*Minor comments: 1. Please note on the images of Fig. 2d what the green fluorescence was. It is very confusing as is, given that it's surrounded by quantifications of c-fos in the figure. *

The label "Prdm13" was added in Fig. 2d.

Please note use a different color for Prdm13 in several figure images (e.g., Fig. 2f, g, 7a,d, and Supplementary 2c). Yellow usually means overlap of red and green.

Since we have four-color images in Fig. 7, we consistently used yellow for Prdm13 throughout the main figures of the paper. At this moment, we would like to keep the current version of images, but we will revise images if the editor of affiliate journal requests this revision.

• Please note the statistic test results on power spectrum graphs. *

We added the statistic test results on power spectrum graphs in Figs. 1d, 4c, and 5d.

• Inconsistency between the graphs in Fig. 3d and the description in the text. Fig. 3d suggests no change in Wake episode duration, significant decrease in Dark phase NREM and significant increase in Dark phase REM, whereas lines 224-227 in the main text state "The duration of wakefulness episodes ... was significantly shorter than control mice during the light period, and the duration of NREM sleep episodes ...was significantly longer ... during the dark period (Fig. 3d)". Which one is correct? Please check. *

We apologize for this typo and unclear description. We revised the sentence regarding Fig. 3d as follows:

On page 10, line 242-246

"The duration of wakefulness episodes in DMH-Prdm13-KO mice was significantly shorter than control mice during the light period between ZT0 to ZT2. The duration of NREM sleep episodes in DMH-Prdm13-KO mice was significantly shorter than control mice during the dark period (Fig. 3d). These results indicate that DMH-Prdm13-KO mice showed mild sleep fragmentation compared with control mice."

• Fig. 5f, Y-axis title should be EEG SWA. * We corrected it.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

---

Referee #2

Evidence, reproducibility and clarity

Summary:

In this study, Tsuji et al. demonstrate that Prdm13 signaling is involved in the regulation of sleep-wake pattern. They also identified Prdm13 as a transcription factor in the DMH neurons.

Major comments:

1. The evidence presented in Fig. 1 of age-related sleep fragmentation is potentially problematic. Although many previous studies have demonstrated fragmented sleep, especially fragmentation of NREM sleep, in aged mice compared to young mice, the data here do not suggest NREM fragmentation, because no change in the NREM bout duration was found. REM, on the other hand, may indeed have fragmentation during the dark phase, but REM only takes a small portion of the total sleep. Therefore, the conclusion that sleep is fragmented in old mice is not fully supported by Fig. 1. I noticed that the authors used 4-6 months old mice as the young group. Mice of this age can hardly be called "young". The females even start to have lowered fertility. This might be one of the reasons for the discrepancy between this and other studies. Repeating these experiments (and others involving the young group) with mice of more appropriate age (usually 2-3 months old) is recommended. Nonetheless, aging-caused sleep change is not new knowledge and has been reported repeatedly. This part of the results should be in the supplementary figures.
2. The sleep phenotypes in aged mice and in Prdm13-KO mice are clearly distinct from each other. In the old mice (Fig. 1), REM sleep is fragmented but the total amount remains unchanged, and NREM sleep is increased (both bout number and total amount), indicating there may be more REM-to-NREM transitions, which the authors should quantify. However, Fig. 3 shows in Prdm13-KO mice, there is no REM fragmentation. In fact, it even seems to stabilize REM. But NREM duration is shorted, and no change in the total NREM or REM sleep time. These results suggest that the sleep alterations caused by aging and Prdm13-KO might have some overlap but are mostly in parallel and likely through different mechanisms. Therefore, the rationale of connecting Prdm13 signaling to aging-caused sleep changes is questionable. Is there a developmental change of Prdm13 expression in DMH between young and old mice? The authors also showed that Prdm13-KO in old mice caused decrease in NREM duration but has no effect on REM sleep, but in normal old mice, it is REM, but not NREM that has a defect. Prdm13 overexpression also only mildly decreased NREM bout number without affecting the episode duration of either NREM or REM, which can hardly be interpreted as "ameliorating sleep fragmentation". To me, all these results just suggest parallel actions of Prdm13 and aging on sleep, with Prdm13 mostly affecting NREM sleep but aging mostly impairing REM sleep.
3. What is the control manipulation for sleep deprivation? The authors need to clarify this in the Methods. Also, sleep deprivation has confounding effects including but not limited to stress, food deprivation (since food was removed during SD), human experimenter (since a gentle-touch method was used). Without proper controls for these variables, the authors should avoid concluding that the changes they saw at cellular level are due to sleep loss.
4. Identification of Prdm13+ cells using neuronal markers should be performed in addition to electrophysiological characterizations.
5. Figs. 6 and 7 seem very disconnected from the main story. Identification of Prdm13 as a transcription factor is potentially interesting, but how does it account for its role in affecting sleep? The criteria of picking Cck, Grp and Pmch out of other candidate genes potentially regulated by Prdm13 and the rationale to investigate these genes seem unclear. More importantly, no evidence was shown regarding how Cck/Grp

Minor comments:

1. Please note on the images of Fig. 2d what the green fluorescence was. It is very confusing as is, given that it's surrounded by quantifications of c-fos in the figure.
2. Please note use a different color for Prdm13 in several figure images (e.g., Fig. 2f, g, 7a,d, and Supplementary 2c). Yellow usually means overlap of red and green.
3. Please note the statistic test results on power spectrum graphs.
4. Inconsistency between the graphs in Fig. 3d and the description in the text. Fig. 3d suggests no change in Wake episode duration, significant decrease in Dark phase NREM and significant increase in Dark phase REM, whereas lines 224-227 in the main text state "The duration of wakefulness episodes ... was significantly shorter than control mice during the light period, and the duration of NREM sleep episodes ...was significantly longer ... during the dark period (Fig. 3d)". Which one is correct? Please check.
5. Fig. 5f, Y-axis title should be EEG SWA.

Significance

General assessment: There are discrepancies in the evidence presented, and the results were poorly organized. I found the main conclusions of the manuscript not very convincing and the causal links among Prdm13, aging and sleep alterations weak.

Advance: The identification of DMH Prdm13 in regulating sleep is potentially interesting and of some novelty, but the underlying mechanism and its causal relationship with aging were not clearly elucidated.

Audience: basic research

My expertise: sleep, social behavior, hypothalamus, dopamine neuromodulation, neural circuit development, synaptic organization.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

---

Referee #1

Evidence, reproducibility and clarity

In this manuscript, the authors report dorsomedial hypothalamus-specific PR-domain containing protein 13-knockout (DMH-Prdm13-KO) mice recapitulated age-associated sleep alterations such as sleep fragmentation and increased sleep attempts during sleep deprivation (SD). These phenotypes were further exacerbated during aging, with increased adiposity and decreased physical activity, resulting in shortened lifespan. Moreover, overexpression of Prdm13 in the DMH ameliorated sleep fragmentation and excessive sleepiness during SD in old mice. They identified maintaining Prdm13 signaling in the DMH might play an important role to control sleep-wake patterns during aging. These findings are interesting and novel and the evidence they provided looks solid.

Major comments

1. The author spent a lot of words on Sirt1 in the introduction. Since Sirt1 regulates Prdm13, is there a link between the two in age-related sleep changes? If so, you can add some results and discussion.
2. In Figure 2e, the author describes n=7-8 in the figure legend, but why do both groups on the column show eight data? Is there something wrong with the statistics? Please check the statistics in the article carefully.
3. DMH is known as one of the major outputs of hypothalamus circadian system and is involved in the circadian regulation of sleep-wakefulness (J.Neurosci. 23, 10691-10702 ; Nat Neurosci 4:732-738). Does Prdm13 correlate with circadian rhythms? The author can add relevant content to the discussion

Minor comments

1. The immunohistochemical diagram in the paper is not representative enough, as shown in FIG. 2b and c.
2. In FIG. 5h, the authors showed that the effect of overexpression of Prdm13 was very obvious, but the expression range of the virus after injection was lacking. Is there a fluorescent gene such as GFP on the virus to directly see the expression of the virus in the brain?
3. Were mice singly housed or housed in groups?
4. The part of sleep analysis needs to be further refined. How can REM and NREM in mice be distinguished and according to what criteria?
5. The authors may consider adding more recent literature related to DMH and sleep, such as DOI: 10.1093/cercor/bhac258

Significance

Akiko Satoh's 2015 article "Deficiency of Prdm13, a dorsomedial hypothalamus-enriched gene, mimics age-associated changes in sleep quality and adiposity "influenced the novelty of the study, but the authors went further in terms of details and mechanisms. The audience of the basic research will be influenced by this research.

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

Learn more at Review Commons

---

Reply to the reviewers

1. General Statements [optional]

We are grateful to the reviewers for highlighting the novelty of the mechanism we describe for P2Y2 in driving RGD-binding integrin-dependent invasion, and acknowledging its potential in cancer therapy. We thank the reviewers for their valuable and detailed comments, which have allowed us to prepare a significantly stronger and clearer manuscript.

2. Point-by-point description of the revisions

Reviewer #1 (Evidence, reproducibility and clarity (Required)):

---

Summary

The study identifies P2Y2 as a purinergic receptor strongly associated with hypoxia, cancer expression and survival. A link is found between P2Y2-integrin interaction and cancer invasion, highlighting this as a novel therapeutic target. The mechanism is interesting and general well explored.

• *

We thank the reviewer for acknowledging the novelty of the therapeutic target presented in this work.

• *

Minor comments

As P2Y2 is highly expressed by other cell types found with tumours, including vascular endothelium and leukocytes, the authors should reflect on this as a confounding factor in the analysis of adrenocarcinoma gene expression analysis. I appreciate the RNAscope work may resolve this issue to some extent.

We agree that P2Y2 is known to be expressed in other cell types. RNAscope did not show convincing staining in PDAC normal adjacent tissue (was similar to negative staining), perhaps due to the challenging nature of pancreatic tissue with respect to RNA degradation. We have resolved this issue by including single cell RNA-seq of normal human pancreas for P2Y2 from Protein Atlas (Sup. Fig. 2B), which shows expression in several cell types, mainly endocrine cells, and macrophages. We now mention this in line 142 : “P2Y2 is known to be expressed at low levels in normal tissues but interestingly RNAscope did not detect this. This data suggest 1) the lower limits of the technique compounded by the challenge of RNA degradation in pancreatic tissue and 2) supports that in tumour tissue where it was detected there was indeed overexpression of P2Y2, in line with the bioinformatic data. Interrogating single cell P2Y2 RNA expression in normal PDAC from proteinatlas.org (Karlsson et al., 2021), expression was found at low levels in several cells types, for example in endocrine cells and macrophages (Sup. Fig. 2B).”

Major comments

• *

The authors correctly identify that the level of ATP in the tumour microenvironment can be very high, typically 100uM or so. However, these concentrations are supramaximal for P2Y2 activation, at which ATP has an approximate EC50 of 100nM. Coupled with the fact that many cell types, including cancer cells, constitutively secrete ATP, there is an opportunity to explore the effects of lower ATP concentrations in some assays, or provide some concentration-response relationship to give more confidence of P2Y2-dependent effects.

• *

We thank the reviewer for raising this point and we agree that 100 uM can be a high concentration, albeit one that is frequently used throughout the literature. We have now included a concentration-response relationship (Sup. Fig. 2D) showing that ATP causes cytoskeletal changes that are P2Y2 dependent most prominently at 100 uM, the concentration that, as the reviewer has also corroborated, is similar to the concentration of ATP found in tumours.

Also, the authors describe the use of cancer cells where P2Y2 has been knocked out using CRISPR. Does this KO have an effect on cancer invasion? The effect of ARC should be absent in these cells and give confidence the effects of ARC are P2Y2-dependent, as some off-target effects of this antagonist have been reported. To explore the influence of constitutive P2Y2 activity, the authors should explore the effects of ARC alone in some assays.

We agree that including more AR-C only experiments would be informative, so we have included a 3D sphere invasion assay with our CRISPR cell line treated with and without AR-C that shows no effect in invasion (p = 0.4413) (Sup. Fig. 3J). We have now also included images of AsPC-1 cells transfected with Lifeact, showing no changes in morphology with AR-C only (Sup. Fig. 2E). We apologise for missing a ‘+’ in one of the supplementary figures which shows AR-C only in AsPC-1 cells has no effect on its own.

The effects of the CRISPR cell line in invasion are shown in Fig. 3F, showing a significant reduction (p = 0.0005) in invasion.

The title of the manuscript implies extracellular ATP drives cancer invasion, though in my opinion this statement is not fully explored. Though ATP/UTP are applied at supramaximal concentrations for P2Y2 activation, the influence of ATP in the cell culture microenvironment without exogenous application is not explored. One would predict that scavenging extracellular ATP with apyrase would negatively impact invasiveness and the proximity of integrin and P2Y2 without ATP/UTP application if constitutively secreted ATP is involved. Pharmacological manipulation of ectonucleotidase activity is an alternative. Experimental route to explore this.

We agree and have changed the title of our article to “Purinergic GPCR-integrin interactions drive pancreatic cancer cell invasion”. Our 3D sphere experiments with the CRISPR cell line show a reduction in invasion without exogenous application of ATP, which we also see to a lesser extent in our siRNA P2Y2 cell line. We have tested our sphere model with apyrase but unfortunately, the buffer used for apyrase to work is not compatible with our gel composition. Pharmacological manipulation would be a very good alternative if the cells used expressed high levels of CD39 or PANX1, which unfortunately they don’t. We hypothesise that most basal extracellular ATP in our 3D spheres comes from hypoxic areas that cause cell death, just as is postulated for tumours.

Immunoprecipitation experiments of native proteins would be more convincing data that P2Y2 and integrin physically interaction, as opposed to being in close proximity. This would also overcome artifacts of interaction that can be attributed to receptor overexpression.

We attempted immunoprecipitation experiments but unfortunately ran into several technical difficulties, including the anti-aV antibody working poorly for Western blot. Immunoprecipitation of these proteins has been reported by others (PMID: 25908848), supporting the proposed interaction.

DNA-PAINT super resolution microscopy allows for quantification of nanoscale distances, and we used this to calculate the distances where physical interaction occurs. The possibility of this close proximity being by chance is accounted for in the computational nearest neighbour distance calculation by calculating points randomly distributed. This random distribution calculation also helps in overcoming artifacts of interaction due to overexpression, as the random distributed points are the same number of points as the proteins detected in each condition for each region of interest. Importantly, we also performed DNA-PAINT in using untransfected AsPC-1 thus endogenous levels (no receptor overexpression or alteration) and saw similar results (Sup. Fig.4A-D), thus we are confident of the interactions reported.

Finally, we alter the RGD motif, which underpins the physical interaction, and see significant changes that match observations in previous publications using the P2Y2 agonist UTP, mentioned in the discussion: Line 398 “Following ATP stimulation, the number of P2Y2 proteins at the plasma membrane decreased significantly after one hour, implying receptor internalisation, in line with previous work showing P2Y2 at the cell surface was reduced significantly after one hour of UTP stimulation (Tulapurkar et al., 2005).” and Line 408: “P2Y2 affecting cell surface redistribution of αV integrin has been reported, with αV integrin clusters observed after 5 min stimulation with UTP (Chorna et al., 2007)”

It is currently not clear what the mechanistic relationship between P2Y2 activity, P2Y2-integrin proximity and RGD motif is. Do the authors suggest the RGD domain becomes exposed upon receptor activation? The mechanism is not fully articulated in the discussion.

We apologise for any lack of clarity in our postulated mechanism, we have now included a more detailed explanation of the mechanism in the discussion : Line 417 “We speculate that by reducing the ability of integrins to bind to the RGD of P2Y2, through receptor internalisation, RGE mutation or through cRGDfV treatment, there is less RGD-triggered integrin endocytosis, hence less integrin recycling and an increase of integrins at the cell surface.”

Reviewer #1 (Significance (Required)):

---

General assessment: A novel mechanism is presented for therapeutic intervention of cancer. The study relies on supramaximal concentrations of agonist and overexpressed receptors. Role of endogenous P2Y2 not fully explored. The study lacks in vivo evidence of the importance of this mechanisms. Cell developed in the study could be used in mouse models to explore effect on tumour growth.

Advance: Integrin and P2Y2 interactions are already documented but not in context of cancer.

Audience: basic research

We thank the reviewer for crediting this work as a novel mechanism for therapeutic intervention of cancer. We trust that the new data provided (as discussed above) have resolved the concerns of the reviewer as we now have provided an explanation for the concentrations used. We do rely on overexpressed receptors for a small portion of our experiments, however, all experiments with overexpressed receptors were then tested in cells with endogenous expression of P2Y2 and used pharmacological means to show the same behaviour. We have now clarified this. We have also included in the discussion a sentence about the mouse experiment performed by Hui et al. with regards to reduced tumour growth when targeting P2Y2: Line 365: “Combination treatment of subcutaneous xenografts of AsPC-1 or BxPC-3 cells with the P2Y2 antagonist AR-C together with gemcitabine significantly decreased tumour weight and resulted in increased survival compared to placebo or gemcitabine monotherapy control (Hu et al., 2019).”

• *

Reviewer #2 (Evidence, reproducibility and clarity (Required)):

Summary:

Considering the fact that most PDAC are characterized by a high level of extracellular purines content, authors decided to study the expression of the 23 genes coding for membrane proteins involved in the binding or transport of purines in available PDAC transcriptomic cohorts. This approach led to the identification of P2Y2, a GPCR, as the best predictor for the worst survival of patients. Using in vitro models, they show that P2Y2 expression is associated with increased invasion capacity of pancreatic cancer cells and that this pro-invasive effect is dependent on the interaction of P2Y2 with αV integrin via the RGD motif.

Major comments:

• It is not clear to me why authors decided at one point to perform a GSEA comparing low and high mRNA expression of P2Y2 and why they decided to focus on the potential interaction of P2Y2 with integrin αV. As a GPCR, activation of P2Y2 leads to the activation of several downstream signaling pathways that may directly impact the adhesion, migration, and invasion properties of cells. Moreover, despite the presence of the RGD motif in P2Y2, it is not excluded that it may bind (maybe more efficiently) to other "cell adhesion" molecules.

We apologise if the link between the GSEA figure and focusing on the potential integrin interaction was not clear. We have now performed GSEA using the panther gene set library, which includes a “Integrin signalling pathway” gene set. This was the top ranked gene set in both cohorts and we have substituted the GSEA figure for this instead (Fig. 2D). We trust that the narrative of the manuscript and our rationale to pursue the importance of integrin interaction is now clear.

We agree with the reviewer and believe that P2Y2 may bind to other molecules important in cell adhesion. We studied integrin interactions due to the clear relationship of P2Y2 and integrins in patient data, which was not as evident with other binding partners. Furthermore, this relationship is unexplored in cancer and offers novel therapeutic strategies.

• Similarly, if αV can regulate P2Y2 signaling, what about the regulation of αV signaling pathways by P2Y2? αV integrin has to bind to a β subunit and, depending on the identity of the β subunit, may have distinct regulations and so different impact on cell invasion. How P2Y2 can interfere with these α/β ratios?

We thank the reviewer for this comment, and have now included western blots showing the impact of P2Y2 treatment on integrin signalling through FAK and ERK (Fig 5). We agree that the β subunit may have distinct regulation and outputs, but this is outwith the scope of our current study.

• While it has been shown in other studies, in this work, there is no real proof of the interaction between P2Y2 and αV. Only in Figure 4I, where the authors look at the NND We thank the reviewer for raising this point as it has made us realise that our chosen NND of * *

• Surprisingly, in the absence of ATP, P2Y2 RGE mutant, which should no more interact with αV, show a 2 to 3 fold more vicinity to αV compared to WT P2Y2. How can the authors explain this?

We agree that this is a suprising, but robust discovery. By altering the RGD motif, there may be less RGD-triggered integrin endocytosis, leading to increased integrins at the surface. We have included this hypothesis in the discussion in Line 417. The RGE mutation has less affinity to integrins, meaning it still retains some ability to bind to integrins. Hence by chance, a higher number of integrins will result in a higher number of interactions with the RGE. We are planning to interrogate the internalisation dynamics in a future study.

• For DNA-PAINT experiments, the authors only focus on membrane proteins whose amounts are balanced by internalization, recycling and export from internal compartment. As claimed, but not demonstrated by the authors, interaction of P2Y2 and αV may interfere with all these steps, thereby increasing or decreasing the cell surface expression of both proteins. Hence, it would be useful to 1) control proteins levels by western blot, especially for the overexpressed P2Y2, to be sure that they are the same, 2) block internalization and/or export to decipher the important steps.

• In fact, all these main questions are raised by the authors in the end of the discussion but so far, they only show that the RGD motif has an impact on the biological role of P2Y2 (cell invasion) and on the membrane dynamic of αV and itself.

We thank the reviewer for the suggestions:

• In the course of our attempts to perform co-IP for P2Y2 and aV we could confirm that P2Y2 expression levels were equivalent (see Fig below – for reviewers only), but the problems with anti-aV antibodies prevented completion of the experiment. We also show IF staining showing similar levels of P2Y2 for both overexpressed conditions (Sup. Fig. 3K).

Figure: Immunoprecipitation of P2Y2 showing similar P2Y2 levels in AsPC-1 P2Y2CRISPR cells trasfected with P2Y2RGD or P2Y2RGE and treated with 100 µM of ATP or control for 1 hour. Antibody used: anti-P2Y2 (APR-010, Alomone Labs).

• As the reviewer highlights, in this work we have focused on the role of P2Y2 in PDAC invasion and have looked at single-molecule resolution membrane dynamics of αV and P2Y2. The different steps of P2Y2 and integrin αV interactions in internalisation, recycling and export are certainly interesting to study but beyond the scope of the current manuscript and in our future aims. We include these ideas in the discussion as suggestions for future research and as a possible explanation for the dynamics observed.

• Fig 2A, authors use RNAscope in order to reveal P2Y2 mRNA expression and distribution in tumor versus normal tissue from 2 patients. They rather show the protein expression, using the antibody they used in other experiments, by standard IHC and in a higher number of patients, including short and long survival, to confirm that the results they obtain by bioinformatics study of transcriptomic data are real.

We now explicitly mention a paper (PMID: 30420446) that performed IHC of P2Y2 in 264 patients showing that P2Y2 was predominantly found in the tumour area, matching our bioinformatics study: Line 141 “matching our findings from larger publicly available cohorts, including P2Y2 IHC data from 264 patients in the Renji cohort (Hu et al., 2019).” and Line 359 “These observations were supported by published immunohistochemical staining of 264 human PDAC samples, showing that P2Y2 localised predominantly in cancer cells in human PDAC…”

• Some figure legends are incorrectly numbered or described, such as the figure 4.

We apologise for the incorrectly described figures in figure 4, this has now been corrected.

• *

Minor comments:

• Can we reasonably talk about OMIC while studying 23 genes? In fact, as described by Timothy A. J. Haystead in 2006 (PMID: 16842150) the purinome is constituted of about 2000 genes coding for proteins binding to purines (including all kinases for example). Author should redefine they pool of genes as perhaps purines receptors/transporter?

We agree with the reviewer and have redefined the pool of genes to ‘purinergic signalling genes’ or ‘(part of the) extracellular purinome’.

• P2Y2 and ADORA2B associated with worse survival while P2Y11 and ADORA2A are associated with better survival (Figure 1B). Would it be more interesting to understand why proteins of the same family act in opposite ways?

We have now included text exploring this idea in the discussion. Both P2Y2 and ADORA2B show increased expression with HIF-1α and/or hypoxia and the inverse happens with ADORA2A, for example. Line 352: “Adenosine A2B receptor requires larger agonist concentrations for activation compared to other receptors in the same family, such as adenosine A2A (Bruns, Lu and Pugsley, 1986; Xing et al., 2016), and receptor expression has been reported to increase when cells are subjected to hypoxia (Feoktistov et al., 2004). Moreover, HIF-1α has been shown to upregulate A2B and P2Y2 expression in liver cancer (Tak et al., 2016; Kwon et al., 2019).”

• Figure 1C, any value for the correlation with Survival? Cause this is not so obvious in the figure.

We agree this correlation needs strengthening with a numeric value, we have now included a Kaplan-Meier curve of high vs low Winter hypoxia score PDAC patients showing significantly lower survival with higher Winter hypoxia score (Sup. Fig. 1B).

• *

• Regarding the correlation of P2Y2 and ADORA2B with hypoxia scores, any HIF1 responsive element in promoter? What happens regarding the expression level of these genes when cells are transferred to low oxygen conditions?

We thank the reviewer for these questions. The relationship of P2Y2 and ADORA2B with hypoxia and/or HIF-1α has been explored in other publications which are now cited in the discussion. Line 356: “Moreover, HIF-1α has been shown to upregulate A2B and P2Y2 expression in liver cancer (Tak et al., 2016; Kwon et al., 2019).” Of note, a HIF1-α responsive element has been reported for A2B, but as yet not for P2Y2.

• Figure 4 E to M are too small.

We apologise and have now increased the size of the graphs and the figure.

• In Supp Figure 4, what are the "Non-altered AsPC-1 cells"?

We apologise for the confusion that may have arisen from calling normal AsPC-1 cells “Non-altered AsPC-1 cells”. We have changed this to ‘Normal AsPC-1 cells (untransfected and unchanged P2Y2 expression).

• *

Reviewer #2 (Significance (Required)):

Strengths: All the data shown are experimentally and statistically strong.

Limitations: This study remains largely descriptive with no real molecular mechanism that could at least partially explain the biological role of P2Y2 regarding cell invasion.

Advance: Limited

We thank the reviewer for noting the experimental strength of the paper.

After the suggested changes, including integrin signalling experiments, and strengthening our DNA-PAINT results, the molecular mechanism presented in this work has been strengthened and clarified significantly. These changes have also helped greatly in the mechanistic explanation of the role of P2Y2 in cell invasion.

• *

Reviewer #3 (Evidence, reproducibility and clarity (Required)):

---

The authors concentrate on the members of the purinome and attempt to identify members of the pathway that are especially relevant for PDAC biology, especially invasion and metastatic spread. Using the in silico analysis of transcriptome data from publicly available PDAC patient cohorts, the authors identify P2Y2 as being the most prominent in terms of cancer cell expression and with highest impact on patient survival. The authors than take an effort in functional characterization of P2Y2 and demonstrate that downregulation/deletion of P2Y2 leads to abrogation of ATP activated invasion in hanging drop spheroid model system in a very reasonable and scientifically good way. Finally, the authors postulate that the P2Y2 actions go over interaction with integrin AlphaV and modulations of the cellular cytoskeleton and show via DNA PAINT that a direct interaction of the 2 molecules. The hypothesis is experimentally elaborated in a sound way mostly using cell culture as a system.

The study is solid communicated, the number of experiments seems to be fine. For my understanding, the study relies much on mRNA data (gene expression in cell lines and patient samples), I would suggest providing evidence on protein level what might have been challenging due to potential lack of specific antibody.

We thank the reviewer for acknowledging our experimentally elaborated hypothesis and our solid communication of the study. As mentioned before, we now explicitly mention a paper (PMID: 30420446) that performed IHC of P2Y2 in 264 patients showing that P2Y2 was predominantly found in the tumour area, matching our bioinformatics study.

Reviewer #3 (Significance (Required)):

---

To strengthen the hypothesis experimentally, I would suggest the experiments listed below:

Figure 1: The authors took a solid bioinformatic effort and analyzed expression of different genes of the purinome pathway in different PDAC patient and cell gene expression databases. In this part, the authors rely a lot on correlation of hypoxia and define high hypoxia scores and low hypoxia scores from previously published datasets. Although hypoxia surely plays an important biological condition in the PDAC, I am not sure I get the connection between purinome pathway and hypoxia. Few sentences give a broad introduction about hypoxia-purinome connection in the discussion part of the manuscript, but I think the readership would benefit from more specific statements (which drug, which hypoxic target, which system-mouse/human/cells, what was the exact discovery) and connect those specific statements to the work that has been done here.

We agree with the reviewer that the study can benefit from more information about the hypoxia-purinergic signalling link. Hence, we have now included more detailed explanations of how hypoxia and purinergic signalling are related in the discussion, giving more information about the cell types and the exact discovery. Line 338: “Purinergic signalling has been associated classically with hypoxia and immune function in cancer (Di Virgilio et al., 2018). One of the first reports of hypoxia inducing ATP release in cells identified an increase of extracellular ATP in rat heart cells when kept in hypoxic conditions (Forrester and Williams, 1977). PDAC is a highly hypoxic cancer, with high levels of ATP reported in the tumour interstitial fluid of human and mouse PDAC tissues compared to healthy tissues (Hu et al., 2019).”

Do the authors attempt to state here that hypoxic PDACs are those with worse prognosis and more aggressive and thus try to associate members of the purine pathway with those "worse" PDACs? Surprisingly, there is relatively little knowledge about hypoxia in PDAC and I would not suggest using it in this context as a predictor. Reports do suggest that hypoxia forces the emerging of resistant phenotypes but if the authors want to use hypoxic signatures, they have to fortify better (with literature) why do they choose hypoxia and what is the hypothesis that connects hypoxia to purinome, what makes this connection worth investigating.

We thank the reviewer for raising the question of PDAC and worse prognosis with hypoxia. We have now included a Kaplan-Meier curve of high vs low Winter hypoxia score PDAC patients showing significantly lower survival with higher Winter hypoxia score (Sup. Fig. 1B). The significant link with poor survival shown with hypoxia and the inclusion of more detailed explanation of the links with hypoxia and purinergic signalling proteins (metioned above), now clarify the reasoning for investigating this connection.

I find the statement "hypoxia in tumor core" a bit tricky, acute and chronic hypoxia can occur anywhere in the tumor, to my knowledge there are no reports saying only the tumor core suffers from hypoxia in PDAC. PDAC being especially rich in stroma in all of its parts is probably more prone to overall hypoxia and not only in tumor core.

We agree that “hypoxia in tumour core” can be a tricky statement. We have changed “tumour core” to tumour cell compartment and have cited data that demonstrate hypo-vascularisation found in the juxta-tumoural stroma, due to PDAC cells inhibiting angiogenesis (PMID: 27288147). This paper supports our hypothesis of distribution of oxygen being reduced in the tumour area. Hence why we hypothesise that purinergic genes would be preferentially expressed in the tumour area: Line 112 “We hypothesised that genes related to high hypoxia scores would be expressed preferentially in the tumour cell compartment, as PDAC cells inhibit angiogenesis, causing hypo-vascularisation in the juxta-tumoural stroma (Di Maggio et al., 2016).”

We would like to clarify that we do not beileve that only the tumour core suffers from hypoxia, we hypothesise that there is more hypoxia in the tumour cell areas. Although there are no reports of only the tumour core suffering from hypoxia, there is evidence of the tumour epithelial region of the cancer having a greater range of hypoxia (1-39%) compared to the stromal (1-13%) (PMID: 26325106). Moreover, all our analyses point to most purinergic genes differentially expressed in patients with high hypoxic scores being also related to cancer cells and the tumour region. These bioinformatic results linking certain genes like P2RY2 and ADORA2B with hypoxia are also supported in published work cited in the discussion (Line 354 and 356).

I would suggest that the authors rely on published subtyping of PDAC

patient cohorts (Collisson et al, 2010; Bailey et al; Moffit et al, 2015; Chan-Sen-Yue, 2020)

and correlate the expression of purinome genes with the QM/basal-like PDAC subtype that has been confirmed multiple times as the "bad predictor" and use those subtypes for correlation with purinome pathway members. In figure 1E is also shown that P2RY2 is high in expression in basal-like subtype.

We thank the reviewer for this suggestion and have included the subtyping of patients in the PAAD-TCGA cohort in Sup. Table 1 and added comments about the genes related to the different subtypes in the text: Line 88 “In the Bailey model, most genes were related to the Immunogenic subtype except for NT5E, ADORA2B, PANX1 and P2RY2, which related to Squamous (Bailey et al., 2016). Collisson molecular subtyping showed several purinergic genes associated mostly to quasimesenchymal and exocrine subtypes (Collisson et al., 2011). The Moffit subtypes were not strongly associated with purinergic genes except for ADA, NT5E, P2RY6, P2RY2 and PANX1 associated with the Basal subtype (Moffitt et al., 2015).” and Line 345 “Expression of most purinergic genes was associated predominantly with immune cells, immunogenic PDAC subtype and low hypoxia scores (Fig. 1C, E). In contrast, expression of genes correlated with worse survival and hypoxia (PANX1, NT5E, ADORA2B and P2RY2) was associated with tumour cells and the squamous PDAC subtype, correlating with hypoxia, inflammation and worse prognosis (Bailey et al., 2016).”

We did not include the subtyping of Chan-Sen-Yue, 2020, due to the similarities with Moffit and the lack of correlation of basal/classical types with purinergic signalling genes as many of them are not expressed in cancer cells.

Figure 2: In further course of the paper the authors elaborate on possible functions of P2RY2 in PDAC. Although the mRNA data is pretty elaborate, the RNA SCOPE ISH has been performed on only 3 (!) patient PDAC samples. To demonstrated the mRNA is really found in tumor and not in normal adjacent tissue or stroma, I would strongly suggest to increase the number of samples here. The authors should perhaps try to co-localize ISH signals with IF/IHC for some other cancer cell marker, e.g. PanCK or GATA6/KRT81 in human samples to differentiate basal-like from classical samples;If possible, I would even suggest to perform immunohistochemistry instead of RNA scope and confirm the presence of the receptor. If there is an issue with the antibody availability, please state so in the manuscript so that it is clear to the readers why mRNA expression is favored over protein.

We thank the reviewer for these suggestions.

RNAscope was used to verify our trascriptomic bioinformatic results of location of expression P2Y2 in the tumour from publicly available data of 60 pairs of laser microdissection of PDAC epithelial and stromal tissue and the PAAD TCGA deconvolution of 177 patients. We have experienced issues with RNAscope due to the RNA degradation in pancreatic tissue and other technical difficulties which unfortunately led to only having 3 samples showing staining with the positive control. All three successful samples showed P2Y2 expression located in cancer cells. The images presented show the location of P2Y2 RNA expression in the tumour region, which was the aim of the RNAscope experiment.

RNAscope only captures mRNA expression above a specific threshold, and we are aware that P2Y2 will be expressed in other cell types in the normal adjacent as seen in the deconvolution. We have now included in supplementary single cell RNAseq data of normal PDAC tissue to counteract this issue (Sup. Fig. 2B).

We also cite a publication that has performed P2Y2 IHC in 264 patients and showed that P2Y2 protein expression was predominantly shown in the epithelial tumour region (PMID: 30420446), hence staining of P2Y2 in a high number of patients has already been performed: Line 359 “These observations were supported by published immunohistochemical staining of 264 human PDAC samples, showing that P2Y2 localised predominantly in cancer cells in human PDAC”

As shown in Fig. 1 E, P2Y2 is associated with basal and classical tumour cells, not just exclusively to basal, hence the staining to differentiate subtypes is not pertinent to the focus of this paper.

The GSEA data indicated that high P2Y2 expression relates to processes of adhesion/ECM/cytoskeleton organization where the authors draw the conclusion (based also on published data mostly on neuronal/astrocyte work) that P2Y2 may interact with integrins over the RGD domain and thus contribute to invasion an migration. Since this is a very important assumption, I would strongly suggest to expand the experiments of figure 2E and 2G on at least 2 more PDAC cell line, if possible include some with originally epithelial morphology (eg. HPAFII, HPAC...).The visualization of filaments can be done with common IF staining, eg. phalloidin, no need for stable expression.

Perhaphs the reviewer missed Sup. Fig. 2F, where data from Figure 2G are recapitulated in 3 different cell lines. We support the idea of the reviewer in including epithelial morphology cells, hence we added an extra cell line to have 2 cells with epithelial morphology, BxPC-3 and CAPAN-2.

We have tried repeating the experiment in Fig. 2E in epithelial cells, but the way the epithelial cells grow in clusters (Sup. Fig. 2F) make it very difficult to evaluate the morphology of individual cells and get quantifiable results. Nonetheless, we show phenotypic similarities of BxPC-3 to AsPC-1 cells in the invasion assays.

I would also be in favor of investigating the expression of EMT markers upon ATP stimulation.

We thank the reviewer for the suggestion, although feel this is out of scope for our study. There have been recent controversies with reference to EMT and cancer metastasis (PMID:31666716) but more importantly we see changes in cell morphology 1 hour after ATP treatment, indicating it is not/not just EMT.

How was 100µM/5µM chosen as a working concentration?

We have now included figures showing different concentrations of ATP (Sup. Fig. 2D) and AR-C (Sup. Fig. 2E) to illustrate how the concentrations were selected based on the greatest change in morphology for ATP and the full recovery of original cell morphology for AR-C.

• *

AsPC-1 is also known as the cell line that gladly migrates and invades, usually used in metastatic modeling of PDAC. Would be interesting to see if another cell line that is not that migrative (HPAF II) presents the same effect...

This is an interesting point, although we haven’t performed experiments with low migrative cells, later on the work, invasion assays with the epithelial cell line BxPC-3, which has a very different migrative nature, presented the same effect (Sup. Fig. 3G, F). We also perform invasion assays with PANC-1 cells, which also recapitulate an invasive phenotype when transfected with P2Y2.

Is treatment with ATP inducing expression of P2RY2 maybe? What is happening with Intergrin expression upon ATP treatment? Since the hypothesis is that extracellular ATP is driving the invasion, I would certainly suggest to investigate if ATP treatment induces expression of P2RY2 in a time and dose dependent manner.

We thank the reviewer for this suggestion. We have now changed the title to “Purinergic GPCR-integrin interactions drive pancreatic cancer cell invasion”, hence shifting from a focus on extracellular ATP and focusing on the effects of the RGD motif in invasion.

Figure 3:

The authors made very good efforts here to provide functional evidence that P2Y2 is really involved and essential for ATP induced invasion in PDAC cells. They performed an 3D hanging drop spheroid model for invasion in co-culture with stellate cells and show that ATP treatment leads to invasive behavior that is than blocked by addition of P2Y2 antagonist or RGD blocking peptides . Although stellate cells are a nice add-on, keeping in mind the very complex tissue microenviroment of the PDAC, I don't rate the presence of stellate cells here as essential. Are the results the same when experiments are performed without stellate cells?

We thank the reviewer for raising this point, as it has allowed us to clarify that the stellate cells are crucial for this assay to work as they are essential for the formation of the cancer spheres due to their matrix deposition. We have included the hanging drop with and without stellate cells to illustrate this point (Sup. Fig. 3A)

EMT markers increase upon ATP stimulation, do not increase under siRNA downregulation of P2Y2?

As mentioned above, we thank the reviewer for the comment, but we are not focusing on EMT, given the rapidity of the phenotype we observe.

Furthermore, the authors downregulate the P2Y2 using the siRNA/CRISPR-Cas9 approach and confirm that the P2Y2 is really involved in the invasive spread also using the specific RGD block. Experiments in the figure 3 are fairly done and provide functional evidence for the hypothesis. I would suggest that for clarity reasons on every panel (A, B,C...) is written which cell line is used (mostly Aspc1) and for the siRNA experiment I would suggest writing directly on the figure the time points (48h-72h post tranfection) and shortly explain in the text why was mRNA evaluated as the measure of siRNA efficacy and not the protein? Probably the antibody problem, though western-blot applicable antibodies are available.

We thank the reviewer for acknowledging that the experiments in figure 3 provide functional evidence for our hypothesis. We agree with the reviewer and for clarity have included the cell line in each panel and the time point post transfection. We now include a Western blot showing protein levels in the siRNA P2Y2 treatment (Sup. Fig. 3I).

Furthermore, for providing higher impact, I would encourage the experiments to be performed (at least in part) in a PDAC cell line with epithelial morphology (eg. HPAF II or any other that expresses the P2Y2 to a reasonable level).

We agree that performing this experiment with an epithelial morphology cell line provides higher impact, hence why we performed the experiment in BxPC-3 cell lines, perhaps missed in Sup. Fig. 3G and H. We now highlight that they are epithelial-like in the text.

Figure 5: By using the DNA-PAINT method, the authors demonstrated that integrin av and P2Y2 physically interact in the cell membrane over the RGD domain and these interactions are essential for ATP induced P2Y2 mediated invasion in Aspc1 cells. The performed work seems plausible, however, I leave the technical evaluation of this experiment to experts in the field.

General suggestion:

I believe the work would benefit from a clinical/patient perspective if the authors show by immunohistochemistry in PDAC tissue samples that P2Y2 is localized at the invasive front/or metastasis. Is there a surrogate marker that can be used to label ATP rich regions in the tumor, are those regions at the invasive front? Are the P2Y2 positive cells those cells at the invasive front?

This is an interesting suggestion but immunostaining has already been performed on a large cohort of 264 PDAC patients (PMID: 30420446) and expression was consistent throughout the tumour cells.

• *

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

---

Referee #3

Evidence, reproducibility and clarity

The authors concentrate on the members of the purinome and attempt to identify members of the pathway that are especially relevant for PDAC biology, especially invasion and metastatic spread. Using the in silico analysis of transcriptome data from publicly available PDAC patient cohorts, the authors identify P2Y2 as being the most prominent in terms of cancer cell expression and with highest impact on patient survival. The authors than take an effort in functional characterization of P2Y2 and demonstrate that downregulation/deletion of P2Y2 leads to abrogation of ATP activated invasion in hanging drop spheroid model system in a very reasonable and scientifically good way. Finally, the authors postulate that the P2Y2 actions go over interaction with integrin AlphaV and modulations of the cellular cytoskeleton and show via DNA PAINT that a direct interaction of the 2 molecules. The hypothesis is experimentally elaborated in a sound way mostly using cell culture as a system.

The study is solid communicated, the number of experiments seems to be fine. For my understanding, the study relies much on mRNA data (gene expression in cell lines and patient samples), I would suggest providing evidence on protein level what might have been challenging due to potential lack of specific antibody.

Significance

To strengthen the hypothesis experimentally, I would suggest the experiments listed below:

Figure 1: The authors took a solid bioinformatic effort and analyzed expression of different genes of the purinome pathway in different PDAC patient and cell gene expression databases. In this part, the authors rely a lot on correlation of hypoxia and define high hypoxia scores and low hypoxia scores from previously published datasets. Although hypoxia surely plays an important biological condition in the PDAC, I am not sure I get the connection between purinome pathway and hypoxia. Few sentences give a broad introduction about hypoxia-purinome connection in the discussion part of the manuscript, but I think the readership would benefit from more specific statements (which drug, which hypoxic target, which system-mouse/human/cells, what was the exact discovery) and connect those specific statements to the work that has been done here.

Do the authors attempt to state here that hypoxic PDACs are those with worse prognosis and more aggressive and thus try to associate members of the purine pathway with those "worse" PDACs? Surprisingly, there is relatively little knowledge about hypoxia in PDAC and I would not suggest using it in this context as a predictor. Reports do suggest that hypoxia forces the emerging of resistant phenotypes but if the authors want to use hypoxic signatures, they have to fortify better (with literature) why do they choose hypoxia and what is the hypothesis that connects hypoxia to purinome, what makes this connection worth investigating. I find the statement "hypoxia in tumor core" a bit tricky, acute and chronic hypoxia can occur anywhere in the tumor, to my knowledge there are no reports saying only the tumor core suffers from hypoxia in PDAC. PDAC being especially rich in stroma in all of its parts is probably more prone to overall hypoxia and not only in tumor core. I would suggest that the authors rely on published subtyping of PDAC patient cohorts (Collisson et al, 2010; Bailey et al; Moffit et al, 2015; Chan-Sen-Yue, 2020) and correlate the expression of purinome genes with the QM/basal-like PDAC subtype that has been confirmed multiple times as the "bad predictor" and use those subtypes for correlation with purinome pathway members. In figure 1E is also shown that P2RY2 is high in expression in basal-like subtype.

Figure 2: In further course of the paper the authors elaborate on possible functions of P2RY2 in PDAC. Although the mRNA data is pretty elaborate, the RNA SCOPE ISH has been performed on only 3 (!) patient PDAC samples. To demonstrated the mRNA is really found in tumor and not in normal adjacent tissue or stroma, I would strongly suggest to increase the number of samples here. The authors should perhaps try to co-localize ISH signals with IF/IHC for some other cancer cell marker, e.g. PanCK or GATA6/KRT81 in human samples to differentiate basal-like from classical samples;<br /> If possible, I would even suggest to perform immunohistochemistry instead of RNA scope and confirm the presence of the receptor. If there is an issue with the antibody availability, please state so in the manuscript so that it is clear to the readers why mRNA expression is favored over protein. The GSEA data indicated that high P2Y2 expression relates to processes of adhesion/ECM/cytoskeleton organization where the authors draw the conclusion (based also on published data mostly on neuronal/astrocyte work) that P2Y2 may interact with integrins over the RGD domain and thus contribute to invasion and migration. Since this is a very important assumption, I would strongly suggest to expand the experiments of figure 2E and 2G on at least 2 more PDAC cell line, if possible include some with originally epithelial morphology (eg. HPAFII, HPAC...). The visualization of filaments can be done with common IF staining, eg. phalloidin, no need for stable expression. I would also be in favor of investigating the expression of EMT markers upon ATP stimulation. How was 100µM/5µM chosen as a working concentration?

AsPC-1 is also known as the cell line that gladly migrates and invades, usually used in metastatic modeling of PDAC. Would be interesting to see if another cell line that is not that migrative (HPAF II) presents the same effect...Is treatment with ATP inducing expression of P2RY2 maybe? What is happening with Intergrin expression upon ATP treatment? Since the hypothesis is that extracellular ATP is driving the invasion, I would certainly suggest to investigate if ATP treatment induces expression of P2RY2 in a time and dose dependent manner.

Figure 3: The authors made very good efforts here to provide functional evidence that P2Y2 is really involved and essential for ATP induced invasion in PDAC cells. They performed an 3D hanging drop spheroid model for invasion in co-culture with stellate cells and show that ATP treatment leads to invasive behavior that is than blocked by addition of P2Y2 antagonist or RGD blocking peptides . Although stellate cells are a nice add-on, keeping in mind the very complex tissue microenviroment of the PDAC, I don't rate the presence of stellate cells here as essential. Are the results the same when experiments are performed without stellate cells? EMT markers increase upon ATP stimulation, do not increase under siRNA downregulation of P2Y2? Furthermore, the authors downregulate the P2Y2 using the siRNA/CRISPR-Cas9 approach and confirm that the P2Y2 is really involved in the invasive spread also using the specific RGD block. Experiments in the figure 3 are fairly done and provide functional evidence for the hypothesis. I would suggest that for clarity reasons on every panel (A, B,C...) is written which cell line is used (mostly Aspc1) and for the siRNA experiment I would suggest writing directly on the figure the time points (48h-72h post tranfection) and shortly explain in the text why was mRNA evaluated as the measure of siRNA efficacy and not the protein? Probably the antibody problem, though western-blot applicable antibodies are available. Furthermore, for providing higher impact, I would encourage the experiments to be performed (at least in part) in a PDAC cell line with epithelial morphology (eg. HPAF II or any other that expresses the P2Y2 to a reasonable level).

Figure 5: By using the DNA-PAINT method, the authors demonstrated that integrin av and P2Y2 physically interact in the cell membrane over the RGD domain and these interactions are essential for ATP induced P2Y2 mediated invasion in Aspc1 cells. The performed work seems plausible, however, I leave the technical evaluation of this experiment to experts in the field.

General suggestion:

I believe the work would benefit from a clinical/patient perspective if the authors show by immunohistochemistry in PDAC tissue samples that P2Y2 is localized at the invasive front/or metastasis. Is there a surrogate marker that can be used to label ATP rich regions in the tumor, are those regions at the invasive front? Are the P2Y2 positive cells those cells at the invasive front?

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

---

Referee #2

Evidence, reproducibility and clarity

Summary:

Considering the fact that most PDAC are characterized by a high level of extracellular purines content, authors decided to study the expression of the 23 genes coding for membrane proteins involved in the binding or transport of purines in available PDAC transcriptomic cohorts. This approach led to the identification of P2Y2, a GPCR, as the best predictor for the worst survival of patients. Using in vitro models, they show that P2Y2 expression is associated with increased invasion capacity of pancreatic cancer cells and that this pro-invasive effect is dependent on the interaction of P2Y2 with αV integrin via the RGD motif.

Major comments:

• It is not clear to me why authors decided at one point to perform a GSEA comparing low and high mRNA expression of P2Y2 and why they decided to focus on the potential interaction of P2Y2 with integrin αV. As a GPCR, activation of P2Y2 leads to the activation of several downstream signaling pathways that may directly impact the adhesion, migration, and invasion properties of cells. Moreover, despite the presence of the RGD motif in P2Y2, it is not excluded that it may bind (maybe more efficiently) to other "cell adhesion" molecules.
• Similarly, if αV can regulate P2Y2 signaling, what about the regulation of αV signaling pathways by P2Y2? αV integrin has to bind to a β subunit and, depending on the identity of the β subunit, may have distinct regulations and so different impact on cell invasion. How P2Y2 can interfere with these α/β ratios?
• While it has been shown in other studies, in this work, there is no real proof of the interaction between P2Y2 and αV. Only in Figure 4I, where the authors look at the NND <20nm between both proteins, we can see that only 1 to 2 % of αV is in close proximity with P2Y2, which seems very low. Surprisingly, in the absence of ATP, P2Y2 RGE mutant, which should no more interact with αV, show a 2 to 3 fold more vicinity to αV compared to WT P2Y2. How can the authors explain this?
• For DNA-PAINT experiments, the authors only focus on membrane proteins whose amounts are balanced by internalization, recycling and export from internal compartment. As claimed, but not demonstrated by the authors, interaction of P2Y2 and αV may interfere with all these steps, thereby increasing or decreasing the cell surface expression of both proteins. Hence, it would be useful to 1) control proteins levels by western blot, especially for the overexpressed P2Y2, to be sure that they are the same, 2) block internalization and/or export to decipher the important steps.
• In fact, all these main questions are raised by the authors in the end of the discussion but so far, they only show that the RGD motif has an impact on the biological role of P2Y2 (cell invasion) and on the membrane dynamic of αV and itself.
• Fig 2A, authors use RNAscope in order to reveal P2Y2 mRNA expression and distribution in tumor versus normal tissue from 2 patients. They rather show the protein expression, using the antibody they used in other experiments, by standard IHC and in a higher number of patients, including short and long survival, to confirm that the results they obtain by bioinformatics study of transcriptomic data are real.
• Some figure legends are incorrectly numbered or described, such as the figure 4.

Minor comments:

• Can we reasonably talk about OMIC while studying 23 genes? In fact, as described by Timothy A. J. Haystead in 2006 (PMID: 16842150) the purinome is constituted of about 2000 genes coding for proteins binding to purines (including all kinases for example). Author should redefine they pool of genes as perhaps purines receptors/transporter?
• P2Y2 and ADORA2B associated with worse survival while P2Y11 and ADORA2A are associated with better survival (Figure 1B). Would it be more interesting to understand why proteins of the same family act in opposite ways? Figure 1C, any value for the correlation with Survival? Cause this is not so obvious in the figure. Regarding the correlation of P2Y2 and ADORA2B with hypoxia scores, any HIF1 responsive element in promoter? What happens regarding the expression level of these genes when cells are transferred to low oxygen conditions?
• Figure 4 E to M are too small.
• In Supp Figure 4, what are the "Non-altered AsPC-1 cells"?

Significance

Strengths: All the data shown are experimentally and statistically strong.

Limitations: This study remains largely descriptive with no real molecular mechanism that could at least partially explain the biological role of P2Y2 regarding cell invasion.

Advance: Limited

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

---

Referee #1

Evidence, reproducibility and clarity

Summary

The study identifies P2Y2 as a purinergic receptor strongly associated with hypoxia, cancer expression and survival. A link is found between P2Y2-integrin interaction and cancer invasion, highlighting this as a novel therapeutic target. The mechanism is interesting and general well explored.

Minor comments

As P2Y2 is highly expressed by other cell types found with tumours, including vascular endothelium and leukocytes, the authors should reflect on this as a confounding factor in the analysis of adrenocarcinoma gene expression analysis. I appreciate the RNAscope work may resolve this issue to some extent.

Major comments

The authors correctly identify that the level of ATP in the tumour microenvironment can be very high, typically 100uM or so. However, these concentrations are supramaximal for P2Y2 activation, at which ATP has an approximate EC50 of 100nM. Coupled with the fact that many cell types, including cancer cells, constitutively secrete ATP, there is an opportunity to explore the effects of lower ATP concentrations in some assays, or provide some concentration-response relationship to give more confidence of P2Y2-dependent effects. Also, the authors describe the use of cancer cells where P2Y2 has been knocked out using CRISPR. Does this KO have an effect on cancer invasion? The effect of ARC should be absent in these cells and give confidence the effects of ARC are P2Y2-dependent, as some off-target effects of this antagonist have been reported. To explore the influence of constitutive P2Y2 activity, the authors should explore the effects of ARC alone in some assays.

The title of the manuscript implies extracellular ATP drives cancer invasion, though in my opinion this statement is not fully explored. Though ATP/UTP are applied at supramaximal concentrations for P2Y2 activation, the influence of ATP in the cell culture microenvironment without exogenous application is not explored. One would predict that scavenging extracellular ATP with apyrase would negatively impact invasiveness and the proximity of integrin and P2Y2 without ATP/UTP application if constitutively secreted ATP is involved. Pharmacological manipulation of ectonucleotidase activity is an alternative. Experimental route to explore this.

Immunoprecipitation experiments of native proteins would be more convincing data that P2Y2 and integrin physically interaction, as opposed to being in close proximity. This would also overcome artifacts of interaction that can be attributed to receptor overexpression.

It is currently not clear what the mechanistic relationship between P2Y2 activity, P2Y2-integrin proximity and RGD motif is. Do the authors suggest the RGD domain becomes exposed upon receptor activation? The mechanism is not fully articulated in the discussion.

Significance

General assessment:

A novel mechanism is presented for therapeutic intervention of cancer. The study relies on supramaximal concentrations of agonist and overexpressed receptors. Role of endogenous P2Y2 not fully explored. The study lacks in vivo evidence of the importance of this mechanisms. Cell developed in the study could be used in mouse models to explore effect on tumour growth.

Advance:

Integrin and P2Y2 interactions are already documented but not in context of cancer.

Audience:

basic research

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

Learn more at Review Commons

---

Reply to the reviewers

The authors do not wish to provide a response at this time.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

---

Referee #3

Evidence, reproducibility and clarity

The authors show that nSMase1 (gene name: SMPD2) knockdown reduces LAMP1 at the mRNA levels, causes inefficient activation of the UPR upon ER stress, arrests cells in the G1 phase, reduces the level of phosphorylated Akt, downregulates the Wnt signaling pathway, and reduces the overall protein translation in both HeLa cells and HCT116 cells. Although these findings are potential interesting, these findings do not define the biological role for nSMase1. Moreover, it is unclear how nSMase1 knockdown causes these changes.

Specific comments:

1. The authors do not provide any evidence showing that "nSMase1 knockdown" actually occurs in HeLa cells or HCT116 cells. Does siRNA reduce the levels of nSMase1 mRNA and protein?
2. Many western blots lack quantification, such as Figures 1A, 1G, 3B, and 4K.
3. Figure 3A shows that the effects of nSMase1 knockdown on cell apoptosis are very modest.
4. Is there any explanation how nSMase1 knockdown dramatically reduces protein translation?
5. It could be better to assess the UPR by performing western bolt for PERK, ATF6 and IRE1.

Significance

The authors show that nSMase1 (gene name: SMPD2) knockdown reduces LAMP1 at the mRNA levels, causes inefficient activation of the UPR upon ER stress, arrests cells in the G1 phase, reduces the level of phosphorylated Akt, downregulates the Wnt signaling pathway, and reduces the overall protein translation in both HeLa cells and HCT116 cells. Although these findings are potential interesting, these findings do not define the biological role for nSMase1. Moreover, it is unclear how nSMase1 knockdown causes these changes.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

---

Referee #2

Evidence, reproducibility and clarity

Summary

In this paper, the authors sought to investigate the biological role of neutral sphingomelinase 1 (nSMase1; SMPD2) in two established cell lines, with a focus on viability and response to cell stress. The authors reduced the level of SMPD2 in HeLa and HCT116 cells, with the use of siRNA and validated the efficiency of this knockdown. They followed up with a characterization on autophagic activity, unfolded protein response (UPR) pathway and cell cycle progression in SMPD2-KD cells. The approach is rational and the statistical methods used are sound.

The authors showed that SMPD2-KD in both cell lines resulted in a significant reduction of LAMP1, a lysosomal-associated protein. However, this reduction did not affect the lysosomal activity of the cells, with the turnover lysosomal-associated proteins, LC3B-II and P62, shown to be unchanged under both starved and normal conditions. Similarly, measurement of lysotracker puncta also revealed no changes in lysosomal acidification. Furthermore, the authors showed that the downregulation of LAMP1 in SMPD2-KD HeLa cells occurs at the transcriptional level, as the inhibition of proteasomal and lysosomal activity, using MG132 and Bafilomycin-A, respectively, did not impact LAMP1 protein levels.

The authors went on to show that the induction of ER stress in HeLa cells using thapsigargin and tunicamycin resulted in the increase in SMPD2 protein. In SMPD2-KD cells, thapsigargin and tunicamycin treatment resulted in lower levels of ER stress markers, spliced version of XBP-1, ATF4 and EDEM mRNA, when compared to 'control' (taken to be SMPD2 intact) cells. Despite the reduced induction of ER stress in SMPD2-KD cells by thapsigargin and tunicamycin, the viability of these cells was found to be significantly lower than thapsigargin- and tunicamycin-treated control cells. These findings led the authors to conclude that an inability to mount a UPR response was detrimental to cell viability. A problematic inference.

The impact of SMPD2 KD in HeLa cells was also validated using annexin V/PI FACS and immunoblotting of cleaved caspase 3 and cleaved caspase 7. FACS analysis showed a significantly lower percentage of viable cells in SMPD2-KD cells but caspase activation did not occur. The authors also showed that this decreased viability could potentially result from cell cycle dysregulation, as the percentage of cells in the G1 phase was found to be significantly higher in SMPD2-KD cells when compared to the control cells. Furthermore, p21 and p27, which are G1 cell cycle arrest protein, were found to be upregulated in SMPD2. Further elucidation revealed a higher level of phosphorylated CHK2 and lower level of phosphorylated AKT, suggesting that cell cycle dysregulation in SMPD2-KD cells could be partially affected by the P13K/Akt pathway. The authors also showed that the impaired cell cycle progression could partially result from the canonical beta-catenin pathway, with SMPD2-KD cells showing lower level of Wnt activity.

Overall, the author concluded that the knockdown of SMPD2 could affect cell viability through dysregulation of cell cycle progression while autophagic activity were unaffected by the loss of SMPD2. Noteworthily, the authors also showed a lower level of UPR response in thapsigargin- and tunicamycin-treated SMPD2-KD cells and concluded that this reduced response is detrimental to the cells and would lead to reduced cell viability.

Major Comments:

1. While the KD of SMPD2 did result in a lowering of nSMase1, the effect of the SMPD2 KD on other SMases remains unclear. Was the compensation from other SMases because of SMPD2 KD?
2. Related to the first, most results are primarily based on siRNA mediated knockdown of SMase1, but there were no rescue experiments conducted to rule out Off-Target effects of the siRNA. This is a major concern as the conclusions on SMase1 role(s) are entirely based on the KD of SMase1. The control for each of the KDs were a generic siRNA pool (siCtrl) purchased from Dharmacon, rather than a scrambled sequence for each specific gene-targeted siRNA. This raises a slight concern.
3. The authors showed lower levels of UPR markers in SMPD2 KD cells exposed to thapsigargin and tunicamycin and concluded that this 'failure' to mount an ER response is responsible for the observed decrease in cell viability. However, there is no conclusive evidence linking the two observations. It becomes more confusing when the inhibition of IRE1 activity with 4µ8C was observed to INCREASE viability in both SMPD2-KD and control cells. Does this not suggest that lowered level of UPR response in SMPD2 cells is beneficial?
4. The Annexin V assay also revealed that there are lower percentage of viable cells in SMPD2-KD cells, even in the absence of thapsigargin or tunicamycin treatment. This suggest that the impact of SMPD2 knockdown on cell viability could be independent of ER stress.
5. The use of established lines that grow extremely rapidly limits the conclusion of the paper. Furthermore, why was the cell cycle analysis done in non-synchronised cells? It would have been cleaner to pre-treat cells with nocodozole for a brief period, before continuing with culture (and treatment). The impact of SMPD2 on cell cycle arrest could be more convincing.
6. The authors also showed that there was a significant decrease in ceramides in SMPD2-KD cells which on its own can induce ER stress (1). The involvement of ceramides in the lowered ER stress response in SMPD2-KD cells is confounding and needs further clarification.
7. The authors showed a higher percentage of early apoptotic cells in SMPD2-KD cells using Annexin V assay but western immunoblotting of cleaved caspases 3 and 7 were inconclusive of apoptosis (or pre-apoptosis) in these cells. A further validation is required, eg. caspase 3/7 activity assay to confirm the immunoblot data.
8. The authors pointed out the endogenous SMPD2 resides in the nuclear matrix, while the shift in subcellular localization to the ER membrane occur when SMPD2 is overexpressed. This premise led to the authors' speculate that upregulation of SMPD2 during ER stress is a crucial event in the maintenance of ER homeostasis. The authors need to validate this (not speculate) by showing SMPD2 localization in the presence, and absence, of thapsigargin and separately tunicamycin.
9. The authors found that p21 and p27 were upregulated in SMPD2-KD cells which then contributed to the cell cycle arrest. But a validation of this conclusion is missing. Are levels of p21 and p27 normalised upon rescue of SMPD2 in KD cells?

Minor Comments

1. There are couple of quantification which could benefit from an increase in N number as the individual points were inconsistent e.g., Fig 2 D-F.
2. The presentation of the results is confusing as work with the two different cell lines were placed in the same figure e.g. Fig 4E-G.
3. The procedure for lysotracker is missing in the materials and methods.

Significance

This paper delves into the role of nSMase1 in the regulation of cell cycle and ER stress response in two cancer cell lines. Previous work had identified nSMase1 as an important initiator of apoptosis when exposed to environmental stressors. Activation of nSMase1 increased ceramide levels, which in turn led to increased apoptosis via the caspase pathway (2). The authors now provide an observation that the knockdown of nSMase1 would also reduce cell viability through dysregulation of cell cycle progression, even in the absence of environmental stressors. However, these findings remain inconclusive in proving that the failure to mount an ER stress response in SMDP2-KD cells leads to G1 phase arrest. The role of nSMase1 in lowering ceramides and using ceramides to link ER stress and cell cycle arrest remains interesting. Ceramide dysregulation in diseases such as diabetes and cardiovascular diseases is perhaps more relevant rather than cancer (use of appropriate cells). Overall, the significance of finding SMDP2-KD cells causing cell cycle arrest is limited because of the cells used. More work needs to be done before realising whether nSMase1 could potentially be a therapeutic target for lowering of ER stress, promoting cell proliferation (and the importance of cellular ceramide in this pathway).

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

---

Referee #1

Evidence, reproducibility and clarity

In this manuscript, the authors report on the role of neutral sphingomyelinase 1 (nSMase1) in regulating cell cycle through ER stress.

Overall, the authors report interesting observations, but the manuscript falls shot to provide a coherent and comprehensive mechanism. Many of the experiments reported have large reproducibility variation and lack further supportive experiments to support the authors' claims. I recommend the authors to perform additional experiments to strengthen their claims and/or to tone down some of their conclusions. The manuscript will require a major revision before it is ready for publication to a specialized and a broad readership.

Major points to address

[Unfortunately, the authors didn't include page numbers nor line numbers so I will refer to the page numbers of the PDF file]

1. In general, the authors report the efficiency of their knockdown after several experiments. It should be reported first before it is used for any of the experiments. For instance, knocking down siSMPD2 should be shown 1st before the experiment and not in panel H of Fig. 1.
2. Some of the reported immunoblots are of poor quality and should be replace by a better replicate from their biological replicates or repeated to meet expected standards. Here are some examples:
   • (a)The band of LAMP1 in Fig. S1B should be strong and obvious based on the commercial antibody they used. Here, we are not even sure which of the 4 bands is LAMP1.
   • (b) In Fig. 1D, I am a bit concerned to see that the loading control GAPDH is uneven through the samples. Do the authors load the same amount of total protein for each sample?
   • (c) The detection of LC3B-II should include LC3B-I. Both are the same protein where the LC3B-II is covalently bound to PE.
3. The authors concluded based on the data presented in Fig. 1D-F that "downsregulated LAMP1 does not affect lysosome function. The lysosomal function is not demonstrated from these experiments so the authors cannot make such conclusion.
4. In Fig. S1G, H, the authors claims that SMPD2 KD affects cellular ceramide levels specifically at the ER. Microscopy is not the best approach to quantify ceramide levels. Ideally, the authors should use a biochemical approach to validate their finding such as TLC or LC/MS/MS.
5. Four-hour treatment with tunicamycin (Tm) or thapsigargin (Tg) is rather small to provide enough time for cells to make sufficient proteins that will be visible by immunoblot. It is best to allow at least 12h of incubation for UPR-regulated proteins. However, 4h would be sufficient to monitor UPR-induced mRNA levels.
6. The authors concluded that nSMase1 protein is increased upon ER stress. However, the increase is small. Was the nSMase1 mRNA level increased as well with Tm and Tg? What is the other protein that has been cut from the immunoblot of nSMase1 on top in DMSO and Tm lanes (Fig. 2A)? Also, nSMase1 predicted MW is 47 so I am bit puzzled why it runs below 40 KDa in Fig. 2A. Is the increase in nSMase1 upon Tm or Tg is specific to the UPR response such IRE1, PERK, or ATF6 branches? The variation between the replicates is enormous especially for PDI.
7. I appreciate that the authors report the replicates for their experiments. However, the variation between biological is rather large for in vitro studies. Also, the authors shouldn't stress that they observe a small different when it is clearly not significant and that the variation is rather large. Many more replicates are needed to distinguish small unsignificant variations. Here are some examples:
   • (a) The authors reported that the upregulated of BiP by Tm or Tg is slightly impaired by SMPD2 KD (Fig. 2C, D). I don't see any significant difference. Large variation between replicates.
   • (b) The variation of spliced XBP1 is extremely high especially siCtrl with Tm (Fig. 2G). It should be very reproducible unless cell confluency was not consistent between replicates or that cells were overconfluent. It should be "XBP1" and not "XBP-1".
   • (c) Replicate variation for BiP, CHOP, SMPD2 is also problematic (Fig. 2).
   • (d) The authors stated "SMPD2 mRNA levels were slightly increased by tunicamycin and thapsigargin treatment (Fig. 2M)". Is the increase significant? It doesn't seem so and the variation between replicate is quite high.
   • (e) I don't see an increase in nSMases1 upon Tm treatment unlike the authors claim in Fig. 2A.
   • (f) The GAPDH band in fig. 4E contains a "bubble" so I am sure how the quantification can be meaningful.
8. ATF4 mRNA level is not a good indicator of UPR activation. ATF4 mRNA is quite constant but the translation of ATF4 is induced upon PERK activation. Therefore, the authors should look at ATF4 protein levels.
9. In Fig. 2, are the increase of all these UPR-upregulated genes significant for Tm and Tg compared to DMSO? Not indicated anywhere.
10. In page 5, the authors stated "under ER stress conditions the LAMP1 mRNA remained significantly downregulated by SMPD2 KD". Is LAMP1 mRNA level significantly upregulated upon ER stress? It doesn't seem to be the case so I am wondering what this statement is implying.
11. For the experiment reported in Fig. S1J, he inhibition should have been shown in the presence of Tm or Tg.
12. What are the evidence that SMPD2 KD failed to activate the UPR upon ER stress? All the data in Fig. 2 demonstrate that the UPR is significantly induced with Tm and Tg in SMPD2 KD cells. Also the authors have to be cautious by using drugs that induce the ER stress as they have side effects. For instance, Tg dramatically increases the calcium levels in the cytosol which could affect SMPD2 KD cells independently of ER stress.
13. I disagree with the authors interpretation of the data "a full-potential UPR signaling activation upon ER stress is not achieved in SMPD2 KD cells, and consequently their cellular fitness is impaired under ER stress conditions.". I am not sure what is their definition of "full-potential UPR" but I don't see any problems in the UPR activation with Tm or Tg in SMPD2 KD cells. Someone has to be very careful to interpret lower UPR activation but still significant activation of the UPR. Overall, the data related to ER stress and the UPR in SMPD2 KD is inconclusive and just a distraction.
14. It should be clear in the text what is detected by flow cytometry for Fig. S2A.
15. How many cells are included in the analysis of Fig. 3D? There should be at least 20 cells so at least 20 data points. It shouldn't be the average of cell diameter for each biological replicate. The difference is very small. Also, diameter is meaningless as most cells are uneven so it would be best to compare the area of each cell.
16. In Fig. 3F, the experiment should include a control that induce cell cycle arrest such as nocodazole.
17. The authors compared the levels of P21 and P27 at 72h and 96h. These 2 time point experiments were not done together so it is difficult to compare and to make any conclusion. Someone would have to analyse several time points to make any conclusion.
18. Can we just say that they grow more slowly instead of claiming temporary cell cycle arrest in page 7? It just means that the cells are spending more time in G1 in KD SMPD2 compared to control.
19. Some of the cell cycle experiments are not done correctly to make any conclusions. For instance, Chk2 is ATM substrate and is phosphorylated upon ATM signaling to enforce checkpoint arrest. Decrease in Chk2 phosphorylation typically means if in DNA damage context, ATR plays predominant role. Usually, ATM and ART are redundant kinases. There is no report of Chk2 phosphorylation from the referred publication.
20. What is the rational of changing cell likes at Fig. 4H?
21. The authors conclude that "SMPD2 KD seems to affect many cellular processes by downregulating their signaling components including Wnt signaling - which could explain the reduction in global protein translation and G1 cell cycle arrest". Global protein transcription and translation inhibition is typical of stressed cells. Therefore, their statement and findings are broad and failed to pinpoint to any major players or mechanism.

Minor points to address

There are some minor points that should be considered below before publication if they haven't been already addressed by the authors.

[Unfortunately, the authors didn't include page numbers nor line numbers so I will refer to the page numbers of the PDF file]

1. Page 3, should be "cancer cell line" and not "cancer line".
2. Page 5, the authors should clarify what the inhibitor refers to in the sentence "We found BiP to be upregulated at the protein level by both inhibitors, which was slightly impaired by SMPD2 KD after 4 h of treatment".
3. Fig. 3A, Y-axis labelling not clear.
4. Fig. 3C, label of x-axis is missing.
5. Page 7, it should be flow cytometry and not FACS.

Significance

The manuscript in its current form fails to provide an advance to the field as there is no coherent mechanism. Therefore, it is difficult to judge the target audience at this premature stage.

My expertise includes endoplasmic reticulum stress, autophagy, lipid synthesis and regulation.

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

Learn more at Review Commons

---

Reply to the reviewers

1. General Statements

We would like to thank the reviewers for their valuable comments. We believe that we can provide all requested revisions.

2. Description of the planned revisions

Reviewer #1 (Evidence, reproducibility and clarity):

Comment 1: The study sought to determine whether hippocampal PSD-95 is involved in extinction of contextual fear memory in mice. Although there is considerable work implicating the hippocampus in contextual fear extinction, this study adds to the literature in an important way by identifying an important role for dendritic PSD-95 in this process. The authors observe changes in PSD-95 expression and phosphorylation in dendritic spines in CA1. Disrupting phosphorylation of PSD-95 attenuated fear extinction. These are interesting data, though the lack of behavioral controls for mere context exposure renders the results difficult to interpret.

ANSWER: The analysis of dendritic spines in Contextual controls (without US) vs 5US (24 hr after fear conditioning) is presented in the manuscript in Supplementary Figure 1. Unlike in the comparison Extinction vs 5US, we found no significant differences here between the groups. In the revised manuscript we will add the analysis of PSD-95 levels in the same group.

Reviewer #1 (Significance):

Comment 2: The strengths of the report include a careful assessment of the role for hippocampal PSD-95 in contextual fear extinction, using several methods. The neurobiological assessments and interventions are robust. The primary concern is with the behavioral methodology, particularly the absence of a context-exposure control (e.g., a non-conditioned group that is treated identically to the current Ext group., or a conditioned group that is exposed to another context). This control is necessary to interpret the initial experiments, because changes in PSD-95 protein and phosphorylation may not be due to extinction learning per se, but rather to exposure to the context (and learning about that context that is independent of extinction). Thought the disruption of PSD-95 phosphorylation in the dorsal hippocampus appears to blunt context extinction with multiple extinction sessions, it did not impede the extinction procedure used in the initial experiments.

ANSWER: The analysis of dendritic spines in Contextual controls (without US) vs 5US (24 hr after fear conditioning) is presented in the manuscript in Supplementary Figure 1. Unlike in the comparison Extinction vs 5US, we found no significant differences here between the groups. In the revised manuscript we will add the analysis of PSD-95 levels in the same group.

Reviewer #2 (Evidence, reproducibility and clarity):

Summary:

Comment 3: In this manuscript, the authors are looking into the effects of PSD-95 phosphorylation at S73 in dorsal CA1 on extinction of fear memories. To do so, they use behavioral, immunofluorescence and electron microscopy approaches. They find that S73 phosphorylation is essential for plasticity related changes mediating fear memory extinction. In general, the data is interesting and experiments appear to have been done with good rigor. However, some experiments lack important controls. Previous literature on the effects of PSD-95 overexpression, which is central to the current study, is not discussed much. Regarding the writing, several typos were found and there are also several instances of overstatements.

Major points:

Comment 4: PSD-95 overexpression is documented to cause an increase in spine density and an increase in spine size (El Husseini, Science, 2000). From the data presented it is not clear that this is happening or not in your animals. The only time where animals injected with a control virus are compared with animals injected with WT PSD-95 and PSD-95 S73A is in figure 5 and no data is shown about PSD-95 amounts in these animals. Moreover, PSD-95 overexpression blocks LTP and enhances LTD (Stein J.Neurosci 2003). This would suggest that animals injected with WT PSD-95 would have deficits in memory acquisition and enhanced extinction. Please comment on this._

ANSWER: The data regarding PSD-95 amounts in the Control, WT and S73A groups are shown on the Supplementary Figure 3. In the revised manuscript we will add the electron microscopy data demonstrating dendritic spine and PSD volume and density in these three groups and discuss the effects of PSD-95 overexpression on LTP and memory. The mentioned literature will be included in the discussion. However, it is important to note that predominantly in vitro studies exist with regards to PSD-95 overexpression. In vivo, although we observed a significant effect of PSD-95 overexpression on dendritic spine density and average PSD volume, the total PSD volume per tissue brick is not affected. Thus compensatory changes may explain why memory formation is not impaired in WT groups.

Comment 5: In figures 1, 2, 3 and 6 PSD-95 immunofluorescence is used to quantify PSD-95 in the dorsal CA1. From these measurements, several metrics are extracted (PSD-95+ density; total PSD-95; PSD-95+, PSD-95+ puncta...) and they slightly differ in the different figures. Some of these are easy to understand but others would benefit from a more detailed description. Please use consistent metrics and/or provide a rationale for using each of the different analysis methods.

ANSWER: We will clearly explain the rationale for each metrics used and explain how they were defined in the methods section.

Comment 6: In Figure 6, immunostaining with the antibody specific for PSD-95 S73 needs to be done in order to link CaMKII to this story.

ANSWER: The immunostaining comparing phospho-S73 levels in WT and T286A mice will be added in the revised manuscript.

Comment 7: Line 220: No differences were observed in PSD-95 levels between mice with WT PSD-95 expression and PSD-95 (S73A). What about differences in PSD-95 levels in mice without viral injection, ie what is the level of overexpression?

ANSWER: We apologize for this mistake in description. We did observe around 40% of overexpression of PSD-95 in WT and S73A groups as compared to the Control. This data is presented in Supplementary Figure 3. In the revised version this will be clearly stated in the results section with the reference to the Supplementary Figure 3.

Comment 8: Line 270: 'The S73A mutation impaired fear extinction-induced downregulation of dendritic spine density as well as dendritic spine and PSD growth'

What about the effect of the S73A mutation on the same metrics (Dendritic spine volume, PSD area and PSD volume)? It looks like the 5US groups are different.

Also, in S73A mice, the PSD area does significantly increase, which is contrary to the statement above; please explain.

ANSWER: PDS surface area is indeed larger in S37A mice after extinction, however not the volume of the dendritic spines and PSDs. This statement will be corrected to precisely state our observations.

Comment 9: In the example picture shown in Figure 1 DEF, spine density and amount of PSD-95 puncta are visibly much lower in the stLM of conditioned animals (5US), this is not what is shown in the quantification at all (Figure 1GHIJ). Please provide an explanation for this and a representative example picture. Also, it would be good to show another example in a supplementary figure.

ANSWER: The quantification is correct. Indeed the stLM 5US image in the current version of the manuscript looks misleading. In the revised version we will submit another picture which better represents the quantification of dendritic spines and PSD-95 in this group.

Minor points:

Comment 10: Figure 4D: Impossible to see what is happening here; please present less (3-5) isolated examples of dendritic spines for each condition.

ANSWER: We will provide isolated reconstructions of dendritic spines.

Comment 11: In the introduction, it is stated at line 75 that: ' Phosphorylation of PSD-

95(S73) enables PSD-95 dissociation from the complex with GluN2B'. Another study found that PSD-95-S73A expression blocked the reduction in the NMDAR/PSD-95 interaction during chemical LTP in a manner that is dependent on CaMKII and calpain (Dore et al Plos One, 2014). This is consistent with the current study and the Steiner et al 2008 paper as well and should thus be mentioned and included in the citations.

ANSWER: We would like to thank the reviewer for reminding us of this important study in line with the current results. We will now include it in the discussion of our results.

Comment 12: Line 268: 'synaptic changes observed in the WT mice resembled the changes found in Thy1-GFP(M) animals after contextual fear extinction'. Please be more specific, sim ilarities were found in stratum oriens for the Thy1-GFP animals, which is where the SBEM experiments were done for Fig.4. What metrics exactly are similar?

ANSWER: This is indeed an imprecise statement and will be corrected. We will indicate that the analysis of dendritic spines by confocal microscopy in Thy1-GFP and EM in WT mice observed decreased density of dendritic spines and increased volume of the remaining dendritic spines in stOri after extinction.

Comment 13: Figure 2A: What is the signification of the H1, H2, H3 samples? Are these different mice? Why is there no band in the H2 sample?

ANSWER: This information will be added.

Comment 14: Line 65: PSD-95 is a major scaffolding protein at glutamatergic synapses

ANSWER: This will be corrected.

Comment 15: Line 106: formation of fear extinction memory => extinction of fear memory

ANSWER: This will be corrected.

Comment 16: Line 112: assess

ANSWER: This will be corrected.

Comment 17: Line 211: 30-minute

ANSWER: This will be corrected.

Comment 18: Line 460: co-localizes

ANSWER: This will be corrected.

Reviewer #2 (Significance):

This paper provides a new molecular mechanism underlying the extinction of fear memories. It should thus be of interest for the general neuroscience community, especially for people working on synaptic plasticity and fear conditioning.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

---

Referee #2

Evidence, reproducibility and clarity

Summary:

In this manuscript, the authors are looking into the effects of PSD-95 phosphorylation at S73 in dorsal CA1 on extinction of fear memories. To do so, they use behavioral, immunofluorescence and electron microscopy approaches. They find that S73 phosphorylation is essential for plasticity related changes mediating fear memory extinction. In general, the data is interesting and experiments appear to have been done with good rigor. However, some experiments lack important controls. Previous literature on the effects of PSD-95 overexpression, which is central to the current study, is not discussed much. Regarding the writing, several typos were found and there are also several instances of overstatements.

Major points:

PSD-95 overexpression is documented to cause an increase in spine density and an increase in spine size (El Husseini, Science, 2000). From the data presented it is not clear that this is happening or not in your animals. The only time where animals injected with a control virus are compared with animals injected with WT PSD-95 and PSD-95 S73A is in figure 5 and no data is shown about PSD-95 amounts in these animals. Moreover, PSD-95 overexpression blocks LTP and enhances LTD (Stein J.Neurosci 2003). This would suggest that animals injected with WT PSD-95 would have deficits in memory acquisition and enhanced extinction. Please comment on this.

In figures 1, 2, 3 and 6 PSD-95 immunofluorescence is used to quantify PSD-95 in the dorsal CA1. From these measurements, several metrics are extracted (PSD-95+ density; total PSD-95; PSD-95+, PSD-95+ puncta...) and they slightly differ in the different figures. Some of these are easy to understand but others would benefit from a more detailed description. Please use consistent metrics and/or provide a rationale for using each of the different analysis methods.

In Figure 6, immunostaining with the antibody specific for PSD-95 S73 needs to be done in order to link CaMKII to this story.

Line 220: No differences were observed in PSD-95 levels between mice with WT PSD-95 expression and PSD-95 (S73A). What about differences in PSD-95 levels in mice without viral injection, ie what is the level of overexpression?

Line 270: 'The S73A mutation impaired fear extinction-induced downregulation of dendritic spine density as well as dendritic spine and PSD growth'<br /> What about the effect of the S73A mutation on the same metrics (Dendritic spine volume, PSD area and PSD volume)? It looks like the 5US groups are different.<br /> Also, in S73A mice, the PSD area does significantly increase, which is contrary to the statement above; please explain.

In the example picture shown in Figure 1 DEF, spine density and amount of PSD-95 puncta are visibly much lower in the stLM of conditioned animals (5US), this is not what is shown in the quantification at all (Figure 1GHIJ). Please provide an explanation for this and a representative example picture. Also, it would be good to show another example in a supplementary figure.

Minor points:

Figure 4D: Impossible to see what is happening here; please present less (3-5) isolated examples of dendritic spines for each condition.

In the introduction, it is stated at line 75 that: ' Phosphorylation of PSD-<br /> 95(S73) enables PSD-95 dissociation from the complex with GluN2B'. Another study found that PSD-95-S73A expression blocked the reduction in the NMDAR/PSD-95 interaction during chemical LTP in a manner that is dependent on CaMKII and calpain (Dore et al Plos One, 2014). This is consistent with the current study and the Steiner et al 2008 paper as well and should thus be mentioned and included in the citations.

Line 268: 'synaptic changes observed in the WT mice resembled the changes found in Thy1-GFP(M) animals after contextual fear extinction'. Please be more specific, similarities were found in stratum oriens for the Thy1-GFP animals, which is where the SBEM experiments were done for Fig.4. What metrics exactly are similar?

Figure 2A: What is the signification of the H1, H2, H3 samples? Are these different mice? Why is there no band in the H2 sample?

Line 65: PSD-95 is a major scaffolding protein at glutamatergic synapses

Line 106: formation of fear extinction memory => extinction of fear memory

Line 112: assess

Line 211: 30-minute

Line 460: co-localizes

Significance

This paper provides a new molecular mechanism underlying the extinction of fear memories. It should thus be of interest for the general neuroscience community, especially for people working on synaptic plasticity and fear conditioning.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

---

Referee #1

Evidence, reproducibility and clarity

The study sought to determine whether hippocampal PSD-95 is involved in extinction of contextual fear memory in mice. Although there is considerable work implicating the hippocampus in contextual fear extinction, this study adds to the literature in an important way by identifying an important role for dendritic PSD-95 in this process. The authors observe changes in PSD-95 expression and phosphorylation in dendritic spines in CA1. Disrupting phosphorylation of PSD-95 attenuated fear extinction. These are interesting data, though the lack of behavioral controls for mere context exposure renders the results difficult to interpret.

Significance

The strengths of the report include a careful assessment of the role for hippocampal PSD-95 in contextual fear extinction, using several methods. The neurobiological assessments and interventions are robust. The primary concern is with the behavioral methodology, particularly the absence of a context-exposure control (e.g., a non-conditioned group that is treated identically to the current Ext group., or a conditioned group that is exposed to another context). This control is necessary to interpret the initial experiments, because changes in PSD-95 protein and phosphorylation may not be due to extinction learning per se, but rather to exposure to the context (and learning about that context that is independent of extinction). Thought the disruption of PSD-95 phosphorylation in the dorsal hippocampus appears to blunt context extinction with multiple extinction sessions, it did not impede the extinction procedure used in the initial experiments.

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

Learn more at Review Commons

---

Reply to the reviewers

1. General Statements

We thank the reviewers for their thorough and insightful evaluations of our manuscript and for their constructive feedback, which have significantly improved the quality of our manuscript. We were pleased to read that all three reviewers found our work novel, interesting, and relevant. In this revised manuscript, we have done our best to address all of the points raised by the reviewers by performing new experiments and revising sections of the text, as requested.

2. Point-by-point description of the revisions

Reviewer #1 (Evidence, reproducibility and clarity):

In this manuscript authors show that extracellular Mtb aggregates can cause macrophage killing in a close contact dependent but phagocytosis independent manner. They showed Mtb aggregates can induce plasma membrane perturbations and cytoplasmic Ca2+ influx with live cell microscopy. Next, the authors show that the type of cell death initiated by extracellular aggregates is pyroptosis and they partially supressed cell death with pyroptosis inhibitors. They also identified that PDIM, EsxA/EsxB and EspB all have a role in uptake-independent killing of macrophages even though their impact varies with respect membrane perturbation and Ca2+ influx. Finally, they used a small molecule inhibitor BTP15 to inhibit the effect of ESX-1 during the contact of the extracellular Mtb aggregates with the macrophages and they observed a substantial decrease in membrane perturbation and macrophage killing.<br /> The work describes a very interesting mechanism by which Mtb can kill macrophages that is possibly relevant in the context of infection.

1. In general, there are two main issues with the experiments and the interpretation: the lack of quantitative analysis showing that in a population of macrophages the ones that are in contact with the aggregates die whereas the ones that are not in contact remain alive. This is currently not shown, and it should be added in figure 1.

All our data are based on the visual inspection and annotation of time-lapse microscopy image series, from which it is conclusive that death happens more often among cells in contact with Mtb aggregates (see movies S3 and S6 for representative examples). However, we acknowledge the reviewer’s suggestion that quantitative data supporting this observation might help to convey this conclusion more effectively. Therefore, we have quantified the percentage of dead cells in: I) macrophages in uninfected controls; II) macrophages that establish contact with an Mtb aggregate; III) bystander macrophages that never contact an Mtb aggregate despite being in the same sample as the infected cells, in experiments with (figure 1D) or without (figure 1Q) cytochalasin D treatment. These data have been incorporated as two additional plots in figure 1 in the revised manuscript. We find that uninfected and bystander cells have similar survival probabilities over the time-course of an experiment, whereas most of the cells that physically interact with Mtb_aggregates die by the end of the experiment. To further validate these observations, we have also plotted the lifespans of infected cells vs. bystander cells without (figure S3A) and with (figure S3B) cytochalasin D treatment. In these plots, the lifespan of an individual cell is represented by a line; the fraction of the line coloured in black corresponds to the time spent as bystander and the fraction of the line in magenta corresponds to the time spent in contact with an _Mtb aggregate. We hope that these new data convincingly show that bystander cells (black lines) survive longer compared to cells that interact with Mtb aggregates (black-magenta lines).

1. The second is the cell death mode, as the markers used are very different and considering different outcomes (e.g., apoptosis vs. necrosis) are relevant for the infection it is unclear what is being measured here and the impact on bacterial replication.

As the reviewer points out, it has previously been shown that different cell death pathways can affect viability and propagation of intracellular bacteria (1, 2). Since in our experiments we are specifically analyzing extracellular bacteria, we cannot directly comment on how cell death affects intracellular bacterial replication. However, to address the reviewer’s comment, we have included additional data in figure S13A of the revised manuscript showing that specific inhibitors of cell death do not affect the growth or replication of extracellular Mtb. These results suggest that while these molecules do not affect Mtb growth per se, the suppression of these specific death pathways also does not significantly affect the microenvironment to alter Mtb growth (i.e., access to nutrients or molecules released by dead cells). In addition, we have included new data in figure S12 demonstrating the responsiveness of our isolated macrophages to the various cocktails of molecules typically used to induce apoptosis, pyroptosis, or necroptosis.

The authors are showing that infection with Mtb aggregates increase the rate of the macrophage killing but how does this impact infection dissemination and replication of the bacterial aggregates? Is it beneficial for the aggregates? Did the authors check the growth rate of Mtb along with cytochalasin D?

A previous study has shown that phagocytosis of Mtb aggregates leads to macrophage death more efficiently than phagocytosis of a similar number of individual bacteria (3). It has also been shown that Mtb growing on the debris of dead host macrophages forms cytotoxic aggregates that kill the newly interacting macrophages (3). These observations suggest a model in which host cell death induced by Mtb aggregates supports faster extracellular growth and propagation of infection (3). This study was cited in the Introduction section of our manuscript, and our data support these observations. In the revised manuscript, we show that single Mtb bacilli or Mtb aggregates induce macrophage death in a dose-dependent manner (figure S7A,B); however, bacterial aggregates kill more efficiently when compared to similar numbers of non-aggregated bacilli (figure S7A,B). We also show that infection with Mtb_aggregates leads to faster bacterial propagation compared to infection with similar numbers of individual bacteria (figure S7C,D). These observations, combined with our data showing that _Mtb aggregation also enhances uptake-independent killing of macrophages (figure 2), suggest that Mtb aggregates induce rapid host cell death, allowing the bacteria to escape intracellular stresses, grow faster outside host cells (figure S1B), and propagate to other cells. To address the reviewer’s concern whether cytochalasin D affects Mtb growth, the revised manuscript includes additional data confirming that cytochalasin D does not affect the growth of Mtb aggregates (figure S6).

1. How did the authors quantify the interactions of Mtb with macrophages in Figure 1D?

The interactions of Mtb with macrophages were quantified through manual annotation of the time-lapse microscopy image series. If the Mtb aggregates disaggregated upon interaction with the macrophage, resulting in redistribution of smaller aggregates of bacteria, we categorized them as “fragmented”. On the other hand, if the aggregates remained clustered, we categorized them as “not fragmented”. Representative snapshots of these two patterns are presented in figure 1E and 1F and we have included additional representative examples in movies S4 and S5 of the revised manuscript. These interactions are quantified and plotted in figure 1N of the revised manuscript (figure 1D in the original version).

1. Is it enough to conclude with one example of SEM that the mycobacteria with different fragmentation discriminates if the bacteria is intracellular or extracellularly localised? Can authors use an alternative quantitative method to confirm the localization of the bacteria by a quantification by 3D imaging of these two phenotypes with a cytoskeleton marker (or may be even with tdTomato-expressing BMDMs)?

In the revised manuscript, we provide additional examples of correlative time-lapse microscopy and SEM images (supplementary figure S5). As suggested by the reviewer, in the revised manuscript we further validate these conclusions using an alternative approach based on correlative time-lapse microscopy followed by confocal 3D imaging. After time-lapse imaging, we fixed the samples and labelled the plasma membrane of the macrophages with a fluorescent anti-CD45 antibody to define the cell boundaries and identify bacteria that are intracellular vs. extracellular. Representative images obtained using this approach have been added to figure 1 and additional examples are shown in supplementary figure S4 of the revised manuscript. The acquisition, processing, and analysis of these 3D images are time-consuming and prevent us from performing an exhaustive quantitative analysis. However, we are confident in our conclusions, since in all of the cells that we analyzed we found that aggregates that are not fragmented within 6 hours of stable interaction with macrophages are visible on the outer side of the plasma membrane.

1. How do we know if the cell is lysed at 30 h in Supplementary Figure 1, did the authors use a marker to detect the cell lysis or is it based on just the observation from the live cell imaging? Movies in supplementary are actually not very informative as there are many ongoing events and it is hard to visualise what the authors claim. A marker of cell death in the movies should be used.

In this study, we used brightfield time-lapse microscopy images to identify cell death. Dying macrophages rapidly change shape, lose membrane integrity, and stop moving. Moreover, the intracellular structures and bacteria also stop moving at the time of death of the host cell. While these events can be difficult to distinguish by examining individual snapshots, they are readily identifiable by careful frame-by-frame examination of time-lapse microscopy image series. To exemplify this process, in the revised manuscript we show in supplementary figure S2A how we identify macrophage cell death events. We also include Draq7 (a live cell-impermeable dye commonly used to identify dead cells by flow cytometry and microscopy) in the growth medium during time-lapse imaging in order to label dead macrophages. The timing of staining validates and confirms our strategy of using brightfield time-lapse images to define the time-of-death of individual cells. To further assist readers, in the revised manuscript we provide the time-lapse microscopy movie used to generate this figure (movie S4). Similar images and movies have also been added for cells treated with cytochalasin D (figure S2B; movie S7). As suggested by the reviewer, we also replaced figure S1A with a new figure that shows a representative example of an Mtb intracellular microcolony that, upon death of the host macrophage, grows and forms a large extracellular aggregate on the debris of the dead cell (Draq7-positive). Movie S2 was used to generate this figure. Finally, we replaced figures 1E,F with new figures incorporating the Draq7 staining to label macrophage cell death and we include the time-lapse microscopy movies used to generate these figures (movies S4, S5).

1. Total macrophage killing after contact in Figure 1L is around 12 hours, whereas it is observed that the macrophage death after contact with cytochalasin D treatment in Figure 1M is even longer than 24 hours. The viability at 12 hours in Figure1M is as fragmented Mtb survival in Figure1L, why there is a difference in timing with respect to macrophage killing?

We thank the reviewer for this interesting observation. Indeed, we find that macrophages treated with cytochalasin D do take longer to die upon establishing stable interaction with Mtb aggregates in comparison to untreated cells. Although we do not have a clear explanation for this difference in timing, we speculate that by inhibiting actin polymerization and consequently cell motility, cytochalasin D might slow the expansion of the macrophage plasma membrane and the establishment of a larger interface of contact between the cell and the bacterial aggregate, which could influence the timing of cell death.

1. Did authors perform statistical tests for Figure 1D and Figure 1N? p-values should be added.

Figure 1D (figure 1N in the revised manuscript) shows the percentage of interactions between macrophages and _Mtb_aggregates that do or do not lead to fragmentation of the aggregate. Each dot represents the percentage of these events in one experimental replicate. We included this plot to show that reproducibly in all our replicates approximately 20% of the interactions do not lead to fragmentation of the aggregate. Since the purpose of this plot is not to compare the “fragmented” and “non-fragmented” populations but rather to highlight the reproducibility of the phenomenon, we do not think it would be appropriate to add a p-value. However, figure 1N (figure 1Q in the revised manuscript) has been updated and modified to include statistical analysis and a p-value.

1. In Figure 3, do the observations indicated in the Figure 3 happen in all the macrophages that are in contact with aggregates? This is unclear and critical to support the conclusions. Do all the macrophages that are in contact with Mtb aggregates become Annexin-V positive? In Supplementary Figure 2 there is some information regarding this question, but it will be important to show it as a percentage.

In response to the reviewer’s suggestion, we have modified the figure to include quantitation of Annexin-V staining. Approximately 75% of the macrophages that interact with an Mtb aggregate show detectable local Annexin V-positive membrane domains at the site of contact with the aggregate during a typical 60 hour-long experiment. Since most of the macrophages show local Annexin V-positive membrane domains within the first 12 hours upon contact with an Mtb_aggregate (figure 3C), we used this criterion for comparison of different conditions or strains (for example, those shown in figure 6F). In addition, we added figure 3D, which shows the behaviour of 105 macrophages upon contact with _Mtb aggregates in a typical experiment. In this plot, each line represents the lifespan of an individual cell; the fraction of the line in black represents the time spent as bystander, the fraction of the line in magenta represents the time spent interacting with an Mtb aggregate, and the fraction in green represents the time upon formation of local Annexin V-positive membrane domains at the site of contact with the Mtb aggregate. We believe that this additional information further supports our conclusions that most of the cells in contact with an Mtb aggregate show local Annexin V-positive membrane domains and that cells that show this pattern die faster than cells that do not develop local Annexin V-positive membrane domains.

1. Did the authors try to stain Mtb aggregates alone with Annexin-V as a control over the duration of the imaging?

We thank the reviewer for suggesting this control. In supplementary figure S8C of the revised manuscript, we include a representative example of a time-lapse microscopy image series showing Mtb aggregates that never interact with a live macrophage althought they are adjacent to a dead cell. As observed in the Annexin V fluorescence images (yellow), these Mtb aggregates never become Annexin-V positive during the course of the experiment (60 hours).

1. In Figure 4, did the authors continue to image the cells interacting with Mtb aggregates that do not die after Ca2+ accumulation in Supplementary Figure 3D? Do these cells recover from the plasma membrane perturbation? Did the authors consider using another marker for plasma membrane perturbation together with BAPTA?

Unfortunately, we are not able to image macrophages stained with Oregon Green 488 Bapta-1 AM for more than 36 hours because they lose fluorescence over time, possibly due to partial dye degradation or secretion. Another issue is that macrophages do not establish synchronous interaction with Mtb aggregates (figure 3D; figure S3B). In order to pool together results from many cells, we analyze all the cells that interact with Mtb within the first 20 hours and we define as timepoint 0 the time at which each individual cell establishes interaction with the bacteria. To compare similar time windows for each cell, we use fluorescence values measured at 16 hours post-interaction with bacteria as a readout. This time window is sufficient to observe formation of local Annexin V plasma membrane domains and death in a relevant number of macrophages (figure 1P; figure 3D). Not all of the contacted cells die within the timeframe of our experiments; however, we believe that if we imaged cells that accumulate Ca2+ for longer durations, we would find that all such cells eventually die. This assumption is consistent with the observation that calcium chelation reduces inflammasome activation and death in macrophages in contact with Mtb aggregates (figure 5D; figure 4E).

With respect to the reviewer’s query whether cells recover from plasma membrane perturbation, in our time-lapse microscopy experiments, we observe that when macrophages form local Annexin V-positive plasma membrane domains at the site of contact with Mtb aggregates, they never revert to an Annexin V-negative status afterwards (figure 3D; movie S7; movie S8). Our SEM data show that Mtb aggregates colocalizing with Annexin V-positive domains are not partially covered by intact membrane, in contrast to those associated with Annexin V-negative macrophages, although they do present vesicles and membrane debris on their surface (figure 3F,G ). In the revised manuscript, we include additional fluorescence microscopy images showing that Annexin V-positive foci colocalize with markers for the macrophages’ plasma membrane (figure S8A,B) as well as with more distal areas of the bacterial aggregates, where we do not observe any positive plasma membrane staining (figure S8B). Similarly, although _Mtb_aggregates that are never in contact with macrophages never become Annexin V-positive (figure S8C), we see that upon macrophage death, aggregates in contact with dead cells retain some Annexin V-positive material on their surface (figure S8C; movie S8). Vesicle budding and shedding is a common ESCRT III-mediated membrane repair strategy that allows removal of damaged portions of the plasma membrane and wound resealing (4). Thus, we think that in our experiments the Annexin V-positive foci might represent both damaged membrane areas and released macrophage plasma membrane vesicles that stick to the hydrophobic surface of the bacterial aggregates. This means that the time of appearance of Annexin V-positive domains marks the time when the macrophage membrane experiences a damaging event. Interestingly, we do not observe a gradual increase in fluorescence intensity of the Annexin V-positive domains, but rather multiple single intensity peaks over time (movie S8). This might suggest that multiple discrete damaging events occur over time.

1. In Figure 5D-G it will be important if the authors include dots for each macrophage events for the contact conditions as well, as it was done for the bystander condition.

We apologize for using a too-pale shade of magenta in the earlier version of the manuscript, which apparently made the dots in these figures hard to visualize. In the revised manuscript, we use a darker shade of magenta to show the dots corresponding to the fluorescence values of the macrophages in contact with Mtb aggregates.

1. How did the authors discriminate between the macrophages that are in contact or not with Mtb aggregates after the staining with Casp-1, pRIP3 and pMLKL? Do the aggregates stay in contact even after the staining procedures? Representative images of the labelling should be included in this figure.

Before fixation, we make sure to remove the medium gently to avoid disrupting the interactions between cells and bacteria. This step most likely removes the floating bacterial aggregates that are not in stable contact with cells but apparently does not detach aggregates that stably interact with cells. Our correlative time-lapse microscopy and immunofluorescence images (figure 1; figure S4), as well as our correlative time-lapse microscopy and SEM images (figure S5; figure 3F,G), confirm that Mtb aggregates that interact with cells during time-lapse imaging are retained on the surface of those cells upon fixation and processing for immunofluorescence or electron microscopy. As we can observe in figure 5B (cell indicated by the white arrow), Mtb aggregates are retained on the debris of dead cells. In figure 5 we distinguish between “in contact” macrophages and “bystander” macrophages by inspecting brightfield images showing the cells and the respective fluorescence images corresponding to the bacteria. If the body of a macrophage identified in the brightfield image overlaps with a bacterial aggregate identified in the fluorescence channel, we define the macrophage as “in contact”; otherwise, it is annotated as “bystander”. We provide representative images in figure S12 and we clarify the definition of “in contact” and “bystander” in the figure legend of figure 5.

1. The labelling of Figure 5H needs to be corrected both in the text and in the figure legend.

We thank the reviewer for bringing our attention to this error, which has been corrected in the revised manuscript.

1. Pyroptosis inhibitors did reduce the percentage of cell death, but did it also reduce the number of Annexin-V positive domains? This is important as AnnexinV is a marker of apoptosis and the outcome for Mtb very different.

As pointed out by the reviewer, Annexin V staining is often used as a marker for apoptosis. Typically, apoptotic cells stain positive for Annexin V but negative for other membrane-impermeable markers such as propidium iodide, because they expose phosphatidylserine (bound by Annexin V) on the outer leaflet of the plasma membrane without losing plasma membrane integrity (5). Apoptotic cells often look round and their plasma membrane is stained homogeneously by fluorescently labelled Annexin V (5). In our experiments, we observe that macrophages in contact with Mtb aggregates become Annexin V-positive; however, this happens only at the site of contact with the bacteria (figure 3A; movie S7). Only when cells die and get stained by membrane-impermeable dies such as Draq7 do they also get stained with Annexin V over the entire membrane debris. We thus use Annexin V staining as a marker for membrane perturbation rather than for cell death. If we were using the Annexin V as a marker for cell death, we would expect to see a reduction in Annexin V-positive cells in samples treated with pyroptosis inhibitors. In these samples, we do observe a reduced percentage of cell death in comparison to untreated controls; however, we still observe a comparable percentage of macrophages that stain positive for Annexin V locally, i.e., at the site of contact with bacterial aggregates (supplementary figure S13B). In line with this observation, treated vs. untreated macrophages in contact with Mtb aggregates accumulate similar levels of intracellular calcium. These observations are consistent with our model suggesting that contact with Mtb aggregates induces membrane perturbation, calcium accumulation, inflammasome activation, and pyroptosis in contacted macrophages. Since the death inhibitors used in our study specifically target pyroptosis effectors, we do not expect them to affect upstream events such as membrane perturbation and calcium accumulation.

1. In Figure 6, The sections for Figure 6 are well described but kept relatively long with too many details, it will be helpful to the reader if the authors can combine the sections in one header.

We agree that the text linked to figure 6 is long. We tried to make these sections as concise as possible; however, we are concerned that combining all of the sections under a single header might be at the expense of clarity. Thus, unless the reviewer objects, we would prefer to maintain the use of multiple headers.

1. Figure 6F does not have a statistical test and p-value, it will be important to include the statistical test in the legend and p-values in the

As recommended by the reviewer, we have analyzed the results in figure 6F by using a one-way ANOVA test and we have added the calculated p-values to the figure.

Reviewer #1 (Significance):

Based on the literature, Mtb infection and replication can trigger different types of cell death and most of the studies have addressed cell death only as an outcome of intracellular replication. This study shows another form of host cell death, associated only to extracellular bacterial aggregates that are in contact with macrophages. Plasma membrane damage initiating pyroptosis has been defined in: "Plasma membrane damage causes NLRP3 activation and pyroptosis during Mycobacterium tuberculosis infection" by K.S. Beckwith et al. (2020). However, the effect of extracellular bacteria on plasma membrane damage was not addressed before and this paper is addressing an important observation with respect to Mtb evasion and dissemination. These observations represent a novel and interesting aspect in the induction of macrophage cell death by Mtb and potentially relevant for the disease. If the authors consider the comments listed above, this manuscript will be a novel and relevant addition to the field of host pathogen interactions in tuberculosis.

We thank the reviewer for their perspective and their positive comments about our work.

Reviewer #2 (Evidence, reproducibility and clarity):

In this work, Toniolo and coworkers use single-cell time-lapse fluorescence microscopy to show that extracellular aggregates of Mycobacterium tuberculosis can evade phagocytosis by killing macrophages in a contact-dependent but uptake-independent manner. The authors further show that this process is dependent on the functionality of the ESX-1 type VII secretion system and the presence of mycobacterial phthiocerol dimycocerosate (PDIM). In essence the authors show that M. tuberculosis can induce macrophage death from the outside of the cell, and dissect the different players that are involved in the process.

Major comments:

1. I was intrigued by all the different findings of this work, which was done by using bone marrow derived murine macrophages, however, my first question to the authors is how they imagine that this process will take under an in vivo situation? Do they have evidence that these mycobacterial clumps may form during the initial infection process in the lungs? It would be important to provide more insights and discussion into this question in order to see how relevant the described details are inside the host organism.

Formation of Mtb aggregates in tuberculosis lesions have been documented in several animal models (6, 7) and in humans (8–11). While it is unclear whether mycobacterial aggregates form during the earliest stages of infection, extracellular bacterial aggregates have been observed in animal models of infection within the first month post-infection, and they are often associated with necrotic foci. Moreover, masses of Mtb growing as pellicle-like aggregates are often observed on the surface of cavities in human tuberculosis patients. These observations confirm that Mtb aggregates can form during a tuberculosis infection and that a significant fraction of bacteria are extracellular during different stages of infection. As we observe that macrophages undergo contact-dependent uptake-independent death also in the absence of cytochalasin D in vitro, we assume that this may also happen in vivo when Mtb aggregates are formed or released outside host cells. This process may promote bacterial propagation at early stages of infection as well as at later stages when necrotic granulomas and cavities are formed.

In the revised manuscript we present and discuss our observations in the context of the in vivo phenotypes reported in the scientific literature and we include additional references showing that extracellular Mtb aggregates are often observed in vivo. We also propose this concept already in the Introduction section to better link our observations to possible in vivo scenarios.

Minor comments:

Line 91: here the authors list the different forms of cell death that is induced by MTB infection, and it would be important to add apoptosis as a reported mechanism as well (References: PMID: 23848406, PMID: 28095608)

As suggested by the reviewer, in the revised manuscript we have modified the Introduction section to include apoptosis as a Mtb-induced mechanism of macrophage death and we have cited the two publications recommended by the reviewer.

1. Line 95: The secretion of EspE was mainly described in M. marinum while in members of the M. tuberculosis complex no virulence phenotype was reported to the best of my knowledge.

In agreement with the reviewer’s comment, we have modified the sentence and removed EspE from the list of virulence factors.

1. Lines 98: In the cited papers it is described that PDIM is required for phagosomal damage/rupture, however, the methods used there do not allow to specifically report about translocation. The wording should be adapted.

We thank the reviewer for this insightful comment and we have modified the text accordingly.

1. Line 206: Here it is described that in Figure 3A the BMDMs were expressing tdTomato fluorescence and the bacteria GFP, and the same is also repeated in the Figure legend of Fig3A. However, on the images, BMDMs are shown green and bacterial clumps purple (as also indicated in the description directly on the images) Please check and explain/correct this discrepancy.

We apologize that the color scheme in figure 3A is confusing. In this figure we used tdTomato-expressing BMDMs and GFP-expressing bacteria; however, we have pseudo-colored the fluorescence images for the sake of consistency with the other figures in the manuscript, which always show bacteria in magenta. We have clarified this point in the figure legend of the revised manuscript.

1. Line 304: Here the authors could mention that this finding is similar to results found previously in reference PMID: 28095608 and opposite to the results reported previously in PMID: 28505176.

As recommended by the reviewer, we have added a sentence comparing our results with previous studies and we have cited the two references suggested by the reviewer.

1. Line 321: It should be mentioned that CFP10 (EsxB) can also be secreted without its EsxA partner (under certain circumstances, i.e. when the EspACD operon is not expressed due to a phoP regulatory mutation (PMID: 28706226)). However, in Figure S7 an EspAdeletion mutant shows loss of EsxB secretion. This should be checked and discussed how the data here compare with data and strains published previously.

We thank the reviewer for pointing out this interesting point. Our proteomics data revealed that both our esxA mutant and our espA mutants abolish secretion of both EsxA and EsxB, in line with previously published data (12–14). We do not know why the espA mutant behaves differently from the MTBVAC strain concerning secretion of EsxA and EsxB (although we note that regulatory mutations may have complex pleiotropic effects). In the revised manuscript, we have modified this section to include references highlighting that secretion of these proteins may be uncoupled in some circumstances.

1. The finding that EspB can substitute the loss of virulence due to loss of EsxA/ESAT-6 secretion is astonishing and also is different to previous observations that strain H37Ra and MTBVAC (two attenuated strains that have no or very little EsxA secretion due to a regulation defect of the espACD operon PMID: 18282096; PMID: 28706226). How does the hypothesis put forward by the authors match with these previously published data ?

We thank the reviewer for this interesting comment. We would like to clarify that we are not claiming that EspB and EsxA are in general redundant and that EspB can substitute EsxA as a virulence factor. In our experiments we show that EspB can induce contact-dependent uptake-independent death in macrophages in contact with Mtb aggregates in vitro even in the absence of EsxA; however, the precise role of EspB during infection in mice or humans remains to be elucidated and is outside the scope of this manuscript. A previous study comparing Mtb ESX-1 mutants with different secretion patterns linked EspB secretion to Mtb virulence in vivo (14); however, the behavior of an isogenic espB_deletion strain _in vivo was not reported. A M. marinum espB mutant was shown to have reduced virulence; however, in contrast to Mtb, deletion of espB also affects secretion of EsxA in this organism (15). As the reviewer points out, the Mtb strains H37Ra and MTBVAC do not secrete EsxA due to a mutated phoP gene. Previous literature has shown that espB expression is also dependent on PhoP (16). We thus speculate that these strains might behave similarly to our espA espB mutant strain in the context of contact-dependent uptake-independent induction of macrophage death, although we think that this point is outside the scope of our manuscript.

1. In the same context, it is to notice that the authors report in the paragraph between lines 310-330 about EsxA/EsxB secretion, however, looking at the Western blots of figure S7, there is no blot showing results using an antibody against EsxA. Given the previously published results that EsxA/EsxB secretion may also be disconnected (PMID: 28706226), the wording of the text in this paragraph should be adapted or the results from Western Blots using EsxA antibodies be added.

We agree with the reviewer’s comment. Unfortunately, we currently do not have access to a good antibody for EsxA. A commercial monoclonal antibody that was previously available for immunoblotting has been discontinued. We tried several other antibodies that were previously shown to work in M. marinum, but none of these antibodies were effective in M. tuberculosis. We agree that analysing secretion of EsxB alone might not be sufficient to support claims about EsxA secretion. For this reason, we performed quantitative proteome analysis of the secretome in all of the relevant mutant strains. In our revised manuscript, we are careful to make sure that whenever we refer to EsxA/EsxB secretion we always provide proteomics data to support our conclusions.

1. Line 395: Here the authors write that BTP15, a small molecule that in a previous study was shown to inhibit EsxA secretion at higher concentrations (starting from 1.5 uM and higher). However, no effect on the expression of EsxA was described for that compound in reference PMID: 25299337. Thus the corresponding sentence in line 395 needs to adapted to that situation.

We thank the reviewer for noticing this error, which we have corrected in the revised manuscript.

1. Moreover, most concentrations of the compounds used are reported in uM, except for BTP15. It would be easier for the reader if the concentration used for BTP15 could also be reported in uM.

As suggested by the reviewer, in the revised manuscript we report the concentration of BTP15 in μM.

1. Line 475 The comment on the pore forming activity has to be made with caution, as recombinant EsxA produced from E. coli cultures has been shown to often retain detergent PMID: 28119503 that may be responsible for pore forming activity of recombinant EsxA observed in quite some studies, whereas EsxA purified from M. tuberculosis cultures did not show the detergent, but still retained membranolytic activity. This point should be clarified and discussed, and the wording adapted, as EsxA is not a classical poreforming toxin, but excerts the membrane-lysing activity together with other partners (PDIM) in a yet unknown way upon cell contact.

We thank the reviewer for this comment. In the revised manuscript, we have modified the text accordingly and included the sugggested reference.

Reviewer #2 (Significance):

The findings in this work extend the current knowledge on cell infection by M. tuberculosis in a significant way and put extracellular M. tuberculosis clumps in a new context. These data obtained by single-cell time-lapse fluorescence microscopy also need to be discussed for predicting the relevance for an in vivo situation inside the host organism.

As suggested by the reviewer, in the revised manuscript we discuss additional examples from the literature showing that Mtb aggregates can form during infection and that many bacteria are extracellular and associated with necrotic foci during different stages of the disease in animal models of infection and in human patients. We believe that these previously published observations support the in vivo relevance of the process we observe in vitro.

Reviewer #3 (Evidence, reproducibility and clarity):

This is an excellent study distinguished by the volume of observations, rigor of analysis and clarity of presentation. The results are novel, biologically interesting and pathophysiologically important. The ability of aggregated M. tuberculosis to kill macrophages has been reported, but the understanding was that proliferation of Mtb within macrophages killed them. Here, the authors observe that macrophages are susceptible to pyroptotic death triggered by contact with extracellular Mtb aggregates, and that this is not recapitulated by contact with a comparable number of Mtb as single bacilli. The authors go some way to tracing the mechanism and uncover a complex inter-dependence on PDIM and on components of the mycobacterial ESX-1 secretory system.

The following comments will helpfully improve the study further.

Major points

1. The chief measurement in this study is death of individual macrophages as judged by the observer in videomicroscopy. However, the criteria for calling a macrophage "dead" are not defined with any morphological detail, beyond noting that the cell stops moving and lyses. Of course a cell will stop moving if it has lysed, but do not some if not most cells stop moving before they lyse? If so, lysis alone would seem to be the time-point marker for cell death. Yet from the images in Fig 1E and F, I cannot tell that the cells called "dead" have lysed. Watching the videos, the time of lysis is not clear to me. Eventually, shrunken cell bodies are obvious but it is not clear if these are residua of cells that had been said to "lyse" at an earlier time.

In this study, we used brightfield time-lapse microscopy images to identify cell death. Dying macrophages rapidly change shape, lose membrane integrity, and stop moving. Moreover, the intracellular structures and bacteria also stop moving at the time of death of the host cell. While these events can be difficult to distinguish by examining individual snapshots, they are readily identifiable by careful frame-by-frame examination of time-lapse microscopy image series. To exemplify this process, in the revised manuscript we show in supplementary figure S2A how we identify macrophage cell death events. We also include Draq7 (a live cell-impermeable dye commonly used to identify dead cells by flow cytometry and microscopy) in the growth medium during time-lapse imaging in order to label dead macrophages. The timing of staining validates and confirms our strategy of using brightfield time-lapse images to define the time-of-death of individual cells. To further assist readers, in the revised manuscript we provide the time-lapse microscopy movie used to generate this figure (movie S4). Similar images and movies have also been added for cells treated with cytochalasin D (figure S2B; movie S7). As suggested by the reviewer, we also replaced figures 1E,F with new figures incorporating the Draq7 staining to label macrophage cell death and we include the time-lapse microscopy movies used to generate these figures (movies S4, S5).

1. The use of BTP15 as a specific inhibitor of ESX-1 is problematic. The source of the compound is not stated.

The BTP15 molecule was kindly provided by Prof. Stewart Cole, the corresponding author of the article describing the identification of this compound and its effect on Esx-1 secretion (17). We have included this information in the Materials and Methods section.

1. The concentration used, 20 ug/mL, is well above the reported IC50 (1.2 uM) for its presumed target, a mycobacterial histidine kinase, and above the concentrations (0.3-0.6 uM) reported to inhibit Mtb's secretion of EsxA almost completely. It is concerning that the concentrations that were reported to work so well on the whole cell are lower than the IC50 for the presumed target, because uptake into Mtb and intrabacterial metabolism will typically lead to a lower potency for an inhibitor against the whole bacterium than against the isolated enzyme; and because 50% inhibition of an enzyme rarely gives a functional effect as complete as what is shown in the cited reference. In other words, it is not clear that the histidine kinase is the functionally relevant target of BTP15 in Mtb. The original report did not consider BTP15's possible effect on mammalian cells and the present authors likewise do not take that into consideration with respect to possible effects on the macrophages. No concentration-response or time course experiments with BTP15 are presented. Most important, unless I missed it, there is apparently no demonstration that the compound inhibited ESX-1-dependent secretion in the present authors' hands, no matter by what mechanism. Without this, I am reluctant to accept that the results with BTP15 demonstrate a dependence of extracellular-aggregate-induced macrophage death on ESX-1-mediated secretion from Mtb. I would recommend that the authors either provide a direct demonstration of BTP15's effect on ESX-1 dependent secretion at concentrations near those that worked on whole cells in the original report, or drop the BTP15 studies from the paper. That said, the genetic experiments remain unequivocal, so the paper's conclusions would not be affected.

We agree with the reviewer that in the original version of our manuscript we did not provide direct evidence demonstrating that BTP15 inhibits ESX-1 secretion and that it does not affect the host cells. We addressed the first issue by quantifying (by Western blot) the secretion of EsxB and EspB in Mtb cultures treated with different concentrations of BTP15. We show that BTP15 reduces secretion of these two proteins in a dose-dependent manner. These data have been included in figures S21A-B of the revised manuscript. In line with this observation, we also show that BTP15 reduces uptake-independent killing of macrophages by Mtb aggregates in a dose-dependent manner (figure 6H). To show that the dose-dependent effect observed in macrophages does not depend on a direct effect of BTP15 on the host cells, we treated Mtb with different concentrations of BTP15 for 48 hours and removed the compound by washing the bacteria prior to infection. We observe that Mtb aggregates that have been treated with BTP15 show reduced uptake-independent killing of macrophages, even when bacteria have been pre-treated and the small molecule is not present during the incubation with the cells (figure S21C). We hope that these additional results provide clear evidence that BTP15 reduces Mtb-mediated contact-dependent uptake-independent killing of macrophages by inhibiting ESX-1 secretion, consistent with our genetic data. We think these results are important because they provide a chemical validation of our genetic data. To the best of our knowledge, BTP15 is the only available compound known to inhibit ESX-1 secretion, and in the revised manuscript we confirm that this compound has the previously described effect on Mtb also in our hands. Unfortunately, we had to use concentrations higher than those previously reported to inhibit ESX-1 secretion in order to achieve the observed effects. As we had access only to prediluted aliquots that had been stored for a long time, we cannot rule out the posibility that the compound might have undergone partial degradation during storage.

1. The experiments, or at least the discussion, could consider what may distinguish single Mtb cells from aggregated Mtb in some way relevant to the present observations. The authors seem to assume that all the Mtb cells in their preparations are biochemically equivalent and that their distribution into single-cell or aggregate subpopulations is stochastic. What if it is deterministic instead? For example, what if these two subpopulations are defined by differential expression of PDIM, so that the greater macrophage-killing effect of aggregates than single cells in equivalent numbers reflects a greater amount of PDIM in the aggregates, rather than some sort of valency-of-contact effect? The authors could compare the PDIM-to-DNA ratio in the single cell and aggregated subpopulations, or at least discuss this possibility.

We thank the reviewer for proposing this extremely interesting idea. In the revised manuscript, we have added a discussion of this point (lines 487-489) and we have floated various possible explanations. However, we believe that experimental dissection of the underlying mechanism could be a very lengthy undertaking and we hope that the reviewer will agree that this is outside the scope of the current manuscript.

Minor points

1. Some of the experiments compare "low", "medium" and "high" numbers of Mtb, but I could not find a definition of these numbers.

We apologize for this oversight. In the revised manuscript, we have clarified the definition of these gates in the figure 2 legend.

1. There seem to be no positive or negative controls for any of the antibodies used for cell staining (anti-cleaved caspase 1, antiphospho RIP3, anti-phospho MLKKL).

As recommended by the reviewer, the revised manuscript includes controls for all of the antibodies used for immunostaining. In figure S12 we provide representative immunostaining images and fluorescence quantification of uninfected untreated macrophages (negative controls) and of uninfected macrophages treated with cocktails of molecules typically used to induce apoptosis, pyroptosis, or necroptosis (positive controls).

Reviewer #3 (Significance):

The results are novel, biologically interesting and pathophysiologically important.

We thank the reviewer for their appreciation of our findings.

References 1. H. Gan, et al., Mycobacterium tuberculosis blocks crosslinking of annexin-1 and apoptotic envelope formation on infected macrophages to maintain virulence. Nature Immunology 9, 1189–1197 (2008). 2. M. Divangahi, et al., Mycobacterium tuberculosis evades macrophage defenses by inhibiting plasma membrane repair. Nature Immunology 10, 899–906 (2009). 3. D. Mahamed, et al., Intracellular growth of Mycobacterium tuberculosis after macrophage cell death leads to serial killing of host cells. eLife 6, e22028 (2017). 4. A. J. Jimenez, et al., ESCRT Machinery Is Required for Plasma Membrane Repair. Science 343, 1247136 (2014). 5. M. van Engeland, L. J. W. Nieland, F. C. S. Ramaekers, B. Schutte, C. P. M. Reutelingsperger, Annexin V-Affinity assay: A review on an apoptosis detection system based on phosphatidylserine exposure. Cytometry 31, 1–9 (1998). 6. D. R. Hoff, et al., Location of Intra- and Extracellular M. tuberculosis Populations in Lungs of Mice and Guinea Pigs during Disease Progression and after Drug Treatment. PLOS ONE 6, e17550 (2011). 7. S. M. Irwin, et al., Presence of multiple lesion types with vastly different microenvironments in C3HeB/FeJ mice following aerosol infection with Mycobacterium tuberculosis. Disease Models & Mechanisms 8, 591–602 (2015). 8. Kaplan, G., et al., Mycobacterium tuberculosis Growth at theCavity Surface: a Microenvironment with FailedImmunity. Infection and Immunity 71, 7099–7108 (2003). 9. J. Timm, et al., A Multidrug-Resistant, acr1-Deficient Clinical Isolate of Mycobacterium tuberculosis Is Unimpaired for Replication in Macrophages. The Journal of Infectious Diseases 193, 1703–1710 (2006). 10. R. L. Hunter, Pathology of post primary tuberculosis of the lung: An illustrated critical review. Tuberculosis 91, 497–509 (2011). 11. G. Wells, et al., Micro–Computed Tomography Analysis of the Human Tuberculous Lung Reveals Remarkable Heterogeneity in Three-dimensional Granuloma Morphology. Am J Respir Crit Care Med 204, 583–595 (2021). 12. S. A. Stanley, S. Raghavan, W. W. Hwang, J. S. Cox, Acute infection and macrophage subversion by Mycobacterium tuberculosis require a specialized secretion system. Proc Natl Acad Sci USA 100, 13001 (2003). 13. S. M. Fortune, et al., Mutually dependent secretion of proteins required for mycobacterial virulence. Proc Natl Acad Sci U S A 102, 10676 (2005). 14. J. M. Chen, et al., Mycobacterium tuberculosis EspB binds phospholipids and mediates EsxA-independent virulence. Mol Microbiol 89, 1154–1166 (2013). 15. L.-Y. Gao, et al., A mycobacterial virulence gene cluster extending RD1 is required for cytolysis, bacterial spreading and ESAT-6 secretion. Mol Microbiol 53, 1677–1693 (2004). 16. V. Anil Kumar, et al., EspR-dependent ESAT-6 Protein Secretion of Mycobacterium tuberculosis Requires the Presence of Virulence Regulator PhoP. Journal of Biological Chemistry 291, 19018–19030 (2016). 17. J. Rybniker, et al., Anticytolytic Screen Identifies Inhibitors of Mycobacterial Virulence Protein Secretion. Cell Host & Microbe 16*, 538–548 (2014).

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

---

Referee #3

Evidence, reproducibility and clarity

This is an excellent study distinguished by the volume of observations, rigor of analysis and clarity of presentation. The results are novel, biologically interesting and pathophysiologically important. The ability of aggregated M. tuberculosis to kill macrophages has been reported, but the understanding was that proliferation of Mtb within macrophages killed them. Here, the authors observe that macrophages are susceptible to pyroptotic death triggered by contact with extracellular Mtb aggregates, and that this is not recapitulated by contact with a comparable number of Mtb as single bacilli. The authors go some way to tracing the mechanism and uncover a complex inter-dependence on PDIM and on components of the mycobacterial ESX-1 secretory system.

The following comments will helpfully improve the study further.

Major points

The chief measurement in this study is death of individual macrophages as judged by the observer in videomicroscopy. However, the criteria for calling a macrophage "dead" are not defined with any morphological detail, beyond noting that the cell stops moving and lyses. Of course a cell will stop moving if it has lysed, but do not some if not most cells stop moving before they lyse? If so, lysis alone would seem to be the time-point marker for cell death. Yet from the images in Fig 1E and F, I cannot tell that the cells called "dead" have lysed. Watching the videos, the time of lysis is not clear to me. Eventually, shrunken cell bodies are obvious but it is not clear if these are residua of cells that had been said to "lyse" at an earlier time.

The use of BTP15 as a specific inhibitor of ESX-1 is problematic. The source of the compound is not stated. The concentration used, 20 mg/mL, is well above the reported IC50 (1.2 uM) for its presumed target, a mycobacterial histidine kinase, and above the concentrations (0.3-0.6 uM) reported to inhibit Mtb's secretion of EsxA almost completely. It is concerning that the concentrations that were reported to work so well on the whole cell are lower than the IC50 for the presumed target, because uptake into Mtb and intrabacterial metabolism will typically lead to a lower potency for an inhibitor against the whole bacterium than against the isolated enzyme; and because 50% inhibition of an enzyme rarely gives a functional effect as complete as what is shown in the cited reference. In other words, it is not clear that the histidine kinase is the functionally relevant target of BTP15 in Mtb. The original report did not consider BTP15's possible effect on mammalian cells and the present authors likewise do not take that into consideration with respect to possible effects on the macrophages. No concentration-response or time course experiments with BTP15 are presented. Most important, unless I missed it, there is apparently no demonstration that the compound inhibited ESX-1-dependent secretion in the present authors' hands, no matter by what mechanism. Without this, I am reluctant to accept that the results with BTP15 demonstrate a dependence of extracellular-aggregate-induced macrophage death on ESX-1-mediated secretion from Mtb. I would recommend that the authors either provide a direct demonstration of BTP15's effect on ESX-1 dependent secretion at concentrations near those that worked on whole cells in the original report, or drop the BTP15 studies from the paper. That said, the genetic experiments remain unequivocal, so the paper's conclusions would not be affected.

The experiments, or at least the discussion, could consider what may distinguish single Mtb cells from aggregated Mtb in some way relevant to the present observations. The authors seem to assume that all the Mtb cells in their preparations are biochemically equivalent and that their distribution into single-cell or aggregate subpopulations is stochastic. What if it is deterministic instead? For example, what if these two subpopulations are defined by differential expression of PDIM, so that the greater macrophage-killing effect of aggregates than single cells in equivalent numbers reflects a greater amount of PDIM in the aggregates, rather than some sort of valency-of-contact effect? The authors could compare the PDIM-to-DNA ratio in the single cell and aggregated subpopulations, or at least discuss this possibility.

Minor points

Some of the experiments compare "low", "medium" and "high" numbers of Mtb, but I could not find a definition of these numbers.

There seem to be no positive or negative controls for any of the antibodies used for cell staining (anti-cleaved caspase 1, antiphospho RIP3, anti-phospho MLKKL).

Significance

The results are novel, biologically interesting and pathophysiologically important.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

---

Referee #2

Evidence, reproducibility and clarity

In this work, Toniolo an coworkers use single-cell time-lapse fluorescence microscopy to show that extracellular aggregates of Mycobacterium tuberculosis can evade phagocytosis by killing macrophages in a contact-dependent but uptake-independent manner. The authors further show that this process is dependent on the functionality of the ESX-1 type VII secretion system and the presence of mycobacterial phthiocerol dimycocerosate (PDIM). In essence the authors show that M. tuberculosis can induce macrophage death from the outside of the cell, and dissect the different players that are involved in the process.

Major comments:

I was intrigued by all the different findings of this work, which was done by using bone marrow derived murine macrophages, however, my first question to the authors is how they imagine that this process will take under an in vivo situation ? Do they have evidence that these mycobacterial clumps may form during the initial infection process in the lungs ? It would be important to provide more insights and discussion into this question in order to see how relevant the described details are inside the host organism.

Minor comments:

Line 91: here the authors list the different forms of cell death that is induced by MTB infection, and it would be important to add apoptosis as a reported mechanism as well (References: PMID: 23848406, PMID: 28095608)

Line 95: The secretion of EspE was mainly described in M. marinum while in members of the M. tuberculosis complex no virulence phenotype was reported to the best of my knowledge.

Lines 98: In the cited papers it is described that PDIM is required for phagosomal damage/rupture, however, the methods used there do not allow to specifically report about translocation.<br /> The wording should be adapted.

Line 206: Here it is described that in Figure 3A the BMDMs were expressing tdTomato fluorescence and the bacteria GFP, and the same is also repeated in the Figure legend of Fig3A. However, on the images, BMDMs are shown green and bacterial clumps purple (as also indicated in the description directly on the images) Please check and explain/correct this discrepancy.

Line 304: Here the authors could mention that this finding is similar to results found previously in reference PMID: 28095608 and opposite to the results reported previously in PMID: 28505176.

Line 321: It should be mentioned that CFP10 (EsxB) can also be secreted without its EsxA partner (under certain circumstances , i.e. when the EspACD operon is not expressed due to a phoP regulatory mutation (PMID: 28706226)). However, in Figure S7 an EspAdeletion mutant shows loss of EsxB secretion. This should be checked and discussed how the data here compare with data and strains published previously.<br /> The finding that EspB can substitute the loss of virulence due to loss of EsxA/ESAT-6 secretion is astonishing and also is different to previous observations that strain H37Ra and MTBVAC (two attenuated strains that have no or very little EsxA secretion due to a regulation defect of the espACD operon PMID: 18282096; PMID: 28706226). How does the hypothesis put forward by the authors match with these previously published data ?<br /> In the same context, it is to notice that the authors report in the paragraph between lines 310-330 about EsxA/EsxB secretion, however, looking at the Western blots of figure S7, there is no blot showing results using an antibody against EsxA. Given the previously published results that EsxA/EsxB secretion may also be disconnected (PMID: 28706226), the wording of the text in this paragraph should be adapted or the results from Western Blots using EsxA antibodies be added.

Line 395: Here the authors write that BTP15, a small molecule that in a previous study was shown to inhibit EsxA secretion at higher concentrations (starting from 1.5 uM and higher). However, no effect on the expression of EsxA was described for that compound in reference PMID: 25299337. Thus the corresponding sentence in line 395 needs to adapted to that situation.<br /> Moreover, most concentrations of the compounds used are reported in uM, except for BTP15. It would be easier for the reader if the concentration used for BTP15 could also be reported in uM.

Line 475 The comment on the pore forming activity has to be made with caution, as recombinant EsxA produced from E. coli cultures has been shown to often retain detergent PMID: 28119503 that may be responsible for pore forming activity of recombinant EsxA observed in quite some studies, whereas EsxA purified from M. tuberculosis cultures did not show the detergent, but still retained membranolytic activity. This point should be clarified and discussed, and the wording adapted, as EsxA is not a classical poreforming toxin, but excerts the membrane-lysing activity together with other partners (PDIM) in a yet unknown way upon cell contact.

Significance

The findings in this work extend the current knowledge on cell infection by M. tuberculosis in a significant way and put extracellular M. tuberculosis clumps in a new context. These data obtained by single-cell time-lapse fluorescence microscopy also need to be discussed for predicting the relevance for an in vivo situation inside the host organism.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

---

Referee #1

Evidence, reproducibility and clarity

n this manuscript authors show that extracellular Mtb aggregates can cause macrophage killing in a close contact dependent but phagocytosis independent manner. They showed Mtb aggregates can induce plasma membrane perturbations and cytoplasmic Ca2+ influx with live cell microscopy. Next, the authors show that the type of cell death initiated by extracellular aggregates is pyroptosis and they partially supressed cell death with pyroptosis inhibitors. They also identified that PDIM, EsxA/EsxB and EspB all have a role in uptake-independent killing of macrophages even though their impact varies with respect membrane perturbation and Ca2+ influx. Finally, they used a small molecule inhibitor BTP15 to inhibit the effect of ESX-1 during the contact of the extracellular Mtb aggregates with the macrophages and they observed a substantial decrease in membrane perturbation and macrophage killing.

The work describes a very interesting mechanism by which Mtb can kill macrophages that is possibly relevant in the context of infection. In general, there are two main issues with the experiments and the interpretation: the lack of quantitative analysis showing that in a population of macrophages the ones that are in contact with the aggregates die whereas the ones that are not in contact remain alive. This is currently not shown, and it should be added in figure 1. The second is the cell death mode, as the markers used are very different and considering different outcomes (e.g., apoptosis vs. necrosis) are relevant for the infection it is unclear what is being measured here and the impact on bacterial replication.

The authors are showing that infection with Mtb aggregates increase the rate of the macrophage killing but how does this impact infection dissemination and replication of the bacterial aggregates? Is it beneficial for the aggregates? Did the authors check the growth rate of Mtb along with cytochalasin D? How did the authors quantify the interactions of Mtb with macrophages in Figure 1D? Is it enough to conclude with one example of SEM that the mycobacteria with different fragmentation discriminates if the bacteria is intracellular or extracellularly localised? Can authors use an alternative quantitative method to confirm the localization of the bacteria by a quantification by 3D imaging of these two phenotypes with a cytoskeleton marker (or may be even with tdTomato-expressing BMDMs)?

How do we know if the cell is lysed at 30 h in Supplementary Figure 1, did the authors use a marker to detect the cell lysis or is it based on just the observation from the live cell imaging? Movies in supplementary are actually not very informative as there are many ongoing events and it is hard to visualise what the authors claim. A marker of cell death in the movies should be used.

Total macrophage killing after contact in Figure 1L is around 12 hours, whereas it is observed that the macrophage death after contact with cytochalasin D treatment in Figure 1M is even longer than 24 hours. The viability at 12 hours in Figure1M is as fragmented Mtb survival in Figure1L, why there is a difference in timing with respect to macrophage killing?

Did authors perform statistical tests for Figure 1D and Figure 1N? p-values should be added.

In Figure 3, do the observations indicated in the Figure 3 happen in all the macrophages that are in contact with aggregates? This is unclear and critical to support the conclusions. Do all the macrophages that are in contact with Mtb aggregates become Annexin-V positive? In Supplementary Figure 2 there is some information regarding this question, but it will be important to show it as a percentage. Did the authors try to stain Mtb aggregates alone with Annexin-V as a control over the duration of the imaging?

In Figure 4, did the authors continue to image the cells interacting with Mtb aggregates that do not die after Ca2+ accumulation in Supplementary Figure 3D? Do these cells recover from the plasma membrane perturbation? Did the authors consider using another marker for plasma membrane perturbation together with BAPTA?

In Figure 5D-G it will be important if the authors include dots for each macrophage events for the contact conditions as well, as it was done for the bystander condition. How did the authors discriminate between the macrophages that are in contact or not with Mtb aggregates after the staining with Casp-1, pRIP3 and pMLKL? Do the aggregates stay in contact even after the staining procedures? Representative images of the labelling should be included in this figure. The labelling of Figure 5H needs to be corrected both in the text and in the figure legend. Pyroptosis inhibitors did reduce the percentage of cell death, but did it also reduce the number of Annexin-V positive domains? This is important as AnnexinV is a marker of apoptosis and the outcome for Mtb very different.

In Figure 6, The sections for Figure 6 are well described but kept relatively long with too many details, it will be helpful to the reader if the authors can combine the sections in one header. Figure 6F does not have a statistical test and p-value, it will be important to include the statistical test in the legend and p-values in the figure.

Significance

Based on the literature, Mtb infection and replication can trigger different types of cell death and most of the studies have addressed cell death only as an outcome of intracellular replication. This study shows another form of host cell death, associated only to extracellular bacterial aggregates that are in contact with macrophages. Plasma membrane damage initiating pyroptosis has been defined in: "Plasma membrane damage causes NLRP3 activation and pyroptosis during Mycobacterium tuberculosis infection" by K.S. Beckwith et al. (2020). However, the effect of extracellular bacteria on plasma membrane damage was not addressed before and this paper is addressing an important observation with respect to Mtb evasion and dissemination. These observations represent a novel and interesting aspect in the induction of macrophage cell death by Mtb and potentially relevant for the disease. If the authors consider the comments listed above, this manuscript will be a novel and relevant addition to the field of host pathogen interactions in tuberculosis.

Transparent Peer Review

---

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

</br>

Transparent Peer Review

---

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

</br>

Transparent Peer Review

---

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

</br>