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Reply to the reviewers

We thank the reviewer for their comments. We are encouraged that the reviewers found our research “important study that addresses the interplay between two major Rho-type small GTPases involved in cell division” and “of interest to those interested in the cell biology of mitotic exit”. We agree with the comments raised by the reviewers and have provided new data as per their recommendation. We have also made changes to the text and format of the paper. We feel that with these changes the manuscript is stronger and we thank the reviewers for their suggestions. Below we provide a detailed response to the reviewers’ comments.

Reviewer #1

*This manuscript focuses on the role of Cdc42 in Rho1 activation during fission yeast cytokinesis. The primary finding is that active Cdc42 and its downstream effector Pak1 prevents accumulation of active Rho1 and the synthesis of cell wall material, at early stages of cytokinesis and despite the local recruitment of the Rho1 GEF Rgf1. The data supporting these conclusions are reasonably sound. *

*Additional experiments are presented to suggest that Cdc42 and Pak1 negatively regulate Rgf1, this conclusion is not as strongly supported (though it may be true) *

*These study relies on a newly described probe for active Rho1. However this probe is not sufficiently well validated. *

*Overall the manuscript was not assembled with sufficient care and rigor, these deficits could be readily corrected. *

The major point of the paper is that Cdc42 and Pak1 negatively regulate Rho1 activation. However, during late cytokinesis, active Cdc42 and active Rho1 co-exist at the division site. Thus, Cdc42 activation induces a delay in Rho1 activation, but how this delay is overcome is not investigated or even discussed. Indeed, while the delay is shown the transience of this inhibition is not explicitly mentioned. At a minimum, the authors should highlight this point for the readers.

We are encouraged by the fact that the reviewer found our data “reasonably sound”. We agree that this manuscript does not provide the molecular details of how Cdc42 inhibits Rho1 activation. Our genetic data suggests that this is likely mediated by multiple pathways possibly involving the regulation of the Rho regulators Rgf1, Rgf2 and Rga5. We are currently investigating the molecular details of this regulation and hope to report it in another manuscript.

Our data shows that while Cdc42 inhibits Rho1, the SIN pathway is essential for Rho1 activation regardless of the presence of Cdc42. While Cdc42 is activated at the division site as the ring completes assembly, the SIN pathway is activated immediately prior to ring constriction similar to that of Rho1 activation. It is possible that once the SIN is activated at the division site, it overcomes Cdc42-mediated Rho1 inhibition. We have highlighted this in the discussion section of this manuscript and are currently investigating the molecular details of this regulation.

*Specific points 1 - RBD probe This probe is central to this manuscript. However, there is insufficient validation of its target. Figure 1 shows the localization and its independence of Rho2. The authors should provide direct evidence that it recognizes Rho1 (for example using a repressible promotor or an anchor away approach).

*

We thank the reviewers for their comments on the RBD probe. We have now provided validation for the RBD-probe. We have used rho1 temperature-sensitive and switch-off mutants to show loss of RBD-probe localization in these mutants. This data is provided I the revised manuscript in Fig1 and Supplementary fig. S1.

At various places in the manuscript the authors refer to this probe as "Rho-probe", RBD-probe, RBD, RBD-(mNG or tdTomato). On page 11 the authors state, "As per our observations, we refer to the Rho-probe signal at the division site as active Rho1 from here onwards." Yet, in the very next paragraph they refer to the localization of the "Rho probe". * This is also an issue with the figures. For example, in figures 4B,C ; 5B,C; 6B; 7B,C the figures are titled either "Rho1 activation at division site", "Rho1-probe at division site"; "Rho1-probe appearance at division site" ; "Rho1-probe in non-constricting rings". *

We agree that these multiple terms to describe the probe is confusing. We have restricted the terms to either “RBD-mNG” or “RBD-tdTomato” when reporting the data and use “Rho-probe” for descriptive purposes.

In fig 3, RBD-nNG is quantified in a graph entitled "localizaton [sic] of Rho1-GEFs at division site"

We thank our reviewers for identifying this error in our labeling of the graph in Fig. 3E. This figure now reads “Localization of Rgf1, Rgf3, active Rho1 at the division site”

In all figures but two, 5c and 7c, the authors quantify Rho1 activation by the presence or absence of the probe, rather than a quantitative measure or the extent of recruitment of the probe. This could be analyzed my quantitatively.

We appreciate this comment and provide this response in order to clarify our reasoning for presenting this data. We quantified the intensities of RBD-mNG or RBD-tdTomato where ever relevant to the question we are addressing for each experiment performed.

Where we look at Rho1 activation at the division site with respect to SPB distances, we are reporting the differences in the timing of Rho activation with respect to mitotic progression. However, in Figures 5c and 7c, and now also Fig 1 of the revised manuscript, we quantified the intensities of the probe as this indicated the changes in overall active Rho1 levels under our experimental conditions. We have added in the text for earlier experiments where we do not report intensity measurements for the active Rho probe that we do not observe any differences in the intensity levels.

*2 - Regulation of Rgf1 by Cdc42 and Pak1. The results shown in figure 8 show that "early Rho1 activation in gef1 mutants is not Rgf3-dependent". Figure 9 establishes "loss of rgf1 prevents premature Rho1 activation in gef1Δ cells restoring it to normal in late anaphase (Fig.9A, B)." This finding indicates that Rgf1, but not Rgf3, is required for Rho1 premature activation. This finding doesn't rule out the possibility that Cdc42 and Pak1 might be required to turn off RhoGAPs to allow active Rho1 to accumulate. This analysis concludes with this unclear and ungrammatical sentence, "While we were unable to assess the Rho-probe in the rgf1Δ rgf2Δ double mutants due to its lethality [sic; is the Rho probe lethal?], our observations suggest that apart from Rgf1 early Rho1 activation in gef1Δ cells is either due to activation of Rgf2 or due to inhibition of Rga5." *

We thank you for your insight and agree with these remarks. We could not investigate Rho1 activation in rgf1Δ rgf2Δ double mutants since the double mutants are inviable. We have re-worded the sentence to reflect our findings appropriately.

*The conclusion that this regulation is due to control of Rgf1 should be toned down. E.g. from the abstract: "We provide functional and genetic evidence which indicates that Pak1 regulates Rho1 activation likely via the regulation of its GEF Rgf1." *

We have now removed this statement from the abstract. We have also clarified in the discussion that the molecular details of how Cdc42 inhibits Rho1 is not known and needs to be investigated. While our data suggests that the regulator Rgf1 and Rga5 may be involved in the process the details are unclear and we are currently investigating this regulation.

*SECTION B - Significance ======================== This manuscript ties together several recent papers from the author's lab on the control of Cdc42 activation during cytokinesis and older papers on the role of Rho1 in Bgs1 activation. It provides missing information into the temporal regulation of septum assembly.

The authors make a point of the similarities of fission yeast cytokinesis to animal cell cytokinesis. Indeed the second sentence reads, "The fission yeast model system divides via an actomyosin-based contractile ring, which is assembled in the medial region of the cell, as in animal cells (Balasubramanian et al., 2004; Pollard, 2010).". However, the authors fail to point out the many differences between yeast and animal cell cytokinesis until the last paragraph of the discussion. If the authors want to include the similarities in the introduction, they should also include the differences. For example, ring assembly is independent of Rho1 activation in fission yeast, but dependent on RhoA activation in animal cells. *

We thank the reviewer for pointing out this deficiency in our writing. We have now amended the introduction to highlight the differences between Rho1 activity in fission yeast and animal cells during cytokinesis. We have added the following text to the Introduction section.

“The animal Rho1 homolog RhoA is required for ring formation and is essential for cytokinesis (Basant and Glotzer, 2018). While in yeast, Rho1 is essential for septum formation, the current literature suggests that it is dispensable for ring formation (Onishi et al., 2013; Yoshida, 2009). In fission yeast where both the actomyosin ring and the septum have important roles in the proper coordination of cytokinesis, Rho1 has no reported roles in ring formation but is essential for septation (Balasubramanian et al., 2004).”

*This work will be of interest to biologists working on yeast cell division. To a lesser extent it will be of interest to biologists interested in cytokinesis and coordination of distinct GTPase pathways.

Additional points*

1 - The text is overly wordy and needs extensive revision. Many of the experiments could be explained more clearly and with somewhat less genetic jargon. The introduction has quite a bit of extraneous information and lacks relevant facts, such as the function of Bgs1, which is central to the results.

We have now modified the text to remove unnecessary genetic jargon. We have also provided additional text to describe the role of Bgs1 in the Introduction.

2 - page 4 "GEFs promote GTP binding, thus keeping the GTPase active while the GAPs increase GTP hydrolysis, thus promoting GTPase inactivation." GEFs promote GTP binding, but they do not keep the GTPase active (an inhibitor of a RhoGAP would do that), they activate the GTPases.

We thank the reviewers for highlighting this error. We have corrected this sentence, which now reads “GEFs promote GTP exchange to activate the GTPase, while the GAPs increase GTP hydrolysis to promote GTPase inactivation.”

*3 - The current literature on animal cell cytokinesis indicates little direct role in cytokinesis, rather than the author's statement, "In larger eukaryotes, the role of Cdc42 activation has been reported mostly in meiotic division events such as polar body extrusion in oocytes, but not much is known about its role in cytokinesis in somatic cell division (Drechsel et al., 1997; Na and Zernicka-Goetz, 2006)." See for example, PMID 10898977, 10871280 which indicate Cdc42 does not play a major role during cytokinesis in at least a few systems where it has been analyzed. *

We thank our reviewer for this observation and agree that this statement can be expanded to further explain the role of Cdc42 in animal cytokinesis. The paragraph has been re-written as follows-

Pg5 - “In animal cells, the direct role of Cdc42 in cytokinesis remains indefinite. In Xenopus embryos and mouse fibroblasts for example, constitutively active Cdc42 impairs cytokinesis completion (Drechsel et al., 1997). However, in other cases such as in mouse embryonic stem cells, Cdc42 was only critical for development but not cytokinesis (Chen et al., 2000). RNA interference in animal cells demonstrate that that while RhoA is required for cytokinesis, Cdc42 is not required for this process (Jantsch-Plunger et al., 2000). Cdc42 also promotes spindle positioning and polar body extrusion in mouse oocytes, but it is not known whether its localization at these spindles affects RhoA (Na and Zernicka-Goetz, 2006). Thus, the role of Cdc42 in the cytokinetic process may be cell-type specific, and these data highlight the importance for more investigation to elucidate Cdc42 regulation in dividing cells (Jordan and Canman, 2012).”

Reviewer #2

*In many fungal cells, including fission yeast, the deposition of a new cell wall (a septum) between daughter cells is essential for cytokinesis. Cell wall synthases are trafficked to and activated at the division site, and dysregulated trafficking and/or synthase activation can lead to cytokinetic defects. In this study, the authors use fluorescent probes for Cdc42 and Rho1 activity and live-cell imaging to investigate the timing and regulation of Rho1 activity in fission yeast, and specifically, the role of Cdc42 in regulating Rho1. Summary of the proposed model: Gef1 -> active Cdc42 -> Pak1 --| Rgf1 -> active Rho1 -> septum formation

Major comments

(1) As far as I can gather from the authors' description in the manuscript and quick literature search, this will be the first publication in S. pombe utilizing the HR1-C2 domain of Pkc2p as fluorescent probe for active Rho1 (RBD-mNG). While a comparable domain of S. cerevisiae Pkc1p (not "pck2" as referenced by the authors in Page 25) has been used for similar purposes, given the importance of this probe and the precedent it sets in the S. pombe literature, it is imperative that proper tests are performed to validate that its localization reflects activity of Rho1 and nothing else (such as membrane binding of the C2 domain or transcriptional regulation of the pkc2 promoter). Such tests should also be independent of the hypotheses central to the current study (i.e., effects of Gef1, Pak1, Rgf1/2 on the timing of RBD-mNG localization). Can the authors provide data to address this point? Examples include, but not limited to, rho1 mutants, expression of constitutively active Rho1, or temporary expression of dominant-negative Rho1.*

We agree with the reviewer and now provide data to show loss of the localization of the Rho-probe RBD-mNG in rho1 mutants. Using temperature-sensitive and switch-off mutants we show that under mutant conditions the RBD-mNG localization is lost at the division site and also from the cell ends. This provides strong evidence that the probe detects active Rho1 in the cells.

*(2) Related, M&M does not provide sufficient details about the amino-acid positions corresponding to the "RBD" domain of Pkc2, thus precluding readers from reproducing the experiments. This needs to be clarified. *

We now provide in the materials and methods the details of how this probe was generated including the base pairs of the budding yeast PKC1 and the fission yeast pck2 promoter.

(3) In Figure 1B, RBD1-mNG localizes clearly to the medial region of rho2∆ cells when the Rlc1-tdTomato ring has not formed. Does this mean that Rho2 has a major role in forming the contractile ring that is independent of Rho1 activation? On this other hand, however, data in Fig. S2BC suggest that RBD-mNG does not localize to the medial region in rho2∆ cells until Rlc1-tdTomato ring forms (the timing of which seems normal). This discrepancy needs to be addressed.

In response to the issue raised here, we do not see active Rho1 at the division site of cells without rings. However, after cytokinesis, while cells are in septation, although the ring has disappeared, active Rho1 lingers at the division site. The cell shown in the panel is a septated cell after ring constriction completes. We have included DIC panels of these cells to show that active Rho1 lingers in septating cells.

*(4) Given the nature of RBD-mNG localization, it seems unavoidable to have some level of arbitrariness in measuring the onset of its localization at the division site. It would be advisable for the authors to be specific in M&M about how they defined the onset of localization, i.e., whether it was based on universal threshold in signal intensity, ratio, etc. or on manual curation (ideally double-blind).

*

We have updated the methods to describe that “onset of localization” was performed via double-blind visual observations.

Minor comments (1) Throughout the manuscript, there are quite a few places where inconsistencies in genetic nomenclature can cause confusion to readers. Below are some examples. Figs. 6B, 7B, 10B: pak1(-ts), shk1, and orb2-34 (including faint labels under category marks in 6B). Fig. 9B (gef1+ rgf1∆) vs 9C (rgf1∆). Wild-type alleles are implicit in some figures, while explicit in others.

We have corrected these inconsistencies.

*(2) The first hypothesis (Fig. 1C) is that the AMR might regulate Rho1 activation. The ring is disrupted with LatA, but Rho remains active. They cite this as evidence that the AMR does not activate Rho1, but were the cells treated before or after the rings formed? If before, then the experiment demonstrates what the authors claim, but if after, it only shows that the AMR is not essential to maintain Rho activity. *

We agree with the reviewer that this is an important distinction. We have modified this statement to “These results indicate that while at the division site the actin cytoskeleton is not required for maintaining Rho1 activation, it is necessary at the growth sites of interphase cells.”

*(3) Page 8: "Time-lapse imaging of cells simultaneously expressing CRIB-3xGFP and RBD-tdTomato [...] while Rho1 is activated ~20 minutes after SPB duplication (Fig. 2B)." This appears to refer to Fig. 2C. *

We thank the reviewers for catching this error in the text. We have now corrected it, showing timelapse as Fig. 2C, and an Image of cells simultaneously expressing CRIB and RBD as Fig. 2B.

*(4) Page 9: "[...] Rgf1 and Rgf3 localize as early as the time of ring assembly at an average SPB distance of 4-5 µm (Fig. 3D)." This sentence is confusing. How was the average calculated over the earliest ring assembly in non-time-lapse data? Fig. 3DE show distances between SPBs as short as 2.5 µm, not 4-5 µm, and average of ~8 µm for all cells at different stages of mitosis. This confusion needs to be clarified. *

We thank the reviewer for observing this mistake in our writing and interpretation. We agree that the text does not reflect the accurate interpretations of the data collected and have now fixed these errors. The current sentence reading “In an asynchronous population of cells, we find that Rgf1 and Rgf3 localize as early as the time of ring assembly at an average SPB distance of 4-5µm.” has now been replaced with the description shown below-

“Using the distance between SPBs of anaphase cells as a proxy for timing of cytokinesis, we find that in most anaphase cells, Rgf1-GFP and Rgf3-eGFP was localized at the division site at very early stages in anaphase (Fig. 3D, E). This can be observed by the short distance between the SPBs of ~2µm (Fig. 3D). We also measured the distance for which active Rho1 appeared at the division site, and find that at the distance between SPBs of ~10µm, active Rho1 was present at the division site in ~50% of the population of control cells (Fig. 3E).”

*(5) Fig. 5. Both the intensity and onset of RBD-mNG localization were affected by cdc42g12v expression. These two may form a causative relationship: reduced overall RBD signal may cause failed detection of early RBD localization. Can the authors compare cells with similar mean RBD-mNG signal intensities (Fig. 5B) and confirm that the timing of appearance at the division site is still delayed in gef1+ cdc42g12v relative to gef1+ empty? *

We thank the reviewers for pointing this out and appreciate the opportunity to further clarify our observations. While there is clear decrease in Rho-probe intensity at the division site of on cells expressing cdc42G12V, we did see some variation in the extent of the decrease likely due to the variation in the expression levels of cdc42g12V. To provide a more accurate analysis of our observation we have shown the changes in the timing and intensities of Rho-probe localization. However, due to the noisy nature of the data we cannot compare the intensities in individual cells at specific spindle pole body distance between cells. As observed cdc42G12V significantly reduces Rho1 activity globally, not just at the division site. To cherry-pick cdc42G12V cells with similar active rho1 intensity to assess time of Rho1 activation may lead to subconscious data manipulation and will not address how early Rho1 activation is regulated.

*Reviewer #3

Onwubiko et al., present a clear and well written manuscript detailing the mechanistic understanding of how Rho1 is activated in a timely manner to ensure cytokinesis occurs in a scheduled manner at the end of telophase. Using fission yeast as a model system, and with the development of a novel Rho1 biosensor, they implicate a series of GTPases, exchange factors, GTPase activating proteins and kinases acting downstream of Cdc42 in the timely activation of Rho1. Specifically, they find that Cdc42 prevents premature Rho1 activation in early anaphase in a manner requiring the kinase Pak1. They observe that the Rho1 activators Rgf1 and Rgf3 localise to the division site in early anaphase, but Rho1 doesn't get activated until late anaphase, suggesting that control mechanisms ensure that these GEFs are held inactive, or that RhoGAP activity is able to balance this activation in early anaphase. This suppression of Rho1 activity in early anaphase requires Cdc42 and Pak1 and implicate (by omission) Rgf1, rather than Rgf3, is the relevant GEF.

I liked this manuscript, it was clearly written the experimental progression was logical and the data were easy to interpret from the figures. The conclusions were precise, believable and not overstated. The manuscript provides novel observations and through good use of a series of rescues/mutants, illuminates a pathway that is held in check by Cdc42 to ensure timely Rho1 activation. The novel Rho1 probe is exciting and shows well differently regulated pools of active Rho1 at the division site and the growing tips. I thought the co-imaging/measurement of ring placement and SBP duplication allowed a really clear understanding of the kinetics during this rapid phase of the cell cycle. A critique of the study is that the the mechanism by which Cdc42 controls Pak1, and by which Pak1 controls Rgf1/Rgf2 is left unclear. I guess there could always be a molecular expansion of these points (e.g., how does Cdc42 control Pak1; how does Pak1 control Rgf1; how is Rgf activity restricted when localised), but I think that would only enhance, rather than change, the level of detail of the paper's message. I think the paper's current conclusions stand on their own, the data is clear and believable, the experiments are well performed. There are a number of observations in the paper that are left open for future studies, and I think this is a positive (e.g., any separable role of Rgf1/Rgf2 and how Rga5 integrates into this pathway. As such, I am tempted to recommend accept with only minor amendments as outlined below.

1. P8 P15: should the call out be to Fig 2C, rather than 2B. *

We thank reviewer for their highlighting this error in our text. We have now fixed it.

*P14 L17: should it be 'gef1+ rgf3-', not 'gef1+, rgf3+' *

We have fixed this error and further clarified the terms for easy understanding.

Structure wise, I thought the section on Rga5 didn't really fit well on P16; it seemed sandwiched between two sections on GEFs. Is there a more appropriate place to place these data - perhaps between the paragraph breaks on P17? Related to this data, the conclusion on P16 suggests 'other' regulators of RhoGAP activity act to repress Rho1 function. Would 'additional' regulators of RhoGAP activity be more appropriate as there is some function contributed by Rga5?

We have now moved this section to the end of the section on Rho1 regulators after we discuss the Rho1 GEFs. We have also modified the text to clarify that multiple regulators are likely involved in the regulation of Cdc42-mediated Rho1 inhibition.

*In Fig 10b, you haven't defined orb2-34. Is it the rgf1-delete?

*

The mutant orb2-34 is a temperature sensitive allele of the pak1 kinase. To avoid confusion, we have replaced the allele name with pak1-ts in figure 10 and in the text.

• I find the sentence at the top of P18: 'Rho1 activation in pak1+ rgf1+....at 25oC and 35.5oc occurred at longer and similar SBP distances' quite hard to interpret. Could you perhaps expand it to make your message clearer? *

We thank the reviewer for pointing this out. These statements have now been re-written for clarity. 'Rho1 activation in pak1+ rgf1+....at 25ºC and 35.5ºC” has been changed, and now reads as follows:

“The timing of RBD-mNG localization at the division site occurs late in cytokinesis during late anaphase as depicted by longer SPB distances in pak1+ rgf1+, pak1-ts rgf1+, and pak1+ rgf1Δ cells at 25ºC (Fig.10B). As previously shown, RBD-mNG localizes to the division site in early anaphase in pak1-ts rgf1+ cells at the restrictive temperature (35.5ºC, Fig. 7A, B). In agreement with our reasoning, early RBD-mNG localization in pak1-ts mutants at 35.5ºC was rescued in the absence of rgf1 (Fig. 10A, B).”

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Referee #3

Evidence, reproducibility and clarity

Onwubiko et al., present a clear and well written manuscript detailing the mechanistic understanding of how Rho1 is activated in a timely manner to ensure cytokinesis occurs in a scheduled manner at the end of telophase. Using fission yeast as a model system, and with the development of a novel Rho1 biosensor, they implicate a series of GTPases, exchange factors, GTPase activating proteins and kinases acting downstream of Cdc42 in the timely activation of Rho1. Specifically, they find that Cdc42 prevents premature Rho1 activation in early anaphase in a manner requiring the kinase Pak1. They observe that the Rho1 activators Rgf1 and Rgf3 localise to the division site in early anaphase, but Rho1 doesn't get activated until late anaphase, suggesting that control mechanisms ensure that these GEFs are held inactive, or that RhoGAP activity is able to balance this activation in early anaphase. This suppression of Rho1 activity in early anaphase requires Cdc42 and Pak1 and implicate (by omission) Rgf1, rather than Rgf3, is the relevant GEF.

I liked this manuscript, it was clearly written the experimental progression was logical and the data were easy to interpret from the figures. The conclusions were precise, believable and not overstated. The manuscript provides novel observations and through good use of a series of rescues/mutants, illuminates a pathway that is held in check by Cdc42 to ensure timely Rho1 activation. The novel Rho1 probe is exciting and shows well differently regulated pools of active Rho1 at the division site and the growing tips. I thought the co-imaging/measurement of ring placement and SBP duplication allowed a really clear understanding of the kinetics during this rapid phase of the cell cycle. A critique of the study is that the the mechanism by which Cdc42 controls Pak1, and by which Pak1 controls Rgf1/Rgf2 is left unclear. I guess there could always be a molecular expansion of these points (e.g., how does Cdc42 control Pak1; how does Pak1 control Rgf1; how is Rgf activity restricted when localised), but I think that would only enhance, rather than change, the level of detail of the paper's message. I think the paper's current conclusions stand on their own, the data is clear and believable, the experiments are well performed. There are a number of observations in the paper that are left open for future studies, and I think this is a positive (e.g., any separable role of Rgf1/Rgf2 and how Rga5 integrates into this pathway. As such, I am tempted to recommend accept with only minor amendments as outlined below.

1. P8 P15: should the call out be to Fig 2C, rather than 2B.
2. P14 L17: should it be 'gef1+ rgf3-', not 'gef1+, rgf3+'
3. Structure wise, I thought the section on Rga5 didn't really fit well on P16; it seemed sandwiched between two sections on GEFs. Is there a more appropriate place to place these data - perhaps between the paragraph breaks on P17? Related to this data, the conclusion on P16 suggests 'other' regulators of RhoGAP activity act to repress Rho1 function. Would 'additional' regulators of RhoGAP activity be more appropriate as there is some function contributed by Rga5?
4. In Fig 10b, you haven't defined orb2-34. Is it the rgf1-delete?
5. I find the sentence at the top of P18: 'Rho1 activation in pak1+ rgf1+....at 25oC and 35.5oc occurred at longer and similar SBP distances' quite hard to interpret. Could you perhaps expand it to make your message clearer?

Significance

I think the advance here is a genetic understanding of control mechanisms that order the exit from mitosis. The interplay between numerous GTPases and kinases must ensure a timely and ordered progression through M-exit, but it is often unclear how these activities are coordinated. A strength of the yeast system is that dependencies can be clearly visualised and the authors do a good job here to order the enzymatic activities needed to activate Rho1 in a timely manner for cytokinesis at the end of telophase.

I think this manuscript will be of interest to those interested in the cell biology of mitotic exit, the interplay between kinases and GTPases and those interested in the systems/netwoek biology of these processes. The description of a new Rho1 biosensor is an excellent tool for the community.

I am a cell biologist (mammalian) with interests in M-exit programmes that ensure a timely and ordered reestablishment of interphase architecture.

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Referee #2

Evidence, reproducibility and clarity

In many fungal cells, including fission yeast, the deposition of a new cell wall (a septum) between daughter cells is essential for cytokinesis. Cell wall synthases are trafficked to and activated at the division site, and dysregulated trafficking and/or synthase activation can lead to cytokinetic defects. In this study, the authors use fluorescent probes for Cdc42 and Rho1 activity and live-cell imaging to investigate the timing and regulation of Rho1 activity in fission yeast, and specifically, the role of Cdc42 in regulating Rho1. Summary of the proposed model: Gef1 -> active Cdc42 -> Pak1 --| Rgf1 -> active Rho1 -> septum formation

Major comments

1. As far as I can gather from the authors' description in the manuscript and quick literature search, this will be the first publication in S. pombe utilizing the HR1-C2 domain of Pkc2p as fluorescent probe for active Rho1 (RBD-mNG). While a comparable domain of S. cerevisiae Pkc1p (not "pck2" as referenced by the authors in Page 25) has been used for similar purposes, given the importance of this probe and the precedent it sets in the S. pombe literature, it is imperative that proper tests are performed to validate that its localization reflects activity of Rho1 and nothing else (such as membrane binding of the C2 domain or transcriptional regulation of the pkc2 promoter). Such tests should also be independent of the hypotheses central to the current study (i.e., effects of Gef1, Pak1, Rgf1/2 on the timing of RBD-mNG localization). Can the authors provide data to address this point? Examples include, but not limited to, rho1 mutants, expression of constitutively active Rho1, or temporary expression of dominant-negative Rho1.
2. Related, M&M does not provide sufficient details about the amino-acid positions corresponding to the "RBD" domain of Pkc2, thus precluding readers from reproducing the experiments. This needs to be clarified.
3. In Figure 1B, RBD1-mNG localizes clearly to the medial region of rho2∆ cells when the Rlc1-tdTomato ring has not formed. Does this mean that Rho2 has a major role in forming the contractile ring that is independent of Rho1 activation? On this other hand, however, data in Fig. S2BC suggest that RBD-mNG does not localize to the medial region in rho2∆ cells until Rlc1-tdTomato ring forms (the timing of which seems normal). This discrepancy needs to be addressed.
4. Given the nature of RBD-mNG localization, it seems unavoidable to have some level of arbitrariness in measuring the onset of its localization at the division site. It would be advisable for the authors to be specific in M&M about how they defined the onset of localization, i.e., whether it was based on universal threshold in signal intensity, ratio, etc. or on manual curation (ideally double-blind).

Minor comments

1. Throughout the manuscript, there are quite a few places where inconsistencies in genetic nomenclature can cause confusion to readers. Below are some examples. Figs. 6B, 7B, 10B: pak1(-ts), shk1, and orb2-34 (including faint labels under category marks in 6B). Fig. 9B (gef1+ rgf1∆) vs 9C (rgf1∆). Wild-type alleles are implicit in some figures, while explicit in others.
2. The first hypothesis (Fig. 1C) is that the AMR might regulate Rho1 activation. The ring is disrupted with LatA, but Rho remains active. They cite this as evidence that the AMR does not activate Rho1, but were the cells treated before or after the rings formed? If before, then the experiment demonstrates what the authors claim, but if after, it only shows that the AMR is not essential to maintain Rho activity.
3. Page 8: "Time-lapse imaging of cells simultaneously expressing CRIB-3xGFP and RBD-tdTomato [...] while Rho1 is activated ~20 minutes after SPB duplication (Fig. 2B)." This appears to refer to Fig. 2C.
4. Page 9: "[...] Rgf1 and Rgf3 localize as early as the time of ring assembly at an average SPB distance of 4-5 µm (Fig. 3D)." This sentence is confusing. How was the average calculated over the earliest ring assembly in non-time-lapse data? Fig. 3DE show distances between SPBs as short as 2.5 µm, not 4-5 µm, and average of ~8 µm for all cells at different stages of mitosis. This confusion needs to be clarified.
5. Fig. 5. Both the intensity and onset of RBD-mNG localization were affected by cdc42g12v expression. These two may form a causative relationship: reduced overall RBD signal may cause failed detection of early RBD localization. Can the authors compare cells with similar mean RBD-mNG signal intensities (Fig. 5B) and confirm that the timing of appearance at the division site is still delayed in gef1+ cdc42g12v relative to gef1+ empty?

Referees cross-commenting

I find all reviewer comments fair and have nothing specific to add.

Significance

The mechanisms governing septum formation during cytokinesis represent a key regulatory step in cytokinesis. Prior work showed that the Rho GTPases Cdc42 and Rho1 together control the timing of septum formation in S. pombe, and in S. cerevisiae, similar antagonistic regulation between Cdc42 and Rho1 has been reported previously (Atkins et al., J. Cell Biol. 2013 202: 231-240; Onishi et al., J Cell Biol. 2013 202: 311-329), but the precise molecular mechanisms remained unclear.

This is an important study that addresses the interplay between two major Rho-type small GTPases involved in cell division of many eukaryotic cells, and highlights their roles outside of the regulation of contractile ring. However, there are some issues that need to be addressed prior to publication, as listed above.

Keywords for the reviewer's field of expertise: S. pombe, S. cerevisiae, cytokinesis, Rho1, Cdc42, septum, MEN/SIN, genetics, cell biology, biochemistry.

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Referee #1

Evidence, reproducibility and clarity

This manuscript focuses on the role of Cdc42 in Rho1 activation during fission yeast cytokinesis. The primary finding is that active Cdc42 and its downstream effector Pak1 prevents accumulation of active Rho1 and the synthesis of cell wall material, at early stages of cytokinesis and despite the local recruitment of the Rho1 GEF Rgf1. The data supporting these conclusions are reasonably sound.

Additional experiments are presented to suggest that Cdc42 and Pak1 negatively regulate Rgf1, this conclusion is not as strongly supported (though it may be true)

These study relies on a newly described probe for active Rho1. However this probe is not sufficiently well validated.

Overall the manuscript was not assembled with sufficient care and rigor, these deficits could be readily corrected.

The major point of the paper is that Cdc42 and Pak1 negatively regulate Rho1 activation. However, during late cytokinesis, active Cdc42 and active Rho1 co-exist at the division site. Thus, Cdc42 activation induces a delay in Rho1 activation, but how this delay is overcome is not investigated or even discussed. Indeed, while the delay is shown the transience of this inhibition is not explicitly mentioned. At a minimum, the authors should highlight this point for the readers.

Specific points

1. • RBD probe This probe is central to this manuscript. However, there is insufficient validation of its target. Figure 1 shows the localization and its independence of Rho2. The authors should provide direct evidence that it recognizes Rho1 (for example using a repressible promotor or an anchor away approach).

At various places in the manuscript the authors refer to this probe as "Rho-probe", RBD-probe, RBD, RBD-(mNG or tdTomato). On page 11 the authors state, "As per our observations, we refer to the Rho-probe signal at the division site as active Rho1 from here onwards." Yet, in the very next paragraph they refer to the localization of the "Rho probe". This is also an issue with the figures. For example, in figures 4B,C ; 5B,C; 6B; 7B,C the figures are titled either "Rho1 activation at division site", "Rho1-probe at division site"; "Rho1-probe appearance at division site" ; "Rho1-probe in non-constricting rings". In fig 3, RBD-nNG is quantified in a graph entitled "localizaton [sic] of Rho1-GEFs at division site"

In all figures but two, 5c and 7c, the authors quantify Rho1 activation by the presence or absence of the probe, rather than a quantitative measure or the extent of recruitment of the probe. This could be analyzed my quantitatively. 2. - Regulation of Rgf1 by Cdc42 and Pak1. The results shown in figure 8 show that "early Rho1 activation in gef1 mutants is not Rgf3-dependent". Figure 9 establishes "loss of rgf1 prevents premature Rho1 activation in gef1Δ cells restoring it to normal in late anaphase (Fig.9A, B)." This finding indicates that Rgf1, but not Rgf3, is required for Rho1 premature activation. This finding doesn't rule out the possibility that Cdc42 and Pak1 might be required to turn off RhoGAPs to allow active Rho1 to accumulate. This analysis concludes with this unclear and ungrammatical sentence, "While we were unable to assess the Rho-probe in the rgf1Δ rgf2Δ double mutants due to its lethality [sic; is the Rho probe lethal?], our observations suggest that apart from Rgf1 early Rho1 activation in gef1Δ cells is either due to activation of Rgf2 or due to inhibition of Rga5." The conclusion that this regulation is due to control of Rgf1 should be toned down. E.g. from the abstract: "We provide functional and genetic evidence which indicates that Pak1 regulates Rho1 activation likely via the regulation of its GEF Rgf1."

Referees cross-commenting

I think reviews are appropriate and speak for themselves.

Significance

This manuscript ties together several recent papers from the author's lab on the control of Cdc42 activation during cytokinesis and older papers on the role of Rho1 in Bgs1 activation. It provides missing information into the temporal regulation of septum assembly.

The authors make a point of the similarities of fission yeast cytokinesis to animal cell cytokinesis. Indeed the second sentence reads, "The fission yeast model system divides via an actomyosin-based contractile ring, which is assembled in the medial region of the cell, as in animal cells (Balasubramanian et al., 2004; Pollard, 2010).". However, the authors fail to point out the many differences between yeast and animal cell cytokinesis until the last paragraph of the discussion. If the authors want to include the similarities in the introduction, they should also include the differences. For example, ring assembly is independent of Rho1 activation in fission yeast, but dependent on RhoA activation in animal cells.

This work will be of interest to biologists working on yeast cell division. To a lesser extent it will be of interest to biologists interested in cytokinesis and coordination of distinct GTPase pathways.

Additional points

1. • The text is overly wordy and needs extensive revision. Many of the experiments could be explained more clearly and with somewhat less genetic jargon. The introduction has quite a bit of extraneous information and lacks relevant facts, such as the function of Bgs1, which is central to the results.
2. • page 4 "GEFs promote GTP binding, thus keeping the GTPase active while the GAPs increase GTP hydrolysis, thus promoting GTPase inactivation." GEFs promote GTP binding, but they do not keep the GTPase active (an inhibitor of a RhoGAP would do that), they activate the GTPases.
3. • The current literature on animal cell cytokinesis indicates little direct role in cytokinesis, rather than the author's statement, "In larger eukaryotes, the role of Cdc42 activation has been reported mostly in meiotic division events such as polar body extrusion in oocytes, but not much is known about its role in cytokinesis in somatic cell division (Drechsel et al., 1997; Na and Zernicka-Goetz, 2006)." See for example, PMID 10898977, 10871280 which indicate Cdc42 does not play a major role during cytokinesis in at least a few systems where it has been analyzed.

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Reply to the reviewers

We thank the reviewers for their enthusiastic support for our work and their insightful comments and suggestions which we believe strengthen the manuscript. Below we detail how we propose to respond to each of the specific points raised by each reviewer.

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

• Summary:*

• In the article entitled "Unique functions of two overlapping PAX6 retinal enhancers", Uttley and coworkers characterize in detail the activity of two conserved human enhancers (i.e. NRE and HS5) previously reported to drive Pax6 expression to the neural retina. By integrating these enhancers in a PhiC31 landing site using a dual enhancer-reporter cassette, they generated a zebrafish stable line in which their activity can be followed by the expression of GFP (NRE) and mCherry (HS5). The authors show that although the enhancers have a partially overlapping activity at early stages (24hpf), later on (48 and 72hpf) they activity domains segregate: to stem cells and differentiated amacrine cells for NRE, and to proliferating progenitors and differentiated Müller glia cells for HS5. To this end they used two different approaches: a scRNA-seq analysis of sorted cells from the transgenic line and a immunofluorescent analysis employing cell specific markers. The authors conclude that their analysis allowed the identification of unique cell type-specific functions.*

• Major comments:*

• In general terms, the article is technically sound (please, see section B for an assessment of the significance of the findings). The methodology used and the data analysis are accurate. The work is well presented, the figures are clear, and the previous literature properly cited. My main concerns are the following:*

• 1) A general concern on the main conclusion of the work "the identification of unique cell type-specific functions for these enhancers". This is in my opinion only partially addressed by the study, as the conclusions are limited due to the absence of genetic experiments: such as deleting the enhancers in their native genomic context (either in human organoids or the homologous sequence in animal models), or at least assessing the effect of mutating their sequence in transgenesis assays in zebrafish. I understand that these functional assays may be out of the scope of the current work, but then the text should be toned down (the word "function" is extensively used) to make clear that the authors mean just expression. I would suggest substituting the word by "activity" in many instances.*

• The absence of further genetic experiments also limits the significance of the study (see section B).*

We appreciate and agree with the reviewer’s concern and would substitute the word “function” with “activity” throughout the manuscript.

2) Whereas the work in general is technically correct (particularly transgenic lines and scRNA-seq data are well described and presented), the co-expression analyses using cell-specific markers (figure 5) need to be improved. There are several issues here. First, the magnification shown is too low to appreciate the colocalization details in the figure. The panels should be replaced by others with higher magnification/resolution (see also minor comment on color-blind compatible images) * In addition, the selection of the markers is suboptimal. Although PCNA is a good general marker of the entire CMZ, it would be advisable to repeat the experiments using more specific markers of the stem cell niche (e.g. rx1, vsx2; Raymond et al 2006; BMC dev Biol) to better define the enhancers expression domain. In addition, HuC/D labels both RGCs and amacrines, and the colocalization could also be refined using amacrine specific markers (e.g. ptf1a : Jusuf & Harris 2009, Neural Dev).*

In the revised version of the manuscript, we would:

1. Provide higher magnification images as suggested by the reviewer

2. Provide additional stainings and justification for our choice of markers used in these colocalizaion experiments Minor comments:

3. 1.- The work includes several figures (1, 2, 5, 6 and S1) showing colocalization experiments in which channels are shown in red and green. I would advise replacing the red channel with magenta (or the green with cyan) in order to make the figures accessible to readers with color-blindness. This also applies to the schematic representations in figure 6.*

We will change the channel colours throughout the manuscript as suggested by the reviewers

2.- It is unclear in the text/images whether the expression driven by the HS5 enhancer is exclusively restricted to temporal retina throughout development (By the way, this differential nasal vs temporal expression should also be included in the final scheme in Figure 6). Does this mean that the expression of Pax6 in proliferating progenitors and Müller glia cells in the nasal retina is not controlled by this enhancer? To which extent is Pax6 needed to maintain the identity of these cell types?

We will modify the figures as suggested and also include more details of expression overlap with PAX6 expression in the text of the revised manuscript.

3.- The following sentence in the Discussion "To the best of our knowledge, ours is the first report where the activities of developmental enhancers have been mapped in vivo at single-cell resolution to reveal distinct patterns of activity" should be removed/rephrased. I would argue that the activity of cis-regulatory regions associated to any developmental gene are genome-wide mapped at single cell resolution in each scATACseq experiment.

We agree that scATAC-seq gives information about potentially active enhancers but it does not define the precise cell-types unless overlapped with expression data. Our method is aimed at ‘defining’ the precise cell-types where the enhancer is active and has the potential to be used to build high resolution maps of cell-type specific enhancer usage for loci with multiple enhancers driving a single gene. We will discuss this in detail in the revised version of the manuscript.

4.- In the methods section: * (a) FACS experiments: Please provide a supplementary Figure to graphically account for all gating/sorting strategies. * (b) ScRNA-seq analysis: Please provide the values of mean reads per cell and median genes per cell as obtained from Cell Ranger. This would be informative for others performing similar experiments

This will be included in the revised version of the manuscript.

**Referees cross-commenting** * I agree with the comments by reviewer #2 on the FACsorting experiments, the description of the landing sites, and the limited significance of the results.*

Reviewer #1 (Significance (Required)):

• As described in the previous section, the technical quality of this work is high in general terms. The experiments presented are clear and the conclusions straightforward. In that sense, the study will be a useful reference for those interested in the regulatory logic of Pax6 during eye development, including mainly developmental biologists and human geneticists. This may be particularly the case if new variants can be associated with these enhancers in microphthalmic patients.*

• The significance and novelty of the findings is however limited by several factors:*

• a) First, although the level of detail described in this article was not achieved previously, the human enhancers NRE and HS5 (or their conserved homologous in other vertebrates) were previously reported to drive Pax6 expression to the neural retina in transgenesis assays.(Kammandel et al 1999; Marquardt et al 2001; McBride et al 2011; Ravi et al 2013; Kim et al 2017).*

We agree that the enhancers we describe in this study have been studied before. However, we would like to argue that ours is the first study where we define precise cell-types for the activity of these enhancers. We will revise the discussion to strengthen this argument.

b) As mentioned in the previous section, the transgenesis assays are not complemented with genetic experiments. The function of the enhancers on retina differentiation and cell fate determination could have been investigated either by deleting them (or their homologous in different species) in their native context, or by exploring their regulatory grammar introducing point mutations or micro-deletions in transgenesis assays.

We agree that the suggested experiments would be useful for unambiguously establishing the functions of these enhancers and we will discuss these prospects in the revised version of the manuscript.

c) For reasons not explained in the text, the analysis focuses only in two of the many cis-regulatory regions controlling Pax6 expression in the retina (Lima Cunha et al 2019, Genes). In the absence of a more comprehensive analysis is difficult assessing the relevance of the findings here described.

We agree that other enhancers for the PAX6 locus should be investigated using similar analysis pipeline to build a complete picture of the enhancer mediated regulation of PAX6. We will discuss this in the revised version of the manuscript.

d) Finally, from a very general methodological point of view, the approach of using scRNA-seq to investigate enhance activity at a single-cell level is valid and original. However, it is unclear to which extent will be a useful method for many studies, particularly if the activity of endogenous elements is being assessed. In such cases, available scATAC-seq data will provide genome-wide information on the activity of any cis-regulatory element with cell resolution with no need for transgenesis assays and sorting experiments. * We thank the reviewer for recognising the novelty of the approach we describe in this manuscript. We will discuss the merits and demerits of our method with scATAC-seq experiments in the revised version of the manuscript.*

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

In this work, Uttley et al fine characterize two previously described Pax6 retinal enhancers (NRE and HS5) by combining QSTARZ transgénesis method in zebrafish (allowing to produce site-specific integrations of a dual enhancer reporter cassette), scRNAseq and co-immunostaining with specific markers for different retinal cell populations. * The work is experimentally very well performed and well presented and only minor considerations are raised below: * - Authors observe that a large fraction Of FACs sorted cells do not display expression of mCherry or EGFP RNAm in their scRNAseq analysis and attribute this to read dropout in the scRNAseq data and/ or to false-positive FAC cell selection. However, a third possibility exists: n fact due to the high stability of the EGFP and mCherry reporters cells or their progeny could maintain relatively high levels of these reporters even after transcriptional downregulation. Accordingly, the two reporters are strongly expressed in retinal precursor at early stages (24hpf). Thus, in my opinion, it is possible that some cells expressing these reporters retained significant EGFP/mCherry protein levels at 48hpf. Could the authors comment on this? Besides, authors could provide the FACsorting data to give an idea of whether only highly EGFP/ mCherry expressing cells were selected or whether also the low or mild expressing ones were included in the scRNAseq analysis. Finally, a combination of HCR/FSH and GFP//mCherry immunostaining could be used to assess whether a discrepancy in the protein vs mRNA distribution of the reporters exists. * - The authors could provide the information on the landing site used for the QSTARZ transgene integration. While from their previous publication (Bhatia et al 2021) I assume it is the chr6 landing site, it would be worth having this information in the manuscript, as well as a genotyping validation of the correct integration.*

We will address these points and provide relevant additional data where needed in the revised version of the manuscript.

**Referees cross-commenting** * I agree with all the points raised by reviewer 1. Particularly I also find that scATACseq experiments already allow testing, to some extent, enhancer activity at cellular level.*

• Reviewer #2 (Significance (Required)):*

• From the biological point of view the work provides only an incremental advance in our understanding of the functions of the HS5 and NRE PAX6 enhancers and of PAX6 regulation in the retina. In fact, unraveling the precise contribution of these enhancers to Pax6 retinal expression and the trans-regulatory code controlling their activity would require complex genetic experiments and would fall out of the scope of this work, requiring an extensive amount of work which could not be addressed in the short term. Thus, this work should be regarded as a methodological resource, with its main strength consisting of the use scRNAseq to fine-characterize enhancer activity.*

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Referee #2

Evidence, reproducibility and clarity

In this work, Uttley et al fine characterize two previously described Pax6 retinal enhancers (NRE and HS5) by combining QSTARZ transgénesis method in zebrafish (allowing to produce site-specific integrations of a dual enhancer reporter cassette), scRNAseq and co-immunostaining with specific markers for different retinal cell populations.

The work is experimentally very well performed and well presented and only minor considerations are raised below:

• Authors observe that a large fraction Of FACs sorted cells do not display expression of mCherry or EGFP RNAm in their scRNAseq analysis and attribute this to read dropout in the scRNAseq data and/ or to false-positive FAC cell selection. However, a third possibility exists: n fact due to the high stability of the EGFP and mCherry reporters cells or their progeny could maintain relatively high levels of these reporters even after transcriptional downregulation. Accordingly, the two reporters are strongly expressed in retinal precursor at early stages (24hpf). Thus, in my opinion, it is possible that some cells expressing these reporters retained significant EGFP/mCherry protein levels at 48hpf. Could the authors comment on this? Besides, authors could provide the FACsorting data to give an idea of whether only highly EGFP/ mCherry expressing cells were selected or whether also the low or mild expressing ones were included in the scRNAseq analysis. Finally, a combination of HCR/FSH and GFP//mCherry immunostaining could be used to assess whether a discrepancy in the protein vs mRNA distribution of the reporters exists.
• The authors could provide the information on the landing site used for the QSTARZ transgene integration. While from their previous publication (Bhatia et al 2021) I assume it is the chr6 landing site, it would be worth having this information in the manuscript, as well as a genotyping validation of the correct integration.

Referees cross-commenting I agree with all the points raised by reviewer 1. Particularly I also find that scATACseq experiments already allow testing, to some extent, enhancer activity at cellular level.

Significance

From the biological point of view the work provides only an incremental advance in our understanding of the functions of the HS5 and NRE PAX6 enhancers and of PAX6 regulation in the retina. In fact, unraveling the precise contribution of these enhancers to Pax6 retinal expression and the trans-regulatory code controlling their activity would require complex genetic experiments and would fall out of the scope of this work, requiring an extensive amount of work which could not be addressed in the short term. Thus, this work should be regarded as a methodological resource, with its main strength consisting of the use scRNAseq to fine-characterize enhancer activity.

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

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Referee #1

Evidence, reproducibility and clarity

Summary:

In the article entitled "Unique functions of two overlapping PAX6 retinal enhancers", Uttley and coworkers characterize in detail the activity of two conserved human enhancers (i.e. NRE and HS5) previously reported to drive Pax6 expression to the neural retina. By integrating these enhancers in a PhiC31 landing site using a dual enhancer-reporter cassette, they generated a zebrafish stable line in which their activity can be followed by the expression of GFP (NRE) and mCherry (HS5). The authors show that although the enhancers have a partially overlapping activity at early stages (24hpf), later on (48 and 72hpf) they activity domains segregate: to stem cells and differentiated amacrine cells for NRE, and to proliferating progenitors and differentiated Müller glia cells for HS5. To this end they used two different approaches: a scRNA-seq analysis of sorted cells from the transgenic line and a immunofluorescent analysis employing cell specific markers. The authors conclude that their analysis allowed the identification of unique cell type-specific functions.

Major comments:

In general terms, the article is technically sound (please, see section B for an assessment of the significance of the findings). The methodology used and the data analysis are accurate. The work is well presented, the figures are clear, and the previous literature properly cited. My main concerns are the following:

1. A general concern on the main conclusion of the work "the identification of unique cell type-specific functions for these enhancers". This is in my opinion only partially addressed by the study, as the conclusions are limited due to the absence of genetic experiments: such as deleting the enhancers in their native genomic context (either in human organoids or the homologous sequence in animal models), or at least assessing the effect of mutating their sequence in transgenesis assays in zebrafish. I understand that these functional assays may be out of the scope of the current work, but then the text should be toned down (the word "function" is extensively used) to make clear that the authors mean just expression. I would suggest substituting the word by "activity" in many instances. The absence of further genetic experiments also limits the significance of the study (see section B).
2. Whereas the work in general is technically correct (particularly transgenic lines and scRNA-seq data are well described and presented), the co-expression analyses using cell-specific markers (figure 5) need to be improved. There are several issues here. First, the magnification shown is too low to appreciate the colocalization details in the figure. The panels should be replaced by others with higher magnification/resolution (see also minor comment on color-blind compatible images) In addition, the selection of the markers is suboptimal. Although PCNA is a good general marker of the entire CMZ, it would be advisable to repeat the experiments using more specific markers of the stem cell niche (e.g. rx1, vsx2; Raymond et al 2006; BMC dev Biol) to better define the enhancers expression domain. In addition, HuC/D labels both RGCs and amacrines, and the colocalization could also be refined using amacrine specific markers (e.g. ptf1a : Jusuf & Harris 2009, Neural Dev).

Minor comments:

1. The work includes several figures (1, 2, 5, 6 and S1) showing colocalization experiments in which channels are shown in red and green. I would advise replacing the red channel with magenta (or the green with cyan) in order to make the figures accessible to readers with color-blindness. This also applies to the schematic representations in figure 6.
2. It is unclear in the text/images whether the expression driven by the HS5 enhancer is exclusively restricted to temporal retina throughout development (By the way, this differential nasal vs temporal expression should also be included in the final scheme in Figure 6). Does this mean that the expression of Pax6 in proliferating progenitors and Müller glia cells in the nasal retina is not controlled by this enhancer? To which extent is Pax6 needed to maintain the identity of these cell types?
3. The following sentence in the Discussion "To the best of our knowledge, ours is the first report where the activities of developmental enhancers have been mapped in vivo at single-cell resolution to reveal distinct patterns of activity" should be removed/rephrased. I would argue that the activity of cis-regulatory regions associated to any developmental gene are genome-wide mapped at single cell resolution in each scATACseq experiment.
4. In the methods section:
   • (a) FACS experiments: Please provide a supplementary Figure to graphically account for all gating/sorting strategies.
   • (b) ScRNA-seq analysis: Please provide the values of mean reads per cell and median genes per cell as obtained from Cell Ranger. This would be informative for others performing similar experiments

Referees cross-commenting I agree with the comments by reviewer #2 on the FACsorting experiments, the description of the landing sites, and the limited significance of the results.

Significance

As described in the previous section, the technical quality of this work is high in general terms. The experiments presented are clear and the conclusions straightforward. In that sense, the study will be a useful reference for those interested in the regulatory logic of Pax6 during eye development, including mainly developmental biologists and human geneticists. This may be particularly the case if new variants can be associated with these enhancers in microphthalmic patients.

The significance and novelty of the findings is however limited by several factors:

• a) First, although the level of detail described in this article was not achieved previously, the human enhancers NRE and HS5 (or their conserved homologous in other vertebrates) were previously reported to drive Pax6 expression to the neural retina in transgenesis assays.(Kammandel et al 1999; Marquardt et al 2001; McBride et al 2011; Ravi et al 2013; Kim et al 2017).
• b) As mentioned in the previous section, the transgenesis assays are not complemented with genetic experiments. The function of the enhancers on retina differentiation and cell fate determination could have been investigated either by deleting them (or their homologous in different species) in their native context, or by exploring their regulatory grammar introducing point mutations or micro-deletions in transgenesis assays.
• c) For reasons not explained in the text, the analysis focuses only in two of the many cis-regulatory regions controlling Pax6 expression in the retina (Lima Cunha et al 2019, Genes). In the absence of a more comprehensive analysis is difficult assessing the relevance of the findings here described.
• d) Finally, from a very general methodological point of view, the approach of using scRNA-seq to investigate enhance activity at a single-cell level is valid and original. However, it is unclear to which extent will be a useful method for many studies, particularly if the activity of endogenous elements is being assessed. In such cases, available scATAC-seq data will provide genome-wide information on the activity of any cis-regulatory element with cell resolution with no need for transgenesis assays and sorting experiments.

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Reply to the reviewers

Response to Reviewers' Comments on Fumagalli et al. "Nirmatrelvir treatment blunts the development of antiviral adaptive immune responses in SARS-CoV-2 infected mice" (Preprint RC-2022-01777).

We wish to thank the reviewers for the scholarly review of our work and the very helpful comments. Based on their constructive suggestions, we have generated substantial new experimental data that, in our opinion, positively address all the major and minor concerns raised. In particular, we have confirmed the negative impact of nirmatrelvir treatment on adaptive immune responses in setting of robust SARS-CoV-2 replication (Delta infection in K18-hACE2 transgenic mice and mouse-adapted SARS-CoV-2 infection of wild-type mice).

One main and one supplemental figure have been added in response to the reviewers' comments. One additional figure – termed Reviewer Figure 1 – has been included in this letter for the reviewers' benefit; while addressing specific comments, we believe that the data depicted in this latter figure remain tangential to the main message of our work and, as such, it should not be incorporated in the final version. To aid the reviewers in the re-evaluation of this study, all relevant passages in the revised text have been written in red. A summary of the changes made to the figures and tables is provided as an appendix at the end of this letter.

Reviewers' comments:

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

In this study, Fumagalli et. al evaluated the impact of Nirmatrelvir drug treatment on the development of SARS-CoV-2-specific adaptive immune responses in a mice model. Nirmatrelvir is one of the component of Paxlovid drug that has been shown to reduce the risk of progression to severe COVID-19 and long COVID. Herein, authors show that nirmatrelvir administration early after infection blunts the development of SARS-CoV-2-specific antibody and T cell responses. Upon secondary challenge, nirmatrelvir-treated mice developed fewer memory T and B cells to the infected lungs and to mediastinal lymph nodes, respectively. Overall, the experimental methods, figures, results, statistical analysis and findings of this study are interesting and convincing.

We wish to thank the reviewer for the overall positive assessment of our work.

CROSS-CONSULATION COMMENTS I agree with the Reviewer 2 comments.

Reviewer #1 (Significance (Required)): It was known that nirmatrelvir reduces the risk of severe covid and long covid but, whether its treatment has any impact on adaptive immune response was not known/evaluated. This study has importantly addressed that impact of nirmatrelvir treatment can impair both T and B cell adaptive immune responses. It would have been impactful to understand the mechanism of T and B cell immune response impairment following nirmatrelvir treatment in mice which they have already mentioned a limitation of the study.

We agree with this reviewer that the mechanism of T and B cell impairment following nirmatrelvir treatment should be addressed in future studies.

Moreover this study provides important implications for clinical management of COVID patients and to revise the treatment strategies to avoid virological and/or symptomatic relapse after Paxlovid/nirmatrelvir treatment completion that have been reported in some individuals.

We thank the reviewer for highlighting the impact of our results.

I am not a mice model expert. Not sure whether the viral dose given to mice in this study was optimal to study the impact of the said drug.

Depending on the virus used, we infected mice with 105-106 TCID50. This is in line with most studies of SARS-CoV-2 infection in mice. It is difficult to know what the average infectious dose in humans is, but the human challenge trial in young adults shows that exposure of individuals to as low as 10 TCID50 of SARS-CoV-2 led to detectable viral RNA in the upper airways (Killingley et al, 2022).

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

Summary:

In this manuscript, the authors show that Paxlovid, a commonly used antiviral for SARS-CoV-2 infections, blunts the adaptive immune response to the virus. Indeed, they show convincing effects on T cell and B cell responses in the K18-hACE2 mouse model infected with Omicron variant. The effect is observed when drug treatment was started at 4, 24, or 48 h post infection. Experiments are well done and the data are presented clearly.

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

However, the early timing of drug administration resulted in minimal virus replication, thus likely limiting innate immune activation and antigenic exposure. Indeed, the authors show that the drug did not decrease adaptive responses to other viral infections, indicating that the effect on adaptive immunity in SARS-CoV-2 infection can be explained by decreased viral antigen production. Whether this is the mechanism by which relapse infections occur in humans after Paxlovid treatment is unclear.

Major comments:

The authors should discuss whether the timing of drug administration in their experiments is relevant to the timing of when Paxlovid is commonly started in humans. Does Paxlovid limit the adaptive immune response when given later in infection?

We thank the reviewer for raising this valid comment. First, we would like to point out that the kinetics of viral replication upon SARS-CoV-2 exposure differ between mice and humans. When mice are exposed to a high-dose (105 TCID50) aerosolized SARS-CoV-2, they show peak viral replication in the airways at day 3 post exposure and viral RNA is undetectable in the upper and lower airways after day 7 (Reviewer Figure 1A and (Fumagalli et al, 2021)). It is difficult to extract precise data in humans, but the human challenge trial in young adults shows that exposure of individuals to an extremely low dose (10 TCID50!) of SARS-CoV-2 led to the detection of viral RNA in the upper airways for longer than 14 days (Reviewer Figure 1B and (Killingley et al, 2022)). Therefore, it is very difficult to estimate what would possibly mimic what is occurring in treated COVID-19 patients, especially in line of the current COVID-19 guidelines for ritonavir-boosted nirmatrelvir that suggests to initiate treatment as soon as possible and within 5 days of symptoms (https://www.covid19treatmentguidelines.nih.gov/therapies/antiviral-therapy/ritonavir-boosted-nirmatrelvir--paxlovid-/). The choice to start treatment 4 hours after infection was motivated by the original paper that reported in vivo antiviral activity of nirmatrelvir against SARS-CoV-2 (Owen et al, 2021). That said, we performed additional experiments whereby we treated mice with nirmatrelvir 24 or 48 hours after infection (at or near the peak of viral replication). As shown in the new Figure 3, such treatment also resulted in blunted adaptive immune responses.

Reviewer Figure 1. (A) K18-hACE2 mice were exposed to a target dose of 2 x 105 TCID50 of aerosolized SARS-CoV-2 (D614G). Quantification of SARS-CoV-2 RNA in the lung after infection. RNA values are expressed as copy numbers per ng of total RNA and the limit of detection is indicated as a dotted line. (B) Healthy adult volunteers were challenged intranasally with SARS-CoV-2. In the infected individuals (n = 18 biologically independent participants). Viral load in twice-daily nose and throat swab samples was measured by qPCR (blue) and focus-forming assay (red) (a). Results are expressed as mean ± SEM. Adapted from ref. (Killingley et al, 2022).

Omicron variant has limited replication in the K18 mouse model and does not cause disease. Thus, the authors are starting from a model with artificially limited viral antigen production. Does Paxlovid limit the adaptive immune response when given during an infection with a variant strain that replicates robustly in the K18 mice?

We thank the reviewer for raising this issue. In the revised manuscript we have now performed experiments where we infected K18-hACE2 transgenic mice with the Delta (B.1.617.2) variant, known to replicate at higher level compared to the Omicron variants (Shuai et al, 2022). Additionally, we have infected WT mice with a mouse-adapted SARS-CoV-2 (rSARS2-N501YMA30)(Wong et al, 2022) that replicates robustly and induces significant disease. These new results, now shown in the new Figure 3 and new Figure S4, confirm that nirmatrelvir treatment blunts the development of antiviral adaptive immune responses regardless of the variants/strain used for infection.

Reviewer #2 (Significance (Required)):

Significance: Nirmatrelvir/Paxlovid is used clinically for treatment of COVID-19. Relapse infections have been reported after courses of the drug. The authors show here that Paxlovid treatment during a mouse model of SARS-CoV-2 infection results in diminished induction of adaptive immunity and immune memory. This is most probably due to decreased production of viral antigenic stimuli due to inhibition of virus replication. The concept that less viral antigen will result in less induction of immunity is not surprising. Further, whether the phenomenon observed here in a mouse model with poor susceptibility to the chosen virus strain is related to relapse infections in humans was not established. Nonetheless, the audience for this work is broad and this work could be of interest due to the common use of Paxlovid and the ongoing SARS-CoV-2 infections across the world.

Although we did not investigate the mechanism underlying the reported observation in depth, we agree with this reviewer that the most likely explanation for the reduced adaptive immune responses is decreased production of viral antigens. In this regard, it is probably not terribly surprising. However, it is worth noting that successful antimicrobial treatment does not inevitably result in reduced adaptive immune responses to any pathogen. For instance, treatment of mice infected with Listeria monocytogenes with amoxicillin early after infection did not significantly impair the development of T cell responses (Corbin & Harty, 2004; Mercado et al, 2000). Furthermore, treatment with antibiotics before L. monocytogenes infection allowed the development of functional antigen-specific memory CD8+ T cells in the absence of contraction (Badovinac et al, 2004). An additional, and possibly more relevant, example was published during the revision process: monoclonal antibody therapy with bamlanivimab during acute COVID-19 did not impact the development of a robust antiviral T cell response (Ramirez et al, 2022).

As per the comment related the poor susceptibility of the mouse model to the Omicron variants of SARS-CoV-2, we believe that the new data obtained with the Delta variant and with the mouse-adapted SARS-CoV-2 (new Figure 3 and S4) convincingly show that nirmatrelvir treatment blunts antiviral adaptive immune responses to SARS-CoV-2 in mice.

List of modifications

New figures:

• Figure 3: new data as per reviewer’s suggestion.
• Figure S4: new data as per reviewer’s suggestion.

References

Badovinac VP, Porter BB & Harty JT (2004) CD8+ T cell contraction is controlled by early inflammation. Nat Immunol 5: 809–817

Corbin GA & Harty JT (2004) Duration of Infection and Antigen Display Have Minimal Influence on the Kinetics of the CD4+ T Cell Response to Listeria monocytogenes Infection. J Immunol 173: 5679–5687

Fumagalli V, Ravà M, Marotta D, Lucia PD, Laura C, Sala E, Grillo M, Bono E, Giustini L, Perucchini C, et al (2021) Administration of aerosolized SARS-CoV-2 to K18-hACE2 mice uncouples respiratory infection from fatal neuroinvasion. Sci Immunol 7: eabl9929

Killingley B, Mann AJ, Kalinova M, Boyers A, Goonawardane N, Zhou J, Lindsell K, Hare SS, Brown J, Frise R, et al (2022) Safety, tolerability and viral kinetics during SARS-CoV-2 human challenge in young adults. Nat Med 28: 1031–1041

Mercado R, Vijh S, Allen SE, Kerksiek K, Pilip IM & Pamer EG (2000) Early Programming of T Cell Populations Responding to Bacterial Infection. J Immunol 165: 6833–6839

Owen DR, Allerton CMN, Anderson AS, Aschenbrenner L, Avery M, Berritt S, Boras B, Cardin RD, Carlo A, Coffman KJ, et al (2021) An oral SARS-CoV-2 Mpro inhibitor clinical candidate for the treatment of COVID-19. Science 374: 1586–1593

Ramirez SI, Grifoni A, Weiskopf D, Parikh UM, Heaps A, Faraji F, Sieg SF, Ritz J, Moser C, Eron JJ, et al (2022) Bamlanivimab therapy for acute COVID-19 does not blunt SARS-CoV-2-specific memory T cell responses. Jci Insight 7

Shuai H, Chan JF-W, Hu B, Chai Y, Yuen TT-T, Yin F, Huang X, Yoon C, Hu J-C, Liu H, et al (2022) Attenuated replication and pathogenicity of SARS-CoV-2 B.1.1.529 Omicron. Nature 603: 693–699

Wong L-YR, Zheng J, Wilhelmsen K, Li K, Ortiz ME, Schnicker NJ, Thurman A, Pezzulo AA, Szachowicz PJ, Li P, et al (2022) Eicosanoid signaling blockade protects middle-aged mice from severe COVID-19. Nature: 1–9

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Referee #2

Evidence, reproducibility and clarity

Summary:

In this manuscript, the authors show that Paxlovid, a commonly used antiviral for SARS-CoV-2 infections, blunts the adaptive immune response to the virus. Indeed, they show convincing effects on T cell and B cell responses in the K18-hACE2 mouse model infected with Omicron variant. The effect is observed when drug treatment was started at 4, 24, or 48 h post infection. Experiments are well done and the data are presented clearly. However, the early timing of drug administration resulted in minimal virus replication, thus likely limiting innate immune activation and antigenic exposure. Indeed, the authors show that the drug did not decrease adaptive responses to other viral infections, indicating that the effect on adaptive immunity in SARS-CoV-2 infection can be explained by decreased viral antigen production. Whether this is the mechanism by which relapse infections occur in humans after Paxlovid treatment is unclear.

Major comments:

The authors should discuss whether the timing of drug administration in their experiments is relevant to the timing of when Paxlovid is commonly started in humans.

Does Paxlovid limit the adaptive immune response when given later in infection?

Omicron variant has limited replication in the K18 mouse model and does not cause disease. Thus, the authors are starting from a model with artificially limited viral antigen production. Does Paxlovid limit the adaptive immune response when given during an infection with a variant strain that replicates robustly in the K18 mice?

Significance

Nirmatrelvir/Paxlovid is used clinically for treatment of COVID-19. Relapse infections have been reported after courses of the drug. The authors show here that Paxlovid treatment during a mouse model of SARS-CoV-2 infection results in diminished induction of adaptive immunity and immune memory. This is most probably due to decreased production of viral antigenic stimuli due to inhibition of virus replication. The concept that less viral antigen will result in less induction of immunity is not surprising. Further, whether the phenomenon observed here in a mouse model with poor susceptibility to the chosen virus strain is related to relapse infections in humans was not established. Nonetheless, the audience for this work is broad and this work could be of interest due to the common use of Paxlovid and the ongoing SARS-CoV-2 infections across the world.

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Referee #1

Evidence, reproducibility and clarity

In this study, Fumagalli et. al evaluated the impact of Nirmatrelvir drug treatment on the development of SARS-CoV-2-specific adaptive immune responses in a mice model. Nirmatrelvir is one of the component of Paxlovid drug that has been shown to reduce the risk of progression to severe COVID-19 and long COVID. Herein, authors show that nirmatrelvir administration early after infection blunts the development of SARS-CoV-2-specific antibody and T cell responses. Upon secondary challenge, nirmatrelvir-treated mice developed fewer memory T and B cells to the infected lungs and to mediastinal lymph nodes, respectively. Overall, the experimental methods, figures, results, statistical analysis and findings of this study are interesting and convincing.

Referees cross-commenting

I agree with the Reviewer 2 comments.

Significance

It was known that nirmatrelvir reduces the risk of severe covid and long covid but, whether its treatment has any impact on adaptive immune response was not known/evaluated. This study has importantly addressed that impact of nirmatrelvir treatment can impair both T and B cell adaptive immune responses. It would have been impactful to understand the mechanism of T and B cell immune response impairment following nirmatrelvir treatment in mice which they have already mentioned a limitation of the study.

Moreover this study provides important implications for clinical management of COVID patients and to revise the treatment strategies to avoid virological and/or symptomatic relapse after Paxlovid/nirmatrelvir treatment completion that have been reported in some individuals.

I am not a mice model expert. Not sure whether the viral dose given to mice in this study was optimal to study the impact of the said drug.

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Reply to the reviewers

1. General Statements We thank the reviewers for their thoughtful comments and suggestions, which have improved the manuscript. We are particularly gratified by their positive comments about the significance of the findings. Our point-by-point responses to the reviewer comments and suggestions are summarized below. Line numbers have been added to the revised manuscript to make it easier to locate the changes.

Point-by-point description of the revisions

Reviewer #1__ __

*1) In the study there is a lack of consideration of other targets. In and of itself this is not a problem, but once the author's identified the T130A mutation as being a key for protection it would have been good to Sanger sequence the other T. gondii myosins - a quick alignment of the TgMyo's A, C, H (class XIV), along with D and E suggests that the motif is highly conserved. This raises the currently unexplored (and exciting!) prospect of a pan-myosin inhibitor, and that there might have been mutations at an equivalent position in the other four KNX002 resistant clones - for example, MyoC has been proposed to provide some level of functional redundancy in the absence of MyoA. *

Because the goal of this work was to evaluate the druggability of TgMyoA, we specifically designed our experiments to identify resistance-conferring mutations in TgMyoA or its light chains, as described on lines 366-372. This strategy yielded the TgMyoA T130A mutation, which enabled us to rigorously determine that inhibiting TgMyoA, and TgMyoA only, was sufficient to slow the progression of disease in vivo. Because we took this targeted approach, our results did not address: (a) the basis of resistance in the 4 resistant clones that did not contain a mutation in TgMyoA or its light chains and (b) whether KNX-002 inhibits any of the other ten parasite myosins.

The most informative way to address (a) would be to do whole genome sequencing on each of the mutants, since resistance might have nothing to do with the other parasite myosins or their light chains. Any potential resistance-conferring mutations identified would need to be regenerated in a non-mutagenized background and functionally characterized, as we have done here for the T130A mutation, to be certain that this particular mutation was responsible for resistance. The most direct way to address (b) would be to individually express each of the other ten parasite myosins together with its specific associated light chains and the myosin co-chaperone protein TgUNC, purify the motor and determine the effect of the compound on motor activity (as we have done for TgMyoA; Fig. 1). These are both major undertakings that are beyond the goals and scope of the current manuscript. Critically, the absence of such data does not impact the conclusions of our phenotypic studies, which used the CRISPR-engineered T130A parasite line.

We nevertheless agree with the reviewer that these are both interesting questions that should be studied further, and we now discuss them on lines 416-422 and 451-453.

2) The fact that T130 is not thought to be the binding site of KNX002 is only introduced quite late on - this also relates to the next point - is the binding pocket conserved??? It is intriguing that the residue and proximal amino acid environment are highly conserved with vertebrates, but that KNX002 does not have an effect on their activity in their screen assay. It would be useful to know if the differences in the structures of the myosins can provide an explanation for this - along the same lines, given that the crystal structure of TgMyoA is available (PMID: 30348763), it would be useful for the authors to provide a molecular model for the binding of the inhibitor to the proposed point of engagement.

These are excellent questions that we unfortunately cannot yet answer. Docking simulations of KNX-002 to the published structure of TgMyoA in its pre-powerstroke state have thus far not yielded any promising results. The site of KNX-002 binding to P. falciparum MyoA was determined by X-ray crystallography (ref. 54); however, the PfMyoA in that study was in the post-rigor state and the coordinates of the co-crystal structure have not yet been made available in the PDB database for homology modeling.

The lack of effect of KNX-002 on the vertebrate muscle myosins may not be surprising. Although the 3D structures of myosins are rather conserved, their primary structures are quite different, which likely contributes to the different effects of KNX-002 on the different myosins. TgMyoA and PfMyoA are more similar to each other than they are to the vertebrate muscle myosins, which may enable the specific targeting of MyoA in apicomplexan parasites (lines 308-310).

*If this is an allosteric site it is possible that the mutation functions indirectly upon binding of KNX002 to the orthosteric site, but this would be useful to help the reader to understand this (and there are bioinformatic prediction tools that will score allostery - which would be interesting to include). This is explained somewhat in the discussion - but this should be introduced much earlier to clarify. What is known about allosteric regulation of Myo function? Is this a known site of regulation? *

Allosteric modulation of human cardiac myosin by small molecules such as omecamtiv is well established, and allosteric effects of the T130A mutation are certainly a possibility. As molecular motors, myosins depend on a complex and highly interconnected network of allosteric interactions to perform their function (for example, see ref. 59). This complexity, combined with the fact that the data on the PfMyoA-KNX-002 structure have not yet been released, makes it very difficult to generate any sort of model that would support meaningful conclusions. A statement to this effect has been added to the Discussion (line 375-382), and the likelihood that T130 is not part of the actual binding site for the compound is now mentioned at the very beginning of the paragraph that discusses the potential mechanism of action of the T130A mutation (lines 375-376).

*3) Introduction: 'Nearly one third of the world's population is infected with the apicomplexan parasite' - given that these data are extrapolated from serology, this should be reworded - it's fairer to say that they 'are or have been infected...' *

Done – line 65.

*4) Page 4: figure 1A - can you provide some explanation for incomplete inhibition in the screen - there seems to be a residual amount (15-20%) of activity that is not inhibited. *

The compound inhibits 80-90% of the motor’s activity at 40uM. We did not test higher concentrations in the ATPase assay; presumably we would see incrementally more inhibition as we increase compound concentration further, but the concentrations used enabled us to construct a reproducible IC50 curve without adding potentially confounding amounts of DMSO (carrier) to the assay.

*5) The authors demonstrate a general effect on growth over 7 days. It would be good to use a replication assay (e.g. parasites/vacuole over a single lytic cycle) to confirm that KNX002 does not affect cellular division. This would further strengthen the argument that the phenotypic effect is primarily via impacts on motility. *

A figure showing the lack of effect of KNX-002 on replication has now been added (new Supplemental Figure 2) and a paragraph describing these data and their implications added to the Results section (lines 137-143).

*6) Page 5: 'selected for parasites resistant to KNX-002 by growth in 40 μM KNX-002.' - could the authors add text to explain why that concentration was chosen. *

40 μM is close to the compound’s IC90 of 37.6 μM and, although we tried a number of different compound concentrations and selection schemes, 40 μM yielded parasites with the greatest shift in IC50. We now include this rationale on lines 606-607, as well as a new figure showing the shift in IC50 curves for all 5 resistant lines (Suppl. Figure 8).

*7) Page 6: 'suggesting that the effects of the T130A mutation on motor function are due to more subtle structural changes' - it's fair to say that there are not gross structural changes based on the data presented, but that does not mean it is therefore a 'more subtle structural change' - surely the mutation could prevent KNX002 binding without effecting TgMyoA structure? *

Based on the residues within P. falciparum MyoA that participate in binding to KNX-002 (ref. 54), it is unlikely that T130 of TgMyoA participates directly in compound binding. Mutation of T130 to alanine therefore seems most likely to impact compound binding through a change in protein structure, discussed more fully now on lines 375-382.

*8) Page 6: 'the proportion of filaments moving' - in the figure it's referred to as the 'fraction of filaments', which makes more sense for the data presented. Please correct to 'fraction' throughout the manuscript (discussion, page 9 - possibly other instances!). Along the same lines, in figure 6 it would be good to change the y axes on the '% moving' to be 'fraction moving' and change the numbers - this would make it easier for the reader to understand the index values presented in the lower panels - if you do the calculation with the % values presented the numbers don't make sense (as fractions they do). The axes for motility also go up to 125% - please correct - based on the data presented there is no need for this to be above 100% (or 1 - see above). *

We thank the reviewer for this suggestion; “percent moving” and “proportion moving” have been changed throughout the manuscript and figures to “fraction moving”. The y-axis labels on the motility and IC50 curves have also been modified as suggested.

*9) Page 7: 'tested whether KNX-002 (20mg/kg, administered intraperitoneally on the day of infection and two days later' - please provide some rationale for the concentration used. *

A preliminary dose tolerance study was conducted prior to the infection experiments, with doses ranging from 5-20 mg/kg. The study showed that two doses of 20 mg/kg, administered two days apart, resulted in minor hepatoxicity without signs of pain or distress. 20mg/kg was therefore considered the maximal tolerated dose. This rationale is now included on lines 715-721.

*10) Page 10: 'the T130A mutation is likely to have long range structural impact that could alter the KNX-002 binding pocket' - this is particularly interesting, and should be addressed with a model - do the authors think that the T130 region be a conserved site of allosteric regulation? This would be good to expand upon in the discussion - mutation of an allosteric site as a mechanism of resistance is unusual, and typically described as being unlikely - and used as justification for the targeted drugging of allosteric sites. *

See response to comment #2 above and the new text on lines 375-382.

Reviewer #2

*1) Considering (i) the moderate effect of KNX-002 on the acute infection process in CBA mice that received tachyzoites intraperitoneally, (ii) the fact that the drug application cannot be envisaged outside of the context of reactivation of cystogenic strains (in particular with respect to cerebral toxoplasmosis as emphasized in the introductive section), which implies the drug would have to be delivered and active in the brain parenchyma, a condition not analyzed here, it would be appropriate to modify the current title. It would be more relevant to highlight the solid body of data on the identification and functional characterization of the compound and derivatives in vitro and in the host mouse model. Apart from the title, the discussion should also recontextualize the in vivo assays and the information these assays bring on the slight delay of the "mortality" of some but not all mice. *

We agree that the major clinical application of any new anti-Toxoplasma chemotherapy would be treatment of a reactivated infection, particularly in the brain (although there could also be a role for treatment of pregnant women), and that the data we present with this compound do not speak directly to clinical efficacy in this context. That said, reactivation leads to an active infection whose pathogenesis requires TgMyoA-dependent motility, invasion and egress, like the active infections analyzed here. The KNX-002 scaffold would likely need to be modified to enable it to cross the blood-brain barrier and access parasites in the brain, but that would be a normal step in any campaign to develop new drugs for toxoplasmosis (which is well beyond the scope of this study; see response to comment #11).

Given these considerations, we gave much thought to how to accurately describe the results from the animal experiments – and we therefore appreciate the reviewer’s comment. For the title, we arrived the word “druggable”, because it has the very specific meaning described on lines 100-101: a protein whose activity is amenable to inhibition by small molecules. In our experiments with mice infected with wild-type parasites, nine of the ten compound-treated animals survived longer than the untreated controls, and 40% of the treated mice were still alive at the end of the experiment. Nevertheless, we stayed away from terms like “therapeutic” or “treatment”, for exactly the reasons the reviewer raises. We believe that the current title is an accurate summary of what we found, since we have indeed shown that MyoA is amenable to inhibition by a small molecule in a well-established animal model of infection (CBA mice infected intraperitoneally). Showing for the first time that the MyoA is druggable, in vivo, provides the rationale for identifying more potent compounds that can access the brain and serve as bona fide leads for drug development.

To the reviewer’s point, we also reviewed all sections of the text where we described the animal experiments, and in the revised manuscript we replaced all instances in the text of “ameliorate disease”, “prevent disease” and “decrease the susceptibility of mice to a lethal infection” with the more circumspect phrases “alter disease progression” or “slow disease progression” (lines 46, 56, 110, 297, 315, Figure 9 legend). We also changed the title of the Results section describing these data from “KNX-002 treatment decreases the susceptibility of mice to lethal infection with T. gondii” to “KNX-002 treatment slows disease progression in mice infected with a lethal dose of T. gondii” (line 284).

*2) Motility analysis: This comment concerns the Figure 7. It seems to the reviewers that the major hypothesis to test in data presented in panel B is that the wild type and the T130A mutant tachyzoite respond differently under similar drug conditions rather than the two populations without drug. These statistics could be added easily, hence it would validate that the proportion of motile mutant parasites is not affected by the drug when compared to vehicle. *

These statistical comparisons have now been added to revised figure 7, as suggested. Since this comparison was between different parasite lines, it required the use of unpaired t-tests (vs. the paired t-tests used for different compound treatments of the same parasite line). We have therefore revised all 3D motility figures (Figures 4 and 7, Suppl. Figures 7 and 12) and their legends to clearly indicate which samples are being compared to which and whether paired or unpaired statistical tests are being used.

However, the statistics shown panel C rather suggest that the drug does impact on the speed of the moving parasites, including when these carry the "resistance" T130 A mutation. It is not clear what we can gain in terms of messages with the motility index except to "slightly reverse" the analysis on panel B and to favor a no-effect of KNX-002 on the mutant parasite motile skills, on which the author might give more explanation. When comparing these quantitative tests with the panel presented above (panel A) it seems that the mutant parasite is still impacted by the MyoA inhibitor. Although there is no doubt for the reviewers that the T130A mutant emerging from the selected T. gondii resistant clones is a valuable probe for assessing drug selectivity: indeed the assays validate KNX-002 as a direct TgMyoA ATPase inhibitor, it might be good to rephrase some sentences and to have a harmonized definition of the parasite motility index throughout the text (Figure 7 legend, result and discussion sections).

The reviewer is correct that there is a decrease in the speed of compound-treated T130A parasites, as the p-values on Figure 7C indicate. This is why we state in the text that “the mutant parasites retain some sensitivity to the compound” (line 263). We were careful throughout the manuscript to refer to the resistance provided by the mutation as “partial”, or to describe it as a “reduced sensitivity”. Partial resistance is still sufficient to establish compound specificity, as noted by the reviewer in this comment.

We present the motility index not to try to “reverse” the effect of the compound on the mutant’s speed, but because the compound has two simultaneous effects on motility -- a decrease in the fraction moving and a decrease in speed of those that do move. Combining these two effects into one value (while still showing each component individually, as we have) enables comparison to the analogous actin filament motility index from the in vitro motility assays, and provides a more complete picture of the impact of compound treatment on parasite motility. This is a similar approach to that used in studies of e.g., phagocytosis, where the widely reported “phagocytic index” corresponds to the fraction of cells that have internalized at least one particle multiplied by the average number of beads internalized. The motility index of the mutant parasites is significantly less impacted by KNX-002 than the motility index of wild-type parasites (Figure 7D).

We have further clarified the definition and rationale for using the parasite motility index throughout, as suggested (lines 233-235, 264-267, 345-348).

*This reviewer's concern was accentuated by the comparison between the actin filament sliding index and the parasite motility index which appears as such far stretch; Aside from the "far stretched claims" easy to re-address in a revised version, the readers have appreciated the writing quality and most figure illustration. The discussion nicely synthetizes the whole dataset, including those related to the 4 T. gondii clones that resisted to KNX-002 but not through mutations targeting any of the myosinA chains. *

We have added additional text to the discussion listing possible reasons for the differential effects of the mutation on the filament and parasite motility indices (lines 403-406).

4) Ab*stract: the concept of "ameliorate disease" in this framework is odd and the objective of the work can be rephrased in a simple way (see below) *

See response to Comment #1; “ameliorate” has been replaced with “alter disease progression” (line 46).

*5) Introduction section: we think that the references on the impairment of invasiveness for the KoMyoA should be included (Bichet et al., BMC Biology 2016) as it has provided proof of an alternative and suboptimal mode of entry in many different cell types, thereby arguing that in absence of MyoA function, parasite invasiveness is not fully abolished and this without considering any MyoC-driven MyoA compensation. *

We thank the reviewer for catching this oversight; the Bichet citation has been added (line 93).

6) Introduction, third paragraph: in the sentence "Because the parasite can compensate for the loss or reduced expression of proteins important to its life cycle [29-31], small-molecule inhibitors of TgMyoA would serve as valuable complementary tools for determining how different aspects of motor function contribute to parasite motility and the role played by TgMyoA in parasite dissemination and virulence ». We definitively agree with this view but saying that, we think it would be worth evaluating (or simply discussing) the potency of the KNX-002 against MyoC, which compensatory contribution has been debated and remains questionable (at least to the reviewers) with respect to cell invasiveness restoration (related to the comment above).

We have included a discussion of a potential compensatory role for MyoC and the value of determining in future studies whether KNX-002 (or its more potent downstream analogs) inhibit any of the other parasite myosins (lines 419-423). Whether or not MyoC can functionally compensate for a lack of MyoA – we agree this is a controversial question – it is important to note (as we do on line 440-442) that “T. gondii engineered to express low levels of TgMyoA … are completely avirulent [28], arguing that sufficiently strong inhibition of TgMyoA is likely, on its own, to be therapeutically useful”.

*7) If we are correct, the screen and the characterization study have been performed with two different products (CK2140597 and KNX-002 the compound library and the re-synthetized one, respectively). Could we make sure that the two have the same potency? *

The source of compound used in each of the assays is now explicitly described on lines 481-490). Commercially obtained compound yielded an IC50 in growth assays of 16.2 and 14.9 μM (Figures 2 and 5, respectively), and compound synthesized by us yielded an IC50 value of 19.7 μM (Figure 3). The 95% confidence intervals of these three independent IC50 determinations with two different sources of compound overlap (lines 484-486).

8) We understood how the authors came to the conclusion that the KNX-002 impact on growth of the parasite and they stated "growth in culture" in the subsection title but then refers to parasite growth. Therefore, it looks a bit confusing for the reader since intracellular growth per se is probably not modified but this feature was not looked at it in this study (we would expect no impact based on published data on MyoA- genetically deficient tachyzoites, except if the drug impacts host cell metabolism for instances). Instead, it is the overall expansion of the parasite population that is analyzed here and clearly shown to be impacted. This decrease in population expansion on a cell monolayer likely results from impaired MyoA-dependent egress and invasiveness upon chemical inactivation of MyoA. Accordingly, it appears difficult to understand what is an IC50 for the "overall" growth in the context of this study. The authors should rephrase for better accuracy when necessary, including in the graph Fig2 legend axis.

While assays that measure parasite expansion in culture are by convention called “growth” assays (e.g., see Gubbels et al, High-Throughput Growth Assay for Toxoplasma gondii Using Yellow Fluorescent Protein AAC 47 (2003) 309, the paper on which our assay was based), we take the reviewer’s point that a reader may incorrectly ascribe the inhibition to some other aspect of the lytic cycle (e.g., intracellular replication), rather than a myosin-dependent motility-based process. We have therefore now: (a) more clearly defined the growth assay as one that measures parasite expansion in culture (lines 132-138); (b) described the myosin-dependent and -independent steps of the lytic cycle (lines 137-140); and (c) added a new figure (new Suppl. Figure 2, lines 140-143) showing that the compound has no effect on intracellular replication.

*9) The authors should clarify for the reader (i) why they use in some case myofibrils and other muscle F actin when measuring the Myosin ATPase activity, (ii) what does mean XX% calcium activation and (iii) why using 75% in these assays which is 3 times higher from the original assays. (iv) Why they did not include non muscle actins in their study since Myosins also extensively work on non muscle actins. *

(i) For both striated and smooth muscle myosins, the assays used here are well established and have identified compounds that have translated into animal models of disease. To assay the activity of myosins from striated muscle types, particularly to determine compound selectivity, myofibril assays are preferred as they recapitulate more of the biology as a more "native", membrane-free preparation and respond cooperatively to calcium activation. For cardiac, fast and slow skeletal muscle it is possible to derive high quality myofibril preparations that can be activated by calcium. A reference describing the value of using myofibrils in assays of striated muscle myosin ATPase activity has been added (ref. 71, line 517).

Smooth muscle, a non-striated tissue, is regulated differently and calcium exerts an effect not through binding to troponin as in the striated muscle but through g-protein signaling, with phosphorylation as an end result, making the contraction slower and also much slower to reverse - in line with the physiological role of the muscle. The only way to reliably reconstitute smooth muscle ATPase activity has been through purification and reconstitution of a more reductionist system. The SMM S1 needs to be crosslinked to the actin to achieve high enough local concentrations to generate robust ATPase activity. A reference describing the use of this assay to identify small molecule inhibitors of SMM is now included (ref. 73, line 522).

(ii, iii) Striated muscle myofibrils are responsive to calcium, as muscle contraction is mediated in vivo through calcium release from the sarcoplasmic reticulum. Titrating calcium can activate the myofibril ATPase activity up to the plateau (100%) and provide optimal signal to noise and sensitivity for the particular activity being assayed. For counterscreening to determine selectivity, we adjusted the assay conditions to a high basal ATPase activity (75% calcium) to provide high sensitivity for detecting inhibition. A sentence explaining this rationale has been added on lines 519-520.

(iv) We used skeletal muscle actin in all of our in vitro assays since we have shown skeletal muscle actin to be a good substrate for TgMyoA (ref 33, cited on line 536) and skeletal muscle actin can be purified in larger quantities than native actin from parasites or functional recombinant protein from insect cells. Others have also shown that the closely related MyoA from P. falciparum moves skeletal muscle actin at the same speeds as recombinant P. falciparum actin (Bookwalter et al [2017] JBC 292:19290).

*10) The protocol of image analysis of the 3D motility assay was increased to 80 seconds for the test of KNX-002 selectivity using wild type and mutant parasites (Fig 7) when compared to the test of KXN-002 concentration effect on wild type tachyzoites (60 sec in the result section, in Fig 4 legend and in the Methods' section). Is there any specific reason? *

The data for Figure 4 were captured earlier in the project than those of Figure 7 and Suppl. Figure 7. In the intervening time we upgraded our Nikon Elements software from v.3.20 to v.5.11 (as already described on lines 583 and 588). With the upgrade to v.5.11, we also began using Nikon’s Illumination Sequence (IS) module, a graphical user interface that provides greater time resolution through a more efficient approach to building the z-stacks and saving the data. With the addition of v.5.11 and the IS module we were able to capture twice the number of image volumes in 80 sec than we were in 60 sec using v.3.20, and that became our standard operating procedure. Other than the improved time resolution, the 60s and 80s assays give indistinguishable relative results. We have now clarified in the methods (line 588-589) that we used the IS module to acquire the data in Figure 7 and Suppl. Figure 7.

*11) In the mouse infection experimental design (Method section), it seems that they were no biological replicates in the case of the drug-treated (parasites + mice) which is not the case for the comparison of virulence between MyoA wild type and T130 mutants. If true, and considering what the authors wish to emphasize as a main message, it is fairly complicated to convincingly conclude about the KNX-002 effectiveness in vivo. Maybe the authors could explain their limitations. *

Since we did not know how the compound and parasites would interact in mice – and in keeping with animal welfare standards – we decided that rather than doing multiple replicates with smaller numbers of infected mice we would do a single experiment with a large enough number of mice per treatment condition to ensure that if any animals died unexpectedly or had to be euthanized prematurely we would still have sufficient numbers for robust statistical comparison. Single experiments with ten treated and ten untreated mice are a generally accepted approach in early studies of drug effectiveness (e.g., Ferrreira et al Parasite 2002, 9:261; Rutaganira et al, J. Med. Chem. 2017, 60: 9976; Zhang et al IJP Drugs and Drug Resistance 2019, 9:27), and power analysis shows that if mortality is 100% in untreated mice and 50% in treated mice, 10 mice per group will provide an 80% probability of detecting the difference with a p value<br /> *We are also not sure why the compound has been injected only twice, at the time of parasite injection and two days after whereas the mice succumbed after 8 to 9 days even without MyoA inhibitors. Although quite difficult to measure, do the authors have any knowledge (based on the chemistry for example) of the compound stability and lipophilicity in blood and tissues? Because the IC50 on free tachyzoites appears significantly higher (5.3 uM, Fig4) than the in vitro molecular assay, when assessed in motility tests, and is increased for intracellular growth (Fig 8), it is somehow expected that the current compound would not work that great in vivo. Did the author try to provide the inhibitor intravenously every day? *

IP injection is a standard method of administration for early drug treatment studies, and two considerations contributed to our decision to inject on days 0 and 2 post-infection: (a) the preliminary dose-tolerance studies, which were done with two IP doses of compound two days apart, showed evidence of mild toxicity so we were hesitant to inject more frequently, inject IV, or use more compound/injection; (ii) we expect the compound to work primarily on egressing and extracellular parasites, and since the parasite’s lytic cycle takes approximately 48 hours, this two-day injection schedule was chosen to maximize exposure of the extracellular parasites to freshly injected compound early in establishment of the infection. This rationale has now been added to the Methods section (lines 715-721).

In terms of the doing systematic studies of dosing, stability, PK/PD, drug partitioning etc., it is important to restate that the primary goal of this work was to test whether inhibiting TgMyoA activity in vivo alters the course of infection. The data reported in the manuscript demonstrated this to be the case. As we state on lines 454-457, “While KNX-002 provided the means to rigorously test the druggability of TgMyoA, it caused weight loss and histological evidence of liver damage in the treated infected mice. Before further animal work, it will therefore be necessary to develop more potent and less toxic analogs that retain specificity for parasite myosin.” Our colleagues at Kainomyx have in fact initiated a drug development campaign based on the KNX-002 scaffold and have already identified a derivative named KNX-115, that is 20-fold more potent against recombinant P. falciparum MyoA (described on lines 356-361). Given Kainomyx’s ongoing efforts we do not believe it makes sense to do any further animal experiments at this time with KNX-002. It will be more informative and ethical to undertake, e.g., dosing, PK and PD studies with the more potent and less toxic derivatives that emerge from the Kainomyx drug development program, once these compounds become publicly available. This does not diminish the importance of the proof-of-principle experiments reported here, which as the reviewer stated, “provide a strong rationale for developing new therapeutic strategies based on targeting MyoA”; rather, it makes it hard to justify doing additional animal studies with a compound that we know will soon be replaced with more potent and less toxic derivatives.

12) Figure 4: 2D and 3D Motility- the authors should comment on the fact that in 2D conditions with 10 uM of KNX-002, circular trajectories (one complete circle so at least 2 parasite lengths but sometimes more) largely dominate over others, whereas in absence of KNX-002 these circular trajectories are barely detectable and helical trajectories predominate. What could that mean as regard to the MyoA functional contribution to either process?

This is an interesting question that we cannot currently answer. Perhaps helical 2D gliding requires more myosin-generated force than circular 2D gliding, but this is pure speculation at this point. Whatever the explanation, the observation is striking and we believe should be reported as it shows a clear effect of the compound on motility in the widely-used 2D trail deposition assay.

*13) Figure 7: Besides the major point raised above for panel C, the information carried by the Figure could be stronger if an additional panel is introduced regarding the interesting assay on the preserved structural stability of the MyoA mutant over the WT MyoA (currently in SupFig7) *

Former Suppl. Figure 7 (now Suppl. Figure 9) addresses one particular explanation for the differential effects of the mutation in the in vitro motility assay (Figure 6) and the parasite 3D motility assay (Figure 7). The data in Suppl. Figures 14A and 14B address two other possibilities. For consistency with the other figures and clarity of the narrative, we would prefer to leave the data in Suppl. Figure 9 as a supplemental figure.

14) Material and Methods - Parasite motility assays: remove the duplicated [16] reference.

Done.

*15) The discussion starts with the ongoing debate on mechanisms underlying zoite motility; We found that the work of Pavlou et al. (ACS nano, 2020) should be part of the references listed there, as it brings evidence that a specific traction polar force is required probably in concert with microtubule storage energy at the focal point, a result that questions the prevailing model. *

This was another oversight; the citation has been added (line 307).

*16) Concerning the C3-20 and C3-21 compounds, the sentence "they have no effect on the activity of the recombinant TgMyoA (AK and GEW, unpublished data)" in the paragraph starting by "There have been only two previously..." should be refrained unless showing the results. *

We have removed reference to this unpublished work, as suggested (lines 338-340).

17) If possible, the authors should expand more on the effect of KNX-002 on Plasmodium falciparum and its homolog PfMyoA.

We have expanded our discussion of these preprint data from others on lines 356-361.

Reviewer #3

*1) The T130A IC50 was done on the mutagenized clone 5. The authors currently don't have data showing IC50 on the independently generated T130A mutant, to see if the IC50s are similar to one another, or if there were additional resistance mutations present in clone 5. *

Because we did not insert the T130A mutation into a fluorescent parasite background, we cannot directly compare its IC50 in the fluorescence-based growth assay to that of the line generated by chemical mutagenesis. Plaque assays do not require fluorescent parasites but, in our hands, these assays lack the sensitivity to reproducibly detect the expected subtle (50. While we agree that it would be interesting to know if the mutant generated by chemical mutagenesis contains any additional resistance-conferring mutations, not having this information does not alter the conclusion that the T130A mutation alone reduces the sensitivity of the motor to KNX-002 (Figures 6-9). See also response to Reviewer 1, comment 1; a discussion of the value of determining what other resistance mechanisms are available to the parasite for this class of compounds is now included on lines 416-422.

*2) For 3D motility assays, it is currently unclear from the data and text what the expected maximal inhibition of motility would be; e.g., would parasites depleted of MyoA display 0% motility. Understanding the dynamic range of this assay could help clarify whether this residual 5% motility explains why parasites treated with 20 uM KNX-002 can still form small plaques. This could be achieved by referencing previous work that assesses 3D motility after depletion of a critical motility factor. *

A small fraction of TgMyoA knockout parasites are still capable of motility in 3D (13% when normalized to wildtype for displacements > 2 μm [ref. 46]), so the dynamic range of the 3D assay for TgMyoA-deficient parasites compared to wild-type parasites is 0.13-1.0. The 13% residual motility of the TgMyoA parasites is now referred to on lines 419-420. Treatment of wild-type parasites with 20μM KNX-002 results in a fractional motility of ~0.24 compared to untreated controls (Figures 4 and 7). This less than complete inhibition compared to the knockout is not surprising, since motor activity is not completely inhibited at 20 μM compound (Figures 1 and 6) and parasite growth as assayed either by the fluorescence-based method (Figure 2A) or plaquing (Figure 8) shows greater inhibition at 40 and 80 μM compound than at 20 μM.

*3) It would be informative for the authors to discuss the rationale for the selected treatment regime. Since many drug-treatments involve daily dosing, was the two-dose regime based on poor tolerance of the compound in mice or other considerations? *

See response to reviewer 2, comment 11; the rationale for this dosing regimen has now been added to the Methods (line 715-721).

*4) Track length is not considered as a parameter in the filament sliding assays (Fig. 6) or the 3D motility assays (Fig. 7). These may be valuable parameters for the authors to examine; however, the time frames analyzed might be insufficient to capture track lengths. Could the authors include analyses of track lengths or discuss the technical limitations of their assays? *

In the in vitro motility assays, almost all of the actin filaments move for the entire 60s of video recording so trajectory length is directly proportional to speed and therefore does not provide any additional information. For the parasite 3D motility assay, we have added a new figure (Suppl. Figure 12) showing the effect of the compound on the displacement of wild-type and T130A parasites, along with new text describing these data (lines 269-273).

*5) When discussing the minor discrepancies between the results with recombinant protein and parasite motility, the authors could consider the relative concentration of motors in the pellicle; i.e. it might be necessary to inhibit a greater % of all the motors to truly block motility, perhaps consistent with the higher compound concentrations needed to affect parasite motility. *

This possible explanation has been added to the Discussion (lines 403-406).

6) The authors should include the IC50 data for all 5 KNX-002 resistant clones in the supplementary data. While the 5/26 clones showed >2.5-fold increase in IC50 for KNX-002, it's unclear how the IC50 of the single clone harboring the T130A MyoA mutation compared to the other resistant clones.

A figure has been added showing these data (new Suppl. Figure 8).

*7) For plaque assays, the authors should indicate how much DMSO was used for 0 KNX-002 conditions. It should presumably be the corresponding concentration at 80 µM drug and if not, that control should be performed to account for effects of DMSO at higher concentrations at all drug concentrations tested. *

In all experiments involving treatment with compound, the compound was serially diluted in DMSO to the appropriate range of concentrations prior to dissolving it in aqueous buffer for the experiment itself, enabling an equivalent amount of DMSO to be added to all samples in that experiment, including the DMSO only vehicle controls. This clarifying statement and the final range of DMSO concentrations in each of the different types of experiments has been added to the Methods section (lines 486-491). * *

*8) Authors should indicate the origins of their hexokinase for counter screens. *

The hexokinase used was from Millipore Sigma (#H6380); the supplier has been added on line 507.

*9) Authors should indicate µM on graphs. *

The μM label has been added to the graphs where it was missing (Figures 2 and 5, Suppl. Figures 4, 6, 8).

*10) In Figures 2A, 5A, and 5B, the use of colored lines (e.g., of different hues) could make the graphs more legible. *

We have experimented with color as a way to discriminate between the different doses on these graphs, but found the use of 8 different colors to be more distracting than helpful. The color-coding approach would be even less useful for readers who have color vision deficiency (including one of our authors). Symbol groupings have been added to the right of all growth curves to improve the legibility of the graphs.

*11) In Figure 2C, it isn't clear which cell line was treated with sodium azide to generate the positive control. *

It was the HFF cells that were treated with azide as a positive control; Figure 2C has been modified to make this clear.

*12) In the discussion, "a" is missing in the phrase "...mutation is likely to have long range structural impact..." *

Done. * *

*13) The abbreviation of species (spp.) should be followed by a period. *

Done.

Other

Further SAR analyses using an optimized actin-dependent myosin ATPase assay resulted in minor changes to Suppl. Figure 3 and Figure 3, with no significant changes to the conclusions. The text has been modified accordingly (lines 155-161, 178-180).

All other changes to the manuscript not noted above were editorial in nature, made to either improve clarity or correct minor errors in the previous version.

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Referee #3

Evidence, reproducibility and clarity

The authors identify a small-molecule inhibitor of T. gondii MyoA, the major apicomplexan myosin that is required for gliding motility and pathogenesis. Reconstitution of MyoA ATPase activity using recombinant protein enabled quantitative fluorescence measurements of actin-activated ATP usage. The authors screened a library of ~50,000 small molecules using their cell-free assay and counter screened hits by measuring their effects on hexokinase ATPase activity. The inhibitor, KNX-002, inhibited MyoA (IC50 of 2.8 µM), but had no effect on the ATPase activity of an array of vertebrate myosins. 15 analogues of KNX-002 were generated for SAR analysis, providing information on the functional groups of the molecule, despite not improving overall potency or specificity. In the context of T. gondii infection in human foreskin fibroblasts (HFFs), KNX-002 inhibited parasite growth (IC50 of 19.7 µM) while host cell viability was unaffected at the highest concentrations tested (80 µM). KNX-002 treatment (20-25 µM) reduced the percentage of motile parasites to approximately 5% compared to 30-40% in vehicle-treated parasites. The authors go on to demonstrate that MyoA is the biologically relevant target of KNX-002 during parasite infection, by generating resistant parasites by directed evolution. One of the resistant clones harbored a T130A mutation in the MyoA motor domain. While KNX-002 treatment of recombinant WT MyoA displayed dose-dependent inhibition of actin filament motility, the T130A proteoform was unaffected at all concentrations tested. T130A mutant parasites were independently generated and indeed conferred partial resistance to KNX-002 in growth and motility assays. Lastly, the therapeutic potential of KNX-002 was assessed during infection of CBA/J mice. At 10 days post-infection, mice treated with KNX-002 displayed a 40% survival rate compared to 0% in the vehicle-treated group. This was in contrast to mice challenged with T130A mutant parasites, in which KNX-002 treatment did not improve survivability. Together, these data indicate that the small molecule KNX-002 can mitigate T. gondii pathogenesis and one mechanism of action of KNX-002 is inhibition of MyoA-mediated gliding motility.

Major comments

• The T130A IC50 was done on the mutagenized clone 5. The authors currently don't have data showing IC50 on the independently generated T130A mutant, to see if the IC50s are similar to one another, or if there were additional resistance mutations present in clone 5.
• For 3D motility assays, it is currently unclear from the data and text what the expected maximal inhibition of motility would be; e.g., would parasites depleted of MyoA display 0% motility. Understanding the dynamic range of this assay could help clarify whether this residual 5% motility explains why parasites treated with 20 uM KNX-002 can still form small plaques. This could be achieved by referencing previous work that assesses 3D motility after depletion of a critical motility factor.
• It would be informative for the authors to discuss the rationale for the selected treatment regime. Since many drug-treatments involve daily dosing, was the two-dose regime based on poor tolerance of the compound in mice or other considerations?
• Track length is not considered as a parameter in the filament sliding assays (Fig. 6) or the 3D motility assays (Fig. 7). These may be valuable parameters for the authors to examine; however, the time frames analyzed might be insufficient to capture track lengths. Could the authors include analyses of track lengths or discuss the technical limitations of their assays?
• When discussing the minor discrepancies between the results with recombinant protein and parasite motility, the authors could consider the relative concentration of motors in the pellicle; i.e. it might be necessary to inhibit a greater % of all the motors to truly block motility, perhaps consistent with the higher compound concentrations needed to affect parasite motility.

Minor comments

• The authors should include the IC50 data for all 5 KNX-002 resistant clones in the supplementary data. While the 5/26 clones showed >2.5-fold increase in IC50 for KNX-002, it's unclear how the IC50 of the single clone harboring the T130A MyoA mutation compared to the other resistant clones.
• For plaque assays, the authors should indicate how much DMSO was used for 0 KNX-002 conditions. It should presumably be the corresponding concentration at 80 µM drug and if not, that control should be performed to account for effects of DMSO at higher concentrations at all drug concentrations tested.
• Authors should indicate the origins of their hexokinase for counter screens.
• Authors should indicate µM on graphs.
• In Figures 2A, 5A, and 5B, the use of colored lines (e.g., of different hues) could make the graphs more legible.
• In Figure 2C, it isn't clear which cell line was treated with sodium azide to generate the positive control.
• In the discussion, "a" is missing in the phrase "...mutation is likely to have long range structural impact..."
• The abbreviation of species (spp.) should be followed by a period.

Significance

This important work substantially advances technical and clinical aspects of studying the motility of apicomplexan pathogens by identifying a new small molecule inhibitor of gliding motility and uncovering its mode of action by inhibition of a motor protein. The evidence supporting the conclusions is convincing, with a new screening assay for myosin motor activity and advanced methods to characterize 3D motility for Toxoplasma gondii. The authors provide compelling evidence that KNX-002 inhibits MyoA, gliding motility, and MyoA-mediated virulence during mouse infection. The evidence suggests that some secondary or off-target effects remain uncharacterized within the parasite and in murine hosts. KNX-002 will enable targeted pharmacological inhibition of the motor complex, as opposed to broad inhibition of actin using cytochalasin D. While other small molecules inhibiting the motor complex have been identified-such as the myosin ATPase inhibitor 2,3-butanedione monoxime (Dobrowolski et al. 1997) and the myosin light chain 1 inhibitor TachypleginA (Heaslip et al. 2010; Leung et al. 2014)-these inhibitors have major off-target effects that render them unsuitable for therapeutic use. These findings are of interest to those studying apicomplexan pathogens, including T. gondii, Plasmodium spp., and Cryptosporidium spp. These include those interested in developing small molecule therapies and those interested in gliding motility. Our relevant expertise during this review process include: chemical genetics, cell biology of apicomplexan parasites, and signaling. We have limited expertise in the SAR strategies implemented to improve the effectiveness of KNX-002.

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Referee #2

Evidence, reproducibility and clarity

Summary

The manuscript by Kelsen et al. reports on the identification of a compound referred to as KNX-002 that inhibits the unconventional MyosinA motor ATPase activity from the intracellular parasite Toxoplasma gondii myosin A (TgMyoA), an unconventional myosin characterized by both its high divergence from those of the parasite host repertoire (i.e., about all homeotherms) and a functional contribution to crucial processes during the parasite life cycle. To identify KNX-002, the authors used assays they had previously developed to produce recombinant active TgMyoA in insect cells combined with those now designed to quantify the TgMyoA ATPase activity. With these tools in hand, the authors screened about 50.000 molecules from a library referred to as small inhibitors and further characterized the potency of the top list MyoA inhibitory compound (i.e., KNX-002) by conducting in vitro, ex vivo and in vivo studies.

The following sections of the manuscripts bring evidence that:

1. the KNX-002 "re-synthetized" compound is at least 10 times more efficient at inhibiting MyoA ATPase activity when compared to muscle actin from different vertebrates in vitro (IC50 data).
2. the KNX-002 can be chemically modified with chemical substitution and structural analysis. Up to 15 derivatives (all with lower potency at that stage) were structurally and functionally tested for comparison with respect to parental KNX-002.
3. exposing the motile-invasive-replicative stage of T. gondii called tachyzoite to the KNX-002 compound (5 to 10 times higher doses than in vitro) impairs in a dose dependent way the tachyzoite's motility and the overall expansion of the population in host cell culture.
4. mice injected intraperitoneally with type I tachyzoites and subsequently with two intraperitoneal injections of KNX-002 (at day 0 and day 2 post infection) showed slightly longer survival time when compared to those injected with parasites and then vehicle, thereby indicating a moderate effectiveness of KXN-002 in mice under the protocol used.

The authors further established the KNX-002 selectivity against TgMyoA parasites by applying chemical mutagenesis and subsequent screening for "resistance" to the KNX-002 compound. Using nucleic acid sequencing of the few genes encoding the myosin motor components (i.e., heavy and light chains), the authors eventually identified a TgMyoA mutation (T130A) which does not concern the predicted pocket to accommodate KNX-002 binding but does confer a 2.9-fold decreased sensitivity to KNX-002. Selectivity towards MyoA was also documented in vitro by analysis Actin filament sliding and ex vivo using CRISPR/Cas9 gene editing to engineer parasites expressing "exclusively" the TgMyoA on its mutated form (T130A mutant). These mutants indeed displayed fairly similar motile skills regardless of the presence of vehicle or KNX-002.

The authors conclude on the interest to consider the potential of KNX-002 (more specifically some promising derivatives) to enrich the limited current therapeutic regimens against T. gondii and other Apicomplexa (in particular Plasmodium) for which MyoA has already been argued as a relevant therapeutic target.

Major comments

The reviewers found the large set of assays appropriately designed, executed and presented to be reproduced by other scientists. The results are also mostly analyzed with enough detail and care throughout the work (except a few minor points raised below), but the reviewers also stress a few overstated conclusions easy to mitigate in particular related to the diseased model.

To the reviewer's opinion, there is no need for additional experiments but we would appreciate some rephrasing for higher accuracy and improved clarity.

Below are listed few major concerns to address.

1. Considering (i) the moderate effect of KNX-002 on the acute infection process in CBA mice that received tachyzoites intraperitoneally, (ii) the fact that the drug application cannot be envisaged outside of the context of reactivation of cystogenic strains (in particular with respect to cerebral toxoplasmosis as emphasized in the introductive section), which implies the drug would have to be delivered and active in the brain parenchyma, a condition not analyzed here, it would be appropriate to modify the current title. It would be more relevant to highlight the solid body of data on the identification and functional characterization of the compound and derivatives in vitro and in the host mouse model. Apart from the title, the discussion should also recontextualize the in vivo assays and the information these assays bring on the slight delay of the "mortality" of some but not all mice.
2. Motility analysis: This comment concerns the Figure 7 It seems to the reviewers that the major hypothesis to test in data presented in panel B is that the wild type and the T130A mutant tachyzoite respond differently under similar drug conditions rather than the two populations without drug. These statistics could be added easily, hence it would validate that the proportion of motile mutant parasites is not affected by the drug when compared to vehicle. However, the statistics shown panel C rather suggest that the drug does impact on the speed of the moving parasites, including when these carry the "resistance" T130 A mutation. It is not clear what we can gain in terms of messages with the motility index except to "slightly reverse" the analysis on panel B and to favor a no-effect of KNX-002 on the mutant parasite motile skills, on which the author might give more explanation. When comparing these quantitative tests with the panel presented above (panel A) it seems that the mutant parasite is still impacted by the MyoA inhibitor. Although there is no doubt for the reviewers that the T130A mutant emerging from the selected T. gondii resistant clones is a valuable probe for assessing drug selectivity : indeed the assays validate KNX-002 as a direct TgMyoA ATPase inhibitor, it might be good to rephrase some sentences and to have a harmonized definition of the parasite motility index throughout the text (Figure 7 legend, result and discussion sections). This reviewer's concern was accentuated by the comparison between the actin filament sliding index and the parasite motility index which appears as such far stretch;

Minor comments

Aside from the "far stretched claims" easy to re-address in a revised version, the readers have appreciated the writing quality and most figure illustration. The discussion nicely synthetizes the whole dataset, including those related to the 4 T. gondii clones that resisted to KNX-002 but not through mutations targeting any of the myosinA chains. Few comments are listed as:

Abstract: the concept of "ameliorate disease" in this framework is odd and the objective of the work can be rephrased in a simple way (see below)

Introduction section: we think that the references on the impairment of invasiveness for the KoMyoA should be included (Bichet et al., BMC Biology 2016) as it has provided proof of an alternative and suboptimal mode of entry in many different cell types, thereby arguing that in absence of MyoA function, parasite invasiveness is not fully abolished and this without considering any MyoC-driven MyoA compensation. Introduction, third paragraph: in the sentence "Because the parasite can compensate for the loss or reduced expression of proteins important to its life cycle [29-31], small-molecule inhibitors of TgMyoA would serve as valuable complementary tools for determining how different aspects of motor function contribute to parasite motility and the role played by TgMyoA in parasite dissemination and virulence ». We definitively agree with this view but saying that, we think it would be worth evaluating (or simply discussing) the potency of the KNX-002 against MyoC, which compensatory contribution has been debated and remains questionable (at least to the reviewers) with respect to cell invasiveness restoration (related to the comment above).<br /> Result section:

1. If we are correct, the screen and the characterization study have been performed with two different products (CK2140597 and KNX-002 the compound library and the re-synthetized one, respectively). Could we make sure that the two have the same potency?
2. we understood how the authors came to the conclusion that the KNX-002 impact on growth of the parasite and they stated "growth in culture" in the subsection title but then refers to parasite growth. Therefore, it looks a bit confusing for the reader since intracellular growth per se is probably not modified but this feature was not looked at it in this study (we would expect no impact based on published data on MyoA- genetically deficient tachyzoites, except if the drug impacts host cell metabolism for instances). Instead, it is the overall expansion of the parasite population that is analyzed here and clearly shown to be impacted. This decrease in population expansion on a cell monolayer likely results from impaired MyoA-dependent egress and invasiveness upon chemical inactivation of MyoA. Accordingly, it appears difficult to understand what is an IC50 for the "overall" growth in the context of this study. The authors should rephrase for better accuracy when necessary, including in the graph Fig2 legend axis.
3. The authors should clarify for the reader (i) why they use in some case myofibrils and other muscle F actin when measuring the Myosin ATPase activity, (ii) what does mean XX% calcium activation and (iii) why using 75% in these assays which is 3 times higher from the original assays. Why they did not include non muscle actins in their study since Myosins also extensively work on non muscle actins.
4. The protocol of image analysis of the 3D motility assay was increased to 80 seconds for the test of KNX-002 selectivity using wild type and mutant parasites (Fig7) when compared to the test of KXN-002 concentration effect on wild type tachyzoites (60 sec in the result section, in Fig 4 legend and in the Methods' section). Is there any specific reason?
5. In the mouse infection experimental design (Method section), it seems that they were no biological replicates in the case of the drug-treated (parasites + mice) which is not the case for the comparison of virulence between MyoA wild type and T130 mutants. If true, and considering what the authors wish to emphasize as a main message, it is fairly complicated to convincingly conclude about the KNX-002effectiveness in vivo. Maybe the authors could explain their limitations. We are also not sure why the compound has been injected only twice, at the time of parasite injection and two days after whereas the mice succumbed after 8 to 9 days even without MyoA inhibitors. Although quite difficult to measure, do the authors have any knowledge (based on the chemistry for example) of the compound stability and lipophilicity in blood and tissues? Because the IC50 on free tachyzoites appears significantly higher (5.3 uM, Fig4) than the in vitro molecular assay, when assessed in motility tests, and is increased for intracellular growth (Fig 8), it is somehow expected that the current compound would not work that great in vivo. Did the author try to provide the inhibitor intravenously every day?

Figure 4: 2D and 3D Motility- the authors should comment on the fact that in 2D conditions with 10 uM of KNX-002, circular trajectories (one complete circle so at least 2 parasite lengths but sometimes more) largely dominate over others, whereas in absence of KNX-002 these circular trajectories are barely detectable and helical trajectories predominate. What could that mean as regard to the MyoA functional contribution to either process?

Figure 7: Besides the major point raised above for panel C, the information carried by the Figure could be stronger if an additional panel is introduced regarding the interesting assay on the preserved structural stability of the MyoA mutant over the WT MyoA (currently in SupFig7)

Material and Methods

Parasite motility assays: remove the duplicated [16] reference.

Discussion:

The discussion starts with the ongoing debate on mechanisms underlying zoite motility; We found that the work of Pavlou et al. (ACS nano, 2020) should be part of the references listed there, as it brings evidence that a specific traction polar force is required probably in concert with microtubule storage energy at the focal point, a result that questions the prevailing model.

Concerning the C3-20 and C3-21 compounds, the sentence "they have no effect on the activity of the recombinant TgMyoA (AK and GEW, unpublished data)" in the paragraph starting by "There have been only two previously..." should be refrained unless showing the results.

If possible, the authors should expand more on the effect of KNX-002 on Plasmodium falciparum and its homolog PfMyoA.

Significance

This multi-disciplinary work timely brings to the research communities (basic research and pre-clinical research) new knowledge and a new reagent quite valuable for future dissection of the T. gondii MyoA contribution across different scales of study. It also provides a strong rationale and preliminary orientations for developing a new compound targeting MyoA in the context of anti-toxoplasma or possibly anti-Apicomplexa therapeutic strategies.

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Referee #1

Evidence, reproducibility and clarity

Summary:

In this study the authors use an in vitro screen to identify a novel inhibitor of the actin-activated ATPase activity of myosin A. The lead molecule, KNX-002, was shown to inhibit parasite growth and motility, with initial SAR studies failing to improve on the potency of the parent molecule. The author's then focus on confirming the expected mechanism of action of KNX-002 in parasites. They use a chemical mutagenesis and drug-pressure protocol to select for a drug-resistant parasite population. They identify a drug-resistant clone harbouring a missense mutation in the TgMyoA locus, converting a threonine at position 130 into an alaninie. These parasites demonstrate resistance to KNX002, and the equivalent mutation introduced de novo into the MyoA locus in otherwise wild-type parasites confirms that the T130A mutation is sufficient to provide protection from the effects of the drug. Finally, they confirm that KNX-002 can protect mice from T. gondii infection.

Overall the manuscript is very well written, with a clear and logical narrative. The data presented broadly support their conclusion, and the general quality of the figures is very high. I would recommend that the manuscript should be accepted once the following points have been addressed:

Major comments:

While the following would strengthen the manuscript, they could be addressed in the discussion or with clarification of the text:

• In the study there is a lack of consideration of other targets. In and of itself this is not a problem, but once the author's identified the T130A mutation as being a key for protection it would have been good to Sanger sequence the other T. gondii myosins - a quick alignment of the TgMyo's A, C, H (class XIV), along with D and E suggests that the motif is highly conserved. This raises the currently unexplored (and exciting!) prospect of a pan-myosin inhibitor, and that there might have been mutations at an equivalent position in the other four KNX002 resistant clones - for example, MyoC has been proposed to provide some level of functional redundancy in the absence of MyoA. The fact that T130 is not thought to the binding site of KNX002 is only introduced quite late on - this also relates to the next point - is the binding pocket conserved???
• It is intriguing that the residue and proximal amino acid environment are highly conserved with vertebrates, but that KNX002 does not have an effect on their activity in their screen assay. It would be useful to know if the differences in the structures of the myosins can provide an explanation for this - along the same lines, given that the crystal structure of TgMyoA is available (PMID: 30348763), it would be useful for the authors to provide a molecular model for the binding of the inhibitor to the proposed point of engagement. If this is an allosteric site it is possible that the mutation functions indirectly upon binding of KNX002 to the orthosteric site, but this would be useful to help the reader to understand this (and there are bioinformatic prediction tools that will score allostery - which would be interesting to include). This is explained somewhat in the discussion - but this should be introduced much earlier to clarify. What is known about allosteric regulation of Myo function? Is this a known site of regulation?

Minor comments:

• Introduction: 'Nearly one third of the world's population is infected with the apicomplexan parasite' - given that these data are extrapolated from serology, this should be reworded - it's fairer to say that they 'are or have been infected...'
• Page 4: figure 1A - can you provide some explanation for incomplete inhibition in the screen - there seems to be a residual amount (15-20%) of activity that is not inhibited.
• The authors demonstrate a general effect on growth over 7 days. It would be good to use a replication assay (e.g. parasites/vacuole over a single lytic cycle) to confirm that KNX002 does not affect cellular division. This would further strengthen the argument that the phenotypic effect is primarily via impacts on motility.
• Page 5: 'selected for parasites resistant to KNX-002 by growth in 40 μM KNX-002.' - could the authors add text to explain why that concentration was chosen.
• Page 6: 'suggesting that the effects of the T130A mutation on motor function are due to more subtle structural changes' - it's fair to say that there are not gross structural changes based on the data presented, but that does not mean it is therefore a 'more subtle structural change' - surely the mutation could prevent KNX002 binding without effecting TgMyoA structure?
• Page 6: 'the proportion of filaments moving' - in the figure it's referred to as the 'fraction of filaments', which makes more sense for the data presented. Please correct to 'fraction' throughout the manuscript (discussion, page 9 - possibly other instances!). Along the same lines, in figure 6 it would be good to change the y axes on the '% moving' to be 'fraction moving' and change the numbers - this would make it easier for the reader to understand the index values presented in the lower panels - if you do the calculation with the % values presented the numbers don't make sense (as fractions they do). The axes for motility also go up to 125% - please correct - based on the data presented there is no need for this to be above 100% (or 1 - see above).
• Page 7: 'tested whether KNX-002 (20mg/kg, administered intraperitoneally on the day of infection and two days later' - please provide some rationale for the concentration used.
• Page 10: 'the T130A mutation is likely to have long range structural impact that could alter the KNX-002 binding pocket' - this is particularly interesting, and should be addressed with a model - do the authors think that the T130 region be a conserved site of allosteric regulation? This would be good to expand upon in the discussion - mutation of an allosteric site as a mechanism of resistance is unusual, and typically described as being unlikely - and used as justification for the targeted drugging of allosteric sites.

Significance

Strengths: Discovery of new parasite-selective inhibitor of TgMyo-A motility will be a valuable tool, and potential therapeutic lead.

Weaknesses: Molecular mode of action on a modelled binding of KNX002 could have strengthened the proposed mechanism of action via a site outside of the expected KNX002 binding pocket.

Advance: the findings advance our understanding of drug-resistance mechanisms, and also validate TgMyoA as a druggable therapeutic target.

Audience: this work will appeal to molecular parasitologists, as also be of broader interest to research communities including myosin structure-function researchers, and drug discovery groups interested in drug-resistance mechanisms.

Expertise: molecular parasitology, chemical biology, proteome engineering

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Reply to the reviewers

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Reply to the Reviewers

I thank the Referees for their...

Referee #1

1. The authors should provide more information when...

Responses + The typical domed appearance of a hydrocephalus-harboring skull is apparent as early as P4, as shown in a new side-by-side comparison of pups at that age (Fig. 1A). + Though this is not stated in the MS 2. Figure 6: Why has only...

Response: We expanded the comparison

Minor comments:

1. The text contains several...

Response: We added...

Referee #2

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Referee #2

Evidence, reproducibility and clarity

This work is is distinguished by impressive technical feats and experimental breadth, and is another excellent contribution from the Zurzolo lab. My comments are advisory regarding more explicit descriptions and qualified conclusions.

Introduction: Please define how filopodia are distinguished from TNTs - is it length only or are there other characteristics? Do filopodia and individual TNTs have the same diameter? There is presumably a functional difference as well?

Please state the number of cells in each array spot?

Paragraph 2 of Results would benefit from a general description of procedure and rationale for assessing protrusions in the artificial setups used in this study of isolated cells. It would help to explicitly state when protrusions were assessed after fixation and when the observations were made with unfixed cells. What are the issues of concern with these methods and what aspects are relevant to general cell behavior? Isn't it important to point out that the conclusions regarding Arp2/3 inhibition and TNT formation are operational for the conditions used?

Ln 148: If filopodia are distinguished/defined by their shorter relative length, the observation that "filopodia lengths showed that a majority of filopodia were far shorter" is not informative. Do cells with TNTs also have filopodia?

Does the negative effect of increasing array separation distance on frequency of TNTs suggest that the observations represent a steady state, and the possibility that the observed frequencies are measures of protrusion stability? If the experiments monitor the steady state, can the authors distinguish between stability and inherent ability to extend filopodia to longer distances? Is the conclusion (ln 151) "there seems to be an upper limit to F-actin-based elongation" justified if stability or relative rates of extension and retraction are factors? Another possibility is that the observations reflect protrusion:protrusion interactions that promote stable TNTs. There is the precedent of cytoneme:cytoneme interactions associated with stable signaling contacts (Gonzalez-Mendez et al PMID: 28825565) as well as previous work from the Zurzolo lab (Sartori-Rupp et al PMID: 30664666). A kinetic analysis in real time might be very informative.

Ln 158: "cells displaying only lamellipodia accounted for 4.1% of [cells with?] TNTs examined"

Significance

This work offers new insights into the cytoskeletal processes that generate long cell protrusions. The implications for understanding cell:cell interactions and signaling are fundamental and important.

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Referee #1

Evidence, reproducibility and clarity

I applaud the authors for the creative experimentation and intriguing findings presented in this work, but the narrative lacks a clear biological question and, as written, comes across more like a collection of thematically related results, rather than a logical point A > B > C progression of studies that significantly advances our understanding of TNT formation beyond the current published literature. In addition, there are a number of inconsistencies and ambiguities in the approach - at least as described - that make it hard to follow the logic flow of the authors. Below I list some of the major inconsistencies and questions I had after reading through the manuscript.

1. From Figure 1b - the micro patterned substrates separated by different distances also appear to be printed as different diameters - is this true? I could find no mention of it in the text. My concern is that changing the contact area between the cell and substrate might impact the cell's ability to extend very long membrane protrusions towards its neighbors. Why not print patterns of exactly the same diameter separated by increasing distances?
2. By using micropatterned substrates the Authors hope to force the extension of TNTs using actin driven mechanisms, while eliminating the possibility that cells are form these connections by "dislodging" from the substrate. The Authors also state that the cells do not wander into the region in between the circular patterns. However, in many images featured in multiple figures, I can find examples where cells clearly extend into the space between the circular patterns (see Fig. 2.a.i for an example). The primary concern here is that the authors did not fully eliminate the possibility that cells are making direct contact and then pulling away from each other/dislodging to form TNTs. In the supplemental material, there were movies showing TNTs that had already formed, but I was unable to find examples of TNTs in the act of forming. If the authors do have timelapse data that clearly shows this, it should be front and center in this data set.
3. The authors make use of CK-666 to inhibit Arp2/3, which is thought to free up actin monomers that can be used to further elongate TNTs. Other orthogonal methods should be employed to corroborate the results from CK-666 treatment. There are other drugs that inhibit Arp2/3 (e.g. arpin, wiskostatin) and of course there is always genetic manipulation (shRNA KD).
4. The authors also employ a formin agonist, IMM-01, and the results from those experiments (Fig. S4) suggest that activating formin mDia could facilitate TNT elongation, but the authors do not follow up with direct molecular manipulation to further test this idea. Are formins (specifically mDia, the target of IMM-01) needed to elongate TNTs?
5. With the experiments shown in Figure 3, the authors apply optical tweezers to pull what they refer to as "nanotubes" from the cell surface under conditions where Arp2/3 is inhibited with CK-666. The panels appear to show that CK-666 allows actin to assemble out into a pulled nanotube more readily than control cells. However, the reporter for actin assembly in these experiments is F-Tractin, and it only partially fills the protrusion. I note here that F-Tractin appears more soluble under the CK-666 condition. Can the Authors rule out the possibility that there is just a larger soluble pool of F-Tractin probe under these conditions?
6. Related to the previous point, it is not clear to me why the Authors need to invoke the application of external forces with an optical trap to study the enhanced elongation of TNTs under CK-666 conditions. Why don't the authors directly visualize actin accumulation and TNT elongation after treatment with this inhibitor?
7. In Figure 4, the Authors move away from TNTs and instead focus on "longer protrusions" (filopodia?) to examine the effects of EPS8 and IRSp53 on growth of these structures. The general findings here are consistent with the role of these factors in protrusion growth from previous studies.
8. The experiments in Figure 5 are performed with a truncated "bundling active" form of EPS8 allegedly lacking actin capping activity. The logic behind this choice is unclear. These experiments need to be repeated in parallel with full length WT EPS8 to allow for a full and clear interpretation of these results. Along these lines, could the Authors stain for endogenous Esp8/IRSp53 complex within TNT-connected cells?
9. Data in Figure 5 show that the Eps8dCAP/IRSp53 complex increases the vesicle transfer within TNT-connected cells from Eps8dCAP/IRSp53 donors to EBFP-H2B acceptors indicating the functionality of TNT. Interestingly, cells with overexpression of Eps8dCAP/IRSp53 and CK-666 treatment do not increase TNT-connections. Could the authors examine the stability of TNTs under this condition? The lack of increased vesicle transfer may be due to the instability of the TNT structure rather the saturation of the system.
10. Figure 5c. The data show that vesicle transfer from EpsdCAP/IRSp53 donors to EBFP-H2B acceptors increases under the overexpression of EpsdCAP/IRSp53. Are the EBFP-H2B acceptors able to form TNT? If so, could the authors show the vesicle transfer in those TNT-connected cells? The images indicate that there is no presence of TNT structures in these conditions.

Significance

This study from Henderson et al. seeks to understand the mechanisms that drive tunneling nanotube (TNT) formation. TNTs are essentially giant filopodia that extend many microns from the cell surface to contact protrusions extending from neighboring cells, to establish channels that allow for exchange of biological material. The authors use an approach that involves micro patterned substrates, various inhibitors/agonists, construct overexpression experiments, and biophysical measurements with optical tweezers. The major insights from these studies are that actin monomer availability is limiting for the formation of long TNTs, and that proteins that are well known to regulate the formation of filopodia and related linear actin structures (namely EPS8 and IRSp53) promote TNT formation. These are interesting findings that are certainly consistent with the previously published literature on actin-based protrusions, and as such they should be of interest to cell biologists.

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Reply to the reviewers

We thank both reviewers for their constructive criticism and the insightful comments on our manuscript. Reviewer 1 states that:

„The strength of this manuscript lies in its comprehensive analysis of Bim1 function, the quality of the results and that the experiments are generally well controlled and interpreted. „

And „the findings of this comprehensive analysis are of great value to the microtubule field, especially for people working in budding yeast. „

• *

While Reviewer 2 adds:

„The current study is indeed rich with new insights into the mechanisms by which these molecules function, and will no doubt prove valuable to a number of people in the microtubule/motor/yeast mitosis fields. As someone who is interested in and studies mitosis in budding yeast, I found the study to be interesting.

• *

Both reviewers conclude that:

“…there are useful data in the manuscript that make this an important contribution and that it should definitely be published”

• *

• *

Both reviewers raised two major areas of concern: 1. A confusing overall structure makes the study hard to follow. 2. A clearer distinction needs to made between what has already been reported in the literature, and what are new insights provided in this study. In this regard, the appropriate citations need to be made at various positions throughout the manuscript.

In this full revision, we have addressed these major points of criticism of the reviewers as follows:

We have re-organized and re-focused the manuscript to make it more accessible and easier to follow for the reader. We have followed a suggestion from reviewer 1 and now present all experiments characterizing mitotic spindle phenotypes and how they can be suppressed consecutively in Figures 2-5 and then finish the manuscript with the characterization of the spindle orientation phenotype. This way of ordering by biological pathway allows for a better flow of the manuscript.

Throughout the text, we have added citations to better indicate the previous state of knowledge and how the presented experiments either confirm or extend the previous findings in the field. This helps to put our current study better into and overall perspective.

In addition, we have addressed the specific points raised by both reviewers in full. Please see below our point-by-point answer.

Reviewer1

There is already a huge body of published information on mitotic spindle positioning via the Kar9 and dynein pathways that grew since the late 1990s. The genetic relationships and molecular interactions between the components of these 2

pathways are well studied (many studies, including Liakopoulos et al. 2003, are not cited by the authors). The authors

should make sure to cite and compare to the relevant primary literature when they report findings that have been

described before. This will help to distinguish novel findings from validation of previous results.

We have added relevant citations throughout the manuscript, please see below.

"The strict dependence of Kar9 and Cik1-Kar3 on the presence of Bim1, as well as the different effects of bim1Δ on

nuclear and cytoplasmic Bik1, may reflect the formation of stable complexes between Bim1 and these binding partners in

cells." I believe this has already been shown (Kumar et al., 2021 and Manatschal et al., 2016). There are several other

instances as well where additional literature should be cited, for example Gardner et al., 2008 and Gardner et al. 2014.

We have now cited the Manatschal and Kumar papers in this section of the revised manuscript. We have also cited the mentioned Gardner papers later in the manuscript.

The selection of targets to study in figure 1 doesn't seem to follow the listed criteria. Many proteins included in the

study were not found by IP-MS, but some perfect targets according to the listed criteria like Duo1 were not included in the

study. In addition, there are more sophisticated ways of finding Bim1 binding motifs in the literature

(https://doi.org/10.1016/j.cub.2012.07.047). I suggest, the authors declare that they rationally chose to study 21 proteins

of interest but remove the claim that their approach was systematic.

We have changed the wording accordingly and removed the claim of systematic target selection.

Much of the microscopy data was acquired after release from alpha factor arrest. What is the reason for this

perturbation? An exponentially growing culture should mostly consist of mitotic cells anyway. Since this treatment affects

cell size and potentially protein levels/concentrations, testing its influence on spindle position as well as levels on MTs for

the most relevant proteins of interest would be important to exclude introduction of artifacts.

In principle that’s correct, but using synchronized cultures has the great advantage that mitotic timing and all the parameters associated related to it (spindle length etc.) can be quantified much better and we obtain larger N and thus get better statistics using this approach. In a typical log culture only one third of the cells are in mitosis and this entails very different states of mitosis. Observations times are limited due to fluorescent bleaching and low signal intensity. We therefore feel the benefits of alpha-factor release outweigh the problems and we compare all mutants under the same conditions.

Some of the results obtained from bim1Δ cells are a challenge to interpret due to the wide range of processes that

involve Bim1 and therefor the potential for many off-target effects- including a global change in microtubule dynamical

behavior in both the cytoplasm and the nucleus that will influence the length distributions and microtubule lifetime (and

thus number). The authors must carefully consider these caveats.

We agree in principle and have therefore not only characterized the bim1 deletion, but also more specific bim1 mutants. We also show that some aspects of the bim1 delta phenotypes, but not others, can be rescued by different strategies.

The results section on page 12 refers to phenotypes of kar9 delete cells with respect to Bim1-GFP on cytoplasmic

microtubules. In the figure 3D,F I only found data for Kar9-AID, though. The authors should refer to supplementary figure

5A or even better include quantification similar to figure 3F.

We have corrected this in the revised text. We refer to the Kar9-AID, for which we have the quantification.

The observation that cytoplasmic Bim1 localization depends on interaction with its cargo Kar9 (figure 3 + 7) fits into the

model that Kumar et al (https://doi.org/10.1016/j.str.2021.06.012) proposed in which Kar9 oligomerization is required for

its Bim1 dependent localization to microtubules. It would be valuable to point that out.

We have now included a sentence that our findings support this model and added the respective citation.

I don't fully understand the model proposed in Figure 5H and discussion page 26. Based on figure 5E, it does not look

like there is a higher concentration of Bik1 along the lattice in bim1 delete. So how would Bik1 increase Kip2 processivity

if its levels are only increased due to a MT length change? If Kip2 was not fully processive, you would rather expect to

see less of it at the tip of a longer microtubule in bim1 delete. The model suggested by Chen et al

(https://doi.org/10.7554/eLife.48627.001) suggests that Kip2 only gets loaded at the minus-end and processively walks

towards the +end without falling off. Are the authors suggesting that bim1 deletion changes this behavior?

We have rephrased this section in results and discussion and more clearly state that there is no increase in Bik1 per MT length unit in the bim1 deletion. We have amended the discussion and grant that we currently cannot explain by which molecular mechanisms Bik1 may contribute to the observed increase in Kip2 plus-end localization under conditions of a bim1 deletion.

I don't see evidence for independent pools of Bik1 in the cytoplasm and nucleus as claimed on top of page 21. Total

Bik1 levels on cytoplasmic microtubules seem to be well explained by their length. Please explain better or remove the

statement.

We have removed the respective statement from the revised manuscript.

The experiments in supplementary figure 7B are difficult to interpret. The localization on cytoplasmic microtubules is

different, but probably explained by the formation of Bim1 heterodimers. Therefore this experiment is difficult to interpret

and should be removed.

As requested, we have removed this experiment from the revised manuscript.

top of page 24: Kar9 localization in metaphase depends exclusively on SxIP, not on LxxPTPh (Manatschal 2016). The

paragraph should be removed as it is not supported by published data or sufficiently by the authors to merit the

conclusion.

We have reformulated this to avoid a misunderstanding. We merely show that in the context of the artificial GCN4 construct a fragment just including the LxxPTPh motif is sufficient for Bim1-dependent localization to microtubules in nucleus and cytoplasm. This makes no statement about localization determinants of the authentic Kar9 protein.

Top of page 26: The genetic interactions between the Kar9 pathway and the dynein pathway were already well known

before this work. Please reformulate accordingly.

We have re-written this section and introduce the two pathways with the respective citations in the very beginning of the section before describing the experiments.

page 27 second paragraph: There is no selective pressure to evolve compensation mechanisms for gene deletions. I

suggest the authors consider that Kar9 and dynein partially redundant, with Kar9 acting to position the spindle prior to

metaphase and dynein to maintain spindle position in the mother and bud compartments in late metaphase and

anaphase. The authors should consider the quantitative analysis of Kar9 and dynein dependent spindle positioning

reported in Shulist et al. 2017 and the method for analysis of spindle length and position in 3D in Meziane et al. 2021.

We have rephrased the section on the partially redundant Kar9 and Dynein pathways. See below our answer for measuring spindle length.

In addition, it is not clear to me which results suggest that the relocalization of Bik1 is required in the bim1 delete. Why

would wild type levels not be sufficient for dynein pathway function? The authors have not conclusively shown that

nuclear migration relies on upregulating the dynein pathway in bim1Δ cells. If there is no supporting data, the paragraph

should be removed.

In this revised manuscript we have phrased our observations more carefully and acknowledge the limitations regarding molecular insights. We present indications for increased levels of Dynein-Dynactin pathway components at plus-ends in the bim1 deletion cells, but it is indeed unclear, whether an increased Bik1 level in the cytoplasm is required to achieve this.

Please provide more details about intensity quantification on page 35. Were these measured on sum or max

projected stacks? What was the method of background subtraction?

Analysed images are optical axis integration scans over 3 μm taken on a Deltavision microscope. This procedure gives a sum projection. Local background was determined for every cell by drawing a line under a signal curve derived by line scan. The background line connects regions that are still within the cell but are outside of spindle (or microtubule). We added a sentence in the materials and methods section under point 2.

Are the spindle lengths in Figure 2E measured in 2D or 3D? Bim1 deletion might lead to more misalignment of the

spindles in z due to inactivation of the Kar9 pathway and thus partially explain the shorter spindles. The measurements

should therefore be performed in 3D.

As we have used optical axis integration (OAIs) on the Deltavision microscope and obtained a sum projection of this virtual stack, the spindles were measured in 2D and we don’t have the information to measure in 3D (this would require a regular stack). We show that there are different ways to restore different aspects of spindle length with alternative strategies. These are unlikely to influence just spindle orientation. In addition, we see that Bim1 deletion has an effect on the size of a nascent bipolar spindle when spindle orientation is similar to wild-type cells. We agree that z-misalignment may affect absolute values of spindle size of Bim1 deletion in late metaphase and it would be better to measure in 3D. However, we think in this case it is unlikely to affect our conclusions in this study.

The authors should try to shorten the text. There is a lot of redundancy between results and discussion sections.

We have to shortened the text to avoid redundancy (before >43000 characters, now around 41000 characters, and we have decreased the number of main figures from 9 to 8.

Data is shown that leads to conclusions that are already supported by the literature should be moved to the

supplementary material.

In the course of re-organizing the manuscript we have tried to do this.

Reviewer 2:

"Robustness of Ndc80 loading might be achieved by the coexistence of multiple kinetochore assembly pathways or

alternatively determined by intrinsic Ndc80 properties." Wouldn't Ndc80 levels be determined by Ndc80 kinetochore

loading, and not by end-binding proteins? This seems to be the more likely means to regulate Ndc80 levels.

We have removed this statement from the revised manuscript.

"Upon analyzing the associations in the cytoplasm, we found that Kar9-3xGFP foci on bud-directed cytoplasmic

microtubules were abolished in the bim1Δ strain, consistent with earlier reports." It would be helpful if the authors

commented on the how the localization of some of these proteins are affected by bim1Δ on the mother-directed plus

ends. Although I understand the need to account for one class of plus end for the sake of consistency (and the distinct

behaviors of the mother vs bud-directed plus end), the text as written leaves me wondering about the other plus end.

We have noticed that the bim1 deletion led to the loss of asymmetric distribution on cytoplasmic microtubules for a number of components. Most prominent are Bik1, Kip2 and proteins of dynein-dynactin complex. We felt that further analysing this phenotype was beyond the scope of this study.

"The CAP-Gly domain construct, expressed from a BIM1 promoter, almost exclusively localized to the spindle of yeast

cells." For clarity, the authors should explicitly state that the CAP-Gly domain in question is from Bik1. Although this can

be deduced, this was not abundantly clear.

We have clarified this in the text and in the figure.

"In addition to Ase1, we followed the kinetochore proteins Ndc80-GFP and Sgo1-GFP which specifically marks

kinetochores that lack tension." This sentence should add "the latter of which..." to clarify that SgoI, but not Ndc80

exhibits this behavior.

We have added the phrase “the latter of which” to clarify this point.

"We observed that bim1Δ cells had mispositioned kinetochores with a bright Sgo1-GFP signal that was much stronger

than in wild-type cells." I don't see the mispositioned kinetochores described here. Are the authors referring to the fact

that Sgo1 is brighter, which suggests tension-free KTs? If so, this should be clearly stated as such, since the authors are

not explicitly assessed kinetochore "positioning".

We have rephrased the sentence to clarify. We refer to a lack of bi-lobed Ndc80 signal and a bright Sgo1-GFP signal as two aspects of the phenotype.

"We speculate that Bim1-Bik1 in a complex with its cargo Cik1-Kar3 is active after bi-polar spindle formation but before

late metaphase and Ase1 can partially substitute for nuclear Bim1 functions." I struggled to grasp the reasoning for these

conclusions. I assume the former point (the timing for Bim1-Bik1-Cik-Kar3) is due to the localization dynamics of Bim1

and Bik1, while the latter (Ase1 can substitute for Bim1) is due to the synthetic interaction between Bim1 and Ase1 (I

needed to look this latter point up myself). Or is this latter point due to the brighter spindle Ase1-GFP intensity? In either

case, the authors should more clearly state their reasoning.

We have clarified this statement in the revised discussion.

The error bars in Figures 3A and 6E (shown as 95% CI) and elsewhere seem very small for the parameters that are

being plotted. Spindle length values as shown in Figure 2E cover a broad range (as would be expected for a biological

process), and it would be more accurate if the error bars in Fig 3A and 6E reflect this, even if it means they start

overlapping each other. I find the error as shown to be misleading to your readers, and unless the authors have very

good reason to use 95% CI (which is not as meaningful as standard deviation), then I would encourage them to use

standard deviation.

We prefer to use CI for the spindle length plots over time for consistency reason and to avoid overlap, which would make the graphs difficult to read. We have changed the text to provide the standard deviation instead of the standard error of the mean for spindle length and metaphase duration, see point below.

The same is true for the values stated throughout the text (e.g., for mitotic timing "47{plus minus}2 min" for metaphase

duration; for distance between SPB and bud neck {plus minus} 0.1 μm, etc). I am highly skeptical that metaphase

duration (for example) ranged from only 46-48 minutes. Please use standard deviation to describe a more accurate

description of the range of values for these parameters.

In the revised manuscript, we now give the mean values plus/minus standard deviation, instead of the standard error of the mean, as requested. In addition, the range of values is directly visible from the individual data points in the plots.

"Unexpectedly, the kar9 deletion mutant displayed a slightly accelerated metaphase progression relative to wild-type

cells (26{plus minus}1 min) (Figure 3C). This could be attributed to an increased level of Bim1 on the metaphase spindle

of kar9Δ (or Kar9-AID) cells." The authors should give us more rationale to explain the "attributing the increased levels of

Bim1" point here. Do they think that the levels of spindle-associated Bim1 impact metaphase duration somehow? If so,

how?

We have added a sentence, speculating about how this could be accomplished.

"Overall, our cell biology data suggested that major nuclear Bim1 functions are conducted in a complex with Cik1-

Kar3, while Bik1 and Kar9 have a smaller impact, probably affecting the nuclear- cytoplasmic distribution of Bim1."

Although I understand and agree with the former conclusion (that Bim1 functions are conducted via Cik1-Kar3"), the latter

was confusing to me. Did the authors mean that "Bim1 impacts Bik1 and Kar9 to a lesser extent", rather than vice versa?

The authors are discussing Bim1 functioning via Cik1, but then switch to discussing how Bik1 and Kar9 affect Bim1.

We have removed the second part of the sentence from the revised manuscript.

"Next, we compared the comparing genetic interaction profile of a bim1 deletion to that of various other factors by reanalyzing the synthetic genetic interaction data..." Remove "comparing".

Thanks for pointing out this typo, we have removed it in the revised manuscript.

As someone who is unfamiliar with the analysis shown in Figure 3H, I think it would be useful to list a Pearson

correlation value for two genes that are not functionally related. This would help define a lower limit for this analysis.

For functionally unrelated genes the Pearson correlation between genetic interaction (GI) profiles is very close to zero. The graph below depicts Pearson correlation between GI profile of Bim1 and GIs of every yeast gene (data used for graph is taken from thecellmap.org).

The axes for the plots in Figure 5E and 5I are very confusing to me. I don't understand what I'm looking at. Why does

it go from 0 to 1, and then back to 0-1 again? I don't see how this can account for MTs of different lengths. Normalizing all MT length values to 1 would do this, no?

We have clarified the labelling in the revised manuscript. The x-axis gives the relative position from either the plus-end, or the Spindle pole body (both set to position 0) in micrometres. This allowed us to compare fluorescent intensities on cytoplasmic microtubules of different lengths in wild-type and bim1 delete.

"These observations are consistent with the idea that Bik1 acts as a processivity factor for Kip2: If more Bik1 is

present on the lattice, then more Kip2 molecules are able to reach plus-ends without detachment." Perhaps I'm

misunderstanding the plot shown in Figure 5E, but it seems to indicate that the levels of lattice-bound Bik1 are the same

in BIM1 and bim1Δ cells (higher SPB-localized levels, though). There are also lower levels of Bik1 at the plus ends in

bim1Δ cells. So, if Bik1 were a processivity factor for Kip2, this would suggest that they would remain bound at plus ends

as well, which these data suggest is not the case…

We have added a section to the discussion that deals with this point and we speculate about the reasons why Kip2 is increased at plus-ends, while Bik1 is not.

"The data on the CH-Cik1 fusion is very compelling, and indeed supports their hypothesis that Bim1's main role in the

nucleus is to target Cik1 to the spindle MT plus ends. That being said, it would be a simple, but important task to ensure

that this fusion behaves as suggested (restores Cik1 plus end binding in cells). Otherwise, it can't' be ruled out that this

fusion is rescuing bim1Δ functions by some other means. However, as stated above, it's unclear how much was already

known about this fusion from the lab's previous work.

In our previous study (Kornakov et al., 2020) we have shown that the CH-Cik1Delta74 fusion indeed is sufficient to enrich Kar3 at plus ends. We expect the same to be true for this slightly different fusion construct. We have added a respective sentence to the results section.

Regarding the p1-p6 promoter data: p6 is missing from Figure S6A, in spite of it being referenced in the text and the

figure.

Thanks for pointing this out, we have corrected that in the revised manuscript and do not refer to p6 anymore.

"Exogenously expressed Ase1 displayed a similar level and kinetics of localization compared to the endogenous

protein, indicating that binding sites for microtubule crosslinkers are not a limiting factor on the budding yeast spindle."

Specifically, the authors show that binding sites for Ase1 may not be limiting (the overlapping 95% CI bars if Fig S6B

suggest this is not significant), not all crosslinkers. The authors should not use such broad language to describe results

from one experiment with one crosslinker.

We have rephrased to make clear that our statement only refers to Ase1.

"We found that all bim1 mutants were less well recruited to the metaphase spindle compared to the wild-type protein,

indicating that Bim1-interacting proteins strongly contribute to Bim1 localization." Can the authors rule out the defects in

localization of these mutants is not compromised MT binding by the Bim1 mutants? Also, regarding this statement: "To

test that the observed recruitment defects of bim1 mutants are not a result of a compromised spindle or microtubule

structure, we examined their localization in a situation when GFP-tagged mutants were covered with the unlabeled wildtype

allele. Indeed, in this situation, the Bim1 mutants displayed very similar localization profiles (Supplementary Figure

7B)." I wasn't sure what these results were similar to: the wild-type protein, or the mutant without the presence of WT

Bim1? The lack of quantitation made this difficult to determine.

At the request of reviewer 1, we have removed the analysis of Bim1-GFP mutants over an unlabelled Bim1 wild-type from the manuscript.

The zoom crops for many of the images (Fig 1F and C, 3D, 5J, etc) are not labeled. I realize the legends indicated

what was what, but it would be much easier for the reader if these panels were labeled in the figure.

We have indicated the channel by a respective frame around the zoom throughout the manuscript. We think this makes orientation easier.

"While in vitro reconstitution experiments have suggested that Bim1 is required to fully reconstitute the Kip2-

dependent loading of the Dynein-Dynactin complex to microtubule-plus ends in vitro (Roberts et al., 2014), our

experiments indicate that it may contribute relatively little to this pathway in cells." Work from other labs have also shown

Bim1 is dispensable for dynein function in cells. This should be noted by the authors, and the appropriate work cited (see

work from Lee and Pellman labs. In fact work from the Lee lab showed that Kip2 is dispensable for plus end binding of

dynein).

We have re-written this section and now also refer to the Markus 2009 paper (Wei-Lih Lee lab).

References are missing throughout the text. I have listed a few examples below:

"We have previously shown that the phenotype of Bim1-binding deficient Cik1 mutants can be rescued by fusing the

CH-domain to this Cik1 mutant (cik1-Δ74)."

We have listed the citation of our 2020 paper (Kornakov et al.)

"We constructed a series of strains expressing an extra copy of Ase1-GFP under different constitutive promoters of

increasing strength (p1 to p6)"; where did these promoters come from?

They were selected based on a systematic analysis of promoter strength in Shaw et al., 2019, DOI: 10.1016/j.cell.2019.02.023 . We have added that citation to the methods section.

"double point mutation exchanging two conserved residues in the EBH domain (bim1 Y220A E228A) is predicted to

eliminate all EBH-dependent cargo interactions, but does not affect protein dimerization."

We have cited the Honnapa 2009 paper here.

"A deletion of the terminal five amino acids is predicted to prevent binding of the CAP-Gly domain of Bik1 to Bim1. The

combination of both mutations is expected to simultaneously prevent both types of interaction."

We have cited the Stangier 2018 paper here.

"Spindle positioning in budding yeast is achieved via two pathways, one relying on the protein Kar9 which interacts

with the actin-based motor Myo2." Yin et al 2000 should be added (in addition to Hwang et al).

We have now included the Yin et al. 2000 citation.

"For nuclear migration to occur efficiently, the Dynein-Dynactin complex must be enriched at the plus-ends of

cytoplasmic microtubules..." Should cite work from the Lee lab here.

We now cite Markus and Lee, 2011 as an example.

"These long microtubules can interact with the bud cortex and initiate pulling events to move the nucleus (Omer et al.,

2018)." Many papers pre-dating the Omer study found this to the case, including work from the Cooper lab (see Adames

et al). These studies should be cited either in place of the Omer study, or in addition.

We have cited additional studies besides the Omer paper.

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Referee #2

Evidence, reproducibility and clarity

The authors of the study performed a systematic assessment of the role of Bim1 in the MT-binding activity and function of a large number of nuclear and cytoplasmic MT-associated proteins (MAPs), as well as their role during mitosis and spindle positioning. For example, they find that the reliance of MT-binding activity of several MAPs varies from complete reliance on Bim1, to almost no role (in some cases, loss of Bim1 even increases MAP-MT binding). The density and quality of the data, and the large number of players analyzed by the study, are certainly impressive, and there is no doubt a lot of valuable information contained within that will be of use to many people in the MAP/mitosis/yeast cell biology community. However, I feel the manuscript can be greatly improved following some significant revisions. In particular, although some of their findings are indeed interesting and useful, and can be used to reliably draw conclusions, it is difficult to parse out what is novel, and what is a rehashing of old data. For instance, the role of Bim1 in Bik1/Kip2 targeting was described years (Carvalho et al), and I was surprised to see that the CH-Cik1 fusion was previously described by the authors' lab a couple years ago (see note below regarding lack of appropriate citations and lack of description of previous knowledge). Also, how much did we already know about the Bim1 truncations shown in Figure 7 and S7, and how they might disrupt binding to partners? Finally, regarding this statement in the Discussion: "Our analysis indicates that Bim1 contributes to both of these processes as part of two key protein complexes (Figure 9A): Bim1-Kar9-Myo2 in the cytoplasm and Bim1- Bik1-Cik1-Kar3 in the nucleus." As far as I know, these things have been known for many years; their work might help to support these findings, but the statement as written misleads the readers in to believing the present work proves these old concepts.

One of the main issues with reading a manuscript with so much data about so many different players and pathways is that this leads to a situation in which each story is only superficially covered, with only minimal depth or detail. This made the paper somewhat difficult for me to follow (and I am a fan of budding yeast mitosis!), especially given the frequent switching from one pathway to another (e.g., the Cik1 section started on page 12 appears to be continued on page.17, only after talking about the spindle orientation story in between the two Cik1 sections). I'm not sure what to suggest, but the manuscript can be improved if the authors try to refocus some of the sections to make it easier to follow one story at a time, for a particular molecule (e.g., Cik1) or pathway (spindle orientation). In addition to explicitly describing what is already known about a particular molecule/pathway, the writing can be greatly improved by introducing their reasoning for the experiments in question. Some of the sections lack sufficient rationale for me to understand the justification for their experiments (e.g., why try to overexpress Ase1 to rescue bim1∆ phenotypes, as described on page 19?).

Although there is likely much to learn from this study, I felt that some conclusions were a little bold (see below), while alternative hypotheses were not addressed (perhaps Bim1 simply competes for MT binding with some of these factors, thus accounting for them increasing their spindle-binding behavior?). For example, the authors make a point that loss of Bim1 enhances dynein-dynactin function. However, it is important to note that mutations in tubulin (tub2-430∆) and other MAPs (Kar9 or Ase1, the latter of which the authors point out) also lead to increased dynein activity (see work by Yeh et al., 2000, and work from the Moore lab). It is unknown whether mutations to these genes affect dynein targeting in cells similar to what the authors describe here. Thus, a direct causal relationship between their bim1∆ phenotypes and enhanced dynein activity is unclear, and at best is speculative. It's also worth noting that overexpression of Bik1 has been shown to actually reduce Dhc1 localization to plus ends in cells (see Markus et al 2011), which would argues against a simple mechanism of increasing Bik1 correlating with increasing dynein localization and activity.

Below are some specific points.

1. "Robustness of Ndc80 loading might be achieved by the coexistence of multiple kinetochore assembly pathways or alternatively determined by intrinsic Ndc80 properties." Wouldn't Ndc80 levels be determined by Ndc80 kinetochore loading, and not by end-binding proteins? This seems to be the more likely means to regulate Ndc80 levels.
2. "Upon analyzing the associations in the cytoplasm, we found that Kar9-3xGFP foci on bud-directed cytoplasmic microtubules were abolished in the bim1Δ strain, consistent with earlier reports." It would be helpful if the authors commented on the how the localization of some of these proteins are affected by bim1∆ on the mother-directed plus ends. Although I understand the need to account for one class of plus end for the sake of consistency (and the distinct behaviors of the mother vs bud-directed plus end), the text as written leaves me wondering about the other plus end.
3. "The CAP-Gly domain construct, expressed from a BIM1 promoter, almost exclusively localized to the spindle of yeast cells." For clarity, the authors should explicitly state that the CAP-Gly domain in question is from Bik1. Although this can be deduced, this was not abundantly clear.
4. "In addition to Ase1, we followed the kinetochore proteins Ndc80-GFP and Sgo1-GFP which specifically marks kinetochores that lack tension." This sentence should add "the latter of which..." to clarify that SgoI, but not Ndc80 exhibits this behavior.
5. "We observed that bim1Δ cells had mispositioned kinetochores with a bright Sgo1-GFP signal that was much stronger than in wild-type cells." I don't see the mispositioned kinetochores described here. Are the authors referring to the fact that Sgo1 is brighter, which suggests tension-free KTs? If so, this should be clearly stated as such, since the authors are not explicitly assessed kinetochore "positioning".
6. "We speculate that Bim1-Bik1 in a complex with its cargo Cik1-Kar3 is active after bi-polar spindle formation but before late metaphase and Ase1 can partially substitute for nuclear Bim1 functions." I struggled to grasp the reasoning for these conclusions. I assume the former point (the timing for Bim1-Bik1-Cik-Kar3) is due to the localization dynamics of Bim1 and Bik1, while the latter (Ase1 can substitute for Bim1) is due to the synthetic interaction between Bim1 and Ase1 (I needed to look this latter point up myself). Or is this latter point due to the brighter spindle Ase1-GFP intensity? In either case, the authors should more clearly state their reasoning.
7. The error bars in Figures 3A and 6E (shown as 95% CI) and elsewhere seem very small for the parameters that are being plotted. Spindle length values as shown in Figure 2E cover a broad range (as would be expected for a biological process), and it would be more accurate if the error bars in Fig 3A and 6E reflect this, even if it means they start overlapping each other. I find the error as shown to be misleading to your readers, and unless the authors have very good reason to use 95% CI (which is not as meaningful as standard deviation), then I would encourage them to use standard deviation.
8. The same is true for the values stated throughout the text (e.g., for mitotic timing "47{plus minus}2 min" for metaphase duration; for distance between SPB and bud neck {plus minus} 0.1 µm, etc). I am highly skeptical that metaphase duration (for example) ranged from only 46-48 minutes. Please use standard deviation to describe a more accurate description of the range of values for these parameters.
9. "Unexpectedly, the kar9 deletion mutant displayed a slightly accelerated metaphase progression relative to wild-type cells (26{plus minus}1 min) (Figure 3C). This could be attributed to an increased level of Bim1 on the metaphase spindle of kar9Δ (or Kar9-AID) cells." The authors should give us more rationale to explain the "attributing the increased levels of Bim1" point here. Do they think that the levels of spindle-associated Bim1 impact metaphase duration somehow? If so, how?
10. "Overall, our cell biology data suggested that major nuclear Bim1 functions are conducted in a complex with Cik1- Kar3, while Bik1 and Kar9 have a smaller impact, probably affecting the nuclear- cytoplasmic distribution of Bim1." Although I understand and agree with the former conclusion (that Bim1 functions are conducted via Cik1-Kar3"), the latter was confusing to me. Did the authors mean that "Bim1 impacts Bik1 and Kar9 to a lesser extent", rather than vice versa? The authors are discussing Bim1 functioning via Cik1, but then switch to discussing how Bik1 and Kar9 affect Bim1.
11. "Next, we compared the comparing genetic interaction profile of a bim1 deletion to that of various other factors by re-analyzing the synthetic genetic interaction data..." Remove "comparing".
12. As someone who is unfamiliar with the analysis shown in Figure 3H, I think it would be useful to list a Pearson correlation value for two genes that are not functionally related. This would help define a lower limit for this analysis.
13. The axes for the plots in Figure 5E and 5I are very confusing to me. I don't understand what I'm looking at. Why does it go from 0 to 1, and then back to 0-1 again? I don't see how this can account for MTs of different lengths. Normalizing all MT length values to 1 would do this, no?
14. "These observations are consistent with the idea that Bik1 acts as a processivity factor for Kip2: If more Bik1 is present on the lattice, then more Kip2 molecules are able to reach plus-ends without detachment." Perhaps I'm misunderstanding the plot shown in Figure 5E, but it seems to indicate that the levels of lattice-bound Bik1 are the same in BIM1 and bim1∆ cells (higher SPB-localized levels, though). There are also lower levels of Bik1 at the plus ends in bim1∆ cells. So, if Bik1 were a processivity factor for Kip2, this would suggest that they would remain bound at plus ends as well, which these data suggest is not the case.
15. "The data on the CH-Cik1 fusion is very compelling, and indeed supports their hypothesis that Bim1's main role in the nucleus is to target Cik1 to the spindle MT plus ends. That being said, it would be a simple, but important task to ensure that this fusion behaves as suggested (restores Cik1 plus end binding in cells). Otherwise, it can't' be ruled out that this fusion is rescuing bim1∆ functions by some other means. However, as stated above, it's unclear how much was already known about this fusion from the lab's previous work.
16. Regarding the p1-p6 promoter data: p6 is missing from Figure S6A, in spite of it being referenced in the text and the figure.
17. "Exogenously expressed Ase1 displayed a similar level and kinetics of localization compared to the endogenous protein, indicating that binding sites for microtubule crosslinkers are not a limiting factor on the budding yeast spindle." Specifically, the authors show that binding sites for Ase1 may not be limiting (the overlapping 95% CI bars if Fig S6B suggest this is not significant), not all crosslinkers. The authors should not use such broad language to describe results from one experiment with one crosslinker.
18. "We found that all bim1 mutants were less well recruited to the metaphase spindle compared to the wild-type protein, indicating that Bim1-interacting proteins strongly contribute to Bim1 localization." Can the authors rule out the defects in localization of these mutants is not compromised MT binding by the Bim1 mutants? Also, regarding this statement: "To test that the observed recruitment defects of bim1 mutants are not a result of a compromised spindle or microtubule structure, we examined their localization in a situation when GFP-tagged mutants were covered with the unlabeled wild-type allele. Indeed, in this situation, the Bim1 mutants displayed very similar localization profiles (Supplementary Figure 7B)." I wasn't sure what these results were similar to: the wild-type protein, or the mutant without the presence of WT Bim1? The lack of quantitation made this difficult to determine.
19. The zoom crops for many of the images (Fig 1F and C, 3D, 5J, etc) are not labeled. I realize the legends indicated what was what, but it would be much easier for the reader if these panels were labeled in the figure.
20. "While in vitro reconstitution experiments have suggested that Bim1 is required to fully reconstitute the Kip2- dependent loading of the Dynein-Dynactin complex to microtubule-plus ends in vitro (Roberts et al., 2014), our experiments indicate that it may contribute relatively little to this pathway in cells." Work from other labs have also shown Bim1 is dispensable for dynein function in cells. This should be noted by the authors, and the appropriate work cited (see work from Lee and Pellman labs. In fact work from the Lee lab showed that Kip2 is dispensable for plus end binding of dynein).
21. References are missing throughout the text. I have listed a few examples below:
    • a. "We have previously shown that the phenotype of Bim1-binding deficient Cik1 mutants can be rescued by fusing the CH-domain to this Cik1 mutant (cik1-Δ74)."
    • b. "We constructed a series of strains expressing an extra copy of Ase1-GFP under different constitutive promoters of increasing strength (p1 to p6)"; where did these promoters come from?
    • c. "double point mutation exchanging two conserved residues in the EBH domain (bim1 Y220A E228A) is predicted to eliminate all EBH-dependent cargo interactions, but does not affect protein dimerization."
    • d. "A deletion of the terminal five amino acids is predicted to prevent binding of the CAP-Gly domain of Bik1 to Bim1. The combination of both mutations is expected to simultaneously prevent both types of interaction."
    • e. "Spindle positioning in budding yeast is achieved via two pathways, one relying on the protein Kar9 which interacts with the actin-based motor Myo2." Yin et al 2000 should be added (in addition to Hwang et al).
    • f. "For nuclear migration to occur efficiently, the Dynein-Dynactin complex must be enriched at the plus-ends of cytoplasmic microtubules..." Should cite work from the Lee lab here.
    • g. "These long microtubules can interact with the bud cortex and initiate pulling events to move the nucleus (Omer et al., 2018)." Many papers pre-dating the Omer study found this to the case, including work from the Cooper lab (see Adames et al). These studies should be cited either in place of the Omer study, or in addition.

Referees cross-commenting

It seems that one of my major concerns is reflected in Reviewer #1's review: that a lot of the findings described in the manuscript have been published elsewhere, and are not novel. In spite of this, I do think there are useful data in this manuscript that make this an important contribution, and that it should definitely be published. However, this would first require a significant re-writing with appropriate description of known vs unknown, and additional citations.

Significance

The current study aims to clarify the role of Bim1 (EB1 homolog in budding yeast) in the various pathways in which it has been implicated. To achieve this aim, the authors assess the localization of numerous other microtubule-associated proteins in cells with and without Bim1. In addition to high quality localization data (e.g., intensity values), the authors perform a number of cell biological assessments (e.g., mitotic spindle length values before, during and after anaphase), genetic assessments (synthetic interaction assays), and in vitro binding assays. The current study is indeed rich with new insights into the mechanisms by which these molecules function, and will no doubt prove valuable to a number of people in the microtubule/motor/yeast mitosis fields. As someone who is interested in and studies mitosis in budding yeast, I found the study to be interesting.

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Referee #1

Evidence, reproducibility and clarity

Kornakov and Westermann provide a comprehensive analysis of the functions of microtubule +end binding proteins (+TIPs) in budding yeast. Bim1 is the EB1 ortholog of budding yeast and serves as a scaffold for other +TIPs. As Bim1 is not essential for cell viability, the authors could use a deletion of Bim1 to evaluate the global loss of function of +TIP proteins on cellular processes involving microtubules in the nucleus and the cytoplasm. During mitosis Bim1 functions almost exclusively in two previously characterized complexes with different functions: 1. The Kar9-Bim1-Bik1 complex which mediates spindle positioning at the bud neck in metaphase and 2. The Kar3-Cik1-Bik1-Bim1 complex which functions in spindle assembly and spindle elongation. Much is already known about Bim1's function in spindle positioning e.g. =TIPs on cytoplasmic microtubules, but the authors provide new insights into Kar9-Bim1 co-dependence in localization to cytoplasmic microtubules and how in turn affects localization of Bik1 and Kip2, both of which act in the dynein dependent spindle positioning pathway. The role of Bim1 in spindle assembly has also been characterized previously. The authors show how Bim1 is required to recruit the kinesin-14 Cik1/Kar3 to mitotic spindles, which interactions are involved, and that spindle elongation is delayed in its absence. Unfortunately, there is considerable overlap between their results and the published literature, and the impact of their finding is therefore reduced. The strength of this manuscript lies in its comprehensive analysis of Bim1 function , the quality of the results and that the experiments are generally well controlled and interpreted.

Major points:

1. There is already a huge body of published information on mitotic spindle positioning via the Kar9 and dynein pathways that grew since the late 1990s. The genetic relationships and molecular interactions between the components of these 2 pathways are well studied (many studies, including Liakopoulos et al. 2003, are not cited by the authors). The authors should make sure to cite and compare to the relevant primary literature when they report findings that have been described before. This will help to distinguish novel findings from validation of previous results.
2. "The strict dependence of Kar9 and Cik1-Kar3 on the presence of Bim1, as well as the different effects of bim1Δ on nuclear and cytoplasmic Bik1, may reflect the formation of stable complexes between Bim1 and these binding partners in cells." I believe this has already been shown (Kumar et al., 2021 and Manatschal et al., 2016). There are several other instances as well where additional literature should be cited, for example Gardner et al., 2008 and Gardner et al. 2014.
3. The selection of targets to study in figure 1 doesn't seem to follow the listed criteria. Many proteins included in the study were not found by IP-MS, but some perfect targets according to the listed criteria like Duo1 were not included in the study. In addition, there are more sophisticated ways of finding Bim1 binding motifs in the literature (https://doi.org/10.1016/j.cub.2012.07.047). I suggest, the authors declare that they rationally chose to study 21 proteins of interest but remove the claim that their approach was systematic.
4. Much of the microscopy data was acquired after release from alpha factor arrest. What is the reason for this perturbation? An exponentially growing culture should mostly consist of mitotic cells anyway. Since this treatment affects cell size and potentially protein levels/concentrations, testing its influence on spindle position as well as levels on MTs for the most relevant proteins of interest would be important to exclude introduction of artifacts.
5. Some of the results obtained from bim1Δ cells are a challenge to interpret due to the wide range of processes that involve Bim1 and therefor the potential for many off-target effects- including a global change in microtubule dynamical behavior in both the cytoplasm and the nucleus that will influence the length distributions and microtubule lifetime (and thus number). The authors must carefully consider these caveats.

Minor points:

1. The results section on page 12 refers to phenotypes of kar9 delete cells with respect to Bim1-GFP on cytoplasmic microtubules. In the figure 3D,F I only found data for Kar9-AID, though. The authors should refer to supplementary figure 5A or even better include quantification similar to figure 3F.
2. The observation that cytoplasmic Bim1 localization depends on interaction with its cargo Kar9 (figure 3 + 7) fits into the model that Kumar et al (https://doi.org/10.1016/j.str.2021.06.012) proposed in which Kar9 oligomerization is required for its Bim1 dependent localization to microtubules. It would be valuable to point that out.
3. I don't fully understand the model proposed in Figure 5H and discussion page 26. Based on figure 5E, it does not look like there is a higher concentration of Bik1 along the lattice in bim1 delete. So how would Bik1 increase Kip2 processivity if its levels are only increased due to a MT length change? If Kip2 was not fully processive, you would rather expect to see less of it at the tip of a longer microtubule in bim1 delete. The model suggested by Chen et al (https://doi.org/10.7554/eLife.48627.001) suggests that Kip2 only gets loaded at the minus-end and processively walks towards the +end without falling off. Are the authors suggesting that bim1 deletion changes this behavior?
4. I don't see evidence for independent pools of Bik1 in the cytoplasm and nucleus as claimed on top of page 21. Total Bik1 levels on cytoplasmic microtubules seem to be well explained by their length. Please explain better or remove the statement.
5. The experiments in supplementary figure 7B are difficult to interpret. The localization on cytoplasmic microtubules is different, but probably explained by the formation of Bim1 heterodimers. Therefore this experiment is difficult to interpret and should be removed.
6. top of page 24: Kar9 localization in metaphase depends exclusively on SxIP, not on LxxPTPh (Manatschal 2016). The paragraph should be removed as it is not supported by published data or sufficiently by the authors to merit the conclusion.
7. Top of page 26: The genetic interactions between the Kar9 pathway and the dynein pathway were already well known before this work. Please reformulate accordingly.
8. page 27 second paragraph: There is no selective pressure to evolve compensation mechanisms for gene deletions. I suggest the authors consider that Kar9 and dynein partially redundant, with Kar9 acting to position the spindle prior to metaphase and dynein to maintain spindle position in the mother and bud compartments in late metaphase and anaphase. The authors should consider the quantitative analysis of Kar9 and dynein dependent spindle positioning reported in Shulist et al. 2017 and the method for analysis of spindle length and position in 3D in Meziane et al. 2021.
9. In addition, it is not clear to me which results suggest that the relocalization of Bik1 is required in the bim1 delete. Why would wild type levels not be sufficient for dynein pathway function? The authors have not conclusively shown that nuclear migration relies on upregulating the dynein pathway in bim1Δ cells. If there is no supporting data, the paragraph should be removed.
10. Please provide more details about intensity quantification on page 35. Were these measured on sum or max projected stacks? What was the method of background subtraction?
11. Are the spindle lengths in Figure 2E measured in 2D or 3D? Bim1 deletion might lead to more misalignment of the spindles in z due to inactivation of the Kar9 pathway and thus partially explain the shorter spindles. The measurements should therefore be performed in 3D.
12. The authors should try to shorten the text. There is a lot of redundancy between results and discussion sections.
13. Data is shown that leads to conclusions that are already supported by the literature should be moved to the supplementary material.

Referees cross-commenting

I am in agreement with reviewer 2

Significance

The role of Bim1 in the Kar9 spindle positioning pathway and in recruiting Kar3/Cik1 to spindles have been extensively characterized in previous publications, however this manuscript adds mechanistic insight into what interactions are essential for localization, what happens to other proteins that have not previously been studied in the context of Bim1 and what are the exact consequences of Bim1 loss with some explanation for the outcomes. Some data presented here was expected from previous work, but never experimentally confirmed and these findings should be the focus of the manuscript. While the manuscript does not provide a huge conceptual advancement, the findings of this comprehensive analysis are of great value to the microtubule field, especially for people working in budding yeast.

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Reply to the reviewers

We would like to thank the reviewers for their thorough and positive assessment of our work. We also thank them for their careful review of our manuscript. Our responses to their specific comments are provided in the lines below.

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

Summary:

The manuscript entitled „Metastatic potential in clonal melanoma cells is driven by a rare, early-invading subpopulation" by Kaur and colleagues provides a phenotypical analysis of the invasive potential of established melanoma cell lines on single cell level. The aim of the study was to answer the question if even homologous tumor cells bear the intrinsic potential to give rise to cells with high invasive (and therefore potentially metastatic) capacity in absence of selection pressure from the tumor microenvironment.

The authors used clones from two different melanoma cell lines (to prevent the accumulation of random (epi)genetic changes during cultivation) and performed invasion assays with Matrigel-coated transwell inlays to differentiate between cells that were able to invade early (up to 8 h, approx. 1% of the total cell population) or late (8-24 h; approx. 3% of the total cell population) after plating. Comparative RNA sequencing of early invaders and non-invaders populations revealed a high expression of SEMA3C in early invaders, which was then established as marker in the used cell lines. Interestingly, in vivo models using NSG mice injected with a mixture of early and late invading melanoma cells revealed that both contributed similarly to the primary tumor, while metastatic cells in the lung consisted almost exclusively of early invaders. Subsequent ATAC sequencing revealed an increase of binding sites for the transcription factor NKX2.2 in the early invaders. Functional analyses revealed that a knockout of NKX2.2. led to an increase in both invasion and proliferation. Finally, the authors showed with different sorted early and late invaders as well as SEMA3Chigh and SEMA3Clow expressers that pro-invasive features go along with reduced proliferation potential in accordance to previously published data. However, they decrease with time, thus demonstrating a reversion of the phenotype and high plasticity.

Major comments:

In general, the paper contains novel and interesting data, is concisely written and supported by replicates. The key conclusion, the presence of a small proportion of highly invasive cells in a seemingly homologous cell population and their striking requirement for lung metastasis, is very convincing. In vitro, SEMA3C was confirmed as a marker for the early invaders in two independent cell lines. However, a few questions remain open, as detailed below:

We thank the reviewer for their positive assessment of our work. We also thank them for their careful review of our manuscript. Our responses to their specific comments are provided in the lines below.

1) The relevance of NKX2.2 in the early invaders is currently unclear to me.

The ATAC sequencing data revealed a high enrichment of accessible NKX2.2 binding sites in early invaders, and data were tested by comparative RNA sequencing of control cells and cells with NKX2.2 ko (Figure 2). The Figure legend of Figure 2 says: "NKX2.2 is a transcription factor that promotes the invasive subpopulation", but the data don`t support this (ko leads to reduced invasion). Accordingly, the authors also state in the Results part "... the direction of the effect is the opposite of what one might have expected".

To set the role of NKX2.2 into context, it would be useful to confirm the actual involvement of NFX2.2 in the invasive phenotype and clarify if NFX2.2. might probably even suppress some pro-invasive genes. I would advise to investigate the protein levels and/or protein localization of NFX2.2 and probably perform ChIp experiments on selected pro-invasive genes that play a role in the early invaders.

The reviewer has raised some excellent points about our studies of NKX2.2 and its role in invasion. Indeed, we were also surprised by the fact that NKX2.2 had the opposite effect as expected (its peaks are enriched for accessibility in the early invaders in FS4, but knockout leads to increased invasion). We elected to include the results because it was a hypothesis we tested, so in the interest of full disclosure of results, we chose to leave the result in.

The reviewer has also made some nice suggestions about how to further explore the role of NKX2.2 in regulation (e.g. ChIP-seq). Owing to the complexity of validating and performing this assay, we felt these experiments were beyond the scope of the current manuscript; we hope to explore these possibilities more fully in the future.

Another excellent suggestion the reviewer made was to look at the regulatory capacity of NKX2.2 to directly demonstrate the link between NKX2.2 regulation and expression differences between early- and late-invading cells. In order to establish this connection, we used a gene set from molecular signatures database (MSigDB: https://www.gsea-msigdb.org/gsea/msigdb/human/geneset/NKX2_2_TARGET_GENES.html) consisting of genes with an NKX2.2 binding site within their promoter (TSS -1000 bp to TSS +100 bp) identified by the gene transcription regulation database (GTRD–paper here: https://pubmed.ncbi.nlm.nih.gov/33231677/). We used the Fisher’s exact test to see if the overlap between these genes regulated by NKX2.2 and genes that are differentially expressed between early-invading cells versus their respective parental population in both cell lines had more overlap than one would expect by chance. Indeed, the p-values using this approach were 3.937e-16 and 0.037 for the FS4 and 1205Lu cell lines, respectively. These results, combined with the motif analysis with our ATAC-seq data, demonstrated that the activity of NKX2.2 is relevant in the early-invading state. We thank the reviewer for the suggestion and feel this additional analysis has improved our conclusions about NKX2.2.

Also, we further checked whether NKX2.2 levels correlated in early versus late invading cells across a panel of cell lines (Fig. 2C). We found that in 4/6 of these lines, NKX2.2 expression was higher in the early invaders. These results further support the case that NKX2.2 is an important positive regulator of invasion in multiple contexts.

“In order to establish the generality of our results, we measured NKX2.2 expression levels across multiple cell lines by single molecule mRNA FISH. We found that the early invaders had higher levels of NKX2.2 expression in four out of the 6 lines tested (Fig. 2C), demonstrating the generality of our results and strengthening the case that NKX2.2 is a potential regulator of early invasiveness. The role of NKX2.2 as a regulator of early invasiveness was further established through comparative analysis between genes with NKX2.2 promoter region binding sites (-1000 bp to +100 bp relative to the transcription start site (TSS) as annotated by the Gene Transcription Regulation Database (GTRD)) and genes differentially expressed in early-invading and parental cells. Analysis using Fisher's exact test revealed a significant overlap between GTRD annotated genes regulated by NKX2.2 and genes expressed in FS4 (****p=3.937e-16) and 1205Lu (*p=0.037) early-invading cells. These results, in complement with our results from ATAC-sequencing motif analysis, further supported the relevance of NKX2.2 regulation in the early-invading state.”

2) The sequencing data are currently accessible via a Dropbox link. They should be deposited instead in a data repository.

We thank the reviewer for noting this problem. We have uploaded all data to the SRA/GEO at the following links:

https://urldefense.com/v3/https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE224772;!!IBzWLUs!SEr5DTViPf08-IBQnv0ml-CoLX3cbaiNlCz-DJbpIKm7UcVXlL9-OD9reVQJs5pm_gzeqJYC_dM-MV8DonwX4c4$

https://urldefense.com/v3/https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE224769;!!IBzWLUs!SEr5DTViPf08-IBQnv0ml-CoLX3cbaiNlCz-DJbpIKm7UcVXlL9-OD9reVQJs5pm_gzeqJYC_dM-MV8DtY6ZB3A$

https://urldefense.com/v3/https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE224771;!!IBzWLUs!SEr5DTViPf08-IBQnv0ml-CoLX3cbaiNlCz-DJbpIKm7UcVXlL9-OD9reVQJs5pm_gzeqJYC_dM-MV8Dq_3ghAU$

Minor comments:

1) The cell line used for Supplementary Figure 4 should be named in the figure legend.

We thank the reviewer for the suggestion. We have included the name of the cell line in the figure legend for Supplementary Figure 4. The text reads as follows:

“A. FS4 melanoma cells were sorted based on SEMA3C expression. Cells were live-imaged for ~10 days every hour and single cells were tracked manually for cell position, cell division and lineage. Lineages were traced manually from single cells. Cell speed was calculated for each cell using the average distance traveled over time.”

2) In Figures 4H-M and Supplementary Figure 4D-I, the authors describe data performed in "sister" and "cousin" cells. It would be useful to provide a definition for both in the main text or figure legend.

This is a very good point. We have provided the following definitions in the main text, and have changed the wording from “sister” to “sibling” to avoid gendered terminology:

“(sibling cells are defined as those that share a common parent cell, and cousin cells are defined as those that share a common grandparent.)”

3) Discussion: "This lack of permanence may reflect the fact that the invasive cells are not subjected to stress-in our case, cells merely pass through a transwell, which may be the reason for the "burning in" of the phenotype in the case of resistance."

This sentence is misleading - please clarify.

We apologize for the confusion caused by this sentence. We have now changed it to the following:

“It is interesting that the early-invading cells eventually revert to the population average even after going through the transwell. Such a result contrasts with our previous work (Shaffer et al., 2017b), in which a rare subpopulation became permanently therapy resistant and did not revert even after several weeks off-treatment. One possibility is that the stress of undergoing therapy treatment induces a transcriptional rewiring, and this rewiring is not induced by the migration through transwells. Further studies will be required to test these hypotheses.”

Furthermore, there are some errors in the reference to the Figures throughout the paper. These which should be corrected:

We thank the reviewer for their detailed reading and finding these issues. We have now fixed them all in our revised manuscript.

4) Results, section "NKX2.2 is a transcription factor that promotes the invasive subpopulation".

Here the authors write: "...we performed RNA sequencing on the NKX2.2 knockout cells and compared the effects on gene expression to the gene expression differences between early vs. non- invaders across the two cell lines." This sentence should contain the reference to Supplementary Figure 3B-D (which is otherwise not referred to).

We thank the reviewer for their detailed reading and noticing this issue. We have now referenced Supplementary Figure 3B-D in the text cited above.

5) Results: "Overexpression of SEMA3C in FS4 cells revealed no changes in invasiveness, suggesting that SEMA3C is a marker with no functional relevance to invasiveness per se; Fig. 1D, Fig. 2A-B)"

The correct reference should be: Suppl. Fig. 1D, Fig. 2A-B. Also, in the current manuscript version the authors jump from Figures 1 to Figure 2 A,B, before coming back to Figure 1. To avoid this, I would advise to shift the current Figure 2A, B to Figure 1 or the supplementary information.

We thank the reviewer for pointing out this error in the reference to these figures. Figure 2A-B is now referenced as “Supp. Fig. 1 E-F”. The figure legend has also been updated.

6) Results: "We then sampled lungs from mice at various times post-injection to look for metastatic cells (Fig.1F, Suppl. Fig. 2B,C)."

As Supplementary Figure 2B, C does not show metastasis, but rather primary tumor growth, I would advise the following wording: "We then sampled lungs from mice at various times post-injection to look for metastatic cells (Fig.1F) and overall tumor growth (Suppl. Fig. 2B,C)."

We thank the reviewer for their advice to reword the sentence cited above. We have now edited the text to read as suggested by the reviewer. In addition, Supp. Fig. 2B,C is not referenced as Supp. Fig. 2C,D.

"We then sampled lungs from mice at various times post-injection to look for metastatic cells (Fig.1F) and overall tumor growth (Supp. Fig. 2C,D)."

7) Results: "Interestingly, NKX2.2 knockout cells showed markedly increased invasion and proliferation (Fig. 2A,B), suggesting a change in regulation of both processes. "

The correct reference is Fig. 2C, D.

The reviewer is right that we only have results in one cell line, and fully agree that the results in FS4 are only correlative. We have now weakened the language in the abstract and the results to emphasize that this result held in 1205Lu cells only.

• Given the robust literature regarding phenotypic switching in melanoma, the NKX2.2 knockout increasing both invasiveness and proliferation (figures 2C, 2D) suggests it may not be involved in phenotype switching. Perhaps NKX2.2 is a negative regulator of cell activity/metabolism. We thank the reviewer for highlighting the possible connections with metabolism. To explore this possibility , we performed metabolic assays on NKX2.2 knockout and AAVS control cells and observed no significant changes in Extracellular acidification rate (B). We did observe some differences in oxygen consumption rate in the cells (A), but the differences do not seem to be large enough or systematic enough to be meaningful given the variation within the controls. We have now included these results in Supp. Fig. 3E-F.

Note, the data previously referenced as Figure 2C,D is now in Figure 2A,B.

“NKX2.2 is a transcriptional repressor and activator essential for the differentiation of pancreatic endocrine cells (Habener et al., 2005). In mice, deletion of NKX2.2 prevents the specification of pancreatic islet cells resulting in the replacement of insulin-expressing β cells and glucagon-expressing α cells with ghrelin-expressing cells; This lack of specification resulted in mortality of newborn mice due to hyperglycemia (Sussel et al. 1998; Prado et al. 2004). Given the link of NKX2.2 with glucose metabolism, we wondered whether NKX2.2 had an effect on metabolic activity prompting us to test the NKX2.2 knockout lines for metabolic differences in the oxygen consumption rate (OCR; an indicator of oxidative phosphorylation) and the extracellular acidification rate (ECAR; an indicator of glycolysis) of the cells. Seahorse assay analysis revealed no systematic differences in metabolic activity (Supp. Fig. 3E,F).”

We thank the reviewer for the correction. The reference has now been corrected in the main text.

Reviewer #1 (Significance (Required)):

Nature and significance of the advance/ literature context:

In their manuscript, the authors provide interesting biological data about the presence of intrinsically and reversibly pro-invasive / pro-metastatic melanoma cells in a seemingly homogenous subpopulation. With SEMA3C, they also provide a marker for early invading cells, which might be useful in future studies to identify therapeutic vulnerabilities for this subgroup. This study sheds further light on the functional effects of phenotypic plasticity, which was previously described particularly in the context of therapy resistance, as mentioned by the authors.

We thank the reviewer for their kind assessment of the impact of our work.

Audience:

The study is interesting for scientists from the melanoma field as well as the cancer metastasis field in general.

Own expertise:

Melanoma, phenotypic switch, metabolism, signal transduction, stress response

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

Metastatic potential in clonal melanoma cells is driven by a rare, early-invading subpopulation

Kaur et al.

In this manuscript the authors highlight a small subpopulation of "early-invading" melanoma cells and functionally characterize the nuances of these early cells compared to their slowly invading counterparts. A cell surface marker, SEMA3C and the transcription factor NKX2.2 were associated with differences in the invasive rates. Importantly, the group demonstrates that existence of the invasive subpopulation is not reliant on genetic changes, and thus exhibits plasticity. While the underlying concept surrounding this paper (phenotypic plasticity) is not novel, highlighting a surface marker and transcription factor that may, at least in part, be associated with phenotype plasticity is interesting. However, the current study seems underdeveloped. Specific points of concern are listed:

Major

• Only two cell lines are used throughout this study. We thank the reviewer for pointing out the need for more cell lines. We have now added two new cell lines to our study, WM793 and WM1799, both of which recapitulate the fundamental phenomenology in question. Although we did not show it in our initial submission, we had originally queried a panel of melanoma cell lines in order to determine their suitability for our study (from which we settled on 1205Lu and FS4). This panel has multiple melanoma cell lines obtained from a variety of melanoma tumor samples from Radial Growth Phase (RGP), Vertical Growth Phase (VGP), and metastatic tissues. We now have included these data in our revised manuscript, since they further support our point.

“We tested a panel of different melanoma cell lines from Radial Growth Phase (RGP), Vertical Growth Phase (VGP), and metastatic tumor types for the existence of fast invading subpopulations. We used four patient-derived melanoma cell lines, FS4, 1205Lu, WM1799, WM793, all of which have BRAF mutations (V600K for FS4, V600E for 1205Lu, WM1799, and WM793) and are known to be highly invasive in vitro and in vivo (27). Out of the 11 melanoma cell lines tested, the FS4 (not shown) and 1205lu cell lines displayed the highest levels of fast invading subpopulations (Supp. Fig. 1A).”

First, we showed that they all have an invasive subpopulation, with 1205Lu and FS4 (not shown) having the most invasive cells. Second, validating a central claim of the manuscript, we showed that many of these cell lines, including WM1799 and WM793, showed much higher levels of both SEMA3C (4/6) and NKX2.2 (4/6) expression in the early invading population as compared to the late invading population.

Together, these data make a strong case that our findings generalize across multiple cell lines, including RGP and VGP models. We have incorporated new text that reads as follows:

“In order to establish the generality of our results, we measured expression of the surface marker SEMA3C across the early and late invading subpopulations of a panel of melanoma cell lines. We found that SEMA3C levels were higher in the early invading subpopulation in 4 of the 6 lines tested (Supp. Fig. 1H). Thus, these results held across a variety of cell lines and, thus, were not a unique feature of a particular patient sample.”

• The in vivo metastasis assay in figure 1 is difficult to interpret and presents a number of concerns. 1) Only ~50% of early invading cells were labeled with GFP, this confounds many aspects of the experiment. The authors comment that in the primary tumor, as expected "...a roughly equal mix of human melanoma cells that were GFP positive and negative." If there was an expectation of equal proliferative rates in the primary tumor of early and late invading cells, given that only 1/2 of the early cells were GFP+, wouldn't we expect only 25% of the human cells to be GFP+?

The reviewer has raised a very important quantitative question about our experiments, which we have now addressed with a more thorough set of analyses. Initially, we quantified GFP positivity post -transduction by looking at fluorescent protein levels, for which the threshold was fairly arbitrary, and potentially could have miscounted many GFP positive cells as GFP negative due to low but non-zero levels of expression. We hence recalculated our positivity rate based on single molecule RNA FISH for GFP and mCherry, given that the technique is sensitive down to even veryl ow levels of expression.

As can be seen in Supp. Fig. 2B, the vast majority of transduced cells did indeed get the transgene and had some level of expression of GFP/mCherry. At a threshold of 5/10 molecules (GFP/mCherry, respectively), we obtained 88% and 96.15% positivity rates for GFP and mCherry, respectively. At these rates of positivity, we would expect much closer to 50% of the cells being GFP positive in the tumors, as observed. We thank the reviewer for noticing this discrepancy, and feel that our new analysis clears up the confusion and strengthens our results. These results are described in the main text as follows:

“We labeled the cells with sufficient virus so that 88% of the early invaders were labeled with GFP and 96.15% of the late invaders were labeled with mCherry (Supp. Fig. 2B). We then sampled lungs from mice at various times post-injection to look for metastatic cells (Fig.1F) and overall tumor growth (Supp. Fig. 2C,D).”

2) The authors note technical difficulties in detecting mCherry in sections. It seems as though this forced them to use a RNA FISH probe to identify human vs. mouse and by extension/negative selection the human FISH positive, GPF negative cell represented a mCherry stained late-invading cell. This is not ideal and seems over complicated. If the population of interest was engineered to express mCherry, why not directly probe for mCherry?

The reviewer has raised an important point about our experimental design. Indeed, we attempted multiple times and in multiple ways to detect mCherry protein directly. We tried multiple times with multiple antibodies, but the signal was simply not detectable. Hence, we arrived at the experimental design we outlined. We felt that a fully transparent disclosure of the issues was preferable, even if it did make the design sound overly complex. We will note that our primary result—that the vast majority of the metastatic cells are GFP positive and hence derived from fast invaders—is robust to any detection issues for mCherry.

3) Given the poor initial labeling/transduction of the early invaders, how can the authors be confident that all human cells without GFP signal are late invaders?

The reviewer raises a great point that is addressed by our GFP and mCherry RNA FISH analysis above, showing that the transduction efficiency was actually quite a bit higher than initially thought due to low but non-zero GFP signal being counted as GFP negative. With the much higher transduction efficiencies we have now validated, we believe that the vast majority of human cells with no GFP signal should be late invaders.

• The authors may have missed an opportunity to study FS4 clone F6 and 1205 clone E11. What is the SEMA3C and NKX2.2 status of these clones? Are they able to revert expressions? The reviewer has pointed out an interesting opportunity for further exploration. Unfortunately, because they were identified as part of an initial screening study, those particular clones were not kept for subsequent analysis. However, in our revised manuscript, we have now worked up multiple additional cell lines (WM1799 and WM793), both of which had high expression levels of both SEMA3C (Supp. Fig. 1H, shown above) and NKX2.2 (Fig. 2C) in the early invading subpopulation. Currently, we do not have data on reversion experiments for these two cell lines, but we would expect them to behave similarly to the other cell lines we examined in this study.

• The lack of statistical analysis/comparisons throughout the paper needs to be addressed. We thank the reviewer for pointing out these deficiencies. We have now added statistical comparisons throughout.

• In figures 1E and 3B, why do the parental (homogenous) cells demonstrate less invasiveness than the selected for the SEMA3C low or "late-invaders" respectively? This is an important point that the reviewer has raised. The finding did occur in every replicate, so we assume it is biologically and not statistical. We have now included the following language in the discussion noting the issue and some possible explanations.

“It is worth noting that, while the SEMA3C-high (early-invading) subpopulation drove the highly invasive phenotype, the SEMA3C-low (late-invading) subpopulation also displayed a somewhat more invasive phenotype than the parental population. It is unclear what the underlying cause of this difference in invasive behavior is between the SEMA3C-low and parental populations. One possibility is that paracrine signaling between cells in the parental population confers them with less invasive potential than when the cells are isolated into early- and late-invading subpopulations. Another possibility is that technical factors associated with the sorting of SEMA3C-low cells from the parental population alter their invasive properties, thus making them distinct from the parental population.”

• Conclusions that NKX2.2 knockout increases invasiveness and proliferation are based on 1 cell line. The comparisons done with FS4 early and late invading cells in Figure 1F may be supportive but is correlative in nature. The reviewer is right that we only have results in one cell line, and fully agree that the results in FS4 are only correlative. We have now weakened the language in the abstract and the results to emphasize that this result held in 1205Lu cells only.

• Given the robust literature regarding phenotypic switching in melanoma, the NKX2.2 knockout increasing both invasiveness and proliferation (figures 2C, 2D) suggests it may not be involved in phenotype switching. Perhaps NKX2.2 is a negative regulator of cell activity/metabolism. We thank the reviewer for highlighting the possible connections with metabolism. To explore this possibility , we performed metabolic assays on NKX2.2 knockout and AAVS control cells and observed no significant changes in Extracellular acidification rate (B). We did observe some differences in oxygen consumption rate in the cells (A), but the differences do not seem to be large enough or systematic enough to be meaningful given the variation within the controls. We have now included these results in Supp. Fig. 3E-F.

Note, the data previously referenced as Figure 2C,D is now in Figure 2A,B.

“NKX2.2 is a transcriptional repressor and activator essential for the differentiation of pancreatic endocrine cells (Habener et al., 2005). In mice, deletion of NKX2.2 prevents the specification of pancreatic islet cells resulting in the replacement of insulin-expressing β cells and glucagon-expressing α cells with ghrelin-expressing cells; This lack of specification resulted in mortality of newborn mice due to hyperglycemia (Sussel et al. 1998; Prado et al. 2004). Given the link of NKX2.2 with glucose metabolism, we wondered whether NKX2.2 had an effect on metabolic activity prompting us to test the NKX2.2 knockout lines for metabolic differences in the oxygen consumption rate (OCR; an indicator of oxidative phosphorylation) and the extracellular acidification rate (ECAR; an indicator of glycolysis) of the cells. Seahorse assay analysis revealed no systematic differences in metabolic activity (Supp. Fig. 3E,F).”

• Given that sorted SEMA3C high levels did not revert to parental FS4 levels, yet the invasive phenotype reverted to parental-like behavior undermines the usefulness of SEMA3C as a marker of invasiveness. The reviewer has brought up an important point. We were able to show that 1205Lu cells had SEMA3C levels revert to those of the parental. The reviewer is right that FS4 did not, which may be because it takes longer for FS4 to revert. It is true that the phenotypic behavior did revert. We have seen similar things in our therapy resistance work (Shaffer et al. 2017, etc.). One possible reason is that the phenotype is governed by multiple factors, and so the phenotype can revert before the expression of SEMA3C. We still think that SEMA3C is a good marker, just perhaps context dependent. We have added text to the discussion to make these important points.

“We note that SEMA3C levels in FS4-SEMA3C-high cells did not revert to the parental levels within two weeks. This incomplete reversion may be because SEMA3C takes longer to revert than the tested time period. Interestingly, the invasive phenotype did revert in this time period, suggesting that there may be multiple factors associated with the phenotype beyond SEMA3C. It may thus be that SEMA3C is a marker of the early-invading population, but only in certain contexts.”

Minor

• How does SEMA3C and/or NKX2.2 expression (here 1.5% of FS4 cells were noted as "SEMA3C high") of metastatic cell lines (FS4 and 1205) compare to RGP and VGP cell lines? The reviewer has asked a great question about radial and vertical growth phase cells. We have tested several other cell lines to determine cell lines that were suitable for transwell assays. We have now included two figures (Supp. Fig. 1H and Fig. 2C) showing the SEMA3C and NKX2.2 status of each of these cell lines (parental cells) and their different subpopulations (early invaders and late invaders)—see also Reviewer #2, Major point 1. We found that the same pattern of SEMA3C-high cells held for both RGP and VGP cell lines.

• There were a number of instances throughout the manuscript that were not clear, colloquial, or simply unnecessary - i.e. description of transwell assay. The reviewer has raised a good point about our language. We have gone through and tried to improve the clarity and precision. As for descriptions of the various assays, we have found that some readers of our papers are unfamiliar with these assays, so we elected to keep those descriptions in. We hope the reviewer does not object too strenuously.

• The authors only analyze/mention lung metastases. Were metastases observed at other sites? The reviewer has posed a very good question about whether metastasis occurred at other locations. We stained additional tissues (liver and kidney) that were collected from the same mice and stained as per our lung invasion assays. As shown in our new Supplemental Fig. 2E, we found a similar pattern with the vast majority of metastatic cells being GFP positive; i.e., early-invaders, just as was the case for lung. We thank the reviewer for this helpful suggestion.

“In the lung, however, we saw predominantly GFP-positive cells, showing that the vast majority of cells that migrated from the primary tumor site were initially early invading cells (Fig. 1I,J). The number of GFP cells in the lung was variable, but generally increased with time. The liver and kidney also showed an enrichment of GFP-positive cells (early invaders), suggesting that the metastatic potential of these cells is not limited to any one particular metastatic location (Supp. Fig. 2E). Thus, we established that the highly invasive subpopulation was able to drive metastasis in vivo.”

• What is PE indicating in Figure 1D? Apologies, PE refers to the channel we used for the sorting on the FACS machine and stands for “Phycoerythrin”. To avoid any confusion, we have omitted the “PE” text on the y-axis of Fig. 1D.

• The number of invaded cells seems to vary quite a bit between experiments - Parental 1205 cells in Fig 2C = ~200, yet 1205 clone F6 and the non-clonal 1205 cell line demonstrate ~10,000. Similar differences observed with Fs4 cells - Parental Fig 1E vs. Empty control Figure 2A. The reviewer has a good eye—indeed, there is a wide variability in the amount of invading cells. We have now remarked on this variability in the results section:

“We note that the number of invading cells varied significantly between experiments. This variability is due to the fact that we employed transwell dishes with different growth areas, ranging from 0.33 cm2 to 4.67 cm2, leading us to collect different cell numbers for individual experiments. The cell density per cm2, however, was kept constant between experiments.”

Note that Figure 2C and Figure 2A are now referenced as Figure 2A and Supplemental Figure 1F, respectively .

Reviewer #2 (Significance (Required)):

This work contributes to the growing fields of phenotypic plasticity and intratumoral heterogeneity. The authors claim to have identified a surface marker SEMA3C and a transcription factor NKX2.2 that may play a role in driving invasive proclivity. Importantly, the group demonstrates that changes in these proteins are not genetic, and therefore represent "intrinsic differences" that are a property of the tumor. Furthermore, the authors indicate how the present observations of early invading cells parallels drug resistance phenomena as their previous works highlights intrinsically resistant subpopulations (Shaffer et al., Nature 2017, Torre et al., Nature Genetics 2021 and others.). Taken together, the current and previous work underscores the importance of cell to cell non-genetic variability in disease progression and response to therapy.

We thank the reviewer for their kind comments on the significance of our manuscript.

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

In this study, Kaur et al. intended to use similar strategy that the same group had developed (https://www.nature.com/articles/nature22794) to identify the subpopulation in melanoma responsible for metastasis. In brief, the melanoma cell population was subjected to the selection of a specific phenotype (transwell migration dubbed as "invasiveness" behavior). By comparing the early and late invaders, a cell maker was identified to allow distinguishing the high-invasive subpopulation. A series of experiments were devised to validate the metastatic function of the high-invasive cells and delineate the signaling that drove this phenotype. The authors concluded that this rare subpopulation was originated from transcriptional fluctuation, and invasiveness is a trade-off of cell growth. Therefore, as the cells growing, overtime the phenotype was reverted to low invasiveness.

Consistency is the most important factor for evaluating observation over temporal and spatial range. Therefore, several controls need to be clarified before further investigation in mechanisms:

1) If the rare invader cells are arising from gene expression fluctuation, the SEMA3C-low population of parental line should generate SEMA3C-high invader subpopulation over time. This should be addressed.

The reviewer has made an excellent point. Indeed, it is the case that the SEMA3C-low population starts to regenerate the high invader subpopulation over time. We have re-graphed Figure 3D to demonstrate this fact more clearly (See Supplemental Fig. 5A,B), showing that the SEMA3C low population regenerates many more SEMA-3C high cells after 14 days.

2) Both early and late invader cells exhibited higher invasiveness than the parental line (Fig. 3B). Therefore, the in vivo metastatic potential of the three lines should be compared to validate the role of the invader cells in the metastatic function.

We thank the reviewer for their comment about testing all three populations in the in vivo context. It is an excellent suggestion, but in order to fully control the experiment, we would need to add all three populations in three separate colors. Given the difficulties we had with getting even the two colors to work together, we think it is beyond the scope of our current efforts to attempt this complex experiment. We have added the following caveat to the text:

“For unknown reasons, the parental population consistently showed lower invasiveness than the early- and late-invading subpopulations. Given that we did not test the parental population for invasiveness in vivo, future studies may address the sources and mechanisms by which the parental population differs and how those differences manifest in vivo.”

3) To evaluate the possible intervention of cellular function by fluorescent proteins (https://doi.org/10.1016/j.ccell.2022.01.015), admix of GFP- and mCherry-labeled populations of early invader cells should be used as a control in Fig. 1F. Noticeably, the labeling ratio of the two populations was not even in Fig. 1F.

The reviewer has brought up an important point about the potential differences brought about by the fluorescent proteins themselves. At this point, it is difficult to redo these complex in vivo experiments, but we can appeal to the fact that the admixture is maintained throughout time as the primary tumor site still has a roughly equal ratio of GFP and mCherry cells in it (Fig. 1I and Supp. Fig. 2E).

4) When the invader cells were expanded and passed, their invasiveness will revert to the level similar to parental line in 14 days (Fig. 3B). The isolated cells were expanded for further testing and manipulation in Fig. 1C and 1F, respectively. How long did was the period for cell expansion in these experiments?

We thank the reviewer for bringing up an important question about the details of cell expansion. For the RNA-seq, the cells were directly processed upon going through the transwell, so there was no expansion period. We have made sure to outline this more carefully in our methods section (see below).

“RNA sequencing and analysis:

RNA collection and library prep: Each treatment/sample was tested in 3 separate biological replicates. Upon passing through the transwell, cells were immediately collected and processed for RNA sequencing. Total RNA isolation was performed using the phenol-chloroform extraction followed by RNA cleanup using RNAeasy Micro (Qiagen 74004) kit. For transwell assays, library preparation was performed using Nebnext single-cell/low input RNA library prep kit (E6420L, NEB). For NKX2.2 CRISPR experiments, library preparation was done using NEBNext Poly(A) mRNA Magnetic Isolation Module (NEB E7490L) integrated with NEBNext Ultra II RNA Library Prep Kit for Illumina (NEB E7770L).

Mouse tumor implantation and growth:

All mouse experiments were conducted in collaboration with Dr. Meenhard Herlyn at The Wistar Institute, Philadelphia, PA. NSG mice (NOD.Cg-Prkdcscid Il2rgtm1Wjl/SzJ) were bred in-house at The Wistar Institute Animal Facility. All experiments were performed under approval from the Wistar Institute Care and Use Committee (protocol 201174). As in the case of RNA sequencing experiments, cells were not expanded prior to injection into the mouse, but were collected and implanted right after passing through the transwell. 50,000 melanoma cells were suspended in DMEM with 10% FBS and injected subcutaneously in the left flank of the mouse.”

5) If invasiveness and growth are trade-off, why did the mCherry-labeled cells not dominate the population of primary tumors in Fig. 1J?

Note that Figure 1J is now referenced as Figure 1I. The reviewer brings up a good point. For potential explanations, first, the difference in growth rate is not large, so we would not necessarily expect mCherry cells to dominate on this timescale. Also, we believe that in vivo, the tradeoff may be mitigated by other factors and cell-cell interactions that are not present in vitro. We have added a note on this point to the results.

“(Note that these numbers were similar despite the slightly increased growth rate of the late-invading subpopulation; we assume this is due to the relatively small difference and cell-cell interactions that could prevent one population from dominating the other.)”

6) In Fig. 1G, why RNA FISH was not used to detect mCherry-labeled cells?

Another excellent point. RNA FISH in tissue sections can often be rather challenging due to various reasons including RNA degradation, and mCherry RNA signal was hard to definitively show in these sections. Hence, we opted for MALAT1, which is very heavily expressed and hence provided a strong and reliable signal.

“For technical reasons, the mCherry cells were not detectable due to the fluorescence of the mCherry protein not being visible in the mouse sections. Nevertheless, we were able to detect late invaders in the population by using a human-specific MALAT1 RNA FISH probe that binds only to human MALAT1 RNA and not mouse MALAT1 RNA (28).”

7) In vivo cycling (harvesting the cells from metastatic site and implanting them to the primary site in mouse models) has been employed to select metastatic sublines from a parental line. Could in vivo cycling make the early invader phenotype fixed?

The reviewer has raised a very interesting point about cycling and selection. Indeed, the 1205Lu cells were derived from repeated cycling of invasive lung cells. That is probably the reason that these cells were useful for our assay, because the percentage of early-invading cells was higher. Nevertheless, the cells still have a significant proportion of late invaders, suggesting that the phenotype has not yet been fixed in the population. Perhaps with further cycling, such a fixation could be achieved. We have now noted this possibility in our discussion.

“It is also possible that repeated cycles of selection, even of non-genetic phenotypes, could lead to an increased fraction of invasive cells. Indeed, 1205Lu cells were derived by exactly such repeated cycles, which presumably are the reason they have a higher percentage of invasive cells; however, despite these repeated rounds of selection, most cells are still not highly invasive, suggesting that it is difficult for this property to fully fix in the population.”

**Referees cross-commenting**

Both reviewers' questions are important for adequate controls.

Reviewer #3 (Significance (Required)):

There are several studies trying to identify subpopulation responsible for the metastasis of melanoma and other types of cancer, and a few mechanisms have been revealed. However, the significance depends on if the results can be validated on clinical data. It is lacking in this study.

We thank the reviewer for their statement of interest in the problem. We agree that it is helpful to link these results to clinical data. We did perform TCGA analyses of several different genes, including SEMA3C, that emerged from our data, and there were no systematic relationships to phenotype. Of course, the relationship to clinical data is complex and many important factors are not obvious from the TCGA data, so we do not think that necessarily diminishes our results. Rather, we think our results raise a conceptual point that there can be rare cells with non-genetic differences that can drive metastasis. Further work will be required to translate these results to the clinic.

We have added the following to the main text:

“We found that the SEMA3C-high cells were far more invasive, intrinsically, than SEMA3C-low cells and the population overall, thus demonstrating that cells vary intrinsically in their invasiveness, and the very invasive subpopulation is marked by the expression of SEMA3C (Fig. 1E). Note, overexpression of SEMA3C in FS4 single cell clones revealed no changes in invasiveness, suggesting that SEMA3C is a marker with no functional relevance to invasiveness per se (Fig. 1D; Supp. Fig. 1E-G). We verified the expression levels of the genes identified in our RNA sequencing study in the The Cancer Genome Atlas (TCGA) data. We combined the list of differentially expressed genes in early invaders with the gene set enrichment analysis (GSEA) “Hallmarks of cancer epithelial-mesenchymal transition” and compared expression in primary vs. metastatic TCGA samples, finding no appreciable difference (Fig. 5A-B). These data suggest that these markers do not have obvious clinical correlates. Moreover, Kaplan Meier analysis comparing the survival time (days to death) between patient cohorts with either high or low SEMA3C expression levels revealed that SEMA3C does not predict survival time post-diagnosis, as both survival curves (p=0.898) follow comparable trends between the two cohorts (Fig. 5C). However, conceptually, our results raise the possibility that a rare, non-genetically defined subpopulation of cells may drive metastasis due to its increased degree of invasiveness, which further data collection efforts in patient samples may help validate.”

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Referee #3

Evidence, reproducibility and clarity

In this study, Kaur et al. intended to use similar strategy that the same group had developed (https://www.nature.com/articles/nature22794) to identify the subpopulation in melanoma responsible for metastasis. In brief, the melanoma cell population was subjected to the selection of a specific phenotype (transwell migration dubbed as "invasiveness" behavior). By comparing the early and late invaders, a cell maker was identified to allow distinguishing the high-invasive subpopulation. A series of experiments were devised to validate the metastatic function of the high-invasive cells and delineate the signaling that drove this phenotype. The authors concluded that this rare subpopulation was originated from transcriptional fluctuation, and invasiveness is a trade-off of cell growth. Therefore, as the cells growing, overtime the phenotype was reverted to low invasiveness.

Consistency is the most important factor for evaluating observation over temporal and spatial range. Therefore, several controls need to be clarified before further investigation in mechanisms:

1. If the rare invader cells are arising from gene expression fluctuation, the SEMA3C-low population of parental line should generate SEMA3C-high invader subpopulation over time. This should be addressed.
2. Both early and late invader cells exhibited higher invasiveness than the parental line (Fig. 3B). Therefore, the in vivo metastatic potential of the three lines should be compared to validate the role of the invader cells in the metastatic function.
3. To evaluate the possible intervention of cellular function by fluorescent proteins (https://doi.org/10.1016/j.ccell.2022.01.015), admix of GFP- and mCherry-labeled populations of early invader cells should be used as a control in Fig. 1F. Noticably, the labeling ratio of the two population was not even in Fig. 1F.
4. When the invader cells were expanded and passed, their invasiveness will revert to the level similar to parental line in 14 days (Fig. 3B). The isolated cells were expanded for further testing and manipulation in Fig. 1C and 1F, respectively. How long did was the period for cell expansion in these experiments?
5. If invasiveness and growth are trade-off, why did the mCherry-labeled cells not dominate the population of primary tumors in Fig. 1J?
6. In Fig. 1G, why RNA FISH was not used to detect mCherry-labeled cells?
7. In vivo cycling (harvesting the cells from metastatic site and implanting them to the primary site in mouse models) has been employed to select metastatic sublines from a parental line. Could in vivo cycling make the early invader phenotype fixed?

Referees cross-commenting

Both reviewers' questions are important for adequate controls.

Significance

There are several studies trying to identify subpopulation responsible for the metastasis of melanoma and other types of cancer, and a few mechanisms have been revealed. However, the significance depends on if the results can be validated on clinical data. It is lacking in this study.

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Referee #2

Evidence, reproducibility and clarity

RC-2022-01474 Metastatic potential in clonal melanoma cells is driven by a rare, early-invading subpopulation Kaur et al.

In this manuscript the authors highlight a small subpopulation of "early-invading" melanoma cells and functionally characterize the nuances of these early cells compared to their slowly invading counterparts. A cell surface marker, SEMA3C and the transcription factor NKX2.2 were associated with differences in the invasive rates. Importantly, the group demonstrates that existence of the invasive subpopulation is not reliant on genetic changes, and thus exhibits plasticity. While the underlying concept surrounding this paper (phenotypic plasticity) is not novel, highlighting a surface marker and transcription factor that may, at least in part, be associated with phenotype plasticity is interesting. However, the current study seems underdeveloped. Specific points of concern are listed:

Major

• Only two cell lines are used throughout this study.
• The in vivo metastasis assay in figure 1 is difficult to interpret and presents a number of concerns.
  • 1.) Only ~50% of early invading cells were labeled with GFP, this confounds many aspects of the experiment. The authors comment that in the primary tumor, as expected "...a roughly equal mix of human melanoma cells that were GFP positive and negative." If there was an expectation of equal proliferative rates in the primary tumor of early and late invading cells, given that only 1/2 of the early cells were GFP+, wouldn't we expect only 25% of the human cells to be GFP+?
  • 2.) The authors note technical difficulties in detecting mCherry in sections. It seems as though this forced them to use a RNA FISH probe to identify human vs. mouse and by extension/negative selection the human FISH positive, GPF negative cell represented a mCherry stained late-invading cell. This is not ideal and seems over complicated. If the population of interest was engineered to express mCherry, why not directly probe for mCherry?
  • 3.) Given the poor initial labeling/transduction of the early invaders, how can the authors be confident that all human cells without GFP signal are late invaders?
• The authors may have missed an opportunity to study FS4 clone F6 and 1205 clone E11. What is the SEMA3C and NKX2.2 status of these clones? Are they able to revert expressions?
• The lack of statistical analysis/comparisons throughout the paper needs to be addressed.
• In figures 1E and 3B, why do the parental (homogenous) cells demonstrate less invasiveness than the selected for the SEMA3C low or "late-invaders" respectively?
• Conclusions that NKX2.2 knockout increases invasiveness and proliferation are based on 1 cell line. The comparisons done with FS4 early and late invading cells in Figure 1F may be supportive but is correlative in nature.
• Given the robust literature regarding phenotypic switching in melanoma, the NKX2.2 knockout increasing both invasiveness and proliferation (figures 2C, 2D) suggests it may not be involved in phenotype switching. Perhaps NKX2.2 is a negative regulator of cell activity/metabolism.
• Given that sorted SEMA3C high levels did not revert to parental FS4 levels, yet the invasive phenotype reverted to parental-like behavior undermines the usefulness of SEMA3C as a marker of invasiveness.

Minor

• How does SEMA3C and/or NKX2.2 expression (here 1.5% of FS4 cells were noted as "SEMA3C high") of metastatic cell lines (FS4 and 1205) compare to RGP and VGP cell lines?
• There were a number of instances throughout the manuscript that were not clear, colloquial, or simply unnecessary - i.e. description of transwell assay.
• The authors only analyze/mention lung metastases. Were metastases observed at other sites?
• What is PE indicating in Figure 1D?
• The number of invaded cells seems to vary quite a bit between experiments - Parental 1205 cells in Fig 2C = ~200, yet 1205 clone F6 and the non-clonal 1205 cell line demonstrate ~10,000. Similar differences observed with Fs4 cells - Parental Fig 1E vs. Empty control Figure 2A.

Significance

This work contributes to the growing fields of phenotypic plasticity and intratumoral heterogeneity. The authors claim to have identified a surface marker SEMA3C and a transcription factor NKX2.2 that may play a role in driving invasive proclivity. Importantly, the group demonstrates that changes in these proteins is not genetic, and therefore represents "intrinsic differences" that is a property of the tumor. Furthermore, the authors indicate how the present observations of early invading cells parallels drug resistance phenomena as their previous works highlights intrinsically resistant subpopulations (Shaffer et al., Nature 2017, Torre et al., Nature Genetics 2021 and others.). Taken together, the current and previous work underscores the importance of cell to cell non-genetic variability in disease progression and response to therapy.

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Referee #1

Evidence, reproducibility and clarity

Summary:

The manuscript entitled „Metastatic potential in clonal melanoma cells is driven by a rare, early-invading subpopulation" by Kaur and colleagues provides a phenotypical analysis of the invasive potential of established melanoma cell lines on single cell level. The aim of the study was to answer the question if even homologous tumor cells bear the intrinsic potential to give rise to cells with high invasive (and therefore potentially metastatic) capacity in absence of selection pressure from the tumor microenvironment. The authors used clones from two different melanoma cell lines (to prevent the accumulation of random (epi)genetic changes during cultivation) and performed invasion assays with Matrigel-coated transwell inlays to differentiate between cells that were able to invade early (up to 8 h, approx. 1% of the total cell population) or late (8-24 h; approx. 3% of the total cell population) after plating. Comparative RNA sequencing of early invaders and non-invaders populations revealed a high expression of SEMA3C in early invaders, which was then established as marker in the used cell lines. Interestingly, in vivo models using NSG mice injected with a mixture of early and late invading melanoma cells revealed that both contributed similarly to the primary tumor, while metastatic cells in the lung consisted almost exclusively of early invaders. Subsequent ATAC sequencing revealed an increase of binding sites for the transcription factor NKX2.2 in the early invaders. Functional analyses revealed that a knockout of NKX2.2. led to an increase in both invasion and proliferation. Finally, the authors showed with different sorted early and late invaders as well as SEMA3Chigh and SEMA3Clow expressers that pro-invasive features go along with reduced proliferation potential in accordance to previously published data. However, they decrease with time, thus demonstrating a reversion of the phenotype and high plasticity.

Major comments:

In general, the paper contains novel and interesting data, is concisely written and supported by replicates. The key conclusion, the presence of a small proportion of highly invasive cells in a seemingly homologous cell population and their striking requirement for lung metastasis, is very convincing. In vitro, SEMA3C was confirmed as marker for the early invaders in two independent cell lines. However, a few questions remain open, as detailed below:

1. The relevance of NKX2.2 in the early invaders is currently unclear to me. The ATAC sequencing data revealed a high enrichment of accessible NKX2.2 binding sites in early invaders, and data were tested by comparative RNA sequencing of control cells and cells with NKX2.2 ko (Figure 2). The Figure legend of Figure 2 says: "NKX2.2 is a transcription factor that promotes the invasive subpopulation", but the data don`t support this (ko leads to reduced invasion). Accordingly, the authors also state in the Results part "... the direction of the effect is the opposite of what one might have expected". To set the role of NKX2.2 into context, it would be useful to confirm the actual involvement of NFX2.2 in the invasive phenotype and clarify if NFX2.2. might probably even suppress some pro-invasive genes. I would advise to investigate the protein levels and/or protein localization of NFX2.2 and probably perform ChIp experiments on selected pro-invasive genes that play a role in the early invaders.
2. The sequencing data are currently accessible via a Dropbox link. They should be deposited instead in a data repository.

Minor comments:

1. The cell line used for Supplementary Figure 4 should be named in the figure legend.
2. In Figures 4H-M and Supplementary Figure 4D-I, the authors describe data performed in "sister" and "cousin" cells. It would be useful to provide a definition for both in the main text or figure legend.
3. Discussion: "This lack of permanence may reflect the fact that the invasive cells are not subjected to stress-in our case, cells merely pass through a transwell, which may be the reason for the "burning in" of the phenotype in the case of resistance." This sentence is misleading - please clarify.

Furthermore, there are some errors in the reference to the Figures throughout the paper. These which should be corrected: 4. Results, section "NKX2.2 is a transcription factor that promotes the invasive subpopulation". Here the authors write: "...we performed RNA sequencing on the NKX2.2 knockout cells and compared the effects on gene expression to the gene expression differences between early vs. non- invaders across the two cell lines." This sentence should contain the reference to Supplementary Figure 3B-D (which is otherwise not referred to). 5. Results: "Overexpression of SEMA3C in FS4 cells revealed no changes in invasiveness, suggesting that SEMA3C is a marker with no functional relevance to invasiveness per se; Fig. 1D, Fig. 2A-B)" The correct reference should be: Suppl. Fig. 1D, Fig. 2A-B. Also, in the current manuscript version the authors jump from Figures 1 to Figure 2 A,B, before coming back to Figure 1. To avoid this, I would advise to shift the current Figure 2A, B to Figure 1 or the supplementary information. 6. Results: "We then sampled lungs from mice at various times post-injection to look for metastatic cells (Fig.1F, Suppl. Fig. 2B,C)." As Supplementary Figure 2B, C does not show metastasis, but rather primary tumor growth, I would advise the following wording: "We then sampled lungs from mice at various times post-injection to look for metastatic cells (Fig.1F) and overall tumor growth (Suppl. Fig. 2B,C)." 7. Results: "Interestingly, NKX2.2 knockout cells showed markedly increased invasion and proliferation (Fig. 2A,B), suggesting a change in regulation of both processes. " The correct reference is Fig. 2C, D.

Significance

Nature and significance of the advance/ literature context:

In their manuscript, the authors provide interesting biological data about the presence of intrinsically and reversibly pro-invasive / pro-metastatic melanoma cells in a seemingly homogenous subpopulation. With SEMA3C, they also provide a marker for early invading cells, which might be useful in future studies to identify therapeutic vulnerabilities for this subgroup. This study sheds further light on the functional effects of phenotypic plasticity, which was previously described particularly in the context of therapy resistance, as mentioned by the authors.

Audience:

The study is interesting for scientists from the melanoma field as well as the cancer metastasis field in general.

Own expertise:

Melanoma, phenotypic switch, metabolism, signal transduction, stress response

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Reply to the reviewers

RC-2022-01661

Response to reviewers:

Review Commons questions and Reviewers’ comments verbatim in plain text.

Authors’ responses in bold text. Line numbers refers to numbers in the marked-up manuscript. In text citations in this document – see bibliography at bottom of this document.

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

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. RESPONSE #1 We appreciate these insightful perspectives. We apologise for not making it clear that the model used is not in fact an overexpression model. This is because, by removing the cumulus-oocyte complex from the follicle and studying it in vitro (oocyte IVM), secretion of these growth factors by the oocyte is notably compromised, so the controls are not exposed to normal physiological levels as suggested by the reviewer. This loss of normal secretion ex vivo is evidenced by: 1) in Mester B. et al _[1]_; Figure 2, we showed the mouse oocytes matured in vitro (i.e. as per the current study) are essentially devoid of the mature domain BMP15 protein, which will therefore be likewise for cumulin as cumulin contains one subunit of BMP15, and 2) mammalian cumulus-oocyte complexes explanted and cultured in vitro by IVM benefit (in terms of developmental competence) from the addition of exogenous oocyte-secreted factors such as BMP15, GDF9 and cumulin, demonstrating that they are rate-limiting under IVM conditions. We were the first to demonstrate this in 2006 _[2]_ which has been subsequently verified in many papers, including in the current paper for cumulin. The exact extent to which the controls are deficient in BMP15 and cumulin is unclear, as there are not yet reliable mouse ELISAs for these, but the model is an add-back model rather than an overexpression model. We have now added text at lines 150-152 and in the Fig 1 legend, to make this point clearer.

Re using complimentary deletion, knock-out or antagonist-type experiments: we agree this would be ideal. However, this is likely impossible as cumulin is a non-covalent heterodimer of BMP15 and GDF9 (as first named and characterised by us: Mottershead DG et al ____[3]____). Hence, to knockout cumulin one needs to knockout either or both of BMP15 and GDF9, making it impossible to discriminate the actions of the heterodimer from the homodimers. In support of this, reviewer #3 made exactly this point, and stated “Such functional analysis cannot be done using gene knockout mouse lines…… only functional work as the one presented in this manuscript can find the mechanisms of action of these hormones”. This issue is further complicated by the fact that BMP15 and GDF9 are thought to exist as homodimers, as well as monomers, including in equilibrium in heterodimeric form as cumulin (also noted by Reviewer #3). Furthermore, there is no cumulin-specific antagonist, e.g. a cumulin-specific neutralizing antibody. Small molecule signaling inhibitors (e.g. Smad2/3 or Smad1/5/8 antagonists) certainly block cumulin actions, but therefore simultaneously also block GDF9 or BMP15 actions. Collectively, these unique (with the TGFβ superfamily) structural peculiarities of cumulin make it complex to interrogate its mechanisms of action, to the extent that others have largely focused on BMP15 or GDF9 homodimer actions only, when in reality, cumulin is likely the key natural protagonist responsible for oocyte paracrine signalling. We have added a paragraph to this effect to the discussion, at lines 417-423, including acknowledging the experimental limitations of the study dictated by having to deal with a noncovalent heterodimer.

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. RESPONSE #2: Indeed, oocyte-granulosa interaction is absolutely essential during oocyte growth, development and meiotic maturation, for healthy oocyte function, including the orchestrated down-regulation of oocyte-granulosa interactions during the latter phase. As pioneered by John Eppig and others, including ourselves ____[4]____ (ref has 673 citations), the master conductor of this dynamic oocyte-granulosa interaction during oocyte meiotic maturation are the oocyte-secreted factors. Hence, these factors are critical at this stage, and we maintain that this is a very important phase of oocyte development to study.

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. RESPONSE #3 This is true. The study is already very large and has many functional experiments (e.g. oocyte respiration, oocyte MS, etc), that follow-up the findings from the proteomic analysis. Hence, the study has taken a global cellular metabolism approach, e.g. we show that cumulin downregulates oxidative phosphorylation globally, c.f. pathways within OXPHOS. We found an abundance of individual proteins altered in this period (see figure 4) and to follow up on the actions and consequences of individual proteins would: 1) at best show small incremental effects, as metabolism of such a cellular syncytium is vastly complex and inter-connected, 2) further increase the size of what is already a large study, and 3) detract from the more important wholistic effects on cumulus-oocyte complex metabolism, which must act as whole, interacting entity, to support the complexities of supporting early life post-fertilization.

__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. __RESPONSE #4 This is not an over-expression study – see RESPONSE #1 above. We have added text in the discussion at lines 417-423, that acknowledges the limitations of the study by the impossibility to conduct a killer knockout experiment of cumulin.

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. RESPONSE #5 The scope of this work was to; (a) identify global changes to protein expression, and (b) to use this data to implement follow-up experiments on some of the lead indicators, such as metabolism (respiration, small molecule metabolic markers) and cellular morphology. This work provides the groundwork, insight and rationale for future additional studies of specific mechanisms of COC interactions. As discussed at RESPONSE# 1, these studies are as close as anyone can probably get currently to mechanistic studies of a NOVEL noncovalent heterodimer, when the noncovalent homodimers are also in play, as also noted by reviewer #3 who specifically references mechanisms: “…… only functional work as the one presented in this manuscript can find the mechanisms of action of these hormones”.

In some instances, in the interests of brevity, we made remarks based on our data, but without specifying details in the text. To redress this, we have now added specific details which illustrate and justify our statements based on the data collected (see RESPONSES #6, #7, #9 below). For greater clarity, we have also restructured our supplementary data set to cover the analysis progression from full raw proteomic data to differentially expressed proteins, to use of differentially expressed proteins in network analysis. The supplementary data set now includes the full proteomics lists for both cells and treatments (Supplementary Tables S1, S2, S3, S4), protein sequences confidently identified by both proteomic software platforms (Supplementary Tables S5, S6), differentially expressed proteomics lists for both cells and treatments (Supplementary Tables S7, S8, S9, S10), differentially expressed protein list used for the network analysis (supplementary Table S11). The Table S11 lists are intended to facilitate use by readers to perform their own analyses, if they so wish, since they can simply copy and paste the list to the on-line STRING platform. Finally, the reanalysed network analysis output, based on the differentially expressed proteins shown in supplementary Table S11, are shown in supplementary Tables S12 and S13.

__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. __RESPONSE #6 There is no mention of mechanism in this sentence at line 40 and we have provided exhaustive evidence that cumulus cell metabolism is remodelled as stated (Figures 4B and 4C). For example, of the 59 upregulated proteins in the cumulus cells of cumulin treated COC (Figure 4C and supplementary Table S11), 38 (i.e. 64%) are involved in primary metabolic processes (supplementary Table S12), including amino acid metabolism (GOT2, SHMT1, CTH, MAT2B), lipid and steroid metabolism (CERS5, DHCR7, HSD17b4), aldehydes metabolism (RDH11), nucleotides biosynthesis (RRM1, GMPR2), glycans biosynthesis and protein glycosylation (UGDH, GFPT2, GALNT2), respiratory chain (mt-ND1). The cellular macromolecule metabolic process is also a significantly enriched network, involving 26 out of the 59 upregulated proteins (i.e. 44%, Figure 4C and supplementary Table S11) and includes processes such as protein complex assembly (TM9sF4, DHX30, AP2M1), RNA metabolism and mRNA processing (DDX17, DDX5, DDX39bPRPF19, PRPF6, HNRNPF, CPSF6). To help clarify the specificity of our findings, we have added this text to the revised manuscript (lines 465-474).

__46: Oocyte-secreted factors downregulate protein catabolic processes, and upregulate DNA binding, translation, and ribosome assembly in oocytes. --No direct evidence is provided. __RESPONSE #7 The proteomic data provides direct evidence that these processes are involved. Sentence modified at lines 47-48 to be more specific re processes. Additional text has been included (revised manuscript lines 434-443) to provide specific details of the differentially expressed proteins involved in each of these processes.

48: Oocyte-secreted factors alter mitochondrial number... --Need to establish that the MitoTracker is a suitable tool to measure the number of mitochondria. RESPONSE #8____ We recognise that total mitochondrial uptake of the MitoTracker Orange dye could be a reflection of either mitochondrial function (polarity) and/or mitochondrial number, given the manufacturer’s (Thermo Fischer) statement that “MitoTracker™ Orange CMTMRos is an orange-fluorescent dye that stains mitochondria in live cells and its accumulation is dependent upon membrane potential”, as we specified in several places in the original manuscript (Lines 354-355, 366-367 and 235 of the marked up manuscript version) . However, we agree that in several places in the manuscript we also indicated that MitoTracker was being used as a measure of mitochondrial number. To avoid this ambiguity, we have made some clarifications in the text (revised manuscript lines 235, 351-352, 377, 481-482, and in Figure 5B legend). Given the extensive and diverse metabolic changes indicated by the proteomic data, our aim was to explore the potential role of mitochondria in response to cumulin and BMP15 treatment of COCs, which we did by use of EM morphology studies (figure 5A), mitochondrial respiration (figures 6B and 6C), quantification of energy metabolites, such as ATP, NAD and related compounds, by mass spectrometry (figure 6D), metabolites identified in multispectral unmixing studies (figure 7) and mitochondrial function using MitoTracker (figure 5B). Collectively this data suggested a modest downturn of energy metabolism, particularly in cumulin treated COCs. This downturn did not cause a change in net energy charge in COCs (figure 6D) despite a reduction in redox ratio in both cells (figure 7A and 7B) and respiration in COCs (Figure 6B and 6C), and could reflect adaptive changes in response to cumulin and BMP15, reflecting metabolic plasticity/Warburg effect, as explained in the discussion (revised manuscript lines 453-551).

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. RESPONSE #9 References have been added (lines 82 and 85 of the revised manuscript)

373: This study provides a detailed exploration of the mechanisms by which oocyte-secreted factors... --No mechanistic studies were performed. RESPONSE #10 We respectfully disagree. One of many mechanisms we have studied here is OXPHOS. We have shown this is how OSFs change metabolism – that is a mechanism. As discussed at RESPONSE #1, these studies are as close as anyone can probably get currently to mechanistic studies of a noncovalent heterodimer, when the noncovalent homodimers are also in play, as also noted by reviewer #3 who specifically references mechanisms: “…… only functional work as the one presented in this manuscript can find the mechanisms of action of these hormones”. Please also refer to the comments in RESPONSE #5.

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. RESPONSE #11 The data in Fig 2C, 2D show exactly that; that the difference between control and treatment (cumulin) is an increase in embryonic development. It does not show fertilizability, so we removed that at lines 41 and 415.

396: suggesting that cumulin affects meiosis in the oocyte and may increase meiotic fidelity... --This statement is highly speculative. RESPONSE #12 We accept this critique - reference to meiosis and meiotic fidelity removed, line 435 (revised manuscript).

409: ...lacks the machinery for amino acid uptake... --Is the oocyte unable to take up any amino acids or only certain amino acids? RESPONSE #13 Thank you for noting this as this sentence is too absolute. Oocytes have a very poor capacity to take up most or even all AAs, which are instead supplied to the oocyte via cumulus cells. Sentence modified at lines 455-456 to be less absolute.

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.

RESPONSE #14____ “metabolic workload” rephrased to “metabolic processes”. Lines 52-53.

67: metabolically 'inept' requires more precision. RESPONSE #15 “metabolically inept” rephrased to “metabolically dependent on surrounding granulosa cells” ____[5]____. Line 69

262: explain 'multispectral analysis' RESPONSE #16 A citation has been added, which explains the technique (ref ____[6]____ at the end of this response letter, which is the same paper as citation [34] in the revised manuscript; lines 111 and 217; revised manuscript). A detailed explanation of this technique has also been added in the supplementary information, under the section “Multispectral microscopy”.

268: how is 'limited' overlap defined. RESPONSE #17 The phrase “distinct profiles, with limited overlap between…” has been rephrased to “distinct profiles, between…” (line 279 of the revised manuscript), as the main point is that the patterns/profiles across treatments are different, and we did not quantify the extent of overlap.

318: define higher workload RESPONSE #18 the phrase “…implying a higher workload for both organelles” has been replaced with a more specific explanation; “We suggest that such changes in morphology may be related to the remarkable increase in a diversity of metabolic processes which we observed (Figure 4C and supplementary Table S12), since ER morphology and architecture is known to be highly dynamic in response to environmental and developmental factors which affect cells” ____[7]____ (Lines 342-345).

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

RESPONSE #19____ Please refer to explanation under RESPONSE #8

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.

RESPONSE #20____ The supplementary information section (Multispectral microscopy, lines 239-258) has been expanded and clarifications provided as to the wavelengths of the channels, the features used and the unsupervised nature of algorithms.

360: intersection of union of 5-22%

RESPONSE #21____ This is a measure of the extent of overlap of data distribution for each class (treatment), i.e. of how different they are. The ellipse (Fig 3D) represents one standard deviation around the central mean value for that data set. The overlap of these ellipses is quantified by their intersection over union (IoU) value, which is the ratio of the area of the two-ellipse intersections, divided by the area of their union (the shape created by their overlap being treated as creating one continuous object). IoU values range from 0 to 100% for fully separated and fully overlapping, respectively. Hence, a 5% IoU represents a low level of overlap of data distribution between treatments. Brief explanatory text has now been added at line 387-388.

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. RESPONSE #22 The panels in Fig 3A and Fig 3B each contain different representations of the information contained within the proteomics dataset, and explain different aspects of the data. The Venn diagram panels in Figure 3B display the level of overlap of specific proteins identified in each cell, treatment and software subgroup. The degree of overlap in each cluster is high (i.e., 76 – 78% for Mascot/scaffold and 95 - 97 % for PD2.4) as would be expected within the same cell type and analysis approach, where the main variable is cell treatment. We agree that the total numbers in the Venn diagrams did not exactly match the total numbers in Figure 3A, which likely resulted from using slightly different parameters during data processing. We have now used exactly the same data set in panels A and B (the full PD2.4 and Mascot/scaffold datasets are shown in the supplementary proteomics summary Excel spreadsheet), so that total numbers are now identical, and will hopefully avoid any confusion in comparing across panels. However, the main conclusion to be drawn from Fig 3B remains unchanged, in that it shows that by far the majority of identified proteins overlap between treatments (control, BMP, cumulin), regardless of cell type or data analysis approach. However, it should be noted that Figure 3B has no information about protein fold change/differential expression, and only represents numbers of proteins confidently identified, and the level of overlap of identified proteins between treatments. Only panel 3A shows differential protein expression relative to the respective control groups.

Fig. 3D. What do the circles represent and how were their parameters (size, position) established? RESPONSE #23 The separation of data distributions for each class is shown by an ellipse for each cluster, which encompasses one standard deviation around the central mean values. This text has now been added to the Fig 3 legend.

Reviewer #1 (Significance (Required)): 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. RESPONSE #24 We thank the reviewer for their perspectives however we respectfully disagree on all accounts. We have rebutted these 2 concerns: point (i) at RESPONSE #1, and point (ii) at RESPONSE #5 above.

Reviewer expertise: growth and meiotic maturation of the mammalian oocyte

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

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

RESPONSE #25____ Network enrichment was performed using the open access software STRING ( ____https://string-db.org/ [8]____), and we have now provided additional information on how we utilised STRING in the supplementary information section, under “Gene Ontology Network Enrichment Analysis” (lines 176-217). STRING utilises information available in the Gene Ontology (GO) database ( ____http://geneontology.org/docs/ontology-documentation/____ ) to determine; (a) how many of the differentially expressed proteins identified in the proteomics experimental data fall into specific networks, (b) how much enrichment this represents relative to a random network of the same size, and (c) whether the enrichment is statistically significant based on the FDR statistic. The size of each GO network within the background set (whole genome or other) will therefore be a major determinant of whether the number of proteins identified in the proteomics experiment represents significant enrichment of a particular network. A few proteins identified within a small background network will represent greater enrichment (and lower FDR score) than the same number of proteins in a much larger network. In fact the “count in network” is often approximately the inverse of the enrichment strength (see supplementary Table S12, within the supplementary dataset Excel spreadsheet). Note that only significantly differentially expressed proteins were used for the network analysis presented in this paper, so even in the case where just 2 proteins are significantly enriched in a network (e.g., “Farnesyl diphosphate metabolic process” identified in the GO biological process section of BMP15 treated cumulus cells) they represent two upregulated proteins in a small network, so the functional/biological significance of this is likely quite high.

In revision of the manuscript we noticed that we had likely originally used the full lists of differentially expressed proteins for network analysis, rather than separating up and downregulated proteins as intended. Furthermore an updated version of STRING is now available, with improvements in the method of correction for multiple testing within the FDR output (STRING version 11.5, current since August 12, 2021). We have therefore revised the STRING network analyses, and have provided a list of the STRING input proteins (supplementary Table S11), STRINGv11.5 gene ontology (GO) functional enrichments for up and downregulated proteins in BMP and cumulin treated cumulus cells and oocytes respectively (supplementary Tables S12 and S13), and replaced the very large Figure 4C and D heatmaps (submitted version) with a summary (new Figure 4C; revised version). The updated heat maps can still be viewed in supplementary Tables S12 and S13 (the heatmaps now being the updated ones, deriving from our review response).

* 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 RESPONSE #26 “Oocyte secreted factors (OSFs)” replaced with BMP15 and cumulin throughout the manuscript where we refer specifically to our treatments, results or discussion of results, except where we refer to “these OSFs” (eg line 34), and not where we refer to the principal of OSF signalling more generically. Re the latter, hence we wish to retain the title as is, as BMP15 and cumulin are prototypical oocyte secreted factors, as the title refers to the principal more generally.

- 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. RESPONSE #27 Addressed next below

• Are the experiments adequately replicated and statistical analysis adequate? *I was not able to access enrichment analysis.

RESPONSE #28____ The method of Network Enrichment is now described in more detail in the supplementary methods section. See previous explanation under RESPONSE #25 above.

*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 RESPONSE #29 Post-hoc test for MitroTracker data was Tukey’s, as already stated in Figure 5 legend. Post-hoc test for metabolite analyses was Kruskal-Wallis – text now added to Figure 6 legend.

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.

RESPONSE #30____ Our apologies for this oversight. We have now provided additional information on how we utilised STRING in the supplementary information, in a new section titled “Gene Ontology Network Enrichment Analysis” (lines 176-217).

* What was the concentration of treatment for the samples used for proteome and mascot/scaffold experiments?

RESPONSE #31____ The two bioinformatic analyses were conducted on common biological samples, so naturally the treatment concentrations were also the same. Text modified at line 175 to make this clearer.

* 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

RESPONSE #32____ The sentences immediately following this (i.e. lines 274-281) elaborated the basis for this statement, particularly where it is explicitly stated “____Proteomic heat maps (Fig. 3C) and multispectral analysis plots (Fig. 3D) both show distinct profiles, between controls, BMP15 and cumulin treated COCs, in both cell types.____”, at lines 277-281.

The data for the two global approaches are shown in Figure 3C (heat maps generated by PD2.4 comparing differences in protein abundance across treatments, shown separately for cumulus cells and oocytes), and Figure 3D (linear discriminant analysis comparing differences in multispectral imaging data across treatments, shown separately for cumulus cells and oocytes). Both of these global analyses show clear differences in distribution pattern between controls (untreated) and treated samples (BMP15 and cumulin), in both oocytes and cumulus cells. The approaches are (a) global, since each relates to analysis of the complete cell extracts (as opposed to targeting a specific component/analyte), and (b) orthogonal because different and unrelated measurement techniques are used i.e., proteomics (mass spectrometry) and multispectral imaging (spectroscopy).____ *I did not understand the discrepancy between the numbers observed in Figure 3A and Figure 3B.

RESPONSE #33____ Refer to RESPONSE #22 above. We have checked the data, and revised the Venn diagrams (Figure 3B) with data analysed using identical parameters, for both Figures 3A and 3B, to avoid confusion over protein numbers. We also noticed and corrected a discrepancy with regard to the number of differentially expressed oocyte proteins under the merged data column of Figure 3A.____ *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? RESPONSE #34 Note: Figures 4C and 4D are now Supplementary Tables S12 and S13. The red shades represent network enrichment analysis of upregulated proteins, while the green shades represent network enrichment analysis of downregulated proteins. The colour gradients in each case follow the numerical values for “count in network”, enrichment strength, and lower FDR, with greater colour intensity for higher numbers (and lower FDR). However, we agree that the original four panels (A, B, C and D) comprising figure 4, made for a very large and potentially overwhelming figure. To simplify the data presentation we have reprocessed the data in STRING (see details under RESPONSE #25 above) and have moved the now considerably shorter network lists (originally displayed as Figures 4C and 4D) to supplementary Tables S12 and S13, and the new Figure 4C provides a network enrichment summary instead. This is likely easier to comprehend, with the marked contrast in networks identified between oocytes and cumulus cells easier to see. The numbers of up and downregulated proteins on which the network analysis is based are also shown in Figure 4C, while the specific proteins used and networks identified are shown in supplementary tables S11, S12 and S13 (original colour coding retained, and also explained within each table). - 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.

RESPONSE #35____ Indeed Figure 4 was large and has now been shortened. We made considerable effort to attempt to present in Fig 4 the vast amount of proteomic data in a summarized, hopefully comprehensible fashion. We have now moved Figs 4C and 4D to the supplementary, and replaced it with the simplified new Fig 4C (tabular format). Pease also see comments under RESPONSES#25 and #34 re this. *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.. RESPONSE #36 Network analysis was performed using only those proteins which were significantly differentially expressed and had a consistent direction of fold change in both mascot/scaffold spectral counting and PD2.4 peak intensity proteomics quantitative approaches. Proteins with no significant expression change (i.e., the majority of proteins, which represented proteins with __Reviewer #2 (Significance (Required)):

• 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. RESPONSE #37 We thank the reviewer for these positive remarks.

• 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.

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

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. RESPONSE #38 See specific responses below

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. RESPONSE #39 We thank the reviewer for these positive comments, especially in relation to the difficulty of getting to the mechanism of actions of a non-covalent heterodimer, and hence the importance of functional experiments in providing mechanistic insights.

Comments:

I really appreciated the reference to auto-symbiosis. We often see the reference to a cellular syncytium but this one is interesting. RESPONSE #40 Thank you.

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. RESPONSE #41 Technical issues – see responses below, as well as responses to other reviewers. We have provided additional methodological information for greater clarity, and added specific observations from our data, to support all statements, to avoid the impression of unsubstantiated overstatements.

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. RESPONSE #42 We agree that shorter protein lists might be expected to result in fewer networks. However, it is interesting to consider the possible reasons for the shorter list:

(1) In our case the amount of protein extracted from oocytes (2-3____m____g) was much less than from cumulus cells (15-17____m____g) as explained in the “Mass Spectrometry for proteomic analysis” section in the Supplementary Information. This is because COCs have many more cumulus cells than oocytes by number as well as total mass. Consequently it was possible to load a larger ____m____g amount of total peptides from cumulus cells onto the nanoLCMSMS system, but it should be noted that on-column loading is not only determined by the total amount of material injected, but also by the limits in capacity of the C18 peptide capture cartridge upstream from the column (which is 1 – 1.5 ug estimated from the binding capacity of C18 with a bed volume of 0.35____m____L, since the trap cartridges have dimensions of 300____m____m ID and 5mm length; ____http://tools.thermofisher.com/content/sfs/manuals/Man-M5001-LC-Nano-Capillary-Micro-Columns-ManM5001-EN.pdf____ and ____https://www.optimizetech.com/opti-pak-trap-columns/____ ). Consequently, the different initial loading of oocyte vs cumulus cell proteins/peptides are likely to have made little if any contribution to proteome coverage, since 2-17____m____g all exceed the trap cartridge binding capacity, and consequently 1 - 1.5____m____g was captured and transferred to the nano-column, while the excess was transferred to waste. Based on the capacity limits of the capture cartridge, there was likely enough peptides/proteins in both oocyte and cumulus cell extracts to reach the saturation point, and therefore much more consistent on-column loadings than the initial ____m____g loadings would imply. We have added some additional information re this to the method section (see the section “Mass Spectrometry for proteomic analysis” in the Supplementary Information).

(2) The expressed proteomes of different cell types may be expected to differ not only in specific proteins expressed but also in the number of different proteins. In a recent study by Marei et al ____[9]____, equal amounts of total protein (9.5ug) from bovine oocytes and matching cumulus cells were prepared for their proteomics comparisons, and interestingly these authors also report about half as many proteins identified in oocytes as compared with cumulus cells, despite equal amounts of total protein used; “A total of 1703 and 1185 proteins were identified in CCs and oocytes, respectively, 679 of which were common.” Furthermore, a transcriptomic study of bovine oocytes and cumulus cells by Moorey et al ____[10]____, showed 69 and 128 differentially expressed genes (DEGs) in oocytes and cumulus cells respectively (comparing small vs large cells in each case), pointing to about double the differential gene expression in cumulus cells than oocytes, again implying a larger cumulus cell vs oocyte transcriptome. Our data support these observations, which collectively suggest a real difference in proteome size between oocytes and cumulus cells. If the difference in proteome size is real, then differences in network enrichment are also likely to have biological relevance, despite differences in size of the differentially expressed proteins lists.

(3) Even if initial protein loading was a contributing factor to the size of the oocyte vs cumulus cell proteomes, it is of note that we observed approximately 2 fold fewer total proteins identified in oocytes as in cumulus cells (Figure 3A, 3B and new Figure 4C), yet the difference between number of identified networks is multiple-fold (a cumulative total of 2 networks identified in BMP15 and cumulin treated oocytes vs 143 networks identified in BMP15 and cumulin treated cumulus cells – see new Figure 4C). Furthermore, there does not seem to be a strictly linear relationship between the number of proteins submitted for network analysis and the numbers of networks identified. For example, 34 upregulated proteins in cumulin treated oocytes identified a single enriched network, while a similar number of 38 upregulated proteins in BMP15 treated cumulus cells, identified a total of 42 networks (new Figure 4C), and similarly cumulin treated cumulus cells had 59 upregulated proteins and 58 downregulated proteins, which resulted in 57 and 23 enriched networks respectively.

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. RESPONSE #43 Please also refer to the explanations under RESPONSE #34 and #42 above. We have added an additional explanation of how we performed the enrichment analysis, in the supplementary information section under the heading “Gene Ontology Network Enrichment Analysis”. In the data presented here we used the whole mouse genome as our background set. The number of total proteins identified by Mascot/Scaffold and ProteomeDiscoverer were similar, but actually considerably more differentially expressed proteins were identified using ProteomeDiscoverer (Fig 3A), as expected using peak intensity vs spectral counting ____[11]____. The spectral counting approaches usually identify fewer differentially expressed proteins, but also with a lower quantitative false positive rate, while peak intensity approaches tend to identify more differentially expressed proteins, but with a higher quantitative false positive rate ____[11]____. Our reasoning was therefore to combine proteins which vary in common across both platforms, to maximise the differentially expressed proteins list while simultaneously minimising the quantitative false positive rate. We thank the reviewer for the suggestion of using our full protein list as the background set. Initially we revised our network enrichment analysis (see comments under RESPONSE #25) still using the mouse whole genome, resulting in fewer overall networks, but improved contrast between oocytes and cumulus cells (see summary in new Figure 4C, and network analysis details in supplementary Tables S12 and S13). We then repeated the network analyses using our full protein list (4450 proteins identified in both oocytes and cumulus cells; see background list in Supplementary Table S11) as the background set. With this we similarly found no enriched GO networks for BMP15 and cumulin treated oocytes, and only 6 and 1 enriched network in BMP15 and cumulin treated cumulus cells. We suggest that detecting network enrichment against a cell specific background list may not give us the same level of “contrast” as can be achieved when comparing against the whole mouse genome.

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 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. RESPONSE #44 We agree and in places we used ambiguous language re mitochondrial function vs mitochondrial number. We have now clarified and corrected this - please refer to detailed comments and manuscript changes under RESPONSE #8.

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. RESPONSE #45 Please refer to comments under RESPONSE #8

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. RESPONSE #46 A version of Figure 3D but with the replicates colour-coded has been added to Supplementary Material (Supplementary Figure 2) and the manuscript text has been revised with the information that data from the three replicates are shown, added to the caption to Figure 3D.

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. RESPONSE #47 We accept this point and agree that, especially the claim re germ-line genomic integrity, is an overstatement. This has been removed. We maintain however that there is ample evidence in our results that there is clear inter-cellular metabolic cooperativity between oocyte and cumulus cells and that this ultimately leads to an oocyte with improved developmental competence. The sentence has been modified to reflect this, line 117-118.

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. RESPONSE #48 Has been rephrased (see line 284 of the revised manuscript)

Line 287: "... and almost a third more significant networks..." Please add values. RESPONSE #49 Section has been deleted (line 291-300 of the revised manuscript)

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. RESPONSE #50 Please refer to detailed explanations under RESPONSES #42 and #43

Line 293: "... resulted in the identification of about double the number..." Please add values. RESPONSE #51 Values added at lines 305-306, and additional detail has been added to this section of the manuscript (lines 305-317 revised manuscript). Line 294: "However, there were 4-5 times as many differentially expressed proteins..." Please add values. RESPONSE #52 Values added and additional detail added to this section (new lines 309-312 of the revised manuscript).

Line 298: "...difference was quite marked..." More factual info should be added. RESPONSE #53 Values added and additional detail has been provided (lines 314-317 of the revised manuscript), as follows; “____Cumulin appeared to have a greater impact on proteomic differential expression in both cell types than BMP15 did, with 59 vs 38 and 34 vs 27 upregulated proteins in cumulin vs BMP15 treated cumulus cells and oocytes respectively, and similarly 14 vs 6 downregulated proteins in cumulin vs BMP15 treated cumulus cells and oocytes respectively (Figure 4C)”.

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. RESPONSE #54 We agree with the reviewers point that it is logical that OSFs should target cumulus cells, with lesser impact on the oocyte. Nonetheless the treatments were performed on COCs, and even though the OSFs are targeting the cumulus cells, however ultimately the cumulus cells response is expected to impact oocytes. Therefore, it is relevant to look at proteomic changes to both cell types and also the related network analysis. We have however rephrased this section, to be more specific as to which data we are reporting, and have included additional citations (lines 325-334 of the revised manuscript).

__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. __RESPONSE #55 These are subjective observations of the typical morphological features seen in response to the different treatments. This is the typical application of TEM. Quantitative features of mitochondria are better assessed using confocal than TEM, which is the complimentary approach we took using MitroTracker in the companion figure 5B, the text for which immediately follows the TEM results. We altered the text at the sentence in question to note that these are subjective observations (line 340).

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. RESPONSE #56 The majority of published papers reporting data dependent analysis (DDA) proteomics results utilise just a single quantitative method (i.e., either spectral counting or peak intensity). This certainly simplifies reporting, and avoids confronting uncomfortable discrepancies between different analytical approaches. However, we reasoned that robust expression change data would maintain consistency, despite the orthogonal quantitative methods. We consider it a notable strength of the approach used here that we have utilised a differentially expressed proteins list which includes only those proteins with consistent direction of fold-change in both the spectral counting and peak intensity workflows. Please also refer to comments under RESPONSE #43, re spectral counting vs peak intensity quantitative methods in data dependent analysis (DDA) proteomics.

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? RESPONSE #57 Refer also to RESPONSE #43. In fact the main question being asked in many/most proteomics experiments is whether there is a real expression change between treatment groups. Therefore fold-change is the most pertinent common ground across disparate quantitative methodology, and indeed commonality of fold-change was the basis for merging the datasets. Since integrating peak areas is a very different approach to counting the number of spectra, then this difference in approach can make a big difference to the p-values, and is the reason why spectral counting is less sensitive to detect differential expression. For similar reasons the fold-change ratio may differ somewhat between these quantitative methodologies. However direction of fold-change is a minimum requirement for demonstrating consistent trends, hence we used this as the common ground for merging the datasets.

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. RESPONSE #58 We see the reviewers point in this subtle difference in wording. We agree that there is a switch in metabolism in untreated COCs during maturation – our point is that that process of changing metabolism is further remodelled by oocyte paracrine signals, to the overall betterment of the oocyte in terms of competence. We have edited this sentence to make this point clearer (line 413-415). Our data on energy charge, respiration, energy metabolite levels (Figure 6), redox potential (Figure 7) and mitotracker intensity (Figure 5) are all presented in comparison with “untreated” cells, and our conclusion that there is remodelling of metabolism is therefore relative to “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).

RESPONSE #59 All reference to Mitotracker in the context of mitochondrial counts only have been altered to Mitotracker being an indicator of mitochondrial function/polarity and/or counts. Accounts for comparisons of gene lists length between cell types: Lines 389-391 and 475-477 and 496-499). RESPONSE #60 Please see comments under RESPONSE # 53 and the new Figure 4C.

Line 395: "... a substantial number of oocyte upregulated proteins... Please provide number. RESPONSE #61 Additional specific proteins have been listed to support our claims of effects on specific processes (see lines 435-443 of the revised manuscript). Also see comments under RESPONSE # 7.

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. RESPONSE #62 Reference to meiosis and meiotic fidelity removed, line 435.

__ __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.

__RESPONSE #63____ Not clear that any change is requested or needed. This sentence is interpreting the significance of the results, as required in a Discussion.

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

RESPONSE #64____ The link to the epigenome was a reference to some published work. However it was linked to observations in the current data, and additional information has now been added to the updated manuscript to explain this further, as follows (currently lines 509-516);

"These included significantly enriched networks of RNA binding, helicase activity, ribonucleoprotein complex biogenesis, and mRNA processing (supplementary Tables S11 and S12; upregulated proteins RNF20, SHMT1, DHX30, DDX17, DDX5, PRPF19, RPS4X, NOP58, DDX39b, HNRNPF, RPS271, NOP56, PRPF6, POLR2b, CPSF6, OOEP), as well as upregulation of key epigenetic regulators (HDAC2 and UHRF1; see supplementary Table S11), histone modifying protein MTA2, and significant network enrichment of the spliceosomal complex (supplementary Table S12; proteins DDX5, PRPF19, HNRNPF, PRPF6, POLR2B), which has been linked to epigenetic regulation ____[12]____.

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.

RESPONSE #65____ Information relating to specific oocyte upregulated proteins and their cellular roles has been added to the updated manuscript (currently lines 434-443).

DNA binding and ribosomal constituents (Fig. 4A, 4C),

In vitro should be in italic because it is Latin. RESPONSE #6____6 corrected throughout

__Lines 125-126: are the batch numbers relevant to anything? __RESPONSE #6____7 We would assume so – for the historical record. These are in-house produced proteins, cumulin is complex to produce and only a few labs worldwide have made it.

__Line 168: Mascor = Mascot __RESPONSE #6____8 Corrected

__Line 168: a reference for the software? __RESPONSE #6____9 URL and published references added (lines 172-175 revised manuscript)

Line 178: need a reference for the software? RESPONSE #70 URL and published references added (line 185)

__Line 187: Need a complete source for "Procure, 812" __RESPONSE #71 Added

Line 188: Need a complete source for "Diatome" RESPONSE #72 Added

Line 197: Need a complete source for "Cell-Tak" RESPONSE #73 Added

Line 232: though = through RESPONSE #74 Corrected

Line 243: define OCR RESPONSE #75 Added

Line 268: If I am not mistaking, it is not a multispectral analysis. The multispectral values were analysed through a principal component analysis. RESPONSE #7____6 Data was analysed through linear discriminative analysis (LDA). This information has been added in Line 278.

Line 363: What is the "behaviour" of an oocyte and cumulus cells? RESPONSE #77 replaced with “function”

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. RESPONSE #78 Edited accordingly; lines 575-577.

Reviewer #3 (Significance (Required)):

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.

References

1. Mester, B., et al., Oocyte expression, secretion and somatic cell interaction of mouse bone morphogenetic protein 15 during the peri-ovulatory period. Reprod Fertil Dev, 2015. 27(5): p. 801-11.
2. Hussein, T.S., J.G. Thompson, and R.B. Gilchrist, Oocyte-secreted factors enhance oocyte developmental competence. Dev Biol, 2006. 296(2): p. 514-21.
3. Mottershead, D.G., et al., Cumulin, an Oocyte-secreted Heterodimer of the Transforming Growth Factor-beta Family, Is a Potent Activator of Granulosa Cells and Improves Oocyte Quality. J Biol Chem, 2015. 290(39): p. 24007-20.
4. Gilchrist, R.B., M. Lane, and J.G. Thompson, Oocyte-secreted factors: regulators of cumulus cell function and oocyte quality. Hum Reprod Update, 2008. 14(2): p. 159-77.
5. Sugiura, K., F.L. Pendola, and J.J. Eppig, Oocyte control of metabolic cooperativity between oocytes and companion granulosa cells: energy metabolism. Dev Biol, 2005. 279(1): p. 20-30.
6. Campbell, J.M., et al., Multispectral autofluorescence characteristics of reproductive aging in old and young mouse oocytes. Biogerontology, 2022. 23(2): p. 237-249.
7. Schwarz, D.S. and M.D. Blower, The endoplasmic reticulum: structure, function and response to cellular signaling. Cell Mol Life Sci, 2016. 73(1): p. 79-94.
8. Szklarczyk, D., et al., STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res, 2019. 47(D1): p. D607-D613.
9. Marei, W.F.A., et al., Proteomic changes in oocytes after in vitro maturation in lipotoxic conditions are different from those in cumulus cells. Sci Rep, 2019. 9(1): p. 3673.
10. Moorey, S.E., et al., Differential Transcript Profiles in Cumulus-Oocyte Complexes Originating from Pre-Ovulatory Follicles of Varied Physiological Maturity in Beef Cows. Genes (Basel), 2021. 12(6).
11. Ramus, C., et al., Benchmarking quantitative label-free LC-MS data processing workflows using a complex spiked proteomic standard dataset. J Proteomics, 2016. 132: p. 51-62.
12. Luco, R.F., et al., Epigenetics in alternative pre-mRNA splicing. Cell, 2011. 144(1): p. 16-26.

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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.

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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.

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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

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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.

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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.

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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.

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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.

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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).

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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.

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Reply to the reviewers

Manuscript number: RC-2021-01111

Corresponding author(s): Esther Stoeckli

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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.

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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.

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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.

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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.

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Manuscript number: RC-2022-01758

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

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1. General Statements [optional]

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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

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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.”

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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.

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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.

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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.

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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.

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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

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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

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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