Author response:

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

Reviewer #1 (Public Review):

Summary:

This study examines the spatial and temporal patterns of occurrence and the interspecific associations within a terrestrial mammalian community along human disturbance gradients. They conclude that human activity leads to a higher incidence of positive associations.

Strengths:

The theoretical framework of the study is brilliantly introduced. Solid data and sound methodology. This study is based on an extensive series of camera trap data. Good review of the literature on this topic.

Weaknesses:

The authors use the terms associations and interactions interchangeably.

This is not the case. In fact, we state specifically that "... interspecific associations should not be directly interpreted as a signal of biotic interactions between pairs of species…" However, co-occurrence can be an important predictor of likely interactions, such as competition and predation. We stand by our original text.

It is not clear what the authors mean by "associations". A brief clarification would be helpful.

Our specific definition of what is meant here by spatial association can be found in the Methods section. To clarify, the calculation of the index of associations is based on the covariance for the two species of the residuals (epsilon) after consideration of all species-specific response to known environmental covariates. These covariances are modelled to allow them to vary with the level of human disturbance, measured as human presence and human modification. After normalization, the final index of association is a correlation value that varies between -1 (complete disassociation) and +1 (complete positive association).

Also, the authors do not delve into the different types of association found in the study. A more ecological perspective explaining why certain species tend to exhibit negative associations and why others show the opposite pattern (and thus, can be used as indicator species) is missing.

Suggesting the ecological underpinnings of the associations observed here would mainly be speculation at this point, but the associations demonstrated in this analysis do suggest promising areas for the more detailed research suggested.

Also, the authors do not distinguish between significant (true) non-random associations and random associations. In my opinion, associations are those in which two species co-occur more or less than expected by chance. This is not well addressed in the present version of the manuscript.

Results were considered to be non-random if correlation coefficients (for spatial association) or overlap (for temporal association) fell outside of 95% Confidence Intervals. This is now stated clearly in the Methods section.  In Figure 3—figure supplement 1-3 and Figure 4—figure supplement 1-3, p<0.01 levels are also presented.

The obtained results support the conclusions of the study.

Anthropogenic pressures can shape species associations by increasing spatial and temporal co-occurrence, but above a certain threshold, the positive influence of human activity in terms of species associations could be reverted. This study can stimulate further work in this direction.

Reviewer #2 (Public Review):

Summary:

This study analyses camera trapping information on the occurrence of forest mammals along a gradient of human modification of the environment. The key hypotheses are that human disturbance squeezes wildlife into a smaller area or their activity into only part of the day, leading to increased co-occurrence under modification. The method used is joint species distribution modelling (JSDM).

Strengths:

The data source seems to be very nice, although since very little information is presented, this is hard to be sure of. Also, the JSDM approach is, in principle, a nice way of simultaneously analysing the data.

Weaknesses:

The manuscript suffers from a mismatch of hypotheses and methods at two different levels.

(1) At the lower level, we first need to understand what the individual species do and "like" (their environmental niche). That information is not presented, and the methods suggest that the representation of each species in the JSDM is likely to be extremely poor.

The response of each species to the environmental covariates provides a window into their environmental niche, encapsulated in the beta coefficients for each environmental covariate. This information is presented in Figure 2.

(2) The hypothesis clearly asks for an analysis of the statistical interaction between human disturbance and co-occurrence. Yet, the model is not set up this way, and the authors thus do a lot of indirect exploration, rather than direct hypothesis testing.

Our JSDM model is set up specifically to examine the effect of human disturbance on co-occurrence, after controlling for shared responses to environmental variables.  It directly tests the first hypothesis, since, if increase in indices of human disturbance had not tended to increase the measured spatial correlations between species as detected by the model, we would have rejected our stated hypothesis that human modification of habitats results in increased positive spatial associations between species.

Even when the focus is not the individual species, but rather their association, we need to formulate what the expectation is. The hypotheses point towards presenting the spatial and the temporal niche, and how it changes, species for species, under human disturbance. To this, one can then add the layer of interspecific associations.

Examining each species one by one and how each one responds to human disturbance would miss the effects of any meaningful interactions between species.  The analysis presented provides a means to highlight associations that would have been overlooked.  Future research could go on to analyze the strongest associations in the community and the strongest effects of human disturbance so as to uncover the underlying interactions that give rise to them and the mechanisms of human impact.  We believe that this will prove to be a much more productive approach than trying to tackle this problem species by species and pair by pair.

The change in activity and space use can be analysed much simpler, by looking at the activity times and spatial distribution directly. It remains unclear what the contribution of the JSDM is, unless it is able to represent this activity and spatial information, and put it in a testable interaction with human disturbance.

The topic is actually rather complicated. If biotic interactions change along the disturbance gradient, then observed data are already the outcome of such changed interactions. We thus cannot use the data to infer them! But we can show, for each species, that the habitat preferences change along the disturbance gradient - or not, as the case may be.

Then, in the next step, one would have to formulate specific hypotheses about which species are likely to change their associations more, and which less (based e.g. on predator-prey or competitive interactions). The data and analyses presented do not answer any of these issues.

We suggest that the so-called “simpler” approach described above is anything but simple, and this is precisely what the Joint Species Distribution Model improves upon.  As pointed out in the Introduction, simply examining spatial overlap is not enough to detect a signal of meaningful biotic interaction, since overlap could be the result of similar responses to environmental variables.  With the JSDM approach, this would not be considered a positive association and would then not imply the possible existence of meaningful interaction.

Another more substantial point is that, according to my understanding of the methods, the per-species models are very inappropriate: the predictors are only linear, and there are no statistical interactions (L374). There is no conceivable species in the world whose niche would be described by such an oversimplified model.

While interaction terms can be included in the JSDM, this would considerably increase the complexity of the models.  In previous work, we have found no strong evidence for the importance of interaction terms and they do not improve the performance of the models.

We have no idea of even the most basic characteristics of the per-species models: prevalences, coefficient estimates, D2 of the model, and analysis of the temporal and spatial autocorrelation of the residuals, although they form the basis for the association analysis!

The coefficient estimates for response to environmental variables used in the JSDM are provided in Figure 2 and Figure 2—source data 1.

Why are times of day and day of the year not included as predictors IN INTERACTION with niche predictors and human disturbance, since they represent the temporal dimension on which niches are hypothesised to change?

Also, all correlations among species should be shown for the raw data and for the model residuals: how much does that actually change and can thus be explained by the niche models?

The discussion has little to add to the results. The complexity of the challenge (understanding a community-level response after accounting for species-level responses) is not met, and instead substantial room is given to general statements of how important this line of research is. I failed to see any advance in ecological understanding at the community level.

We agree that the community-level response to human disturbance is a complex topic, and we believe it is also a very important one.  This research and its support of the spatial compression hypothesis, while not providing definitive answers to detailed mechanisms, opens up new lines of inquiry that makes it an important advance.  For example, the strong effects of human disturbance on certain associations that were detected here could now be examined with the kind of detailed species by species and pair by pair analysis that this reviewer appears to demand.

Reviewer #1 (Recommendations For The Authors):

L27 indicates instead of "idicates".

We thank the reviewer for catching that error.

L64 I would refer to potential interactions or just associations. It is always hard to provide evidence for the existence of true interactions.

We have revised to “potential interactions” to qualify this statement.

L69 Suggestion: distort instead of upset.

We thank the reviewer for catching that error.

L70-71 Here, authors use the term associations. Please, be consistent with the terminology throughout the manuscript.

We thank the reviewer for raising this important point.  The term “co-occurrence” appears to be used inconsistently in the literature, so we have tried to refer to it only when referencing the work of us. For us, co-occurrence means “spatial overlap” without qualification as to whether it is caused by interaction or simply by similar responses to environmental factors (see Blanchet et al. 2020, Argument 1). In our view, interactions refer to biotic effects like predation, competition, commensalism, etc., while associations are the statistical footprint of these processes.   In keeping with this understanding, in Line 73, we changed "association" to the stronger word "interaction," but in Line 76, we keep the words "spatiotemporal association", which is presumed to be the result of those interactions. In Line 91, we have changed “interactions” to “associations,” as we do not believe interactions were demonstrated in that study. 

L76 "Species associations are not necessarily fixed as positive or negative..." This sentence is misleading. I would say that species associations can vary across time and space, for instance along an environmental gradient.

We thank the reviewer for pointing out the potential for confusion.  In Line 79, we have changed as suggested.

L78 "Associations between free-ranging species are especially context-dependent" Loose sentence. Please, explain a bit further.

We have changed the sentence to be more specific; ”Interactions are known to be context-dependent; for example, gradients in stress are associated with variation in the outcomes of pairwise species interactions.”

L83-85 This would be a good place to introduce the 'stress gradient' hypothesis, which has also been applied to faunal communities in a few studies. According to this hypothesis, the incidence of positive associations should increase as environmental conditions harden.

In our review of the literature, we find that the stress gradient hypothesis is somewhat controversial and does not receive strong support in vertebrates.  We have added the phrase “…the controversial stress-gradient hypothesis predicts that positive associations should increase as environmental conditions become more severe…”

L86-88 Well, overall, the number of studies examining spatiotemporal associations in vertebrates is relatively small. That is, bird associations have not received much more attention than those of mammals. I find this introductory/appealing paragraph a bit rough. I think the authors can do better and find a better justification for their work.

We thank the reviewer for the comments.  We have rewritten the paragraph extensively to make it clearer and to provide a stronger justification for the study.

L106 "[...] resulting in increased positive spatial associations between species" I'd say that habitat shrinking would increase the level of species clustering or co-occurrence, but in my opinion, not necessarily the incidence of positive associations. It is not clear to me if the authors use positive associations as a term analogous to co-occurrence.

We thank the reviewer for raising this very important distinction.  Habitat shrinking would increase levels of species co-occurrence, but this is not particularly interested.  We wanted to test whether there were effects on species interactions, as revealed by associations.  We find that the terms association and co-occurrence are used somewhat loosely in the literature and so have made some new effort to clarify and systematize this in the manuscript.  For example, there appear to be a differences in the way “co-occurrence” is used in Boron 2023 and in Blanchet 2020. We do not use the term "positive spatial association" as analogous to "spatial co-occurrence.". Spatial co-occurrence, which for us has the meaning of spatial overlap, could simply be the result of similar reactions to environmental co-variates, not reflecting any biotic interaction. Joint Species Distribution Models enable the partitioning of spatial overlap and segregation into that which can be explained by responses to known environmental factors, and that which cannot be explained and thus might be the result of biotic interactions.  It is only the latter that we are calling spatial association, which can be positive or negative.   These associations may be the statistical footprint of biotic interactions.

Results:

Difference between random and non-random association patterns. It is not clear to me if the reported associations are significant or not. The authors only report the sign of the association (either positive or negative) but do not clarify if these associations indicate that two species coexist more or less than expected by chance. In my opinion, that is the difference between true ecological associations (e.g., via facilitation or competition effects) and random co-existence patterns. This is paramount and should be addressed in a new version of the manuscript.

This information is provided in Figure 3—figure supplement 1,2,3 and Figure 4—figure supplement 1,2,3.  This is referenced in the text as follows, “… correlation coefficients for 18 species pairs were positive and had a 95 % CI that did not overlap zero, and the number increased to 65 in moderate modifications but dropped to 29 at higher modifications" and so on. This criterion for significance (ie., greater than expected by chance) is now stated at the end of the Materials and methods.  In Figure 3—figure supplement 1,2,3 and Figure 4—figure supplement 1,2,3, those correlations that were significant at p<0.01 are also shown.

I am also missing a more ecological explanation for the observed findings. For instance, the top-ranked species in terms of negative associations is the red fox, whereas the muntjac seems to be the species whose presence can be used as an indicator for that of other species. What are the mechanisms underlying these patterns? Do red foxes compete for food with other species? Do the species that show positive associations (red goral, muntjac) have traits or a diet that are more different from those of other species? More discussion on these aspects (role of traits and the trophic niche) would be necessary to better understand the obtained results.

The purpose of this paper was to test the compression hypotheses, and we have tried to keep that as the focus.  However, the analysis does open up interesting lines of inquiry for future research to decipher the details of the interactions between species and the mechanisms by which human disturbance facilitates or disrupts these interactions. The reviewer raises some interesting possibilities, but at this point, any discussion along these lines would be largely speculation and could lengthen the paper without great benefit. 

Reviewer #2 (Recommendations For The Authors):

The manuscript should be accompanied by all data and code of analysis.

All data and RScripts have been made available in Science Data Bank: https://doi.org/10.57760/sciencedb.11804.

The sentence "not much is known" is weak: it suggests the authors did not bother to quantify what IS known, and simply waved any previous knowledge aside. Surely we have some ideas about who preys on whom, and which species have overlapping resource requirements (e.g., due to jaw width). For those, we would expect a particularly strong signal, if the association is indeed indicative of interactions.

We believe that the reviewer is referring to the statement in Line 90-92 about the lack of understanding of the resilience of terrestrial mammal associations to human disturbance.  We have added a reference to one very recent publication that addresses the issue (Boron et al., 2023), but otherwise we stand by our statement. We have, however, added a qualifier to make it clear that we did indeed look for previous knowledge; "However, a review of the literature indicates that ...."

Figures:

Fig. 1. This reviewer considers that this is too trivial and should be deleted.

This is a graphical statement of the hypotheses and may be helpful to some readers.

Fig. 2. Using points with error bars hides any potential information.

Done as suggested.

That only 4 predictors are presented is unacceptably oversimplified.

Only 4 predictors are included because, in previous work, we found that adding additional predictors or interactions did little to improve the model’s performance (Li et al. 2018, 2021 and 2022) and could lead to over-fitting.

Fig. 5. and 6. aggregate extremely strongly over species; it remains unclear which species contribute to the signal, and I guess most do not.

The number of detection events presented in Table 1 should help to clarify the relative contribution of each species to the data presented in Figures 5 and 6.

This reviewer considers that the introduction 'oversells' the paper.

L55: can you give any such "unique ecological information"

L60: Lyons et al. (Kathleen is the first name) has been challenged by Telford et al. (2016 Nature) as methodologically flawed.

The first name has been deleted.  The methodological flaw has to do with interpretation of the fossil record and choice of samples, not with the need to partition shared environmental preferences and interactions.

L61 contradicts line 64: Blanchet et al. (2022, specifying some arguments from Dormann et al. 2018 GEB) correctly point out that logically one cannot infer the existence or strength from co-occurrence data. It is thus wrong to then claim (citing Boron et al.) that such data "convey key information about interactions". The latter statement is incorrect. A tree and a beetle can have extremely high association and nothing to do with each other. Association does not mean anything in itself. When two species are spatially and temporally non-overlapping, they can exhibit perfect "anti-association", yet, by the authors' own definition, cannot interact.

We believe that the reviewer’s concerns arise from a misunderstanding of how we use the term association.  In our usage, an association is not the same as co-occurrence or overlap, which may simply be the result of shared responses to environmental variables.  The co-occurring tree and beetle would not be found to have any association in our analysis, only shared environmental sensitivities.  In contrast, associations can be the statistical footprint of interactions, and would be overlaid onto any overlap due to similar responses to the environment.  In the case of negative associations, such as might be the result of competitive exclusion or avoidance of predators, the two species would share environmental responses but show lower than expected spatial overlap.  Even though they might be only rarely found in the same vicinity, they would indeed be interacting when they were together.

Joint Species Distribution Models "allow the partitioning of the observed correlation into that which can be explained by species responses to environmental factors... and that which remains unexplained after controlling for environmental effects and which may reflect biotic interactions." (Garcia Navas et al. 2021). It is the latter that we are calling “associations.”

L63: Gilbert reference: Good to have a reference for this statement.

This point is important, but the reviewer’s comments below have made it clear that it is even more important to point out that strong interactions should be expected to lead to significant associations.  We have added a statement to clarify this.

L70-72: Incorrect, interactions play a role, not associations (which are merely statistical).

In this, we agree, and we have revised the statement to refer to interactions, not associations. In our view, an interaction is a biological phenomenon, while an association is the resulting statistical signal that we can detect.

L75: Associations tell us nothing, only interactions do. Since these can not be reliably inferred, this statement and this claim are wrong.

We thank the reviewer for raising this point, but we beg to disagree. Strong interactions should be expected to lead to significant associations that can be detected in the data. Associations, which can be measured reliably, are the evidence of potential interactions, and hence associations can tell us a great deal.  We have added a note to this effect after the Gilbert reference above to clarify this point.

However, we do accept that associations must be interpreted with caution. As Blanchet et al. 2020 explain, " …the co-occurrence signals (e.g. a significant positive or negative correlation value) estimated from these models could originate from any abiotic factors that impact species differently. Therefore, this correlation cannot be systematically interpreted as a signal of biotic interactions, as it could instead express potential non-measured environmental drivers (or combinations of them) that influence species distribution and co-distribution.”  Or alternatively an association could be the result of interaction with a 3rd species. 

L87: Regarding your claim, how would you know you DO understand? For that, you need to formulate an expectation before looking at the data and then show you cannot show what you actually measure. (Jaynes called this the "mind-projection fallacy".)

We are not sure if the reviewer is criticizing our paper or the entire field of community ecology.  Perhaps it is the statement that “….resilience of interspecific spatiotemporal associations of terrestrial mammals to human activity remains poorly understood….”  Since we are confident that the reviewer believes that mammals do interact, we guess that it is the term “association” that is questioned.  We have revised this to “…the impacts of human activity on interspecific interactions of terrestrial mammals remains poorly understood…” 

In this particular case, we did formulate an expectation before looking at the data, in the form of the two formal hypotheses that are clearly stated in the Introduction and illustrated in Figure 1. If the hypotheses had not been supported, then we would have accepted that we do not understand. But as the data are consistent with the hypotheses, we submit that we do understand a bit more now.

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Reply to the reviewers

We would like to thank all reviewers for their detailed and constructive feedback, which substantially helped improve the manuscript. We apologise for the time taken for the revisions, which was partially due to the first author (successfully) writing and defending her PhD thesis in the same time frame. We would like to point out already here that, based on reviewers' feedback, main figure 6 is completely redone and the conclusions of this figure have changed substantially. We no longer suggest RNA chaperoning activity (it was identified as being due to the high concentration of TEV protease, in a control suggested by the reviewers). Instead, our refined assay conditions with lower TEV protease concentration identified ribonuclease activity of membrane-bound full-length 2C, which is consistent with a publication from 2022 (PMID: 35947700).

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

Evidence, reproducibility, and clarity

Summary:

In this study by Shankar and colleagues, the authors aim to understand the structure and function of the enterovirus 2C protein, a putative viral helicase with AAA+ ATPase activity. Using poliovirus (as a model enterovirus) 2C, the author's propose the protein contains two amphipathic helices (AH1 and AH2) at the N-terminus that are divided by a conserved glycine. Using purified MBP-tagged 2C and N-terminal 2C truncations, their data suggests AH1 is primarily responsible for clustering at membranes, whilst AH2 is the main mediator of 2C oligmerisation and membrane binding. Furthermore, 2C was suggested to be able to recruit RNA to membranes, with a preference for dsRNA, and the author's data implies that the helicase activity of 2C is ATP-independent. Instead, the ATP activity appears to be required for 2C hexamer formation or chaperone activity. The manuscript is generally well written /presented and the author's present very interesting data which raises several questions, some of which require additional experimentation to help support the author's conclusions. Specific comments are as follows.

We thanks the reviewer for the overall positive assessment, as well as the specific comments below.

Major Comments:

1. The authors use four main constructs throughout the paper: full-length 2C, 2C with deletion of AH1 (ΔAH1), 2C with both AH1 and AH2 deleted (ΔMBP) and 2C with an extended N-terminal deletion. From this, the author's draw conclusions on the function of both AH1 and AH2. One of the author's main conclusions is that AH2 is the main mediator of 2C membrane association (e.g., in line 169). However, is it possible to conclude the relative importance of AH1 vs AH2 without testing a construct containing the deletion of AH2 only (ΔAH2)? This should be generated and used alongside this data to fully define the relative importance of AH1 and AH2 in these assay and remove the possibility that the deletion of AH1 changes the structure and/or function of AH2, which could also result in the observed differences.

This was a very good suggestion. We expressed and purified the ΔAH2 protein requested by the reviewer and characterized its oligomeric state as well as its membrane binding. It turns out, as suspected, that the ΔAH2 protein behaves very similarly to the ΔMBD protein (i.e. it does not form higher order oligomers and does not bind membranes). The changes in the manuscript due to this addition are many but can primarily be found in main figures 2-3 and their associated supplementary figures.

Previous structural predictions of 2C do not appear to have two separate AHs at the N-terminus. Are the AH1 and AH2 structures predicted to be formed in the context of the entire 2C protein, 2BC precursors and polyprotein? Are there structural approaches that could provide experimental evidence for two separate AH at the N-terminus?

This is a good point. Previous predictions were not that detailed, partially since they were done in the pre-alphafold era. Unfortunately, we cannot think of a tractable experimental method that could verify the split nature of the amphipathic helix in the only context that would matter: the protein bound to a membrane. A long-term goal would be in situ structures of full-length 2C on membranes using cryo-electron tomography, but our current sample and data sets are not sufficient for this. We added a mention of the long-term need for experimental structures of full-length 2C on lines 315-318 in the discussion.

Why are the 2C dimers (lines 137-138) not apparent on the mass photometry data presented (figure 2)?

Different constructs were measured by mas photometry and SEC-MALS. Also, the required concentration is 100-1000x lower for mass photometry which will affect a dynamic equilibrium in case the same construct were measured by the two methods.

It appeared that binding of ΔMBD-2C was better when POPS is in the membrane (line 174). What is the explanation for this and was this finding significant?

Well spotted. It may mean that 2C has a second, lower affinity membrane-binding site which is charge-dependent somewhere outside the MBD. We now added a mention of this in the discussion, lines 321-323.

From the author's data on lipid drop clustering they conclude ΔAH1 is more effective for clustering, however, the ΔAH1 construct produces pentamers not hexamers (from Figure 2). Is formation of hexamers related to or required for membrane clustering?

ΔAH1 is LESS effective at clustering, not more. As for the mention of pentamers in the original submission: we now think this was an unfortunate choice of words. The mass photometry data for 2C(ΔAH1) could more parsimoniously be interpreted as a mix of hexamers and other (unknown to us) smaller oligomers such as trimers. We have removed all mentions of pentamers.

The replicon data presented in Figure 7 should include a replication-defective control (e.g., polymerase mutant), in order to compare how defective in replication ΔAH1 and ΔMBP deletions are compared to a fully-defective construct. Likewise, deletion of ΔAH1 in this construct is likely to affect processing of the viral polyprotein where several previous studies with picornaviruses have demonstrated that the residues in the P2'-P4' positions can change cleavage efficiency (e.g., PMID: 2542331), or the structure of 2C, leading to the reduction of replication.

Thanks for these good comments. We made the polymerase-dead (GDD-to-GAA) replicon and remeasured it side by side with the 2C replicons. It has a similar luciferase activity indicating that no replication takes place in the 2C deletion replicons. This is shown in the new figure 7. As for the possibility or processing defects, we mentioned this in the original discussion and have now cited the reference suggested by the reviewer in this context (line 324).

How does the author's model of ATPase-independent helicase activity and an APT-dependent required RNA chaperone activity fit with 2 step model for RNA binding and ATPase activity suggested by Yeager et al (PMID: 36399514)?

Acting upon comments from other reviewers, we completely redid the "helicase assay" in the revised manuscript. It turns out that the ATP-independent unwinding activity in the original submission was an artefact of the assay conditions (specifically, of the TEV protease at the higher concentration we used in the old assay). In our improved assay we neither see helicase activity nor ATP-independent RNA chaperoning activity.

Optional major comments that would increase the significance of the work:

All of the optional comments below are exceptionally interesting. But given the long time needed for the several major changes to this manuscript (e.g. the ΔAH2 protein characterization and reoptimisation of the helicase assay) we believe it is more sensible to address them in future studies, for which the 2C reconstitution system can be used.

The preference for dsRNA over ssRNA appears to be quite small (Figure 5d). In the context of a viral infection where ssRNA is likely to outnumber dsRNA at different times during infection is this preference physiologically relevant? In relation to this, what size stretch of dsRNA is required for preference, and could this correspond to cis-acting RNA structural elements, dsRNA as it escapes 3D polymerase or as part of the RF and RI forms (PMID: 9343205)? What is the proposed mechanism of how dsRNA outcompetes membrane tethering of 2C? OPTIONAL The author's study has been conducted in the absence of other viral non-structural proteins. What is the physiological importance of the observations, such as membrane interaction/clustering or RNA binding when presented in the context of the other replication machinery. OPTIONAL Do 2C monomers, dimers and hexamers have different functions in viral replication perhaps at different stages of replication and which of these forms are relevant during viral infection or can they all be detected during infection? Can any suggested separate functional arrangements be separated by genetic complementation experiments? OPTIONAL

Minor comments:

1. The author's appear to interchange between naming/nomenclature of the constructs which makes it confusing to follow (for example, ΔMBD is the same as 2C(41-329) likewise, 2C(Δ115) is sometimes called 2C(116-329)). It would be much easier to follow if the naming of constructs was consistent throughout (unless I am misunderstanding some subtlety in the difference between such constructs).

Thanks very much for spotting this. We have fixed it.

The author's suggest a pentamer arrangement for the ΔAH1 construct, however in the mass photometry data (figure 2D), a hexamer is indicated with the arrow. It would be helpful to change the label to indicate the size of the pentamer where this is being generated, not the hexamer.

As mentioned above, we think the "pentamer" designation of the original manuscript was unfortunate. It is more parsimonious to interpret this as a mix of states, hexamer and undefined snaller.

In most figures, data for full-length 2C, ΔAH1 and ΔMBP is shown. However data for ΔMBP is missing in Figure 4. Using ΔMBP may demonstrate even lower clustering, hinting that AH2 is also involved in this process.

Thanks for this comment. In our view, it can be derived from figure 3 (which shows lack of binding to PC/PE membranes) that the ΔMBD construct would not cluster membranes under the conditions of the assay (clustering requires concomitant binding to two membranes). We now describe our rationale for this on lines 220-222. However, we did include the ΔMBD protein in the new negative staining TEM supplementary figure where it and ΔAH2 show no signs of clustering (figure S10).

I think it would be better for normalise the data in the flotation experiments such that the percentage of 2C in the upper faction is presented as relative to the amount of lipid in the upper fraction (presented in Figure S4).

The change suggested by the reviewer would make it impossible to show the important no-liposome control (leftmost bar in Fig. 3C) in the same plot as the other measurements. We believe that would unnecessarily complicate the figure. Thus, we opted to keep the measurement that are normalised by lipid fluorescence in the supplementary figure. Instead, we now added another mention of this supplementary figure in the legend to main figure 3.

At several places (e.g., lines 232 and 272) the author's refer to "realistic systems". I think the term "physiologically relevant" might be more appropriate.

Agreed and changed throughout.

Line 237: I think "y" is a typo and should read "by".

Thanks. This text was reworked due to the major changes to figure 6.

Reviewer #1 (Significance (Required)):

Significance

I have limited expertise with structural biology but specialise my research on positive-sense RNA virus replication, structure and function. This research is of interest to a broad audience of researchers investigating many positive-sense RNA viruses, which extends beyond the viral family studied here. The work utilises novel techniques to begin to understand the specific roles of 2C in poliovirus replication. The author's data add important incremental new insight into recent studies on viral helicase proteins as referenced in the study, however, a key limitation is understanding the importance/relevance of their observations during a viral infection.

We thanks the reviewer for this positive and nuanced appraisal of our work.

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

The authors present an alternative assay system to investigate picornavirus 2C, a protein that is tricky to analyze biochemically in its full length form because of an amphipathic helix at the N-terminus. Poliovirus 2C is expressed with an N-terminal MBP tag, a 50kD protein that helps with solubility as is commonly used for 2C investigations. A difference here is that liposomes are included to mimic membranes for 2C attachment. The key findings are that 2C induces clustering of of liposomes, that double stranded RNA binding by 2C impacts this clustering effect and that a free N-terminus (after cleavage of MBP by TEV protease) is needed for RNA binding and an ATP independent (ie non helicase) RNA duplex separation activity.

Major:

In the floatation assays in figure 3 the authors use a system where MBP-2C is fluorophore-labeled with ATTO488 on exposed cysteines. Poliovirus and other enterovirus 2C has a very well characterized zinc finger domain that has cysteines coordinating a zinc ion. Mutation experiments previously showed that these cysteines are necessary for viral replication and 2C stability. Have the authors controlled for disruption of the zinc finger domain by the labelling of cysteines with ATT0488 and checked if the protein remains folded?

We completely agree with the reviewer and apologise for the omission in the original submission. We have now included a Zn content measurement, which shows unchanged levels between labelled and unlabelled 2C protein (Figure S7). Also, we now in the revised manuscript explicitly describe our original reasoning for labelling on native cysteines: the presence of two cysteines which are not necessary for viral replication and which are more solvent exposed-exposed (and thus more likely to be labelled) in the crystal structure of the soluble fragment of 2C (lines 176-181).

In the analysis of the amphipathic helix, did the authors include membranes in their structural predictions o just the free helix? How does inclusion of membranes impact the predictions? In the predictions in Figure D, only 2 of 4 show a kink and there doesn't seem to be a correlation between those that predict a kink or not and whether the hydrophobic side is aligned in Figure S1.

Unfortunately, predicting a protein structure with the interacting membrane is beyond what is currently doable with protein prediction methods (one would have to combine protein structure predictions with molecular dynamics simulations including a membrane). Based on general principles of protein structure, it is likely that there is some flexibility around G17. Thus there may not be a single "kink angle" for any given virus, but we believe that the presence of the kink (and offset hydrophobic surfaces) for a number of viruses lends credibility and robustness to the observation. We added some descriptions of this thinking on lines 126-127.

Based on previous structures of 2C from different viruses the N-terminal amphipathic helix containing region is predicted to localize on one face of the predicted hexametric structure tethering 2C to the membrane. How does the authors hypothesized model explain 2C dependent clustering? is there evidence that 2C hexamers can oligomerize further into dodecamers for example, maintaining separate faces to enable N-terminal interaction with different membranes? What is the distance between the liposomes in figure 4 at the points of density attributed to 2C? How does this compare to the size of 2C determined in previous structural studies? Is it consistent with one hexamer/2 hexamers sitting on top of one another?

These are very interesting questions but we believe it is prudent to limit our speculation at this point. Eventually, we hope that larger data sets of cryo-electron tomography, coupled to subtomogram averaging, may provide a more definitive answer. What we managed to do with our current cryo-electron tomography data set is to estimate the volume of individual protein densities, and from the volume calculate an estimated molecular mass of the individual complexes seen in the tomograms. This correlates very well with 2C hexamers (new figure 4D).

In the Discussion lines 278-285 the authors suggest that having MBP attached may reflect the polyprotein condition. Can they make a construct with MBP-2B2C to examine interaction with liposomes and assess 2C function?

This is a highly relevant question, but the biochemistry of 2BC is even more challenging than 2C, and we are unfortunately nowhere near being able to work with purified 2BC at the moment.

Discussion lines 293-296, the possibility of two different populations of 2C, binding RNA or membranes cannot be excluded, there is much more 2C around late in infection that present in early infection- the model in figure 8 doesn't acknowledge/capture this.

We have changed the model figure such that more 2C is seen later, and the clustering function is also seen late in infection. The original discussion text referred to (which is unchanged) talks about a "preferential role in RNA replication and particle assembly at later time points" specifically for this reason. We hope the new figure 8 is better at conveying this message.

Discussion lines 313-317, the authors don't reference a study where a mutant of foot-and-mouth disease virus 2C lacking the n-terminal amphipathic helix that could bind but not hydrolyze ATP, hexamerized in the presence of RNA that seems pertinent here (PMID: 20507978).

Thanks for the suggestion. However, after the extensive changes we made to the revised to figure 6 based on excellent reviewer comments (essentially: the RNA chaperoning activity turned out to be an artefact, the improved assay shows no sign of RNA unwinding but instead of 2C-mediated ribonuclease activity), these sentence of the original discussion lost most of their context and we opted to remove them.

Some evidence of MBP-2C cleavage by TEV in the different assays used should be presented as this is a major focus of discussion and currently no gels show TEV cleavage is happening.

Thanks for the suggestion - we agree. We now show these in the new supplementary figures S5 and S12.

Reviewer #2 (Significance (Required)):

The work presents an additional methodology to investigate a a protein that has previously been difficult to study. The authors acknowledge that there is still a lot of 2C biology that remains to be discovered.

Thanks, we agree.

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

The manuscript provides insights into the role of the N-terminus in membrane binding and its importance in the various functions of 2C.

Major issues

Line 103-119. Is this novel? I thought people had done a lot of bioinformatic analysis of PV 2C (especially Wimmer) who also did mutational work to analyse the importance of various amino acids in the N-terminal helix. I feel like the paper in general, and this section in particular, underplays the large body of work that has been done on the amphipathic helix by various groups.

We apologise if our original manuscript didn't sufficiently acknowledge previous work in the field. In the first sentence of the mentioned paragraph (now lines 112-113) , we did however cite several papers that have previously addressed the amphipathic nature of the N-terminus of 2C. We have now added two more references along the same line, and changed the wording in a way that we hope better bring across that the amphipathic nature per se has been studies before. We would be happy to add more specific references if the reviewer has any suggestions. However, the rest of our analysis IS indeed novel for the following reasons: (i) we show that the amphipathic region is not a simple, single amphipathic helix, but instead has a conserved glycine (helix breaker/destabiliser residue) and two distinct amphipathic stretches before and after this region, (ii) we use alphafold2 (not available at the time of the earlier work) to provide the first reliable structural models of the membrane-binding domain. These models consistently, across several enterovirus 2C proteins, reveal that the hydrophobic surfaces of the first and second amphipathic regions, on either side of the conserved glycine 17, are offset from one another. This lends additional credibility to the distinct nature of these regions which have not previously been identified as such and which we also show in the biochemical assays to be functionally distinct. We have now also added a clarification to the Discussion that the N-terminus of 2C had previously been identified as its membrane-binding domain and we cite references for this. We hope that these changes will sufficiently acknowledge earlier work in the field while clearly pointing out the advance that our paper makes.

Line 132. Did you validate your column with known MW standards? The peak for full length and deltaAH1 look fairly standard for 2C, in that you have a mixture of species. Not sure you can say it is a hexamer when it is such a broad peak. C doesn't really help you too much since the counts at 400 (pentamer) and 480 (hexamer) are almost the same with quite large error bars. Like most people that have worked with 2C I think the best you can say is that you are making some kind of oligomerized 2C that includes hexamer, pentamer, etc. Why no dimer for MBP-2C and MBP-2C(delta AH1) when compared to the other constructs?

We did not calibrate the gel filtration column since the outcome would anyway be a more crude estimate of molecular mass than the mass photometry and SEC-MALS measurements. But we do agree with the reviewer on the broad mass photometry peaks. To address this experimentally, we compared the existing MBP-2C spectra to new recordings on apoferritin, a highly stable homomultimeric protein complex of a similar mass to aa MBP-2C hexamer. The apoferritin mass estimate is overlayed with the full-length MBP-2C in the new figure 2D and the corresponding supplementary figure S3. This indeed shows that the MBP-2C peak is broader, i.e. consistent with a mix of species which are predominantly but not only hexamers. We describe and discuss this on lines 145-149. As for the mention of pentamers in the original submission: we now think this was an unfortunate choice of words. The mass photometry data for 2C(ΔAH1) could more parsimoniously be interpreted as a mix of hexamers and other (unknown to us) smaller oligomers such as trimers. We have removed all mentions of pentamers.

Line 143. Does your data show that there are two amphipathic helices? Bioinformatics suggests it but your experiments just show the importance of the two areas in oligomerization, not that it is forming two helices.

We agree that the choice of words was not idea and have now changed it to "structure predictions indicate" (lines 162).

Figure S2. Your preps are still relatively dirty, which isn't ideal for biochemical assays. Especially lane 3, where you are looking at 50-60% purity. I don't want you to re-run experiments but I think you need to comment on the purity of the protein you are working with. Also I don't like that you removed the top and bottom of the SDS-PAGE. How much protein never entered the gel. Is there a big fat band at 20 kDa? You need to have the full gel here. Did you measure 260 nm of the preps as well to see if you had bound RNA to the 2C?

Thanks for the comment, we agree that our original submission lacked detail in the description of the protein purification. This is now addressed with the new figure S2 which shows size exclusion chromatograms of the fluorophore-labelled proteins (same chromatograms as in figure 2) and the corresponding uncropped gels imaged both in the stain-free channel (showing all proteins) and in the fluorescence channel. The A260/A280 ratio measured for all proteins shows that they are free of nucleic acids at the point of imaging. The protein preps are not 100% homogeneous but we do believe that they are more than 50-60% pure.

Lines 170. Wasn't this done in the recent "An Amphipathic Alpha-Helix Domain from Poliovirus 2C Protein Tubulate Lipid Vesicles"? I don't see it referenced. What is novel about the current work when compared to that paper? Any differences?

Thanks for pointing this out. The referenced study worked with a synthesized, isolated peptide corresponding to AH2 (i.e. not with full protein). An amphipathic peptide outside the context of its protein cannot be expected to recapitulate the properties of the entire protein, e.g. since it is not spatially constrained in how it interactis with membranes. As one example (relating to the title of that paper) we don't see full-length 2C protein tubulating membranes the way the isolated peptide does. As for the reviewer's question about novelty, the paper mentioned does not identify the split nature of the amphipathic region, does not consider the role of AH1, does not characterise the membrane-binding properties of full-length 2C with respect to liposome membrane composition and size, does not identify and characterise the membrane clustering properties of 2C, nor its interactions with nucleic acid when bound to a membrane. However, we do agree that we should have cited the paper in our manuscript. We now cite it in the discussion, lines 320-321.

I'm surprised by the lack of electron microscopy (negative stain mostly) of both the oligomerized 2C and the various liposomes. I know the Carlson group is a microscopy group so why the lack of validation using electron microscopy of the various DLS experiments? I know you did cryo-ET for one of the constructs but I think negative stain electron microscopy of other constructs would be useful.

Thanks for the suggestion. As suggested, we have now expanded the analysis with negative staining EM of several more constructs studied by DLS. It can be found in the new supplementary figure S10.

Figure 4C. What evidence is there that this is 2C apart from you added it to the liposomes? It also comes back to the relative impurity of your protein prep. Could this be E.coli contamination?

Thanks for this comment. We have now added a new supplementary figure (S5) showing SDS-PAGE gels of the reactions used for flotation and DLS assays - which are identical to the cryo-ET samples. In addition, we estimated the molecular mass of the individual, putative 2C desities in the cryo-electron tomograms by measuring their volume. This analysis, which can be found in the new figure 4D, shows that the estimated mass of individual protein densities is consistent with a hexamer of full-length 2C. In addition, we mention in the discussion the long-term need to determine high-resolution structures of membrane-bound 2C using cryo-ET and subtomogram averaging (lines 315-318).

Figure 8. Is this model supported by the data in this paper? Your cryo-ET says that 2C is there but that isn't supported by any other data. How is the dsRNA protected from the innate immune system in this model? is it just sat out in the cytosol? How is the nascent ssRNA packeged into the capsid? Is there competition between the dsRNA and capsid for 2C binding (which your model suggests)? I know it sounds like I am being overly critical of the model but in my opinion there are still too many unanswered questions in the field to come up with a half decent model.

Thanks for this comment. We are the first to agree that our understanding of the roles of 2C is far from complete! We should have been more clear that the model figure represents some of the roles of 2C identified to date, and does not claim to be complete. However we do feel that a model figure serves a purpose of putting our findings into a context, and also providing testable hypotheses for future research . As for the question, some of the roles of 2C shown in the model figure (in particular, particle assembly) are rather supported but earlier work of ourselves and others. We have now produced a new model figure and changed the figure legend to better reflect the incompleteness of the current understanding, and the origin of the different parts of the model figure. In addition, we extended the final paragraph of the discussion (which lists still-unknown aspects of 2C) with the reviewer's mention of dsRNA shielding from innate immunity (lines 374-375). The other aspects mentioned by the reviewer as not yet fully understood are already mentioned in that paragraph.

Minor issues

Lines 43-45: I feel like you underplay the success of the poliovirus vaccination program. Approximately 30 of WPV1 in 2022 and the full eradication of WPV2 and 3. Vaccine derived polio is still an issue but even that is relatively low compared to where the world was in the 1950s.

We agree that the previous wording was not ideal. We replaced it and added another recent reference - related to the type 2 vaccine switch (lines 47-49).

Line 66. I agree there are 11 individual proteins but I feel like this leaves out the fact that some of the uncleaved precursors appear to have some functions, for example 2BC.

Good point. We have now added a mention of 2BC and the fact that it has distinct functions to the introduction (lines 70-71). 2BC is also mentioned in the legend of the model figure (figure 8).

Line 56: LD needs to be defined.

Well spotted thanks. Since the abbreviation was not used anywhere else we opted to spell it out instead (line 59).

Line 75. I think you have misrepresented Xia et al here. They clearly say that in their study that they show helicase and chaperone activity. I never managed to repeat that work but you should still report what they claim. One major thing is that they used insect expressed protein, whereas most people (including myself and in the paper under review) use E.coli expressed protein. Do post translational modifications play an important role in function?

You are right that the reference to their paper for this statement was incorrect. We have now made this part of the introduction more explicit (lines 82-83) and we also in the new discussion mention the possibility of e.g. post-translational modifications affecting 2C helicase activity, under reference to Xia et al (lines 359-361)

Line 103. Need to make it clear here it is poliovirus 2C.

Thanks, we added it (line 112).

Line 135. I assume you mean kDa instead of uM?

It should actually be μM. It is the solution concentration at which the assay was performed. We added some words to clarify this (line 154).

Figure 3. What do you mean by "Only 2C"? Is that MBP-2C? Maybe I am reading the data wrong but adding TEV does nothing? How do you know TEV is removing the MBP? It looks like MBP-2C binds to the liposomes just the same as cleaved MBP-2C. I see in line 165 you acknowledge this. Could an alternative conclusion for line 168 be that MBP isn't being cleaved off but that AH2 is too small to be exposed in that construct? Did you do that construct without MBP being cleaved? I think you need to confirm that MBP is being cleaved off.

Thanks for spotting this mistake. It should indeed be MBP-2C (in the absence of liposomes). We corrected figure 3. Also, in response to this comment and similar ones, we have now added a new supplementary figure showing SDS-PAGE gels of the reaction loaded onto flotation assays and DLS (figure S5). It shows that MBP-2C is cleaved.

Line 184. Is there a reason you use the 2019 paper as a reference instead of the far earlier Bienz et al papers? I'd suggest they are the seminal papers on 2C membrane association. Once again how is this work different from the recent "An Amphipathic Alpha-Helix Domain from Poliovirus 2C Protein Tubulate Lipid Vesicles" paper?

See our response above of the paper mentioned here (which we have now cited). As for why we cite the 2019 paper here: our statement pertains specifically to the contact sites between lipid droplets and replication organelles, not to the membrane binding of 2C per se. We have now added a more general mention of membrane remodelling by non-structural proteins in the introduction, where we cite on of the Bienz papers (lines 75-77).

Figure 5D. So only 1-3% of RNA is found in the upper fraction? Is that significant enough to say that dsRNA was recruited significantly more than ssRNA? How confident are you in your quantification of the starting amounts of RNA?

We agree that the fraction is low, however, the fluorescence signal is very clearly above background. We are thus confident in the measurement. The low percentage at the end of the experiment likely has a simple physico-chemical explanation: in a dynamic equilibrium in a density gradient, whatever RNA dissociates during the run will migrate away from the 2C-vesicle fraction and not be able to rebind. We still tried to address this concern by a complementary experiment where we used fluorescence anisotropy to measure binding of RNA to 2C on vesicles. While the measurements showed the same tendency, they curves were not clean enough to be published, which we think is due to the complex system with 2C bound to vesicles and clusters of vesicles. Still, in view of the relatively low percentage of measured recruitment we opted to adjust the paper title and the title of figure 5 (including the subheading related to figure 5) to put less emphasis on the dsRNA recruitment.

Line 223. Any idea why the MBP needs to be cleaved off? Clearly the MDB is accessible or it would not bind to the liposomes.

Since we have no data directly supporting this we prefer not to speculate in the paper. But one guess would be that the NTD of 2C, as implicated by previous publications, has a dual role in membrane binding and RNA binding. It may be that it can bind membrane while conjugated to MBP, but needs MBP to be removed in order to simultaneously bind membrane and RNA.

Line 237: missing "b" in "by"

Thanks. This paragraph was rewritten in the light of the changes to figure 6.

Figure 6. I don't fully understand the results here. Earlier you showed that the delta MBD didn't really bind SUV. So presumably it isn't really membrane bound. Why does it have similar activity to full-length MBP in your helicase assay if membrane is important? Did you do SUV and TEV protease only control?

We are very grateful to this reviewer (and others) for pointing out the need for a TEV control. When performing the control, we found that the TEV protease, at the high concentrations initially used, surprisingly had an artefactual RNA chaperone-like effect on its own. We then proceeded to titrate down the TEV protease concentration to the point where it no longer interfered. At this TEV protease concentration, although 2C was substantially cleaved (see the new supplementary figure S12), we could no longer detect an RNA chaperone activity. Thus, the contents of the new figure 6, and its conclusions, have been substantially changed. We now focused our attention on the remaining effect that 2C has on RNA: single-strand ribonuclease activity. These experiments were all conducted in the presence of RNase inhibitors, and the presence of Mg2+-dependent ribonuclease activity parallels a recent publication that found this for truncated 2C from hepatitis A and several enteroviruses.

Line 257: "staring"?

Thanks, corrected. A staring glycine would indeed be something strange.

Line 336. Need to change the u to mu.

Thanks, corrected.

Any discussion on your observation in Figure 1D that EV71 and CVB3 don't appear to have AH1 and AH2 or do you think that the domains are conserved across the different viruses?

Thanks for bringing this up. Based on this and a comment from another reviewer, we have now clarified our thinking around this. Since the glycine will introduce some flexibility between AH1 and AH2, we cannot say from the single alphafold predictions that this is THE kink angle. The presence of the kink in the predictions of several MBDs lends more credibility to the robustness of the observation, but most importantly the hydrophobic surfaces in AH1 and AH2 are non-aligned for ALL sequences we looked at. This is now described on lines 126-128.

Table 1 (and possibly elsewhere): an apostrophe is not the prime symbol. 5' compared to 5′.

Thanks, we corrected this throughout.

Line 702 "and" should be "an".

Thanks, corrected.

I couldn't open one of the movies (140844_0_supp_2820374_a2g272.avi).

Sorry to hear this, we will check the movie again.

Reviewer #3 (Significance (Required)):

Overall I liked the paper and is worth publishing. One of the issues in the 2C field is the difficulty in making pure 2C and carrying out in vitro assays that correlate with what is observed in the natural infection. I think this paper suffers from similar struggles with a 2C preparation that doesn't appear that pure. I think it also suffers from not having 2C from a wild-type infection. I don't think that it is feasible to get that kind of 2C but by once again using a recombinant protein from E.coli we are left with another manuscript that provides conflicting evidence of the functions of 2C without a definitive answer. The experiments are well done, although are missing some controls and the manuscript is laid out in a logical manner and is relatively easy to follow.

We thanks the reviewer for these comments. We believe that we have now provided better information regarding the purification of the recombinant 2C protein, and we do think that the controls present in the original manuscript and the revised manuscript alleviate the concerns about lack of specificity. Of course, isolating 2C vesicles from wildtype infection would be another interesting way of approaching its function, but such an approach would come with its own set of challenges related e.g. to the presence of confounding host factors.

Reviewer #4 (Evidence, reproducibility and clarity (Required)):

This is an interesting manuscript that reports the development of an in vitro membrane assay for probing the biochemical functions of the enterovirus 2C protein. The technique is interesting because it can be applied to 2C proteins from other members of the picornavirus family, an important group of mammalian pathogens. It has the capacity to probe different functions (e.g. membrane clustering, ATPase activity, RNA-binding and manipulation activities).

Overall, the manuscript is well written and gives a clear account of the work undertaken. It adds insight to previous studies of enteroviral (and picornaviral) 2C proteins, providing confirmation of some earlier work in a more physiological context and some new insights, particularly into the membrane and RNA binding aspects of 2C.

That said, there are a number of places where some amendment of the claims made is required to provide a more precise statement of the findings of this work. These are listed below.

We thank the reviewer for this positive feedback on our work, as well as for the specific comments below.

Line 21 (Abstract) - The authors claim to have shown that a conserved glycine divides the N-terminal membrane-binding domain into 2 helices. I would suggest instead what they have produced are computational predictions that this is the case - some way short of an experimental demonstration. Sequence analysis predicts helical secondary structure in the N-terminus and indeed Alphafold2 also predicts a helical structure, but these predictions require experimental verification. The authors should therefore rewrite sections that claim to have shown the presence of 2 helices. In doing so, they should perhaps also comment on the fact that Alphafold2 does not predict 2 helices in this region for all enteroviruses (see Fig 1D). Moreover, the sequence analysis in Fig. S1 shows the presence of two Lys residues in the segment 17-38; it would be interesting for the reader to have these indicated in the figures showing the Alphafold2 prediction - do they in any way interrupt the hydrophobic face of the predicted helix?

Thanks very much for this comment, which is in line with what other reviewers also wrote. We agree, and changed the abstract sentence. We have also rewritten the manuscripts in several places to address the limits of structure predictions and the eventual need for an experimental structure of full-length membrane-bound 2C (lines 126-128 and 315-318).

Line 82 (Introduction) - The authors write that the membrane binding domain (MBD) of poliovirus has been shown to mediate hexamerisation, citing Adams et al (2009) - reference 43. However, that is not what this paper shows. Rather it provides evidence of aggregation of an MBP-2C fusion protein into forms that ranged from tetramer to octamer, but no evidence that these aggregates assume functional forms (e.g. the presumed hexameric ring structure characteristic of the AAA+ ATPase family to which 2C belongs). As far as I am aware the first demonstration of hexameric ring formation by a picornaviral 2C protein was for the 2C of foot-and-mouth disease virus (see Sweeney et al, JBC, 2010). Although this is not an enterovirus, this finding was later confirmed for Echovirus 30 (ref 51). I should declare an interest here: the Sweeney paper is from my lab. I will leave it to the editor and the authors to determine how to write a more precise account of the early observations of hexamerisation in picornaviral and enteroviral 2C proteins.

Thanks very much for this insightful comment. As a response to this and other similar comments, we are much more cautious about our wording in the revised manuscript (see also response to comment below. In the part of the introduction discussed here (now lines 89-91) we now use the original wording of the Adams paper ("oligomerization"). In the context of that new text we didn't feel that Sweeney et al paper was a suitable reference, but we now cite it in the later mention of 2C's oligomeric/hexameric state in the first part of the Results (lines 137-138 ).

Line 132 - the authors used mass photometry to investigate oligomeric forms of their MBP-2C constructs and state that for the full length 2C protein "the high-mass peak closely corresponds to a hexamer". While it is true that the peak shown in Fig 2C aligns with the expected MW for an MBP-2C hexamer, the peak is very broad, indicative of the presence of other oligomeric states with lower and higher numbers of monomers. This should be commented on. Indeed, the finding seems to echo the early findings of Adams et al (ref 43) with poliovirus MBP-2C.

Thanks for this comment, which was also made by another reviewer. We cite here what we replied to that reviewer

...we do agree with the reviewer on the broad mass photometry peaks. To address this experimentally, we compared the existing MBP-2C spectra to new recordings on apoferritin, a highly stable homomultimeric protein complex of a similar mass to aa MBP-2C hexamer. The apoferritin mass estimate is overlayed with the full-length MBP-2C in the new figure 2D and the corresponding supplementary figure S3. This indeed shows that the MBP-2C peak is broader, i.e. consistent with a mix of species which are predominantly but not only hexamers. We describe and discuss this on lines 145-149.

Line 143 - for the reasons given above, this summary paragraph represents too strong a statement of what has been observed.

We agree, and changed the paragraph. It now only refers to "oligomerization" (lines 162-164).

Line 197 - I note that the authors did not test the membrane clustering capabilities of the 2C(41-329) construct. Although the 2C(deltaAH1) construct had already shown a significant loss of activity, the shorter construct could still have been a useful control. I don't think it is necessary for this experiment to be done, but if the authors have a rationale for not performing the experiment, perhaps they could include it in a revised manuscript.

Thanks for the suggestion. The rationale is that a protein that doesn't bind a membrane in the first place will also not cluster them (an action that requires binding TWO membranes). We now describe our reasoning on lines 220-222. Nevertheless, we did test these constructs in the new supplementary figure showing negative staining TEM (figure S10).

Line 223 - typo. I think you mean MBD.

Thanks! Corrected (now line 257).

Line 215 - the authors observed that the presence of ssDNA reduced membrane clustering and conclude that "nucleic acid binding partially outcompetes membrane tethering activity". Two things: (1) although I agree is it likely that this effect is due to binding of DNA to 2C, binding has not been demonstrated experimentally so the authors should be more careful in how they describe their result; (2) there is no data presented to show that RNA binding reduces membrane tethering so at best I think the conclusion has to be that the data are consistent with the notion that DNA binding reduces membrane tethering. It would of course be interesting to see the effects of RNA and I'm curious to know why the assay was not performed.

Thanks for the comment. The honest answer is that previous publications (primarily Yeager et al, NAR 2022) convinced us that the outcome should be near-identical with DNA, so we chose DNA oligos because they are cheaper and easier to work with. But we agree with the reviewer that RNA is of course more relevant. We now present a comparison at 5 μM of ssDNA and ssRNA, which in fact shows a slightly stronger effect on membrane clustering by RNA (figure 5C). In the light of this additional experiment, we feel that some of the text changes suggested by the reviewer may no longer be necessary.

Line 237 - typo: by, not y

Thanks. In the light of the extensive changes to figure 6 this text was removed.

Line 284 - the authors claim that 2C may only bind RNA after the N-terminus is liberated from 2B in infected cells, since cleavage of the MBP tag from their construct was needed for 2C to bind RNA in their in vitro assay. However, this does not automatically follow given the large structural differences between MBP and 2B and the fact that the authors have not tested the RNA binding capacity of a 2BC fusion protein. Their claim here is too strong and should be re-written.

We agree, and have added a discussion along the lines suggested by the reviewer (line 330-332).

Line 293 - The authors speculate that RNA binding might cause a shift between the membrane clustering activities and the role of the protein in RNA replication. However, since they have not shown that RNA binding reduces membrane clustering, this is too speculative.

In our revised manuscript we have studied the effect of RNA on membrane binding, thus we feel that this text is relevant in the context of the extended experiments.

Line 299-317 - within this discussion is the assumption that in their assay system enterovirus 2C adopts the ring-like hexameric structure typical of AAA+ ATPases. While I agree this may well be the case, it has not been demonstrated in this study so the authors should make clear they are making this assumption. The same applies to the legend of Fig 8.

This part of the discussion was extensively rewritten after our changes to figure 6. We now only refer to "hexamer" once in the corresponding part of the discussion, where we talk about structural models of hexamers produced by other groups who have crystallised fragments of 2C. There we believe we should refer to hexamers to accurately cite their work.

We are not sure what the reviewer is referring to when it comes to the legend for figure 8: the original legend had no reference to the oligomeric state of 2C. We have substantially changed figure 8 and its legend and the new figure and legend make no references to hexamers/oligomers.

Line 302 - the authors claim to have shown that 2C is 'selective' for dsRNA. I think at best they have shown a preference for binding dsRNA over ssRNA.

We changed the wording (line 349). We have also changed the title of the paper where we removed "double-stranded".

Line 313 - The sentence starting "A recent study..." needs a reference.

The revised discussion no longer contains this sentence.

Line 332 - the full sequence of the synthetic gene used in this study should be made available (e.g. as supplementary information or a deposited sequence with an accession number). This is a critical point before the paper can be published.

We will of course submit the sequences as supplementary data. Thanks for the reminder.

Line 362 - the authors should describe the likely points of attachment of fluorophores and comment on how this labelling might affect 2C function.

Thanks for the comment. In response to this and a similar comment from another reviewer, we discuss the likely conjugation site of the fluorophore (lines 175-181), and also (due to the proximity to the Zn finger) provide a new measurement showing that equal amounts of Zn can be detected in the labelled and unlabelled protein (figure S7).

Line 372 - Is a single protein standard (BSA) sufficient to calibrate the SEC-MALS system?

Yes, it is the recommended procedure (note that SEC-MALS is only dependent on scattering, not elution volumes etc).

Reviewer #4 (Significance (Required)):

As stated above this is an interesting study that presents findings from a novel assay. It will be of interest to picornavirologists and the wider community interested in the mechanisms of AAA+ ATPases.

We thanks the reviewer for this positive appraisal of our work.

Author response:

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

Reviewer #1:

We thank the reviewer for their careful reading of our manuscript and have taken all of their grammatical corrections into account.

Reviewer #2 (Public Review):

Weaknesses: 

The paper contains multiple instances of non-scientific language, as indicated below. It would also benefit from additional details on the cryo-EM structure determination in the Methods and inclusion of commonly accepted requirements for cryo-EM structures, like examples of 2D class averages, raw micrographs, and FSC curves (between half-maps as well as between rigid-body fitted (or refined) atomic models of the different polymorphs and their corresponding maps). In addition, cryo-EM maps for the control experiments F1 and F2 should be presented in Figure 9.

We tried to correct the non-scientific language and have included the suggested data on the Cryo-EM analyses including new Figures 11-17.  We did not collect data on the sample used for the seeds in the cross seeding experiments because we had already confirmed in multiple datasets that the conditions in F1 and F2 reproducibly produce fibrils of Type 1 and Type 3, respectively. We have now analyzed cryo-EM data for 6 more samples at pH 7.0 and found that several kinds of polymorphs (Types 1A, 1M, 2A, 2B and 5) are accessible at this pH, however the Type 3 polymorphs are not formed at pH 7.0 under the conditions that we used for aggregation.

Reviewer #2 (Recommendations For The Authors):

Remove unscientific language: "it seems that there are about as many unique atomicresolution structures of these aggregates as there are publications describing them"   

We have rephrased this sentence.

For same reason, remove "Obviously, " 

Done

What does this mean? “polymorph-unspecific” 

Rephrased as non-polymorph-specific

What does this mean? "shallow amyloid energy hypersurface"  

By “shallow hypersurface” we mean that the minimum of the multi-dimensional function that describes the energy of the amyloid is not so deep that subtle changes to the environment will not favor another fold/energy minimum. We have left the sentence because while it may not be perfect, it is concise and seems to get the point across.

"The results also confirm the possibility of producing disease-relevant structure in vitro." -> This is incorrect as no disease-relevant structure was replicated in this work. Use another word like “suggest”.

We have changed to “suggest” as suggested.

Remove "historically" 

Done

Rephrase “It has long been understood that all amyloids contain a common structural scaffold” 

Changed to “It has long been established that all amyloids contain a common structural scaffold..” 

"Amyloid polymorphs whose differences lie in both their tertiary structure (the arrangement of the beta-strands) and the quaternary structure (protofilamentprotofilament assembly) have been found to display distinct biological activities [8]" -> I don't think this is true, different biological activities of amyloids have never been linked to their distinct structures.  

We have added 5 new references (8-12) to support this sentence.

Reference 10 is a comment on reference 9; it should be removed. Instead, as for alphasynuclein, all papers describing the tau structures should be included.  

We have removed the reference, but feel that the addition of all Tau structure references is not merited in this manuscript since we are not comparing them.

Rephrase: "is not always 100% faithful"

Removed “100%”

What is pseudo-C2 symmetry? Do the authors mean pseudo 2_1 symmetry (ie a 2-start helical symmetry)?

Thank for pointing this out.  We did indeed mean pseudo 21 helical symmetry.  

Re-phrase: "alpha-Syn's chameleon-like behavior" 

We have removed this phrase.

"In the case of alpha-Syn, the secondary nucleation mechanism is based on the interaction of the positively charged N-terminal region of monomeric alpha-Syn and the disordered, negatively charged C-terminal region of the alpha-Syn amyloid fibrils [54]" -> I would say the mechanisms of secondary nucleation are not that well understood yet, so one may want to tune this down a bit. 

We have changed this to “mechanism has been proposed to be”

The paragraphs describing experiments by others are better suited for a Discussion rather than a Results section. Perhaps re-organize this part? 

We have left the text intact as we are using a Results and Discussion format.

A lot of information about Image processing seems to be missing: what steps were performed after initial model generation? 

We have added more details in the methods section on the EM data processing and model analysis.

Figure 1: Where is Type 4 on the pH scale?

We have adjusted the Fig 1 legend to clarify that pH scale is only applicable to the structures presented in this manuscript. 

Figure 2: This might be better incorporated as a subpanel of Figure 1.

We agree that this figure is somewhat of a loner on its own and we only added it in order to avoid confusion with the somewhat inconsistent naming scheme used for the Type 1B structure. However, we prefer to leave it as a separate figure so that it does not get dilute the impact of figure 1.

Figure 3: What is the extra density at the bottom of Type 3B from pH 5.8 samples 1 and 2. pH 5.8 + 50mM NaCl (but not pH 5.8 + 100 mM NaCl)? Could this be an indication of a local minimum and the pH 5.8 + 100 mM NaCl structure is correct? Or is this a real difference between 0/50mM NaCl and 100 mM NaCl? 

We did not see the extra density to which the reviewer is referring, however the images used in this panel are the based on the output of 3D-classification which is more likely to produce more artifacts than a 3D refinement. With this in mind, we did not see any significant differences in the refined structures and therefore only deposited the better quality map and model for each of the polymorph types.

Figure 3: To what extent is Type 3B of pH 6.5 still a mixture of different types? The density looks poor. In general, in the absence of more details about the cryo-EM maps, it is hard to assess the quality of the structures presented.

In order to improve the quality of the images in this panel, a more complete separation of the particles from each polymorph was achieved via the filament subset selection tool in RELION 5. In each case, an unbiased could be created from the 2D classes via the relion_helix_inimodel2D program, further supporting the coexistence of 4 polymorphs in the pH 6.5 sample. The particles were individually refined to produce the respective maps that are now used in this figure.

Many references are incorrect, containing "Preprint at (20xx)" statements.  

This has been corrected.

Reviewer #3 (Public Review):

Weaknesses: 

(1) The authors reveal that both Type 1 monofilament fibril polymorph (reminiscent of JOSlike polymorph) and Type 5 polymorph (akin to tissue-amplified-like polymorph) can both form under the same condition. Additionally, this condition also fosters the formation of flat ribbon-like fibril across different batches. Notably, at pH 5.8, variations in experimental groups yield disparate abundance ratios between polymorph 3B and 3C, indicating a degree of instability in fibrillar formation. The variability would potentially pose challenges for replicability in subsequent research. In light of these situations, I propose the following recommendations: 

(a) An explicit elucidation of the factors contributing to these divergent outcomes under similar experimental conditions is warranted. This should include an exploration of whether variations in purified protein batches are contributing factors to the observed heterogeneity.

We are in complete agreement that understanding the factors that lead to polymorph variability is of utmost importance (and was the impetus for the manuscript itself). However the number of variables to explore is overwhelming and we will continue to investigate this in our future research. Regarding the variability between batches of purified protein, we also think that this could be a factor in the polymorph variability observed for otherwise “identical” aggregation conditions, particularly at pH 7 where the largest variety of polymorphs have been observed. However, even variation between identical replicates (samples created from the same protein solution and simply aggregated simultaneously in separate tubes) can lead to different outcomes (see datasets 15 and 16 in the revised Table 1) suggesting that there are stochastic processes that can determine the outcome of an individual aggregation experiment. While our data still indicates that Type 1,2 and 3 polymorphs are strongly selected by pH, the selection between interface variants 3B vs. 3C and 2A vs. 2B might also be affected by protein purity. Our standard purification protocol produces a single band by coomassie-stained SDS-PAGE however minor truncations and other impurities below a few percent would go undetected and, given the proposed roles of the N and C-termini in secondary nucleation, could have a large effect on polymorph selection and seeding. In line with the reviewer’s comments we now include a batch number for each EM dataset. While no new conclusions can be drawn from the inclusion of this additional data, we feel that it is important to acknowledge the possible role of batch to batch variability. 

(b) To enhance the robustness of the conclusions, additional replicates of the experiments under the same condition should be conducted, ideally a minimum of three times.  

The pH 5.8 conditions that yield Type 3 fibrils has already been repeated several times in the original manuscript. Since the pH 7.4 conditions produce the most common a-Syn polymorph (Type 1A) and were produced twice in this manuscript (once as an unseeded and once as a cross-seeded fibrilization) we decided to focus on the intermediate condition where the most variability had been seen (pH 7.0). The revised table 1 now has 6 new datasets (11-16) representing 6 independent aggregations at pH 7.0 starting from two different protein purification batches. The results is that we now produce the type 2A/B polymorphs in three samples and in two of these samples we once again observed the type 1M polymorph.  The other samples produced Type 1A or non-twisted fibrils.

(c) Further investigation into whether different polymorphs formed under the same buffer condition could lead to distinct toxicological and pathology effects would be a valuable addition to the study.  

The correlation of toxicity with structure would in principle be interesting. However the Type 1 and Type 3 polymorphs formed at pH 5.8 and 7.4 are not likely to be biologically relevant. The pH 7 polymorphs (Type 5 and 1M) would be more interesting because they form under the same conditions and might be related to some disease relevant structures. Still, it is rare that a single polymorph appears at 7.0 (the Type 5 represented only 10-20% of the fibrils in the sample and the Type 1M also had unidentified double-filament fibrils in the sample). We plan to pursue this line of research and hope to include it in a future publication.

(2) The cross-seeding study presented in the manuscript demonstrates the pivotal role of pH conditions in dictating conformation. However, an intriguing aspect that emerges is the potential role of seed concentration in determining the resultant product structure. This raises a critical question: at what specific seed concentration does the determining factor for polymorph selection shift from pH condition to seed concentration? A methodological robust approach to address this should be conducted through a series of experiments across a range of seed concentrations. Such an approach could delineate a clear boundary at which seed concentration begins to predominantly dictate the conformation, as opposed to pH conditions. Incorporating this aspect into the study would not only clarify the interplay between seed concentration and pH conditions, but also add a fascinating dimension to the understanding of polymorph selection mechanisms.

A more complete analysis of the mechanisms of aggregation, including the effect of seed concentration and the resulting polymorph specificity of the process, are all very important for our understanding of the aggregation pathways of alphasynuclein and are currently the topic of ongoing investigations in our lab.

Furthermore, the study prompts additional queries regarding the behavior of cross-seeding production under the same pH conditions when employing seeds of distinct conformation. Evidence from various studies, such as those involving E46K and G51D cross-seeding, suggests that seed structure plays a crucial role in dictating polymorph selection. A key question is whether these products consistently mirror the structure of their respective seeds. 

We thank the reviewer for reminding us to cite these studies as a clear example of polymorph selection by cross-seeding. Unfortunately, it is not 100% clear from the G51D cross seeding manuscript (https://doi.org/10.1038/s41467-021-26433-2) what conditions were used in the cross-seeding since different conditions were used for the seedless wild-type and mutant aggregations… however it appears that the wildtype without seeds was Tris pH 7.5 (although at 37C the pH could have dropped to 7ish) and the cross-seeded wild-type was in Phosphate buffer at pH 7.0. In the E46K cross-seeding manuscript, it appears that pH 7.5 Tris was used for all fibrilizations (https://doi.org/10.1073/pnas.2012435118).  In any event, both results point to the fact that at pH 7.0-7.5 under low-seed conditions (0.5%) the Type 4 polymorph can propagate in a seed specific manner.

(3) In the Results section of "The buffer environment can dictate polymorph during seeded nucleation", the authors reference previous cell biological and biochemical assays to support the polymorph-specific seeding of MSA and PD patients under the same buffer conditions. This discussion is juxtaposed with recent research that compares the in vivo biological activities of hPFF, ampLB as well as LB, particularly in terms of seeding activity and pathology. Notably, this research suggests that ampLB, rather than hPFF, can accurately model the key aspects of Lewy Body Diseases (LBD) (refer to: https://doi.org/10.1038/s41467-023-42705-5). The critical issue here is the need to reconcile the phenomena observed in vitro with those in in-vivo or in-cell models. Given the low seed concentration reported in these studies, it is imperative for the authors to provide a more detailed explanation as to why the possible similar conformation could lead to divergent pathologies, including differences in cell-type preference and seeding capability.  

We thank the reviewer for bring this recent report to our attention. The findings that ampLB and hPFF have different PK digestion patterns and that only the former is able to model key aspects of Lewy Body disease are in support of the seed-specific nature of some types of alpha-synuclein aggregation.  We have added this to the discussion regarding the significant role that seed type and seed conditions likely play in polymorph selection.

(4) In the Method section of "Image processing", the authors describe the helical reconstruction procedure, without mentioning much detail about the 3D reconstruction and refinement process. For the benefit of reproducibility and to facilitate a deeper understanding among readers, the authors should enrich this part to include more comprehensive information, akin to the level of detail found in similar studies (refer to:

https://doi.org/10.1038/nature23002).

As also suggested by reviewer #2, we have now added more comprehensive information on the 3D reconstruction and refinement process.

(5) The abbreviation of amino acids should be unified. In the Results section "On the structural heterogeneity of Type 1 polymorphs", the amino acids are denoted using three-letter abbreviation. Conversely, in the same section under "On the structural heterogeneity of Type 2 and 3 structures", amino acids are abbreviated using the one-letter format. For clarity and consistency, it is essential that a standardized format for amino acid abbreviations be adopted throughout the manuscript.

That makes perfect sense and had been corrected.

Reviewing Editor:

After discussion among the reviewers, it was decided that point 2 in Reviewer #3's Public Review (about the experiments with different concentrations of seeds) would probably lie outside the scope of a reasonable revision for this work. 

We agree as stated above and will continue to work on this important point.

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

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Reply to the reviewers

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

This manuscript provides a detailed analysis of RNA and protein dynamics during transmission of the rodent malaria model P. yoelii from the mouse host to an in vitro ookinete culture setting (mimicking the mosquito midgut environment). This group and others have shown experimentally that a substantial number of mRNAs is stored in the female Plasmodium gametocyte, ready to be translated following initiation of ookinete development. The process is akin to maternal deposition of mRNA in oocytes of metazoans. With this manuscript the authors provide a significant contribution to the field of translational control in Plasmodium parasites as they explore the translational activation during the early hours of zygote-to-ookinete development. The paper presents RNAseq and mass-spec analyses of female gametocytes and for the first time for 6-hour zygotes (ie a fertilized female gamete); the zygote datasets are much improved and more comprehensive than the only other performed in 2008 in P. gallinaceum. Using comparative analyses of transcriptome and proteome data (including published datasets) the authors arrive at a list of 198 transcripts that are translationally repressed in the gametocyte and translated within 6 hours of fertilization in the zygote. Many of these mRNAs are known to be involved in zygote to ookinete transformation. BioID is finally used to explore changes in mRNP protein composition between the female gametocyte and the zygote.

The paper is generally well written. The authors present a lot of data (also in comparison with published data). Sometimes perhaps the main message could be simplified / streamlined in section titles (Quantitative Proteomics by DIA-MS is not very informative. The outcome of the analysis would be more telling).

Response: We have revised section headers to clarify the content.

A considerable proportion of the DIA mass-spec proteomics results section is very technical. The paper describes a biological phenomenon rather than a technical mass-spec advance. Can these technical details be moved to the methods section?

Response: As this is one of the first published instances of using DIA-MS to Plasmodium, we want to keep this information in the main text to help our community adopt these approaches. While these details are highly technical, they are also some of the major advances of this project.

On the other hand, a bit more detail could be provided in the main text. For example, the age of the zygotes is never mentioned. This is important, please add this. The main manuscript text has 16 mentions of the word "many". As the authors are in possession of the data, please provide, if missing, (in parenthesis) the absolute numbers, maybe in an "x out y" format. Please clearly state the number of biological and/or technical replicates used for transcriptome and proteome analyses in the main text, figures and/or figure legends. How many protein coding genes are encoded in the P. yoelii genome?

Response: Several of these requested details are noted in the materials and methods. We have added this information to the main manuscript now as well. We have also revised the manuscript to replace some instances of “many” with specific numbers unless it adversely impacted the flow of the sentence to do so.

The authors claim that only zygotes (fertilized females) have surface-exposed Pys25 (a surface protein they use to affinity-purify zygotes) but not gametocytes. I could not find the experimental data for this in the paper. The cited reference #22 also does not appear to show this. In Figure 2C Pys25 is shown to be translated in gametocytes. In this context it may be important to note that in the related P. berghei the related protein P28 is expressed even in the absence of fertilization (Billker 2004; DOI: 10.1016/s0092-8674(04)00449-0). It may not be relevant whether translation requires fertilization, but the authors claim it affects trafficking of the Pys25 protein to the surface, so it needs to be shown. A reference to an infertile P. yoelii line would be great.

Response: We have corrected the reference supporting the surface exposure of p25 on zygotes. The observation by Billker and colleagues about Pbs28 is also of interest, but outside of the scope of this study as we did not investigate the fertilization event itself here.

It is highly commendable that all data is provided throughout the manuscript. For readability, may I suggest that the authors add labels to individual sheets within an excel file from A to Z, and do so also within the manuscript. That would really help; the most relevant data sets could then be identified quickly. For example, line 184 refers to 276 zygote proteins in which sheet of which table?

Response: While this labeling system would also be effective, we have provided a README tab for our files that quickly directs the reader to the relevant tab (as we do for our previous publications).

Section 176 onwards: here the authors combine P. falciparum and P. yoelii proteomics data. Please explain why you excluded any of the available P. berghei proteome data such as the male and female gametocyte proteome? The same question applies to 294 onwards.

Response*: We compared our datasets with those of Lasonder et al. NAR 2016 because that study was also focused on translational repression of mRNAs and provided both RNA-seq and proteomic datasets of female gametocytes (although not of zygotes). *

The comparative transcriptome-proteome analysis arrives at 198 translationally repressed mRNAs. Could the authors provide one or two alternatives using less stringent parameters? The list in P. falciparum and P. berghei is considerably larger (500+ and 700+).

Response: We could have reduced the stringency of our thresholds to arrive at a far larger number, but prefer to retain higher confidence in those we are scoring as translationally repressed and then released for translation. We provide all of the pertinent data in the supplemental files if readers would like to adjust these thresholds to see which additional mRNAs may also be regulated.

The turboID data is informative but somewhat speculative in regard to spatial rearrangements within these mRNPs. Figure 6 presents the RNA helicase to bind the 5' end of mRNAs that are associated with polyribosomes and I assume being translated. Is this association realistic? The RNA helicase DOZI homolog of yeast (Dhh1) is also involved in decapping. Response: We provide Figure 6 as our working model of how the reorganization of the DOZI/CITH/ALBA complex could occur based on available data from this study and others. Future studies are warranted to determine if DOZI remains associated with monosomes vs. polysomes, but current data indicate that DOZI can bind to eIF4E when translational repression is not imposed.

Specific comments:

title Is global the appropriate word? Some transcripts appear to be translated later.

Response: We believe it does apply appropriately to these data.

Line 30/32 Please re-phrase the sentence. There is: Cell Host Microbe 2012 Jul 19;12(1):9-19. doi: 10.1016/j.chom.2012.05.014.

Response: We conclude that the sentence is correct as written, even in considering Sebastian et al. Cell Host & Microbe 2012.

30 Perhaps add ookinete that establishes infection rather than the zygote. For a general readership, a brief description of the sexual life cycle might be useful

Response: It is not possible to get into these nuances in the Abstract. This information is covered in the main text and the works that are cited.

32 DOZI/CITH/ALBA complex would require some explanation for a more general reader

Response: It is not possible to get into these nuances in the Abstract. This information is covered in the main text and the works that are cited.

36-37 I believe zygotes were collected 6 hours after fertilization. Does that qualify as soon after fertilization? Motile ookinetes are generated within 20 hours and motility can be seen before that.

Response: Yes, we think this qualifies as the process is not synchronous, but relies on when male gametes encounter and fuse with female gametes.

37 Essential functions for what?

Response: It is not possible to get into these nuances in the Abstract. This information is covered in the main text and the works that are cited.

39 Is the spatial arrangement of this mRNP known?

Response*: Some interactions of members of this complex were known (DOZI with eIF4E, ALBA4 with PABP1), but not the overall spatial arrangement. These findings are novel to this study. *

40 Can you briefly allude to the "recent, paradigm-shifting models of translational control"

Response: It is not possible to get into these nuances in the Abstract. This information is covered in the main text and the works that are cited.

44 Products = mRNA

Response: We have stated it as products because the maternal cell provides more than just mRNAs that are essential to further development post-fertilization.

45 Oocyte in metazoans ?

Response: Yes, this is the correct term. The context here is in higher eukaryotes.

60/62 Please re-phrase the sentence. There is: Cell Host Microbe 2012 Jul 19;12(1):9-19. doi: 10.1016/j.chom.2012.05.014.

Response: We conclude that the sentence is correct as written, even in considering Sebastian et al. Cell Host & Microbe 2012.

81 PbDozi Plasmodium berghei DOZI

Response: We have added this clarifying text here as suggested.

84/85 Please rephrase and cite Nucleic Acids Res. 2008 Mar;36(4):1176-86. doi: 10.1093/nar/gkm1142. Epub 2007 Dec 23. and Cell Host Microbe 2012 Jul 19;12(1):9-19. doi: 10.1016/j.chom.2012.05.014.

Response: As noted above for other comments, we hold that the current phrasing is accurate even when considering these important publications.

88 Please define the timepoints throughout this manuscript. What age are the zygotes? How many hours post-induction? Please define the time for ookinete development somewhere in the introduction

Response: The timepoint used for zygote collection is now included in the main text in addition to its previous inclusion in the Materials and Methods section. As we have not studied the ookinete stage here, we have opted to keep the introduction focused on the key details for this study.

104 Please add the age (in hours) of these zygotes from the time of starting the in vitro cultures. From the methods section it looks like 6 hours.

Response: The timepoint used for zygote collection is now included in the main text in addition to its previous inclusion in the Materials and Methods section.

103/105 I can find no evidence for P25 (Pys25) expression relying on fertilization in the cited paper (22). The SOM has no reference to Pys25 either. Please show data or reference published data that there is no translation and trafficking of Pys25 in unfertilized female gametes, ie those that are placed in ookinete medium. In this respect it may be important to note that unfertilized Plasmodium berghei females placed in ookinete medium translate P28, the P25 paralog (https://www.sciencedirect.com/science/article/pii/S0092867404004490?via%3Dihub)

Response: We have corrected the reference supporting the surface exposure of p25 on zygotes. The observation by Billker and colleagues about Pbs28 is also of interest, but outside of the scope of this study as we did not investigate the fertilization event itself here.

104 What cell line was used for the zygotes?

Response*: The PyApiAP2-O::GFP transgenic parasite line was used here. These details are included in the manuscript and supporting information. *

114 The number of transcripts detected in gametocytes is quite small compared to the twice as large proteomics dataset. See for example also Lasonder 2016 for P. falciparum detected transcripts: 4477 different sense transcripts were identified, 98% of which were shared between MG and FG.

Response: Yes, the number of mRNAs or proteins scored as detected differs based on thresholds applied. We prefer to err on the side of higher stringency as noted above.

117 Does the 194 up-in-gametocytes dataset include the 81 not found in zygotes?

Response: No, these 194 are detected in both datasets, but are more abundant in gametocytes than zygotes.

117 Could you indicate some of the genes in the plot?

Response: Several hits of special note are described in the text. We have opted to keep the figure clear and streamlined.

Fig1 How were the upregulated transcripts identified? 1647 are shown to be specific to zygotes in 1B, yet only 685 are shown in 1C to be upregulated. Do the transcripts found exclusively in zygotes not count? Are these transcripts likely the result of de novo transcription? How old are these zygotes when the libraries are made?

Response: The details of the RNA-seq processing are provided in the MakeFile, the supplementary tables, and the manuscript. The README tab provides descriptions of what processing occurred between sequential tabs. As noted above, zygotes were collected at 6 hours.

132 Many? How many? Please provide a precise number.

Response: These details are now in the revised manuscript.

134 Please explain why p28 would be differentially abundant in the zygote rather than the female gametocyte. That would require de novo transcription of this gene. If there is experimental evidence for the de novo transcription of p28 and other translationally repressed transcripts in the zygote please cite the references. Can you name a few more examples here? P25 for example, ap2-o, or anything published and experimentally validated. What about AP2-o and AP2-Z? Both are known to be translationally repressed.

Response: We state in the original manuscript that there is not a significantly different mRNA abundance of pys28.

139 Please define how many members of the IMC?

Response*: We have now replaced “many” with the number of IMC members we have detected, which is also shown in supporting tables. *

156 Can you provide a number of how many parasites were used in total or per run. And how many biological and technical replicates were analysed?

Response: These details are provided in the Materials and Methods.

169 The number of proteins detected in the gametocyte sample is twice the size of transcripts. IS this to be expected?

Response*: This reflects the sensitivity of the assays run for transcriptomics and proteomics. *

170 How many samples were analyzed? One gametocyte and one zygote sample?

Response: Yes, for the creation of the DIA-MS spectral library, a single biological replicate was used in addition to in silico library approaches. This information is provided in the next sentence.

176 Why did you not include P. berghei in the meta-analysis?

Response: We compared these results to all of the published Plasmodium proteomes in PlasmoDB.

184 Please refer to an excel table here.

Response: We have pointed to the relevant supporting files in this section.

184 145 proteins: do you mean orthologs in general or orthologs with a gene/protein annotation other than unknown function?

Response: We use the standard form of ortholog throughout the manuscript.

190 142 proteins: do they all have orthologs in P. falciparum?

Response: No, not all proteins in our dataset have unambiguous orthologues in P. falciparum, and this is accounted for in our data processing approaches.

Figure 2C P25 is not exclusive to zygotes here and also found in the gametocyte sample.

Response: That is correct. It is known that p25 is expressed in female gametocytes, but that the localization changes in the zygote.

190 shortlist

Response: The spelling of “short list” as two words is an appropriate American spelling of this term.

219 onwards Does the list of 198 transcripts exclusively arise from your RNAseq and proteomics comparison? Or does it include falciparum data as outline in section 176 onwards, ie the list of 276 proteins that only are detected in zygotes?

Response: Yes, this list of 198 mRNAs is derived from our datasets only using our defined thresholds. The details of this are provided in the manuscript.

224 Early zygote? At 6 hours do the parasites not start to transform, elongate?

Response: This process is not synchronous, as it is affected by the timing of gamete fusion.

225 >5-fold. Is this an arbitrary decision?

Response: This threshold has been used by our group and others in prior studies, and was partially informed by the behavior of previously characterized transcripts.

227 1417 mRNAs: they are from which dataset?

Response: These are from our datasets with P. yoelii, as described in the manuscript.

228/229 Please explain why DOZI and CITH are in the list of 198 repressed transcripts? They are present in the gametocyte. Are they upregulated>5 fold?

Response: Yes, they meet our criteria for this regulation, and in the manuscript we note that we believe that they are self-regulated and likely have continuing roles in early mosquito stage development.

259 ... as they are already translated in the gametocyte?

Response: Yes. Translational repression allows for the existence of some of the protein in the initial timepoint. This differs from translational silencing which does not.

295 Is this from the 198 TR list S4?

Response: No. Transcripts that remain repressed would not be in the list of 198, as the protein was not detected in zygotes.

294 onwards How many putatively falciparum transcripts are there? How many were identified in P. berghei? How many are common to all? A Venn diagram perhaps to compare the different studies

Response: There is substantial overlap between the species with respect to the presence of syntenic orthologues in this dataset. However, because we did not conduct experiments with P. falciparum or P. berghei here, we do not want to make claims that they are similarly regulated or potentially have a reader misinterpret a figure to that effect.

301 How many transcripts were found associated with Plasmodium berghei DOZI and/or CITH in female gametocytes? How many of those were abundantly detected as protein in zygotes, or had no difference in protein abundance between gametocytes and zygotes, or even greater abundance in female gametocytes?

Response: These details are now provided in the revised manuscript.

303/305 Please indicate the numbers of translationally repressed transcripts identified for P. falciparum and berghei.

Response: These data are provided in Supporting Information Table 4.

317/319 Please add the promoter used for tid-GFP

Response: We have now added this information to the Materials and Methods.

320 Please elaborate on the spatial organization of the DCA complex.

Response: This has not been previously characterized, and this entire section is dedicated to the experimental data and interpretations of how the DOZI/CITH/ALBA complex may be organized.

321/322 Have precise binding sites of DOZI and ALBA4 really been shown experimentally in the cited papers? In relation to 5' and 3' ends of the mRNA? Please cite Braks et al. paper.

Response: Yes. The association of DOZI with eIF4E and ALBA4 with PABP1 are established in the literature, in some cases by multiple independent laboratories. The Braks publication does not address the binding of these proteins, and thus is not cited.

323 What is the first generation BioID enzyme? BirA*

Response: Yes. The first generation enzyme is called BirA*

323 Please cite relevant Kyle Roux and Alice Ting for the original enzymes

Response: We have now added these citations to this sentence.

327 Could you show images of ALBA4::TurboID::GFP, DOZI::TurboID::GFP and cytosolic (free) TurboID? Perhaps stained with fluorescently labelled streptavidin and / or against GFP? In the gametocyte and zygote samples?

Response: We attempted to stain with monoclonal antibodies that are reactive against biotin and there was insufficient specificity, hence why such data is not included. We conclude that all of the other data that supports this approach suffices to demonstrate its rigor.

331 What is the age of these zygotes? Where they affinity purified?

Response: As throughout the manuscript, zygotes were collected at 6 hours. Details of experimental purifications are provided in the materials and methods.

Fig S4 Please indicate whether ALBA4 and DOZI were tagged endogenously

Response: Yes. The endogenous loci for both ALBA4 and DOZI were modified to include the C-terminal TurboID and GFP tags.

421/430 Please add a few references here

Response: We do not believe that specific references are warranted for these general statements.

429 translational repression?

Response: Yes. These statements set the stage for the use of translational repression.

445 966 proteins in gallinaceum? The zygote cultures in that study were 2-3 hours. How old were the cultures in your study?

Response: As throughout the manuscript, zygotes were collected at 6 hours.

481 Please explain / cite why repression is energetically costly.

Response: These details are provided in both the introduction and discussion sections. The energetic cost of translational repression is both the cost to produce the transcripts without immediately/fully utilizing it for translation, in addition to the energetic cost to impose the regulation.

501 Please add the time-point of RNA and protein sampling. How many hours into ookinete development? What is the time from cardiac puncture through FACS sampling of gametocytes.

Response: We have provided all of these details in the materials and methods for female gametocytes and zygotes. We did not look at ookinetes in this study.

711/713 Do you have any images that show the successful purification of zygotes away from gametocytes? Secondly, please provide a reference for the statement that unfertilized female gametocyte do not express surface exposed Pys25.

Response*: We do not have captured images of these zygotes, but confirmed them during collection using microscopy. The reference for surface exposure of Pbs25 is now provided earlier in the manuscript as well. *

711/716 Were parasites lysed and mechanically homogenised?

Response: We have provided all of these details in the materials and methods for female gametocytes and zygotes.

Figure 6 What is the evidence that DOZI stays associated with mRNA that is being translated? Rather than mRNA that is being decapped. Please add the references that unequivocally show that DOZI and ALBA4 bind to opposite ends of repressed mRNAs.

Response: This is our working model of these data. It is feasible that these complexes could form off of mRNA as well. Publications describing the interactions of DOZI with eIF4E and ALBA4 with PABP1 are provided in the manuscript. It is well established that eIF4E binds to the m7G cap of the 5’ end of mRNAs, and PABP1 binds to the poly(A) tail at the 3’ end of mRNAs.

Reviewer #1 (Significance (Required)):

The experiments in the manuscript are carefully conducted. Apart from a P. gallinaceum study from 2009 this is the first comprehensive analysis of the transcriptome and proteome of a Plasmodium zygote (developing ookinete) at 6 hours post-fertilization. The data are used to explore the temporal aspect of activation of translation during the first quarter of the 20-24 hour ookinete developmental period. The study will be of interest to the field, specifically those scientists working to understand translational control, ookinete development, and those developing intervention strategies to prevent mosquito infection and thus malaria transmission.

Response: We appreciate Reviewer 1’s extensive feedback and positive remarks about the significance of our study. We have revised our manuscript to reflect this constructive feedback.

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

Main findings

Taking a multi-omic approach, the authors provide quantitative evidence for translation repression of ~200 mRNAs in Plasmodium yoelii female gametocytes. These mRNAs are then translated, and proteins detected by 6 hours after activating gametocytes. They accomplish this by performing a comparative global analysis of the transcriptome and proteome between female gametocytes and early zygotes that provides an intresting resource. The authors also use proximity labelling of the DOZI/CITH/ALBA4 repression complex, and these data suggest the complex may disassemble in the zygote or change its composition.

Major points

Line 181-184: The authors state that there is no evidence of how the DCA complex selects specific mRNAs for translation repression. While the exact mechanisms have not been fully elucidated, Braks et al (2008, doi:10.1093/nar/gkm1142) suggested a role of the untranslated regions (UTRs) in translation repression of transcripts in Plasmodium berghei female gametocytes. They identified a uridine-rich 47-base element in the 5'UTR and or 3'UTR that was associated with translationally repressed transcripts and validated it experimentally. Considering this finding, I would recommend an amendment of the statement and to include the earlier work. I would also like to see additional analysis to check if this U-rich motif or other motifs are associated with the translationally repressed transcripts identified in the current study. The current study should be better powered to conduct such an analysis.

Response: We have now added a comment and citation in the revised text about this study in Lines 86-88. Understanding the full importance of this element is challenging, as the Plasmodium transcriptome is highly enriched in A’s and U’s due to the highly skewed A/T content of its genome. Perhaps for this reason, we did not see an association of this motif with the identified mRNAs.

The authors used zygotes that expressed GFP tagged AP2-O, however, there is no explanation of the significance of using this line.

Response: This line is described in the Materials and Methods and supporting information. It was used to provide further validation of the production of zygotes.

Minor points

In line 106-107, the authors refer to figure SI, this figure is about genomic locus and genotyping PCR for the PyApiAP2-O::GFP parasites but there is no intext description of why this specific line was used.

Response: We have provided this information in the revised manuscript.

Statement in line 122-124 "It is likely that....." should go into the discussion not results.

Response: We have placed this single sentence immediately after presenting these data here to aid reader comprehension.

Statement in line 171-175: "In addition to providing confirmatory...." Should be in the discussion not on the results.

Response: We view this sentence as a concluding remark of this section of data that also places this information in context for the reader.

In Fig. 4 A and B, could the colour scheme be changed so that the proteins that are not in both samples (and probably contain many unspecifically detected proteins) appear less prominent?

Response: We appreciate this suggestion and have adjusted these plots accordingly in the revised manuscript.

Reviewer #3 (Significance (Required)):

Why is the paper interesting. Translation repression of mRNA at a global level in the female gametocytes has been studied previously in rodent malaria parasites investigated, but prior to the current study, the release of mRNA from translation repression in the mosquito stages has only been demonstrated for specific transcripts. By characterizing and quantitating changes in protein abundance between macrogamete and zygote, coupled with transcriptomic analysis, the current work broadens our understanding of zygotic translation activation that is key to successful malaria parasite transmission to the mosquito.

This dataset provides a useful resource for the Plasmodium research community as it provides a more comprehensive view of how transcripts behave during the transitions from the mammalian host to the vector. It is one step in a broader endeavour towards finding genes crucial for parasite transmission that could be targeted for interventions.

How translational repression and derepression is regulated remains unknown, although some of the molecular players have been identified. This paper shows proximity labelling and expansion microscopy data of the ribonuclear protein complex thought to mediate repression. Although the specific mechanistic insights provided by the experiments shown here remain relatively limited, the work demonstrates interesting new avenues for how translational derepression in Plasmodium can be studied.

Response: We also appreciate Reviewer 3’s excellent feedback and positive remarks about the significance of our study. The revised manuscript addresses these comments, and we believe it is further strengthened because of it.

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

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Reply to the reviewers

1. General Statements [optional]

We thank all the reviewers for their constructive and critical comments. We provide a point-by-point response to the reviewers' comments, as detailed below. By responding to them, we believe that our revised manuscript will significantly improve so that it will be of interest for researchers in the field of cell biology, signaling pathways, physiology and nutrition.

Reply to the reviewers

Reviewer #1 (Evidence, reproducibility and clarity):

Summary: The manuscript by Yusuke Toyoda and co-workers describes that the phosphorylation of the a-arrestin Aly3 downstream of TORC2 and GAD8 (AKT) negatively regulates endocytosis of the hexose transporter Ght5 in S.pombe under glucose limiting growth conditions.

To arrive at these conclusions, the researchers define a set of redundant c-terminal phosphorylation sites in Aly3 that are downstream by GAD8. Phosphorylation of these sites reduces Ght5 ubiquitination and endocytosis. For ubiquitination, Aly3 interacts with the ubiquitin ligases Pub1/3.

We thank the reviewer for his/her time and reporting advantages and issues of this study.

Major points:

Figure 3B: it would be interesting to compare Aly3 migration pattern (and hence potential phosphorylation) under glucose replete or limiting growth conditions. Can the authors provide direct evidence that Aly3 phosphorylation changes in response to glucose availability? Also please explain the 'smear' in lanes aly3(4th Ala), aly3(4th Ala, A584S), aly3(4th Ala, A586T).

While it is an interesting possibility that the Aly3 migration pattern changes in response to glucose concentrations in medium, we think that this is unlikely and that examining this possibility is beyond the scope of this study. Because a phospho-proteomics study reported by Dr. Paul Nurse's lab showed Tor1-dependent phosphorylation of Aly3 at S584 under high glucose (2%) conditions (Mak et al, EMBO J, 2021), the Aly3 phosphorylation (migration) pattern is likely to be constant regardless of glucose conditions. Glucose conditions affect the mRNA and protein levels of Ght5, but supposedly not its endocytosis to vacuoles (Saitoh et al, Mol Biol Cell, 2015; Toyoda et al, J Cell Sci, 2021).

As for the smear in Aly3(4th A), Aly3(4th A;A584S), Aly3(4th A; A586T), we suspect that some posttranslational modification occurs on these mutant Aly3 proteins, but the identity of the modification is unclear. We did not mention the smear signals in the original manuscript, because the presence or absence of the smear did not necessarily correlate with cell proliferation in low glucose and thus vacuolar localization of Ght5, which is the main topic of this study. In the revised manuscript, we will mention this point more clearly.

Figure 4: Ght5 localization should be analyzed + / - thiamine and in media with different glucose levels. Also, a co-localization with a vacuolar marker (FM4-64) would be nice (but not necessary). Ideally, the authors should add WB analysis of Ght5 turnover to complement the imaging data. Also, would it be possible to measure directly the effects on glucose uptake (using eg: 2-NBDG).

In this revision, we plan to observe Ght5 localization under the conditions indicated by the reviewer (+/- thiamine and high/low glucose levels) to unambiguously show that the vacuolar localization of Ght5 occurs in a manner dependent solely on expression of the mutant Aly3 protein.

We thank the reviewer for the suggestion of co-staining with FM4-64. Indeed, because we previously reported that the cytoplasmic Ght5 signals were surrounded by FM4-64 signals in the TORC2-deficient tor1Δ mutant cells (Toyoda et al, J Cell Sci, 2021), the cytoplasmic Ght5-GFP signals in Figure 4 are very likely to co-localize with vacuoles. We will modify the text to clarify this point.

As suggested, we plan to add Western blot analysis of Ght5 turnover in Aly3-expressing cells, to complement the imaging data (Figure 4) in the revised manuscript. Persistent appearance of GFP in Western blot would be a good support for vacuolar transport of Ght5-GFP.

While regulation of glucose uptake is an important issue, measurement of Ght5-dependent glucose uptake using 2-NBDG was very difficult in our hands. Another reviewer (Reviewer #2) also mentioned the difficulty of this measurement in the Referees cross-commenting section.

Figure 5: Given the localization of Ght5 shown in Figure 4, I'm surprised that it is possible in to detect full length Ght5, and its ubiquitination in the phospho-mutants of Aly3. I expected that the majority of Ght5 would be constitutively degraded, and that one would need to prevent endocytosis and/or vacuolar degradation to detect full length Ght5 and ubiquitination. Please explain the discrepancy. Also it seems that the quantification in B was performed on a single experiment.

As the aim of Figure 5 is to compare the ubiquitinated species of Ght5 among the samples expressing different species of Aly3, the loading amount of each sample was adjusted so that the abundance of immunoprecipitated Ght5 is same across them. Therefore, as the reviewer points out, before the adjustment, abundance of the full-length Ght5 might be different in these samples. In the revised manuscript, we will add explanation on this point; why the anti-GFP blot of Figure 5A has the similar intensities in those samples.

In the revised manuscript, we will add two additional replicates of the same experiment as Figure 5 in Supplementary material to show reproducibility of the result.

Figure 6: Which PPxY motif of Aly3 is used for interaction with Pub1/3 and does their interaction depend on (de)phosphorylation?

In the revised manuscript, we will discuss that "both PY motifs of Aly3 might be required for full interaction with Pub1/3," by citing the following published knowledge:

(a) Mutation of both PPxY motif of budding yeast Rod1 and Rog3 (Aly3 homologs) diminished their interaction with the ubiquitin ligase Rsp5 (Andoh et al, FEBS Lett, 2002).

(b) Mutating either one of two PPxY motifs of budding yeast Cvs7/Art1 greatly decreased interaction with WW domain, and mutating both abolished the interaction (Lin et al, Cell, 2008).

Our preliminary results indicated that Pub3 interacted with Aly3, Aly3(4th A) and phospho-mimetic Aly3(4th D), and thus suggested that the Aly3-Pub1/3 interaction does not depend on the phosphorylation status of Aly3. Consistently, budding yeast Rod1 reportedly interacts with Rsp5 regardless of its phosphorylation status (e.g. Becuwe et al, J Cell Biol, 2012). While we have partially mentioned this point in the original manuscript (L499-503), we will discuss this point more clearly in the revised manuscript.

Reviewer #1 (Significance):

The results are well presented and clear cut (with few exceptions, please see major points). They provide further evidence that metabolic cues instruct the phosphorylation of a-arrestins. Phosphorylation then negatively regulates a-arrestin function in selective endocytosis and is essential to adjust nutrient uptake across the plasma membrane to the given biological context.

We thank the reviewer for finding significance of our study. We believe that adding new results of the requested experiments and responding to the raised comments will clarify the significance of our revised manuscript.

Reviewer #2 (Evidence, reproducibility and clarity):

**Summary / background. This paper focuses on the regulation of endocytosis of the hexose transporter, Ght5, in S. pombe by nutrient limitation through the arrestin-like protein Aly3. Ght5 is induced when glucose is limiting and is required for growth and proliferation in these conditions. ght5+ encodes the only high-affinity glc transporter from fission yeast. ght5+ is induced in low glucose conditions at the transcriptional level and is translocated to the plasma membrane to allow glc import. Ght5 is targeted to the vacuole in conditions of N limitation. Mutations in the TORC2 pathway lead to the same process, thus preventing growth on low glucose medium, as shown in the gad8ts mutant, mutated for the Gad8 kinase acting downstream of TORC2. Previously, the authors demonstrated that the vacuolar delivery of Ght5 in the gad8ts mutant is suppressed by mutation of the arrestin-like protein Aly3. Arrestin-like proteins are in charge of recognising and ubiquitinating plasma membrane proteins to direct their vacuolar targeting by the endocytosis pathway. This suggested that Aly3 is hyperactive in TORC2 mutants, and accordingly, Ght5 ubiquitination was increased in gad8ts.

**Overall statement This study aims at deepening our understanding of the regulation of endocytosis by signalling pathways through arrestin-like proteins. Ght5 is a nice model to study a physiological regulation, and the authors have a great set of tools at hand. However, I think the conclusions are not always rigorous and the conclusions are sometimes far-reaching. The main problem is that much of the conclusions concern a potential phosphorylation of Aly3 which is not experimentally addressed. An additional issue is the fact that they look at Ght5 ubiquitination by co-immunoprecipitation in native conditions (or at least, it seems to me) which cannot be conclusive. Overall, I think some experiments should be performed to address (at least) these 2 points before the manuscript can be published, see detailed comments below.

We thank the reviewer for pointing both advantages and issues of our manuscript.

We admit that phosphorylation of Aly3 was not experimentally shown in our manuscript, although its phosphorylation has already been shown in phospho-proteomic studies by other groups. For this issue, we plan to add an experiment and modify the text, as explained below.

The other major issue raised by this reviewer is that detection of Ght5 ubiquitination by immunoprecipitation in a native condition cannot be conclusive. Although we noticed that many studies perform affinity purification after denaturing and precipitating proteins with TCA or acetone to detect ubiquitination of the affinity-purified protein (e.g. Lin et al, Cell, 2008), we disagree with this opinion of the reviewer #2. In a review article describing methods to study ubiquitination by immunoblotting (Emmerich and Cohen, Biochem Biophys Res Comm, 2015), affinity purification of the protein of interest in a native condition is mentioned as one major choice. Moreover, a denaturing condition was not applicable to detect ubiquitinated Ght5 because the Ght5 protein that is once denatured and precipitated with TCA cannot be re-solubilized for immune-purification and -blotting. As the reviewer points out, a pitfall of detection of ubiquitinated Ght5 in a native condition is the presence of co-immunoprecipitated proteins. In our previous study (Toyoda et al, J Cell Sci, 2021), we purified GFP-tagged Ght5 and showed that a 110 kDa band detected in an anti-Ub immunoblot was also recognized by an anti-GFP antibody, confirming that the detected 110 kDa band corresponded to an ubiquitinated species of Ght5, but not a co-immunoprecipitated protein. Similarly, in the revised manuscript, we will add a panel of high-contrast (over-exposed) anti-GFP immunoblot, in which the indicated 110 kDa band was clearly detected by an anti-GFP antibody, in Figure 5A.

We appreciate these issues raised by the reviewer #2. By responding to them, we believe that conclusions of our study will be more rigorous and undoubtful in the revised manuscript.

**Major statements and criticism.

*Fig 1. Based on the hypothesis that TORC2-mediated phosphorylation regulate Ght5 endocytosis, the authors first considered a possible phosphorylation of Ght5. They mutagenised 11 **possible** phosphorylation sites on the Ct of Ght5, but none affected the growth on low glucose in the absence of thiamine, suggesting that they don't contribute to the observed TORC2-mediated regulation. However, I disagree with the statement that "phosphorylation of Ght5 is dispensable for cell proliferation in low glucose", given that the authors do not show 1- that Ght5 is phosphorylated and 2-that this is abolished by these mutations. They should either provide data on this or tone down and say that these residues are not involved in the regulation, without implying phosphorylation which is not proven.

Although we did not experimentally test whether these 11 residues of Ght5 was phosphorylated in our hand, these residues have been shown to be phosphorylated in phospho-proteomics studies by other groups (Kettenbach et al, Mol Cell Proteomics, 2015; Swaffer et al, Cell Rep, 2018; Tay et al, Cell Rep, 2019; Halova et al, Open Biol, 2021; Mak et al, EMBO J, 2021). In the revised manuscript, we plan to be more precise by replacing this conclusion with the following statement: "11 Ser/Thr residues of Ght5, which are reportedly phosphorylated, are not essential for cell proliferation in low glucose."

In the presence of Thiamine (Supp fig 1), it seems that the ST/A mutant grows better in low glucose, and this is not explained nor commented. Since the transporter is not expressed, could the authors provide an explanation to this? If the promoter is leaky and some ght5-ST/A is expressed, it may be more stable and allow better growth than the WT, which would tend to indicate that impairing phosphorylation prevents endocytosis (which is classical for many transporters, see the body of work on CK1-mediated phosphorylation of transporters). Have the authors tried to decrease glc concentration lower than 0.14% in the absence of thiamine to see if this also true when the transporters is strongly expressed? (OPTIONAL)

Improved growth of Ght5(ST11A)-expressing cells in the presence of thiamine was mentioned in the legend of Supplementary Figure 1A. In the revised manuscript, we will mention this observation also in the main text for better description of the results.

Adding thiamine to medium does not completely shut off transcription from the nmt1 promoter but allows some transcription, as previously reported (Maundrell, J Biol Chem, 1990; Forsburg, Nuc Acid Res, 1993). In the revised manuscript, we will mention this "leakiness" of the nmt1 promoter and, by citing the suggested studies, will discuss a possibility that the ST11A mutations might prevent endocytosis of Ght5 and consequently promote cell proliferation in low glucose conditions.

We found that, in the absence of thiamine, cells expressing ght5+ and ght5(ST11A) proliferated to the comparable extent on medium containing 0.08% glucose. This result will be added to the revised manuscript.

*Fig 2. The authors then follow the hypothesis that TORC2 exerts its Ght5-dependent regulation through the phosphorylation of Aly3. They mutagenised 18 **possible** phosphorylation sites on Aly3. This led to a strong defect in growth in low-glc medium. Mutation of the possible Gad8 site (S460) did not recapitulate this phenotype, suggesting that it is not sufficient, however, mutations of 4 ST residues in a CT cluster (582-586) mimicked the full 18ST/A mutation, suggesting these are the important residues for Ght5 endocytosis.

We thank the reviewer for appreciating the results in Fig. 2. As we explain below, we plan to perform an additional experiment to show that the Aly3 C-terminus is phosphorylated. With this result, our model will gain another experimental support.

*Fig 3A. Further dissection did not allow to pinpoint this regulation to a specific residue, beyond the dispensability of the T586 residue. Fig 3B. The authors look at the effects of mutation of Aly3 on these sites at the protein level. They had to develop an antibody because HA-epitope tagging did not lead to a functional protein (Supp fig 2). Whereas I agree that the mutations causing a phenotype lead to a change in the migration pattern, I disagree with the statement that "This observation indicated that slower migrating bands were phosphorylated species of Aly3" (p.9 l.271). First, lack of phosphorylation usually causes a slower mobility on gel, which is not clear to spot here. Second, a smear appears on top of the mutated proteins (eg. 4th Ala) which is possibly caused by another modification. There are many precedents in the literature about arrestins being ubiquitinated when they are not phosphorylated (see the work on Bul1, Rod1, Csr2 in baker's yeast from various labs). My gut feeling is that lack of phosphorylation unleashes Aly3 ubiquitination leading to change in pattern. All in all, it is impossible to state about the phosphorylation of a protein without addressing its phosphorylation properly by phosphatase treatment + change in migration, or MS/MS. Thus, whereas the data looks promising, this hypothesis that Aly3 is phosphorylated at the indicated sites is not properly demonstrated.

We disagree with the reviewer's opinion that a lack of phosphorylation usually causes slower mobility on gel. There are many examples in which phosphorylation causes slower mobility on gel, including budding yeast Rod1 (Alvaro et al, Genetics, 2016), and mammalian TXNIP (Wu et al, Mol Cell, 2013). In the revised manuscript, we will cite these reports to support our interpretation that the slower migrating bands are likely phosphorylated species of Aly3 (L270-271).

Smear-like signals in Aly3(4th Ala), Aly3(4th A;A584S) and Aly3(4th A;A586T) might result from some modification, but identity of the modification is unknown. As the reviewer #2 mentioned, phosphorylation on Aly3 might negatively regulate another modification. The precedent studies revealed that budding yeast Rod1 and Rog3 arrestins tend to be ubiquitinated in snf1/AMPK-deficient cells (Becuwe et al, J Cell Biol, 2012; O'Donnell et al, Mol Cell Biol, 2015), and that Bul1 arrestin is dephosphorylated and ubiquitinated in budding yeast cells deficient in Npr1 kinase (Merhi and Andre, Mol Cell Biol, 2012). Also, budding yeast Csr2 arrestin is deubiquitinated and phosphorylated upon glucose replenishment, while non-phosphorylated Csr2 is ubiquitinated and activated by Rsp5 (Hovsepian et al, J Cell Biol, 2012). While the smear-like signals are interesting, we noticed that the smear-like signals did not necessarily correlate with cell proliferation defects in low glucose. We therefore think that clarifying the identity of the smear-like signals is beyond the scope of this study. We will discuss the smear-like signals only briefly in the revised manuscript, and would address this issue in our future work, hopefully.

While the 4 S/T residues at the C-terminus of Aly3 as well as the other 14 S/T residues have been already shown to be phosphorylated in the precedent studies (Kettenbach et al, Mol Cell Proteomics, 2015; Tay et al, Cell Rep, 2019; Halova et al, Open Biol, 2021), we will confirm that the slower migrating Aly3 is indeed phosphorylated by phosphatase treatment in the revised manuscript. This planned experiment will further strengthen our study and support our conclusion and model.

*Fig 4. The authors now look at the functional consequences of these mutations on ALy3 on Ght5 localisation. The data clearly shows that mutation of the 4 identified S/T residues (Aly3-4th A) causes aberrant localisation of the transporter to the vacuole, likely to cause the observed growth defect on low glucose. There is a nice correlation between the vacuolar localisation and growth in low-glucose for the various aly3 mutants. (A final proof could be to express this in the context of an endocytic mutant, which should restore membrane localisation and suppress the aly3-4thA phenotype - OPTIONAL). However, I still disagree with the statement that "These results indicate that phosphorylation of Aly3 at the C-terminal 582nd, 584th, and/or 585th serine residues is required for cell-surface localization of Ght5." given that phosphorylation was not properly demonstrated.

While phosphorylation of the 582nd, 584th and/or 585th serine residues of Aly3 is not experimentally demonstrated in our hands, they have been shown to be phosphorylated in phospho-proteomics studies by other groups (Kettenbach et al, Mol Cell Proteomics, 2015; Tay et al, Cell Rep, 2019; Halova et al, Open Biol, 2021; Mak et al, EMBO J, 2021). Among them, the 584th serine residue (S584) was reported to be phosphorylated in a TORC2-dependent manner (Mak et al, EMBO J, 2021), consistent with our model. To explicitly demonstrate that S584 is phosphorylated, we plan to make a strain expressing a mutant Aly3 protein in which all the possible phosphorylation sites except S584 are replaced with alanine, namely Aly3(ST17A;S584). Hopefully, we can properly show the phosphorylation of S584 by measuring the mobility of the Aly3(ST17A;S584) on gel with/without phosphatase treatment or gad8 mutation.

We thank the reviewer for suggestion of the experiment using an endocytic mutant. Previously we reported that vacuolar localization of Ght5 in gad8 mutant cells was suppressed by mutations in not only aly3 but also genes encoding ESCRT complexes (Toyoda et al, J Cell Sci, 2021). We therefore think that in cells expressing Aly3(ST18A) or Aly3(4th Ala), Ght5 is subject to endocytosis and ensuing selective transport to vacuoles via endosome-localized ESCRT complexes. We will discuss this point in the revised manuscript.

*Fig 5. Here, the authors question the role of Aly3 mutations on Ght5 ubiquitination. They immunoprecipitate Ght5 and address its ubiquitination status in various Aly3 mutants. The data is encouraging for a role in Aly3 phosphorylation (?) in the negative control of Ght5 ubiquitination. My main problem with this experiment is that it seems that Ght5 immunoprecipitations were made in non-denaturing conditions, which leads to the question of what is the anti-ubiquitin revealing here (Ght5 or a co-immunoprecipitated protein, for example Aly3 itself, or the Pub ligases, or an unknown protein). It seems that this protocol was previously used in their previous paper, but I stand by my conclusion that ubiquitination of a given protein can only be looked in denaturing conditions. The experiments should be repeated in buffers classical for the study of protein ubiquitination to be able to conclude unambiguously that we are looking at Ght5 ubiquitination itself, especially in the absence of a non-ubiquitinable form of Ght5 as a negative control. Could the authors comment on the fact that S-A or S-D mutations display the same phenotype regarding the possible Ght5 ubiquitination?

As mentioned above, immunoprecipitation of Ght5 in denaturating conditions is not feasible. Ght5 can be affinity-purified only in a non-denaturing condition. In addition, affinity purification in a native condition is considered as a major choice to examine its ubiquitination according to a literature by Emmerich and Cohen (Emmerich and Cohen, Biochem Biophys Res Comm, 2015). A drawback of native condition is, as the reviewer points out, that the affinity-purified fraction might include non-bait (non-Ght5) proteins. The 110 kDa band indicated by an arrow in Fig. 5A was confirmed to be Ght5, not a non-bait protein, as a band at the identical position was detected in the immunoblot with anti-GFP antibody. Because this band in the anti-GFP immunoblot was too faint to be visible in Fig. 5A of the original manuscript, we will add an additional panel showing the contrast-enhanced anti-GFP immunoblot in which the 110 kDa band is clearly visible.

As for the result that "S-A or S-D mutations display the same phenotype regarding the possible Ght5 ubiquitination," we are afraid that the reviewer #2 misunderstood the labels of the samples. We apologize for confusing notational system of the sample name. Full description of samples is as follows; In Aly3(4th A), all of S582, S584, S585 and T586 are replaced with A; In Aly3(4th A;A584S), S582, S585 and T586 are replaced with A, whereas S584 remains intact; In Aly3(4th A;A584D), S582, S585 and T586 are replaced with A, and S584 is replaced with phospho-mimetic D. Because cells expressing Aly3(4th A;A584S) and Aly3(4th A;A584D) exhibited similarly low levels of Ght5 ubiquitination, we speculated that phosphorylation at S584 of Aly3 negatively regulates ubiquitination of Ght5.

In the revised manuscript, we plan to add a table showing amino acid sequence of each species of Aly3 (just like Figure 3A) to avoid confusion.

*Fig 6. The authors want to document the model whereby Aly3 may interact with some of the Nedd4 ligases (Pub1/2/3) to mediate its Ght5-ubiquitination function. They actually use the Aly3-4thA mutant, it should have been better with the WT protein. But the results indicate a clear interaction with at least Pub1 and Pub3. By the way, are the Pub1/2/3 fusions functional? Nedd4 proteins are notoriously affected in their function by C-terminal tagging and are usually tagged at their N-terminus (See Dunn et al. J Cell Biol 2004).

We plan to test whether Pub1-myc is functional by comparing proliferation of the Pub1-myc-expressing strain and pub1Δ strain, as pub1Δ cells reportedly show proliferation defects at a high temperature (Tamai and Shimoda, J Cell Sci, 2002). As deletion of pub2 or pub3 reportedly exhibited no obvious defects (Tamai and Shimoda, J Cell Sci, 2002; Hayles et al, Open Biol, 2013), it is not easy to assess functionality of the myc-tagged genes.

Please note that C-terminally tagged Pub1/2/3 proteins have been widely used in studies with fission yeast. Both Pub1-HA and non-tagged Pub1 were reported to be ubiquitinated (Nefsky and Beach, EMBO J, 1996; Strachan et al, J Cell Sci, 2023). Pub1-GFP, which complemented the high temperature sensitivity of pub1Δ, localized to cell surface and cytoplasmic bodies (Tamai and Shimoda, J Cell Sci, 2002). Pub2-GFP, overexpression of which arrested cell growth just like overexpression of non-tagged Pub2, localized to cell surface, and consistently Pub2-HA was detected in membrane-enriched pellet fractions after ultracentrifugation (Tamai and Shimoda, J Cell Sci, 2002). They also reported ubiquitin conjugation of the HECT domain of Pub2 fused with myc epitope at its C-terminus. Pub3-GFP localized to cell surface (Matsuyama et al, Nat Biotech, 2006).

Regardless of functionality of the myc-tagged Pub1/2/3, we believe that results of this experiment (Figure 6) support our model, because the aim of this experiment, which is to identify the HECT-type and WW-domain containing ubiquitin ligase(s) that interact with Aly3, is irrelevant to functionality of the myc-tagged Pub proteins.

*Fig 7. The authors want to provide genetic interaction between the Pub ligases and the growth defects in low glc due to alterations in Ght5 trafficking. It is unclear how the gad8ts pub1∆ mutant was generated since it doesn't seem to grow on regular glc concentration (Supp fig 5), could the authors provide some information about this? It is also not clear whether it can be stated thatches mutant is "more sensitive" to glc depletion because of the low level of growth to begin with (even at 3%). Altogether, the data show that deletion of pub3+ is able to suppress the growth defect of the gad8ts mutant on low glc medium, suggesting it is the relevant ligase for Ght5 endocytosis. This is confirmed by microscopy observations of Ght5 localisation. However, I would again tone down the main conclusion, which I feel is far-reaching: "Combined with physical interaction data, these results strongly suggest that Aly3 recruits Pub3, but not Pub2, for ubiquitination of Ght5." Work on Rsp5 in baker's yeast has shown that Rsp5 function goes beyond cargo ubiquitination, including ubiquitination of arrestins (which is often required for their function as mentioned in the introduction) or other endocytic proteins (epsins, amphyphysin etc). I agree that the data are compatible with this model but there are other possible explanations. Anything that would block endocytosis would supposedly suppress the gad8ts phenotype.

gad8ts pub1Δ was produced at 26 {degree sign}C, a permissive temperature of the gad8ts mutant. While this is described in the Methods section of the original manuscript, we will mention this more clearly in the Results section of the revised manuscript.

We did not conclude low glucose sensitivity of gad8ts pub1Δ cells in the indicated part (L376-377). Rather, we compared proliferation of gad8ts single mutant and pub1Δ single mutant cells in low glucose, and we found that the pub1Δ single mutant exhibited the higher sensitivity. In the revised manuscript we will correct the text to clarify that we compared proliferation of two single mutants (but not gad8ts pub1Δ mutant).

We agree with the opinion that the recruited Pub3 may ubiquitinate proteins other than Ght5. In the revised manuscript, we will correct our conclusion of the Figure 7 experiment (L388-390), not to limit the possible ubiquitination target(s) to Ght5.

In a genetic screen, we found that mutations in aly3+ and genes encoding ESCRT complexes suppressed low-glucose sensitivity and vacuolar transport of Ght5 of gad8ts mutant cells (Toyoda et al, J Cell Sci, 2021). This finding appears consistent with the reviewer's opinion that blocking endocytosis would supposedly suppress the gad8ts phenotype. We will mention this point in the revised manuscript.

*Discussion Some analogy with the regulation of the Bul arrestins by TORC1/Npr1 and PP2A/Sit4 could be mentioned (Mehri et al. 2012), at the discretion of the authors. The possibility that phosphorylation may neutralise a basic patch on Aly3 Ct, possibly involved in electrostatic interactions with Ght5 is very interesting. Regarding the effect of the mutations on Aly3 localisation (p.15 l.498), did the authors tag Aly3 with GFP? There are examples where proteins tagged with HA are not functional whereas tagging with GFP does not alter their function (eg. Rod1, Laussel et al. 2022) - and here Supp Fig 2 only relates to HA-tagging. Proof of a change in Aly3 localisation upon mutation would definitely be a plus (OPTIONAL).

We thank the reviewer for the suggestion of a reference. In the revised manuscript, we will cite the indicated report in the corresponding part for an additional support of TORC1-mediated control of Aly3 (de)phosphorylation.

While examining localization of Aly3 by GFP-tagging is interesting, we do not believe that it is necessary in this study. We would like to produce Aly3-GFP and to examine its functionality and localization in our future study. We thank the reviewer's insightful suggestion.

**Minor comments.

*Introduction: - I believe the text corresponding to the work on TXNIP is incorrect (p.5 l.127). TXNIP is degraded after its phosphorylation, not "rectracted" from the surface.

In the revised manuscript, we will correct the text accordingly.

• For the sake of completion, the authors could add other references concerning the regulation of Rod1 in budding yeast such as Becuwe et al. 2012 J Cell Biol and O'Donnell et al. 2015 Mol Cell Biol, in addition to Llopis-Torregrosa et al. 2016.

In the revised manuscript, we will add the suggested references and correct the text in the corresponding part of the Introduction (L123-138).

• Other examples of the requirement for arrestin ubiquitination beyond Art1 (p.5 l.136-137) are listed in the ref cited: Kahlhofer et al. 2021.

We will cite the indicated review to navigate readers for more examples of arrestin ubiquitination (and transporter ubiquitination).

*Figures: In general, I think it would be clearer if the authors showed on the figures that the background strain in which the XXX gene is added (or its mutant forms) is a xxx∆ strain.

We will modify the figures to clearly show the genetic background of the strains used.

**Referees cross-commenting**

Cross review of Reviewer 1 - *I don't believe that the authors "define a set of redundant c-terminal phosphorylation sites in Aly3", because phosphorylation is not proven. *I thinks the points raised for Fig 3B are valid but the authors should focus on making their story conclusive before expanding to other data (except for the explanation of the smear, see my review). Also, I don't think 2NBDG actually works to measure Glc uptake. * same for Fig 6 - not sure the interaction site mapping between Aly3 and Pubs would bring much value since there are more urgent things to do to make the story solid.

As mentioned above, we will experimentally show phosphorylation of the Aly3 C-terminus in the revised manuscript. Such experiments would make our story more solid and conclusive. We truly appreciate the comments and suggestions.

We agree with the comments on difficulty of measuring glucose uptake using 2-NBDG. In fact, we tried and failed measuring Ght5-mediated glucose uptake using 2-NBDG.

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Cros review of Reviewer 3 - we have many overlaps, so briefly : *I agree that the bibliography is incomplete (mentioned in my review) *I agree that there is no demonstration of the phospho-status of Aly3, and it is a problem *I agree that the results can be better quantified, esp. in the light of the points raised by this referee concerning the variability of expression of ST18A Other specific comments : *I agree that the statement that dephosphorylation activates alpha-arresting should be toned down - this was observed in several instances but there are examples of arrestin-mediated endocytosis which does not require their prior dephosphorylation. *I fully agree that efforts could be made regarding the classification/nomenclature of arrestins in S. pombe, this had escaped my attention

As detailed in the individual point raised by the reviewers, we will add the suggested references and accordingly correct the text in the revised manuscript.

In addition to experimentally showing Aly3 phosphorylation, we will quantify the immunoblot result.

Our statement that dephosphorylation activates alpha-arrestins might be too generalized. We will mention reports in which arrestin-mediated endocytosis does not require prior dephosphorylation (e.g. O'Donnell et al, Mol Biol Cell, 2010; Gournas et al, Mol Biol Cell, 2017; Savocco et al, PLoS Biol, 2019), and modify the text precisely.

Reviewer #2 (Significance):

*strengths and limitations This study aims at deepening our understanding of the regulation of endocytosis by signalling pathways through arrestin-like proteins in S. pombe. Ght5 is a nice model to study a physiological regulation, and the authors have a great set of tools at hand, including the discovery of Aly3 as the main arrestin for this regulation, and a signalling pathway (TORC2/Gad8) acting upstream. The main question is now to understand at the mechanistic level how TORC2 signaling impinges on the regulation of this arrestin.

Overall, the authors nicely demonstrate that C-terminal Ser/Thr residues are crucial for the function of Aly3 in Ght5 endocytosis. They propose a model whereby Aly3 phosphorylation by an unknownn kinase inhibits its function on Ght5 ubiquitination, which would favour its endocytosis. However, I think the conclusions are not always rigorous and the conclusions are sometimes far-reaching. The main problem is that much of the conclusions concern a potential phosphorylation of Aly3 which is not experimentally addressed. An additional issue is the fact that they look at Ght5 ubiquitination by co-immunoprecipitation in native conditions (or at least, it seems to me) which cannot be conclusive. Overall, I think some experiments should be performed to address (at least) these 2 points before the manuscript can be published, see detailed comments above.

*Advance

This study, if completed carefully, would provide among the first examples of mapping of phosphorylation sites on arrestins, which are usually phosphorylated at many sites and are thus difficult to study. Few studies went down to this level in this respect (see Ivshov et al. eLife 2020). There are no changes in paradigms or new conceptual insights, but this work is a nice example of the conservation of these regulatory mechanisms.

We appreciate that this study is highly evaluated by this reviewer. We understand the main problems raised by the reviewer, and as we detailed above, we plan to perform an experiment and make explanation to respond to the problems. With the raised issues answered, we believe that conclusions of the revised manuscript will be more rigorous.

Our study reveals mechanisms regulating vacuolar transport of the Ght5 hexose transporter via the TORC2 pathway in fission yeast. The serine residues at the Aly3 C-terminus (582nd, 584th and 585th serine residues), which are probably phosphorylated in a manner dependent on the TORC2 pathway, are required for sustained Ght5 localization to cell surface and cellular adaptation to low glucose. To our knowledge, there is no such study, and thus we think that this study is novel. By responding to the reviewers' comments and adding new data as explained above, the revised manuscript will be able to present novelty of our study more clearly. Comparison of our study in fission yeast to related studies in other model organisms may reveal the conservation and diversity of these regulatory mechanisms.

*Audience Should be of interest for people studying basic research in the field of cell biology, signalling pathways, transporter regulation by physiology. Reviewer background is on the regulation of transporter endocytosis by signalling pathways and arrestin-like proteins.

Reviewer #3 (Evidence, reproducibility and clarity): (Authors' response in blue)

In this manuscript, the authors work to address how phospho-regulation of a-arrestin Aly3 in S. pombe regulates the glucose transporter Ght5. The authors use a series of phospho-mutants in Aly3 and assess function of these mutants using growth assays and localization of Ght5. My main concerns with the manuscript are that 1) there is a lack of appreciation for the similar work that has been done in S. cerevisiae to define a-arrestin phospho-regulation, which is evidenced by the severe lack of referencing throughout the document, 2) the sites mutated on Aly3 are not demonstrated to change phospho-status of Aly3 and so all interpretations of these mutants need to be better contextualized and 3) almost none of the findings are quantified (imaging or immunoblots) making it difficult to assess the rigor of the outcomes. More detailed comments are provided below.

We thank the reviewer for thorough reading of the manuscript and the detailed comments. As explained below, we will respond to the points raised by the reviewer and accordingly modify the manuscript.

Minor Comments

Immunoblotting or immunostaining to define the levels and localization of phospho-mutants - In Figure 1, an immunoblot or immunostaining to define the abundance/localization of WT Ght5 vs its ST11A mutant would be appreciated. It is very difficult to know if ST11A is as functional as WT or not without an assessment of the levels and localization of the WT and mutant proteins to accompany the spot assays. Perhaps a version of Ght5 that is a phospho-mimetic would be more useful here as well since that version should not be dephosphorylated and then presumably would be internalized and not allow for growth on low glucose medium.

We plan to add fluorescence microscopy data of WT Ght5 and Ght5(ST11A) in the revised manuscript, to compare the localization and abundance of these two Ght5 species. In our preliminary observation, those of two Ght5 species seemed to be indistinguishable.

We'd like to emphasize that the primary aim of this study is to reveal mechanisms regulating Ght5 localization and consequently ensuring cell proliferation in low glucose. While analyzing a phospho-mimetic Ght5 mutant (e.g. Ght5(ST11D)) is interesting in terms of understanding of the nature of Ght5, we believe that such an analysis is out of the scope on this study. As Ght5(ST11A)-expressing cells proliferated comparably to Ght5(WT)-expressing cells and WT and ST11A Ght5 indistinguishably localize on the cell surface, phosphorylation of the ST residues of Ght5 is not likely to be the primary mechanism regulating Ght5 localization and function. We would like to assess a phospho-mimetic Ght5 mutant protein in our future studies.

For the Aly3 mutants where the abundance of Aly3 appears lower via immunoblotting (i.e., 4thA-A582S or S582A) how is the near perfect functional readout explained when the levels of the protein are much lower than WT? For the ST18A mutant, this is a particularly important point since the authors indicate on lines 194-197 that based on the functional data for ST18A, some of these ST residues are needed for phospho-regulation of Aly3. However, in Figure 3B the authors clearly show that there is very little ST18A protein in cells, and so these mutations have impacted Aly3 stability, which may or may not be linked to its phospho-status. The authors should be upfront about this finding on lines 194-197 and should not present this phospho-model as the only reason for why ST18A may not be functional. On lines 265-276 for the authors indicate that ST18A is expressed equivalently to WT Aly3, which is just not the case in Figure 3B. Perhaps quantification of replicate data would help clarify this issue. Further, if the authors wish to conclude that the upper MW bands in Figure 3B are due to phosphorylation, perhaps they should perform phosphatase treatments of their extracts to collapse these bands. However, most certainly the overall abundance of the single band for ST18A is reduced compared to the total bands of WT Aly3.

We disagree with the opinion that the levels of the mutant Aly3 are much lower than WT. For semi-quantitative measurement of the protein abundance, 2-fold dilution series of the WT Aly3 sample were loaded in the leftmost 3 lanes of Figure 3B. Although the levels of Aly3(4th A;A582S), Aly3(S582A) and Aly3(ST18A) were lower than that of WT Aly3, those are 50% or more of the WT, judging from the intensities of the serially-diluted WT samples. To clearly show that the expression of these Aly3 proteins is within comparable levels, we plan to add a column chart of the quantified expression levels and to mention abundances of the Aly3 proteins more quantitatively in the revised text. We do not think that replicate data (of Western blots as in Figure 3B) helps clarify this issue, because nmt1 promoter-driven gene transcription is induced with a small variation (Forsburg, Nuc Acid Res, 1993). We will cite this report and mention this point in the revised text.

We are afraid that this reviewer seems to consider that Aly3(ST18A) is not functional, but it is not a case and we do not intend to claim so. While deletion of aly3 did not interfere with cell proliferation in low glucose (see vector controls in Figures 2B, 2C and 3A, -Thiamine), expression of the ST18A mutant clearly hinders cell proliferation in low glucose, indicating that the ST18A performs dominant negative function to inhibit cell proliferation. That is, even though the expression level and/or stability of the ST18A is reduced, it is still sufficiently abundant to perform the dominant negative function. We propose the phospho-model not due to dysfunctionality of ST18A, but its dominant negative functionality. The 18 S/T residues of Aly3, which are shown to be phosphorylated in precedent phospho-proteomics studies, seem to be required to down-regulate Aly3's function to inhibit cell proliferation in low glucose. We apologize for this confusion, and we will modify the text and figures to clarify these points in the revised manuscripts.

To obtain an experimental support for our description that the slower migrating bands in Figure 3B are due to phosphorylation, we plan to perform a phosphatase treatment experiment as suggested.

Figure 2A - how do the phosphorylation sites identified in Aly3 compare to those identified in Rod1 from S. cerevisiae? See PMID 26920760 or SGD for more information. I am confused as to why the Aly3 protein has an arrowhead at the C-terminus. What does this denote?

We will mention reported phosphorylation sites of Aly3 and budding yeast Rod1/Art4 in the revised manuscript, by referring to the indicated report and database. It should be noted that similarity between amino acid sequences of Aly3 and S. cerevisiae Rod1 is not so high and limited in Arrestin-N and -C domains. The C-terminal half of Aly3, in which most of the potential phosphorylation sites are found, is not similar to Rod1. Thus, these sites are unlikely to be conserved between them.

An arrowhead indicates the direction of transcription (from N to C-terminus). We will describe it explicitly in the revised figure legend.

Figure 2 - The WT and Aly3-ST18A are expressed in S. pombe from a non-endogenous locus under the control of the Nmt1 promoter. However, are these mutants present in cells that contain WT copies of Aly3 at other genomic loci? If so, this would surely muddy the interpretations of these data as a- and b-arrestins are capable of multimerizing and the effect of multimerization on their activities can vary.

As mentioned in L188, an aly3 deletion mutant strain (aly3Δ) was used as a host, and thus all strains harboring an nmt1-driven aly3 gene lack the endogenous aly3 gene. We will add an illustration clearly showing that the host strain lacks the endogenous aly3+ gene and modify the legend of Figure 2.

Functional readouts for Aly3 using Ght5 localization - The reduced surface levels of Ght5 does correspond to the spot assay growth in low glucose for the various Aly3 mutants used. However, it would be useful if these assays incorporated an endocytosis inhibitor to help prevent the activities of these Aly3 plasmids to see if the transporter is retained at the PM. At the end of these mutational analyses, the authors conclude that phosphorylation of Aly3 at any of 3 sites is required for Ght5 trafficking to the vacuole in low glucose, however no experiment is done to demonstrate that these sites are phosphorylated residues. A phosphatase assay would be useful to help demonstrate that the modifications in 3B really are phosphorylation and a quantification of the phosphorylated bands in these WBs would also be useful to solidify the statement made on lines 306-309.

We thank the reviewer for suggestion of the experiment using an endocytosis inhibitor. Previously we reported that vacuolar localization of Ght5 in gad8ts mutant cells was suppressed by mutations in not only aly3 but also genes encoding ESCRT complexes (Toyoda et al, J Cell Sci, 2021). We therefore think that, in cells expressing Aly3(ST18A) or Aly3(4th Ala), Ght5 is subject to endocytosis and subsequent selective transport to vacuoles via ESCRT complexes. We will mention these previous findings in the revised manuscript.

As mentioned in responses to the comments above and other reviewer's, we will perform a phosphatase treatment experiment and its quantification in the revised manuscript. Here, we'd like to emphasize that these 3 sites have been shown to be phosphorylated in phospho-proteomic studies by other researchers (Kettenbach et al, Mol Cell Proteomics, 2015; Tay et al, Cell Rep, 2019; Halova et al, Open Biol, 2021; Mak et al, EMBO J, 2021), although we do not show it directly in this study.

Phosphorylation assessments - in general, it would be good to not only build the non-phosphorylatable versions of Aly3 but also the phospho-mimetic forms.

We produced a phospho-mimetic mutant Aly3 (i.e. Aly3(4th A;A584D)), and showed the result in Figure 5A; cells expressing Aly3(4th A;A584D) exhibited a low ubiquitination of Ght5, similarly to Aly3(WT)- and Aly3(4th A;A584S)-expressing cells. According to our experiences, replacing S/T with D/E does not necessarily mimic phosphorylation. Thus, we do not believe that systematic production of phospho-mimetic Aly3 mutants would help achieve the aim of this study.

Pub1, 2, and 3 - It would be helpful if the authors indicated what genes Pubs 1-3 correspond to in S. cerevisiae, where Rsp5 is the predominant Ub ligase interacting with a-arrestins. Is there no ortholog of Rsp5 in S. pombe?

Pub1, Pub2 and Pub3 are regarded as orthologs of budding yeast Rsp5, according to the fission yeast database PomBase. We will perform a homology search for these E3 proteins, and based on the result, we will add a description in the revised manuscript.

Pub-Aly3 interactions - could the authors please comment on the reason why so very little Aly3 is copurified with Pub1 or Pub2? Can any clear conclusion be drawn about pub2 given how very little Pub2 is present in the IPs? Based on my understanding of these data I do not think that this can be cleanly interpreted. What is is the identity of the ~50kDa MW band in Figure 6 in the upper MYC detection panel?

We do not have an accurate answer for the result that a small amount of Aly3 is copurified with Pub1 or Pub3. The Pub1/3-Aly3 interaction may be weak or transient. We will discuss this point in the revised manuscript.

Regarding whether Aly3 interacts with Pub2, we agree with the reviewer. As described in the Results (L360-362), we could not conclude anything about Aly3-Pub2 interaction by this immunoprecipitation experiment alone. On the other hand, the genetic interaction experiment (Figure 7A) suggests that pub2+ is not involved in defects caused by the gad8ts mutation (while pub3+ and aly3+ are). By this experiment, we think that Pub2 is not a partner of Aly3.

In the revised manuscript, we will describe that Pub2 is not a partner of Aly3 in a paragraph describing the Figure 7A experiment.

Because the 50 kDa band found in the IP fraction of all the samples appears even in "beads only" (Figure 6), those are supposedly derived from mouse IgG dissociated from the beads used for immunoprecipitation. We will mention this in the legend of Figure 6.

Phosphorylation and ubiquitination of a-arrestins - The paragraph from lines 123-138 is very superficial in addressing what is known about phosphorylation and ubiquitination of a-arrestins. The way this section is written, it feels misleading to the reader as it omits many of the details for regulation that would help place the current study in context. The discussion of Rod1 phosphorylation by AMPK for example, which is directly relevant to this study, is underdeveloped. I would recommend splitting this into two paragraphs and providing a more in depth, and accurate, view of the literature on this topic, with a focus on the regulation that is relevant for the ortholog of Aly3 in S. cerevisiae. For example, Rod1 phosphorylation by AMPK is greatly expanded upon in the following papers (PMID 22249293 and 25547292) and AMPK regulation of C-tail phosphorylation of a-arrestins is defined further in PMID 26920760. These references are each particularly important to compare with the current findings presented in this manuscript. Torc2 regulation ofa-arrestins is also reviewed in PMID 36149412 and references therein should be considered.

Because the primary aim of this study is to reveal mechanisms regulating Ght5 localization in fission yeast, but not to dissect modification and regulation of α-arrestins, we decided not to get into the details of phosphorylation and ubiquitination of α-arrestins. Furthermore, although budding yeast Rod1 and Rog3 are found to be downstream of the TORC2-Ypk1 signaling in the context of internalization of the Ste2 pheromone receptor, it is not clear whether TORC2-Ypk1 signaling also regulate α-arrestin-mediated internalization of hexose transporters in budding yeast. For these reasons, we focused on limited literatures essential for interpretation of the results and omitted many references describing the details of α-arrestin regulation. However, as this reviewer commented, we realize that our decision makes the discussion superficial and misleading to the reader. We sincerely apologize for this inconvenience.

In the revised manuscript, we will reorganize the paragraphs in the discussion and include the suggested references. Regarding budding yeast Rod1, we will cite the study reporting Ypk1-mediated phosphorylation on Rod1 in mating pheromone response via regulation of Ste2 endocytosis (Alvaro et al, Genetics, 2016). We will also mention other reports (Becuwe et al, J Cell Biol, 2012; O'Donnell et al, Mol Cell Biol, 2015) about AMPK-dependent phosphorylation of Rod1 in the corresponding part (e.g. L129-130). In addition, we will mention that Aly2, Rod1 and Rog3 α-arrestins were found downstream of the TORC2-Ypk1 signaling (Muir et al, eLife, 2014; Thorner, Biochem J, 2022).

As a further detailed example, there is far more work done on ubiquitination of a-arrestins in S. cerevisiae than the single citation provided by the authors on line 137. The way this section is written it feels misleading. Considerable effort has been spent on defining how mono- and poly-ubiquitination regulate a-arrestins and the authors should consider the data provided in the following citations and revise the two sentences they provide in this introduction to better reflect the breadth of our understanding rather than simply indicate that the 'mechanisms that regulate functions of a-arrestisn are not fully understood'. (PMIDs 23824189; 22249293; 17028178; 28298493)

Ubiquitination of α-arrestin itself is not the topic of this study, and physiological consequences of ubiquitination of Aly3 remain unknown. Because of these reasons, we did not describe the details of ubiquitination of α-arrestins in the original manuscript. However, we never intend to mislead the reader, and thus to avoid it, we will revise the indicated sentences and cite the suggested literatures (O'Donnell et al, J Biol Chem, 2013; Becuwe et al, J Cell Biol, 2012; Kee et al, J Biol Chem, 2006; Ho et al, Mol Biol Cell, 2017) in the revised manuscript.

Context of the findings and lack of citations - The referencing in this manuscript is very poor as many of the key papers that report analogous findings in the budding yeast Saccharomyces cerevisiae are not cited. This oversight in citing the appropriate literature must be remedied before this manuscript can be considered further for publication. Examples of these omissions occur at the following places:

We will modify the text and carefully cite more literatures describing analogous finding in budding yeast and other organisms in the revised manuscript. We appreciate the insightful suggestions by this reviewer. It should be noted, however, that it is not evident whether budding yeast Rod1 and Rog3 are orthologous to fission yeast Aly3. Although Rod1 and Aly3 share overlapping roles, amino acid sequence similarity of them is not high and limited only in domains which are generally conserved among α-arrestin-family proteins.

Line 90 - The Puca and Brou citations is one example of this but the first examples come from Daniela Rotin's work looking at Rsp5 interactions in budding yeast, which is where the association between HECT-domain Ub ligases and a-arrestins is also documented by Scott Emr and Hugh Pelham's labs. Here are some PMID numbers to improve the citations of this section (PMID 17551511; 18976803; 19912579) and each of these references long predates the Puca and Brou publication.

In the revised manuscript, we will improve the citations by including the suggested studies (Gupta et al, Mol Syst Biol, 2007; Lin et al, Cell, 2008; Nikko and Pelham, Traffic, 2009).

Lines 123-126 - Phosphorylation can also increase vacuole-dependent degradation of alpha-arrestins as demonstrated in PMID 35454122. The interaction with 14-3-3 proteins that is driven by phosphorylation of a-arrestins was first demonstrated by the Leon group in PMID 22249293). Lines 129-132 - Here again the Leon reference that helps demonstrate the 14-3-3 inhibition of Rod1 is lacking (PMID 22249293).

We will cite the suggested studies in description of these topics (Bowman et al, Biomolecules, 2022; Becuwe et al, J Cell Biol, 2012).

Lines 130-132 - Please include references for the statement that dephosphorylation activates a-arrestin activity. There are no citations on this statement and there are many to choose from and I would urge the authors to cite the primary literature on these points.

We will cite studies for the statement "Conversely, dephosphorylation is thought to activate α-arrestins and to promote selective endocytosis of transporter proteins" (L130-132).

These are just a few examples from the Introduction, but the Discussion is similarly wrought with issues in referencing and framing the experimental results within the context of the larger field, including what is known about Rod1/Rog3 regulation in S. cerevisiae. For example, the Llopis-Torregrosa et al reference and statement on lines 508-510 is incorrect. There are other phosphorylation sites defined in the C-terminus of Rod1, as described in Alvaro et al. PMID: 26920760.

We will carefully correct Discussion by citing the suggested references (e.g. Alvaro et al, Genetics, 2016) and framing the obtained results within the context of the larger field.

Of note, a combination of α-arrestin, upstream kinase(s) and distinct phosphorylation sites appears to determine the target transporter (Kahlhofer et al, Biol Cell, 2021; Thorner, Biochem J, 2022), and it has not been explicitly proved that TORC2-Ypk1 signaling also regulate α-arrestin-mediated internalization of hexose transporters in budding yeast. For these reasons, we stated "S. cerevisiae Rod1 and Rog3 are phosphorylated solely by Snf1p/AMPK" in the context of internalization of hexose transporters. We will also discuss this point in the revised manuscript.

Minor Comments Clarification needed - Lines 107-121 - The relationship between the S. pombe arrestins and those in other organisms is somewhat unclear. Frist, all the arrestins in humans and S. cerevisiae can be sorted into the alpha, beta and Vps26 classes. However, the authors indicate that the S. pombe genome has 11 arrestin-like proteins but only 4 of these are a-arrestins. What classes do the other 7 arrestins belong to? It would be appreciated if this point was clarified.

To our knowledge, fission yeast arrestins are not well classified yet. We will perform a phylogenetic tree analysis to classify them, and modify the description of the indicated part accordingly. We will also cite our previous report (Toyoda et al, J Cell Sci, 2021), in which the overall protein structure and domains of 11 fission yeast arrestin-like proteins were reported.

Next, for the 4 a-arrestins identified in S. pombe the authors indicate that Aly3 is the homolog of Rod1/Art4 and Rog3/Art7 from S. cerevisiae. What is the relationship of Rod1 in S. pombe to Rod1 in S. cerevisiae? Are these also homologs? You can see how the nomenclature is confusing and, given the functional overlap of S. cerevisiae Rod1/Rog3 proteins it is important to know if Aly3 is the only version of these a-arrestins or if there is an additional counterpart in S. pombe. This point becomes somewhat more confusing when on lines 134-136 the authors talk about Arn1/Any1 as an arrestin related protein in S. pombe yet this protein was not included on the list of a-arrestins in the preceding section. What class of arrestin is this protein?

According to PomBase, both Aly3 and Rod1 are assigned as the orthologue of budding yeast Rod1 and Rog3. However, as mentioned in responses above, it is unclear whether Aly3 is really orthologous to budding yeast Rod1/Rod3. In the revised manuscript, we will perform a homology search for these 4 proteins, and add information on how much these arrestins share homology.

Arn1/Any1 is regarded as a β-arrestin (Nakase et al, J Cell Sci, 2013). We will also mention this in the revised manuscript.

Alpha-arrestin homology - On lines 127-129 the authors indicate that TXNIP is the mammalian homolog of Aly3. To my knowledge, there are no evolutionary analyses that can draw these lines of homology between the a-arrestins in humans and those in yeasts. It would be appreciated if the authors could cite the work that leads to this conclusion or revise the sentence to more accurately reflect what is known on this topic. It certainly appears that, given their functional overlap in regulating glucose transporters, Txnip and Rod1/Rog3 in humans and S. cerevisiae are functionally connected. I urge the authors to use more caution when describing this protein family.

Among human α-arrestins, ARRDC2 (22%) but not TXNIP (20%) has the highest amino acid identity to Aly3 (Toyoda et al, J Cell Sci, 2021). However, as TXNIP has been reported to regulate endocytosis of hexose transporters, GLUT1 and 4 (Wu et al, Mol Cell, 2013; Waldhart et al, Cell Rep, 2017), we think that TXNIP and Aly3 share physiological roles. We will revise the sentence (L127-129) more accurately.

Text editing - The text could use editing as there are awkward and grammatically incorrect sentences in several places. Here are a few examples to help the authors:

Please note that the original manuscript is edited by a professional editor, who is a native English (American) speaker and has edited thousands of research papers, before initial submission. We will ask an editor to check the revised draft again before submission.

Lines 57-60 - the protein is not expressed over the entire cell surface, but is localized to the entire cell surface.

We will correct this wording.

Lines 80-83 - this sentence is very confusing

We will correct this part by changing the phrase "Unlike TORC1," into a clause.

Line 86 - Is there more than one gene encoding Aly3 in S. pombe?

No, there is only one gene encoding Aly3. We will correct this part so as to avoid being misunderstood.

Line 88, 109, - these sentences need to start with a capitol so either capitalize the A in arrestin or write out Alpha with a capitol A.

We will correct the sentence as suggested.

Lines 145-148 - unclear as written

We will clarify the meaning of the sentence by changing the voice.

Line 224 - why are these amino acids being referred to as hydroxylated? Perhaps hydroxyl-containing amino acids or 18 amino acids with hydroxyl side chains would be better choices?

We will correct the word as suggested.

Line 300 - very confusing sentence structure

We will correct this part by simplifying the structure of the sentence.

And elsewhere....

We will carefully check the revised text before submission.

Reviewer #3 (Significance):

The authors provide some information as to the residues needed in the Aly3 C-tail for Ght5 trafficking in S. Pombe. These results are not places in the context of similar phosphor-regulatory work done for a-arrestins in S. cerevisiae, and this is needed for appreciation of the significance of the study.

Overall, it appears that the model put forth is very similar to the one already proposed in S. cerevisiae where phosphorylation impedes a-arrestin-mediated trafficking of glucose transporters. It is interesting to see this similarity hold in S. Pombe, but it does not dramatically alter our appreciation of a-arrestin biology.

The significance of the findings are somewhat underscored by the fact that very little quantification of data are presented, making the rigor of the work difficult to assess.

We thank the reviewer for careful reading and evaluation of our study. As the reviewer states, the results are not placed in the context of similar phospho-regulatory works done for α-arrestins in S. cerevisiae. This may partly come from the fact that it remains unclear whether internalization of hexose transporters is regulated by TORC2-dependent phosphorylation in S. cerevisiae. We believe that our study is novel and significant for this reason. By performing the additional experiments/quantification and revising the text as suggested by the reviewers, the manuscript will be further strengthened, and we will be able to clearly conclude that TORC2-dependent phosphorylation of Aly3 regulates localization of the Ght5 hexose transporter and cellular responses to glucose shortage stress.

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Referee #2

Evidence, reproducibility and clarity

Summary/background.

This paper focuses on the regulation of endocytosis of the hexose transporter, Ght5, in S. pombe by nutrient limitation through the arrestin-like protein Aly3. Ght5 is induced when glucose is limiting and is required for growth and proliferation in these conditions. ght5+ encodes the only high-affinity glc transporter from fission yeast. ght5+ is induced in low glucose conditions at the transcriptional level and is translocated to the plasma membrane to allow glc import. Ght5 is targeted to the vacuole in conditions of N limitation. Mutations in the TORC2 pathway lead to the same process, thus preventing growth on low glucose medium, as shown in the gad8ts mutant, mutated for the Gad8 kinase acting downstream of TORC2. Previously, the authors demonstrated that the vacuolar delivery of Ght5 in the gad8ts mutant is suppressed by mutation of the arrestin-like protein Aly3. Arrestin-like proteins are in charge of recognising and ubiquitinating plasma membrane proteins to direct their vacuolar targeting by the endocytosis pathway. This suggested that Aly3 is hyperactive in TORC2 mutants, and accordingly, Ght5 ubiquitination was increased in gad8ts.

Overall statement

This study aims at deepening our understanding of the regulation of endocytosis by signalling pathways through arrestin-like proteins. Ght5 is a nice model to study a physiological regulation, and the authors have a great set of tools at hand. However, I think the conclusions are not always rigorous and the conclusions are sometimes far-reaching. The main problem is that much of the conclusions concern a potential phosphorylation of Aly3 which is not experimentally addressed. An additional issue is the fact that they look at Ght5 ubiquitination by co-immunoprecipitation in native conditions (or at least, it seems to me) which cannot be conclusive. Overall, I think some experiments should be performed to address (at least) these 2 points before the manuscript can be published, see detailed comments below.

Major statements and criticism.

• Fig 1. Based on the hypothesis that TORC2-mediated phosphorylation regulate Ght5 endocytosis, the authors first considered a possible phosphorylation of Ght5. They mutagenised 11 possible phosphorylation sites on the Ct of Ght5, but none affected the growth on low glucose in the absence of thiamine, suggesting that they don't contribute to the observed TORC2-mediated regulation. However, I disagree with the statement that "phosphorylation of Ght5 is dispensable for cell proliferation in low glucose", given that the authors do not show 1- that Ght5 is phosphorylated and 2-that this is abolished by these mutations. They should either provide data on this or tone down and say that these residues are not involved in the regulation, without implying phosphorylation which is not proven. In the presence of Thiamine (Supp fig 1), it seems that the ST/A mutant grows better in low glucose, and this is not explained nor commented. Since the transporter is not expressed, could the authors provide an explanation to this? If the promoter is leaky and some ght5-ST/A is expressed, it may be more stable and allow better growth than the WT, which would tend to indicate that impairing phosphorylation prevents endocytosis (which is classical for many transporters, see the body of work on CK1-mediated phosphorylation of transporters). Have the authors tried to decrease glc concentration lower than 0.14% in the absence of thiamine to see if this also true when the transporters is strongly expressed? (OPTIONAL)
• Fig 2. The authors then follow the hypothesis that TORC2 exerts its Ght5-dependent regulation through the phosphorylation of Aly3. They mutagenised 18 possible phosphorylation sites on Aly3. This led to a strong defect in growth in low-glc medium. Mutation of the possible Gad8 site (S460) did not recapitulate this phenotype, suggesting that it is not sufficient, however, mutations of 4 ST residues in a CT cluster (582-586) mimicked the full 18ST/A mutation, suggesting these are the important residues for Ght5 endocytosis.
• Fig 3A. Further dissection did not allow to pinpoint this regulation to a specific residue, beyond the dispensability of the T586 residue. Fig 3B. The authors look at the effects of mutation of Aly3 on these sites at the protein level. They had to develop an antibody because HA-epitope tagging did not lead to a functional protein (Supp fig 2). Whereas I agree that the mutations causing a phenotype lead to a change in the migration pattern, I disagree with the statement that "This observation indicated that slower migrating bands were phosphorylated species of Aly3" (p.9 l.271). First, lack of phosphorylation usually causes a slower mobility on gel, which is not clear to spot here. Second, a smear appears on top of the mutated proteins (eg. 4th Ala) which is possibly caused by another modification. There are many precedents in the literature about arrestins being ubiquitinated when they are not phosphorylated (see the work on Bul1, Rod1, Csr2 in baker's yeast from various labs). My gut feeling is that lack of phosphorylation unleashes Aly3 ubiquitination leading to change in pattern. All in all, it is impossible to state about the phosphorylation of a protein without addressing its phosphorylation properly by phosphatase treatment + change in migration, or MS/MS. Thus, whereas the data looks promising, this hypothesis that Aly3 is phosphorylated at the indicated sites is not properly demonstrated.
• Fig 4. The authors now look at the functional consequences of these mutations on ALy3 on Ght5 localisation. The data clearly shows that mutation of the 4 identified S/T residues (Aly3-4th A) causes aberrant localisation of the transporter to the vacuole, likely to cause the observed growth defect on low glucose. There is a nice correlation between the vacuolar localisation and growth in low-glucose for the various aly3 mutants. (A final proof could be to express this in the context of an endocytic mutant, which should restore membrane localisation and suppress the aly3-4thA phenotype - OPTIONAL). However, I still disagree with the statement that "These results indicate that phosphorylation of Aly3 at the C-terminal 582nd, 584th, and/or 585th serine residues is required for cell-surface localization of Ght5." given that phosphorylation was not properly demonstrated.
• Fig 5. Here, the authors question the role of Aly3 mutations on Ght5 ubiquitination. They immunoprecipitate Ght5 and address its ubiquitination status in various Aly3 mutants. The data is encouraging for a role in Aly3 phosphorylation (?) in the negative control of Ght5 ubiquitination. My main problem with this experiment is that it seems that Ght5 immunoprecipitations were made in non-denaturing conditions, which leads to the question of what is the anti-ubiquitin revealing here (Ght5 or a co-immunoprecipitated protein, for example Aly3 itself, or the Pub ligases, or an unknown protein). It seems that this protocol was previously used in their previous paper, but I stand by my conclusion that ubiquitination of a given protein can only be looked in denaturing conditions. The experiments should be repeated in buffers classical for the study of protein ubiquitination to be able to conclude unambiguously that we are looking at Ght5 ubiquitination itself, especially in the absence of a non-ubiquitinable form of Ght5 as a negative control. Could the authors comment on the fact that S-A or S-D mutations display the same phenotype regarding the possible Ght5 ubiquitination?
• Fig 6. The authors want to document the model whereby Aly3 may interact with some of the Nedd4 ligases (Pub1/2/3) to mediate its Ght5-ubiquitination function. They actually use the Aly3-4thA mutant, it should have been better with the WT protein. But the results indicate a clear interaction with at least Pub1 and Pub3. By the way, are the Pub1/2/3 fusions functional? Nedd4 proteins are notoriously affected in their function by C-terminal tagging and are usually tagged at their N-terminus (See Dunn et al. J Cell Biol 2004).
• Fig 7. The authors want to provide genetic interaction between the Pub ligases and the growth defects in low glc due to alterations in Ght5 trafficking. It is unclear how the gad8ts pub1∆ mutant was generated since it doesn't seem to grow on regular glc concentration (Supp fig 5), could the authors provide some information about this? It is also not clear whether it can be stated thatches mutant is "more sensitive" to glc depletion because of the low level of growth to begin with (even at 3%). Altogether, the data show that deletion of pub3+ is able to suppress the growth defect of the gad8ts mutant on low glc medium, suggesting it is the relevant ligase for Ght5 endocytosis. This is confirmed by microscopy observations of Ght5 localisation. However, I would again tone down the main conclusion, which I feel is far-reaching: "Combined with physical interaction data, these results strongly suggest that Aly3 recruits Pub3, but not Pub2, for ubiquitination of Ght5." Work on Rsp5 in baker's yeast has shown that Rsp5 function goes beyond cargo ubiquitination, including ubiquitination of arrestins (which is often required for their function as mentioned in the introduction) or other endocytic proteins (epsins, amphyphysin etc). I agree that the data are compatible with this model but there are other possible explanations. Anything that would block endocytosis would supposedly suppress the gad8ts phenotype.

Discussion

Some analogy with the regulation of the Bul arrestins by TORC1/Npr1 and PP2A/Sit4 could be mentioned (Mehri et al. 2012), at the discretion of the authors. The possibility that phosphorylation may neutralise a basic patch on Aly3 Ct, possibly involved in electrostatic interactions with Ght5 is very interesting. Regarding the effect of the mutations on Aly3 localisation (p.15 l.498), did the authors tag Aly3 with GFP? There are examples where proteins tagged with HA are not functional whereas tagging with GFP does not alter their function (eg. Rod1, Laussel et al. 2022) - and here Supp Fig 2 only relates to HA-tagging. Proof of a change in Aly3 localisation upon mutation would definitely be a plus (OPTIONAL).

Minor comments.

Introduction:

• I believe the text corresponding to the work on TXNIP is incorrect (p.5 l.127). TXNIP is degraded after its phosphorylation, not "rectracted" from the surface.
• For the sake of completion, the authors could add other references concerning the regulation of Rod1 in budding yeast such as Becuwe et al. 2012 J Cell Biol and O'Donnell et al. 2015 Mol Cell Biol, in addition to Llopis-Torregrosa et al. 2016.
• Other examples of the requirement for arrestin ubiquitination beyond Art1 (p.5 l.136-137) are listed in the ref cited: Kahlhofer et al. 2021.

Figures: In general, I think it would be clearer if the authors showed on the figures that the background strain in which the XXX gene is added (or its mutant forms) is a xxx∆ strain.

Referees cross-commenting

Cross review of Reviewer 1

• I don't believe that the authors "define a set of redundant c-terminal phosphorylation sites in Aly3", because phosphorylation is not proven.
• I thinks the points raised for Fig 3B are valid but the authors should focus on making their story conclusive before expanding to other data (except for the explanation of the smear, see my review). Also, I don't think 2NBDG actually works to measure Glc uptake.
• same for Fig 6 - not sure the interaction site mapping between Aly3 and Pubs would bring much value since there are more urgent things to do to make the story solid.

Cros review of Reviewer 3 - we have many overlaps, so briefly :

• I agree that the bibliography is incomplete (mentioned in my review)
• I agree that there is no demonstration of the phospho-status of Aly3, and it is a problem
• I agree that the results can be better quantified, esp. in the light of the points raised by this referee concerning the variability of expression of ST18A

Other specific comments :

• I agree that the statement that dephosphorylation activates alpha-arresting should be toned down - this was observed in several instances but there are examples of arrestin-mediated endocytosis which does not require their prior dephosphorylation.
• I fully agree that efforts could be made regarding the classification/nomenclature of arrestins in S. pombe, this had escaped my attention

Significance

strengths and limitations

This study aims at deepening our understanding of the regulation of endocytosis by signalling pathways through arrestin-like proteins in S. pombe. Ght5 is a nice model to study a physiological regulation, and the authors have a great set of tools at hand, including the discovery of Aly3 as the main arrestin for this regulation, and a signalling pathway (TORC2/Gad8) acting upstream. The main question is now to understand at the mechanistic level how TORC2 signaling impinges on the regulation of this arrestin.

Overall, the authors nicely demonstrate that C-terminal Ser/Thr residues are crucial for the function of Aly3 in Ght5 endocytosis. They propose a model whereby Aly3 phosphorylation by an unknownn kinase inhibits its function on Ght5 ubiquitination, which would favour its endocytosis. However, I think the conclusions are not always rigorous and the conclusions are sometimes far-reaching. The main problem is that much of the conclusions concern a potential phosphorylation of Aly3 which is not experimentally addressed. An additional issue is the fact that they look at Ght5 ubiquitination by co-immunoprecipitation in native conditions (or at least, it seems to me) which cannot be conclusive. Overall, I think some experiments should be performed to address (at least) these 2 points before the manuscript can be published, see detailed comments above.

Advance

This study, if completed carefully, would provide among the first examples of mapping of phosphorylation sites on arrestins, which are usually phosphorylated at many sites and are thus difficult to study. Few studies went down to this level in this respect (see Ivshov et al. eLife 2020). There are no changes in paradigms or new conceptual insights, but this work is a nice example of the conservation of these regulatory mechanisms.

Audience

Should be of interest for people studying basic research in the field of cell biology, signalling pathways, transporter regulation by physiology. Reviewer background is on the regulation of transporter endocytosis by signalling pathways and arrestin-like proteins.

Author response:

Reviewer #1 (Public Review):

Summary and Strengths:

The ability of Wolbachia to be transmitted horizontally during parasitoid wasp infections is supported by phylogenetic data here and elsewhere. Experimental analyses have shown evidence of wasp-to-wasp transmission during coinfection (eg Huigins et al), host to wasp transmission (eg Heath et al), and mechanical ('dirty needle') transmission from host to host (Ahmed et al). To my knowledge this manuscript provides the first experimental evidence of wasp to host transmission. Given the strong phylogenetic pattern of host-parasitoid Wolbachia sharing, this may be of general importance in explaining the distribution of Wolbachia across arthropods. This is of interest as Wolbachia is extremely common in the natural world and influences many aspects of host biology.

Weaknesses:

The first observation of the manuscript is that the Wolbachia strains in hosts are more closely related to those in their parasitoids. This has been reported on multiple occasions before, dating back to the late 1990s. The introduction cites five such papers (the observation is made in other studies too that could be cited) but then dismisses them by stating "However, without quantitative tests, this observation could simply reflect a bias in research focus." As these studies include carefully collected datasets that were analysed appropriately, I felt this claim of novelty was rather strong. It is unclear why downloading every sequence in GenBank avoids any perceived biases, when presumably the authors are reanalysing the data in these papers.

Thank you for bringing this to our attention, and we will make the necessary amendments in our revised manuscript.

I do not doubt the observation that host-parasitoid pairs tend to share related Wolbachia, as it is corroborated by other studies, the effect size is large, and the case study of whitefly is clearcut. It is also novel to do this analysis on such a large dataset. However, the statistical analysis used is incorrect as the observations are pseudo-replicated due to phylogenetic non-independence. When analysing comparative data like this it is essential to correct for the confounding effects of related species tending to be similar due to common ancestry. In this case, it is well-known that this is an issue as it is a repeated observation that related hosts are infected by related Wolbachia. However, the authors treat every pairwise combination of species (nearly a million pairs) as an independent observation. Addressing this issue is made more complex because there are both the host and symbiont trees to consider. The additional analysis in lines 123-124 (including shuffling species pairs) does not explicitly address this issue.

We concur with your observation regarding the non-independence of the data due to phylogenetic relationships. While common phylogenetic correction methods are indeed not directly applicable to wsp distances between species pairs, we are investigating the potential of phylogenetic mixed models to address this issue. We hope to include a revised analysis using this approach in our revised manuscript.

The sharing of Wolbachia between whitefly and their parasitoids is very striking, although this has been reported before (eg the authors recently published a paper entitled "Diversity and Phylogenetic Analyses Reveal Horizontal Transmission of Endosymbionts Between Whiteflies and Their Parasitoids"). In Lines 154-164 it is suggested that from the tree the direction of transfer between host and parasitoid can be inferred from the data. This is not obvious to me given the poor resolution of the tree due to low sequence divergence. There are established statistical approaches to test the direction of trait changes on a tree that could have been used (a common approach is to use the software BEAST).

Thank you for your insightful comments regarding the transfer direction of Wolbachia between whiteflies and their parasitoids. We acknowledge the concern about the resolution of the phylogenetic tree and the inference of the direction of Wolbachia transmission based on the available data. We considered the high infection frequency and obligate nature of Wolbachia in En. formosa, which exhibits a 100% infection rate, as a strong indicator that recent transmission of Wolbachia in this clade likely occurred from En. formosa to B. tabaci. We appreciate your recommendation and will ensure that our conclusions are supported by a more statistically sound approach. As you suggested, we will employ the software BEAST to rigorously test the direction of transmission, and we will revise our statements accordingly.

Reviewer #2 (Public Review):

The paper by Yan et al. aims to provide evidence for horizontal transmission of the intracellular bacterial symbiont Wolbachia from parasitoid wasps to their whitefly hosts. In my opinion, the paper in its current form consists of major flaws.

Weaknesses:

The dogma in the field is that although horizontal transmission events of Wolbachia occur, in most systems they are so rare that the chances of observing them in the lab are very slim.

For the idea of bacteria moving from a parasitoid to its host, the authors have rightfully cited the paper by Hughes, et al. (2001), which presents the main arguments against the possibility of documenting such transmissions. Thus, if the authors want to provide data that contradict the large volume of evidence showing the opposite, they should present a very strong case.

In my opinion, the paper fails to provide such concrete evidence. Moreover, it seems the work presented does not meet the basic scientific standards.

We are grateful for your critical perspective on our work. Nonetheless, we are confident in the credibility of our findings regarding the horizontal transmission of Wolbachia from En. formosa to B. tabaci. Our study has documented this phenomenon through phylogenetic tree analyses, and we have further substantiated our observations with rigorous experiments in both cages and petri dishes. The horizontal transfer of Wolbachia was confirmed via PCR, with the wsp sequences in B. tabaci showing complete concordance with those in En. formosa. Additionally, we utilized FISH, vertical transmission experiments, and phenotypic assays to demonstrate that the transferred Wolbachia could be vertically transmitted and induce significant fitness cost in B. tabaci. All experiments were conducted with strict negative controls and a sufficient number of replicates to ensure reliability, thereby meeting basic scientific standards. The collective evidence we present points to a definitive case of Wolbachia transmission from the parasitoid En. formosa to the whitefly B. tabaci.

My main reservations are:

• I think the distribution pattern of bacteria stained by the probes in the FISH pictures presented in Figure 4 looks very much like Portiera, the primary symbiont found in the bacterium of all whitefly species. In order to make a strong case, the authors need to include Portiera probes along with the Wolbachia ones.

We are very grateful for your critical evaluation regarding the specificity of FISH in our study. We assure the reliability of our FISH results based on several reasons.

1) We implemented rigorous negative controls which exhibited no detectable signal, thereby affirming the specificity of our hybridization. 2) The central region of the whitefly nymphs is a typical oviposition site for En. formosa. Post-parasitism, we observed FISH signals around the introduced parasitoid eggs, distinct from bacteriocyte cells which are rich in endosymbionts including Portiera (FIG 3e-f). This observation supports the high specificity of our FISH method. 3) In the G3 whiteflies, we detected the presence of Wolbachia in bacteriocytes in nymphs and at the posterior end of eggs in adult females (FIG 4). This distribution pattern aligns with previously reported localizations of Wolbachia in B. tabaci (Shi et al., 2016; Skaljac et al., 2013). Furthermore, the distribution of Wolbachia in the whiteflies does indeed exhibit some overlap with that of Portiera (Skaljac et al., 2013; Bing et al., 2014). 4) The primers used in our FISH assays have been widely cited (Heddi et al., 1999) and validated in studies on B. tabaci and other systems (Guo et al., 2018; Hegde et al., 2024; Krafsur et al., 2020; Rasgon et al., 2006; Uribe-Alvarez et al., 2019; Zhao et al., 2013). Taking all these points into consideration, we stand by the reliability of our FISH results.

References:

Bing XL, Xia WQ, Gui JD, Yan GH, Wang XW, Liu SS. 2014. Diversity and evolution of the Wolbachia endosymbionts of Bemisia (Hemiptera: Aleyrodidae) whiteflies. Ecol Evol, 4(13): 2714-37.

Guo, Y, Hoffmann, AA, Xu, XQ, Zhang X, Huang HJ, Ju JF, Gong JT, Hong XY. 2018. Wolbachia-induced apoptosis associated with increased fecundity in Laodelphax striatellus (Hemiptera: Delphacidae). Insect Mol Biol, 27: 796-807.

Heddi A, Grenier AM, Khatchadourian C, Charles H, Nardon P. 1999. Four intracellular genomes direct weevil biology: Nuclear, mitochondrial, principal endosymbiont, and Wolbachia. Proc Natl Acad Sci USA, 96: 6814-6819.

Hegde S, Marriott AE, Pionnier N, Steven A, Bulman C, Gunderson E, et al. 2024. Combinations of the azaquinazoline anti-Wolbachia agent, AWZ1066S, with benzimidazole anthelmintics synergise to mediate sub-seven-day sterilising and curative efficacies in experimental models of filariasis. Front Microbiol, 15: 1346068.

Krafsur AM, Ghosh A, Brelsfoard CL. 2020. Phenotypic response of Wolbachia pipientis in a cell-free medium. Microorganisms, 8: 1060.

Rasgon JL, Gamston, CE, Ren X. 2006. Survival of Wolbachia pipientis in cell-free medium. Appl Environ Microbiol, 72: 6934-6937.

Shi P, He Z, Li S, An X, Lv N, Ghanim M, Cuthbertson AGS, Ren SX, Qiu BL. 2016. Wolbachia has two different localization patterns in whitefly Bemisia tabaci AsiaII7 species. PLoS One, 11: e0162558.

Skaljac M, Zanić K, Hrnčić S, Radonjić S, Perović T, Ghanim M. 2013. Diversity and localization of bacterial symbionts in three whitefly species (Hemiptera: Aleyrodidae) from the east coast of the Adriatic Sea. Bull Entomol Res, 103(1): 48-59.

Uribe-Alvarez C, Chiquete-Félix N, Morales-García L, Bohórquez-Hernández A, Delgado-Buenrostro N L, Vaca L, et al. 2019. Wolbachia pipientis grows in Saccharomyces cerevisiae evoking early death of the host and deregulation of mitochondrial metabolism. MicrobiologyOpen, 8: e00675.

Zhao DX, Zhang XF, Chen DS, Zhang YK, Hong XY, 2013. Wolbachia-host interactions: Host mating patterns affect Wolbachia density dynamics. PLoS One, 8: e66373.

• If I understand the methods correctly, the phylogeny presented in Figure 2a is supposed to be based on a wide search for Wolbachia wsp gene done on the NCBI dataset (p. 348). However, when I checked the origin of some of the sequences used in the tree to show the similarity of Wolbachia between Bemisia tabaci and its parasitoids, I found that most of them were deposited by the authors themselves in the course of the current study (I could not find this mentioned in the text), or originated in a couple of papers that in my opinion should not have been published to begin with.

We appreciate your meticulous examination of the sources for our sequence data. All the sequences included in our phylogenetic analysis were indeed downloaded from the NCBI database as of July 2023. The sequences used to illustrate the similarity of Wolbachia between B. tabaci and its parasitoids include those from our previously published study (Qi et al., 2019), which were sequenced from field samples. Additionally, some sequences were also obtained from other laboratories (Ahmed et al., 2009; Baldo et al., 2006; Van Meer et al., 1999). We acknowledge that in our prior research (Qi et al., 2019), the sequences were directly submitted to NCBI and, regrettably, we did not update the corresponding publication information after the article were published. It is not uncommon for sequences on NCBI, with some never being followed by a published paper (e.g., FJ710487- FJ710511 and JF426137-JF426149), or not having their associated publication details updated post-publication (for instance, sequences MH918776-MH918794 from Qi et al., 2019, and KF017873-KF017878 from Fattah-Hosseini et al., 2018). We recognize that this practice can lead to confusion and apologize for the oversight in our work.

References:

Ahmed MZ, Shatters RG, Ren, SX, Jin GH, Mandour NS, Qiu BL. 2009. Genetic distinctions among the Mediterranean and Chinese populations of Bemisia tabaci Q biotype and their endosymbiont Wolbachia populations. J Appl Entomol, 133: 733-741.

Baldo L, Hotopp JCD, Jolley KA, Bordenstein SR, Biber SA, Choudhury RR, et al. 2006. Multilocus sequence typing system for the endosymbiont Wolbachia pipientis. Appl Environ Microbiol, 72: 7098-110.

Fattah-Hosseini S, Karimi J, Allahyari H. 2014. Molecular characterization of Iranian Encarsia formosa Gahan populations with natural incidence of Wolbachia infection. J Entomol Res Soc, 20: 85–100.

Qi LD, Sun JT, Hong XY, Li YX. 2019. Diversity and phylogenetic analyses reveal horizontal transmission of endosymbionts between whiteflies and their parasitoids. J Econ Entomol, 112(2): 894-905.

Van Meer MM, Witteveldt J, Stouthamer R. 1999. Phylogeny of the arthropod endosymbiont Wolbachia based on the wsp gene. Insect Mol Biol, 8: 399-408.

• The authors fail to discuss or even acknowledge a number of published studies that specifically show no horizontal transmission, such as the one claimed to be detected in the study presented.

Thank you for bringing this to our attention. We will address and discuss the published studies that report no evidence of horizontal transmission, as you've highlighted, in the revised version of our manuscript.

Reviewer #3 (Public Review):

This is a very ordinary research paper. The horizontal of endosymbionts, including Wolbachia, Rickettsia etc. has been reported in detail in the last 10 years, and parasitoid vectored as well as plant vectored horizontal transmission is the mainstream of research. For example, Ahmed et al. 2013 PLoS One, 2015 PLoS Pathogens, Chiel et al. 2014 Enviromental Entomology, Ahmed et al. 2016 BMC Evolution Biology, Qi et al. 2019 JEE, Liu et al. 2023 Frontiers in Cellular and Infection Microbiology, all of these reported the parasitoid vectored horizontal transmission of endosymbiont. While Caspi-Fluger et al. 2012 Proc Roy Soc B, Chrostek et al. 2017 Frontiers in Microbiology, Li et al. 2017 ISME Journal, Li et al. 2017 FEMS, Shi et al. 2024 mBio, all of these reported the plant vectored horizontal transmission of endosymbiont. For the effects of endosymbiont on the biology of the host, Ahmed et al. 2015 PLoS Pathogens explained the effects in detail.

Thank you very much for your insightful comments and for highlighting the relevant literature in the field of horizontal transmission of endosymbionts, including Wolbachia and Rickettsia. After careful consideration of the studies you have mentioned, we believe that our work presents significant novel contributions to the field. 1) Regarding the parasitoid-mediated horizontal transmission of Wolbachia, most of the cited articles, such as Ahmed et al. 2013 in PLoS One and Ahmed et al. 2016 in BMC Evolutionary Biology, propose hypotheses but do not provide definitive evidence. The transmission of Wolbachia within the whitefly cryptic species complex (Ahmed et al. 2013) or between moths and butterflies (Ahmed et al. 2016) could be mediated by parasitoids, plants, or other unknown pathways. 2) Chiel et al. (2014 in Environmental Entomology reported “no evidence for horizontal transmission of Wolbachia between and within trophic levels” in their study system. 3) The literature you mentioned about Rickettsia, rather than Wolbachia, indirectly reflects the relative scarcity of evidence for Wolbachia horizontal transmission. For example, the evidence for plant-mediated transmission of Wolbachia remains isolated, with Li et al. 2017 in The ISME Journal being one of the few reports supporting this mode of transmission. 4) While the effects of endosymbionts on their hosts are not the central focus of our study, the effects of transgenerational Wolbachia on whiteflies are primarily demonstrated to confirm the infection of Wolbachia into whiteflies. Furthermore, the effects we report of Wolbachia on whiteflies are notably different from those reported by Ahmed et al. 2015 in PLoS Pathogens, likely due to different whitefly species and Wolbachia strains. 6) More importantly, our study reveals a mechanism of parasitoid-mediated horizontal transmission of Wolbachia that is distinct from the mechanical transmission suggested by Ahmed et al. 2015 in PLoS Pathogens. Their study implies transmission primarily through host-feeding contamination, without the need for Wolbachia to infect the parasitoid, suggesting host-to-host transmission at the same trophic level. In contrast, our findings demonstrate transmission from parasitoids to hosts through unsuccessful parasitism, which represents cross-trophic level transmission. To our knowledge, this is the first experimental evidence that Wolbachia can be transmitted from parasitoids to hosts. We believe these clarifications and the novel insights provided by our research contribute valuable knowledge to the field.

References:

Ahmed MZ, De Barro PJ, Ren SX, Greeff JM, Qiu BL. 2013. Evidence for horizontal transmission of secondary endosymbionts in the Bemisia tabaci cryptic species complex. PLoS One, 8: e53084.

Ahmed MZ, Li SJ, Xue X, Yin XJ, Ren SX, Jiggins FM, Greeff JM, Qiu BL. 2015. The intracellular bacterium Wolbachia uses parasitoid wasps as phoretic vectors for efficient horizontal transmission. PLoS Pathog, 10: e1004672.

Ahmed MZ, Breinholt JW, Kawahara AY. 2016. Evidence for common horizontal transmission of Wolbachia among butterflies and moths. BMC Evol Biol, 16: 118. doi.org/10.1186/s12862-016-0660-x.

Caspi-Fluger A, Inbar M, Mozes-Daube N, Katzir N, Portnoy V, Belausov E, Hunter MS, Zchori-Fein E. 2012. Horizontal transmission of the insect symbiont Rickettsia is plant-mediated. Proc Biol Sci, 279(1734): 1791-6.

Chiel E, Kelly SE, Harris AM, Gebiola M, Li X, Zchori-Fein E, Hunter MS. 2014. Characteristics, phenotype, and transmission of Wolbachia in the sweet potato whitefly, Bemisia tabaci (Hemiptera: Aleyrodidae), and its parasitoid Eretmocerus sp. nr. emiratus (Hymenoptera: Aphelinidae). Environ Entomol, 43(2): 353-62.

Chrostek E, Pelz-Stelinski K, Hurst GDD, Hughes GL. 2017. Horizontal transmission of intracellular insect symbionts via plants. Front Microbiol, 8: 2237.

Li SJ, Ahmed MZ, Lv N, Shi PQ, Wang XM, Huang JL, Qiu BL. 2017. Plantmediated horizontal transmission of Wolbachia between whiteflies. ISME J, 11: 1019-1028.

Li YH, Ahmed MZ, Li SJ, Lv N, Shi PQ, Chen XS, Qiu BL. 2017. Plant-mediated horizontal transmission of Rickettsia endosymbiont between different whitefly species. FEMS Microbiol Ecol, 93(12). doi: 10.1093/femsec/fix138.

Liu Y, He ZQ, Wen Q, Peng J, Zhou YT, Mandour N, McKenzie CL, Ahmed MZ, Qiu BL. 2023. Parasitoid-mediated horizontal transmission of Rickettsia between whiteflies. Front Cell Infect Microbiol, 12: 1077494. DOI: 10.3389/fcimb.2022.1077494

Qi LD, Sun JT, Hong XY, Li YX. 2019. Diversity and phylogenetic analyses reveal horizontal transmission of endosymbionts between whiteflies and their parasitoids. J Econ Entomol, 112: 894-905.

Shi PQ, Wang L, Chen XY, Wang K, Wu QJ, Turlings TCJ, Zhang PJ, Qiu BL. 2024. Rickettsia transmission from whitefly to plants benefits herbivore insects but is detrimental to fungal and viral pathogens. mBio, 15(3): e0244823.

Weaknesses:

In the current study, the authors downloaded the MLST or wsp genes from a public database and analyzed the data using other methods, and I think the authors may not be familiar with the research progress in the field of insect symbiont transmission, and the current stage of this manuscript lacking sufficient novelty.

We appreciate your critical perspective on our study. However, we respectfully disagree with the viewpoint that our manuscript lacks sufficient novelty.

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Reply to the reviewers

Reviewer #1

i) "Enhancers dependent on TPR during senescence are enriched for binding sites of inflammatory transcription factors". *Proximity to genes does not confirm an enhancer role for that gene, although Tasdemir et al., 2016 suggested this. At that time, HI-C and Hi-CHiP techniques were not well-established. Nowadays, without combining HI-C and H3K27ac ChIP, Hi-ChIP alone cannot definitively identify actual enhancer regions. If we repeatedly use the Tasdemir et al., 2016 map, we risk incorrect mapping of enhancers of SASP. The authors should either use other public Hi-C databases to map the enhancer of SASP or temper their conclusions about enhancers. Otherwise, this could set a precedent for the SASP enhancer region that might not be entirely accurate. *

The enhancer mapping for SASP is outdated, as advancements in Hi-C have significantly developed this area. Therefore, the claimed enhancers of SASP may not be accurate.

__Response: __We agree with the reviewer that enhancers are not easy to define, or to pair with their target gene(s). Indeed, we would argue that even combined HI-C and H3K27ac does not define enhancers or enhancer-gene pairs and that the gold-standard evidence for an enhancer is genetics – does its deletion/mutation abrogate gene activation. We would also point out that we did not actually use the Tasdemir data to call enhancers. In response to the reviewer’s comment, we will temper our terminology and now refer to our inter-and intra-genic ATAC-seq peaks only as “putative enhancers”.

ii) “Many of these include putative enhancers located close to key SASP genes, such as IL1B and IL8 (Figure 1D).” I have the same concern as mentioned above (i). However, I am interested in knowing the other key SASP genes where DNA is accessible near the genes. A supplementary table listing key SASP genes along with their distances to the TSS and affected by TPR knock-down would be helpful.

__Response: __We thank the reviewer for this suggestion. We will provide tables listing the TPR dependent, senescent specific ATAC-seq peaks that are close to genes associated with the ‘positive regulation of inflammatory response’, ‘cytokine activity’ and ‘cytokine receptor binding’ gene ontology terms which were significant in the GREAT analysis, and which includes many SASP genes. We will also provide distances of these regions from the associated genes.

iii) "As we previously reported, knockdown of TPR (siTPR) in RAS cells blocks SAHF formation, but it also results in reduced nuclear localisation (decreased nucleocytoplasmic ratio) of NF-κB, consistent with decreased NF-κB activation (Figure 2A and B, Figure S2A)." TPR is required for CCF, SASP, and SAHF. The relationship between CCF and SASP is well established, but the relationship between SAHF and CCF/SASP remains elusive. Both SAHF and CCF are enriched with heterochromatin markers, suggesting that CCF might originate from SAHF. However, this has not been confirmed. Do the authors think that SAHF is a prerequisite for CCF in the OIS model, or is it an independent event?

Response: __We agree with the reviewer that CCFs likely originate from SAHF. Whilst we cannot definitively prove thisin our ER-Ras OIS model, in the revised manuscript we intend to further investigate the relationship between SAHF and CCF by knocking down HMGA1 during RAS-induced senescence. Like TPR, HMGA1 depletion is known to lead to loss of SAHF (Narita et al., Cell, 2006) but, unlike TPR, HMGA1 is a chromatin protein enriched on heterochromatin itself. We will assess whether loss of HMGA1 also abrogates CCF formation.__

iv) The authors suggested that "it is plausible that the decrease in CCFs produced during the early phases of OIS upon TPR knockdown may be caused by an increase in the stability of the nuclear periphery due to the heterochromatin that remains there when SAHF are not formed." I do not completely agree with this explanation because CCF starts forming at day 3-4 but culminates at later time points. According to Figure 5A, only 5-6% of cells are positive for CCFs on day 5. What happens on day 8? By day 8, the percentage of CCF-positive cells could be 20-25%, or the number of CCFs per cell might be 0.2-0.3. If TPR is not required for CCF formation at this stage, then linking CCF to SASP at day 8 becomes critical. This suggests that another mechanism might be driving SASP expression and that TPR could be regulating downstream signaling of CCF. It is possible that changes in nuclear pore density affect the localization of cGAS from the nucleus to the cytoplasm.

Response: __In our hands and using this IMR90 ER-RAS system, CCF formation decreases later in senescence (d8 - only 2% of cells) hence our focus on early timepoints after oncogenic RAS activation. At later timepoints, cGAS activation is also mediated by retrotransposons (de Cecco et al., Nature, 2019; Liu et al., Cell, 2023), as well as leakage of mitochondrial DNA (Victorelli et al., Nature, 2023; Chen et al., Nat. Comms, 2024), and so it is difficult to disentangle the net contribution of these three inputs.__

v) Additionally, the authors did not address what happens in the later stages of CCF formation in the absence of TPR. If TPR is not required for CCF formation at later stages, it fails to explain the downstream processes at these time points adequately. This suggests that TPR may also have another mechanism of SASP regulation independent of CCF formation.

__Response: __In our cellular system CCFs precede the SASP - CCFs are already present at day 3 but SASP factors are not secreted until day 5. However, CCFs are not necessarily required for maintenance of the SASP. Once initiated the SASP is maintained by cytokine feedback loops.

…………

Reviewer #2:

1. The claim that TPR knockdown does not affect NFkappaB nuclear translocation indeed stands, but it would be nice if the authors also compared data across conditions in Fig. 2F, i.e. siCTRL+Ras CM versus siTPR+Ras CM in RAS cells and provided a p-value as it seems to me that there is some dampening of translocation intensity, which is clearly not the case for STOP cells. The authors focus on this for d3 and d5, but it seems to be also the case for later time points.

__Response: __As basal NF-κB translocation is lower in RAS cells on TPR knockdown, we would expect a dampening in NF-κB translocation between siCTRL+RAS CM and siTPR+Ras CM regardless of whether there is a transportation defect. Consistent with this, the p-value for this comparison is significant, but we did not show it because it is not important in considering whether NF-κB nuclear translocation is impeded by TPR knockdown, which is the focus here. We will add a table with median nuclear:cytoplasmic NF-κB ratios and 95% confidence intervals to make the changes in basal level (treatment with STOP CM) clearer.

Also, a comment based on literature or from the authors previous work on TPR, on the extent to which the structural integrity of the nuclear basket is at all affected upon TPR depletion would be helpful for data interpretation.

__Response: __In the revised manuscript we will refer to the literature showing that TPR is the final component added to the nuclear pore and that its absence does not affect localisation of NUP153 to the nuclear basket (Hase and Cordes., Mol. Biol. Cell 2003; Aksenova et al., Nat Comms, 2020).

Magnification of representative cells per each condition in Fig. 2E would be welcome.

__Response: __We will provide a revised figure 2E with the magnifications as requested.

Regarding the data in Figs 3 and S3: I am a bit confused about how the obviously decreased NFkappaB nuclear signal (e.g., in Fig. 3D) does not translate into a skewed N/C ratio (e.g., in Fig. 3C)? The western blots indicate that overall NFkappaB levels remain essentially unchanged? Am I missing something?

Response: __As stated in the Methods section, we used a 50-pixel expansion of the detected nuclear area as our cytoplasmic area in the analysis (see image below). This was because we found detecting and segmenting the whole cytoplasmic area in the NF-κB channel to be unreliable. At day 3 and 5, the decrease in NF-κB nuclear signal in RAS cells on TPR knockdown was accompanied by a decrease in signal in the portion of the cytoplasm closest to the nucleus. This led to no change in the nuclear:cytoplasmic ratio. We believe the redistribution of NF-κB closer to the nucleus in the RAS siCTRL sample indicates early activation and will make this clearer in the revised text. We will also quantify the NF-κB western blots (see point 5), to help clarification of this issue.____ __

Also, along these lines, d8 western blots seem to portray an overall drop in NFkappaB levels. Is this indeed so? Can the authors maybe quantify their blots' replicates and provide a box plot and statistical testing?

Response: __We will provide quantification for the NF-κB western blots, though box plots would not be appropriate as we only have two replicates.__

Regarding the ATAC-seq data from d3, I think it could be mined a bit more. For example, compare to d8 (which the authors have apparently done, but don't present in detail) and discuss which are these early regions that also become accessible by d3 and what kind of genes and motifs are associated with them. Moreover, the focus in Fig. S3E is on ATAC sites shared with d8; how about d3-specific ones? How many of these are there (if any) and how might they be affected?

__Response: __As shown in Table S2, TPR knockdown did not cause any changes in chromatin accessibility at day 3, so there are no day 3 specific TPR dependent peaks. We will edit the text to make this clearer. We will carry out motif analysis and GREAT analysis on the day 3 peaks that become accessible in RAS cells but are not accessible in STOP (RAS-specific peaks).

I trust that the authors quantified their STING blots for the conclusions they present, but since it is difficult to assess these confidently by eye, again, some quantification plots would be welcome in Figs 4C,D and S4D,E.

__Response: __We will provide quantification for the STING western blots.

As controls for Fig. 5, it would be interesting to see if active histone readouts also mark CCFs in this system.

__Response: __Ivanov et al., J. Cell Biol., 2013 showed the absence of H3K9 acetylation from chromatin in CCFs. Further exploration of the types of chromatin/sequences in CCFs is outside the scope of our current manuscript.

*The POM121 channel in Fig. 5C appears to have some small signal foci in the cytoplasm; could these be small CCFs? More generally, the authors focus on these large blobs that only appear in

__Response: __The small signal foci the reviewer is highlighting are background from the POM121 antibody staining rather than CCFs – they do not show DAPI staining, and similar foci are evident in non-senescent cells where CCFs are generally not present. Our unpublished data (see response to Reviewer 1, point iv) from day 8 cells shows that only ~2% of senescent cells are positive for CCF regardless of TPR knockdown, which is a similar number to that observed in non-senescent cells at earlier timepoints. Thus, in our hands CCF formation occurs earlier, triggering the SASP, rather than at day 8 when the SASP is already established and reinforced through positive feedback cytokine signalling.

I wonder if there is a simple experiment the authors could do to test if this mechanism is only linked to senescence, specifically oncogene-induced senescence? I don't think this is needed to support the conclusions drawn here, but it could significantly broaden the scope of their discovery of, for example, this was true in other senescence models or during proinflammatory activation in general?

__Response: __These are interesting suggestions, but setting up, characterising and quantifying other senescence models will take a substantial amount of time that would be outside the scope of our current manuscript.

………….

Reviewer #3

1. The study uses a single cell strain IMR90 undergoing a single form of senescence, induced by activated Ras. To show the generalizability of the finding, the authors are advised to inhibit TPR in other forms of senescence in addition to IMR90. For example, IR or etoposide induces greater amount of CCF than in OIS of IMR90. BJ, MEFs, and ARPE-19 senescence also show prominent CCF.

__Response: __These are interesting suggestions, but as we responded to reviewer 2, setting up, characterising and quantifying other senescence models will take a substantial amount of time that would be outside the scope of our current manuscript.

To convincing show the CCF pathway is involved, the authors need to measure the activity of cGAS-STING pathway. Including cGAMP ELISA will be informative.

__Response: __We thank the reviewer for this suggestion, and we will try to include this assay in our revised manuscript.

The authors used conditioned media to show that TPR KD does not directly affect NFkB nuclear translocation. While this is helpful, conditions other than senescence will be more direct. For example, TNFa treatment or poly I:C transfection induces efficient NFkB nuclear translocation in IMR90 cells.

__Response: __This experiment (Fig. 2EF) was designed to simply show that knocking down TPR does not impair the ability of activated NFkB to enter the nucleus, it is not about senescence per se. Indeed, this is why we included the addition of SASP (RAS) conditioned media to non-senescence STOP cells in Fig. 2. We do not think investigating other methods of activating NFkB would add more to the question of whether TPR loss abrogates NFkB nuclear import.

Fig. 4C and Fig. S4D are identical.

Response: Though these STING immunoblots look similar; in fact they are not identical. Below we attach the raw original image in which both biological replicates (Fig 4C and S4D) for Day 3 were run on the same gel as proof of this claim.

Figure legend for Fig. S4F is mislabeled.

__Response: __We will correct this.

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Referee #1

Evidence, reproducibility and clarity

DNA damage triggers senescence, inducing chromatin reorganization and SASP activation. The authors previously demonstrated that the TPR nucleoprotein at nuclear pores is crucial for both SAHF formation and SASP activation during senescence. Here they also showed that TPR is required for the formation of cytoplasmic chromatin fragments (CCF), which activate cGAS-STING-TBK1-NF-kB signaling to express SASP. While the mechanistic regulation of CCF formation by TPR remains unclear, their study provides compelling evidence of downstream processes involving CCF. This study offers new insights into CCF formation, suggesting a promising direction for further research. I endorse the manuscript; however, there are several concerns that need addressing before acceptance.

i) "Enhancers dependent on TPR during senescence are enriched for binding sites of inflammatory transcription factors".

Proximity to genes does not confirm an enhancer role for that gene, although Tasdemir et al., 2016 suggested this. At that time, HI-C and Hi-CHiP techniques were not well-established. Nowadays, without combining HI-C and H3K27ac ChIP, Hi-ChIP alone cannot definitively identify actual enhancer regions. If we repeatedly use the Tasdemir et al., 2016 map, we risk incorrect mapping of enhancers of SASP. The authors should either use other public Hi-C databases to map the enhancer of SASP or temper their conclusions about enhancers. Otherwise, this could set a precedent for the SASP enhancer region that might not be entirely accurate.

ii) Many of these include putative enhancers located close to key SASP genes, such as IL1B and IL8 (Figure 1D).

I have the same concern as mentioned earlier about enhancers. However, I am interested in knowing the other key SASP genes where DNA is accessible near the genes. A supplementary table listing key SASP genes along with their distances to the TSS and affected by TPR knock-down would be helpful.

iii) "As we previously reported, knockdown of TPR (siTPR) in RAS cells blocks SAHF formation, but it also results in reduced nuclear localisation (decreased nucleocytoplasmic ratio) of NF-κB, consistent with decreased NF-κB activation (Figure 2A and B, Figure S2A)." TPR is required for CCF, SASP, and SAHF. The relationship between CCF and SASP is well established, but the relationship between SAHF and CCF/SASP remains elusive. Both SAHF and CCF are enriched with heterochromatin markers, suggesting that CCF might originate from SAHF. However, this has not been confirmed. Do the authors think that SAHF is a prerequisite for CCF in the OIS model, or is it an independent event?

iv) The authors suggested that "it is plausible that the decrease in CCFs produced during the early phases of OIS upon TPR knockdown may be caused by an increase in the stability of the nuclear periphery due to the heterochromatin that remains there when SAHF are not formed." I do not completely agree with this explanation because CCF starts forming at day 3-4 but culminates at later time points. According to Figure 5A, only 5-6% of cells are positive for CCFs on day 5. What happens on day 8? By day 8, the percentage of CCF-positive cells could be 20-25%, or the number of CCFs per cell might be 0.2-0.3. If TPR is not required for CCF formation at this stage, then linking CCF to SASP at day 8 becomes critical. This suggests that another mechanism might be driving SASP expression and that TPR could be regulating downstream signaling of CCF. It is possible that changes in nuclear pore density affect the localization of cGAS from the nucleus to the cytoplasm.

Significance

The authors previously demonstrated that the TPR nucleoprotein at nuclear pores is crucial for both SAHF formation and SASP activation during senescence. Here they also showed that TPR is required for the formation of cytoplasmic chromatin fragments (CCF), which activate cGAS-STING-TBK1-NF-kB signaling to express SASP. While the mechanistic regulation of CCF formation by TPR remains unclear, their study provides compelling evidence of downstream processes involving CCF. This study offers new insights into CCF formation, suggesting a promising direction for further research.

However, there are some limitations to this study. The enhancer mapping for SASP is outdated, as advancements in Hi-C have significantly developed this area. Therefore, the claimed enhancers of SASP may not be accurate. Additionally, the authors did not address what happens in the later stages of CCF formation in the absence of TPR. If TPR is not required for CCF formation at later stages, it fails to explain the downstream processes at these time points adequately. This suggests that TPR may also have another mechanism of SASP regulation independent of CCF formation.

I think the opening lines of the poem are the first of many examples of Choi employing Parallelism in this poem. I think the repetitive nature of this parallelism may be a commentary on how society pushes us to to fit in to a mold both in our daily routine, as well as in our identities.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

(1) The manuscript by Lu et al aims to study the effects of tubulin post-translational modification in C. elegans touch receptor neurons. Authors use gene editing to engineer various predicted PTM mutations in a-tubulin MEC-12 and b-tubulin MEC-7. Authors generate and analyze an impressive battery of mutants in predicted phosphorylation site and acetylation site of b-tubulin MEC-7, K40 acetylation site in a-tubulin MEC-12, enzymatic site of the a-tubulin acetyltransferase MEC-17, and PTM sites in the MEC-12 and MEC-7 C-tails (glutamylation, detyrosination, delta-tubulin). This represents a lot of work, and will appeal to a readership interested in C. elegans touch receptor neurons. The major concern/criticism of this manuscript is whether the introduced mutation(s) directly affects a specific PTM or whether the mutation affects gene expression, protein expression/stability/localization, etc. As such, this work does convincingly demonstrate, as stated in the title, that "Editing of endogenous tubulins reveals varying effects of tubulin posttranslational modifications on axonal growth and regeneration." 

We thank the reviewer for the constructive comments. With regards to the major concern or criticism, we like to point out that we have previously characterized ~100 missense mutations in mec-7 and mec-12 (Zheng et al., 2017, PMID: 28835377; Lee et al., 2021, PMID: 33378215). So, we are familiar with the phenotypes associated with mutations that affect gene expression or protein stability, which mostly result in a null phenotype. When analyzing the PTM site mutants, we compared their phenotypes with the previously categorized phenotypes of null alleles, neomorphic mutations that increase microtubule stability, and antimorphic mutations that prevent polymerization or disrupt microtubule stability. For example, in the case of mec-7 S172 mutations, we found that S172P mutants had the same phenotype as the mec-7 knockout (mild neurite growth defects), suggesting that S172P likely affects protein folding or stability, resulting in the loss of MEC-7. In contrast, S172A and S172E mutations showed phenotypes similar to neomorphic alleles (the emergence of ectopic ALM posterior neurite) and antimorphic alleles (the severe shortening of all neurites in the TRNs), respectively. These phenotypic differences suggested to us that the effects of S172A and S172E mutations cannot be simply attributed to the loss of protein expression and stability. Similar logic was applied to the studies of other PTM-inactivating or -mimicking mutations.

(2) For example, the authors manipulate the C-terminal tail of MEC-12 and MEC-7, to test the idea that polyglutamylation may be an important PTM. These mutants displayed subtle phenotypes. The authors show that branch point GT335 and polyglutamyation polyE recognizing antibodies stain cultured embryonic touch receptor neurons (TRNs), but did not examine staining in C. elegans TRNs in situ. To my knowledge, these antibodies have not been shown to stain the TRNs in any published papers, raising the question of how these "glutamylation" mutations are affecting mec-12 and -7. The rationale for using cultured embryonic TRNs and the relevance of the data and its interpretation are not clear. 

The GT335 and polyE antibodies were used by previous studies (O’Hagan et al., 2011, PMID: 21982591; and O’Hagan et al., 2017, PMID: 29129530) to detect the polyglutamylation signals in the sensory cilia of C. elegans. We initially tried to stain the whole animals using these antibodies but could not get clear and distinct signals in the TRNs. We reason that the tubulin polyglutamylation signals in the TRNs may be weak, and the in situ staining method which requires the antibodies to penetrate multiple layers of tissues (e.g., cuticles and epidermis) to reach the TRN axons may be not sensitive enough to detect the signal. In fact, the TRN axons are located deeper in the worm body compared to the sensory cilia that are mostly exposed to the environment. Another reason could be that the tissues (mostly epidermis) surrounding the TRN axons also have polyglutamylation staining, which makes it difficult to recognize TRN axons. This is a situation different from the anti-K40 acetylation staining, which only occurs in the TRNs because MEC-12 is the only a-tubulin isotype that carries K40. Due to these technical difficulties, we decided to use the in vitro cultured TRNs for the staining experiment, which allows both easy access of the antibodies (thus higher sensitivity) and the dissociation of the TRNs from other tissues. The fact that we were able to observe reduced staining in the ttll mutants and the tubulin mutants that lost the glutamate residues suggest that these antibodies indeed detected glutamylation signals in the cells.

(3) The final paragraph of the discussion is factually incorrect. The C. elegans homologs of the CCP carboxypeptidases are called CCPP-1 and CCPP-6. There are several publications on their functions in C. elegans.

We thank the reviewer for pointing out the mistake in the text. We intended to say that “there is no C. elegans homolog of the known tubulin carboxypeptidases that catalyze detyrosination”, which is true given that the detyrosinase vasohibins (VASH1/VASH2) homologs cannot be found in C. elegans. We are aware of the publications on CCPP-1 and CCPP-6; CCPP-1 is known to regulate tubulin deglutamylation in the cilia of C. elegans (O’Hagan et al., 2011 and 2017), while CCPP-6 may function in the PLM to regulate axonal regeneration (Ghosh-Roy et al., 2012). In the revised manuscript, we have corrected the error.

Reviewer #2 (Public Review):

Summary:

The tubulin subunits that make up microtubules can be posttranslationally modified and these PTMs are proposed to regulate microtubule dynamics and the proteins that can interact with microtubules in many contexts. However, most studies investigating the roles of tubulin PTMs have been conducted in vitro either with purified components or in cultured cells. Lu et al. use CRISPR/Cas9 genome editing to mutate tubulin genes in C. elegans, testing the role of specific tubulin residues on neuronal development. This study is a real tour de force, tackling multiple proposed tubulin modifications and following the resulting phenotypes with respect to neurite outgrowth in vivo. There is a ton of data that experts in the field will likely reference for years to come as this is one of the most comprehensive in vivo analyses of tubulin PTMs in vivo.

This paper will be very important to the field, however would be strengthened if: 1) the authors demonstrated that the mutations they introduced had the intended consequences on microtubule PTMs, 2) the authors explored how the various tubulin mutations directly affect microtubules, and 3) the findings are made generally more accessible to non C. elegans neurobiologists.

(1) The authors introduce several mutations to perturb tubulin PTMs, However, it is unclear to what extent the engineered mutations affect tubulin in the intended way i.e. are the authors sure that the PTMs they want to perturb are actually present in C. elegans. Many of the antibodies used did not appear to be specific and antibody staining was not always impacted in the mutant cases as expected. For example, is there any evidence that S172 is phosphorylated in C. elegans, e.g. from available phosphor-proteomic data? Given the significant amount of staining left in the S172A mutant, the antibody seems non-specific in this context and therefore not a reliable readout of whether MTs are actually phosphorylated at this residue. As another example, there is no evidence presented that K252 is acetylated in C. elegans. At the very least, the authors should consider demonstrating the conservation of these residues and the surrounding residues with other organisms where studies have demonstrated PTMs exist. 

We thank the reviewer for the comments. To our knowledge, there are very few phosphor-proteome data available for C. elegans. We searched a previously published dataset (Zielinska et al., 2009; PMID: 19530675) and did not find the S172 phosphorylation signal in MEC-7. This is not surprising, given that only six touch receptor neurons expressed MEC-7 and the abundance of MEC-7 in the whole animal lysate may be below the detection limit. However, this phosphorylation site S172 is highly conserved across species and tubulin isotypes (Figure 1-figure supplement 1 in the revised manuscript), suggesting that this site is likely phosphorylated in MEC-7.

In the case of K252, the potential acetylation site and the flanking sequences are extremely conserved across species and isotypes. In fact, the 20 amino acids from 241-260 a.a. are identical among the tubulin genes of C. elegans, fruit flies, Xenopus, and humans (Figure 4-figure supplement 1B). Thus, although K252 acetylation was found in the HeLa cells, this site can possibly be acetylated. 

In the case of K40, we observed sequence divergence at the PTM site and adjacent sequences among the tubulin isotypes in C. elegans. MEC-12 is the only C. elegans a-tubulin isotype that has the K40 residue, and the 40-50 a.a. region of MEC-12 appears to be more conserved than other isotypes when compared to Drosophila, frog, and human a-tubulins (Figure 4-figure supplement 1A).

(2) Given that the authors have the mutants in hand, it would be incredibly valuable to assess the impact of these mutations on microtubules directly in all cases. MT phenotypes are inferred from neurite outgrowth phenotypes in several cases, the authors should look directly at microtubules and/or microtubule dynamics via EBP-2 when possible OR show evidence that the only way to derive the neurite phenotypes shown is through the inferred microtubule phenotypes. For example, the effect of the acetylation or detyrosination mutants on MTs was not assessed. 

We thank the reviewer for the suggestions. In this study, we created >20 tubulin mutants. Due to limited time and resources, we were not able to examine microtubule dynamics in every mutant strain using EBP-2 kymographs. We assessed the effects of the tubulin mutations mostly based on the changes on neurite growth pattern. From our previous experience of analyzing ~100 mec-7 and mec-12 missense mutations (Zheng et al., 2017, MBoC; Lee et al., 2021, MBoC), we found that the changes in microtubule dynamics are correlated with the changes in neuronal morphologies. For example, the growth of ectopic ALM-PN is correlated with fewer EBP-2 comets and potentially reduced microtubule dynamics; this correlation holds true for several mec-7 neomorphic missense alleles we examined before (Lee et al., 2021, MBoC) and the PTM site mutants [e.g., mec-7(S172A) and mec-12(4Es-A)] analyzed in this study. Similarly, the shortening of TRN neurites is correlated with more EBP-2 comets and increased microtubule dynamics. For the mutants that don’t show neurite growth defects, our previous experience is that they are not likely to show altered microtubule dynamics in EBP-2 tracking experiments. So, we did not analyze the acetylation mutants (which had no defects in neurite growth) and the detyrosination mutants (which had weak ALM-PN phenotype). Nevertheless, we agree with the reviewer that we could not rule out the possibility that there may be some slight changes to microtubule dynamics in these mutants.

Using tannic acid staining and electron microscopy (EM), we previously examined the microtubule structure in several tubulin missense mutants (Zheng et al., 2017, MBoC) and found that the loss-of-function and antimorphic mutations significantly reduced the number of microtubules and altered microtubule organizations by reducing protofilament numbers. These structural changes are consistent with highly unstable microtubules and defects in neurite growth. On the other hand, neomorphic mutants had only slight decrease in microtubule abundance, maintained the 15-protofilament structure, and had a more tightly packed microtubule bundles that filled up most of the space in the TRN neurite (Zheng et al., 2017, MBoC). These structural features are consistent with increased microtubule stability and ectopic neurite growth. Although we did not directly examine the microtubule abundance and structure using EM in this study, we would expect similar changes that are correlated with the neurite growth phenotypes in the PTM mutants. We agree with the reviewer, it will be informative to conduct more comprehensive analysis on these mutants using EM and other structural biology methods.

(3) There is a ton of data here that will be important for experts working in this field to dig into, however, for the more general cell biologist, some of the data are quite inaccessible. More cartoons and better labeling will be helpful as will consistent comparisons to control worms in each experiment.

Response: We thank the reviewer for the comment. In the revised manuscript, we added some cartoons to Figure 2G to show the location of the synaptic vesicles. The neurite growth phenotype should be quite straightforward. Nevertheless, we added one more Figure (Figure 8) to summarize all the results in the study with cartoons that depicted the changes to neuronal morphologies.

(4) In addition, I am left unconvinced of the negative data demonstrating that MBK does not phosphorylate tubulin. First, the data described in lines 207-211 does not appear to be presented anywhere. Second, RNAi is notoriously finicky in neurons, thus necessitating tissue-specific degradation using either the ZF/ZIF-1 or AID/TIR1 systems which both work extremely well in C. elegans. Third, there appears to be increasing S172 phosphorylation in Figure 3 Supplement 2 with added MBK-2, but there is no anti-tubulin blot to show equal loading, so this experiment is hard to interpret.

We added the results of mbk-1, mbk-2, and hpk-1 mutants and cell-specific knockdown of MBK-2 into Figure 3-figure supplement 1D. Considering the reviewer’s suggestion, we attempted to use a ZIF-1 system to remove the MBK-2 proteins specifically in the TRNs using a previously published method (PMID: 28619826). We fused endogenous MBK-2 with GFP by gene editing and then expressed an anti-GFP nanobodies fused with ZIF-1 in the TRNs to induce the degradation of MBK-2::GFP. To our surprise, unlike the mbk-2p::GFP transcriptional reporter, the MBK-2::GFP did not show detectable expression in the TRNs, although expression can be seen in early embryos, which is consistent with the “embryonic lethal” phenotype of the mbk-2(-) mutants (Figure 3-figure supplement 2A-B in the revised manuscript). We reason that either endogenous MBK-2 is not expressed in the TRNs or is expressed at a very low level. We then crossed mbk-2::GFP with ItSi953 [mec-18p::vhhGFP4::Zif-1] to trigger the degradation of any potential MBK-2 proteins and did not observe the ectopic growth of ALM-PN (Figure 3- figure supplement 2C). These results suggest that MBK-2 is not likely to regulate tubulin phosphorylation in the TRNs, which is consistent with the results of other genetic mutants and the RNAi experiments.

For Figure 3 Supplement 2 (Figure 3-figuer supplement 3 in revised manuscript), because we added the same amount of purified MEC-12/MEC-7 to all reactions and had established equal loading in Figure 3E, we did not do the anti-tubulin staining in this experiment. Since higher concentration (1742 nM) of MBK-2 did not produce stronger signal than the condition with 1268 nM, we don’t think the 1268 nM band represents true phosphorylation. Moreover, the signal is not significantly stronger than the control without MBK-2 and is much lower than the signal generated by CDK1 in Figure 3E. Based on these results, we concluded that MBK-2 is not likely to phosphorylate MEC-7.  

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

General:

A summary table would help the reader digest the vast amount of phenotypic data.

Cartoons to help a non-C. elegans reader understand the figures. 

We added Figure 8 to summarize and illustrate the effects of the various mutants analyzed in this study.

Specific:

The authors engineered mutations into the predicted phosphorylation site of b-tubulin mec-7. These CRISPR-alleles mutations phenocopied previously identified loss-of-function, gain-of-function, and neomorphic mec-7 alleles identified in genetic screens by the Chalfie lab. Next, the authors sought to identify the responsible kinase, taking a candidate gene approach. The most likely family - minibrain - had no effect when knocked down/out. The authors showed that cdk-1 mutants displayed ectopic ALM-PN outgrowth. Whether cdk-1 specifically acts in the TRNs was not demonstrated, calling into question whether CDK-1 phosphorylates S172 in vivo. In their introduction (lines 45-59), the authors built a case for engineering PTM mutations directly into tubulins, because the PTM enzymes may have multiple substrates. This logic applies to the cdk-1 experiment and its interpretation. 

The reviewer is right. Since CDK1 and minibrain kinase are the only known kinases that catalyze S172 phosphorylation, our results suggest that CDK-1 is more likely to catalyze S172 phosphorylation in the TRNs compared to MBK-1/2. Genetic studies found that cdk-1(-); mec-7(S172A) double mutants did not show stronger phenotype than the two single mutants, suggesting that they function in the same pathway. Nevertheless, we could not rule out the possibility that other kinases may also control S172 phosphorylation, and the effect of CDK-1 is indirect. We mentioned this possibility in the revised manuscript.

For a-tubulin MEC-12, acetyl-mimicking K40Q and unmodifiable K40R mutants failed to stain with the anti-acetyl-a-tubulin (K40) antibody and displayed subtle TRN phenotypes. The enzymatically dead MEC-17 had phenotypes similar to those described by Topalidou (2012), confirming the Chalfie lab finding that MEC-17 has functions in addition and independent of its acetyltransferase activity. The authors moved onto a predicted acetylation site in MEC-7 and observed TRN developmental defects, and acknowledged that this may be due to tubulin instability and not a PTM. This is a concern for all mutants, as there is no way to measure whether the protein is expressed, stable, or localized properly. 

We acknowledge that this is a caveat of mutational studies. An amino acid substitution at the PTM site may have multiple effects, including the change of the PTM state and potential alteration of protein conformation. Without direct evidence for enzymatic modification of the PTM site in the neurons, we could not rule out the possibility the phenotype we observed is not related to PTM and instead is the result of abnormal protein conformation and function caused by the mutation.

Nevertheless, as stated in our above response to the first point in the public review, we can phenotypically differentiate loss-of-function and gain-of-function mutants. If the mutation reduces expression or general protein stability, it is more likely to cause a loss-of-function phenotype. For most PTM site mutants, this is not the case. We observed mostly gain-of-function phenotype, suggesting that the missense mutations did not simply inactivate the tubulin protein and instead affected the functional properties of the protein.

From here, the authors manipulate the C-terminal tail of MEC-12 and MEC-7, testing the idea that polyglutamylation may be an important PTM. These mutants displayed subtle phenotypes. The authors show that branch point GT335 and polyglutamyation polyE recognizing antibodies stain cultured embryonic TRNs, but did not examine staining in TRNs. To my knowledge, these antibodies have not been shown to stain the TRNs in any published papers (see next point). The rationale for using cultured embryonic TRNs is not clear. 

See our response to the second point in the public review.

Lines 548-553 There are several publications on CCPP-1 and CCPP-6 functions in TRNs and ciliated sensory neurons. See

PMID: 20519502

PMID: 21982591

PMID: 21943602

PMID: 23000142

PMID: 29129530

PMID: 33064774

PMID: 36285326

PMID: 37287505 

We thank the reviewer for pointing out these references, some of which were cited in the revised manuscript. We made a mistake in the Discussion by saying that there are no C. elegans homologs of tubulin carboxypeptidases while we intended to state that there is no homolog of tubulin detyrosinase in C. elegans. We are aware of the studies of CCPP-1 and CCPP-6 and have corrected the mistake in revised manuscript (also see our response to the third point in the public review).

Reviewer #2 (Recommendations For The Authors):

Figures: 

As stated in the public review, more cartoons and better labeling will be helpful as will consistent comparisons to control worms in each experiment. A good example of this issue is demonstrated in Figure 2 and Figure 4: 

(1) Figure 2: Please label images with what is being probed in each panel. 

We added labels to the panels.

(2) Figure 2G is very hard to interpret - cartoon diagramming what is being observed would be helpful. 

We added cartoons to help illustrate the images.

(3) Line 182-185: is this referring to your data or to Wu et al? It is not clear in this paragraph when the authors are describing published work versus their own data presented here. 

It is from our data. We have made it clear in the revised manuscript.

(4) Figure 2 - 2K is not well described. What experiment is being done here? What is dlk-1 and why did you look at this mutant? 

Figure 2K showed that both wild-type animals and S172A mutants could reconnect the severed axons after laser axotomy. Previous studies have found that dlk-1(-) mutants were not able to regenerate axons due to altered microtubule dynamics (PMID: 19737525; PMID: 23000142). We used dlk-1(-) mutants as a negative control, because DLK-1 promotes microtubule growth following axotomy, and the DLK-1 pathway is essential for regeneration (PMID: 23000142). We want to highlight the phenotypic difference between dlk-1(-) mutants and the S172E mutants. Although both mutants showed similar regrowth length, dlk-1(-) mutants showed unbranched regrowth probably due to the lack of microtubule polymerization, whereas the S172E mutants showed a mesh-like regrowth pattern likely due to highly dynamic and unstable microtubules. We explained the different phenotypes in the revised manuscript.

(5) Figure 4C: this phenotype is hard to interpret. Where is the wt control? Where is the quantification? 

In the Figure legend, we have referred the readers to Figure 1G for the wild-type image. Quantification is provided in the text (~20% of the animals showed the branching defects).

(6) There are no WT comparison images in Figure 4I, making the quantification difficult to interpret 

In the Figure legend, we have referred the readers to Figure 1A for the wild-type control. Moreover, we included a new Figure 8 to summarize the phenotypes of all mutants.

Experimental:

(1) Is it clear that only MEC-7/MEC-12 are the only a- and b-tubulin present in the TRNs? The presence of other tubulins not mutated would complicate the interpretation of the results. 

According to the mRNA levels, the expression of MEC-7 and MEC-12 are >100 fold higher than other tubulin isotypes. For example, single-cell transcriptomic data (Taylor et al., 2021) showed that mec-7 mRNA is at 135,940 TPM in ALM neurons, whereas two other tubulin isotypes, tbb-1 and tbb-2, have expression value of 54 and 554 TPM, respectively in the ALM. So, even if there are some other tubulin isotypes, their abundance is much lower than mec-7 and mec-12 and are not likely to interfere with the effects of the mec-7 and mec-12 mutants.

(2) The in vitro kinase assays should be quantified. 

We have added the quantification.

(3) The idea that Cdk1 phosphorylates tubulin in interphase is surprising and I am left wondering how the authors propose that Cdk1 is activated in interphase. Is cyclin B (or another cyclin) present in interphase in this cell type? Expression but not activation of Cdk1 is not discussed. 

CDK1 can work with cyclin A and cyclin B. C. elegans has one cyclin A gene (cya-1) and four cyclin B genes (cyb-1, cyb-2.1, cyb-2.2, and cyb-3). According to single-cell transcriptomic data of L4 animals, cya-1 and cyb-1 showed weak expression in many postmitotic neurons (including the ALM neurons), while cyb-2.1, cyb-2.2, and cyb-3 had no expression in neurons. So, it is possible that cya-1/cyclin A and cyb-1/cyclin B has low level of expression in the TRNs. A previous study also found the expression of cell cycle regulators (including cyclins) in postmitotic neurons in mouse brain (Akagawa et al., 2021; PMID: 34746147).

(4) What is the significance of neurite swelling and looping in Figure 4H? The underlying cause of this phenotype is not described. 

The neurite swelling and looping phenotype of mec-17(-) mutants were described by Topalidou et al., (2012; PMID: 22658602) and were caused by the bending of the microtubules. It appears that the loss of the a-tubulin acetyltransferase altered the organization of microtubules in the TRNs. These defects were partially rescued by the enzymatically dead MEC-17, suggesting that MEC-17 may play a non-enzymatic (and likely structural) role in regulating microtubule organization. We added more explanation in the revised manuscript.

(5) It is quite surprising that polyglutamylation is not affected in the quintuple ttll mutant. Since the authors made the sextuple ttll mutant, could they demonstrate whether polyglutamylation is further reduced in this mutant via GT335 staining? 

We did not make the comparison of the quintuple and sextuple ttll mutants because they were crossed with TRN markers with different colors for technical reasons. The quintuple mutants CGZ1475 carried uIs115 [mec-17p::TagRFP] IV, whereas the sextuple mutants CGZ1474 carried zdIs5 [mec-4p::GFP] I. As a result, we need to use different secondary antibodies for the antibody staining, which makes the results not compatible.

Polyglutmaylation signal in the cell body was strongly affected by the ttll mutations. In fact, in the ttll-4(-); ttl-5(-); ttll-12(-) triple mutants, the signal is significantly reduced in the cell body of the TRNs, as well as the cell body of other cells. What’s surprising is that the signal in the axons persisted in the ttll triple and quintuple mutants. As the reviewers suggested, we also stained the sextuple mutants and found similar pattern as the triple and quintuple mutants (new Figure 6-figure supplement 1C in the revised manuscript), although the results are not quantitatively comparable due to the use of secondary antibodies with different fluorophores.

Writing:

(1) The beginning of the results section is quite jarring. The information in lines 96-104 should be in the Introduction. 

Due to the nature of this paper, each section deals with a particular PTM. We think it is helpful to discuss some background information before describing our results on each PTM rather than giving all in the introduction. Nevertheless, we modified the beginning of the results to make it more coherent and more connected with the preceding paragraphs.

(2) Line 122-126: conclusions are not supported by the data: it is suggested from previous experiments, but authors do not look at MTs directly. 

We have rephrased the statement to acknowledge that we made such conclusion based on phenotypic similarity with mutants we previously examined.

(3) I am confused by the usage of both mec-12(4EtoA) and mec-12(4Es-A). Are these the same mutations? If so, there needs to be consistency. If not, each case needs to be defined. 

They are the same. We have corrected the mistake and are now using mec-12(4Es-A) to refer to the mutants.

Line 105: phosphor --> phospho 

Line 187: were --> was 

Line 298: is --> are

The above typos are corrected.

Author response:

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

Reviewer #1 (Recommendations For The Authors):

I still find it really impressive that the Purkinje cell stimulation so closely mimics the pathogenic phenotypes - in my opinion, the strongest part of the paper. I would like just a little clarification on some of my previous questions.

Major points:

(1) Can the authors clarify where the new units came from? Are these units that were recorded before the initial submission and excluded, but are now included? If so, why were they excluded before? Or are these units that were recorded since the original submission?

The number of units increased in Figure 1 for three reasons: 1) We have now plotted the classifier results in Figure 1 instead of the validation results, which have been moved to Figure 1 Supplement 3. 2) In response to reviewer comments, we no longer include units that had >60 s of recording in both our model creation and validation. We had previously used 30 s for creating the model and a different 30 s for validating the model, if an additional 30 s were available. 3) We changed our model creation and validation strategy based on previous reviewer comments. The new units in Figures 2-4 were taken from our pool of previously collected but unanalyzed data (we collect neural data on a rolling basis and thus these data were not initially available). We were fortunate to have these data to analyze in order to address the concerns about the number of cells included in the manuscript. The number of units increased in Figure 5 because new units were recorded in response to reviewer comments.

(2) Why did some of the neuron counts go down? For example, in Pdx1Cre;Vglut2fl/fl mice, the fraction of units with the control signature went from 11/21 to 7/23. Is this because the classifier changed between the original submission and the revision?

Yes, the proportion of cells matching each classification changed due to the different parameters and thresholds used in the updated classifier model.

Minor points:

In the Discussion: "We find some overlap and shared spike features between the different disease phenotypes and show that healthy cerebellar neurons can adapt multiple disease-associated spike train signatures." I think "adapt" should be "adopt"

In the Discussion: "compare" is misspelled as "compared"

Thank you for bringing these typos to our attention. We will upload a new version of the text with the typos corrected.

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

We would like to thank the Reviewers for providing excellent and constructive suggestions that have enabled us to strengthen our overall presentation of our data. We have addressed each of the comments by altering the text, providing additional data, and revising the figures, as requested.

Below are our explanations for how we have altered the manuscript in this revised version.

Recommendations for the authors:

I think you will have seen from the comments that there was great enthusiasm for the importance of this study. There were also shared concerns about how the classifier may be inadequate in its current format, as well as specific suggestions to consider to improve. I hope that you will consider a revision to really amplify the impact of the importance of this study.

Reviewer #1 (Recommendations For The Authors):

Distinct motor phenotypes are reflected in different neuronal firing patterns at different loci in motor circuits. However, it is difficult to determine if these altered firing patterns: 1) reflect the underlying neuropathology or phenotype, 2) whether these changes are intrinsic to the local cell population or caused by larger network changes, and 3) whether abnormal firing patterns cause or reflect abnormal movement patterns. This manuscript attempts to address these questions by recording neural firing patterns in deep cerebellar nucleus neurons in several models of cerebellar dysfunction with distinct phenotypes. They develop a classifier based on parameters of single unit spike trains that seems to do an inconsistent job of predicting phenotype (though it does fairly well for tremor). The major limitation of the recording/classifier experiments is the low number of single units recorded in each model, greatly limiting statistical power. However, the authors go on to show that specific patterns of Purkinje cell stimulation cause consistent changes in interposed nucleus activity that map remarkably well onto behavioral phenotypes. Overall, I did not find the recording/classifier results to be very convincing, while the stimulation results strongly indicate that interposed nucleus firing patterns are sufficient to drive distinct behavioral phenotypes.

We thank the reviewer for their comments. We describe below how we have addressed the major concerns.

Major concerns:

(1) I don't think it's legitimate to use two 30-second samples from the same recording to train and validate the classifier. I would expect recordings from the same mouse, let alone the same unit, to be highly correlated with each other and therefore overestimate the accuracy of the classifier. How many of the recordings in the training and validation sets were the same unit recorded at two different times?

We previously published a paper wherein we measured the correlation (or variability) between units recorded from the same mouse versus units recorded from different mice (see: Van der Heijden et al., 2022 – iScience, PMID: 36388953). In this paper we did not find that nuclei neuron recordings from the same mouse were more correlated or similar to each other than recordings from different mice. 

Upon this reviewer comment, however, we did observe strong correlations between the two 30-second samples from the same recording units. We therefore decided to no longer validate our classifier based on a training and validation sets that had overlapping units. Instead, we generated 12 training sets and 12 non-overlapping validation sets based on our entire database. We then trained 12 classifier models and ranked these based on their classification ability on the validation sets (Figure 1 – supplemental Figure 3). We found that the top two performing classifier models were the same, and used this model for the remainder of the paper. 

(2) The n's are not convincing for the spike signature analyses in different phenotypic models. For example, the claim is that Pdx1Cre;Vglut2fl/fl mice have more "control" neurons than ouabain infusion mice (more severe phenotype). However, the numbers are 11/21 and 7/20, respectively. The next claim is that 9/21 dystonic neurons are less than 11/20 dystonic neurons. A z-test for proportions gives a p-value of 0.26 for the first comparison and a pvalue of 0.44 for the second. I do not think any conclusions can be drawn based on these data.

We included more cells in our analyses and found that the z-test for n the proportion of cells with the “control” and “dystonia” signature is indeed statistically significant. 

(3) Since the spiking pattern does not appear to predict an ataxic phenotype and the n's are too small to draw a conclusion for the dystonic mice, I think the title is very misleading - it does not appear to be true that "Neural spiking patterns predict behavioral phenotypes...", at least in these models.

We have changed the title to: “Cerebellar nuclei cells produce distinct pathogenic spike signatures in mouse models of ataxia, dystonia, and tremor.” We feel that this new title captures the idea that we find differences between spike signatures associated with ataxia, dystonia, and tremor and that these signatures induce pathological movements.

(4) I don't think it can be concluded from the optogenetic experiments that the spike train signatures do not depend on "developmental changes, ...the effect of transgene expression, ... or drug effects outside the cerebellum." The optogenetic experiments demonstrate that modulating Purkinje cell activity is sufficient to cause changes in DCN firing patterns and phenotypes (i.e., proof-of-principle). However, they do not prove that this is why DCN firing is abnormal in each model individually.

Thank you for highlighting this section of the text. We agree that the optogenetic experiments cannot explain why the DCN is firing abnormally in each model. We have edited this section of the text to prevent this conclusion from being drawn by the readers.

Minor points:

(1) It would be nice to see neural recordings in the interposed nucleus during Purkinje terminal stimulation to verify that the firing patterns observed during direct Purkinje neuron illumination are reproduced with terminal activation. This should be the case, but I'm not 100% certain it is.

We have edited the text to clarify that representative traces and analysis of interposed nucleus neurons in response to Purkinje terminal stimulation are the data in Figure 5.

(2) How does the classifier validation (Fig. 1E) compare to chance? If I understand correctly, 24/30 neurons recorded in control mice are predicted to have come from control mice (for example). This seems fairly high, but it is hard to know how impressive this is. One approach would be to repeat the analysis many (1000s) of times with each recording randomly assigned to one of the four groups and see what the distribution of "correct" predictions is for each category, which can be compared against the actual outcome.

We have now also included the proportion of spike signatures in the entire population of neurons and show that the spike signatures are enriched in each of the four groups (control, ataxia, dystonia, tremor) relative to the presence of these signatures in the population (Figure 1E). 

(3) I don't think this is absolutely necessary, but do the authors have ideas about how their identified firing patterns might lead to each of these phenotypes? Are there testable hypotheses for how different phenotypes caused by their stimulation paradigms arise at a network level?

We have added some ideas about how these spike signatures might lead to their associated phenotypes to the discussion.

Reviewer #2 (Recommendations For The Authors):

(1) As mentioned earlier, my main concern pertains to the overall architecture and training of the classifier. Based on my reading of the methods and the documentation for the classifier model, I believe that the classifier boundaries may be biased by the unequal distribution of neurons across cerebellar disease groups (e.g., n=29 neurons in control versus n=19 in ataxics). As the classifier is trained to minimize the classification error across the entire sample, the actual thresholds on the parameters of interest may be influenced by the overrepresentation of neurons from control mice. To address this issue, one possible solution would be to reweight each group so that the overall weight across classes is equal. However, I suggest a better strategy might be to revise the classifier architecture altogether (as detailed below).

We have retrained the classifier model based on equal numbers of ataxic, dystonic, and tremor cells (n=20) but we intentionally included more control cells (n=25). We included more control cells because we assume this is the baseline status for all cerebellar neurons and wanted to avoid assigning disease signatures to healthy neurons too easily. 

(2) As the authors make abundantly clear, one mouse model of disease could potentially exhibit multiple phenotypes (e.g., a mouse with both ataxia and tremor). To address this complexity, it might be more valuable to predict the probability of a certain CN recording producing specific behavioral phenotypes. In this revised approach, the output of the classifier wouldn't be a single classification (e.g., "this is an ataxic mouse") but rather the probability of a certain neural recording corresponding to ataxia-like symptoms (e.g., "the classifier suggests that this mouse has a 76% likelihood of exhibiting ataxic symptoms given this CN recording"). This modification wouldn't require additional data collection, and the exemplar disease models could still be used to train such a revised network/classifier, with each mouse model corresponding to 0% probability of observing all other behavioral phenotypes except for the specific output corresponding to the disease state (e.g., L7CreVgat-fl/fl would be 0% for all categories except ataxia, which would be trained to produce a score of 100%). This approach could enhance the validation results across other mouse models by allowing flexibility in a particular spike train parameter to produce a diverse set of phenotypes.

This is a great comment. Unfortunately, our current dataset is constrained to fully address this comment for the following reasons:

- We have a limited number of neurons on which we can train our classifier neurons. Further dividing up the groups of neurons or complicating the model limited the power of our analyses and resulted in overfitting of the model on too few neurons.

- The recording durations (30 seconds) used to train our model are likely too short to find multiple disease signatures within a single recording. We feel that the complex phenotypes are likely resulting from cells within one mouse exhibiting a mix of disease signatures (as in the Car8wdl/wdl mice).

We think this question would be great for a follow-up study that uses a large number of recordings from single mice to fully predict the mouse phenotype based on the population spike signatures. 

To limit confusion about our classifier model, we have also altered the language of our manuscript and refer to the cells exhibiting a spike signature instead of predicting a phenotype. 

However, the paper falls short in terms of the classifier model itself. The current implementation of this classifier appears to be rather weak. For instance, the crossvalidated performance on the same disease line mouse model for tremor is only 56%. While I understand that the classifier aims to simplify a high-dimensional dataset into a more manageable decision tree, its rather poor performance undermines the authors' main objectives. In a similar vein, although focusing on three primary features of spiking statistics identified by the decision tree model (CV, CV2, and median ISI) is useful for understanding the primary differences between the firing statistics of different mouse models, it results in an overly simplistic view of this complex data. The classifier and its reliance on the reduced feature set are the weakest points of the paper and could benefit from further analysis and a different classification architecture. Nevertheless, it is commendable that the authors have collected high-quality data to validate their classifier. Particularly impressive is their inclusion of data from multiple mouse models of ataxia, dystonia, and tremor, enabling a true test of the classifier's generalizability.

We intentionally simplified our parameter space from a high-dimensional dataset into a more manageable decision tree. We did this for the following reasons:

- The parameters, even though all measuring different features, are highly correlated (see Figure 1 – supplemental Figure 2). Further, we were training our dataset on a limited number of recordings. We found that including all parameters (for example using a linear model) caused overfitting of the data and poor model performance.

- Describing the spike signatures using a lower number of parameters allowed us to design optogenetic parameters that would mimic this parameter space. This would be infinitely more complex with a bigger parameter space. 

We agree with the reviewer that inclusion of multiple mouse models in addition to the optogenetics experiments provide the classifier’s generalizability. 

Minor Comments:

(1) The blown-up CN voltage traces in Figures 5C and Supplementary Figure 2B appear more like bar plots than voltage traces on my machine.

Thank you for bringing this to our attention. We have improved the rendering of the traces.

(2) The logic in lines 224-228 is somewhat confusing. The spike train signatures are undoubtedly affected by all the factors mentioned by the authors. What, I believe, the authors intend to convey is that because changes in CN firing rates can be driven by multiple factors, it is the CN firing properties themselves that likely drive disease-specific phenotypes.

We agree that our discussion of the CN firing needs clarification. We have made the appropriate edits in the text.

Reviewer #3 (Recommendations For The Authors):

It's quite astounding that this can be done from single spike trains from what are almost certainly mixed populations of neurons. Could you add something to the discussion about this? Some questions that could be addressed would be would multiple simultaneous recordings additionally help classify these diseases, or would non-simultaneous recordings from the same animal be useful? Also more discussion about which cells you are likely recording from would be useful.

Thank you for this suggestion. We have added discussion about multiple recordings, simultaneous vs non-simultaneous recordings, and our thoughts on the cell population recorded in this work.

Data in figure 2 is difficult to understand - it appears that the majority of dysregulated cells in 2 ataxic models are classified as dystonia cells, not ataxic cells. This appears surprising as it seems to be at odds with earlier data from Fig 1. In my opinion, it is not discussed adequately in the Results or Discussion section.

We have added further discussion of the ataxia models represented in Figures 1 and 2.

Minor comment:

The colours of the subdivisions of the bars in 2C and 3C, and the rest of the paper appear to be related to the groups in the middle (under "predicted"), but the colours are much paler in the figure than in the legend, although the colours in the bars and the legends match in the first figure (1E). Does this signify something?

These figures were remade with the same colors across the board.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

The study by Prieto et al. faces the increasingly serious problem of bacterial resistance to antimicrobial agents. This work has an important element of novelty proposing a new approach to control antibiotic resistance spread by plasmids. Instead of targeting the resistance determinant, plasmid-borne proteins are used as antigens to be bound by specific nanobodies (Nbs). Once bound plasmid transfer was inhibited and Salmonella infection blocked. This in-depth study is quite detailed and complex, with many experiments (9 figures with multiple panels), rigorously carried out. Results fully support the authors' conclusions. Specifically, the authors investigated the role of two large molecular weight proteins (RSP and RSP2) encoded by the IncHI1 derivative-plasmid R27 of Salmonella. These proteins have bacterial Ig-like (Big) domains and are expressed on the cell surface, creating the opportunity for them to serve as immunostimulatory antigens. Using a mouse infection model, the authors showed that RSP proteins can properly function as antigens, in Salmonella strains harboring the IncHI1 plasmid. The authors clearly showed increased levels of specific IgG and IgA antibodies against these RSP proteins proteins in different tissues of immunized animals. In addition, non-immunized mice exhibited Salmonella colonization in the spleen and much more severe disease than immunized ones. 

However, the strength of this work is the selection and production of nanobodies (Nbs) that specifically interact with the extracellular domain of RSP proteins. The procedure to obtain Nbs is lengthy and complicated and includes the immunization of dromedaries with purified RPS and the construction of a VHH (H-chain antibody variable region) library in E. coli. As RSP is expressed on the surface of E. coli, specific Nbs were able to agglutinate Salmonella strains harboring the p27 plasmid encoding the RSP proteins. 

The authors demonstrated that Nbs-RSP reduced the conjugation frequency of p27 thus limiting the diffusion of the amp resistance harbored by the plasmid. This represents an innovative and promising strategy to fight antibiotic resistance, as it is not blocked by the mechanism that determines, in the specific case, the amp resistance of p27 but it targets an antigen associated with HincHI- derivative plasmids. Thus, RPS vaccination could be effective not only against Salmonella but also against other enteric bacteria. A possible criticism could be that Nbs against RSP proteins reduce the severity of the disease but do not completely prevent the infection by Salmonella.

It is true that vaccina2on of mice with purified RSP protein did not provide complete protec2on against infec2on with a Salmonella strain harboring an IncHI plasmid. As this finding is based on an animal model, further inves2ga2on is required to evaluate its clinical efficacy. In any case, even par2al protec2on provided by nanobodies or by a vaccine could poten2ally improve survival rates among cri2cally ill pa2ents infected with a pathogenic bacterium harboring an IncHI plasmid. An addi2onal beneficial aspect of our approach is that it will reduce dissemina2on of IncHI plasmids among pathogenic bacteria, which would reduce the presence of an2bio2c resistance plasmids in the environment and in the bacteria infec2ng pa2ents. 

Reviewer #2 (Public Review):

Summary:

This manuscript aims to tackle the antimicrobial resistance through the development of vaccines. Specifically, the authors test the potential of the RSP protein as a vaccine candidate. The RSP protein contains bacterial Ig-like domains that are typically carried in IncHl1 plasmids like R27. The extracellular location of the RSP protein and its role in the conjugation process makes it a good candidate for a vaccine. The authors then use Salmonella carrying an IncHl plasmid to test the efficacy of the RSP protein as a vaccine antigen in providing protection against infection of antibioticresistant bacteria carrying the IncHl plasmid. The authors found no differences in total IgG or IgA levels, nor in pro-inflammatory cytokines between immunized and non-immunized mice. They however found differences in specific IgG and IgA, attenuated disease symptoms, and restricted systemic infection.

The manuscript also evaluates the potential use of nanobodies specifically targeting the RSP protein by expressing it in E. coli and evaluating their interference in the conjugation of IncHl plasmids. The authors found that E. coli strains expressing RSPspecific nanobodies bind to Salmonella cells carrying the R27 plasmid thereby reducing the conjugation efficacy of Salmonella. 

Strengths:

The main strength of this manuscript is that it targets the mechanism of transmission of resistance genes carried by any bacterial species, thus making it broad.

The experimental setup is sound and with proper replication.

Weaknesses:

The two main experiments, evaluating the potential of the RSP protein and the effects of nanobodies on conjugation, seem as parts of two different and unrelated strategies.

In preparing our manuscript, we were aware that we included two different strategies to combat an2microbial resistance. However, we deemed it valuable to include both in the paper. The development of new vaccines and the inhibi2on of the transfer of an2bio2c resistance determinants are currently considered relevant approaches to combat an2microbial resistance. Our inten2on in the ar2cle is to integrate these two strategies. 

The survival rates shown in Figure 1A and Figure 3A for Salmonella pHCM1 and non-immunized mice challenged with Salmonella, respectively, are substantially different. In the same figures, the challenge of immunized mice and Salmonella pHCM1 and mice challenged with Salmonella pHCM1 with and without ampicillin are virtually the same. While this is not the only measure of the effect of immunization, the inconsistencies in the resulting survival curves should be addressed by the authors more thoroughly as they can confound the effects found in other parameters, including total and specific IgG and IgA, and pro-inflammatory cytokines.

Overall the results are inconsistent and provide only partial evidence of the effectiveness of the RSP protein as a vaccine target.

To address the concerns regarding the disparities in survival rates depicted in Figures 1A and 3A, it is important to refer to several factors that contribute to these variations. Firstly, it should be noted that the data depicted in these figures stem from distinct experimental sets conducted at different times employing different batches of mice. Despite the use of the same strain and supplier, individual animals and their batches can exhibit variability in susceptibility to infection due to inherent biological differences.

Unlike in vitro cell culture experiments, which can achieve high replicability due to the homogeneity of cell lines, in vivo animal studies often exhibit greater variability. This variability is influenced not only by genetic variations within animal populations, even if originating from the same supplier, but also by environmental factors within the animal facility. These factors include temperature variations, the concentration y of non-pathogenic microorganisms in the facility, which can modify the immune responses, or the density of animals in the environment, consequently affecting human traffic and generating potential disturbances. 

When designing experiments with animals, it is desirable for the results to be consistent across different animal batches. If one bacterial strain exhibits higher mortality rates than another across multiple experimental series, this pattern should be reproducible despite the inherent variability in in vivo studies. It is more important to demonstrate consistency in trends than to focus on absolute figures when validating experimental results. 

It is also important to clarify that when we refer to survival rates, it doesn’ t necessarily mean that the animals were found deceased. The animal procedures were approved by the Ethics Committee of Animal Experimentation of the Universitat de Barcelona, which include an animal monitoring protocol. Our protocol requires close daily monitoring of several health and behavioral parameters, each evaluated according to specific criteria. When an animal reaches a predetermined score threshold indicating severe distress or suffering, euthanasia is administered to alleviate further suffering. At this point, biological samples are collected for subsequent analysis.

The conjugative experiments use very long conjugation times, making it harder to assess if the resulting transconjugants are the direct result of conjugation or just the growth of transconjugants obtained at earlier points in time. While this could be assessed from the obtained results, it is not a direct or precise measure.

In the conjuga2on experiments we u2lized a reduced number of donor cells expressing the RSP protein and of recipient cells, as well as long conjuga2on 2mes, to reflect more accurately a situa2on that may occur naturally in the environment. Short conjuga2on 2mes are efficient in controlled laboratory condi2ons using high densi2es of donor and recipient cells, but these condi2ons are not commonly found in the environment. For the interference of the conjuga2ve transfer of the IncHI plasmid we used an E. coli strain displaying the nanobody binding RSP to simulate a process that could be also scaled-up in a natural environment (i.e., a probio2c strain in a livestock farm) and that could be cost effec2ve. See discussion sec2on, lanes 326-328.   

While the potential outcomes of these experiments could be applied to any bacterial species carrying this type of plasmids, it is unclear why the authors use Salmonella strains to evaluate it. The introduction does a great job of explaining the importance of these plasmids but falls short in introducing their relevance in Salmonella.

The prevalence of IncHI plasmids in Salmonella was indicated in the introduc2on sec2on, lanes 65-67. Nevertheless, we understand the reviewer’s cri2cisms and have modified both these sentences in the introduc2on sec2on and also added comments in the results sec2on (lanes 118-128).

Recommendations for the authors:

Reviewer #2 (Recommendations For The Authors):

I understand working with mice can be challenging in terms of repeating experiments to further support the study's claims. For this reason, I think the authors need to discuss more thoroughly the following things:

Can the authors comment on why the presence of Ampicillin leads to a lower upregulation of proinflammatory cytokines in the spleen despite harboring resistance against ampicillin?

At the intestinal level, physiological inflammatory responses play a crucial role in enabling the host to identify foreign and commensal bacterial antigens and initiate a highly regulated and "controlled" immune response (Fiocchi, 2008. Inflamm Bowel Dis. 2008, 14 Suppl 2:S77-8). The administration of antibiotics such as ampicillin, reduces the load of intestinal resident microbiota, thereby lowering the extent of intestinal immune activation. This decline in immune activation extends to systemic levels, potentially accounting for the reduced expression of proinflammatory cytokines observed in the spleen.

There are inconsistent results in the survival rates in Figures 1A and 3A, please discuss how this could alter the observed differences in total and specific IgG and IgA, and pro-inflammatory cytokines.

To address the reviewer concerns regarding the discrepancies in survival rates shown in Figures 1A and 3A, and how these differences might influence the observed variations in total and specific IgG and IgA, as well as pro-inflammatory cytokines, it is important to clarify the terminology used in our study. In our context, "survival" does not solely refer to mortality per se, but encompasses the endpoints defined by our animal welfare protocols, which are rigorously supervised by the Animal Experimentation Ethics Committee of the University of Barcelona. Our protocol mandates close daily monitoring of several health and behavioral parameters, each scored according to specific criteria. When an animal reaches a predefined score threshold indicating severe distress or suffering, euthanasia is conducted to prevent further distress, at which point we collect biological samples for analysis.

In contrast to in vitro cell culture experiments, which often achieve high replicability thanks to the homogeneity of cell lines, in vivo animal studies frequently display greater variability. This variability stems not only from genetic differences within animal populations, even if originating from the same supplier, but also from environmental factors within the animal facility. These factors encompass variations in temperature, the presence of non-pathogenic microorganisms in the facility (capable of altering immune responses) and the density of animals, which can impact human traffic and potentially lead to disturbances. 

The experiments depicted in Figs. 1A and 3A were separated in time, and hence may be influenced by environmental factors within the animal facility. Nevertheless, in the comparative analysis performed between immunized and non-immunized animals, experiments were performed simultaneously and hence under similar environmental conditions in the animal facility. For several parameters (i.e., immunoglobulins and proinflammatory cytokines) statistically significant differences were observed. 

Regarding the conjugation assays, it is not entirely clear to me why the conjugation times are so long. It would be beneficial to have more data about the conjugation efficacy between the donor and recipient without any E. coli expressing the nanobodies at different time intervals. This would help to differentiate between transconjugants and transconjugants obtained from early conjugation events.

This comment is par2ally answered in a previous response, regarding the numbers of donor and recipient cells and dura2on of conjuga2on.  We note here that in fig. 9, the requested experiment with donor and recipient cells without E. coli interferent cells is already present, corresponding to the label “none”. To avoid confusion, we have modified the legend in fig. 9.

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

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Reply to the reviewers

*Reviewer #1 *

1. The authors conclude that RFP-Ac expression is restricted to emerging SOPs and surroundings cells at 18h APF, indicating that Ac is activated later than Sc. Can the authors provide images for RFP-Ac expression at 10h and 16h APF similar to GFP-Sc as shown in their figures. Do the SOPs that contain high levels of both Ac and Sc (as some SOPs have Sc expression but not Ac) undergo fate divergence and SB faster than the SOPs containing higher levels of only Sc?

We are now showing the expression pattern of GFP-SC and RFP-Ac/GFP-Ac in fixed samples stained also for E-cad at 13h, 16h and 18h APF (Fig 1I-K' and Fig S1E-G'). Ac and Sc were found to be activated around the same time. However, Ac appeared to accumulate at lower levels than Sc prior to SOP selection in the central domain of the ADHN (Fig 1J-K'). We also confirmed that Ac was more strongly expressed in SOPs. Additionally, SOPs appeared to accumulate both Ac and Sc, i.e. SOPs with high levels of GFP-Sc also showed a strong RFP-Ac signal (Fig S1H-H'). Finally, since RFP-Ac was not detectable in living pupae, possibly due to the rapid turn-over of Ac and the slow maturation of RFP, we could not study more precisely the relative dynamics of Ac and Sc. For the same reason, we could not address whether the rate of fate divergence (measured using GFP-Sc) varied with the level of Ac.

2. It would be interesting to see the spatial and temporal dynamics of Ac and Sc in Notch mutants or even Notch dynamics in Sc and Ac mutants to better understand the progression fate divergence and its effect on lateral inhibition in real time.

Following the reviewer's suggestion, we examined the expression pattern of NRE-deGFP, a Notch activity reporter, in ac sc double mutant pupae at 16h and 24h APF (Fig S3A-D). This showed that the initial pattern of NRE-deGFP at 16h APF (signal detected in posterior ADHN cells as well as in the ADHN) did not depend on Ac and Sc. By contrast, the second phase of NRE-deGFP expression (in cells of the proneural ADHN domain, around emerging SOPs) was found to depend on the activity of Ac and Sc. Thus, strong Notch activation observed in cells surrounding emerging SOPs was found to depend on the activity of Ac-Sc, presumably because Ac and Sc are required for SOP specification and SOPs produce Delta, serving as the local source to activate Notch (see also our response to reviewer 3, point #6). Thus, since NRE-deGFP was not up-regulated in the proneural ADHN domain of sc10-1 ac3 mutant pupae, a quantitative analysis of the dynamics of NRE-deGFP may not be informative.

The reviewer also suggested us to study the dynamics of GFP-Sc in Notch mutants. One can easily predict that most Notch mutant cells would accumulate GFP-Sc, as observed in the notum (PMID: 28386027). Therefore, analysis of fate symmetry breaking is unlikely to be useful in that context. Likewise, a FDI analysis would not be relevant. From a technical point of view, live imaging of GFP-Sc would have to be performed in Notch mutant clones. This is because RNAi against Notch (strong 10xUAS-Notch hp2 construct, PMID: 19487563) driven by escargot-Gal4 to knock down Notch in larval histoblasts only led to a partial loss of Notch function (our unpublished data). Generation of Notch mutant clones in the abdomen would require constructing appropriate GFP-Sc Notch FRT recombinant chromosome as well as generating a new FRT GFP-Sc chromosome with an infrared marker (not currently available) to compare the relative dynamics of GFP-Sc in wild-type and mutant cells. In sum, this proposed experiment would take a significant amount of time and is unlikely to shed new light. Given that this experiment is not essential to support the claims of the paper and that it is not clear to us what would be learnt from this experiment, we opted for not performing this experiment.

Minor comments * 1. In figure 1F and F', the authors mention GFP-Sc is not expressed prior to 14h, however, there is still GFP signal detected in their imaging. Can the authors comment what would be the cause of this GFP signal or was it due to non-specific background signal during their imaging analysis?*

We thank the referee for raising this issue. Yes, a strong autofluorescence signal was detected prior to the onset of GFP-Sc expression. We provide below the results of our analysis of the autofluorescence signal (Fig R1) relative to the nuclear signal (Fig R2), and how normalization of the signal was used to measure the specific GFP-Sc signal.

Analysis of the autofluorescence signal over time

To estimate the autofluorescence signal, we measured the average intensity of the signal acquired in the GFP channel for each frame and plotted these values over time. The results are shown in Fig R1 below:

*Fig R1: temporal profile of the autofluorescence signal *

Each measurement corresponds to the average intensity measured in the GFP channel over the entire field at each z-section and for each time point. Mean and SD values of measured are shown over time in black and grey, respectively. Time is in frame number (dt is 2.5 min). The data shown above corresponds to movie 1 (see also Fig 2).

This plot indicates that the autofluorescence signal was progressively bleached. We therefore excluded from our analysis the first 50 time points when the autofluorescence signal was initially strong. No nuclear GFP-Sc signal was detectable in these first 50 frames in the cells of the central area of the ADHN which are studied here (see Fig 2A', t=1:12, time frame #29).

While revising the manuscript, we realized that t=0 corresponded to two distinct time points in the first version of our manuscript: it corresponded to the onset of imaging in Fig 2A-D', and to t=2:08 (time frame #51) in all other figures showing data following removal of the first 50 time points. We have now fixed this issue and are presenting all data with t=0 corresponding to the onset of imaging.

Analysis of the nuclear fluorescence signal over time

To detect the nuclear GFP-Sc signal, we measured the average intensity of the signal acquired in the GFP channel (raw intensity values corresponding to the sum of the GFP-Sc and autofluorescence signals) in segmented nuclei (in 3D, within the entire z-stack). These values were plotted over time (pink curve in Fig R2 below; the autofluorescence is plotted in black, as in Fig R1, for the sake of comparison). This showed that the intensity of the signal measured in nuclei was initially identical to the mean intensity measured across the entire field of view, indicative of autofluorescence only. A specific increase in signal intensity in nuclei (relative to the entire field of view) was detectable after 2h of imaging (time frame 48 in Fig R1; dt is 2.5 min). Importantly, mean intensity values of the autofluorescence signal appeared to be approximately 10-fold stronger than the mean intensity associated with the nuclear GFP-Sc signal.

Fig R2: temporal profile of the GFP-Sc signal

*The plot in pink corresponds to the average intensity in the GFP channel (raw intensity values corresponding to the GFP-Sc and/or autofluorescence signals) per nucleus (within the entire z-stack) for each time point. Mean and SD values measured in each nucleus are shown over time (in pink; these data correspond to movie 1; shown also in Fig 3). This plot (pink) should be compared with the plot shown in Fig R1 (also in black in Fig R2). The intensity difference between the pink and black curves was attributed to the specific GFP-Sc signal. *

Signal normalization and analysis of the GFP-Sc signal

In our study, we normalized the GFP-Sc signal by dividing the averaged value measured in each single nucleus (data corresponding to the pink curve in Fig R2) by the mean value of the signal measured at the same time point in the same channel in the entire image stack (data corresponding to the black curve in Fig R1/R2). Given the low intensity of the GFP-Sc signal, and the small number of pixels corresponding to Scute-expressing nuclei over the entire field of view, this value should closely reflect the autofluorescence noise. Thus, the background autofluorescence signal should be close to 1. This was experimentally verified by measuring the normalized intensity values of the PDHN nuclei that did not express Scute: a mean intensity value of 0.96 +/- 0.10 was measured (at time frame #51; see Fig R1 below). In contrast, the normalized GFP-Sc values measured several hours before SB were found to be close to 1.1 (see Fig 3D). Whether these values reflect very low levels of nuclear GFP-Sc that cannot be detected visually or result from imperfect normalization of the signal remain unclear. Given the intensity and non-uniformity of the autofluorescence signal, we cannot exclude the latter. For this reason, we chose to not over-interpret the initial low intensity values of GFP-Sc.

In the materials and methods, the authors mention that prior to imaging the larvae and pupae are grown at 18, 21 or 25{degree sign}C. Is there a reason why the larvae and pupae are grown at different temperatures for different experiments? Can the authors specify (i.e. in the figure legends) in which experiments different temperatures were used?

Larvae and pupae were grown at different temperatures for convenience, i.e. to adapt the time interval between staging at 0h APF and mounting for live imaging. Indeed, it is much easier to obtain 10-14h APF pupae by collecting staged pupae at 0h APF the day before and incubating them overnight at lower temperature to slow-down development. However, all live imaging experiments were performed at 23-25{degree sign}C, and we have no reason to think that this prior incubation would affect the process studied here.

The citations need to have a better format as they show up as each citation within a single bracket which makes it a little hard to read when multiple references are cited in a single sentence. fixed

In the abstract, the sentence 'Unexpectedly, we observed at low frequency (10%) pairs of cells that are in direct contact at the time of SB'. SB should be replaced with "Symmetry breaking", as it appeared for the first time in the manuscript and should be written out in full. fixed

Throughout the manuscript there are instances where the abbreviations are written in full with the abbreviation in brackets after they have already been introduced in the introduction which can be changed to just the abbreviation itself. fixed

In the discussion on page 11, 'our observation...', our needs to be changed to Our. fixed

7. It would be nice to have arrow heads or dotted lines around the cells or areas on interest in both, all the figures and movies, so that it will be easier to follow the results. The videos have a lot of background due to fragmented apoptotic nuclei, etc. as mentioned by the authors, hence arrow heads or dotted lines would bring viewers focus on the areas of interest.

fixed (see for instance Fig 1D, Fig 2A, Fig 5B, Fig 7A, Fig S3D, etc...)

8. It would be helpful to have anterior - posterior axis (i.e. with an arrow) shown on top of all the figures.

In our earlier version, we indicated that 'In this and all other figures, dorsal is up and anterior is left' in the legend of Fig 1B. We have now moved this sentence at the end of Fig 1 to have it more apparent. Additionally, the AP axis is now clearly indicated in Fig 1C. We believe that it is not necessary to repeat this orientation in all figures.

Scale bars are missing in all figures, videos, and figure legends. Added

Only movies 1 and 3 are referenced in the text. All movies are now referenced in the text

Keeping the colors in the movies and figures consistent and same would be helpful. For example, Movie 2 Histone3.3-mIFP marker is in blue but in figure 3 it is in magenta. fixed (H3.3-mIFP in magenta in this movie, now numbered 3)

As mentioned above, it would be helpful if the authors have arrow heads or dotted lines around the cells or areas of interest in both the figures and movies for better representation of their data. For example, movie 1 shows a larger area of imaging than shown in figure 2A, which makes it hard to follow the cells of interest in the movie.

An additional movie corresponding to the SOP shown in Fig 2A is now provided (new movie 2).

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Reviewer #2

1. Despite "symmetry breaking" being the main focus of the paper, in the Introduction, the authors do not explain what this term means and do not provide any description of this process. This is a critical point that makes understanding of the goals of the paper difficult. Therefore, the authors are encouraged to provide more information and a clear description of this term/phenomenon. We thank the reviewer for this suggestion, we are now stating in the introduction what symmetry breaking means in the context of lateral inhibition: 'To describe and study the process of SOP selection, we studied fate SB. The latter refers to the transition point when one cell, the future SOP, starts to stably accumulate a higher level of GFP-Sc relative to its immediate neighbors.'

The role of Achaete in the story is not clear. Even though both factors are required for SOP determination, the authors mainly focus on Scute, so it is not very clear what the role of Achaete in this process is, if there is any. As shown in the paper, Achaete is expressed later when heterogeneity is promoting cell fate divergence. Is Achaete maybe contributing to cell heterogeneity/ cell fate divergence?

We thank the reviewer for raising this point. We now show in Fig S1A-D that abdominal bristles develop in a protein null allele of sc (scM6 ) as well as in an ac mutant corresponding to a 45 kb deletion that removes ac but not sc (PMID: 16216235)). Together with our analysis of sc10-1 ac3 __mutant flies, we can now conclude that __Sc and Ac act redundantly for SOP specification in the pupal abdomen. We have also further studied the expression of Ac relative to Sc and E(spl)HLH-m3 (see our response above to point #1 of reviewer 1). We fully agree with the reviewer that cell-to-cell variations in Ac expression might contribute to proneural heterogeneity and SB. This is now briefly discussed.

Minor points: * * 1. Symmetry Breaking (SB) should be abbreviated in the Abstract. The authors initially use the full term without abbreviation, and only on page 5, the abbreviation is finally defined; however, it should be introduced much earlier.

fixed

The second-to-last sentence in the abstract, "These lateral inhibition defects were correlated via cellular rearrangements," is unclear regarding what defects the authors are referring to.

This sentence was rewritten: 'Live imaging showed that these patterning defects were corrected via cellular rearrangements associated with global tissue fluidity, not via cell fate change.'

For clarity, being more specific in the text in regards to description of the figure panels would be beneficial (e.g. page 3 Fig 1C-E); referring to C-E together makes it hard to understand what does each panel shows.

fixed

In many instances, the movies are not properly referenced (e.g. on page 5, third row simply states "movies"), making it difficult to discern which movie should be checked. On page 8, when authors refer to movie 3, they likely meant movie 5.

fixed

Figure S1 requires some corrections.

We thank the reviewer for helping us improve the presentation of our results.

The authors use the short name "scute" initially and then switch to the shortened version "sc'.

fixed

Additionally, the nlsRFP (blue) is difficult to see; adjusting the levels or changing colors/showing separate channels may improve visibility.

The authors mention clone borders, but none are shown. It would greatly help to outline the borders in all figures.

The ubiquitous nlsRFP marker is now shown in magenta in Fig S1I that now shows only 2 channels to outline the ADHN (white dotted line) and the clones (yellow dotted lines).

We also outlined the clone borders in Fig 4C,C'.

Genotypes of the samples should be indicated, and clarification is needed regarding what "n" represents (number of cells, clones, or flies).

The genotype studied in Fig S1 and Fig 4 (which is the only complex genotype studied here) is now indicated in the Methods section. We have clarified what the different 'n' meant, in Fig 4 (see text) and elsewhere (see legend of Fig S2 for instance).

What do the arrows in the panel B show?

Thanks for pointing this out. The arrows in Fig S1I' indicate Cut/Hnt-positive cells (SOPs) within the clones (as now explained in the legend).

It is also recommended to display important channels as separate black and white images.

Separate channels are now shown in Fig S1 and S3.

Additionally, the use of RNAi against GFP instead of RNAi against scute should be justified; using RNAi GFP as the genotype on the graph could be interpreted as a control genotype rather than downregulation of scute.

A RNAi construct against GFP was used because this construct was known to very efficient and specific. Indeed, a strong knock-down of GFP-Sc was obtained by this approach (see Fig 4C'). We did not test sc RNAi constructs in the context of GFP-Sc. To avoid confusion, we are now indicating Sc downregulation (gfp RNAi) in Fig 4C'.

In the Figure 2 Legend, the authors use "std" as an abbreviation to define standard deviation. Typically, this is abbreviated as SD.

fixed

In Figure 4E, the authors do not explain on why there are points on the x-axis that correspond to a decimal number of cells.

Since heterogeneity was calculated over a 20 min interval, we likewise calculated the number of neighbors over the same time interval. Thus, the number of neighbors for each SOP corresponds to an averaged value calculated over this time interval. This is now explained in the legend.

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Reviewer #3

1. First and foremost, the authors should state in the first paragraph of the Results that scGFP is a CRISPR knockin and thus it's the only source of Sc protein in the animals imaged (this is stated only in the Methods section). Thanks for this comment, we agree that this is one of the strengths of our work that we should emphasize. We now state in the results section: 'GFP-Sc is produced from the endogenous locus such that all Sc molecules produced in these pupae are GFP-tagged'

The magnitude of the Sc increase should be commented on. Based on the intensity and FDI plots in Fig. 3B-E, an increase of 15-17% in the amount of Sc is suggested (the FDI plateaus at 0.08, which gives 1.08/0.92 = 1.17x the level of Sc in the SOP vs the surrounding cells). However, in the stills shown in Fig. 2BCD and in Fig. 3A, the intensity differential between SOPs and neighbors seems at least 100% (ie at least double the intensity, which would yield an FDI of >1/3 =0.33). Why is this high contrast never seen in the quantitative measurements?

Thanks for asking about the fold change of GFP-Sc levels in SOPs, from SB to its plateau. This fold change can be seen in Fig 3D: the normalized value of GFP-Sc is 1.12 at SB, and 1.26 three hours after SB (when the FDI plateaus), indicative of a 2.2x fold increase of GFP-Sc in SOPs (0.26/0.12= 2.2, following background subtraction; see our detailed response to reviewer #1, minor point 1, about background signal analysis and normalization of the signal). This fold-change value is now indicated in the legend of Fig 3D. Obviously, this fold-change value is highly sensitive to signal normalization. Since the autofluorescence signal was stronger than the GFP-Sc signal (see Fig R2 above) and varied over time (due to bleaching; see Figs R1 and R2 above), we feel that this fold-change value should be taken with a grain of salt.

From Fig. 2A-D it appears that the ScGFP fluorescence intensity is at the same level or weaker than nearby autofluorescence. Please state (1) how you confirmed that the histoblast nest has lower autofluorescence than the larval epidermis and (2) how you corrected for histoblast nest autofluorescence in your quantifications.

As detailed above (our response to reviewer #1, minor point 1), the specific GFP-Sc signal is ten-times lower than the autofluorescence signal. We did not compare the autofluorescence signal produce by larval and imaginal cells (but note that larval epidermal cells had a stronger autofluorescence signal; see the yellow dots in Fig 2A). Normalization of the signal to correct for autofluorescence was explained in the Methods (and is also detailed above in our response to reviewer #1, minor point 1).

The paradoxical result of Fig. S1B should be discussed. On the one hand it is stated that "Ac and Sc specify the fate of the Sensory Organ Precursor cells (SOPs)" (p.2) and on the other S1B shows SOP specification in the absence of Sc. Are the SOPs shown in Fig S1B rare exceptions? Do the authors believe that these rare exceptions are there because of inefficient RNAi (since in comparison with S1A, in the null condition almost no SOPs should be formed)? Or they are the SOPs in RNAi clones as rare as the occasional bristles in S1A?

We do not see the result of Fig S1B as paradoxical but interpreted this result assuming that Ac and Sc were redundant for SOP determination. We now provide clear genetic evidence in support of this view (see our response above to reviewer #2, point 2). Otherwise, we found that RNAi is efficient (see loss of the GFP signal in clone in Fig. 4C'). In adult males, the density of bristles appeared to be quite normal over clonal patches of gfp RNAi cells (not shown), consistent with Ac being redundant with Sc

One figure that is not straightforward to interpret is Fig. 4E. It plots ScGFP heterogeneity vs. number of RNAi neighbors. Each point in the plot must be an individual SOP (165 total). Therefore, its neighbors (the x-axis) should take integral (not decimal) values. How can a single SOP have a decimal number of RNAi neighbors, especially since heterogeneity was sampled over a 10min time-window, when not much cell rearrangement can take place? Please explain.

Since heterogeneity was calculated over a 20 min interval, we likewise calculated the number of neighbors over the same time interval. Thus, the number of neighbors for each SOP corresponds to an averaged value calculated over this time interval. This is now explained in the legend: 'Note that the number of neighbors was likewise calculated over this time interval, and the resulting number of neighbors may not take an integral value.'

I found the discussion of the Notch reporter dynamics (Fig. 7) confusing in several places. * * (6a) Whereas it's clear that there is plenty of Notch signaling going on before SBN, the authors repeatedly imply that Notch signaling starts after SBN. For example, in the Results (p.9) they state "Thus, this quantitative approach failed to detect a phase of reciprocal Notch signaling during which proneural cluster cells would both send and receive a Delta-Notch signal prior to SOP emergence." The fact that the NRE-deGFP gave a robust signal before the start of the movies clearly means that mutual inhibition was going on for quite some time before SB. In fact, an FDI of 0 for >4h prior to SBN (Fig. 7G) means exactly this: that the level of Notch response among the cluster cells is equivalent ("mutual inhibition" lasts for at least 4h before SBN). (6b) In the first paragraph of this section (p.8) they comment that the pre-existence of Notch signaling is unexpected - why? I interpret it to simply be mutual inhibition (see above). Then they go on to quantitate the average Notch response intensity over the entire posterior ADHN (please define the borders the "posterior" ADHN). I question the informational value of this analysis (averaging over a large region), when Notch signaling is known to have intense local cell-to-cell variability (also evident in the stills shown in Fig. 7A,B,C).

We apologize for not describing well enough the data shown in Fig 7E, and for not explaining clearly our interpretation of the NRE-deGFP signal.

While the observation of a strong NRE-deGFP signal indeed indicates that Notch signaling had been active prior to the time of observation (in this sense, Notch is indeed active long before SBN), this does not necessarily imply that Notch is still active at that time. This is because the deGFP protein produced by the NRE-deGFP reporter is stable relative to the time scale of the studied process. Its measured half-life in S2 cells cultured at 25{degree sign}C is 2h (PMID: 31140975). Based on this data, the NRE-deGFP signal is likely to remain detectable several hours after Notch signaling has been switched off. If the rate of production of deGFP is lower than its rate of degradation, then the NRE-deGFP signal is expected to progressively decay over time. We believe that this is what we observed in our movies: while a strong signal was detected over the posterior half of the ADHN at 14-15h APF, this signal decreased over time (Fig 7D). To interpret the temporal dynamics of NRE-deGFP signal in terms of instantaneous Notch activity, we examined the Rate of Change (ROC): an increase of the NRE-deGFP signal over time (positive ROC) would indicate that Notch activity is increasing (more precisely, the production rate of deGFP is higher than its rate of degradation), whereas a decrease (negative ROC) indicates that Notch becomes less active (or inactive if the rate of decrease approximates the decay rate of the deGFP protein). Our data shown in Fig 7D showed that the NRE-deGFP signal (measured in the area indicated with a dotted line in Fig 7A,B; this area was defined by the initial pattern of NRE-deGFP) decreased over time (negative ROC) between t=1 and t=6.5h. We therefore conclude that Notch signaling is decreasing to reach a minimum at t=~3.5h, indicating that the level of Notch activity is at its lowest around the time of SB. At this minimum, the decay rate corresponds to a protein half-life of 4.4h, which is not so different from the measured half-time of deGFP in S2 cells (particularly if one assumes a 1.4x difference between the decay rates measured at 22 and 25{degree sign}C, based on the known temperature-dependent speed of development). This is why we conclude that Notch signaling is very low at this stage. Additionally, no NRE-deGFP signal was detected before t=4:30h (movie 7) in the initially NRE-deGFP negative cells (located anterior to the area indicated with a dotted line in Fig 7A). This indicated that Notch was activated late in this area. Together, our observations are not consistent with the view that Notch mediates a strong mutual inhibition signal over a prolonged time interval prior to SB.

To further study the pattern of Notch activity, we have monitored over time the accumulation pattern of GFP-tagged E(spl)m3-HLH (GFP-m3) (PMID: 31375669) in fixed sample (Fig S3F-G'). This confirmed that Notch was active in posterior ADHN cells and in the PDHN prior to 14h APF, i.e. prior to the onset of Ac and Sc, and that Notch activation extended to the central ADHN domain at 17-18h APF (Fig S3E-E' and G-G', and Fig 7I-I''), coinciding with SOP emergence.

Otherwise, the reviewer is correct when stating that a FDI value close to 0 indicates that the level of measured fluorescence among the different cells of the considered cluster is similar. Such a FDI value would be measured if cells did not express NRE-deGFP or had decreasing but similar levels of NRE-deGFP. This FDI value does not, per se, imply that Notch is active.

And then they move on to a (much more informative) cell-by-cell analysis, without even changing paragraphs, making it hard for the reader to follow. (6c) The conclusion at the end of the second paragraph (p. 9) "It also showed that SB was detected soon after the onset of Notch-mediated inhibitory signaling." is nowhere supported by data. If I understand well, SB refers to Sc and "the onset of Notch-mediated inhibitory signaling" refers to SBN (which is the onset of ASYMMETRY in Notch signaling, not the onset of Notch signaling, which has been going on for hours earlier). I don't see any data comparing SB with SBN. In fact, this is an important question to address (see below - comment 10).

We apologize for the lack of clarity in our writing, we meant: "It also showed that SBN was detected soon after the onset of Notch-mediated inhibitory signaling."

Yes, SBN refers to the onset of asymmetry in Notch signaling, as measured using NRE-deGFP. As explained above (but see also our response to point #7 below), our data do not provide evidence for a detectable Notch signal prior to SBN.

We agree that comparing SB and SBN would be nice. Unfortunately, our current tools do not permit a detailed comparison (see our detailed response below, point #10).

Mutual inhibition amongst neighboring cells has been proposed to involve (besides mutual Notch signaling) an increase in Sc levels in 2-3 cells in a cluster before the singularization of a single SOP. The authors seem rather biased against such a transient Sc hike based on their results in Fig. 2D, where the neighboring cells stay at rather constant basal Sc levels for several hours, while the Sc SB event happens. However, looking at an individual SOP in Fig. 2B, I do detect a mild hike in the pink curve right around SB in the blue curve. Could the average result from 160 SOPs (in Fig 2D) simply blur such transient Sc hikes, if they happen with different kinetics for different SOPs? Couldn't the 10% of SOP twins (shown in Fig. 6) represent a special case of this transient "subcluster" Sc hike? I would appreciate some discussion on this point. [Whether Sc is transiently upregulated or not, however, does not change my firm conclusion - from the data presented - that Notch-mediated mutual inhibition has been going on long before SBN.]

First, our data are consistent with the notion that a few proneural cells progressively accumulate higher level of Scute prior to SB (as proposed above). Indeed, the moderate increase in both GFP-Sc levels and coefficient of variation values (GFP-Sc heterogeneity) seen prior to SB correspond to what the reviewer has in mind (higher levels of GFP-Sc in a few proneural cluster cells). We also appreciate the reviewer's comment about the plot shown in Fig 2D. However, we strongly feel that our quantitative analysis of a large dataset is a strength. Thus, we do not find useful to discretize a continuous process by introducing the notion of 'subclusters' of 2-3 cells. Likewise, we believe that it is more informative to focus our analysis on the entire dataset using average and SD values and do not wish to base our interpretation of the process based on selected tracks (the one shown in Fig 2B only served as an illustration of how we performed our analysis and has no interpretation value).

The reviewer also states that "mutual inhibition amongst neighboring cells has been proposed to involve an increase in Sc levels in 2-3 cells in a cluster before the singularization of a single SOP". Since there is no published description of the pattern of accumulation of Scute in abdominal histoblats (to our best knowledge), we hypothesize that this statement applies to the proneural clusters in the developing wing disc. This is because the accumulation pattern of Sc has been studied in detail in that context by the Modollel and Carroll labs (PMID: 2044965, PMID: 2044964). However, their description of the accumulation pattern of Scute (in fixed samples, using anti-Sc antibodies) did not refer to sub-clusters of 2-3 cells. We would appreciate if the reviewer could direct us to the relevant published observation.

Finally, we are not sure to follow the reviewer when she/he firmly concluded from our data that Notch-mediated mutual inhibition has been going on long before SBN. Instead, our data clearly showed that the ADHN region that produced SOPs exhibited two distinct NRE-deGFP patterns, with Notch signaling being active prior to imaging (i.e. prior to 14h APF) and decreasing to reach a minimum of Notch activation around 17h APF (i.e. around the time of SB, as determined by GFP-Sc imaging) in the posterior area of the ADHN.

Thus, our data do not show that mutual inhibition does not take place in this tissue but rather imply that the phase of mutual inhibition (or competition) must be relatively short, or transient, and that competition amongst proneural cluster cells operate at low Notch and Sc levels (probably contrary to what many people have in mind).

Some minor points: * * 8. Please change Cad-GFP to Ecad-GFP or shg-GFP, as Cad misdirects to caudal.

Thanks, changed into Ecad-GFP and Ecad-mKate

What is c in "(x,y,z,c,t) movies"? (a fifth independent variable?)

c stands for channel. This is relatively standard nomenclature.

The authors show that Sc displays a SB event leading to FDI of 0.08 and the Notch reporter displays another SB (SBN) leading to a much more pronounced FDI of -0.2. Are these two events (the hike of Sc levels and the plummeting of Notch signal) contemporaneous or does one precede the other? Having both tagged with GFP makes it impossible to image simultaneously, but the authors could register each reporter's dynamics relative to the time of SOP division (as done in Fig. 5C) to get a sense of their relative order.

We do agree with the reviewer that it would be nice to be able to align in time these two data sets. Unfortunately, the temporal correlation between SB and the SOP division is too variable (4.7 +/- 1.1) to confidently align these two datasets using this event as a time reference. New tools are needed (see our response to point #11 below).

Where in the above timeline is the SOP fate definitively adopted? neur-nlsGFP, Ac-RFP, m3Cherry and Sens detection in Figs. 1 and 7 give us a rough idea that these other markers appear around the time of Sc FDI peaking, around 3h after the initial SB. But this is not presented in an organized fashion - the reader collects this information sporadically. A reanalysis of the already existing data attempting to place these various markers in an integrated timeline would be of great importance in understanding the details of this cell fate specification process. Which is the earliest SB event? sc, neur or Notch? How long does it take from that early SB until definitive SOP markers (Sens) first appear?

We agree with the reviewer, it would be interesting to extend the approach reported here for Scute to characterize SB and rate of FDI for other key factors governing the selection of SOPs. As pointed out by the reviewer (point #10 above), it would also be important to register in time these various events. Unfortunately, the maturation time of RFP, mCherry, FP670, etc... appeared to be too slow relative to the rapid turnover of the Ac, Sc and E(spl)-HLH factors prevented us from performing two-color imaging. Hence, current tools do not permit to determine which is the earliest SB event.

More genetic perturbations could be performed to solidify the model of cell-cell communication during lateral inhibition. Two obvious ones come to mind: (a) How would the Sc-GFP dynamics change in a Notch-RNAi background? (b) How would the NRE-deGFP dynamics change in a sc-RNAi background?

See our detailed response to reviewer #1, major point #2.

Author response:

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

Your editorial guidance, reviews, and suggestions have led us to make substantial changes to our manuscript. While we detail point-by-point responses in typical fashion below, I wanted to outline, at a high level, what we’ve done.

(1) Methods. Your suggestions led us to rethink our presentation of our methods, which are now described more cohesively in a new methods section in the main text.

(2) Model Validation & Robustness. Reviewers suggested various validations and checks to ensure that our findings were not, for instance, the consequence of a particular choice of parameter. These can be found in the supplementary materials.

(3) Data Cleaning & Inclusion/Exclusion. Finally, based on feedback, our new methods section fully describes the process by which we cleaned our original data, and on what grounds we included/excluded individual faculty records from analysis.

eLife assessment

Efforts to increase the representation of women in academia have focussed on efforts to recruit more women and to reduce the attrition of women. This study - which is based on analyses of data on more than 250,000 tenured and tenure-track faculty from the period 2011-2020, and the predictions of counterfactual models - shows that hiring more women has a bigger impact than reducing attrition. The study is an important contribution to work on gender representation in academia, and while the evidence in support of the findings is solid, the description of the methods used is in need of improvement.

Reviewer #1 (Public Review):

Summary and strengths

This is an interesting paper that concludes that hiring more women will do more to improve the gender balance of (US) academia than improving the attrition rates of women (which are usually higher than men's). Other groups have reported similar findings but this study uses a larger than usual dataset that spans many fields and institutions, so it is a good contribution to the field.

We thank the reviewer for their positive assessment of the contributions of our work.

Weaknesses

The paper uses a mixture of mathematical models (basically Leslie matrices, though that term isn't mentioned here) parameterised using statistical models fitted to data. However, the description of the methods needs to be improved significantly. The author should consider citing Matrix Population Models by Caswell (Second Edition; 2006; OUP) as a general introduction to these methods, and consider citing some or all of the following as examples of similar studies performed with these models:

Shaw and Stanton. 2012. Proc Roy Soc B 279:3736-3741

Brower and James. 2020. PLOS One 15:e0226392

James and Brower. 2022. Royal Society Open Science 9:220785 Lawrence and Chen. 2015.

[http://128.97.186.17/index.php/pwp/article/view/PWP-CCPR-2015-008]

Danell and Hjerm. 2013. Scientometrics 94:999-1006

We have expanded the description of methods in a new methods section of the paper which we hope will address the reviewer’s concerns.

We agree that our model of faculty hiring and attrition resembles Leslie matrices. In results section B, we now mention Leslie matrices and cite Matrix Population Models by Caswell, noting a few key differences between Leslie matrices and the model of hiring and attrition presented in this work. Most notably, in the hiring and attrition model presented, the number of new hires is not based on per-capita fertility constants. Instead, population sizes are predetermined fixed values for each year, precluding exponential population growth or decay towards 0 that is commonly observed in the asymptotic behavior of linear Leslie Matrix models.

We have additionally revised the main text to cite the listed examples of similar studies (we had already cited James and Brower, 2022). We thank the reviewer for bringing these relevant works to our attention.

The analysis also runs the risk of conflating the fraction of women in a field with gender diversity! In female-dominated fields (e.g. Nursing, Education) increasing the proportion of women in the field will lead to reduced gender diversity. This does not seem to be accounted for in the analysis. It would also be helpful to state the number of men and women in each of the 111 fields in the study.

We have carefully examined the manuscript and revised the text to correctly differentiate between gender diversity and women’s representation.

We have additionally added a table to the supplemental materials (Tab. S3) that reports the estimated number of men and women in each of the 111 fields.

Reviewer #2 (Public Review):

Summary:

This important study by LaBerge and co-authors seeks to understand the causal drivers of faculty gender demographics by quantifying the relative importance of faculty hiring and attrition across fields. They leverage historical data to describe past trends and develop models that project future scenarios that test the efficacy of targeted interventions. Overall, I found this study to be a compelling and important analysis of gendered hiring and attrition in US institutions, and one that has wide-reaching policy implications for the academy. The authors have also suggested a number of fruitful future avenues for research that will allow for additional clarity in understanding the gendered, racial, and socioeconomic disparities present in US hiring and attrition, and potential strategies for mitigating or eliminating these disparities.

We thank the reviewer for their positive assessment of the contributions of our work.

Strengths:

In this study, LaBerge et al use data from over 268,000 tenured and tenure-track faculty from over 100 fields at more than 12,000 PhD-granting institutions in the US. The period they examine covers 2011-2020. Their analysis provides a large-scale overview of demographics across fields, a unique strength that allows the authors to find statistically significant effects for gendered attrition and hiring across broad areas (STEM, non-STEM, and topical domains).

LaBerge et al. find gendered disparities in attrition-using both empirical data and their counterfactual model-that account for the loss of 1378 women faculty across all fields between 2011 and 2020. It is true that "this number is both a small portion of academia... and a staggering number of individual careers," as ." - as this loss of women faculty is comparable to losing more than 70 entire departments. I appreciate the authors' discussion about these losses-they note that each of these is likely unnecessary, as women often report feeling that they were pushed out of academic jobs.

LaBerge et al. also find-by developing a number of model scenarios testing the impacts of hiring, attrition, or both-that hiring has a greater impact on women's representation in the majority of academic fields in spite of higher attrition rates for women faculty relative to men at every career stage. Unlike many other studies of historical trends in gender diversity, which have often been limited to institution-specific analyses, they provide an analysis that spans over 100 fields and includes nearly all US PhD-granting institutions. They are able to project the impacts of strategies focusing on hiring or retention using models that project the impact of altering attrition risk or hiring success for women. With this approach, they show that even relatively modest annual changes in hiring accumulate over time to help improve the diversity of a given field. They also demonstrate that, across the model scenarios they employ, changes to hiring drive the largest improvement in the long-term gender diversity of a field.

Future work will hopefully - as the authors point out - include intersectional analyses to determine whether a disproportionate share of lost gender diversity is due to the loss of women of color from the professoriate. I appreciate the author's discussion of the racial demographics of women in the professoriate, and their note that "the majority of women faculty in the US are white" and thus that the patterns observed in this study are predominately driven by this demographic. I also highly appreciate their final note that "equal representation is not equivalent to equal or fair treatment," and that diversifying hiring without mitigating the underlying cause of inequity will continue to contribute to higher losses of women faculty.

Weaknesses

First, and perhaps most importantly, it would be beneficial to include a distinct methods section. While the authors have woven the methods into the results section, I found that I needed to dig to find the answers to my questions about methods. I would also have appreciated additional information within the main text on the source of the data, specifics about its collection, inclusion and exclusion criteria for the present study, and other information on how the final dataset was produced. This - and additional information as the authors and editor see fit - would be helpful to readers hoping to understand some of the nuance behind the collection, curation, and analysis of this important dataset.

We have expanded upon the description of methods in a new methods section of the paper.

We have also added a detailed description of the data cleaning steps taken to produce the dataset used in these analyses, including the inclusion/exclusion criteria applied. This detailed description is at the beginning of the methods section. This addition has substantially enhanced the transparency of our data cleaning methods, so we thank the reviewer for this suggestion.

I would also encourage the authors to include a note about binary gender classifications in the discussion section. In particular, I encourage them to include an explicit acknowledgement that the trends assessed in the present study are focused solely on two binary genders - and do not include an analysis of nonbinary, genderqueer, or other "third gender" individuals. While this is likely because of the limitations of the dataset utilized, the focus of this study on binary genders means that it does not reflect the true diversity of gender identities represented within the professoriate.

In a similar vein, additional context on how gender was assigned on the basis of names should be added to the methods section.

We use a free, open-source, and open-data python package called nomquamgender (Van Buskirk et al, 2023) to estimate the strengths of (culturally constructed) name-gender associations. For sufficiently strong associations with a binary gender, we apply those labels to the names in our data. We have updated the main text to make this approach more apparent.

We have also added language to the main text which explicitly acknowledges that our approach only assigns binary (woman/man) labels to faculty. We point out that this is a compromise due to the technical limitations of name-based gender methodologies and is not intended to reinforce a gender binary.

I do think that some care might be warranted regarding the statement that "eliminating gendered attrition leads to only modest changes in field-level diversity" (Page 6). while I do not think that this is untrue, I do think that the model scenarios where hiring is "radical" and attrition is unchanged from present (equal representation of women and men among hires (ER) + observed attrition (OA)) shows that a sole focus on hiring dampens the gains that can otherwise be addressed via even modest interventions (see, e.g., gender-neutral attrition (GNA) + increasing representation of women among hires (IR)). I am curious as to why the authors did not include an additional scenario where hiring rates are equal and attrition is equalized (i.e., GNA + ER). The importance of including this additional model is highlighted in the discussion, where, on Page 7, the authors write: "In our forecasting analysis, we find that eliminating the gendered attrition gap, in isolation, would not substantially increase representation of women faculty in academia. Rather, progress towards gender parity depends far more heavily on increasing women's representation among new faculty hires, with the greatest change occurring if hiring is close to gender parity." I believe that this statement would be greatly strengthened if the authors can also include a comparison to a scenario where both hiring and attrition are addressed with "radical" interventions.

Our rationale for omitting the GNA + ER scenario in the presented analysis is that we can reason about the outcomes of this scenario without the need for computation; if a field has equal inputs of women and men faculty (on average) and equal retention rates between women and men (on average), then, no matter the field’s initial age and gender distribution of faculty, the expected value for the percentage of women faculty after all of the prior faculty have retired (which may take 40+ years) is exactly 50%. We have updated the main text to discuss this point.

Reviewer #3 (Public Review):

This manuscript investigates the roles of faculty hiring and attrition in influencing gender representation in US academia. It uses a comprehensive dataset covering tenured and tenure-track faculty across various fields from 2011 to 2020. The study employs a counterfactual model to assess the impact of hypothetical gender-neutral attrition and projects future gender representation under different policy scenarios. The analysis reveals that hiring has a more significant impact on women's representation than attrition in most fields and highlights the need for sustained changes in hiring practices to achieve gender parity.

Strengths:

Overall, the manuscript offers significant contributions to understanding gender diversity in academia through its rigorous data analysis and innovative methodology.

The methodology is robust, employing extensive data covering a wide range of academic fields and institutions.

Weaknesses:

The primary weakness of the study lies in its focus on US academia, which may limit the generalizability of its findings to other cultural and academic contexts.

We agree that the U.S. focus of this study limits the generalizability of our findings. The findings that we present in this work will only generalize to other populations–whether it be to an alternate industry, e.g., tech workers, or to faculty in different countries–to the extent that these other populations share similar hiring patterns, retention patterns, and current demographic representation. We have added a discussion of this limitation to the manuscript.

Additionally, the counterfactual model's reliance on specific assumptions about gender-neutral attrition could affect the accuracy of its projections.

Our projection analysis is intended to illustrate the potential gender representation outcomes of several possible counterfactual scenarios, with each projection being conditioned on transparent and simple assumptions. In this way, the projection analysis is not intended to predict or forecast the future.

To resolve this point for our readers, we now introduce our projections in the context of the related terms of prediction and forecast, noting that they have distinct meanings as terms of art: On one hand, prediction and forecasting involve anticipating a specific outcome based on available information and analysis, and typically rely on patterns, trends, or historical data to make educated guesses about what will happen. Projections are based on assumptions and are often presented in a panel of possible future scenarios. While predictions and forecasts aim for precision, projections (which we make in our analysis) are more generalized and may involve a range of potential outcomes.

Additionally, the study assumes that whoever disappeared from the dataset is attrition in academia. While in reality, those attritions could be researchers who moved to another country or another institution that is not included in the AARC (Academic Analytics Research Centre) dataset.

In our revision, we have elevated this important point, and clarified it in the context of the various ways in which we count hires and attritions. We now explicitly state that “We define faculty hiring and faculty attrition to include all cases in which faculty join or leave a field or domain within our dataset.” Then, we enumerate the number of situations that could be counted as hires and attritions, including the reviewer’s example of faculty who move to another country.

Reviewer #1 (Recommendations For The Authors):

Section B: The authors use an age structured Leslie matrix model (see Caswell for a good reference to these) to test the effect of making the attrition rates or hiring rates equal for men and women. My main concern here is the fitting techniques for the parameters. These are described (a little too!) briefly in section S1B. Some specific questions that are left hanging include:

A 5th order polynomial is an interesting choice. Some statistical evidence as to why it was the best fit would be useful. What other candidate models were compared? What was the "best fit" judgement made with: AIC, r^2? What are the estimates for how good this fit is? How many data points were fitted to? Was it the best fit choice for all of the 111 fields for men and women?

We use a logistic regression model for each field to infer faculty attrition probabilities across career ages and time, and we include the career age predictor up to its fifth power to capture the career-age correlations observed in Spoon et. al., Science Advances, 2023. For ease of reference, we reproduce the attrition risk curves in Fig S4.

We note that faculty attrition rates start low and then reach a peak around 5-7 years after earning PhD, and then decline until around 15-20 years post-PhD, after which, attrition rates increase as faculty approach retirement.

This function shape starts low and ends high, and includes at least one local minimum, which indicates that career age should be odd-ordered in the model and at least order-3, but only including career age up to its 3rd order term tended to miss some of the overserved career-age/attrition correlations. We evaluated the fit using 5-fold cross validation with a Brier score loss metric, and among options of polynomials of degree 1, 3, 5, or 7, we found that 5th order performed well overall on average over all fields (even if it was not the best for every field), without overfitting in fields with fewer data. Example fits, reminiscent of the figure from Spoon et al, are now provided in Figs S4 and S5.

While the model fit with fifth order terms may not be the best fit for all 111 fields (e.g., 7th order fits better in some cases), we wanted to avoid field-specific curves that might be overfitted to the field-specific data, especially due to low sample size (and thus larger fluctuations) on the high career age side of the function. Our main text and supplement now includes justifications for our choice to include career age up to its fifth order terms.

You used the 5th order logistic regression (bottom of page 11) to model attrition at different ages. The data in [24] shows that attrition increases sharply, then drops then increases again with career age. A fifth order polynomial on its own could plausibly do this but I associate logistic regression models like this as being monotonically increasing (or decreasing!), again more details as to how this worked would be useful.

Our first submission did not explain this point well, but we hope that Supplementary Figures S4 and S5 provide clarity. In short, we agree of course that typical logistic regression assumes a linear relationship between the predictor variables and the log odds of the outcome variable. This means that the relationship between the predictor variables and the probability of the outcome variable follows a sigmoidal (S-shaped) curve. However, the relationship between the predictor variables and the outcome variable may not be linear.

To capture more complex relationships, like the increasing, decreasing and then increasing attrition rates as a function of career age, higher-order terms can be added to the logistic regression model. These higher-order terms allow the model to capture nonlinear relationships between the predictor variables and the outcome variable — namely the non-monotonic relationship between rates of attrition and career age — while staying within a logistic regression framework.

"The career age of new hires follows the average career age distribution of hires" did you use the empirical distribution here or did you fit a standard statistical distribution e.g. Gamma?

We used the empirical distribution. This information has been added to the updated methods section in the main text.

How did you account for institution (presumably available)? Your own work has shown that institution types plays a role which could be contributing to these results.

See below.

What other confounding variables could be at play here, what is available as part of the data and what happens if you do/don't account for them?

A number of variables included in our data have been shown to correlate with faculty attrition, including PhD prestige, current institution prestige, PhD country, and whether or not an individual is a “self-hire,” i.e., trained and hired at the same institution (Wapman et. al., Nature, 2022). Additional factors that faculty self-report as reasons for leaving academia include issues of work-life balance, workplace climate, and professional reasons, and in some cases to varying degrees between men and women faculty (Spoon et. al., Sci. Adv., 2023).

Our counterfactual analysis aims to address a specific question: how would women’s representation among faculty be different today if men and women were subjected to the same attrition patterns over the past decade? To answer this question, it is important to account for faculty career age, which we accept as a variable that will always correlate strongly with faculty attrition rates, as long as the tenure filter remains in place and faculty continue to naturally progress towards retirement age. On the other hand, it is less clear why PhD country, self-hire status, or any of the other mentioned variables should necessarily correlate with attrition rates and with gendered differences in attrition rates more specifically. While some or all of these variables may underlie the causal roots of gendered attrition rates, our analysis does not seek to answer causal questions about why faculty leave their jobs (e.g., by testing the impact of accounting for these variables in simulations per the reviewers suggestion). This is because we do not believe the data used in this analysis is sufficient to answer such questions, lacking comprehensive data on faculty stress (Spoon et. al., Sci. Adv., 2023), parenthood status, etc.

What career age range did the model use?

The career age range observed in model outcomes are a function of the empirically derived attrition rates for faculty across academic fields. The highest career age observed in the AARC data was 80, and the faculty career ages that result from our model simulations and projections do not exceed 80.

We have also added the distribution of faculty across career ages for the projection scenario model outputs in the supplemental materials Fig. S3 (see response to your later comment regarding career age for further details). Looking at these distributions, it is observed that very few faculty have career age > 60, both in observation and in our simulations.

What was the initial condition for the model?

Empirical 2011 Faculty rosters are used as the initial conditions for the counterfactual analysis, and 2020 faculty rosters are these as the initial conditions for the projections analysis. This information has been added to the descriptions of methods in the main text.

Starting the model in 2011 how well does it fit the available data up to 2020?

Thank you for this suggestion. We ran this analysis for each field starting in 2011, and found that model outcomes were statistically indistinguishable from the observed 2020 faculty gender compositions for all 111 academic fields. This finding is not surprising, because the model is fit to the observed data, but it serves to validate the methods that we used to extract the model's parameters. We have added these results to the supplement (Fig. S2).

What are the sensitivity analysis results for the model? If you have made different fitting decisions how much would the results change? All this applied to both the hiring and attrition parameters estimates.

We model attrition and hiring using logistic regression, with career age included as an exogenous variable up to its fifth power. A natural question follows: what if we used a model with career age only to its first or third power? Or to higher powers? We performed this sensitivity analysis, and added three new figures to the supplement to present these findings:

First, we show the observed attrition probabilities at each career age, and four model fits to attrition data (Supplementary Figs S4 and S5). The first model includes career age only to its first power, and this model clearly does not capture the full career age / attrition correlation structure. The second model includes career age to its third power, which does a better job of fitting to the observed patterns. The third model includes career age up to its fifth power, which appears to very modestly improve upon the former model. The fourth model includes career age up to its seventh power, and the patterns captured by this model are largely the same as the 5th-power model up to career age 50, beyond which there are some notable differences in the inferred attrition probabilities. These differences would have relatively little impact on model outcomes because the vast majority of faculty have a career age below 50.

Second, we show the observed probability that hires are women, conditional on the career age of the hire. Once again, we fit four models to the data, and find that career age should be included at least up to its fifth order in order to capture the correlation structures between career age and the gender of new hires. However, limited differences result from including career age up to the 7th degree in the model (relative to the 5th degree).

As a final sensitivity analysis, we reproduce Fig. 2, but rather than including career age as an exogenous variable up to its fifth power in our models for hiring and attrition, we include career age up to its third power. Findings under this parameterization are qualitatively very similar to those presented in Fig. 2, indicating that the results are robust to modest changes to model parameterization (shown in supplement Fig. S6).

Far more detail in this and some interim results from each stage of the analysis would make the paper far more convincing. It currently has an air of "black box" too much of the analysis which would easily allow an unconvinced reader to discard the results.

We have added more detailed descriptions of the methods to the main text. We hope that the changes made will address these concerns.

Section C: You use the Leslie model to predict the future population. As the model is linear the population will either grow exponentially (most likely) or dwindle to zero. You mention you dealt with this by scaling the average value of H to keep the population at 2020 levels? This would change the ratio of hiring to attrition. How did this affect the timescale of the results. If a field had very minimal attrition (and hence grew massively over the time period of the dataset) the hiring rate would have to be very small too so there would be very little change in the gender balance. Did you consider running the model to steady state instead?

We chose the 40 year window (2020-2060) for this projection analysis because 40 years is roughly the timespan of a full-length faculty career. In other words, it will take around 40 years for most of the pre-existing faculty from 2020 to retire, such that the new, simulated faculty will have almost entirely replaced all former faculty by 2060.

For three out of five of our projection scenarios (OA, GNA, OA+ER), the point at which observed faculty are replaced by simulated faculty represents steady state. One way to check this intuition is to observe the asymptotic behavior of the trajectories in Fig. 3B; the slopes for these 3 scenarios nearly level out within 40 years.

The other two scenarios (OA + IR, GNA+IR) represent situations where women’s representation among new hires is increasing each year. These scenarios will not reach steady state until women represent 100% of faculty. Accordingly, the steady state outcomes for these scenarios would yield uninteresting results; instead, we argue that it is the relative timescales that are interesting.

What did you do to check that your predictions at least felt realistic under the fitted parameters? (see above for presenting the goodness of fit over the 10 years of the data).

We ran the analysis suggested in a prior comment (Starting the model in 2011 how well does it fit the available data up to 2020?) and found that model outcomes were statistically indistinguishable from the observed 2020 faculty gender compositions for all 111 academic fields, plus the “All STEM” and “All non-STEM” aggregations.

You only present the final proportion of women for each scenario. As mentioned earlier, models of this type have a tendency to lead to strange population distributions with wild age predictions and huge (or zero populations). Presenting more results here would assuage any worries the reader had about these problems. What is the predicted age distribution of men and women in the long term scenarios? Would a different method of keeping the total population in check have yielded different results? Interim results, especially from a model as complex as this one, rather than just presenting a final single number answer are a convincing validation that your model is a good one! Again, presenting this result will go a long way to convincing readers that your results are sound and rigorous.

Thank you for this suggestion. We now include a figure that presents faculty age distributions for each projection scenario at 2060 against the observed faculty age distribution in 2020 (pictured below, and as Fig. S3 in the supplementary materials). We find that the projected age distributions are very similar to the observed distributions for natural sciences (shown) and for the additional academic domains. We hope this additional validation will inspire confidence in our model of faculty hiring and attrition for the reviewer, and for future readers.

In Fig S3, line widths for the simulated scenarios span the central 95% of simulations.

Other people have reached almost identical conclusions (albeit it with smaller data sets) that hiring is more important than attrition. It would be good to compare your conclusions with their work in the Discussion.

We have revised the main text to cite the listed examples of similar studies. We thank the reviewer for bringing these relevant works to our attention.

General comments:

What thoughts have you given to non-binary individuals?

Be careful how you use the term "gender diversity"! In many countries "Gender diverse" is a term used in data collection for non-binary individuals, i.e. Male, female, gender diverse. The phrase "hiring more gender diverse faculty" can be read in different ways! If you are only considering men and women then gender balance may be a better framework to use.

We have added language to the main text which explicitly acknowledges that our analysis focuses on men and women due to limitations in our name-based gender tool, which only assigns binary (woman/man) labels to faculty. We point out that this is a compromise due to the technical limitations of name-based gender methodologies and is not intended to reinforce a gender binary.

We have also taken additional care with referring to “gender diversity,” per reviewer 1’s point in their public review.

Reviewer #2 (Recommendations For The Authors):

Data availability: I did not see an indication that the dataset used here is publicly available, either in its raw format or as a summary dataset. Perhaps this is due to the sensitive nature of the data, but regardless of the underlying reason, the authors should include a note on data availability in the paper.

The dataset used for these analyses were obtained under a data use agreement with the Academic Analytics Research Center (AARC). While these data are not publicly available, researchers may apply for data access here: https://aarcresearch.com/access-our-data.

We also added a table to the supplemental materials (Tab. S3) that reports the estimated number of men and women in each of the 111 fields.

Additionally, a variety of summary statistics based on this dataset are available online, here: https://github.com/LarremoreLab/us-faculty-hiring-networks/tree/main

Gender classification: Was an existing package used to classify gender from names in the dataset, or did the authors develop custom code to do so? Either way, this code should be cited. I would also be curious to know what the error rate of these classifications are, and suggest that additional information on potential biases that might result from automated classifications be included in the discussion, under the section describing data limitations. The reliability of name-based gender classification is particularly of interest, as external gender classifications such as those applied on the basis of an individual's name - may not reflect the gender with which an individual self-identifies. In other words, while for many people their names may reflect their true genders, for others those names may only reflect their gender assigned at birth and not their self-perceived or lived gender identity. Nonbinary faculty are in particular invisibilized here (and through any analysis that assigns binary gender on the basis of name). While these considerations do not detract from the main focus of the study - which was to utilize an existing dataset classified only on the basis of binary gender to assess trends for women faculty-these limitations should be addressed as they provide additional context for the interpretation of the results and suggest avenues for future research.

We use a free, open-source, and open-data python package called nomquamgender (Van Buskirk et al, 2023) to estimate the strengths of (culturally constructed) name-gender associations. For sufficiently strong associations with a binary gender, we apply those labels to the names in our data. We have updated the main text to make this approach more apparent.

We have also added language to the main text which explicitly acknowledges that our approach only assigns binary (woman/man) labels to faculty. We point out that this is a compromise due to the technical limitations of name-based gender methodologies and is not intended to reinforce a gender binary.

As we mentioned in response to the public review, we use a free and open source python package called nomquamgender to estimate the strengths of name-gender associations, and we apply gender labels to the names with sufficiently strong associations with a binary gender. This package is based on a paper by Van Buskirk et. al. 2023, “An open-source cultural consensus approach to name-based gender classification,” which documents error rates and potential biases.

We have also added language to the main text which explicitly acknowledges that our approach only assigns binary (woman/man) labels to faculty. We point out that this is a compromise due to the technical limitations of name-based gender methodologies and is not intended to reinforce a gender binary.

Page 1: The sentence beginning "A trend towards greater women's representation could be caused..." is missing a conjunction. It should likely read: "A trend towards greater women's representation could be caused entirely by attrition, e.g., if relatively more men than women leave a field, OR entirely by hiring..."

We have edited the paragraph to remove the sentence in question.

Pages 1-2: The sentence beginning "Although both types of strategy..." and ending with "may ultimately achieve gender parity" is a bit of a run-on; perhaps it would be best to split this into multiple sentences for ease of reading.

We have revised this run-on sentence.

Page 2: See comments in the public review about a methods section, the addition of which may help to improve clarity for the readers. Within the existing descriptions of what I consider to be methods (i.e., the first three paragraphs currently under "results"), some minor corrections could be added here. First, consider citing the source of the dataset in the line where it is first described (in the sentence "For these analyses, we exploit a census-level dataset of employment and education records for tenured and tenure-track faculty in 12,112 PhD-granting departments in the United States from 2011-2020.") It also may be helpful to include context here (or above, in the discussion about institutional analyses) about how "departments" can be interpreted. For example, how many institutions are represented across these departments? More information on how the authors eliminated the gendered aspect of patterns in their counterfactual model would be helpful as well; this is currently hinted at on page 4, but could instead be included in the methods section with a call-out to the relevant supplemental information section (S2B).

We have added a citation to Academic Analytics Research Center’s (AARC) list of available data elements to the data’s introduction sentence. We hope this will allow readers to familiarize themselves with the data used in our analysis.

Faculty department membership was determined by AARC based on online faculty rosters. 392 institutions are represented across the 12,112 departments present in our dataset. We have updated the main text to include this information.

Finally, we have added a methods section to the main text, which includes information on how the gendered aspect of attrition patterns were eliminated in the counterfactual model.

Page 2: Perhaps some indication of how many transitions from an out-of-sample institution might be helpful to readers hoping to understand "edge cases."

In our analysis, we consider all transitions from out-of-sample institutions to in-sample institutions as hires, and all transitions away from in-sample institutions–whether it be to an out of sample institution, or out of academia entirely–as attritions. We choose to restrict our analysis of hiring and attrition to PhD granting institutions in the U.S. in this way because our data do not support an analysis of other, out-of-sample institutions.

I also would have liked additional information on how many faculty switched institutions but remained "in-sample and in the same field" - and the gender breakdowns of these institutional changes, as this might be an interesting future direction for studies of gender parity. (For example, readers may be spurred to ask: if the majority of those who move institutions are women, what are the implications for tenure and promotion for these individuals?)

While these mid-career moves are not counted as attritions in the present analysis, a study of faculty who switch institutions but remain (in-sample) as faculty could shed light on issues of gendered faculty retention at the level of institutions. We share the reviewer’s interest in a more in depth study of mid-career moves and how these moves impact faculty careers, and we now discuss the potential value of such a study towards the end of the paper. In fact, this subject is the topic of a current investigation by the authors!

Page 3: I was confused by the statement that "of the three types of stable points, only the first point represents an equitable steady-state, in which men and women faculty have equal average career lengths and are hired in unchanging proportions." Here, for example, computer science appears to be close to the origin on Figure 1, suggesting that hiring has occurred in "unchanging proportions" over the study interval. However, upon analysis of Table S2, it appears that changes in hiring in Computer Science (+2.26 pp) are relatively large over the study interval compared to other fields. Perhaps I am reading too literally into the phrase that "men and women faculty are hired in unchanging proportions" - but I (and likely others) would benefit from additional clarity here.

We had created an arrow along with the computer science label in Fig. 1, but it was difficult to see, which is likely the source of this confusion. This was our fault, and we have moved the “Comp. Sci.” label and its corresponding arrow to be more visible in Figure 1.

Changes in women’s representation in Computer Science due to hiring over 2011 - 2020 was +2.26 pp as the reviewer points out, but, consulting Fig. 1 and the corresponding table in the supplement, we observe that this is a relatively small amount of change compared to most fields.

Page 3: If possible it may be helpful to cite a study (or multiple) that shows that "changes in women's representation across academic fields have been mostly positive." What does "positive" mean here, particularly when the changes the authors observe are modest? Perhaps by "positive" you mean "perceived as positive"?

We used the term positive in the mathematical sense, to mean greater than zero. We have reworded the sentence to read “women's representation across academic fields has been mostly increasing…” We hope this change clarifies our meaning to future readers.

Page 3: The sentence that ends with "even though men are more likely to be at or near retirement age than women faculty due to historical demographic trends" may benefit from a citation (of either Figure S3 or another source).

We now cite the corresponding figure in this sentence.

Page 4: The two sentences that begin with "The empirical probability that a person leaves their academic career" would benefit from an added citation.

We have added a citation to the sentences.

Figure 3: Which 10 academic domains are represented in Panel 3B? The colors in appear to correspond to the legend in Panel 3A, but no indication of which fields are represented is provided. If possible, please do so - it would be interesting and informative to be able to make these comparisons.

This was not clear in the initial version of Fig. 3B, so we now label each domain. For reference, the domains represented in 3B are (from top to bottom):

● Health

● Education

● Journalism, Media, Communication

● Humanities

● Social Sciences

● Public Administration and Policy

● Medicine

● Business

● Natural Sciences

● Mathematics and Computing

● Engineering

Page 6: Consider citing relevant figure(s) earlier up in paragraph 2 of the discussion. For example, the first sentence could refer to Figure 1 (rather than waiting until the bottom of the paragraph to cite it).

Thank you for this suggestion, we now cite Fig. 1 earlier in this discussion paragraph.

Page 10: A minor comment on the fraction of women faculty in any given year-the authors assume that the proportion of women in a field can be calculated from knowing the number of women in a field and the number of men. This is, again, true if assuming binary genders but not true if additional gender diversity is included. It is likely that the number of nonbinary faculty is quite low, and as such would not cause a large change in the overall proportions calculated here, but additional context within the first paragraph of S1 might be helpful for readers.

We have added additional context in the first paragraph of S1, explaining that an additional term could be added to the equation to account for nonbinary faculty representation if our data included nonbinary gender annotations. Thank you for making this point.

Page 10: Please include a range of values for the residual terms of the decomposition of hiring and attrition in the sentence that reads "In Figure S1 we show that the residual terms are small, and thus the decomposition is a good approximation of the total change in women's representation."

These residual terms range from -0.51pp to 1.14pp (median = 0.2pp). We have added this information to the sentence in question.

Page 12: It may be helpful to readers to include a description of the information contained in Table S2 in the supplemental text under section S3.

We refer to table S2 twice in the main text (once in the observational findings, and once for the counterfactual analysis), and the contents of table S2 are described thoroughly in the table caption.

Reviewer #3 (Recommendations For The Authors):

(1) There is a potential limitation in the generalizability of the findings, as the study focuses exclusively on US academia. Including international perspectives could have provided a more global understanding of the issues at hand.

The U.S. focus of this study limits the generalizability of our findings, as non-U.S. other faculty may exhibit differences in hiring patterns, retention patterns, and current demographic representations. We have added a discussion of this limitation to the manuscript. Unfortunately, our data do not support international analyses of hiring and attrition.

(2) I am not sure that everyone who disappeared from the AARC dataset could be count as "attrition" from academia. Indeed, some who disappeared might have completely left academia once they disappeared from the AARC dataset. Yet, there's also the possibility that some professors left for academic positions in countries outside of the US, or US institutions that are not included in the AARC dataset. These individuals didn't leave academia. Furthermore, it is also possible that these scholars who moved to an institution outside of US or not indexed by AARC are gender specific. Therefore, analyses that this study conducts should find a way to test whether the assumption that anyone who disappeared from AARC is indeed valid. If not, how will this potentially challenge the current conclusions?

The reviewer makes an important point: faculty who move to faculty positions in other countries and faculty who move to non-PhD granting institutions, or to institutions that are otherwise not included in the AARC data are all counted as attritions in our analysis. We intentionally define hiring and attrition broadly to include all cases in which faculty join or leave a field or domain within our dataset.

The types of transitions that faculty make out of the tenure track system at PhD granting institutions in the U.S. may correlate with faculty attributes, like gender. For example, women or men may be more likely to transition to tenure track positions at non-U.S. institutions. Nevertheless, these types of career transition represent an attrition for the system of study, and a hire for another system. Following this same logic, faculty who transition from one field to another field in our analysis are treated as an attrition from the first field and a hire into the new field.

By focusing on “all-cause” attrition in this way, we are able to make robust insights for the specific systems we consider (e.g.,, STEM and non-STEM faculty at U.S. PhD granting institutions), without being roadblocked by the task of annotating faculty departures and arbitrating which should constitute “valid” attritions.

(3) It would be very interesting to know how much of the attribution was due to tenure failure. Previous studies have suggested that women are less likely to be granted tenure, which makes me wonder about the role that tenure plays in the gendered patterns of attrition in academia.

We note that faculty attrition rates start low and then reach a peak around 5-7 years after earning PhD, and then decline until around 15-20 years post-PhD, after which, attrition rates increase as faculty approach retirement. The first local maximum appears to coincide roughly with the tenure clock timing, but we can only speculate that these attritions are tenure related. Our dataset is unfortunately not equipped to determine the causal mechanisms driving attrition.

We reproduce the attrition risk curve in the supplementary materials, Fig. S4:

(4) The dataset used doesn't fully capture the complexities of academic environments, particularly smaller or less research-intensive institutions (regional universities, historically black colleges and universities, and minority-serving institutions). This could be potentially added to the manuscript for discussions.

We have added this point to the description of this study’s limitations in the discussion.

Reviewer #1 (Public Review):

Summary:

In "Changes in wing morphology..." Roy et al investigate the potential allometric scaling in wing morphology and wing kinematics in 8 different hoverfly species. Their study nicely combines different new and classic techniques, investigating flight in an important, yet understudied alternative pollinator. I want to emphasize that I have been asked to review this from a hoverfly biology perspective, as I do not work on flight kinematics. I will thus not review that part of the work.

Strengths:

The paper is well-written and the figures are well laid out. The methods are easy to follow, and the rationale and logic for each experiment are easy to follow. The introduction sets the scene well, and the discussion is appropriate. The summary sentences throughout the text help the reader.

Weaknesses:

The ability to hover is described as useful for either feeding or mating. However, several of the North European species studied here would not use hovering for feeding, as they tend to land on the flowers that they feed from. I would therefore argue that the main selection pressure for hovering ability could be courtship and mating. If the authors disagree with this, they could back up their claims with the literature. On that note, a weakness of this paper is that the data for both sexes are merged. If we agree that hovering may be a sexually dimorphic behaviour, then merging flight dynamics from males and females could be an issue in the interpretation. I understand that separating males from females in the movies is difficult, but this could be addressed in the Discussion, to explain why you do not (or do) think that this could cause an issue in the interpretation.

The flight arena is not very big. In my experience, it is very difficult to get hoverflies to fly properly in smaller spaces, and definitely almost impossible to get proper hovering. Do you have evidence that they were flying "normally" and not just bouncing between the walls? How long was each 'flight sequence'? You selected the parts with the slowest flight speed, presumably to get as close to hovering as possible, but how sure are you that this represented proper hovering and not a brief slowdown of thrust?

Your 8 species are evolutionarily well-spaced, but as they were all selected from a similar habitat (your campus), their ecology is presumably very similar. Can this affect your interpretation of your data? I don't think all 6000 species of hoverflies could be said to have similar ecology - they live across too many different habitats. For example, on line 541 you say that wingbeat kinematics were stable across hoverfly species. Could this be caused by their similar habitat?

Author response:

Reviewer 1:

Summary:

In this manuscript by Bimbard et al., a new method to perform stable recordings over long periods of time with neuropixels, as well as the technical details on how the electrodes can be explanted for follow-up reuse, is provided. I think the description of all parts of the method is very clear, and the validation analyses (n of units per day over time, RMS over recording days...) are very convincing. I however missed a stronger emphasis on why this could provide a big impact on the ephys community, by enabling new analyses, new behavior correlation studies, or neurophysiological mechanisms across temporal scales

Strengths:

Open source method. Validation across laboratories. Across species (mice and rats) demonstration of its use and in different behavioral conditions (head-fixed and freely moving).

Weaknesses:

Weak emphasis on what can be enabled with this new method that didn't exist before.

We thank the reviewer for highlighting the limited discussion around scientific impact. Our implant has several advantages which combine to make it much more accessible than previous solutions. This enables a variety of recording configurations that would not have been possible with previous designs, facilitating recordings from a wider range of brain regions, animals, and experimental setups. In short, there are three key advances:

(1) Adaptability: The CAD files can be readily adapted to a wide range of configurations (implantation depth, angle, position of headstage, etc.). Labs have already, modified the design to optimise for their needs, and re-shared with the community.

(2) Weight:  Because of the lightweight design, experimenters can i) perform complex and demanding freely moving tasks as we exemplify in the manuscript, and ii) implant female and water restricted mice while respecting animal welfare weight limitations.

(3) Cost: At ~$10, our implant is significantly cheaper than published alternatives, which makes it affordable to more labs and means that testing modifications is cost-effective.

We will make these features clearer in the manuscript.

Reviewer 2:

Summary:

This work by Bimbard et al., introduces a new implant for Neuropixels probes. While Neuropixels probes have critically improved and extended our ability to record the activity of a large number of neurons with high temporal resolution, the use of these expensive devices in chronic experiments has so far been hampered by the difficulty of safely implanting them and, importantly, to explant and reuse them after conclusion of the experiment. The authors present a newly designed two-part implant, consisting of a docking and a payload module, that allows for secure implantation and straightforward recovery of the probes. The implant is lightweight, making it amenable for use in mice and rats, and customizable. The authors provide schematics and files for printing of the implants, which can be easily modified and adapted to custom experiments by researchers with little to no design experience. Importantly, the authors demonstrate the successful use of this implant across multiple use cases, in head-fixed and freely moving experiments, in mice and rats, with different versions of Neuropixels probes, and across 8 different labs. Taken together, the presented implants promise to make chronic Neuropixel recordings and long-term studies of neuronal activity significantly easier and attainable for both current and future Neuropixels users.

Strengths:

- The implants have been successfully tested across 8 different laboratories, in mice and rats, in head-fixed and freely moving conditions, and have been adapted in multiple ways for a number of distinct experiments.

- Implants are easily customizable and the authors provide a straightforward approach for customization across multiple design dimensions even for researchers not experienced in design.

- The authors provide clear and straightforward descriptions of the construction, implantation, and explant of the described implants.

- The split of the implant into a docking and payload module makes reuse even in different experiments (using different docking modules) easy.

- The authors demonstrate that implants can be re-used multiple times and still allow for high-quality recordings.

- The authors show that the chronic implantations allow for the tracking of individual neurons across days and weeks (using additional software tracking solutions), which is critical for a large number of experiments requiring the description of neuronal activity, e.g. throughout learning processes.

- The authors show that implanted animals can even perform complex behavioral tasks, with no apparent reduction in their performance.

Weaknesses:

- While implanted animals can still perform complex behavioral tasks, the authors describe that the implants may reduce the animals' mobility, as measured by prolonged reaction times. However, the presented data does not allow us to judge whether this effect is specifically due to the presented implant or whether any implant or just tethering of the animals per se would have the same effects.

The reviewer is correct: some of the differences in mouse reaction time could be due to the tether rather than the implant. As these experiments were also performed in water-restricted female mice with the heavier Neuropixels 1.0 implant, our data represent the maximal impact of the implant, and we will highlight this in the revision.

- While the authors make certain comparisons to other, previously published approaches for chronic implantation and re-use of Neuropixels probes, it is hard to make conclusive comparisons and judge the advantages of the current implant. For example, while the authors emphasize that the lower weight of their implant allows them to perform recordings in mice (and is surely advantageous), the previously described, heavier implants they mention (Steinmetz et al., 2021; van Daal et al., 2021), have also been used in mice. Whether the weight difference makes a difference in practice therefore remains somewhat unclear.

The reviewer is correct: without a direct comparison, we cannot be certain that our smaller, lighter implant improves behavioural results (although this is supported by the literature, e.g. Newman et al, 2023). However, the reduced weight of our implant is critical for several laboratories represented in this manuscript due to animal welfare requirements. Indeed, in Daal et al the authors “recommend a [mouse] weight of >25 g for implanting Neuropixels 1.0 probes.” This limit precludes using (the vast majority of) female mice, or water-restricted animals. Conversely, our implant can be routinely used with lighter, water-restricted male and female mice. We will emphasise this point in the revision.

- The non-permanent integration of the headstages into the implant, while allowing for the use of the same headstage for multiple animals in parallel, requires repeated connections and does not provide strong protection for the implant. This may especially be an issue for the use in rats, requiring additional protective components as in the presented rat experiments.

We apologise for not clarifying the various headstage options in the manuscript and we will address this in the revision. Our repository has headplate holder designs (in the XtraModifications/Mouse_FreelyMoving folder). This allows leaving the headstage on the implant, and thus minimize the number of connections (albeit increasing the weight for the mouse). Indeed, mice recorded while performing the task described in our manuscript had the head-stage semi-permanently integrated to the implant, and we will highlight this in the revision.

Reviewer 3:

Summary:

In this manuscript, Bimbard and colleagues describe a new implant apparatus called "Apollo Implant", which should facilitate recording in freely moving rodents (mice and rats) using Neuropixels probes. The authors collected data from both mice and rats, they used 3 different versions of Neuropixels, multiple labs have already adopted this method, which is impressive. They openly share their CAD designs and surgery protocol to further facilitate the adaptation of their method.

Strengths:

Overall, the "Apollo Implant" is easy to use and adapt, as it has been used in other laboratories successfully and custom modifications are already available. The device is reproducible using common 3D printing services and can be easily modified thanks to its CAD design (the video explaining this is extremely helpful). The weight and price are amazing compared to other systems for rigid silicon probes allowing a wide range of use of the "Apollo Implant".

Weaknesses:

The "Apollo Implant" can only handle Neuropixels probes. It cannot hold other widely used and commercially available silicon probes. Certain angles and distances are not possible in their current form (distance between probes 1.8 to 4mm, implantation depth 2-6.5 mm, or angle of insertion up to 20 degrees).

We appreciate the reviewer’s points, but as we will discuss in the revised manuscript, one implant accommodating the diversity of the existing probes is beyond the scope of this project. However, because the design is adaptable, groups should be able to modify the current version of the implant to adapt to their electrodes’ size and format (and can highlight any issues in the Github “Discussions” area).

With Neuropixels, the current range of depths covers practically all trajectories in the mouse brain. In rats, where deeper penetrations may be useful, the experimenter can attach the probe at a lower point in the payload module to increase the length of exposed shank. We now specify this in the Github repository.

We have now extended the range of inter-probe distances from a maximum of 4 mm to 6.5 mm, and this will be reflected in the revised manuscript. Distances beyond this may be better served by 2 implants, and smaller distances could be achieved by attaching two probes on the same side of the docking module. In the next revision, we will add these points to the discussion.

Reviewer #1 (Public Review):

Summary:

This paper presents a mechanistic study of rDNA origin regulation in yeast by SIR2. Each of the ~180 tandemly repeated rDNA gene copies contains a potential replication origin. Early-efficient initiation of these origins is suppressed by Sir2, reducing competition with origins distributed throughout the genome for rate-limiting initiation factors. Previous studies by these authors showed that SIR2 deletion advances replication timing of rDNA origins by a complex mechanism of transcriptional de-repression of a local PolII promoter causing licensed origin proteins (MCMcomplexes) to re-localize (slide along the DNA) to a different (and altered) chromatin environment. In this study, they identify a chromatin remodeler, FUN30, that suppresses the sir2∆ effect, and remarkably, results in a contraction of the rDNA to about one-quarter it's normal length/number of repeats, implicating replication defects of the rDNA. Through examination of replication timing, MCM occupancy and nucleosome occupancy on the chromatin in sir2, fun30, and double mutants, they propose a model where nucleosome position relative to the licensed origin (MCM complexes) intrinsically determines origin timing/efficiency. While their interpretations of the data are largely reasonable and can be interpreted to support their model, a key weakness is the connection between Mcm ChEC signal disappearance and origin firing. While the cyclical chromatin association-dissociation of MCM proteins with potential origin sequences may be generally interpreted as licensing followed by firing, dissociation may also result from passive replication and as shown here, displacement by transcription and/or chromatin remodeling. Moreover, linking its disappearance from chromatin in the ChEC method with such precise resolution needs to be validated against an independent method to determine the initiation site(s). Differences in rDNA copy number and relative transcription levels also are not directly accounted for, obscuring a clearer interpretation of the results. Nevertheless, this paper makes a valuable advance with the finding of Fun30 involvement, which substantially reduces rDNA repeat number in sir2∆ background. The model they develop is compelling and I am inclined to agree, but I think the evidence on this specific point is purely correlative and a better method is needed to address the initiation site question. The authors deserve credit for their efforts to elucidate our obscure understanding of the intricacies of chromatin regulation. At a minimum, I suggest their conclusions on these points of concern should be softened and caveats discussed. Statistical analysis is lacking for some claims.

Strengths are the identification of FUN30 as suppressor, examination of specific mutants of FUN30 to distinguish likely functional involvement. Use of multiple methods to analyze replication and protein occupancies on chromatin. Development of a coherent model.

Weaknesses are failure to address copy number as a variable; insufficient validation of ChEC method relationship to exact initiation locus; lack of statistical analysis in some cases.

Additional background and discussion for public review:

This paper broadly addresses the mechanism(s) that regulate replication origin firing in different chromatin contexts. The rDNA origin is present in each of ~180 tandem repeats of the rDNA sequence, representing a high potential origin density per length of DNA (9.1kb repeat unit). However, the average origin efficiency of rDNA origins is relatively low (~20% in wild-type cells), which reduces the replication load on the overall genome by reducing competition with origins throughout the genome for limiting replication initiation factors. Deletion of histone deacetylase SIR2, which silences PolII transcription within the rDNA, results in increased early activation or the rDNA origins (and reduced rate of overall genome replication). Previous work by the authors showed that MCM complexes loaded onto the rDNA origins (origin licensing) were laterally displaced (sliding) along the rDNA, away from a well-positioned nucleosome on one side. The authors' major hypothesis throughout this work is that the new MCM location(s) are intrinsically more efficient configurations for origin firing. The authors identify a chromatin remodeling enzyme, FUN30, whose deletion appears to suppress the earlier activation of rDNA origins in sir2∆ cells. Indeed, it appears that the reduction of rDNA origin activity in sir2∆ fun30∆ cells is severe enough to results in a substantial reduction in the rDNA array repeat length (number of repeats); the reduced rDNA length presumably facilitates it's more stable replication and maintenance.

Analysis of replication by 2D gels is marginally convincing, using 2D gels for this purpose is very challenging and tricky to quantify. The more quantitative analysis by EdU incorporation is more convincing of the suppression of the earlier replication caused by SIR2 deletion.

To address the mechanism of suppression, they analyze MCM positioning using ChEC, which in G1 cells shows partial displacement of MCM from normal position A to positions B and C in sir2∆ cells and similar but more complete displacement away from A to positions B and C in sir2fun30 cells. During S-phase in the presence of hydroxyurea, which slows replication progression considerably (and blocks later origin firing) MCM signals redistribute, which is interpreted to represent origin firing and bidirectional movement of MCMs (only one direction is shown), some of which accumulate near the replication fork barrier, consistent with their interpretation. They observe that MCMs displaced (in G1) to sites B or C in sir2∆ cells, disappear more rapidly during S-phase, whereas the similar dynamic is not observed in sir2∆fun30∆. This is the main basis for their conclusion that the B and C sites are more permissive than A. While this may be the simplest interpretation, there are limitations with this assay that undermine a rigorous conclusion (additional points below). The main problem is that we know the MCM complexes are mobile so disappearance may reflect displacement by other means including transcription which is high is the sir2∆ background. Indeed, the double mutant has greater level of transcription per repeat unit which might explain more displaced from A in G1. Thus, displacement might not always represent origin firing. Because the sir2 background profoundly changes transcription, and the double mutant has a much smaller array length associated with higher transcription, how can we rule out greater accessibility at site A, for example in sir2∆, leading to more firing, which is suppressed in sir2 fun30 due to greater MCM displacement away from A?

I think the critical missing data to solidly support their conclusions is a definitive determination of the site(s) of initiation using a more direct method, such as strand specific sequencing of EdU or nascent strand analysis. More direct comparisons of the strains with lower copy number to rule out this facet. As discussed in detail below, copy number reduction is known to suppress at least part of the sir2∆ effect so this looms over the interpretations. I think they are probably correct in their overall model based on the simplest interpretation of the data but I think it remains to be rigorously established. I think they should soften their conclusions in this respect.

This describes the approach of Sellars, Brandom, and Brassier, all of which Bakker has criticized in the blog.

They admit that science has priority in the scientific realm, but what we think we are is not something that can be true or false, but are games, rules, things we play, a game of "pretend as if we are persons".

This is a much better position. It does not attempt to tell science that science is a building founded upon the ground of philosophy (unlike Kant, or Heidegger), and does not try to make scientifically testable predictions and get embarrassed in the process (unlike those who sought to study the "quantum of consciousness" because they thought free will is real, thus something quantum-mechanical must be true of the brain, or that philosopher who argued that Anton syndrome is impossible because it is philosophically impossible, or those psychoanalysts that try to interpret Cotard's syndrome as some manifestation of childhood trauma).

The problem with this position is as follows:

1. Is science really based on a game of giving and taking reasons? If not, then there's no guarantee that science would protect the game of "let's pretend we are persons who make decisions, has plans, hopes for love, etc". The juggernaut of science may eventually crush the "manifest image of man" under its wheels, migrate to a society of unconscious biorobots, and run even faster as a result!

2. Philosophers are unable to figure out what rules, games, normativity, etc, are! They can't agree, after centuries of disputation. Any working consensus will have to come from science, and what if science finally shows that rules and games are nothing like what Sellars, Brandom, etc, thought they are? If not, then not only is the manifest image not the scientific image, not only is it unnecessary for working scientists, it is even not what the philosophers say it is. It is as if the philosophers have been stuck in Plato's Cave, mistaking the shadow-play for optical-science.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

…I find the concept and execution of the study very interesting and elegant. The paper is also commendably clear and readable. The differences between primary and higher cortex are compelling and I am largely convinced by the authors' claim that they have found evidence that broadly supports a mixed selectivity model of neural disentanglement along the lines of Rigotti et al (2013). I think that the increasing body of evidence for these kinds of representations is a significant development in our understanding of higher sensory representations. I also think that the dDR method is likely to be useful to researchers in a variety of fields who are looking to perform similar types of neural decoding analysis.

Thanks! We agree that questions around population coding and high-level representations are critical in the field of sensory systems.

Reviewer #2 (Public Review):

... This is a well-carried out study with thoughtful analyses which in large part achieves its aims to evaluate how task-engagement changes neural activity across multiple auditory regions. As with all work, there are several caveats or areas for future study/analysis. First, the sounds used here (tones, and narrow-band noise) are relatively simple sounds; previous work suggests that exactly what activity is observed within each region (e.g., sensory only, decision-related, etc) may depend in part upon what stimuli are used. Therefore, while the current study adds importantly to the literature, future work may consider the use of more varied stimuli. Second, the animals here were engaged in a behavioral task; but apart from an initial calculation of behavioral d', the task performance (and its effect on neural activity) is largely unaddressed.

The reviewer makes several important points that we hope we addressed in the specific changes detailed below. Indeed, it is important to recognize the possibility that the specific stimuli involved in a task may interact with the effects of behavioral state and that variability in task performance should be considered as an important aspect of behavioral state.

Reviewer #1 (Recommendations For The Authors):

I have a few minor comments and criticisms:

(1) Figure 1c. The choice of low-contrast grey text (e.g. "Target vs. target" is unfortunate, especially when printed, and should be replaced (e.g. with dark grey).

We have edited the figure to use a higher contrast (dark grey). Thanks for catching this.

(2) Figure 2 and Supplementary Figure 3. I think some indication of error or significance is required in all panels. Without this, it's hard to interpret any of these panels.

Thank you for this feedback. Including significance here was clarifying and helps to strengthen our claim that state-dependent changes in neural activity were smaller and more diverse for single neurons than at the population level. We modified Figure 2b-c to indicate whether each neuron’s response to the target stimulus was significantly different than its response to the catch stimulus. The same test was performed in Supplementary Figure 3. Additionally, we added a statistical test in Figure 2d-e to indicate, for each pair of target/catch stimuli, whether discrimination (d-prime) changed significantly between active and passive conditions. Furthermore, we modified the text of the second paragraph under the results heading: “Diverse effects of task engagement on single neurons in primary and non-primary auditory cortex” to reference and interpret the results of these significance tests. The new text reads as follows (L. 121):

“Sound-evoked spiking activity was compared between active and passive states to study the impact of task engagement on sound representation. In both A1 and dPEG, responses to target and catch stimuli were significantly discriminable for a subset of single neurons (about 25% in both areas, Figure 2A-C, Supplemental Figures 3-5, bootstrap test). This supports the idea that stimulus identity can be decoded in both brain regions, regardless of task performance. However, the fact that the responses of most neurons in both brain areas could not significantly discriminate target vs. catch stimuli also highlights the diversity of sound encoding observed at the level of single neurons. The accuracy of catch vs. target discrimination for each neuron was quantified using neural d-prime, the z-scored difference in target minus catch spiking response for each neuron (Methods: Single neuron PSTHs and d-prime (Niwa et al., 2012a)). Task engagement was associated with significant changes in catch vs. target d-prime for roughly 10% of neurons in both A1 (40 / 481 neurons, bootstrap test) and dPEG (33 / 377 neurons, bootstrap test). This included neurons that both increased their discriminability and decreased their discriminability (Figure 2D-E). Thus, the effects of task engagement at the level of single neurons were relatively mild and inconsistent across the population; many neurons showed no significant change and of those that did, effects were bidirectional (Figure 2D-E).”

We also included an additional methods paragraph in the “Statistical tests” section to describe the bootstrapping procedure used for these significance tests (L. 644):

“The one exception to this general approach is in Figure 2, where we analyzed the sound discrimination abilities of single neurons. In this case, we computed p-values for each neuron and stimulus independently. First, for each neuron and catch vs. target stimulus pair, we measured d-prime (see Methods: Single neuron evoked activity and d-prime). We generated a null distribution of d-prime values for each neuron-stimulus pair, under each experimental condition by shuffling stimulus identity across trials before computing d-prime (100 resamples). A neuron was determined to have a significant d-prime for a given target vs. catch pair if its actual measured d-prime was greater than the 95th percentile of the null d-prime distribution. Second, for each neuron and catch vs. target stimulus pair, we tested if d-prime was significantly different between active and passive conditions. To test this, we followed a similar procedure as above, however, rather than shuffle stimulus identity, we shuffled active vs. passive trial labels. This allowed us to generate a null distribution of active vs. passive d-prime difference for each neuron and stimulus pair. A neuron was determined to have a significant change in d-prime between conditions if the actual Δ d-prime lay outside the 95% confidence interval of the null Δ d-prime distribution.”

For Figure 2a, we chose not to indicate significance on the figure to avoid clutter, since the significance for all neurons in the population are shown in panels b-c anyway. Additionally, the difference plot shown in panel a is in units of z-scores, which we believe already gives a raw sense of the significance of the target vs. catch response change per neuron in this example dataset.

(3) Figure 2 and Supplementary Figure 3. I would consider including some more examples as a Supplementary Figure (and perhaps combining Supp Fig 3 with Fig 2 as a main figure).

We found no significant or apparent difference in single-neuron properties between A1 and dPEG. Therefore, we decided it is not helpful to plot both A1 and PEG examples in the main text. However, we agree that the ability to see more examples of the raw data could be useful. Therefore, we compiled two supplementary figures (Supplementary Figures 4 and 5) that replicate Figure 2a for all datasets, encompassing A1 and PEG.

(4) Figure 2a and Supp Fig 3a. I was initially confused that the "delta-spk/sec (z-score)" values had themselves been z-scored, but now I think that they are simply the differences of the two left hand sub-panels. This could be made clear in the figure legend.

The figure legends have been modified to state the procedure for computing “delta-spk/sec” more clearly. Specifically, we added the following information to the legend (L. 141):

“Difference is computed as the z-scored response to the target minus the z-scored catch response (resulting in a difference shown in units of z-score).”

(5) Figure 2b-e and Supp Fig 3b-e. Indicate the time window over which the responses were measured, and the number of neurons.

Figure legends have been modified to include a sentence clearly stating the time window over which responses were measured. The number of neurons is also now included in the legend and on the figure itself. Furthermore, a brief description of the new statistical testing procedure has been added here (L. 144).

“Responses were defined as the total number of spikes recorded during the 300 ms of sound presentation (area between dashed lines in panel A). Neurons with a significantly different response to the catch vs. target stimulus are indicated in black and quantified on the respective figure panel.”

(6) Figure 2. "singe" should read "single"

Typo in figure label has been fixed.

(7) Line 144. Figure number is missing (Figure 3B-C).

The missing figure number has been added to the text.

(8) Figure 3. Again, the low-contrast grey should be replaced.

The low-contrast grey has been replaced with dark grey.

Reviewer #2 (Recommendations For The Authors):

This study really nicely compares the activity and effects on activity in two areas of the auditory cortex in respect to task-engagement; I think it is, for the most part, very well done.

A couple of specific recommendations:

(1) Although I understand 'inf dB' as the SNR, including the actual dB level used in the experiments, would be useful, especially in the case of the inf dB.

Thank you for this feedback. We agree that clarification about the overall sound level used here would be helpful. We have modified the methods section “Behavioral paradigm” to include the following sentence (L. 450):

“That is, the masking noise (and distractor stimuli) were always presented with an overall sound level of 60 dB SPL. Infinite (inf) dB trials corresponded to trials where the target tone was presented at 60 dB SPL without any masking noise present, 0 dB to trials where the target was 60 dB SPL, -5 dB to trials where the target was presented at 55 dB SPL etc.”

In addition, we have modified the main text (L. 82):

“Animals reported the occurrence of a target tone in a sequence of narrowband noise distractors by licking a piezo spout (Figure 1A, Methods: Behavioral paradigm, distractor stimulus sound level: 60 dB SPL). … We describe SNR as the overall SPL of the target relative to distractor noise level. Thus, an SNR of –5 dB corresponds to a target level of 55 dB SPL while an Inf dB SNR corresponds to a target tone presented without any masking noise.”

And Figure legend 1 now explicitly states the sound level used in the experiments (L. 104):

“Variable SNR was achieved by varying overall SPL of the target relative to the fixed (60 dB SPL) distractor noise, e.g., -5 dB SNR corresponds to a 55 dB SPL target with 60 dB SPL masking noise. Infinite (inf) dB SNR corresponds to a target tone presented in isolation (60 dB SPL).”

(2) I very much appreciate the attempt to disentangle task engagement from generalized arousal state, and specifically, addressing this through the use of pupillometry. However, by focusing the discussion of pupil dynamics solely on the arousal-state aspects of pupil size, the paper doesn't address the increasing evidence suggests that pupil size may fluctuate based upon a lot of other things, including perceptual events (see Kronemer et al, 2022 for a recent human paper; for auditory: Zekveld et al 2018 (review) and Montes-Lourido et al, 2021; but many many others, too). It would be nice to see either a bit more nuanced discussion of what pupil size may be indicating (easier), or analyzing the behavior in the context of pupil dynamics (a heavier lift).

This is a good point. We agree that it is worth mentioning these more nuanced aspects of cognition that may be reflected by pupil size. Therefore, we also analyzed pupil size in the context of behavioral performance (see Supplemental Figure 6) and added the following text to the results (L. 193).

“In addition to reflecting overall arousal level, pupil size has also been reported to reflect more nuanced cognitive variables such as, for example, listening effort (Zekveld et al., 2014). Furthermore, rodent data suggests that optimal sensory detection is associated with intermediate pupil size (McGinley et al., 2015), consistent with the hypothesis of an inverted-U relationship between arousal and behavioral performance (Zekveld et al., 2014). To determine if this pattern was true for the animals in our task, we measured the dynamics of pupil size in the context of behavioral performance. Across animals, task stimuli evoked robust pupil dilation that varied with trial outcome (Supplemental Figure 6b-c). Notably, pre-trial pupil size was significantly different between correct (hit and correct reject), hit, and miss trials (Supplemental Figure 6b-c), recapitulating the finding of an inverted-U relationship to performance in rodents (McGinley et al., 2015).  Since we focused only on correct trials in our decoding analysis, these outcome-dependent differences in pupil size are unlikely to contribute to the emergent decoding selectivity in dPEG.”

(3) I think it would make this paper shine that much more if behavioral performance were not subsumed into the overall label of task engagement. You've already established you have performance that varies as a function of SNR; I would love to see the neural d' and covariability related to the behavioral d' (in the comparisons where this is possible). I would also love to see a more direct measure of choice for those stimuli that show variable behavior (e.g., a choice probability analysis or something of the like would seem to be easily applied to the target SNRs of -5 and 0 dB); and compare task engaged activity of hits vs misses vs passive listening to those same stimuli. You discuss previous studies looking at choice-related/decision-related activity and draw parallels to this work-given that there is the opportunity with this data set to *directly* assess choice-related activity, the absence of such an analysis seems like a missed opportunity.

Thank you for this feedback. We agree that “task engagement” is not a unimodal state and that a more fine-grained analysis of task-engaged neural activity, according to behavioral choice, could be informative.

First, we would like to point out that in Figure 4 we did already compare behavioral d’ to delta neural d’. We found that the two were significantly correlated in dPEG, but not in A1. This suggests that task-dependent changes in stimulus decoding in dPEG, but not A1, are predictive of behavioral performance. This is consistent with the finding that task-relevant stimulus representations were selectively enhanced in dPEG, but not in A1.

Second, we added a choice decoding analysis to address whether auditory cortex represents the animal’s choice in our task. The results of this analysis are summarized in Supplemental Figure 8 and are discussed under the results section: “Behavioral performance is correlated with neural coding changes in non-primary auditory cortex only.” (L. 226):

“The previous analysis suggests that the task-dependent increase in stimulus information present in dPEG population activity is predictive of overall task performance. Next, we asked whether the population activity in either brain region was directly predictive of behavioral choice on single hit vs. miss trials. To do this, we conducted a choice probability analysis (Methods). We found that in both brain regions choice could be decoded well above chance level (Supplemental Figure 8). Choice information was present throughout the entire trial and did not increase during the target stimulus presentation. This suggests that the difference in population activity primarily reflects a cognitive state associated with the probability of licking on a given trial, or “impulsivity” rather than “choice.” This interpretation is consistent with our finding that baseline pupil size on each trial is predictive of trial outcome (Supplemental Figure 6b).”

To keep our decoding approach consistent throughout the manuscript, we followed the same approach for choice decoding as we did for stimulus decoding (perform dDR then calculate neural d-prime in the dimensionality reduced space). To make the results more interpretable, we converted choice d-prime to a choice probability (percent correctly decoded choices) using leave-one-out cross validation. (We note that d-prime and percent correct are very highly correlated statistics.) This is described in the methods as follows (L. 550):

“We performed a choice decoding analysis on hit vs. miss trials. We followed the same procedure as described above for stimulus decoding, where instead of a pair of stimuli our two classes to be decoded were “hit trial” vs. “miss trial”. That is, for each target stimulus we computed the optimal linear discrimination axis separating hit vs. miss trials (Abbott and Dayan, 1999) in the reduced dimensionality space identified with dDR (Heller and David, 2022). For the sake of interpretability with respect to previous work we reported choice probability as the percentage of correctly decoded trial outcomes rather than d-prime. Percent correct was calculated by projecting the population activity onto the optimal discrimination axis and using leave-one-out cross validation to measure the number of correct classifications.”

(4) It would also be interesting to look at population coding across sessions (although the point is taken that within a session allows the opportunity to assess covariability). Minorly self-servingly but very much related to the above point, Christison-Lagay et al, 2017 employed a similar detect-in-noise task, analyzed single neurons and population level activity, and looked at putative choice-related activity. The current study has the opportunity to expand on that kind of analysis that much more by looking across multiple sites vs within a given recording site; and compare across regions.

Thank you for highlighting this point, we agree that it is important. When studying population coding it is critical to consider the impact of covariability between neurons. Therefore, it is worthwhile to revisit our interpretations of prior results, e.g., Christison-Lagay et al, 2017, which studied population coding by combining neurons across different sessions, given that we now have access to simultaneously recorded population data.

First, we would like to point out that this was the primary motivation for our simulation analyses presented in Figure 5. Using simulations, we found that task-dependent gain modulation (which can be observed across sessions) was sufficient to explain our primary finding – selective enhancement in decoding of behaviorally relevant sound stimuli in dPEG.

Second, to address the question about how covariability affects choice-related information in auditory cortex and compare our findings with prior studies, we performed the same set of simulations for choice probability analysis. We found that, again, choice-dependent gain modulation was sufficient to explain our findings. That is, simulations with hit- vs. miss-dependent gain changes, but fixed covariability, closely mirrored the choice probability we observed in the raw data. An additional simulation where covariability between all neurons was set to zero also recapitulated our findings in the raw data. Collectively, this suggests that covariability does not play a significant role in shaping the choice information present in A1 and dPEG during this task. We have added the following text to the manuscript to summarize this finding (L. 293):

“Finally, we used the same simulation approach to determine what aspects of population activity carry the “choice” related information we observed in A1 and dPEG (Figure 4 – figure supplement 1). Similar to our findings for stimulus decoding, we found that gain modulation alone was sufficient to recapitulate the choice information present in the raw data for this task. This helps frame prior work that pooled neurons across sessions to study population coding of choice in similar auditory discrimination tasks (Christison-Lagay et al, 2017).”

Author response:

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

eLife assessment

This manuscript presents a solid and generally convincing set of experiments to address the question of whether the lateral parafacial area (pFL) is active in controlling active expiration, which is particularly important in patient populations that rely on active exhalation to maintain breathing (eg, COPD, ALS, muscular dystrophy). This study presents a valuable finding by pharmacologically mapping the core medullary region that contributes to active expiration and addresses the question of where these regions lie anatomically. Results from these experiments will be of value to those interested in the neural control of breathing and other neuroscientists as a framework for how to perform pharmacological mapping experiments in the future.

Thanks for the positive feedback on our study, as well as the assessment of the novelty of our investigation and the advancements to the field that these results will bring in the future.

We have addressed the specific comments and made changes to the manuscript as indicated below.

Public Reviews:

Reviewer #1 (Public Review):

The main focus of the current study is to identify the anatomical core of an expiratory oscillator in the medulla using pharmacological disinhibition. Although expiration is passive in normal eupneic conditions, activation of the parafacial (pFL) region is believed to evoke active expiration in conditions of elevated ventilatory demands. The authors and others in the field have previously attempted to map this region using pharmacological, optogenetic, and chemogenetic approaches, which present their own challenges.

In the present study, the authors take a systematic approach to determine the precise anatomical location within the ventral medulla's rostrocaudal axis where the expiratory oscillator is located. The authors used a bicuculline (a GABA-A receptor antagonist) and fluorobeads solution at 5 distinct anatomical locations to study the effects on neuronal excitability and functional circuitry in the pFL. The effects of bicuculline on different phases of the respiratory cycle were characterized using a multidimensional cycle-by-cycle analysis. This analysis involved measuring the differences in airflow, diaphragm electromyography (EMG), and abdominal EMG signals, as well as using a phase-plane analysis to analyze the combined differences of these respiratory signals. Anatomical immunostaining techniques were also used to complement the functional mapping of the pFL.

Major strengths of this work include a robust study design, complementary neurophysiological and immunohistochemical methods, and the use of a novel phase-plane analysis. The authors construct a comprehensive functional map revealing functional nuances in respiratory responses to bicuculline along the rostrocaudal axis of the parafacial region. They convincingly show that although bicuculline injections at all coordinates of the pFL generated an expiratory response, the most rostral locations in the lateral parafacial region play the strongest role in generating active expiration. These were characterized by a strong impact on the duration and strength of ABD activation and a robust change in tidal volume and minute ventilation. The authors also confirmed histologically that none of the injection sites overlapped grossly with PHOX2B+ neurons, thus confirming the specificity of the injections in the pFL and not the neighboring RTN.

Collectively, these findings advance our understanding of the presumed expiratory oscillator, the pFL, and highlight the functional heterogeneity in the functional response of this anatomical structure.

Thanks for the positive feedback on the results presented in the current manuscript.

Reviewer #2 (Public Review):

Summary:

Pisanski and colleagues map regions of the brainstem that produce the rhythm for active expiratory breathing movements and influence their motor patterns. While the neural origins of inspiration are very well understood, the neural bases for expiration lag considerably. The problem is important and new knowledge pertaining to the neural origins of expiration is welcome.

The authors perturb the parafacial lateral (pFL) respiratory group of the brainstem with microinjection of bicuculline, to elucidate how disinhibition in specific locations of the pFL influences active expiration (and breathing in general) in anesthetized rats. They provide valuable, if not definitive, evidence that the borders of the pFL appear to extend more rostrally than previously appreciated. Prior research suggests that the expiratory pFL exists at the caudal pole of the facial cranial nucleus (VIIc). Here, the authors show that its borders probably extend as much as 1 mm rostral to VIIc. The evidence is convincing albeit with caveats.

Strengths:

The authors achieve their aim in terms of showing that the borders of the expiratory pFL are not well understood at present and that it (the pFL) extends more rostrally. The results support that point. The data are strong enough to cause many respiratory neurobiologists to look at the sites rostral to the VIIc for expiratory rhythmogenic neurons and characterize their properties and mechanisms. At present my view is that most respiratory neurobiologists overlook the regions rostral to VIIc in their studies of expiratory rhythm and pattern.

Weaknesses:

The injection of bicuculline has indiscriminate effects on excitatory and inhibitory neurons, and the parafacial region is populated by excitatory neurons that are expiratory rhythmogenic and GABA and glycinergic neurons whose roles in producing active expiration are contradictory (Flor et al. J Physiol, 2020, DOI: 10.1113/JP280243). It remains unclear how the microinjections of bicuculline differentially affect all three populations. A more selective approach would be able to disinhibit the populations separately. Nevertheless, for the main point at hand, the data do suggest that we should reconsider the borders of the expiratory pFL nucleus and begin to examine its physiology up to 1 mm rostral to VIIc.

The control experiment showed that bicuculline microinjections induced cFos expression in the pFL, which is good, but again we don't know which neurons were disinhibited: glutamatergic, GABAergic, or glycinergic.

Thanks for sharing your excitement on the results of our study, and appreciating the thorough investigation performed with the use of bicuculline, an approach that was originally used in Pagliardini et al, 2011, PMID: 21414911) and then used by many other groups to generate and study active expiration in vivo.

In the current study we used the well known effect of Bicuculline to systematically test the area that is more sensitive to such a pharmacological effect, and hence may be the core for generating active expiration. While the use of GABA receptor antagonists may have an indiscriminate effect on GABA receptor expressing neurons with various phenotypes, anatomical assessment of inhibitory cells has shown very little distribution of GABAergic and glycinergic cells in the parafacial area (Tanaka et.al, 2003; PMID: 14512139) and it has been inferred in multiple publications (Huckstepp et al., 2015, PMID: 25609622; Huckstepp et al. 2016 PMID: 27300271; Huckstepp et al., 2018, PMID: 30096151; Flor et al., 2020, PMID: 32621515; Britto & Moraes, 2017; PMID: 28004411; Silva et al. 2016; PMID: 26900003) and demonstrated recently (Magalhaes et al.,  2021; PMID: 34510468) that late-E neurons in the parafacial region are excitatory and have a glutamatergic phenotype. We can’t exclude that a small fraction of neurons in the pFL area are inhibitory, and that they could influence recruitment of adjacent late-E expiratory neurons. A more selective activation of neuronal populations with different phenotype would be indeed interesting, nonetheless, if local inhibitory neurons have a role in the generation of active expiration, then their disinhibition could have either an inhibitory effect on late-E activity or stimulate expiration in a more indirect fashion.

While the effect of bicuculline on active expiration has been reported and replicated in multiple manuscripts, the source of inhibition across different phases of the respiratory cycle is still under investigation. Some studies suggest that GABAergic and glycinergic inhibition is not originated in pFL but rather in the BötC and preBötC areas (Flor et al., 2020, PMID: 32621515; Magalhaes et al., 2021; PMID: 34510468) and the effects of this inhibition across the respiratory cycle is debated. Future studies will be key to identify the source of pFL inhibition.

The manuscript characterizes how bicuculline microinjections affect breathing parameters such as tidal volume, frequency, ventilation, inspiratory and expiratory time, as well as oxygen consumption. Those aspects of the manuscript are a bit tedious and sometimes overanalyzed. Plus, there was no predictive framework established at the outset for how one should expect disinhibition to affect breathing parameters. In other words, if the authors are seeking to map the pFL borders, then why analyze the breathing patterns so much? Does doing so provide more insight into the borders of pFL? I did not think it was compellingly argued.

We have edited the introduction to address this comment and emphasize the rationale for the study. We also edited the results section to summarize our findings.

We continue to report our in-depth analysis of the perturbations induced by bicuculline injection over the various respiratory characteristics as this will be fundamental to determine the effects of our experiment not only on the activation of pFL and active expiration, but also on the respiratory network in general. In order to be fair and open about our findings we have reported the results of our analysis in detail. Of note, all sites generated active expiration, but since the objective of the study was to determine the sites with the most significant changes, a finer and multilevel analysis has been used.

Further, lines 382-386 make a point about decreasing inspiratory time even though the data do not meet the statistical threshold. In lines 386-395, the reporting appears to reach significance (line 388) but not reach significance (line 389). I had trouble making sense of that disparity.

The statistics were confirmed, and the lines edited as follows: “Interestingly, the duration of inspiration during the response was found to decrease in all groups relative to baseline respiration (Ti response = 0.279 ± 0.034s, Ti baseline = 0.318 ± 0.043s, Wilcoxon rank sum: Z = 3.24, p = 0.001). Contrary to this decrease in inspiratory duration, the total expiratory time was observed to increase in all groups and remained elevated compared to baseline (TE response = 1.313 ± 0.188s, TE baseline = 1.029 ± 0.161s, Wilcoxon rank sum: Z = 4.49, p = 0.001).”

The other statistical hiccups include "tended towards significance" (line 454), "were found to only reach significance for a short portion of the response" (line 486-7), "did not reach the level of significance" (line 506), which gives one the sense of cherry picking or over-analysis. Frankly, this reviewer finds the paper much more compelling when just asking whether the microinjections evoke active expiration. If yes, then the site is probably part of the pFL.

Statistical “tendencies” have been eliminated throughout the manuscript.

We have analyzed in details our results in order to determine changes and differential effects on respiration when comparing the 5 sites of injections. Although the presentation of the results may seem tedious, it has allowed us to highlight some interesting effects: first, the effects on respiratory frequency. It has been shown in the past that optogenetic stimulation of this area causes an increase in respiratory frequency (Pagliardini et al., 2011, PMID: 21414911), whereas a dishinibition with this same approach or stimulation of AMPAreceptor in pFL have shown a reduction in frequency or not a significant change in the response (Pagliardini et al., 2011, PMID: 21414911; Huckstepp et al., 2015, PMID: 25609622; Huckstepp et al. 2016 PMID: 27300271; Huckstepp et al., 2018, PMID: 30096151). Here, we suggest that the reduction in respiratory frequency is observed only in the caudal sites and could be attributed to BötC effects rather than the stimulation of the core of the pFL since no respiratory change was observe where the effect was more potent (rostral side). Another interesting point was the effects on O2 consumption, although difficult to interpret at this point, we found very interesting that hyperventilation occurred only at the most rostral injection sites.

I encourage the authors to consider the fickleness of p-values in general and urge them to consider not just p but also effect size.

Thank you for the feedback on our description of the statistical results and the suggestion of incorporating effect size. We have now included measurements of effect size in the results section.  Specifically, we calculated the effect size within each ANOVA using the value of eta squared for all data shown in Figures 3 and 4. Please note that in our phase-plane analysis (Fig. 5-6) the Mahalanobis distance is itself an effect size measure for multidimensional data. We also note that statistical evaluation using non-parametric analyses do not involve effect sizes.

Reviewer #3 (Public Review):

Summary:

The study conducted by Pisanski et al investigates the role of the lateral parafacial area (pFL) in controlling active expiration. Stereotactic injections of bicuculline were utilized to map various pFL sites and their impact on respiration. The results indicate that injections at more rostral pFL locations induce the most robust changes in tidal volume, minute ventilation, and combined respiratory responses. The study indicates that the rostrocaudal organization of the pFL and its influence on breathing is not simple and uniform.

Strengths:

The data provide novel insights into the importance of rostral locations in controlling active expiration. The authors use innovative analytic methods to characterize the respiratory effects of bicuculline injections into various areas of the pFL.

Weaknesses:

Bicuculline injections increase the excitability of neurons. Aside from blocking GABA receptors, bicuculline also inhibits calcium-activated potassium currents and potentiates NMDA current, thus insights into the role of GABAergic inhibition are limited.

Increasing the excitability of neurons provides little insights into the activity pattern and function of the activated neurons. Without recording from the activated neurons, it is impossible to know whether an effect on active expiration or any other respiratory phase is caused by bicuculline acting on rhythmogenic neurons or tonic neurons that modulate respiration. While this approach is inappropriate to study the functional extent of the conditional "oscillator" for active expiration, it provides valuable insights into this region's complex role in controlling breathing.

We have included a reflection of the weaknesses of our studies in the technical consideration section to address the possibility that bicuculline may induce active expiration through other mechanisms. Please note that the use of bicuculline was not to gain further insight on GABAergic inhibition of pFL but to adopt a tool to generate active expiration that has been extensively validated by our group and others.

Multiple studies have shown recruitment of excitatory late expiratory neurons with bicuculline injections. Although we did not record from late-E neurons in this study, we infer from the body of literature that disinhibition of neurons in this area will activate late-E neurons (as previously demonstrated) and generate active expiration. Although we see value in recording activity of single neurons (especially to study mechanisms of rhythmogenesis), we opted to measure the physiological response from respiratory muscles as an indication of active expiration recruitment in vivo. Recording from single neurons after bicuculline injections in each site would confirm the presence of expiratory neurons along the parafacial area, which is probably not surprising, since every site tested promoted active expiration. The focus of the study though was to determine the site with the strongest physiological response to disinhibition. Future studies will be key to determine whether all neurons along this column have similar electrophysiological rhythmic properties to the ones recently reported (Magalhaes et al., 2021; PMID: 34510468), or some of them simply provide tonic drive to late-E neurons located elsewhere.

We have discussed the issue as follows:

“Our experiments focused on determining the area in the pFL that is most effective in generating active expiration as measured by ABD EMG activity and expiratory flow. We did not attempt to record single cell neuronal activity at various locations as previously shown in other studies (Pagliardini et al 2011; Magalhaes et al., 2021), as this approach would most likely find some late-E neurons across the pFL and thus not effectively discriminate between areas of the pFL. Future studies involving multi-unit recordings or imaging of cell population activities will help to determine the firing pattern and population density of bicuculline-activated cells and further determine differences in distribution and function of late-E neurons across the region of the pFL.”

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Overall, the manuscript addresses an important question in the field, the anatomical location of the expiratory oscillator. I commend the authors for a well-thought-out and clearly presented study. However, a few small concerns deserve attention to improve the clarity of the report.

(1) The figures would benefit from a rostral-to-caudal representation of results instead of a caudal-to-rostral orientation. Example, Figure 2.

We opted for a caudal to rostral representation to progressively move away from the inspiratory oscillator (preBötC) and the anatomical reference point (the caudal tip of the facial nucleus) with our series of injections. 

(2) A discussion about how expiratory responses generated by these pharmacological approaches would compare to endogenous baseline conditions. The authors mention that bicuculline injections elicited a late-E downward inflection that was absent in baseline conditions. Thus, this raises the point of how these findings compare to awake freely moving animals or during different conditions of increased ventilatory demand.

This is an interesting question that has not yet been address in the field. As far as we know, there are no recordings of pFL neurons in freely behaving animals although recordings of pFL late-E neurons under elevated PaCO2 have shown a late-E activity in in situ preparations (Britto & Moraes, 2017; PMID: 28004411; Magalhaes et al., 2021; PMID: 34510468).

We have clarified this in the discussion as follows:

“At rest, respiratory activity does not present with active expiration (i.e, expiratory flow below its functional residual capacity in conjunction with expiratory-related ABD muscle recruitment) and expiratory flow occurs due to passive recoil of chest wall with no contribution of abdominal activity. Active expiration and abdominal recruitment can be spontaneously observed during sleep (in particular REM sleep, Andrews and Pagliardini, 2015; Pisanski et al., 2019) and can be triggered during increased respiratory drive (e.g. Hypercapnia, RTN stimulation, Abbott et al., 2011). Although never assessed in freely moving, unanesthetized rodents, bicuculline has been extensively used to generate active expiration and late-E neuron activity in both juvenile and adult anesthetized rats (Pagliardini et al., 2011; Huckstepp et al., 2015 Huckstepp et al., 2016; Huckstepp et al., 2018; De Britto and Moraes, 2017; Magalhaes et al., 2021). “

(3) In Figure 2A, there appears to be an injection site in the top right quadrant of the image, very distant from the intended site. Could the authors confirm if this is an artifact?

Yes, it is an artifact of image acquisition, we should have marked that in the figure. To avoid confusion and follow other reviewers’ suggestions we have edited he figure.

(4) A stylistic suggestion would be to include the subpanel of Figure 2C saline control injection as a graph of its own and also include the control anatomical location in 2B.

Thanks for the suggestion. Because of the complex organization of the figure we opted to leave it as a subpanel in order to not distract the reader from the 5 injection sites, but still provide information about vehicle injection and their lack of changes in respiratory response.

(5) The authors note that DIAm Area (norm.) during the inspiratory phase is increased in the +6 and +8mm groups. However, Figure 5E shows that the +8mm group is significantly reduced as compared to the +6mm group. Please clarify.

During the inspiratory phase we did not observe any significant change in the DIA Area (norm.). We realize that the description of this part of the results was confusing and therefore we have eliminated that section.

Reviewer #2 (Recommendations For The Authors):

I encourage the authors to consider the fickleness of p-values in general and urge them to consider not just p but also effect size. There is a valuable editorial in this week's J Physiology (https://doi.org/10.1113/JP285575) that may provide helpful guidance.

Thanks for this comments and the general assessment. We realized that the results section was dense and with a lot of information. We significantly slimmed the description of the results in order to facilitate the appreciation of the results and avoid confounding statement about significant vs non- significant results.

We have now included measurements of effect size in the results section.  Specifically, we calculated the effect size within each ANOVA using the value of eta squared for all data shown in Figures 3 and 4. Please note that in our phase-plane analysis (Fig. 5-6) the Mahalanobis distance is itself an effect size measure for multidimensional data. We also note that statistical evaluation using non-parametric analyses do not involve effect sizes.

The equipment and resources should be clearly identified and use RRIDs whenever possible. Resources like antibodies and other reagents (e.g., cryoprotectants) should be identified, not just by manufacturer, but also by specific part or product numbers or identifiers.

Manuscript has been edited to add these details.

The manuscript makes reference to ImageJ and Matlab routines, which must be public through GitHub or another stable repository.

Thanks for pointing this out. Image J analysis has been performed following scripts already available to users (no custom scripts). The Matlab scripts used for the multivariate analysis is now available at: https://github.com/mprosteb/Pisanski2024

The way that ABD-DIA coupling was assessed was unclear from the Methods.

The following text has been added to the methods: “The coupling between ABD and DIA signals was measured as a ratio and analyzed by quantifying the number of bursts of activity observed for the ABD and DIA EMG signals during the first 10 minutes of the response, excluding time bins at end of the response (due to fading and waning of the ABD response in those instances).”

Fig. 1A was never cited in the text.

It has been cited now.

Fig. 1A-C appears to be exactly the same as Fig. 5A-C.

The reviewer is correct. We have used figure 1 to describe and explain our analytical methods with sample data and Figure 5 describes our results. We have clarified that in: “Figure 5: Rostral injections elicit more prominent changes to respiration in each signal and sub-period. A-C: Is the same as Method Figure 1, has been included here for further clarity when analyzing the results.”

Late Expiratory airflow is given in units of volts (V) in lines 358-363 (Fig. 4C) but then in units of volts-seconds (V•s) in lines 363-367. Both units are problematic because the voltage is neither an air volume nor an air volume per unit time. Is there some conversion factor left out?

In this section of the results we describe the changes in expiratory peak amplitude (V) and expiratory peak flow (V•s). Since calibration of airflow was performed on the positive flow and for larger volumes, we prefer to use the original units to guarantee precise assessment of the change and avoid introducing potential errors. Since the analysis considers changes from baseline readings, converting to ml or ml*s would not affect our analysis.

Reviewer #3 (Recommendations For The Authors):

The study conducted by Pisanski et al investigates the role of the lateral parafacial area (pFL) in respiratory control, specifically in modulating active expiration. The precise location of this expiratory oscillator within the ventral medulla remains uncertain, with some studies indicating that the caudal tip of the facial nucleus (VIIc) forms the core while others propose more rostral areas. Bicuculline injections were utilized at various pFL sites to explore the impact of these injections on respiration. The authors use innovative and impressive analytic methods to characterize the effect on respiratory activity. The results indicate that injections at more rostral pFL locations induce the most robust changes in tidal volume, minute ventilation, and combined respiratory responses. The study will contribute to an enhanced understanding of the neural mechanisms controlling active expiration. The main message of the study is that the rostro-caudal organization of the pFL is not simple and uniform. The data provides novel insights into the importance of rostral locations in controlling active expiration (see e.g. lines 738-740).

The data and results of the paper are intriguing, and it appears that the experiments are well-managed and executed. However, there are several major and minor comments and suggestions that should be addressed by the authors:

(1) The study relies heavily on local injections into specific areas that are confirmed histologically. One potential concern is the injection volume of 200 nL in such a tiny area. The authors suggest that the drug did not spread to rostral/caudal areas outside the specified coordinate partly based on their cFOS staining. For example, the lack of cFOS activation in TH+ cells and Phox2B cells is interpreted as proof that bicuculline did not spread to these somas (Figure 2). The authors seem to use a similar argument as evidence that the pFL does not include Phox2B neurons in the RTN as discussed in the Discussion section (lines 830-847). However, it is very surprising that bicuculline injections into an area that is known to contain Phox2B and Th+ neurons do not activate these neurons as assessed by the cFOS staining. It seems puzzling to me that none of their injections shown in Figure 2 activated Phox2B or Th neurons. I assume that in targeting the pFL the authors must have sometimes hit areas that included neurons that define the RTN, which would have activated Phox2B or Th+ neurons. Did the authors find that these activations did not activate active expiration? Such negative "controls" would strengthen their argument that pFL is a separate and distinct region that selectively controls active expiration.

Thanks for the positive feedback on the manuscript. As it has been demonstrated and discussed in several previous publications, PHOX2B expressing neurons in this area of the brain are part of the RTN Neuromedin B positive neurons (more densely located in the ventral paraFacial rather than the lateral parafacial, our site of injection), the TH+ C1 neurons (located in a somewhat more caudal and medial position compared to our sites of injection, around the BötC/ preBötC area) and the large Facial MN (easily identifiable by their large size and compact location). Given this differential spatial distribution, and the controls described below, we believe we have reduced the possibility of the direct activation of these neurons, although we can’t exclude it in full.

There is now strong evidence about lack of PHOX2B expression in late E neuron in juvenile and adult rats (Magalhaes et al., 2021; PMID: 34510468). We realize that the microinjected solution could potentially diffuse in the brain and hit other areas, but we combined two strategies to verify our intention for a focal injection activating only a restricted area of the brain (i.e., the pFL): i) localization of fluorobeads that were diluted in the Bicuculline solution; ii) expression of cFos combined with anatomical markers, to identify activated cells. Fluorobeads have a very limited spread in the brain and therefore informed us of the site of the injection to differentiate between the five injections locations. Although we can’t assume that Bicuculline will have a similar spread (and it will also be quickly degraded in the tissue), the combination of this analysis with the localized expression of cFos cells has helped us to differentiate between injections site. Because of the proximity of PHOX2B cells in RTN and C1 neurons, we also combined cFos expression with immunohistochemistry to determine whether bicuculline activation was also visible in these two neuronal populations. Our results indicate that there is baseline cfos activity in RTN neurons (see vehicle injection) but the fraction of PHOX2B activated cells did not increase with bicuculline injections suggesting that these neurons were not the target of our injections. Please note that cfos expression has been extensively used to determine RTN neuron activation, especially following chemoreflex responses. 

(2) The authors refer to "the expiratory oscillator" throughout the manuscript (e.g. lines 58, 62, 65) as if there is only one expiratory oscillator i.e. "the expiratory oscillator". For some reason, the authors avoided citing and mentioning PiCo (Anderson et al. 2016), which is considered the oscillator for postinspiration. Since the present study focuses on the role of expiration, and since the authors describe convincing effects on postinspiration, considering this oscillator which is located dorsomedial to the VRC seems relevant for the present study.

Due to the limited and controversial literature that is currently present describing Pico as a third oscillator and the fact that our studies do not directly assess the post-inspiratory activity (as measure by the V nerve or laryngeal muscles) or Pico activity and location (which would be even more distant than the RTN, for example), we prefer to avoid commenting on the effects of this injection on Pico or the connectivity between Pico and pFL.

We have added this to the discussion:

“Therefore, although it has previously been described, it is currently unknown the exact mechanism by which this post-I activity in the ABD muscles is generated. For example the interplay between the rostral pFL and brainstem structures generating post-inspiratory activity, such as the proposed post-inspiratory oscillator (PiCo; Anderson et al., 2016) or pontine respiratory networks, could be reasonably involved in this process.”

(3) The authors do not specify what type of bicuculline they injected. Bicuculline is known to have significant effects on potassium channels. Thus, the effects reported here could be due to a non-specific change in excitability, rather than caused by a specific GABAergic blockade.

The authors also do not know what effects these injections cause in the neurons in vivo, since the injections are not accompanied by recordings from the respiratory neurons that they activate. This together with the non-specific bicuculline effects will affect the interpretation of the results. Thus, the authors need to be more careful when interpreting their effects as "GABAergic". The use of more specific blockers like gabazine could partly address this concern. The authors have to discuss this in a "limitation section".

Thanks for pointing that out, we have now clarified in the methods section that we used bicuculline methochloride. We can’t exclude that some side- effects could be present due to the use of this drug. For the purpose of this study though, we focused on using bicuculline as a tool to consistently generate active expiration since it has been extensively used by multiple laboratories to induce abdominal muscle recruitment and active expiration, as well as to directly record late-E neurons in this same area.

We have included in the discussion the following statement:

“Technical considerations

Bicuculline methiodide has previously been observed to exhibit inhibitory effects on Ca2+ activated K+ currents inducing non-specific potentiation of NMDA currents (Johnson and Seutin, 1997). Consequently, caution is warranted in attributing our findings solely to the GABAa antagonist properties of bicuculline. Previous work has demonstrated a temporal correlation between the onset of late-E neuron activity in the caudal parafacial region and ABD activity in response to bicuculline (Pagliardini et al., 2011; de Britto and Moraes, 2017; Magalhaes et al., 2021) as well as GABAergic sIPSCs in late-E neurons (Magalhaes et al., 2012). However, it is essential to note that the current study lacks single unit recording, preventing us from definitively confirming whether the observed activity stems from late-E neuronal GABAergic dishinibition or excitation through non GABAergic mechanisms.”

(4) I also caution the authors when stating that the bicuculline injections will reveal the precise location and functional boundaries of "the" expiratory oscillation within the pFL. Increasing the excitability with bicuculline is inappropriate to study the functional boundaries of an oscillator. It is particularly inappropriate to identify the boundaries of the pFL, a network that is normally inactive and activated only under certain behavioral and metabolic conditions. Because the injections are increasing the neuronal excitability unspecifically, and because the authors are not recording the activity of the neurons in the pFL region it is unclear what kind of neurons are activated. The cFOS staining may help to define whether these neurons are Phox2B or Th positive or negative, but they will not provide insights into the activity patterns of the activated neurons. Thus, it is fair to assume that these injections will likely include also tonic neurons that might indirectly control the activity of pFL neurons under certain metabolic or behavioral conditions without actually being involved in the rhythmogenesis of active expiration. Many of the effects peak after several minutes, and different regions cause differential effects with different time courses, which is difficult to interpret functionally. Thus, the "core" identified in the present study could consist of tonic neurons as opposed to rhythmic neurons generating active expiration.

We agree with the reviewer that our local injections may have activated an heterogeneous population of neurons. We do not claim that we only activated late-E rhythmogenic neurons but that our multiple sites of injections revealed the area that is generating the strongest excitation of ABD muscles and active expiration.

While the use of GABA receptor antagonists may have an indiscriminate effect on GABA receptor expressing neurons with various phenotypes, anatomical assessment of inhibitory cells has shown very little distribution of GABAergic and glycinergic cells in the parafacial area (Tanaka et.al, 2003; PMID: 14512139) and it has been inferred in multiple publications (Huckstepp et al., 2015, PMID: 25609622; Huckstepp et al. 2016 PMID: 27300271; Huckstepp et al., 2018, PMID: 30096151; Flor et al., 2020, PMID: 32621515; Britto & Moraes, 2017; PMID: 28004411; Silva et al. 2016; PMID: 26900003) and demonstrated recently (Magalhaes et al.,  2021; PMID: 34510468) that late-E neurons in the parafacial region are excitatory and have a glutamatergic phenotype

As suggested by the reviewer, it is possible that the bicuculline injection may have activated some tonic non rhythmogenic neurons which could activate the expiratory oscillator located elsewhere.

We have edited the introduction as follows:

“By strategically administering localized volumes of bicuculline at multiple rostrocaudal levels of the ventral brainstem, we aimed to selectively enhance the excitability of neurons driving active expiration, thereby revealing the extension of the pharmacological response and the most efficient site in generating active expiration.”

We have edited the results as follows:

“Importantly, the group with injection sites at +0.6 mm from VIIc exhibited the swiftest response onset, suggesting that this area is the most critical for the generation of active expiration, either through direct activation of the expiratory oscillator or, alternatively, for providing a strong tonic drive to late-E neurons located elsewhere.”

In the introduction, it should also be emphasized that the pharmacological approach used in the present study complements the existing elegant chemogenetic studies, rather than emphasizing primarily the limitations of the chemogenetic inhibitions. The conclusion should be that these studies together provide different, yet complementary insights: The chemogenetic approach by inhibiting neurons, the present study by exciting neurons, and all studies come with their own limitations.

Thanks for the suggestion, we have updated the manuscript as follows:

“Although both of these elegant chemogenetic studies have contributed extensively to our understanding of the pFL, the existing evidence suggests that the expiratory oscillator may expand beyond the limits of the viral expression achieved in said studies, as proposed by Huckstepp et al., (2015).”

Throughout the manuscript, the authors have to be cautious when implying that an excitatory effect relates to the activity of rhythmogenic pFL neurons. For example, on line 710 the authors state that "it is conceivable to infer that the rostral pFL is in the closest proximity to the cells responsible for the generation of active expiration". While it may indeed be "conceivable", the bicuculline injections themselves provide no insights into the location of neurons responsible for rhythmogenesis. It is equally "conceivable" that the excited neurons provide a tonic drive to the neurons without being involved in the generation of active expiration. These tonic neurons could be located at a distance from the presumed rhythmogenic core.

We have included the possibility of tonic excitation in the technical considerations section:

“However, our study did not include recording from late-E neurons following bicuculline injections, preventing us from definitively confirming whether the observed activity stems from late-E neuronal excitation or the potentiation of a tonic drive, particularly in the rostral areas.”

(5) It is intriguing that some of their injections (Fig.2D) evoked postinspiratory activity. This interesting finding should be discussed as it could provide important insights into the coordination of the different phases of expiration.

Thanks for the suggestion. We have included the following to the discussion:

“Therefore, although it has previously been described, the exact mechanism by which this post-I ABD activity is generated is unclear. This late-E/post-I pattern of activity is similar to what has been observed in in vitro preparations and in vivo recordings in juvenile rats (Janczewski et al., 2002; Janczewski et al., 2006).

“Therefore, although it has previously been described, it is currently unknown the exact mechanism by which this post-I activity in the ABD muscles is generated. For example the interplay between the rostral pFL and brainstem structures generating post-inspiratory activity, such as the proposed post-inspiratory oscillator (PiCo; Anderson et al., 2016) or pontine respiratory networks, could be reasonably involved in this process.”

(6) The authors conducted bilateral disinhibition of the pFL, but only a unilateral photomicrograph was shown. Figure 2 should include a representative bilateral photomicrograph along with a scatter plot for clarity and completeness.

We have edited figure 2 to include representative images of bilateral injections.

(7) Regarding the Bicuculline injections in the Methods section: Aside from specifying exactly what type of bicuculline was used, the authors should provide more information about the pFL location and landmarks used, including the missing medial-lateral coordinate. The fluorobead spread of approximately ~300 µm, as observed in Figure 2C, is crucial for the interpretation of the results and should be detailed. An alternative approach could involve e.g. calculating the area covered by fluorobeads in each group.

We have included the following in the text:

“Each rat was injected at 2.8 mm lateral from the midline and at a specific RC coordinate based on the following groups: -0.2 mm from the caudal tip of the facial nucleus (VIIc) (n=5), +0.1 mm from VIIc (n=7), +0.4 mm from VIIc (n=5), +0.6 mm from VIIc (n=6), +0.8 mm from VIIc (n=5)”

“These findings strongly suggest that bicuculline specifically activated cells within the vicinity of the injection sites which spread ~300 ìm (Figure 2C, horizontal lines) and did not activate PHOX2B+ cells in the RTN area, beyond their baseline level of activity.”

(8) In the Experimental Protocol, the authors should provide more details on how the parameters were determined. For example, specify the number of cycles included for Dia frequency/amplitude, Abd frequency/amplitude, and with regards to the averaging process, the authors should specify over how many cycles they obtained an average for Dia/Abd activity time and AUC. The authors should also provide information on the number of bicuculline injections that they repeated to average these values and they should report the coefficient of variation for repeated injections. Please clarify the method used to calculate AUC, considering the non-linear nature of the activity.

Only one bicuculline injection per rat was performed and the number of rats used for each injection site is indicated in the methods as follows:

“Each rat was injected at 2.8 mm lateral from the midline and at a specific RC coordinate based on the following groups: -0.2 mm from the caudal tip of the facial nucleus (VIIc) (n=5), +0.1 mm from VIIc (n=7), +0.4 mm from VIIc (n=5), +0.6 mm from VIIc (n=6), +0.8 mm from VIIc (n=5), and CTRL (n=7). We recorded the physiological responses to the injection for 20-25 min.”

We have clarified in the methods section the following:

“Respiratory data was tracked in time bins of 2-minute duration from the baseline period prior to injections and spanned 20 min of recording post-injection. Mean-cycle measurements for each signal were computed by averaging values across all cycles within a given time bin.”

Additional clarifications have been added:

“We then used the average calculations of respiratory rate (RR), tidal volume (VT), Minute Ventilation (Ve), expiratory ABD amplitude, expiratory ABD area, VO2, VE/VO2 to obtain values relative to the baseline period. Peak responses were identified as the time bin that produced the strongest changes relative to baseline.”

“Mean-cycle measurements for each signal were computed by averaging across all cycles within a given time bin. (~300 cycles in baseline, ~100 cycles per response time bin). We then used the average calculations of respiratory rate (RR), tidal volume (VT), Minute Ventilation (Ve), expiratory ABD amplitude, expiratory ABD area, VO2, VE/VO2 to obtain values relative to the baseline period. Peak responses were identified as the time bin that produced the strongest changes relative to baseline.”

“The Area under the curve (AUC) was measured during baseline and was subtracted from the corresponding AUC of the response for each time bin (Figure 1C). This AUC measure was computed as the sum of the signal in a given respiratory phase as all signals were sampled at the same rate. Note that areas calculated below the zero- (0) line, as would be expected from a negative airflow during expiration, yields negative AUC values.”

(9) The authors should explain how oxygen consumption was calculated-did it involve the Depocas & Hart (1957) formula? Please provide information on expiratory CO2, whether ventilation was adjusted to achieve consistent CO2 levels across animals, and ideally specify the end-tidal CO2 range for the experiments. Discuss the rationale behind the chosen CO2 levels and whether CO2-dependent pFL activity could have influenced results.

We have clarified in the measurement in the methods as follows:

“The gas analyzer measured fractional concentration of O2. Based on this and the flow rate at the level of the trachea (minute ventilation), we calculated O2 consumption according to Depocas and Hart (1957).”

We have also added to the methods section:

“During the entire experimental procedure, rats breathed spontaneously and end tidal CO2 was not adjusted through the experimental protocol.”

In terms of the CO2-dependent pFL activity possibly influencing the results: by inducing active expiration in conditions in which there is no physiological demand for it (i.e. no hypoxia or hypercapnia), it is likely that pCO2 is reduced, overall decreasing the drive for ABD activity which would suggest that our results are likely an underestimation of the response that would have been produced if we maintained the CO2 levels constant.

(10) The authors should address the discrepancy in fos-activated neurons between the control (44 neurons) and experimental animals (90-120 neurons per hemisection). Please explain the activation in the control group. Please also provide insights into how the authors interpret this difference in cfos-activated neurons between control and experimental groups.

The following paragraph has been added to the discussion:

“The assessment of cellular activity, quantified through cFos staining, unveiled the existence of basal activity in control rats. This observed baseline activity is likely emanating from subthreshold physiological processes within the parafacial area which do not culminate in ABD activity. Analysis of the cFos staining confirmed focal activation of neurons in the pFL of rats injected with bicuculline and minimal cFos expression in the PHOX2B+ cells in all groups as compared to the control group. These results confirm the very limited mediolateral spread of the drug from the core site of injection and back previous findings supporting the hypothesis that the majority of PHOX2B+ cells are more ventrally located in the parafacial area (pFV, Huckstepp et al., 2015) and PHOX2B+ cell recruitment is not necessary for active expiration (de Britto & Moraes, 2017; Magalhães et al., 2021).”

(11) In Figure 8, the authors plotted the relationship of each cycle correlated to the normalized area. Have you also calculated the same late-E, inspiratory, and post-I to fR or VT separately?

No, we only did the separated breathing phase (late-E, I, Post-I) analysis in the calculations of the DIA, airflow and ABD area, as well as on the Euclidean and Mahalanobis distances.

Minor comments:

Is there any specific reason for conducting these experiments exclusively in males?

No, we usually use male rats for this type of experiments. We use both male and female rats for other studies that concern the effects of sex hormones but in this case, we performed experiments only in male rats.

Page 13, Line 320: What is the duration of the bicuculline-induced effects?

This information is included in the results section as follows:

“Similarly, the ABD response duration was longer at the two most rostral locations (+0.6 mm = 17.6 ± 2.7 min; +0.8 = 17.1 ± 3.3 min) compared to the most caudal group (-0.2 mm = 2.4 ± 1.1 min; One-Way ANOVA p = 0.043; Tukey -0.2 mm vs +0.6 mm: p = 0.048; -0.2 mm vs +0.8 mm: p = 0.041; Figure 3E).”

Page 16, Line 400: Is there a rationale for the high tidal volume (VT) observed in these animals? A baseline VT of 7 ml/kg appears notably elevated.

Please note that rats were vagotomised and spontaneously breathing, hence the tidal volume is increased compared to non-vagotomised rats as seen in previous studies (Ouahchi et al., 2011).

Figure 2D: Could you provide longer recordings? Additionally, incorporating diaphragm (Dia) recordings would enhance the interpretation of abdominal (Abd) recordings.

Figure 3 A has a representative example of the 20 minute recordings for each location.

Page 18, Line 458: Please rectify "Dunn: p , 0.001" to the appropriate format, perhaps "Dunn: p < 0.001."

Thank you, edited.

Author response:

eLife assessment

“…The evidence however is incomplete, since the tai loss-of-clone phenotype is based on one allele and the mechanism involved in cell competition through Dlp and Wg lacks adequate supporting data.”

We agree with the need for a second allele and are adding supporting data from a new tai lof allele we have generated by Crispr.

We also agree that additional functional data would help demonstrate that differences in Dlp levels are required for the mechanism of Tai cell competition. Experiments are ongoing to test whether normalizing Dlp levels across clonal boundaries rescues elimination of Tai-low clones.

Reviewer #1:

Overall Statements:

“There is some data in the supplementary materials suggesting that Tai promotes dlp mRNA expression, but this was not compelling.”

We are currently testing effects on Tai on dlp and dally transcription using qPCR and reporter transgenes. As noted below, the effects of Tai on Dlp trafficking are ‘strong’, so resolving effects on Dlp transcription will complement this localization data.

“The authors don't further examine Dlp protein in tai clones.”

As noted by the Reviewer, we do examine Dlp levels and localization in tai-low clones (see Figure 9), but these experiments are challenging due to their very small size and the hypomorphic nature of the tai allele (tai[k15101]) that was used. Experiments are in progress to examine the effect of our Crispr null allele of tai on Dlp levels and localization in wing clones.

“In sum, the authors have uncovered some interesting results, but the story has some unresolved issues that, if addressed, could boost its impact. Additionally, the preprint seems to have 2 stories, one about tai and cell competition and the other about tai and Wg distribution. It would be helpful to reorder the figures and improve the narrative so that these are better integrated with each other.”

We agree. The results of our modifier screen required that we first understand how Tai regulates the Wg pathway before could apply this to understanding the competitive mechanism. Thus, the paper is composed of three sections: 1. the screen, 2. the Tai-Dlp-Wg connection in the absence of competition, and 3. the contribution of Dlp-Wg to the tai[low] ‘loser’ phenotype. These sections use different techniques (e.g., clonal mosaics with genomic alleles, Gal4/UAS and RNAi to define the effect of Tai loss on Wg and Dlp). Ongoing experiments return to clonal mosaics to test whether elevating Dlp can rescue tai lof clones in the same manner as Apc/Apc2 alleles (see Figs. 2-3), which elevate Wg pathway activity.

Specifics:

“It would be good to know whether the authors can rescue tai-low clones by over-expression UAS-Dlp.”

As noted above, experiments are ongoing to test whether normalizing Dlp levels across clonal boundaries rescues elimination of Tai-low clones.

“The data on Wg distribution seems disjointed from the data about cell competition. The authors could refocus the paper to emphasize the cell competition story. The role of Dlp in Wg distribution is well established, so the authors could remove or condense these results. The story really could be Figs 1, 2, 3 and 7 and keep the paper focused on cell competition. The authors could then discuss Dlp as needed for Wg signaling transduction, which is already established in the literature.”

We appreciate the suggestion to reorganize the figures to focus the first part of the story on competition, and then follow with the role of Tai in controlling Dlp. We will consider this approach pending the results of ongoing experiments.  

“The model of tai controlling dlp mRNA and Dlp protein distribution is confusing. In fact, the data for the former is weak, while the data for the latter is strong. I suggest that the authors focus on the altered Dlp protein distribution on tai-low clones. It would also be helpful to prove the Wg signaling is impeded in tai clones (see #5 below).”

We agree but are currently testing how dlp reporters and mRNA respond to Tai in order to rigorously test a Dlp transcriptional mechanism. To complement the ‘strong’ evidence that Tai regulates Dlp distribution, we are testing Dlp in clones of our Tai Crispr null. Since submission, we have also assessed the effect of blocking the endocytic factor shibire/dynamin in Dlp distribution in Tai deficient cells to complement the data on Pentagone that is already in the paper (see Fig. S3).

“I don't know if the Fz3-RFP reported for Wg signaling works in imaginal discs, but if it does then the authors could make clones in this background to prove that cell-autonomous Wg signaling is reduced in tai-low clones.”

We thank the reviewer for this suggestion, which we are now testing.

Reviewer #2

Overall Comments:

“While the authors present good evidence in support of most of their conclusions, there are alternative explanations in many cases that have not been excluded.”

We appreciate this point and are conducting experiments for a revised submission that will help test alternative mechanisms and clarify our conclusions.

Specifics:

“However, the experiments have been done with a single allele, and these experiments do not exclude the possibility that there is another mutation on the same chromosome arm that is responsible for the observed phenotype. Since the authors have a UAS-tai stock, they could strengthen their results using a MARCM experiment where they could test whether the expression of UAS-tai rescues the elimination of tai mutant clones. Alternatively, they could use a second (independent) allele to demonstrate that the phenotype can be attributed to a reduction in tai activity.”

As noted above, we agree with the need for a second allele and are adding supporting data from a new tai lof allele we have generated by Crispr.

The tai[k15101] allele acts as a tai hypomorph and has been shown to produce weaker phenotypes than the 61G1 strong lof in a number of papers (Bai et al, 2000; König et al, 2011, Luo et al, 2019, and Zhang et al, 2015). We agree that rescue of tai[k1501] with a UAS-Tai transgene would help rule out effects of second site mutations. We are currently pursuing the reviewer’s second suggestion of phenocopy with a different allele, our new tai Crispr lof.   

“The authors have screened a total of 21 chromosomes for modification and have not really explained which alleles are nulls and which are hypomorphs. The nature of each of the alleles screened needs to be explained better.”

We will update the text to better reflect what type of alleles were chosen. In most cases we preferred amorphs or null alleles over hypomorphs, however when the amorph option was not available, we used hypomorphs.

“Also, the absence of a dominant modification does not necessarily exclude a function of that gene or pathway in the process. This is especially relevant for the Spz/Toll pathway which the authors have previously implicated in the ability of tai-overexpressing cells to kill wild-type cells.”

We thank the reviewer for this completely accurate point. The dominant screen does not rule out effects of other pathways such as Spz/Toll. Indeed, we were surprised by the lack of dominant effects by Spz/Toll alleles on tai[low] competition given our prior work. The reciprocally clear dominant effect of Apc/Apc2 led us to consider that Wg signaling plays a role in this phenomenon, which then became the starting point of this study.

“The most important discovery from this screen is the modification by the Apc alleles. This part of the paper would be strengthened by testing for modification by other components of the Wingless pathway. The authors show modification by Apc[MI01007] and the double mutant Apc[Q8] Apc2[N175A]. Without showing the Apc[Q8] and Apc2[N175A] alleles separately, it is hard to know if the effect of the double mutant is due to Apc, Apc2,` or the combination.”

We agree that testing for modification with other components of the Wg pathway would be helpful to strengthen the connection between Tai low clonal elimination and Wg pathway biology. We also agree that separating Apc [Q8] and Apc2 [N175A] would be a good idea to check if both Apc proteins are equally important for rescuing Tai low cell death, and future experiments for the lab could investigate this distinction.

“RNAi of tai seems to block the formation of the Wg gradient. If so, one might expect a reduction in wing size. Indeed, this could explain why the wings of tai/Df flies are smaller. The authors mention briefly that the posterior compartment size is reduced when tai-RNAi is expressed in that compartment. However, this observation merits more emphasis since it could explain why tai/Df flies are smaller (Are their wings smaller?).”

We agree that this is an exciting possibility. Growth effects of Tai linked to interactions with Yorkie and EcR could be due to a distinct role in promoting Wg activity. Alternatively, Tai may cooperate with Yorkie or EcR to control Wg pathway. These are exciting possibilities that we are pursuing in future work

With regard to the “small size” effect of reducing Tai, we have previously shown that RNAi of Tai using engrailed-Gal4 causes the posterior compartment to shrink (Zhang et al. 2015, Figure 1C-F, H). In this paper, we also showed that tai[k15101]/Df animals are proportionally smaller than wildtype animals and quantified this by measuring 2D wing size (Zhang et al. 2015, Figure 1A and 1B)

“In Figure 7, the authors show the effect of manipulating Tai levels alone or in combination with increasing Dlp levels. However, they do not include images of Wg protein distribution upon increasing Dlp levels alone.”

We thank the reviewer for this reminder and have already generated these control images to include in a revised submission paper.

“In Figure 8, there is more Wg protein both at the DV boundary and spreading when tai is overexpressed in the source cells using bbg-Gal4. However, in an earlier experiment (Figure 5C) they show that the wg-lacZ reporter is downregulated at the DV boundary when tai is overexpressed using en-Gal4. They therefore conclude that wg is not transcriptionally upregulated but is, instead secreted at higher levels when tai is expressed in the source cells. Wg protein is reduced in the DV stripe with tai is overexpressed using the en-Gal4 driver (Figure 6B') and is increased at the same location when tai is overexpressed with the bbg-Gal4 driver. (Figure 8) I don't know how to reconcile these observations.”

We thank the reviewer for pressing us to develop an overall model explaining our results and how we envision Tai regulating Dlp and Wg. We are preparing a graphic abstract that illustrates this model and will be included in our revision.

Briefly, we favor a model in which Tai controls the rate of Wg spread via Dlp, without a significant effect on wg transcription. For example, the induction of Dlp across the ‘engrailed’ domain of en>Tai discs (Fig 7B-B”) allows Wg to spread rapidly across the flanks and moderately depletes it from the DV margin (Fig 6B-B”) as noted by the reviewer. Adding a UAS-Dlp transgene in the en>Tai background dramatically accelerates Wg spread and causes it to be depleted from the DV margin and build up at the far end of the gradient adjacent to the dorsal and ventral hinge. Significantly blocking endocytosis of Wg in en>Tai discs with a dominant negative shibire transgene also causes Wg to build up in the same location (new data to be added in a revision) consistent with enhanced spreading. The difference in the bbg-Gal4 experiment is that Tai is only overexpressed in DV margin cells, which constrains and concentrates Wg within this restricted domain; we are in the process of testing whether this effect on Wg is blocked by RNAi of Dlp in bbg>Tai discs.

“In Figure 9, the tai-low clones have elevated levels of Dlp. How can this be reconciled with the tai-RNAi knockdown shown in Figure 7C' where reducing tai levels causes a strong reduction in Dlp levels?”

We apologize for not explaining this data well enough. First, the tai[k15101] allele is a weak, viable hypomorph (as shown in our Zhang et al, 2015 paper) whereas the Tai RNAi line is lethal with most drivers (including en-Gal4) and thus a stronger lof. Second, Tai RNAi lower Dlp levels (Fig 7C) while tai[k15101] causes Dlp to accumulate intracellularly (see Fig. 9A-C). These data indicate that reduced Tai leads to a defect in Dlp intracellular trafficking while its loss reduces Dlp overall levels; these data can be explained by a single role for Tai in Dlp traffic to or from the cell membrane, or two roles, one in trafficking and one Dlp expression. As noted, we are investigating both possibilities using dlp reporter lines and our new tai null Crispr allele.

Reviewer #3:

Overall Weaknesses:

“The study has relatively weak evidence for the mechanism of cell competition mediated by Dlp and Wg.”

The screen and middle section of the paper provide genetic evidence that elevating Wg pathway activity rescues Tai[low} loser cells and that Tai controls levels/localization of Dlp and distribution of Wg in the developing wing disc. Our current work is focused on linking these two finding together in Tai “loser” clones.

“More evidence is required to support the claim that dlp transcription or endocytosis is affected in tai clones.”

As noted above, we are testing whether normalizing Dlp levels across clonal boundaries rescues tai[low] loser clones and assessing effects of Tai on dlp transcription and Dlp trafficking.

Specifics:

“Most of the rest of the study is not in the clonal context, and mainly relies on RNAi KD of tai in the posterior compartment, which is a relatively large group of cells. I understand why the authors chose a different approach to investigate the role of tai in cell competition. However because ubiquitous loss of tai results in smaller organs, it is important to determine to what extent reducing levels of tai in the entire posterior compartment compares with clonal elimination i.e. cell competition. This is important in order to determine to what extent the paradigm of Tai-mediated regulation of Dlp levels and by extension, Wg availability, can be extended as a general mechanism underlying competitive elimination of tai-low clones. If the authors want to make a case for mechanisms involved in the competitive elimination of tai clones, then they need to show that the KD of tai in the posterior compartment shows hallmarks of cell competition. Is there cell death along the A/P boundary? Or is the compartment smaller because those cells are growing slower?”

Based on data that cell competition does not occur over compartment boundaries (e.g., see review by L.A. Johnston, Science, 2009), we chose not to use UAS-Gal4 to assess competition, but rather to investigate underlying biology occurring between Tai, Wg, and Dlp.

“Are the levels of Myc/DIAP1, proteins required for fitness, affected in en>tai RNAi cells?”

This is, of course, an interesting question given that Myc is a well-studied competition factor and is proposed to be downstream of the Tai-interacting protein Yki. We are not currently focused on Myc, but plan to test its role in the Tai-Dlp-Wg pathway in future work.

“The authors do not have direct/strong evidence of changes in dlp mRNA levels or intracellular trafficking. To back these claims, the authors should look for dlp mRNA levels and provide more evidence for Dlp endocytosis like an antibody uptake assay or at the very least, a higher resolution image analysis showing a change in the number of intracellular Dlp positive punctae. Also, do the authors think that loss of tai increases Dlp endocytosis, making it less available on the cell surface for maintaining adequate extracellular Wg levels?”

As noted above, have added experiments using a dominant-negative shibire/dynamin allele to test whether Tai controls Dlp endocytosis. These data will be added to a revised manuscript. We have also gathered reagents to test effects of Tai gain/loss on Dlp secretion.

“The data shown in the last figure is at odds with the model (I think) the authors are trying to establish: When cells have lower Tai levels, this reduces Dlp levels (S2) presumably either by reducing dlp transcription and/or increasing (?) Dlp endocytosis. This in turn reduces Wg (availability) in cells away from source cells (Figure 6). The reduced Wg availability makes them less fit, targeting them for competitive elimination. But in tai clones, I do not see any change in cell-surface Dlp (9B) (I would have expected them to be down based on the proposed model). The authors also see more total Dlp (9A) (which is at odds with S2 assuming data in S2 were done under permeabilizing conditions.).”

As noted above (under Rev #2 comments), we apologize for not explaining this data well enough. First, the tai[k15101] allele is a weak, viable hypomorph (as shown in our Zhang et al, 2015 paper) whereas the Tai RNAi line is lethal with most drivers (including en-Gal4) and thus a stronger lof. Second, Tai RNAi lower Dlp levels (Fig 7C) while tai[k15101] causes Dlp to accumulate intracellularly (see Fig. 9A-C). These data indicate that reduced Tai leads to a defect in Dlp intracellular trafficking while its loss reduces Dlp overall levels; these data can be explained by a single role for Tai in Dlp traffic to or from the cell membrane, or two roles, one in trafficking and one Dlp expression. We are investigating both possibilities using dlp reporter lines and our new tai null Crispr allele.

“As a side note, because Dlp is GPI-anchored, the authors should consider the possibility that the 'total' Dlp staining observed in 9A may not be actually total Dlp (and possibly mostly intracellular Dlp, since the permeabilizing membranes with detergent will cause some (most?) Dlp molecules to be lost, and how this might be affecting the interpretation of the data. I think one way to address this would be to process the permeabilized and non-permeabilized samples simultaneously and then image them at the same settings and compare what membrane staining in these two conditions looks like. If membrane staining in the permeabilized condition is decreased compared to non-permeabilized conditions, and the signal intensity of Dlp in permeabilized conditions remains high, then the authors will have evidence to support increased endocytosis in tai clones. Of course, these data will still need to be reconciled with what is shown in S2.

We thank the reviewer for this excellent suggestion and are generating mosaic discs to test the proposed approach of synchronous analysis of total vs. intracellular Dlp.

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

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Reply to the reviewers

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

Summary: Authors performed a metatranscriptomic analysis from publicly-available datasets of whole blood from 3 places in Indonesia. Their goal was to explore which pathogens were present on the blood of those 117 healthy individuals. It was interesting that reads from Flaviviridae and Plasmodium were detected in asymptomatic subjects.

Major comments: 1) How did the authors assess and correct batch-effects between different datasets?

Our response: We have sequencing batch information for the Indonesian dataset and saw no clear clustering based on batches in the first 8 PCs. We recognize that sampling variations may exist between islands, though the taxa matrix we acquired from the unmapped reads are very scarce that such variations did not have a strong enough effect to introduce batch effects in our microbiome analyses, and that the signals were driven by pathogenic reads. For our comparative analyses between datasets, we made sure that all three datasets shared similar processing (collected using Tempus Blood RNA Tubes and went through globin depletion method) and have trimmed both Indonesian and Malian reads to match the length of the UK reads (75BP).

2) Did the RNA-seq capture poly-A mRNAs? If so... these reads that did not map the human genome were captured because of internal priming. Can they find internal poly A sequences in the genome of Flaviviridae and Plasmodium pathogens? I would like to know that to understand the source of the reads and which other pathogens may be missing (due to the lack of internal priming).

__Our response: __No, our dataset did not capture poly-A mRNAs. We performed ribosomal RNA (rRNA) and globin mRNA depletion.

3) Principal coordinates analysis (PCoA) is often utilized in metagenomics analysis. Although they are equivalent, is there a reason for using PCA?

Our response: Since we used CLR transformation, the resulting matrix lies in Euclidian space. PCA is just a form of PCoA in Euclidian space.

Minor comments: 1) "Indonesia is a country with large numbers of endemic and emerging infectious diseases [16], making it a crucially important location to monitor and understand the effects of pathogens on human hosts." Is there any epidemiological data that shows differences in infectious diseases across these 3 places? Can the authors provide a map and better explanation about the importance in comparing these 3 areas?

__Our response: __We have added references to malaria infection being more prevalent in the eastern side of Indonesia in the discussion section.

2) Why is it so hard to try to identify (only for Flaviviridae reads) reads that map to very relevant viruses, such as Zika, Dengue, and Yellow Fever? Why did the authors state that they "were unable to refine this assignment further" if this is one of the most interesting finding?

__Our response: __Our reanalysis showed a small percentage of the Flaviviridae reads to be assigned to the Pegivirus genus. As more diverse microbial genomes are added to reference databases and identical regions become more common between them, it becomes harder for the classifer to further define reads to species level (https://link.springer.com/article/10.1186/s13059-018-1554-6). Flaviviridae has distinct species spread across six different genera (https://www.ncbi.nlm.nih.gov/Taxonomy/Browser/wwwtax.cgi?id=11050). In comparison, despite Plasmodiidae having more species recorded compared to Flaviviridae, an overwhelming majority of the species is part of the Plasmodium genus, hence we were able to refine them down to species-level (https://www.ncbi.nlm.nih.gov/Taxonomy/Browser/wwwtax.cgi?id=1639119).

3) Is the script available at https://gitlab.unimelb.edu.au/igr-lab/Epi_Study ? This reviewer could not access it. __Our response: __We thank Reviewer 1 for pointing this out and have amended the link, now accessible here: https://gitlab.svi.edu.au/muhamad.fachrul/indo_blood_microbiome

Reviewer #1 (Significance (Required)):

Interesting paper that enable to extract additional knowledge from whole blood RNA-seq data. There are already several papers that do this and I think authors could go one step forward (for instance, PCR validation of additional individuals). I don't think this can be used for surveillance if it cannot identify species, it is more expensive than running targeted assays, and that may be many false negative pathogens in the samples.

__Our response: __We thank Reviewer 1 for their comments. We have updated our manuscript to reflect our updated analyses which minimizes false positive taxa and the project’s significance not as a mainline surveillance tool, but a retrospective one.

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

Summary:

Bobowik and colleagues perform a computational analysis of whole blood RNA-seq datasets from healthy individuals of three different regions of Indonesia. Their goal is to identify infecting pathogens and other microbes and correlate their abundances to host gene expression patterns or health characteristics in these populations. They find a broad range of bacterial, viral and microeukaryote taxa. When comparing the three Indonesian populations, they find that the Korowai population is the most diverse and different from the other two, possibly driven by the higher prevalence and abundance of Plasmodium (Apicomplexa) in this population.

Then, the authors conduct a statistical decomposition of human gene expression in these samples in independent factors using ICA, and correlate each of these factors to the abundances of the microbial taxa detected. This analysis allows researchers to associate specific patterns of gene expression, such as immune-related pathways, to the presence of members of the Apicomplexa and Kitrinoviricota phyla.

Lastly, the authors use previously published data from other two cohorts (from Mali and the UK) to contextualize their blood microbiome findings. They find microbial reads in all datasets. The Mali cohort is characterized by a large abundance of archaea, not found in the other two populations, while the UK cohort has the lower diversity. Altogether, the authors propose the use of RNA-seq data from human whole blood as a way to study the blood microbiome and establish potential associations between blood resident microbes and host gene expression

Major comments:

1) The methodology to filter and remove reads from potential contaminants needs to be more stringent to ensure the results do not contain spurious contaminants and that the conclusions are correct. It has been described that genomic databases are heavily contaminated with human sequences (Steinegger and Salzberg, 2020), and in this manuscript, even after a two-pass alignment with STAR, reads mapping to helminths also corresponded to the human genome. Additionally, ad-hoc removal of specific taxa (Metazoa and Viridiplantae) was only performed after suspicion of contamination. However, this ad-hoc removal cannot be performed with microbial (bacterial, viral, etc.) contaminants as there is a risk of removing actual bacteria from the samples. But it has been confirmed that many microbial assemblies also suffer from human contamination. Possible actions to take are the following: a.Perform the human mapping with more lenient parameters to avoid human reads to map to other (likely contaminated) genomes in genome databases. b.Remove common contaminants that have been documented, for instance in blood (Chrisman et al., 2022). c.Run a tool to detect contaminated contigs in the database used to map reads to microbes and remove these problematic contigs from further analysis.

Our response: We thank Reviewer 2 for the suggestions, especially to address contaminants. We have reanalyzed our data which resulted in much fewer taxa yet still retained the main pathogenic findings.

2) In line with the above, removing singletons (as I have understood these are taxa that are represented by a single read), is a way to minimize the risk of contamination. To take advantage of the functional profiling of RNA-seq, a measure to ensure that microbes found in blood are active would be to include in the analysis only taxa for which expression of more than a few genes is detected. This type of filtering has been previously applied in studies where very low microbial loads are expected (Lloréns-Rico et al., 2021). In this study, it has only been applied to the specific case of the archaeal taxon Methanocaldococcaceae. However, I would expect cleaner results if applied consistently to all taxa detected.

__Our response: __We have reanalyzed the data and applied this to all taxa detected.

3) The specificity of Methanocaldococcaceae in the samples from Mali is very striking. I am highly suspicious that this only occurs due to a batch effect, even though the authors were highly selective in their cohorts to avoid these. In fact, I extracted the genes spanning the regions highlighted in Supplementary Figure 9 of the Methanocaldococcus jannaschii genome. A BLAST search of these sequences returned, among Methanocaldococcus hits, hits from the ERCC synthetic spike-in sequences, used as internal controls in many RNA-seq experiments. ERCC synthetic spike-in hits appeared for all 4 regions in the genome of M. jannaschii highlighted in this figure. In the original publications of this dataset, there is no reference to the use of these ERCC controls, but given the observed matches, I suggest the authors to perform an extra step in their filtering pipeline to remove all reads mapping to these ERCC standards in all their three cohorts to prevent these sort of batch effects.

__Our response: __We thank Reviewer 2 for pointing this out. Our reanalysis, which now used proper 2-pass mapping and further downstream classification with both pairs of the reads, no longer detected any archaea.

4) I am puzzled by the inconsistencies shown between forward and reverse reads when mapping paired-end data. I expect these inconsistencies at lower taxonomic ranks (species or genus level) due to incomplete genomes, but not at higher taxonomic ranks. I wonder if, by performing more stringent filtering of contaminants as suggested above, the consistency between forward and reverse reads increases and both mates can be used, making the mapping more reliable.

__Our response: __We have reanalyzed the data using both pairs of the reads for classification, resulting in less detected taxa. We believe the new results are more robust as it no longer includes taxa that are not typically found in humans (such as the archae Methanocaldococcus and other environmental bacteria).

In summary, my main concerns regarding this manuscript involve the possibility that contaminants in the sequencing data may be the cause of some of the results presented, and I tried to propose ways of dealing with these contaminants. While some of the results may not be affected by detection of contaminants (i.e. the association between Apicomplexa and some ICs), others such as the diversity measures or the comparison across cohorts may be severely affected. I will consider these results highly preliminary until a more thorough and stringent approach for contaminant removal is applied.

Our response: We thank Reviewer 2 for the suggestions and have updated our manuscript with results updated analyses that are more stringent towards contaminants, as can be seen from our updated findings.

Minor comments:

1) I would appreciate some of the analyses done at lower taxonomic levels if the sparsity of the data allows it, after removing contaminants. Given that the CLR transformation does not allow for zeros, other alternatives such as GMPR (Chen et al., 2018) or adding a pseudocount would allow these analyses?

__Our response: __After our reanalysis, we ended up with even sparser data and therefore could not perform the analyses at lower taxonomic levels.

2) In the PCA shown in figure 1, does the number of microbial reads detected correlate with any of the first two components?

__Our response: __Yes Plamosdiidae correlates well with PCs 1 and 2 (0.66 & 0.73) and Flaviviridae correlates very strongly with PC1 (0.917). We have added this detail in the results section.

3) In Figure 1C, the x axis is wrongly named PC2.

__Our response: __We thank Reviewer 2 for pointing this out and have amended this detail.

4) There is a typo in the legend of Figure 1A ("showeing")

__Our response: __We thank Reviewer 2 for pointing this out and have amended this detail.

5) In the alpha diversity estimates comparison across the three different cohorts, after subsampling each population to achieve similar sample size in each cohort, it is stated that "after subsampling, each population had similar diversity estimates". However, the numbers shown afterwards corresponding to the mean values of alpha diversity, without confidence intervals or a boxplot/violin plot together with an accompanying statistical test, are not enough to assess similarity. I would appreciate a figure (similar to Figure 3E and F) or a test accompanying these mean values.

__Our response: __We thank Reviewer 2 for pointing this out and have amended this detail.

6) In the volcano plots (Figure 3A, B and others throughout the manuscript) it would help the reader to add lines for the thresholds chosen for the effect size and -log10(p-value) to separate significant results.

__Our response: __We thank Reviewer 2 for pointing this out and have amended this detail.

7) In Figure 3E and F, I would appreciate having bars for the statistically significant comparisons.

__Our response: __We thank Reviewer 2 for pointing this out and have amended this detail.

Author response:

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

eLife assessment

The manuscript constitutes an important contribution to antimalarial drug discovery, employing diverse systems biology methodologies; with a focus on an improved M1 metalloprotease inhibitor, the study provides convincing evidence of the utility of chemoproteomics in elucidating the preferential targeting of PfA-M1. Additionally, metabolomic analysis effectively documents specific alterations in the final steps of hemoglobin breakdown. These findings underscore the potential of the developed methodology, not only in understanding PfA-M1 targeting but also in its broader applicability to diverse malarial proteins or pathways. Revisions are needed to further enhance overall clarity and detail the scope of these implications.

We thank the editor and reviewers for recognising the contribution our work makes to understanding the selective targeting of aminopeptidase inhibitors in malaria parasites and the wider impact this multi-omic strategy can have for anti-parasitic drug discovery efforts. The reviewers have provided constructive feedback and raised important points that we have taken on-board to improve our manuscript. In particular, we have revised aspects of the text and figures to enhance clarity, performed additional analysis on the other possible MIPS2673 interacting proteins and more comprehensively analysed the effect of MIPS2673 on parasite morphology. NB: Specific responses to comments in the public reviews are provided within responses to the specific recommendations to authors.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The article "Chemoproteomics validates selective targeting of Plasmodium M1 alanyl aminopeptidase as a cross-species strategy to treat malaria" presents a series of biochemical methods based on proteomics and metabolomics, as a means to:

(1) validate the specific targeting of biologically active molecules (MIPS2673) towards a defined (unique) protein target within a parasite and (2) to explore whether by quantifying the perturbations generated at the level of the parasite metabolome, it is possible to extrapolate which metabolic pathway has been disrupted by using this biologically active molecule and whether this may further confirm selective targeting in parasites of the expected (or in-vitro targeted) enzyme (here PfA-1).

The inhibitor used in this work by the authors (MIPS2673) is to my knowledge a novel one, although belonging to a chemical series previously explored by the authors, which recently enabled them to discover a specific PfA-M17 inhibitor, MIPS2571 (Edgard et al., 2022, ref 11 of this current work). Indeed, inhibitors specifically targeting either PfA-M1 or PfA-M17 (and not both, as currently done in the past) are scarce today, and highly needed to functionally characterize these two zinc-aminopeptidases. MIPS2673, blocks the development of erythrocytic stages of Plasmodium falciparum with an EC50 of 324 nM, blocks the parasite development at the young trophozoite stage at 5x EC50 (but at ring stages at 10xEC50, figure 1E), and inhibits the enzymatic activity of PfA-M1 (and its ortholog Pv-M1) but not of the related malarial metallo-aminopeptidases (M17 and M18 families) nor the human metalloenzymes from closely related enzymatic families, supporting its selective targeting of PfA-M1 (and Pv-M1).

All experiments are carried out in vitro (e.g. biochemical studies such as enzymology, proteomics, metabolomics) and on cultured parasites (erythrocyte stages of Plasmodium falciparum and several gametocytes stages obtained in vitro); there are no in vivo manipulations. The work related to Plasmodium vivax, which justifies the "cross-species" indication in the title of the article, is restricted to using a recombinant form of the M1-family aminopeptidase in enzymatic assays. The rest of the work concerns only Plasmodium falciparum. While I found globally that this work is original and brings new data and above all proposes chemical validation approaches that could be used for other target validations under similar limiting conditions (impossibility of KO of the gene), I have some specific questions to address to the authors.

Strengths and weaknesses:

- The chemoproteomic approach, that explores the ability of MIPS2673 to more significantly "protect" the putative target (PfA-M1) against thermal degradation or enzymatic attack (by proteinase K), to document its selective targeting towards PfA-M1 (the inhibitor, once associated with its target, is expected to stabilize its structure or prevent the action of end proteases), uses several concentrations of MIPS2673 and provides convincing results. My main criticism is that these tests are carried out with parasite extracts enriched in 30-38 hours old forms, and restricted to the fraction of soluble proteins isolated from these parasitic forms, which still limits the scope of the analysis. It is clear that this methodological approach is a choice that can be argued both biologically (PfA-M1 is well expressed in these stages of the parasite development) and biochemically (it is difficult to do proteomic analyses on insoluble proteins) but I regret that the authors do not discuss these limitations further, notably, I would have expected (from Figure 1E) some targets to be also present at ring stages.

- The metabolomic approach, by documenting the ability of MIPS2673 to selectively increase the number of non-hydrolyzed dipeptides in treated versus untreated parasites is another argument in favor of the selective targeting of PfA-M1 by MIPS2673, in particular by its broad-spectrum aminopeptidase action preferentially targeting peptides resulting from the degradation of hemoglobin by the parasite. The relative contribution of peptides derived from host hemoglobin versus other parasite proteins is, however, little discussed.

The work as a whole remains highly interesting, both for the specific topic of PfA-M1's role in parasite biology and for the method, applicable to other malarial drug contexts.

Reviewer #2 (Public Review):

In this manuscript, the authors first developed a new small molecular inhibitor that could target specifically the M1 metalloproteases of both important malaria parasite species Plasmodium falciparum and P. vivax. This was done by a chemical modification of a previously developed molecule that targets PfM1 as well as PfM17 and possibly other Plasmodial metalloproteases. After the successful chemical synthesis, the authors showed that the derived inhibitor, named MIPS2673, has a strong antiparasitic activity with IC50 342 nM and it is highly specific for M1. With this in mind, the authors first carried out two large-scale proteomics to confirm the MIPS2673 interaction with PfM1 in the context of the total P. falciparum protein lysate. This was done first by using thermal shift profiling and subsequently limited proteolysis. While the first demonstrated overall interaction, the latter (limited proteolysis) could map more specifically the site of MIPS2673-PfM1 interaction, presumably the active site. Subsequent metabolomics analysis showed that MIPS2673 cytotoxic inhibitory effect leads to the accumulation of short peptides many of which originate from hemoglobin. Based on that the authors argue that the MIPS2673 mode of action (MOA) involves inhibition of hemoglobin digestion that in turn inhibits the parasite growth and development.

Reviewer #3 (Public Review):

This is a manuscript that attempts to validate Plasmodium M1 alanyl aminopeptidase as a target for antimalarial drug development. The authors provide evidence that MIPS2673 inhibits recombinant enzymes from both Pf and Pv and is selective over other proteases. There is in vitro antimalarial activity. Chemoproteomic experiments demonstrate selective targeting of the PfA-M1 protease.

This is a continuation of previous work focused on designing inhibitors for aminopeptidases by a subset of these authors. Medicinal chemistry explorations resulted in the synthesis of MIPS2673 which has improved properties including potent inhibition of PfA-M1 and PvA-M1 with selectivity over a closed related peptidase. The compound also demonstrated selectivity over several human aminopeptidases and was not toxic to HEK293 cells at 40 uM. The activity against P. falciparum blood-stage parasites was about 300 nM.

Thermal stability studies confirmed that PfA-M1 was a binding target, however, there were other proteins consistently identified in the thermal stability studies. This raises the question as to their potential role as additional targets of this inhibitor. The authors dismiss these because they are not metalloproteases, but further analysis is warranted. This is particularly important as the authors were not able to generate mutants using in vitro evolution of resistance strategies. This often indicates that the inhibitor has more than one target.

The next set of experiments focused on a limited proteolysis approach. Again several proteins were identified as interacting with MIPS2673 including metalloproteases. The authors go on to analyze the LiP-MS data to identify the peptide from PfA-M1 which putatively interacts with MIPS2673. The authors are clearly focused on PfA-M1 as the target, but a further analysis of the other proteins identified by this method would be warranted and would provide evidence to either support or refute the authors' conclusions.

The final set of experiments was an untargeted metabolomics analysis. They identified 97 peptides as significantly dysregulated after MIPS2673 treatment of infected cells and most of these peptides were derived from one of the hemoglobin chains. The accumulation of peptides was consistent with a block in hemoglobin digestion. This experiment does reveal a potential functional confirmation, but questions remain as to specificity.

Overall, this is an interesting series of experiments that have identified a putative inhibitor of PfA-M1 and PvA-M1. The work would be significantly strengthened by structure-aided analysis. It is unclear why putative binding sites cannot be analyzed via specific mutagenesis of the recombinant enzyme.

In the thermal stability and LiP -MS analysis, other proteins were consistently identified in addition to PfA-M1 and yet no additional analysis was undertaken to explore these as potential targets.

The metabolomics experiments were potentially interesting, but without significant additional work including different lengths of treatment and different stages of the parasite, the conclusions drawn are overstated. Many treatments disrupt hemoglobin digestion - either directly or indirectly and from the data presented here it is premature to conclude that treatment with MIPS2673 directly inhibits hemoglobin digestion.

Finally, the potency of this compound on parasites grown in vitro is 300 nM - this would need improvements in potency and demonstration of in vivo efficacy in the SCID mouse model to consider this a candidate for a drug.

Summary:

Overall, this is an interesting series of experiments that have identified a putative inhibitor of the Plasmodium M1 alanyl aminopeptidases, PfA-M1 and PvA-M1.

Strengths:

The main strengths include the synthesis of MIPS2673 which is selectively active against the enzymes and in whole-cell assay.

Weaknesses:

The weaknesses include the lack of additional analysis of additional targets identified in the chemoproteomic approaches.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Question 1. Line 737 (and elsewhere). Why are Plasmodium vivax orthologs of PfA-M1 and PfA-M17 called Pv-M1 and Pv-M17 and not PvA-M1 and PvA-M17, where A stands for Aminopeptidase? I would recommend changing the names if possible, although the mention of Pv-M1 and Pv-M17 is now current in the literature (which is kind of regrettable). See also Supplemental Table S1 where PfA-M1 is named Pf-M1.

Supplemental Table S1 was updated to PfA-M1. Nomenclature for the Plasmodium vivax aminopeptidase orthologs was amended to PvA-M1 and PvA-M17 as suggested by the reviewer.

Question 2. Figure 1. Observation of parasite culture slide smears in Figure 1E strongly suggests that an important target of MIPS2673 appears to be expressed at the ring stage or very young trophozoites, whereas the authors, in their proteomic and metabolomic analyses, performed studies focused on late trophozoites stages (30-38h post-invasion). This difference in the targeting of Plasmodium stages puzzles me and deserves some explanations from the authors, and is related to my question 3.

As the reviewer indicates, ring-stage parasite growth appears to be affected at high concentrations (5x and 10x EC50) of MIPS2673. Under these conditions, parasite growth appears to stall during late rings/early trophs at ~16-22 h post invasion when haemoglobin digestion is increasing and when one presumes PfA-M1 (the primary target of MIPS2673) is increasing in both expression and activity (see references 26 and 28 of this manuscript). Thus, whilst it is unsurprising that MIPS2673 has some activity against ring-stage parasites, we focused on the trophozoite stage for our proteomics studies as we showed this to be the stage most susceptible to MIPS2673 (Fig. 1D) and reasoned that we would most likely identify the primary MIPS2673 target, and other interacting proteins, from a complex biological mixture at this stage. The same reasoning underpinned our decision to perform metabolomics on drug-treated trophozoites, as we reasoned we would see a greater functional effect on this stage. Furthermore, performing these experiments on trophozoites rather than rings minimises the interference from the host red blood cell. While we cannot rule out additional targets in rings, repeating all experiments during this parasite stage is beyond the scope of this study.

Question 3. Figure 2. Although Figure 2 is insightful and somehow self-explanatory, I think it misses two specific pieces of information. First, it is indicated in line 618 (M&M) that parasite material for thermal stability and limited proteolysis studies correspond to synchronized parasites (30-38h post-invasion) but this information is not given in Figure 2. In addition, if I fully understand the experimental protocol of obtaining parasite extracts, they strictly correspond to the soluble protein fraction of the erythrocytic stages of plasmodium at the late trophozoite stage, and not to all parasitic proteins as the scheme of Figure 2 might suggest. I would appreciate it very much if these two points (parasite stages and soluble proteins) were clearly indicated in the scheme as indeed, not the whole parasite blood stage proteome is investigated in the study but just a part of it (~47%, as the authors indeed indicate line 406). Please, edit also the legend of the figure accordingly.

This is correct, the soluble protein fraction from synchronised trophozoites was used in our proteomics studies. These details have been included in an updated Figure 2 and in the corresponding figure legend.

Question 4. Thermal stabilization. Figure 3B. Could the authors explain how they calculated or measured "absolute" protein abundances, and how this refers to a number of parasites in initial assays as this is not clear to me. Notably, abundance for PfA-M1 is much higher than for PF3D7_0604300, which are interesting "absolute" values.

Protein abundance was calculated using the mean peptide quantity of the stripped peptide sequence, with only precursors passing the Q-value threshold (0.01) considered for relative quantification. Within independent experiments, normalisation was based on total protein amount (determined by the BCA assay) rather than the initial number of parasites.

PfA-M1 is known to be a highly abundant protein and PF3D7_0604300 (as well as the other protein hits identified by thermal stability proteomics) are likely less abundant. It is noted that abundance is also dependent on ionisation efficiency and trypsin digestion efficiency. Therefore, we avoid comparing absolute abundances across proteins and use relative differences across conditions instead.

NB: the word “absolute” in the text (“absolute fold-change”) refers to the absolute value of the fold-change (i.e. positive or negative), and not to absolute quantification of proteins. The preceding text in each case clarifies that these are based on “relative peptide abundance”.

Question 5. Figure 5A. How do the authors explain peptides whose abundances are decreasing instead of increasing? Figure 5C. Could the authors provide digital cues (aa numbers or positions) on the ribbon representation of the PfA-M1 sequence? It is difficult to correlate the position of the 3D domains with respect to the primary structure of the protein. Also, the "yellow" supposed to show the "drug ligand" is really not very visible.

LiP-MS is based on the principle that ligand binding alters the local proteolytic susceptibility of a protein to a non-specific protease (in this case proteinase K, PK). In this sense, in LiP-MS we are not looking at variations in the stability of whole proteins (as is the case with thermal stability proteomics, where proteins detected with significantly higher abundance in treated relative to control samples reflects thermal stabilisation of the target due to ligand binding), but differences in peptide patterns between treated and control samples that reflect a change in the ability of PK to cleave the target. Thus, in the bound state, the ligand prevents proteolysis with PK. This results in decreased abundance of peptides with non-tryptic ends (as PK cannot access the region around where the ligand is bound) and increased abundance of the corresponding fully tryptic peptide, when compared to the free target. This concept is demonstrated in Fig. 4A and is explained in the text (lines 279-282) and Fig. 4 figure legend.

To aid visualisation, we have not added amino acid positions on the PfA-M1 sequence in Fig. 5, but have provided amino acid positions for all peptides in Supplementary File 3. We have also changed the colour of the ligand in Fig. 5C to blue and increased transparency of the binding and centre of mass neighbourhoods.

Question 6. Gametocyte assays. Line 824 states that several compounds were used as positive controls for anti-gametocyte activity (chloroquine, artesunate, pyronaridine, pyrimethamine, dihydroartemisinin, and methylene blue) and line 821 states that the biological effects are measured against puromycin. This is not very clear to me, could the authors comment on this?

This wording has been clarified in the methods to reflect that 5 µM puromycin was used as the positive control to calculate percent viability, whereas the other antimalarials were run in parallel as reference compounds with known anti-gametocyte activity (line 862).

Question 7. Metabolomics. Metabolomic assays were done on parasites at 28h pi, incubated for 1h with 3x EC50 of MIPS2673. You mention applying the drug on 2x10E8 infected red blood cells (line 838) but you do not explain how you isolate these infected red blood cells from non-infected red blood cells. Could you please specify this?

Metabolomics studies were performed such that cultures at 2% haematocrit and 6% trophozoite-stage parasitaemia (representing 2 x 108 cells in total, rather than 2 x 108 infected cells) were treated with compound or vehicle and after 1 h metabolites were extracted. This methodological detail has been clarified in the methods (line 875).

Question 8. Figure 3B. Does this diagram come from the experimental 3D structure created by the authors (8SLO) or from molecular modeling? Please specify in the legend (line 1305).

The diagram showing the binding mode of MIPS2673 bound to PfA-M1 comes from the experimentally determined 3D structure (PDB ID: 8SLO). This has now been stated in the figure legend. Note that the structural diagram refers to Fig. 1B (not Fig. 3B as indicated by the reviewer). The experimentally determined PfA-M1 structure with MIPS2673 bound (PDB ID: 8SLO) was also used to map LiP peptides and estimate the MIPS2673 binding site in Fig. 5, which is also now reflected in the appropriate section of the text (line 308) and Fig. 5 legend.

Question 9. Line 745. Why not indicate µm concentration for this H-Leu-NHMec substrate while it is indicated for the other substrates mentioned in the rest of the paragraph (H-Ala-NHMec, 20 μM, etc..). Also in this section (Enzyme assays) the pH at which the various enzymatic assays were done is missing.

All enzyme assays were performed at pH 8.0. The concentration of H-Leu-NHMec varied depending on the enzyme assayed, as follows: 20 µM for PfA-M1, 40 µM for PvA-M1 and 100 µM for ERAP1 and ERAP2. This information is now clearly stated in the methods section (lines 782 and 787) and as a footnote for Supplemental Table S1.

Question 10. Line 830, please define FBS.

Fetal bovine serum (FBS) has been added where appropriate (line 867).

Question 11. The authors mention in the title the targeting of several plasmodium species, but the only experimental study on the Plasmodium vivax species concerns the use of the recombinant enzyme Pv-M1. Authors also mention "multi-stage targets", but ultimately only look at erythrocyte stages and three different gametocyte stages.

We have now removed the words “cross-species” and “multi-stage” from the manuscript title and abstract so as not to overstate these findings. We have also added the word “potential” in the manuscript text to clarify that selective M1 inhibition could offer a potential multistage and cross species strategy for malaria.

Question 12. Supplemental Table S1. I would suggest replacing "Percent inhibition by MIPS2673 of PfA-M1 and Pv-M1 aminopeptidases compared to selected human M1 homologues" with "Percent inhibition by MIPS2673 of PfA-M1 and Pv-M1 aminopeptidase activities compared to selected human M1 homologues".

Done.

Question 13. Supplemental Table S3. Here you indicate IC50 while in text and Figure 1 you quote EC50. Why this difference?

This has now been changed to EC50 in Supplemental Table S3.

Reviewer #2 (Recommendations For The Authors):

Amendments that I would recommend in order to improve the presentation include all four parts of the study:

(1) In vitro antiparasitic activity of MIPS2673.

The authors showed that MIPS2673 inhibits parasite growth with IC50 of 324nM measured by a standard drug sensitivity assay, Fig 1C. This is all well and good, but it would be helpful to include at least one if not more other compounds such as antimalaria drugs and/or their earlier inhibitors (e.g. inhibitor 1) for comparisons. This is typically done to show that the assay in this manuscript is fully compatible with previous studies. It will also give a better view of how the selective inhibition of PfM1 kills the parasite, specifically.

Alongside MIPS2673, we also analysed the potency of the known antimalarial artesunate, which was found to have an EC50 of 4 nM. This value agrees with the expected potency of artesunate and indicates our MIPS2673 value of 324 nM is indeed compatible with previous studies. We have now reported the artesunate EC50 value for reference (lines 197-198 and Fig. S1).

Next, the authors proceeded to investigate the stage-specific effect of MIPS2673 but this time doing a survival assay instead of proper IC50 estimations (Figure 1. I wonder why? Drug survival assays have typically very limited information content and measuring proper IC50 in stage-specific wash-off assays would be much more informative.

We performed single concentration stage specificity assays to determine the parasite asexual stage at which MIPS2673 is most active. This involved washing off the compound after a 24 h exposure in rings or trophozoites and determining parasite viability in the next asexual lifecycle. While a full dose response curve would allow generation of an EC50 value against the respective parasite stages, this information is unlikely to change the interpretation that MIPS2673 is more active against trophozoites stages than against rings.

Finally, in Figure 1E, the authors present the fact that the MIPS2673 arrests the parasite development. This is done by presenting a single (presumably representative) cell per time point. This is in my view highly insufficient. I recommend this figure be supplemented by parasite stage counts or other more comprehensive data representation. Also, the authors mention that while there is a growth arrest, hemoglobin is still being made. From the cell images, I can not see anything that supports this statement.

We thank the reviewer for this constructive comment and they are correct in their assessment that these are representative parasite images at the respective time points. To address the reviewers concerns we have now provided cell counts from each treatment condition (Fig. 1E) at selected time points, which shows parasite stalling at the ring to trophozoite transition under drug treatment. On reflection, we agree that it is difficult to determine the presence of haemozoin from our images and have removed this statement.

(2) Protein thermal shift profiling. In the next step, the authors proceed to carry out cellular thermal shift profiling to show that PfM1 indeed interacts with MIPS2673, this time in the context of the total protein lysates from P. falciparum. This section of the study is in my view quite solid and indeed it is nice to see that the inhibitor causes a thermal shift of PfM1 which further supports what was already expected: interaction.

I have no problem with this study in terms of the technical outcome but I would urge the authors to tone down the interpretation of these results in two ways.

Four other proteins were found to be shifted by the inhibitor which also indicates interactions. Calling it simply "off-target" interactions might not represent the truth. The authors should explore and in some way comment that interactions with these proteins could contribute to the MIPS2673 MOA. I do not suggest conducting any more studies but simply acknowledge this situation. Identifying more than one target is indeed very common in CETSA studies and it would be helpful to acknowledge this here as well.

We agree that identifying binding proteins in addition to the “expected” target is commonplace, and is indeed one of the benefits of this unbiased and proteome-wide approach. In the results and discussion, we have now amended our language to refer to these additional hits as MIPS2673-interacting proteins. In our original manuscript we dedicate a paragraph in the discussion to these additional interacting proteins and the likelihood of them being targets that contribute to antimalarial activity. Of these four additional interacting proteins, only the putative AP2 domain transcription factor (PF3D7_1239200) is predicted to be essential for blood stage growth and is therefore the only protein from this additional four that would likely contribute to antimalarial activity. These points are explicitly stated in the discussion (lines 530-550). Notably, all of the other interacting proteins identified in our thermal stability dataset were detected in our LiP-MS experiment but were not identified as interacting proteins by this method. The remaining three proteins were two non-essential P. falciparum proteins with unknown functions (PF3D7_1026000 and PF3D7_0604300) that are poorly described in the literature and a human protein (RAB39A). Further analysis of these other thermal stability proteomics hits in our LiP-MS dataset (see responses to Reviewer #3) identified none or only 1 significant LiP peptide from these proteins across our LiP-MS datasets, indicating they are likely to be false positive hits. Caveats around identifying protein targets by different deconvolution methods are also now addressed (lines 545-550).

At some point, the author argues that causing shifts of only four/five proteins including PfM1 shows that MIPS2673 does not interact with other (off) targets. Here one must be careful to present the lack of shifts in the CETSA as proof of no interaction. There are many reasons why thermal shifts are not observed including the physical properties of the individual proteins, detection limit etc. Again I suggest adjusting these statements accordingly.

We thank the reviewer for raising this important point and have now included additional discussion around this comment (lines 545-550).

Finally, I am not convinced that Figure 2 presents nothing more than the overall experimental scheme with not much new information. Many of such schemes were published previously in the original publication of thermal profiling. I would suggest omitting it from the main text and shifting it into supplementary methods etc.

We agree that similar schemes have been published previously, especially for thermal proteome profiling, and acknowledge the reviewer’s suggestion of moving this figure to the supplemental material. However, we have kept Fig. 2 in the main text as this scheme also incorporates a LiP-MS workflow for malaria drug target deconvolution (the first to do so) and also to satisfy the additional details requested for this figure by Reviewer #1 (question 3).

(3) Identification of MIPS2673 target proteins using LiP-MS. In the next step, the authors carried out the limited proteolysis analysis with the rationale that protein peptides that are near the inhibitor binding site will exhibit higher resilience to proteolysis. The authors did a very good job of showing this for PfM1-MISP2673 interaction. This part is very impressive from a technological perspective, and I congratulate the authors on such achievement. I imagine these types of studies require very precise optimizations and performance.

Here, however, I struggle with the meaning of this experiment for the overall flow of the manuscript. It seems that the binding pocket of MIPS2673 is less known since the inhibitor was designed for it. In fact, the authors mentioned that the crystal structure of PfM1 is available. From this perspective, the LiP-MS study represents more of a technical proof of concept for future drug target analysis but has limited contribution to the already quite well-established PfM1-MISP2673 interaction. Perhaps this could be presented in this way in the text.

We thank the reviewer for this comment and they are correct that we solved the crystal structure of PfA-M1 bound to MIPS2673. We wish to highlight that the primary reason for performing the LiP-MS study was as an independent and complementary target deconvolution method to narrow down the shortlist of targets identified with thermal stability proteomics, and validate with high confidence that PfA-M1 is indeed the primary target of MIPS2673 in parasites. The use of a complementary approach based on a different biophysical principle (proteolytic susceptibility vs thermal stability) would also allow us to identify MIPS2673 interacting proteins that may not be detectable by thermal stability proteomics, for example targets that do not alter their thermal stability upon ligand binding. The text in the results and discussion has been amended to clarify these points (lines 266-268 and 545-550).

Furthermore, we agree that correctly predicting the MIPS2673 binding site on PfA-M1 using our LiP-MS peptide data is a technical proof of concept. Indeed, we wished to highlight the potential utility of LiP-MS for identifying both the protein targets of drugs and predicting their binding site, which is not possible with many other target deconvolution approaches. This point has been updated in the text (lines 303-304, 459-461).

(4) Metabolomic profiling of MIPS2673 inhibition showed a massive accumulation of short peptides which clearly indicates that this inhibitor blocks some proteolytic activity of short peptides, presumably products of upstream proteolytic activities. Here the authors argue, that because many of these detected short (di-/tri-) peptides could be mapped on the hemoglobin protein sequence, this must be their origin. Although this might be the case the author could not exclude the fact that at least some of these come from other sources (e.g. Plasmodium proteins). It would be quite helpful to comment on such a possibility as well. In particular, it was mentioned that the main subcellular localization of PfM1 is in the cytoplasm while most if not all hemoglobin digestion occurs in the digestive vacuole...?

Indeed, we agree that Pf_A-M1 is likely processing both Hb and non-Hb peptides and do not definitively conclude that all dysregulated peptides must be derived from haemoglobin. A subset of dysregulated peptides cannot be mapped to haemoglobin and must have an alternative source such as other host proteins or turnover of parasite proteins. We have amended the discussion to better reflect these possible alternate peptide sources (480-482). Although the peptides detected in the metabolomics study (2-5 amino acids) are too short to be definitively assigned to any specific parasite or RBC protein, it is important to note that our analysis strongly indicates that the majority, but not all, of dysregulated peptides are more likely to originate from haemoglobin than other human or parasite proteins. This is based on sequence mapping, which was aided by acquiring MS/MS data for a subset of dysregulated peptides from which we derive accurate sequences (as opposed to residue composition inferred from total peptide mass) to more directly link dysregulated peptides to haemoglobin. We further quantified the sequence similarity of dysregulated peptides to all detectable proteins in the _P. falciparum infected erythrocyte proteome (~4700 proteins), showing that these peptides are statistically more similar to haemoglobin than other host or parasite proteins.

The apparent disconnect between PfA-M1 localisation (cytosol) and the predominant site of haemoglobin digestion (digestive vacuole, DV) is explained by the fact that peptides originating from digestion of haemoglobin in the DV are required to be transported into the cytoplasm for further cleavage by peptidases, including PfA-M1. This point has now been clarified in the discussion (lines 473-474).

Reviewer #3 (Recommendations For The Authors):

(1) Thermal stability studies confirmed that PfA-M1 was a binding target, however, there were other proteins consistently identified in the thermal stability studies. This raises the question as to their potential role as additional targets of this inhibitor. The authors dismiss these because they are not metalloproteases, but further analysis is warranted. This is particularly important as the authors were not able to generate mutants using in vitro evolution of resistance strategies. This often indicates that the inhibitor has more than one target.

We thank the reviewer for this comment. The possibility of other targets contributing to MIPS2673 activity was also raised by Reviewer #2 (question 2) and is addressed above. Further to our response to Reviewer #2, we agree that the inability to generate resistant parasites in vitro could indicate that inhibition of multiple essential parasite proteins (including PfA-M1) contribute to MIPS2673 activity and do not rule out this possibility. It may also indicate the target has a very high barrier for resistance and is unable to tolerate resistance causing mutations as they are deleterious to function. Indeed, previous attempts to mutate PfA-M1 (references 12 and 50), and our own attempts to generate MIPS2673 resistant parasites in vitro (unpublished), were unsuccessful. It is important to note that of the hits reproducibly identified using thermal stability proteomics, only PfA-M1 and a putative AP2 domain transcription factor (PF3D7_1239200) are predicted to be essential for blood stage growth. We have explicitly stated that PF3D7_1239200 could also contribute to activity (line 533 and 537).

As we identified multiple hits with thermal stability proteomics we employed the complementary LiP-MS method to further investigate the target landscape of MIPS2673. PfA-M1 was the only protein reproducibly identified as the target through this approach. Importantly, the five proteins identified as hits by thermal stability proteomics were also detected in our LiP-MS datasets, but only PfA-M1 was identified as a target by both target deconvolution methods, strongly indicating it is the primary target of MIPS2673 in parasites. An important caveat is that we profiled the soluble proteome (we did not include detergents necessary for extracting membrane proteins as they may interfere with these stability assays) and other factors (e.g. the biophysical properties of the protein) will impact on whether ligand induced stabilisation events are detected. We have added additional text in the discussion around the above points (lines 545-550).

While we do not definitively rule out other MIPS2673 interacting proteins existing in parasites (that possibly also contribute to activity), our metabolomics studies indicated no functional impact by MIPS2673 outside of elevated levels of short peptides. This is indicative of aminopeptidase inhibition and the profile of peptide accumulation was distinct from a known PfA-M17 inhibitor, and other antimalarials, further pointing to selective inhibition of the PfA-M1 enzyme by MIPS2673 being responsible for antimalarial activity.

(2) The next set of experiments focused on a limited proteolysis approach. Again several proteins were identified as interacting with MIPS2673 including metalloproteases. The authors go on to analyze the LiP-MS data to identify the peptide from PfA-M1 which putatively interacts with MIPS2673. The authors are clearly focused on PfA-M1 as the target, but a further analysis of the other proteins identified by this method would be warranted and would provide evidence to either support or refute the authors' conclusions.

As PfA-M1 was the only protein reproducibly identified as an interacting protein across both LiP-MS experiments (and by thermal stability proteomics) we focused our analysis on this protein. However, we agree that further analysis of the other putative interacting proteins would be valuable. Additional analysis was performed  (see new figure S4) on the other interacting proteins identified by thermal stability proteomics and the other interacting proteins identified in LiP-MS experiment one, as no other proteins (apart from PfA-M1) were identified as hits in the second LiP-MS experiment (lines 314-318, 495-505, 740-762 and Fig. S4). Using the common peptides detected across both LiP-MS experiments we mapped significant LiP peptides to the structures of the other putative MIPS2673-interacting proteins, where a structure was available and significant LiP-MS peptides were detected, and measured the minimum distance to expected binding sites. It is noted that when using the same criteria for a significant LiP peptide that we used for our PfA-M1 analysis, only one significant LiP peptide is identified from these other putative interacting proteins (YSPSFMSFK from PfADA). Therefore, we used a less stringent criteria for defining significant LiP peptides for these other proteins (see methods and Fig. S4 legend) in order to identify significant LiP peptides to map to structures. This analysis showed that, with the exception of PfA-M17, significant LiP-MS peptides for these other proteins are not significantly closer to binding sites than all other detected peptides, supporting our assertion that these other proteins are likely to be false positives or not functionally relevant MIPS2673 interacting proteins. Although significant peptides from PfA-M17 were closer to the binding site, our thermal stability and metabolomics data, combined with our previous work on the PfA-M17 enzyme, argue against this being a functionally relevant target (see lines 362-374 and 486-529 for a more detailed discussion). Another possible explanation for this result is that peptide substrates accumulating due to primary inhibition of PfA-M1 interact with PfA-M17, leading to structural changes around the enzyme active site that are detected by LiP-MS.

(3) The final set of experiments was an untargeted metabolomics analysis. They identified 97 peptides as significantly dysregulated after MIPS2673 treatment of infected cells and most of these peptides were derived from one of the hemoglobin chains. The accumulation of peptides was consistent with a block in hemoglobin digestion. This experiment does reveal a potential functional confirmation, but questions remain as to specificity.

As indicated, the accumulation of short peptides identified by metabolomics suggests MIPS2673 perturbs aminopeptidase function. Many of these peptides (but not all) likely map to haemoglobin and are more haemoglobin-like than other proteins in the infected red blood cell proteome. An effect on a subset of non-haemoglobin peptides is also apparent and we have added this to our discussion (also refer to our response to question 4 from Reviewer #2). A direct comparison to our previous metabolomics analysis of a specific PfA-M17 inhibitor (MIPS2571, reference 11) revealed MIPS2673 induces a unique metabolomic profile. The extent of peptide accumulation differed and a subset of short basic peptides (containing Lys or Arg) were elevated only by MIPS2673, consistent with the broad substrate preference of PfA-M1. Importantly, the metabolomics profile induced by MIPS2673 is the opposite of many other antimalarials, which cause depletion of haemoglobin peptides. Taken together, the profile of short peptide accumulation induced by MIPS2673 is consistent with specific inhibition of PfA-M1.

(4) Overall, this is an interesting series of experiments that have identified a putative inhibitor of PfA-M1 and PvA-M1. The work would be significantly strengthened by structure-aided analysis. It is unclear why putative binding sites cannot be analyzed via specific mutagenesis of the recombinant enzyme.

Contrary to this comment we solved the crystal structure of PfA-M1 bound to MIPS2673, determining its binding mechanism to the enzyme. This was further supported through proteomics-based structural analysis by LiP-MS. Undertaking site specific mutagenesis would be interesting to further probe the binding dynamics of MIPS2673 to the M1 protein. However, we believe it is beyond the scope of this study and would not change our conclusion that MIPS2673 binds to PfA-M1, which we have shown using multiple unbiased proteomics-based methods, enzyme assays and X-ray crystallography.

(5) In the thermal stability and LiP -MS analysis, other proteins were consistently identified in addition to PfA-M1 and yet no additional analysis was undertaken to explore these as potential targets.

As addressed in our previous responses, across independent thermal stability proteomics experiments we consistently identified 5 interacting proteins, including the expected target PfA-M1. In contrast, only PfA-M1 was reproducible across independent LiP-MS experiments. While several plausible putative targets (including aminopeptidases and metalloproteins) were identified in one of our LiP-MS experiment, they appear to be false discoveries and not responsible for the antiparasitic activity of MIPS2673, as peptide-level stabilisation was not consistent across independent LiP-MS experiments, and an interaction is refuted by our thermal stability, metabolomics and recombinant enzyme inhibition data. We have now performed further analysis of these other putative interacting proteins, which also argues against them being likely interacting proteins (see also response to question 2). We have also added to our existing discussion on possible MIPS2673 targets and the likelihood of these proteins contributing to antimalarial activity (lines 486-550).

(6) The metabolomics experiments were potentially interesting, but without significant additional work including different lengths of treatment and different stages of the parasite, the conclusions drawn are overstated. Many treatments disrupt hemoglobin digestion - either directly or indirectly and from the data presented here it is premature to conclude that treatment with MIPS2673 directly inhibits hemoglobin digestion.

Our metabolomics studies were performed using typical experimental conditions for investigating the antimalarial mechanisms of compounds by metabolomics (see references 11, 39, 40 and 55-57). We used a short 1 h incubation at 3x EC50 allowing us to profile the primary parasite pathways affected by MIPS2673 and avoid a nonspecific death phenotype associated with longer incubations. As addressed in our response to Reviewer #1 (question 2) we focused on trophozoite infected red blood cells as this is the stage most susceptible to MIPS2673 and when one presumes the greatest functional impact would be seen. It is possible that an expanded kinetic metabolomics analysis may reveal secondary mechanisms involved in MIPS2673 activity and we have now acknowledged this in the manuscript (lines 515-516). However, even though secondary mechanisms may become apparent at longer incubations it also becomes difficult to uncouple drug specific responses from nonspecific death effects. We believe any additional information provided by an expanded metabolomics analysis is unlikely to outweigh the significant extra financial cost associated with this type of experiment.

It is correct that many antimalarial compounds appear to disrupt haemoglobin digestion when analysed by metabolomics. However, as indicated in our manuscript (lines 369-373) and previous responses, the profile of elevated haemoglobin peptides induced by MIPS2673 is substantially different to the profile caused by other antimalarials. For example, artemisinins and mefloquine cause haemoglobin peptide depletion (references 55-57) and chloroquine results in increased levels of a different subset of non-haemoglobin peptides (see Creek et al. 2016). While there is some overlap in profile with a selective M17 inhibitor (our previous work, reference 11), the level of enrichment of these peptides is different and MIPS2673 also induces accumulation of a distinct set of basic peptides consistent with the substrate preference of the PfA-M1 enzyme. As we show that MIPS2673 does not inhibit other parasite aminopeptidases, a likely explanation for the profile overlap is that the build-up of substrates that cannot be processed by PfA-M1 leads to secondary dysregulation of other aminopeptidases. Our analyses (sequence mapping, MS/MS analysis and sequence similarities to all infected red blood cell proteins) strongly indicate that the majority of elevated peptides (but not all) originate from haemoglobin. Combined with our proteomics and recombinant enzyme data indicating direct engagement of PfA-M1, and with previous literature indicating the enzyme functions to cleave amino acids from haemoglobin-derived peptides, our data indicates MIPS2673 likely directly perturbs the haemoglobin digestion pathway through PfA-M1 inhibition.

(7) Finally, the potency of this compound on parasites grown in vitro is 300 nM - this would need improvements in potency and demonstration of in vivo efficacy in the SCID mouse model to consider this a candidate for a drug.

We do not propose MIPS2673 as an antimalarial candidate. The experiments presented here were centred on target validation rather than identification of an antimalarial lead, which may be the focus of future studies. To avoid this confusion, we have amended the manuscript title and language throughout to clarify this point.

Author response:

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

eLife assessment

This study advances our understanding of the allosteric regulation of anaerobic ribonucleotide reductases (RNRs) by nucleotides, providing valuable new structural insight into class III RNRs containing ATP cones. The cryo-EM structural characterization of the system is solid, but some open questions remain about the interpretation of activity/binding assays and the newly incorporated HDX-MS results. The work will be of interest to biochemists and structural biologists working on ribonucleotide reductases and other allosterically regulated enzymes.

Public Reviews:

Reviewer #1 (Public Review):

The goal of this study is to understand the allosteric mechanism of overall activity regulation in an anaerobic ribonucleotide reductase (RNR) that contains an ATP-cone domain. Through cryo-EM structural analysis of various nucleotide-bound states of the RNR, the mechanism of dATP inhibition is found to involve order-disorder transitions in the active site. These effects appear to prevent binding of substrate and a radical transfer needed to initiate the reaction.

Strengths of the manuscript include the comprehensive nature of the work - including both numerous structures of different forms of the RNR and detailed characterization of enzyme activity to establish the parameters of dATP inhibition. The manuscript has been improved in a revision by performing additional experiments to help corroborate certain aspects of the study. But these new experiments do not address all of the open questions about the structural basis for mechanism. Additionally, some questions about the strength of biochemical data and fit of binding or kinetic curves to data that were raised by other referees still remain. Some experimental observations are not consistent with the proposed model. For example, why does dATP enhance Gly radical formation when the proposed mechanism of dATP inhibition involves disorder in the Gly radical domain?

The work is impactful because it reports initial observations about a potentially new mode of allosteric inhibition in this enzyme class. It also sets the stage for future work to understand the molecular basis for this phenomenon in more detail.

We express our gratitude to the reviewer for dedicating time to review our work and for the overall favorable assessment. We agree that the question of exactly how much the glycyl radical domain becomes more mobile without losing the glycyl radical entirely is an unresolved one but we also think that our work sets a solid basis for future experiments by us and others.

Reviewer #3 (Public Review):

The manuscript by Bimai et al describes a structural and functional characterization of an anaerobic ribonucleotide reductase (RNR) enzyme from the human microbe, P. copri. More specifically, the authors aimed to characterize the mechanism by how (d)ATP modulates nucleotide reduction in this anaerobic RNR, using a combination of enzyme kinetics, binding thermodynamics, and cryo-EM structural determination, complemented by hydrogen-deuterium exchange (HDX). One of the principal findings of this paper is the ordering of a NxN 'flap' in the presence of ATP that promotes RNR catalysis and the disordering (or increased protein dynamics) of both this flap and the glycyl radical domain (GRD) when the inhibitory effector, dATP, binds. The latter is correlated with a loss of substrate binding, which is the likely mechanism for dATP inhibition. It is important to note that the GRD is remote (>30 Ang) from the binding site of the dATP molecule, suggesting long-range communication of the structural (dis)ordering. The authors also present evidence for a shift in oligomerization in the presence of dATP. The work does provide evidence for new insights/views into the subtle differences of nucleotide modulation (allostery) of RNR, in a class III system, through long-range interactions.

The strengths of the work are the impressive, in-depth structural analysis of the various regulated forms of PcRNR by (d)ATP using cryo-EM. The authors present seven different models in total, with striking differences in oligomerization and (dis)ordering of select structural features, including the GRD that is integral to catalysis. The authors present several, complementary biochemical experiments (ITC, MST, EPR, kinetics) aimed at resolving the binding and regulatory mechanism of the enzyme by various nucleotides. The authors present a good breadth of the literature in which the focus of allosteric regulation of RNRs has been on the aerobic orthologues.

The addition of hydrogen-deuterium exchange mass spectrometry (HDX-MS) complements the results originating from cryo-EM data. Most notably, is the observation of the enhanced exchange (albeit quite subtle) of the GRD domain in the presence of dATP that matches the loss of structural information in this region in the cryo-EM data. The most pronounced and compelling HDX results are seen in the form of dATP-induced protection of peptides immediately adjacent to the b-hairpin at the s-site, where dATP is expected to bind based on cryo-EM. It is clear that the presence of dATP increases the rigidity of this region.

We are happy that both reviewers find the HDX-MS experiments to be a valuable addition to the existing data.

Weaknesses:

The discussion of the change in peptide mobility in the N-terminal region is complicated by the presence of bimodal mass spectral features and this may prevent detailed interpretation of the data, especially for select peptide region that shows opposite trends upon nucleotide association.

Further, the HDX data in the NxN flap is unchanged upon nucleotide binding (ATP, dATP, or CTP), despite changes observed in the cryo-EM data.

We are grateful to the reviewer for the comprehensive feedback on the HDX-MS part and for identifying areas for improvement. The HDX analysis was of course undertaken with the intention of identifying differences in disorder of the NxN flap and GRD region. From an HDX perspective both regions were found to be highly susceptible to HDX regardless of state/ligand, due to surface accessibility and/or very fast dynamics. However, this does not mean that there is no difference in the degree of order of these regions upon ligand addition, simply that we with HDX-MS, in the limited time span of 30-3000 seconds, could not conclusively support an increased disorder. We have rephrased the discussion text to reflect this fact

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

On page 5 (and throughout the manuscript) there are some inconsistencies in how dissociation constants for effectors and inhibitors are described - for example, D in KD is sometimes subscripted and sometimes not.

Thank you for noticing these remaining errors. We hope that we have fixed all of them now.

Reviewer #3 (Recommendations For The Authors):

The authors addressed many of the initial concerns raised. The addition of the HDX-MS data in this revision is a welcomed contribution to the work and complements the cryo-EM data. In select cases, the data may be over-interpreted. This reviewer suggests that the authors revise the text in this section so that it is more consistent with the presented data.

Specific points:

(1) The bimodal mass spectral features in the N-terminal domain complicate the data interpretation. Specifically for peptides in 81-99 region, the fast exchanging feature shows protection in the presence of (d)ATP/CTP, but the opposite trend is observed for the slow exchanging species. It is therefore advisable to not make absolutes about the HDX results in this region, as the data are complicated.

As stated by the reviewer, it is not possible from the presented HDX data to deduce if this is a result of 50% loaded dimer or the oligomerization state of the protein. We have remedied this by removing mentions of a difference between the dATP and ATP in bimodality. Also, we have addressed this in the text by stating that the main reason is most likely the different oligomerization states present in solution. Nevertheless, it is clear from the HDX data that the N-terminal region and 81-99 are very interesting, and it was somewhat disappointing that due to the dynamics of the oligomerization it was not possible to SEC-purify pure dimer or tetramer samples for HDX-MS, in order to deconvolute the cause.

(2) Related to #1, the authors assign the bimodal HDX behavior to EX1 mechanism, but this is not necessarily (and unlikely) true based on the limited time points. The authors also state that it originates from the heterogeneity of the sample: "a mixture of states" which could reflect the mixture of oligomerization states. The authors should be careful assigning EX1 mechanism unless there are compelling results to support it.

We apologize for the unfortunate phrasing. It was not our intention to imply that the bimodality is due to true EX1 kinetics. See the above answer. The mention of EX1 has been removed from the discussion text.

(3) The deuterium uptake for peptide 118-126 is very small (~1Da) compared to the length of the peptide. The change in deuterium uptake (<0.25Da) from dATP is very small; the authors should proceed with caution when presenting interpretations of such small differences.

We agree with the reviewer that extra caution should be taken when dealing with such a small difference. However, the 118-126 peptide has been significance tested in both HDExaminer and Deuteros 2.0, and we also observed this for more than one run. The difference in uptake is small but increases to significance at the longer labelling times. The proximity to the NxN flap makes it interesting in context of an allosteric conformational change. i.e the dynamics of the NxN might be too fast so we can only see some secondary effects. We would like to keep the data  in Figure 10 for reasons of transparency. In essence this is similar to the observed bimodality mentioned above: we cannot fully explain the observation but present the data as it was observed.

(4) On p. 22, the authors should consider revising the following statement: "confirming dATP binding to the s-site." Even though the HDX data are most compelling for the protection of peptides 178-204 and 330-348 that are adjacent to the beta-hairpin at the s-site, these data cannot "confirm" a binding site for a small molecule, such as dATP.

We appreciate that the reviewer has pointed out that the statement can be misleading, and we agree that the binding site of small molecules can’t be confirmed based solely on HDX data. The sentence reformulated to clarify that the binding site was confirmed based on the combined evidence of HDX data and the previously presented biochemical and structural data on the s-site.

Author response:

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

eLife assessment

This manuscript reports important in vitro biochemical and in planta experiments to study the receptor activation mechanism of plant membrane receptor kinase complexes with non-catalytic intracellular kinase domains. Several lines of evidence convincingly show that one such putative pseudokinase, the immune receptor EFR achieves an active conformation following phosphorylation by a co-receptor kinase, and then in turn activates the co-receptor kinase allosterically to enable it to phosphorylate down-stream signaling components. This manuscript will be of interest to scientists focusing on cell signalling and allosteric regulation.

We wish to clarify that EFR is itself, not a pseudokinase. We could show in previous work (Bender et al., 2021; https://doi.org/10.1073/pnas.2108242118 ) that EFR has catalytic activity in vitro. This catalytic activity is, however, not required for elf18-induced immune signaling in planta.

Public Reviews:

Reviewer #1 (Public Review):

Summary

The authors use an elegant but somewhat artificial heterodimerisation approach to activate the isolated cytoplasmic domains of different receptor kinases (RKs) including the receptor kinase BRI1 and EFR. The developmental RK BRI1 is known to be activated by the co-receptor BAK1. Active BRI1 is then able to phosphorylate downstream substrates. The immune receptor EFR is also an active protein kinase also activated by the co-receptor BAK1. EFR however appears to have little or no kinase activity but seems to use an allosteric mechanism to in turn enable BAK1 to phosphorylate the substrate kinase BIK1. EFR tyrosine phosphorylation by BAK1 appears to trigger a conformational change in EFR, activating the receptor. Likewise, kinase activating mutations can cause similar conformational transitions in EFR and also in BAK1 in vitro and in planta.

We wish to clarify that we make no strong link between tyrosine phosphorylation and the conformational change leading to activation of the complex. Rather, the HDX-MS data demonstrate the structural importance of Tyr836 for the activation mechanism. At present, we do not know how phosphorylation of the residue would affect the activation process.

Strengths:

I particularly liked The HDX experiments coupled with mutational analysis (Fig. 2) and the design and testing of the kinase activating mutations (Fig. 3), as they provide novel mechanistic insights into the activation mechanisms of EFR and of BAK1. These findings are nicely extended by the large-scale identification of EFR-related RKs from different species with potentially similar activation mechanisms (Fig. 5).

Weaknesses:

In my opinion, there are currently two major issues with the present manuscript. (1) The authors have previously reported that the EFR kinase activity is dispensible for immune signaling (https://pubmed.ncbi.nlm.nih.gov/34531323/) but the wild-type EFR receptor still leads to a much better phosphorylation of the BIK1 substrate when compared to the kinase inactive D849N mutant protein (Fig. 1). (2) How the active-like conformation of EFR is in turn activating BAK1 is poorly characterized, but appears to be the main step in the activation of the receptor complex. Extending the HDX analyses to resting and Rap-activated receptor complexes could be a first step to address this question, but these HDX studies were not carried out due to technical limitations.

Overall this is an interesting study that aims to advance our understanding of the activation mechanisms of different plant receptor kinases with important functions in plant immunity.

Reviewer #2 (Public Review):

Summary:

Transmembrane signaling in plants is crucial for homeostasis. In this study, the authors set out to understand to what extent catalytic activity in the EFR tyrosine kinase is required in order to transmit a signal. This work was driven by mounting data that suggest many eukaryotic kinases do not rely on catalysis for signal transduction, relying instead on conformational switching to relay information. The crucial findings reported here involve the realisation that a kinase-inactive EFR can still activate (ie lead to downstream phosphorylation) of its partner protein BAK1. Using a convincing set of biochemical, mass spectrometric (HD-exchange) and in vivo assays, the team suggest a model in which EFR is likely phosphorylated in the canonical activation segment (where two Ser residues are present), which is sufficient to generate a conformation that can activate BAK1 through dimersation. A model is put forward involving C-helix positioning in BAK1, and the model extended to other 'non-RD' kinases in Arabidopsis kinases that likely do not require kinase activity for signaling.

We prefer not to describe EFR as a tyrosine kinase. It may be the case that EFR can function under certain conditions as a dual-specificity protein kinase, but this has never been demonstrated experimentally. We therefore describe EFR as a Ser/Thr protein kinase, since it is known that the isolated cytoplasmic domain can phosphorylate on Ser and Thr residues (Wang et al., 2014; https://doi.org/10.1016/j.jprot.2014.06.009).

Strengths:

The work uses logical and well-controlled approaches throughout, and is clear and convincing in most areas, linking data from IPs, kinase assays (including clear 32P-based biochemistry), HD-MX data (from non-phosphorylated EFR) structural biology, oxidative burst data and infectivity assays. Repetitions and statistical analysis all appear appropriate.

Overall, the work builds a convincing story and the discussion does a clear job of explaining the potential impact of these findings (and perhaps an explanation of why so many Arabidopsis kinases are 'pseudokinases', including XPS1 and XIIa6, where this is shown explicitly).

Weaknesses:

No major weaknesses are noted from reviewing the data and the paper follows a logical course built on solid foundations; the use of Tables to explain various experimental data pertinent to the reported studies is appreciated.

(1) The use of a, b,c, d in Figures 2C and 3C etc is confusing to this referee, and is now addressed in the latest version

(2) The debate about kinase v pseudokinases is well over a decade old. For non-experts, the kinase alignments/issues raised are in PMID: 23863165 and might prove useful if cited.

We have cited the suggested reference in the second paragraph of the discussion.

(3) Early on in the paper, the concept of kinases and pseudokinases related to R-spine (and extended R-spine) stability and regulation really needs to be more adequately introduced to explain what comes next; e.g. some of the key work in this area for RAF and Tyr kinases where mutual F-helix Phe amino acid changes are evaluated (conceptually similar to this study of the E-helix Tyr to Phe changes in EFR) should be cited (PMID: 17095602, 24567368 and 26925779).

As an alternative, we have amended the text in several places to focus on conformational toggling between active/inactive states rather than R-spine stability. We think that this keeps the message of our manuscript focused. We hope that the reviewer finds this acceptable.

(4) In my version, some of the experimental text is also currently in the wrong order (and no page numbers, so hard for me to state exactly where in the manuscript); However, I am certain that Figure 2C is mentioned in the text when the data are actually shown in Figure 3C for the EFR-SSAA protein.

Indeed, some references to Figure 2 in the text were incorrect. We have corrected these. References in the text to Figure 3 and the data reported therein are correct.

(5) Tyr 156 in PKA is not shown in Supplement 1, 2A as suggested in the text; for readers, it will be important to show the alignment of the Tyr residue in other kinases; this has been updated in the second version. Although it is clearly challenging to generate phosphorylated EFR (seemingly through Codon-expansion here?), it appears unlikely that a phosphorylated EFR protein, even semi-pure, couldn't have been assayed to test the idea that the phosphorylation drives/supports downstream signaling. What about a DD or EE mutation, as commonly used (perhaps over-used) in MEK-type studies?

Our aim with codon expansion was to generate recombinant protein carrying high-stoichiometry phosphorylation at sites which we have previously documented to be required for downstream signaling (Macho et al., 2014; Bender et al., 2021). We additionally demonstrated previously that a DD mutant of the activation loop sites in EFR does not fully complement the efr-1 mutant (Bender et al., 2021), suggesting that the Asp mutations are not good phospho-mimics in this context. We therefore did not generate DD or EE mutations for in vitro studies.

Impact:

The work is an important new step in the huge amount of follow-up work needed to examine how kinases and pseudokinases 'talk' to each other in (especially) the plant kingdom, where significant genetic expansions have occurred. The broader impact is that we might understand better how to manipulate signaling for the benefit of plants and mankind; as the authors suggest, their study is a natural progression both of their own work, and the kingdom-wide study of the Kannan group.

Reviewer #3 (Public Review):

The study presents strong evidence for allosteric activation of plant receptor kinases, which enhances our understanding of the non-catalytic mechanisms employed by this large family of receptors.

Plant receptor kinases (RKs) play a critical role in transducing extracellular signals. The activation of RKs involves homo- or heterodimerization of the RKs, and it is believed that mutual phosphorylation of their intracellular kinase domains initiates downstream signaling. However, this model faces a challenge in cases where the kinase domain exhibits pseudokinase characteristics. In their recent study, Mühlenbeck et al. reveal the non-catalytic activation mechanisms of the EFR-BAK1 complex in plant receptor kinase signaling. Specifically, they aimed to determine that the EFR kinase domain activates BAK1 not through its kinase activity, but rather by utilizing a "conformational toggle" mechanism to enter an active-like state, enabling allosteric trans-activation of BAK1. The study sought to elucidate the structural elements and mutations of EFR that affect this conformational switch, as well as explore the implications for immune signaling in plants. To investigate the activation mechanisms of the EFR-BAK1 complex, the research team employed a combination of mutational analysis, structural studies, and hydrogen-deuterium exchange mass spectrometry (HDX-MS) analysis. For instance, through HDX-MS analysis, Mühlenbeck et al. discovered that the EFR (Y836F) mutation impairs the accessibility of the active-like conformation. On the other hand, they identified the EFR (F761H) mutation as a potent intragenic suppressor capable of stabilizing the active-like conformation, highlighting the pivotal role of allosteric regulation in BAK1 kinase activation. The data obtained from this methodology strengthens their major conclusion. Moreover, the researchers propose that the allosteric activation mechanism may extend beyond the EFR-BAK1 complex, as it may also be partially conserved in the Arabidopsis LRR-RK XIIa kinases. This suggests a broader role for non-catalytic mechanisms in plant RK signaling.

The allosteric activation mechanism was demonstrated for receptor tyrosine kinases (RTKs) many years ago. A similar mechanism has been suggested for the activation of plant RKs, but experimental evidence for this conclusion is lacking. Data in this study represent a significant advancement in our understanding of non-catalytic mechanisms in plant RK signaling. By shedding light on the allosteric regulation of BAK1, the study provides a new paradigm for future research in this area.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

The authors have considered points 1-5 raised in my initial review and the revised manuscript contains a more balanced discussion and limitation section. No additional experiments have been performed to substantiate the envisioned allosteric activation mechanism of the co-receptor kinase BAK1 by the receptor EFR. I rewrote the public statement accordingly.

Reviewer #2 (Recommendations For The Authors):

Thanks for responding to my comments.

Reviewer #3 (Recommendations For The Authors):

The revised manuscript has fully addressed my previous concerns and is now suitable for publication in eLife.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary: 

Using concurrent in vivo whole-cell patch clamp and dendritic calcium imaging, the authors characterized how functional synaptic inputs across dendritic arborizations of mouse primary visual cortex layer 2/3 neurons emerge during the second postnatal week. They were able to identify spatially and functionally separated domains of clustered synapses in these neurons even before eye-opening and characterize how the clustering changes from P8 to P13. 

Strengths: 

The work is technically challenging and the findings are novel. The results support previous EM and immunostaining studies but provide in vivo evidence on the time course and the trajectory of how functional synaptic input develops. 

Weaknesses: 

There are some missing details about how the experiments were performed, and I also have some questions about the analyses. 

We have now added a more detailed description of the methods and added new supplemental figures and descriptions to clarify our analyses. Please find our responses to the specific points of this reviewer in the section “Recommendations for the authors” below.

Reviewer #2 (Public Review):

In this study, Leighton et al performed remarkable experiments by combining in-vivo patch-clamp recording with two-photon dendritic Ca2+ imaging. The voltage-clamp mode is a major improvement over the pioneer versions of this combinatorial experiment that has led to major breakthroughs in the neuroscience field for visualizing and understanding synaptic input activities in single cells in-vivo (sharp electrodes: Svoboda et al, Nature 1997, Helmchen et al, Nature Neurosci 1999; whole-cell current-clamp: Jia et al, Nature 2010, Chen et al, Nature 2011. I suggest that these papers would be cited). This is because in voltage-clamp mode, despite the full control of membrane voltage in-vivo not being realistic, is nevertheless most effective in preventing back-propagation action potentials, which would severely confound the measurement of individual synaptically-induced Ca2+ influx events. Furthermore, clamping the cell body at a strongly depolarized potential (here the authors did -30mV) also facilitates the detection of synaptically-induced Ca2+ influx. As a result, the authors successfully recorded high-quality Ca2+ imaging data that can be used for precise analysis. To date, even in view of the rapid progress of voltage-sensitive indicators and relevant imaging technologies in recent years, this very old 'art' of combining single-cell electrophysiology and two-photon imaging (ordinary, raster-scanned, video-rate imaging) of Ca2+ signals still enables measurements of the best level precision. 

We thank the reviewer for reminding us of these important previous studies that we cite now in the revised manuscript. 

On the other hand, the interpretation of data in this study is a bit narrow-minded and lacks a comprehensive picture. Some suggestions to improve the manuscript are as follows: 

(1) The authors made a segregation of 'spine synapse' and 'shaft synapse' based solely on the two photon images in-vivo. However, caution shall be taken here, because the optical resolution under in vivo imaging conditions like this cannot reliably tell apart whether a bright spot within or partially overlapping a segment of the dendrite is a spine on top of (or below) it. Therefore, what the authors consider as a 'shaft synapse' (by detecting Ca2+ hotspots) has an unknown probability of being just a spine on top or below the dendrite. If there is other imaging data of higher axial resolution to validate or calibrate, the authors shall take some further considerations or analysis to check the consistency of their data, as the authors do need such a segregation between spine and shaft synapses to show how they evolve over the brain development stages. 

We agree with the reviewer that the differentiation between spine and sha synapses can be difficult for those spines that are located above or below the dendric sha in the z-dimension because of the lower resolution of 2-photon microscopy in the z-dimension compared to the image plane. We have now added a new paragraph to the Methods section to describe in more detail how we identify spine and sha synapses and provide more examples in a new supplementary figure (Fig S5). We believe that we can identify spine and sha synapses reliably in most cases, but added a cautionary note to make the reader aware of potential misidentifications.

(2) The use of terminology 'bursts of spontaneous inputs' for describing voltage-clamp data seems improper. Conventionally, 'burst' refers to suprathreshold spike firing events, but here, the authors use 'burst' to refer to inward synaptic currents collected at the cell body. Not every excitatory synaptic input (or ensemble of inputs) activation will lead to spike firing under naturalistic conditions, therefore, these two concepts are not equivalent. It is recommended to use 'barrage of inputs' instead of 'burst of inputs'. Imagine a full picture of the entire dendritic tree, the fact that the authors could always capture spontaneous Ca2+ events here and there within a few pieces of dendrites within an arbitrary field-of-view suggests that, the whole dendritic tree must have many more such events going on as a barrage while the author's patch electrode picks up the summed current flow from the whole dendritic tree. 

We agree with the reviewer that “barrage” is a clearer term for multiple synaptic inputs occurring simultaneously and therefore we changed the terminology throughout the manuscript.

(3) Following the above issue, an analysis of the temporal correlation between synaptic (not segregating 'spine' or 'shaft') Ca2+ events and EPSCs is absent. Again, the authors drew arbitrary time windows to clump the events for statistical analysis. However, the demonstrated example data already shows that the onset times of individual synaptic Ca2+ events do not necessarily align with the beginning of a 'barrage' inward current event. 

The reviewer writes that “an analysis of the temporal correlation between synaptic calcium events and EPSCs is absent”. We would like to point out that we did determine the percentage of calcium transients that occurred during barrages of synaptic inputs (~60%, page 7). This is important, since the barrages in our patch-clamp recordings most likely reflect spontaneous network events as described in the developing cortex previously by us and many other labs . The time window we chose was not “arbitrary” as the reviewer suggests, but based on the duration of the barrages of synaptic inputs as defined in the Methods section. 

The reason, why we did not perform a more in-depth analysis of the temporal relationship between synaptic calcium transients and synaptic input currents is that it is essentially impossible to relate calcium transients at individual synapses to specific synaptic input events. First, during barrages of synaptic inputs many synapses are active simultaneously, both in the mapped dendrites as well as in the un-observed parts of the dendric arborization as the reviewer notes above. Thus, barrages cannot be broken down into individual synaptic transmission events. Second, since our acquisition frequency is ~10 Hz, we can identify the onset of individual synaptic calcium transients with 100-200 ms precision (1 or 2 frames). However, throughout any 100-200 ms period of recording, several synapses are active across the entire dendric arborization such that we cannot assign a given calcium transient to a specific EPSC within a 100-200 ms epoch. Third, due to the limited clamping capacity of in vivo patch recordings, we cannot be certain that individual transmission events in distal dendrites can be resolved in the patch recording.

(4) The authors claim that "these observations indicate that the activity patterns investigated here are not or only slightly affected by low-level anesthesia". It would be nice to show some of the recordings in this work without any anesthesia to support this claim. 

Indeed, the conclusion that the patterns of activity are only slightly affected by low levels of anesthesia is based on our previous recordings on the network level. Unfortunately, we are still not able to record calcium imaging with single synapse resolution in unanesthezed developing mice (and no one else is as far as we know), because the skull of these young animals is not firm, yet. As a consequence, movements cannot be reduced sufficiently for patching and imaging with single synapse resolution. Our previously published (Siegel et al., 2012) and unpublished work on the cellular level suggests that activity patterns during light anesthesia are very similar to those during sleep in mouse pups at this age.

Reviewer #3 (Public Review):

Summary: 

There is a growing body of litterature on the clustering of co-active synapses in adult mice, which has important implications for understanding dendritic integration and sensory processing more broadly. However, it has been unclear when this spatial organization of co-active synapses arises during development. In this manuscript, Leighton et al. investigate the emergence of spatially organized, coactive synapses on pyramidal dendrites in the mouse visual cortex before eye-opening. They find that some dendrite segments contain highly active synapses that are co-active with their neighbors as early as postnatal day (P) 8-10, and that these domains of co-active synapses increase their coverage of the dendritic arbor by P12-13. Interestingly, Leighton et al. demonstrate that synapses co-active with their neighbors are more likely to increase their activity across a single recording session, compared to synapses that are not co-active with their neighbors, suggesting local plasticity driven by coincident activity before eye-opening. 

The current manuscript includes some replication of earlier results from the same research group (Winnubst et al., 2015), including the presence of clustered, co-active synapses in the visual cortex of mouse pups, and the finding that synapses co-active with their neighbors show an increase in transmission frequency during a recording session. The main novelty in the current study compared to Winnubst et al. (2015) is the inclusion of younger animals (P8-13 in the current study compared to P10-15 in Winnubst et al., 2015). The current manuscript is the first demonstration that active synapses are clustered on specific dendrite segments as early as P8-10 in the mouse visual cortex, and the first to show the progression in active synapse distribution along the dendrite during the 2nd postnatal week. These results from the visual cortex may help inform our understanding of sensory development more broadly. 

Strengths: 

The authors ask a novel question about the emergence of synaptic spatial organization, and they use well-chosen techniques that directly address their questions despite the challenging nature of these techniques. To capture both structural and functional information from dendrites simultaneously, the authors performed a whole-cell voltage clamp to record synaptic currents arriving at the soma while imaging calcium influx at individual synaptic sites on dendrites. The simultaneous voltage clamp and calcium imaging allowed the authors to isolate individual synaptic inputs without their occlusion by widespread calcium influx from back-propagating action potentials. Achieving in vivo dendrite imaging in live mice that are as young as P8 is challenging, and the resulting data provides a unique view of synaptic activity along individual dendrites in the visual cortex at an early stage in development that is otherwise difficult to assess. 

The authors provide convincing evidence that synapses are more likely to be co-active with their neighbors compared to synapses located farther away (Fig. 6F-H), and that synapses co-active with their neighbors increase their transmission frequency during a recording session (Figure 7C). These findings are particularly interesting given that the recordings occur before eye-opening, suggesting a relationship between co-activity and local synaptic plasticity even before the onset of detailed visual input. These results replicate previously published findings from P10-15 pups (Winnubst et al., 2015), increasing confidence in the reproducibility of the data. 

The authors also provide novel data documenting for the first time spatially organized, co-active synapses in pups as young as P8. Comparing the younger (P8-10) and older (P12-13) pups, provides insight into how clusters of co-active synapses might emerge during development. 

Weaknesses: 

This manuscript provides insufficient detail for assessing the rigor and reproducibility of the methods, particularly for age comparisons. The P8-10 vs P12-13 age comparisons are the primary novel finding in this manuscript, and it is, therefore, critical to avoid systematic age differences in the methods and analysis whenever possible. Specific concerns related to the age comparisons are listed below: 

(1) Given that the same research group previously published P12-13 data (Winnubst et al., 2015), it is unclear whether both age groups in the current study were imaged/analyzed in parallel by the same researcher(s), or whether previous data was used for the P12-13 group. 

While indeed the approach in the present study is similar to that of our previous study (Winnubst et al. 2015), the data set presented here is entirely new. The current study was made possible by a new microscope that allows combining resonant scanning with piezo-focusing to image large fractions of the dendric arborization. In fact, we could now image almost 10 times larger dendric segments including branch points than in our previous study. One author contributed to the experiments in both studies. Image analysis of all experiments was performed by the first author of the present study who was not involved in the Winnubst et al. work.

(2) The authors mention that they used 2 different microscopes, and used a fairly wide range of imaging frame rates (5-15 Hz). It is unclear from the current manuscript whether the same imaging parameters were used across the two age groups. If data for the two experimental groups was collected separately, perhaps at different times, by a different person, or on a different microscope, there is a concern that some differences between the groups may not necessarily be due to age. 

The reviewer mentions that the experimental settings are not identical across the experiments of this study. In the original manuscript we erroneously reported in the Methods section that 2 different setups were used for this study; however, all experiments were performed on the same microscope. We have corrected this in the new manuscript. We took timelapse recordings of small stacks of varying depth to cover as many dendrites as possible in each recording, therefore, we needed to adjust the rate of acquired stacks within a certain range as the reviewer points out. The data were acquired by two scientists during an overlapping period. And while the different ages were not recorded in a strictly randomized fashion, they were not acquired in sequence according to ages, but rather involved many attempts on animals of different ages from many different litters. For each litter a small percentage of animals would generate successful recordings, and the ages of these successes were random. Therefore, we believe that neither the collection of data nor the analysis (see point above) affected the differences we describe here for the two age groups.

(3) It is unclear whether the image analysis was performed blind to age. Blinding to age during analysis is particularly important for this study, in which it was not possible to blind to age during imaging due to visible differences in size and developmental stage between younger and older pups. 

The analysis was not setup to be performed blind to age. Not only is the age of the animal apparent at the stage (as the reviewer points out), also the number of spines and the activity levels clearly show differences between neurons only a few days apart. However, all age-related findings reported in this study - except the increase in synapse density and activity - became apparent to us only after the full set of synaptic transmission events was determined and the analysis was performed on the entire data set, making it very unlikely that event detection was biased.

(4) The relatively low N (where N is the number of dendrites or the number of mice) in this study is acceptable due to the challenging nature of the techniques used, but unintentional sampling bias is a concern. For example, if higher-order dendrites from the apical tuft were imaged at P12-13, while more segments of the apical trunk were imaged at P8-10, this could inadvertently create apparent age differences that were in fact due to dendrite location on the arbor or dendrite depth. 

The reviewer points out that sampling bias with respect to synapse location along dendrites in the dataset could lead to falsely apparent age differences. In all experiments we imaged dendrites of layer 2/3 neurons that were relatively close to the cortical surface to optimize image quality. In addition, we confirmed that the mean distance of the imaged dendric stretches from the cell body was similar between the dendrites of each age group (Young: 392 +/-  104 µm, Old: 323 +/- 118 µm; mean +/- STD). Therefore, we do not think that sampling bias affected these results.

Additional general methodological concerns, which are not specifically related to the age comparisons, are listed below: 

(5) The authors assert that clustered, co-active synapses emerge in the visual cortex before eye-opening, which is an important finding in that it suggests this phenomention is driven by spontaneous activity rather than visual input. However, this finding hinges on the imaged cells being reliably located in the visual cortex, which is difficult to identify with certainty in animals that have not yet opened their eyes and therefore cannot undergo intrinsic signal imaging to demarcate the boundaries of the visual cortex. If the imaged cells were in, for example, nearby somatosensory cortex, then the observed spatial organization could be due to sensory input rather than spontaneous activity. 

The reviewer argues that if the neurons included in our analysis were located in non-visual sensory cortex, e.g. the somatosensory cortex, sensory experience might have shaped clustered inputs instead of spontaneous activity. We are, however, certain that the neurons were located inside the primary visual cortex. In previous experiments where we performed the same craniotomies, we mapped spontaneous activity across the sensory areas in the occipital neocortex and we know the exact location of V1 which is already very consistent during the second postnatal week. (See for example Supplemental Figure 4 in Leighton et al., 2021).  

(6) It is unclear how the authors defined a synaptic transmission event in the GCaMP signal (e.g. whether there was a quantitative deltaF/F threshold). 

In the revised manuscript, we describe the procedure of identifying synaptic calcium transients in more detail and added a new supplemental figure to clarify this aspect of the analysis. In short, we use an automated detection with a 2x standard deviation threshold and a subsequent manual control and selection step. Please, find all details in the Methods section and Figure S4 of the revised manuscript.

(7) The authors' division of synapses into spine vs shaft is unconvincing due to the difficulty of identifying Z-projecting spines in images from 2-photon microscopy, where the Z resolution is insufficient to definitively identify Z-projecting spines, and the fact that spines in young animals may be thin and dim. The authors' examples of spine synapses (e.g. in Fig. 2A) are convincing, but some of the putative shaft synapses may in fact be on spines. 

We agree with the reviewer that the differentiation between spine and sha synapses can be difficult for those spines that are located above or below the dendric sha in the z-dimension because of the lower resolution of 2-photon microscopy in the z-dimension compared to the image plane (see also response to Reviewer 2, point 1). We have now added a new paragraph to the Methods section to describe in more detail how we identify spine and sha synapses and provide more examples in a new supplementary figure (Fig S5). We believe that we can identify spine and sha synapses reliably in most cases, but added a cautionary note to make the reader aware of potential misidentifications.

Reviewer #1 (Recommendations For The Authors):

I think the experiments performed were very technically challenging (probably one of the few labs that can do this in the field), and the findings provide in vivo evidence on how structured synaptic inputs are assembled during development that has never been reported. 

I suggest improving the writing and presentation and really explaining how they conducted the experiments and how they defined shaft synapses. 

Line 96: 12 dendritic areas from 11 mice at ages between postnatal day 8 to 13. 

- Do the authors know how many neurons were imaged? It is unclear if the authors patch on all the imaged neurons and only imaged (or analyzed) the dendrites of those patched neurons. If yes, how sparse are the neurons labelled from IUE? From 1B, it looks like there are two cells adjacent to each other. Can the authors really distinguish whether the imaged dendrites are from the patched neuron? 

The reviewer wonders whether we can tell apart dendrites of patched cells from those of neighboring neurons that were not patched. This is actually very straight forward: the experiment included a depolarization step (see Methods section) which leads to an immediate, but temporary, increase in fluorescence in all of the patched neurons’ dendrites, but none of the neighboring dendrites. We have added this information to the Methods section of the new manuscript and provide now an example (Fig S3). Furthermore, as these cells normally fire frequently, it would immediately become clear that an unpatched cell is being imaged if backpropagating action potentials are predominantly observed rather than synaptic signals. The visualization of these synaptic signals is only possible due to the blockade of Na+ channels with QX314 in the intracellular solution (see Methods). 

- In the methods section, it says 'dendrites were imaged in single plane or small stacks with plane...'. How do the authors do calcium imaging with small stacks of plane using Nikon MP scope? 

Small stacks were acquired by using the piezo focusing device of our Nikon A1 microscope. Since we combined this fast focusing approach with resonant scanning, we were able to acquire z-stacks of 3-5 frames at a rate of up to 15 Hz (per stack).

- I also assume this is not chronic imaging, and there are different mice for each postnatal day. If it's true, this is somewhat important for all the correlation analysis as there are only 2 mice for each postnatal day (other than day 12) and day 13 only has 1 animal. 

Yes, indeed these are not chronic experiments and dendrites imaged on different days are from different neurons and different mice. We agree with the reviewer that if it had been possible to image the same neurons across these developmental stages, we would have detected even clearer correlations. Therefore, we see our results as conservative estimates of the developmental trajectory of the analyzed parameters.

Line 104 - 109: I don't understand why the authors need to hold at -30mV to facilitate calcium influx through NMDA receptors? I assume this helps them to visualize as many synapses as possible? but wouldn't that also make the 'event frequency' not reflect the true value? 

Indeed depolarizing the imaged neurons to -30 mV was necessary to get sufficient calcium influx to map synaptic inputs. We don’t think that this affects the frequency of inputs, because the frequency of synaptic inputs is determined by the presynaptic firing rate and the release probability of the presynaptic terminal, which are not affected by the depolarization of the dendrite.

Figure 2A - It says in the method section that ROIs are manually selected. However, it's not explained what the criteria are. For spine synapses, it's easy to define but for shaft synapses like in Fig 2B, why are there 2 synapses on the shaft? And in Fig 4a, 5a, Fig S1 P13, some of the dendrites are packed with ROIs. What's the distance between those shaft synapses? Can the imaging resolution really separate them? 

The reviewer asks for a better description of how we identified individual ROIs and thus synapse locations and whether this is actually feasible. We have now added a more detailed description of how we select synaptic sites based on the occurrence of synaptic calcium transients. In addition, we have added a new supplemental Figure (S4) to give the reader an impression of the image quality and the ability to locate individual synapses reliably. We find that separating sha synapses was possible for inter-synapse distances of ~4 µm or more. The mean sha synapse distance in our data set is 21 µm.

- Similar issue applies to Figure 4A that I'm not sure what's the resolution of each 'hot spot'. They all seem very close together. Maybe additional raw dendrite images with fluorescence changes like 1C or 2A could be helpful (or movies in the supplementary?) 

As the reviewer suggests, we have added now additional supplemental figures to illustrate better how we identify synaptic transmission events as well as spine and sha synapses.

- Also for line 164, it says that 76% of high-activity synapses were located on spines. This could also maybe support that only the spine synapses are real synapses and many shaft synapses are actually not synapses and they were just categorized as shaft synapses from manual ROI? 

We are actually quite sure that sha synapses are real synapses based on our analysis, since they show repeated synaptic calcium transients that co-occur with barrages of synaptic inputs as measured by patch-clamp recordings. Indeed one would expect to see a number of excitatory synapses on dendric shas of pyramidal neurons at these ages based on previous EM studies (Miller and Peters, 1981; Wildenberg et al., 2023).

- While this might not impact the overall novelty of the paper, I would be curious to know if the authors can still observe the same findings if they only analyze spine synapses. 

We repeated several analyses with a dataset that contained only spine synapses. For most analyses we observed the expected result: the effect sizes were similar compared to the entire data set, but the power was reduced. For example the effect of distance to closest high-activity neighbor and own activity (Fig 5E, F) was similar, but p-values were around 0.1 (Similar results for Figure 7B). In contrast, the co-activity with synapses within a domain was significantly higher than the co-activity with synapses in other domains also for the spine-synapse only data set. 

Fig 6 - Does the domain co-activity also contribute to the synaptic current recorded (related to Fig 4). 

Yes, the synaptic activity measured by calcium imaging contributes to the recorded EPSCs. However, the exact relationship between synaptic inputs measured by calcium imaging and those measured by patch-clamping is complicated by 3 factors: first, during barrages of synaptic inputs many synapses are active simultaneously, both in the mapped dendrites as well as in the un-observed parts of the dendric arborization. Thus, barrages cannot be broken down into individual events. Second, since our acquisition frequency is ~10 Hz, we can identify the onset of individual synaptic calcium transients with 100-200 ms precision (1 or 2 frames). However, throughout any 100-200 ms period of recording several synapses are active across the entire dendric arborization such that we cannot assign a given calcium transient to a specific EPSC within a 100-200 ms epoch. Third, due to the limited clamping capacity of in vivo patch recordings, we cannot be certain that individual transmission events in distal dendrites can be resolved in the patch recording as EPSCs.

Reviewer #2 (Recommendations For The Authors):

(1) I suggest the authors should provide the number of cells and mice recorded in the figure legends. 

The number of dendrites and mice is the same across all analyses: 12 dendrites from 11 mice for all experiments, 6/6 for P8-10 and 6/5 for P12-13. All dendrites and synapses (and their ages) are shown in the supplemental figures S1 and S2. We mention the number of imaged dendrites now at the beginning of the Results section and when we split ages for the first me.

(2) Instead of showing only cartoon illustrations of dendrites in Figures 3-6, I suggest showing the two photon images as well together with the cartoon. 

The 2-photon images of all dendrites of the dataset are available in Figure S1. To allow the reader to compare the cartoon representations in the main figures and the 2-photon images of each neuron, we have now labeled each dendrite in the dataset (D1-D12, see figures S1 and S2). For every figure, where we show example neurons (cartoons or zoom ins) we now provide this identifier.

Reviewer #3 (Recommendations For The Authors):

To address the weaknesses outlined above, we recommend that the authors do the following: 

• To address concerns about the rigor and reproducibility of the methods specifically related to age comparisons, please confirm the following: 

- Both age groups were run in parallel by the same researcher(s). 

Experiments were run partly overlapping and experiments from different age groups were performed in parallel by both researchers.

- Both age groups were imaged on the same microscope, or animals from each age group were imaged on both microscopes. If it was necessary to use different microscopes for the different age groups for biological or practical reasons, please explain. 

All experiments were run on the same microscope, a Nikon A1 2-photon microscope. In the original methods description we erroneously mentioned two microscopes (copy and paste error from a previous publication). We corrected that in the revised manuscript.

- There was no difference in imaging frame rates or other imaging parameters between age groups. If it was necessary to use different parameters for different age groups for biological reasons, please explain. 

We varied the frame rates somewhat to allow larger z-stacks for some experiments where dendrites traversed different depths; however the mean frame rates were similar between the experiments in P8-10 vs P12-13 dendrites, 8.5 vs 10 Hz, respectively.

- Images were analyzed blind to age. 

The analysis was not setup to be performed blind to age. The number of spines and the activity levels clearly show obvious differences between neurons only a few days apart. However, all findings reported in this study related to age - except the increase in synapse density and activity - became apparent to us only after the full set of synaptic transmission events was determined and the analysis was performed on the entire data set, making it unlikely that event detection was biased.

- There was no difference in the location of analyzed dendrites (e.g. depth from the pia, branch order) between age groups. 

In all experiments we imaged dendrites of layer 2/3 neurons that were relatively close to the cortical surface to optimize image quality. In addition, we determined the mean distance of the imaged dendric stretches from the cell body and found that this distance was similar between the dendrites of each age group (Young: 392 +/-  104 µm, Old: 323 +/- 118 µm; mean +/- STD). Therefore, we do not think that sampling bias affected these results.

• To address general methodological concerns, please provide additional description of the following points: 

- Please clarify how the visual cortex was identified in P8-13 pups. If there was ambiguity about identifying the visual cortex in these pups, please discuss the implications of this ambiguity. 

The reviewer asks how we identified V1 in these experiments. We are indeed certain that the neurons were located inside the primary visual cortex. We have ample experience with mapping V1 in these animals based on patterns of spontaneous activity as well as post-hoc stainings. V1 is quite large already at these ages (> 2 mm long and > 1 mm wide) and its extent very consistent across animals. Thus, we would argue it is actually hard to miss.

- Please clarify how synaptic transmission events were identified in the GCaMP signal. 

We have now added a more detailed description of how we identify synaptic calcium transients. In addition, we have added a new supplemental Figure (S3) to give the reader an impression of the image quality and the ability to locate individual synapses reliably. 

- It is acceptable to use the spine vs shaft analysis despite the inevitable difficulty resolving Z-projecting spines, but this caveat should be mentioned in the discussion of the spine vs shaft results. 

We added a more detailed description of spine and sha synapse identification, a new supplemental figure (S5) and we now mention the caveat related to the limited z-resolution of 2-photon microscopy in the revised manuscript.

• Two additional minor details should be clarified in the text of the manuscript: 

- Please specify the volume of DNA solution injected into each embryo. 

The injected volume was 1 µl. We added this information in the Methods section of the revised manuscript.

- In Fig S1, please specify whether the scale bar applies to all images. 

The scale bar applies to all images. This information was added to the figure legend.

References

Leighton AH, Cheyne JE, Houwen GJ, Maldonado PP, De Winter F, Levelt CN, Lohmann C. 2021. Somatostatin interneurons restrict cell recruitment to renally driven spontaneous activity in the developing cortex. Cell Rep 36:109316. doi:10.1016/j.celrep.2021.109316

Miller M, Peters A. 1981. Maturation of rat visual cortex. II. A combined Golgi-electron microscope study of pyramidal neurons. JComp Neurol 203:555–573.

Siegel F, Heimel JA, Peters J, Lohmann C. 2012. Peripheral and central inputs shape network dynamics in the developing visual cortex in vivo. Current Biology 22:253–258.

Wildenberg G, Li H, Sampathkumar V, Sorokina A, Kasthuri N. 2023. Isochronic development of cortical synapses in primates and mice. Nat Commun 14:8018. doi:10.1038/s41467-02343088-3

Author response:

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

Reviewer #1 (Public Review):

This is an interesting and well-written paper reporting on a novel approach to studying cerebellar function based on the idea of selective recruitment using fMRI. The study is well-designed and executed. Analyses are sound and results are properly discussed. The paper makes a significant contribution to broadening our understanding of the role of the cerebellum in human behavior.

We thank the reviewer for the positive assessment of our paper.

(1) While the authors provide a compelling case for the link between BOLD and the cerebellar cortical input layer, there remains considerable unexplained variance. Perhaps the authors could elaborate a bit more on the assumption that BOLD signals mainly reflect the input side of the cerebellum (see for example King et al., elife. 2023 Apr 21;12:e81511).

Our paper is based on the assumption that the cerebellar BOLD signal reflects solely the input to the cerebellum and does not reflect the changes in firing rates of Purkinje cells. This assumption relies on two lines of arguments: Studies that have directly looked at the mechanism of vasodilation in the cerebellum, and studies that try to infer the contributions of different neurophysiological mechanisms to overall cerebellar metabolism (Attwell and Iadecola, 2002).

Vasodilatory considerations: The mechanisms that causes vasodilation in the cerebellum, and hence BOLD signal increases, has been extensively studied: Electrical stimulation of mossy fibers (Gagliano et al., 2022; Mapelli et al., 2017), as well as parallel fibers (Akgören et al., 1994; Iadecola et al., 1996; Mathiesen et al., 1998; Yang and Iadecola, 1997) lead to robust increases in cerebellar blood flow. In contrast to the neocortex, the regulation of blood flow in the cerebellum depends nearly purely on the vasodilator Nitric Oxide (NO) (Akgören et al., 1994; Yang and Iadecola, 1997) with stellate cells playing a key role in the signaling cascade (Yang et al., 2000).

Electrical (Mathiesen et al., 2000) and pharmacological (Yang and Iadecola, 1998) stimulation of climbing fibers also leads to robust increases in blood flow. Simultaneous parallel and climbing fiber stimulation seems to combine sub-additively to determine the blood flow changes (K. Caesar et al., 2003).

Importantly, even dramatic changes in spiking rate of Purkinje cells do not lead to changes in vasodilation. For starters, parallel fiber stimulation leads to blood flow increases, even though the net effect on Purkinje cell firing is inhibitory (Mathiesen et al., 1998). More importantly, complete inhibition of the Purkinje cell using a GABA agonist does not change baseline cerebellar blood flow (Kirsten Caesar et al., 2003). Conversely, even a 200-300% increase in simple (and complex) spike firing rate through application of a GABA antagonist does not show any measurable consequences for blood flow, even though it clearly increases the metabolic rate of oxygen consumption in the tissue (Thomsen et al., 2009, 2004).

In sum, this extensive set of studies clearly argues that the cerebellar blood flow response is mostly dictated by synaptic input, and that the firing rate of Purkinje cells does not influence vasodilation. Because the BOLD signal is caused by an supply of oxygen over and above the level of oxygen consumption, this would argue that increases in Purkinje cell firing would not lead to BOLD increases. What is less clear is the degree to which changes in BOLD signal during normal activity are determined by changes in mossy fiber or climbing fiber input. Disruption of either pathway leads to 60-70% reductions in the evoked blood flow response during whisker stimulation (Yang et al., 2000; Zhang et al., 2003) – but it remains unclear to what degree this reflects the distribution of contributions in the healthy animal, as these powerful disruptions may have a number of side-effects.

Metabolic considerations: To estimate the relative contributions climbing fiber / mossy fiber input to the variations in BOLD signal under natural conditions, it is useful to consider the contributions of different cerebellar processes to the overall metabolism of the cerebellum. Assuming an average firing rate of 40Hz for mossy fibers, ~3Hz for Granule cells, and 1Hz for climbing fibers, Howarth et al. (Howarth et al., 2012, 2010) estimated that the transmission from mossy fibers to granular cells, dominates the energy budget with 53%. The subsequent stage, encompassing the transfer of information from Granular cells to Purkinje cells, accounts for 32% of energy expenditure. In contrast, integration within Purkinje cells and the spiking (simple and complex) of these cells represents only 15% of the total energy consumption.

More important for the BOLD signal, however, are the activity-induced variations in metabolic consumption: Purkinje cells fire relatively constantly at a very high frequency (~50Hz) both during awake periods and during sleep (Shin et al., 2007). When providing a signal to the neocortex, firing rate decreases, actually lowering the metabolic demand. Climbing fibers normally fire at ~0.5 Hz and even during activity rarely fire much above 2Hz (Streng et al., 2017). In contrast, granule cells show a low firing rates during rest (typically <1hz) and can spike during activity well above 100Hz. Combined with the sheer number of granule cells, these considerations would suggest that the vast majority of the variation in metabolic demand are due to mossy fiber input and granule cell activity.

Overall, we therefore think it is likely that the main determinant of the cerebellar cortical BOLD signal is mossy fiber input and the transmission of information from mossy fibers to granule cells to Purkinje cells. We admit that the degree to which climbing fiber input contribute to BOLD signal changes is much less clear. We can be quite certain, however, that the firing rate of Purkinje cells does not contribute to the cerebellar BOLD signal, as even dramatic changes in the firing rate do not cause any changes in vasodilation.  We have clarified our line of reasoning in the paper, and hope this more extensive response here will give the reader a better overview over the pertaining literature.

(2) The current approach does not appear to take the non-linear relationships between BOLD and neural activity into account.

Thank you for raising this concern. We did not stress this point in the paper, but one big advantage of our selective recruitment approach is that it is – to some degree- robust against non-linearities in the relationship between neural activity and BOLD signal. This is the case, as long as the shape of the non-linearity is similar in the cerebellum and the neocortex. The results of our motor task (Figure 3) provide a clear example of this: The BOLD signal both in the neocortex and cerebellum incases non-linearly as a function of force – the increase from 2.5N to 6N (a 3.5N increase) is larger than the increase from 6N to 10N (a 4N increase). A similar non-linearity can be observed for tapping speed (6, 10 to 18 taps / s). However, within each condition, the relationship between cortical and cerebellar activity is nearly perfectly linear, reflecting the fact that the shape of the non-linearity for the cerebellum and cortex is very similar.

Most importantly, even if the non-linearity across the two structures is different, any non-linear relationship between neural activity and BOLD signal (of vasodilatory nature) should apply to different conditions (here force and speed increases) similarly. Therefore, if two conditions show overlapping activity levels (as observed for force and speed across medium and high levels, Figure 3), a offset between conditions cannot be caused by a non-linearity in the relationship of cortical and cerebellar activity. Because all conditions are subject to the same non-linearity, all points should lie on a single (likely monotonically increasing) non-linear function. Both for the motor and working memory task, the pattern of results clearly violates this assumption.

(3) The authors may want to address a bit more the issue of closed loops as well as the underlying neuroanatomy including the deep cerebellar nuclei and pontine nuclei in the context of their current cerebello-cortical correlational approach. But also the contribution of other brain areas such as the basal ganglia and hippocampus. 

Cortical-cerebellar communication is of course bi-directional. As discussed in King at al., (2023), however, we are restricting our model to the connections from the neocortex to the cerebellum for the following reasons: First, cerebellar BOLD activity likely reflects mostly neocortical input (see our answer to pt. 1), whereas neocortical activity is determined by a much wider array of projections, including striato-thalamo-cortical and cortico-cortical connections. Secondly, the output of the cerebellum cannot be predicted from the BOLD signal of the cerebellar cortex, as it is unlikely that the firing rate of Purkinje cells contribute to cerebellar BOLD signal (see pt. 1). For these reasons we believe that the relationship between neocortical and cerebellar activity patterns is mostly dictated by the connectivity from cortex to cerebellum, and is therefore best modelled as thus. This is now more clearly discussed in a new paragraph (line 318-323) of the revised manuscript.

We are also ignoring other inputs to the cerebellum, including the spinal chord, the basal ganglia (Bhuvanasundaram et al., 2022; Bostan and Strick, 2018) hippocampus (Froula et al., 2023; Watson et al., 2019), and amygdala (Farley et al., 2016; Jung et al., 2022; Terburg et al., 2024). In humans, however, the neocortex remains the primary source of input to pontine nuclei. Consequently, it stands as the main structure shaping activity within the cerebellar cortex. While it is an interesting question to what degree the consideration of subcortical structures can improve the prediction of cerebellar activity patterns, we believe that considering the neocortex provides a good first approximation.

Reviewer #1 (Recommendations):

(4)  A few sentences to clarify the used models as was done in the King et al. (2024) paper may improve readability.

We have now added the sentences in the introduction (line 25ff):

To approach this problem, we have recently developed and tested a range of cortical-cerebellar connectivity models (King et al., 2023), designed to capture fixed, or task-invariant, transmission between neocortex and cerebellum. For each cerebellar voxel, we estimated a regularized multiple regression model to predict its activity level across a range of task conditions (King et al., 2019) from the activity pattern observed in the neocortex for the same conditions. The models were then evaluated in their ability to predict cerebellar activity in novel tasks, again based only on the corresponding neocortical activity pattern. Two key results emerged from this work. First, while rs-FC studies (Buckner et al., 2011; Ji et al., 2019; Marek et al., 2018) have assumed a 1:1 mapping between neocortical and cerebellar networks, models which allowed for convergent input from multiple neocortical regions to a single cerebellar region performed better in predicting cerebellar activity patterns for novel tasks. Second, when given a cortical activation pattern, the best performing model could predict about 50% of the reliable variance in the cerebellar cortex across tasks (King et al., 2023).

(5) To what extent does this paper demonstrate the limitations of BOLD in neuroscientific research? 

The primary objective of this study was to shed light on the problems of interpreting BOLD activation within the cerebellum. The problem that the BOLD signal mostly reflect input to a region is not unique to the cerebellum, but also applies (albeit likely to a lesser degree) to other brain structures. However, the solution we propose here critically hinges on three features of the cerebellar circuitry: a) the mossy fiber input for the cerebellar hemispheres mostly arise from the neocortex, b) the BOLD signal is likely dominated by this mossy fiber input (see pt. 1), and c) there is very little excitatory recurrent activity in the cerebellum, so output activity in the cerebellum does not cause direct activity in other parts of the cerebellum.

These features motivate us to use a directed cortex->cerebellum connectivity model, which does not allow for any direct connectivity within the cerebellum. While the same approach can also be applied to other brain structures, it is less clear that the approach would yield valid results here. For example, due the local excitatory recurrent connectivity within neocortical columns, the activity here will also relate to local processing.

(6) What if the authors reversed their line of reasoning as in that cerebellum activity is matched to map changes in cerebral cortical activity? Perhaps this could provide further evidence for the assumed directional specificity of the task-dependent gating of neocortical inputs. 

Given (a) that the cerebellar BOLD signal tells us very little about cerebellar output signals (b) that there are many other input signals to the neocortex that are more powerful than cerebellar inputs, and c) that there strong cortical-cortical connections, we believe that this model would be hard to interpret (see also our answer to pt. 3).

Therefore, while the inversion of the linear task-invariant mapping between cortical and cerebellar activity is a potentially interesting exercise, it is unclear to us at this point what strong predictions we would be able to test with this approach.

(7) The statement that cerebellar fMRI activity may simply reflect the transmission of neocortical activity through fixed connections can be better explained. Also in the context of using the epiphenomenon (on page 11) in the paper. To what extent is the issue of epiphenomenon not a general problem of fMRI research?

We have rephrased the introduction of this idea (line 17):

This means that increases in the cerebellar BOLD signal could simply reflect the automatic transmission of neocortical activity through fixed anatomical connections. As such, whenever a task activates a neocortical region, the corresponding cerebellar region would also be activated, regardless of whether the cerebellum is directly involved in the task or not.

Epiphemonal activity: This is indeed a general problem in fMRI research (and indeed research that uses neurophysiological recordings, rather than manipulations of activity). Indeed, we have discussed similar issues in the context of motor activity in ipsilateral motor cortex (Diedrichsen et al., 2009). However, given that we only offer a possible approach to address this issue for the cerebellum (see pt. 5), we thought it best to keep the scope of the discussion focused on this structure.

Reviewer #2 (Public Review):

Summary:

Shahshahani and colleagues used a combination of statistical modelling and whole-brain fMRI data in an attempt to separate the contributions of cortical and cerebellar regions in different cognitive contexts.

Strengths:

The manuscript uses a sophisticated integration of statistical methods, cognitive neuroscience, and systems neurobiology.

The authors use multiple statistical approaches to ensure robustness in their conclusions.

The consideration of the cerebellum as not a purely 'motor' structure is excellent and important. <br />

We thank the reviewer for their positive evaluation.

Weaknesses:

(1) Two of the foundation assumptions of the model - that cerebellar BOLD signals reflect granule cells > purkinje neurons and that corticocerebellar connections are relatively invariant - are still open topics of investigation. It might be helpful for the reader if these ideas could be presented in a more nuanced light.

Please see response to the comment 1 of Reviewer 1 for a more extensive and detailed justification of this assumption. We have now also clarified our rationale for this assumption better in the paper on line 10-14. Finally, we now also raise explicitly the possibility that some of the violations of the task-invariant model could be caused by selectively increase of climbing fiber activity in some tasks (line 340).

(2) The assumption that cortical BOLD responses in cognitive tasks should be matched irrespective of cerebellar involvement does not cohere with the idea of 'forcing functions' introduced by Houk and Wise. 

We are assuming that you refer to the idea that cerebellar output is an important determinant of the dynamics (and likely also of the magnitude) of neocortical activity. We agree most certainly here. However, we also believe that in the context of our paper, it is justified to restrict the model to the connectivity between the neocortex and the cerebellum only (see reviewer 1, comment 3).

Furthermore, if increased cerebellar output indeed occurs during the conditions for which we identified unusually high cerebellar activity, it should increase neocortical activity, and bring the relationship of the cerebellar and cortical activity again closer to the predictions of the linear model. Therefore, the identification of functions for which cerebellar regions show selective recruitment is rather conservative.

Reviewer #2 (Recommendations):

(3) One of the assumptions stated in the abstract -- that the inputs to the cerebellum may simply be a somewhat passive relay of the outputs of the cerebral cortex -- has been challenged recently by work from Litwin-Kumar (Muscinelli et al., 2023 Nature Neuroscience), which argues for complex computational relationships between cortical pyramidal neurons, pontine nuclei and granule cells, which in turn would have a non-linear impact on the relationship between cortical and cerebellar BOLD. The modelling is based on empirical recordings from Wagner (2019, Cell) which show that the synaptic connections between the cortex and granule cells change as a function of learning, further raising concerns about the assumption that the signals inherent within these two systems should be identical. Whether these micro-scale features are indicative of the macroscopic patterns observed in BOLD is an interesting question for future research, but I worry that the assumption of direct similarity is perhaps not reflective of the current literature. The authors do speak to these cells in their discussion, but I believe that they could also help to refine the authors' hypotheses in the manuscript writ large.

We absolutely agree with your point. However, we want to make extremely clear here that our hypothesis (that the inputs to the cerebellum are a linear task-invariant function of the outputs of the cerebral cortex) is the Null-hypothesis that we are testing in our paper. In fact, our results show the first empirical evidence that task-dependent gating may indeed occur. In this sense, our paper is consistent with the theoretical suggestion of (Muscinelli et al., 2023).

You may ask whether a linear task-invariant model of cortical-cerebellar connectivity is not a strawman, given that is most likely incorrect. However, as we stress in the discussion (line 298-), a good Null-model is a useful model, even if it is (as all models) ultimately incorrect. Without it, we would not be able to determine which cerebellar activity outstrips the linear prediction. The fact that this Null-model itself can predict nearly 50% of the variance in cerebellar activity patterns across tasks at a group level, means that it is actually a very powerful model, and hence is a much more stringent criterion for evidence for functional involvement than just the presence of activity.

(4) Further to this point, I didn't follow the authors' logic that the majority of the BOLD response in the cerebellum is reflective of granule cells rather than Purkinje cells. I read through each of the papers that were cited in defense of the comment: "The cerebellar BOLD signal is dominated by mossy fiber input with very little contribution from the output of the cerebellar cortex, the activity of Purkinje cells" and found that none of these studies made this same direct conclusion. As such, I suggest that the authors soften this statement, or provide a different set of references that directly confirm this hypothesis. 

Please see response to the comment 1, Reviewer 1. We hope the answer provides a more comprehensive overview over the literature, which DOES show that spiking behavior of Purkinje cells does not influence vasodilation (as opposed to mossy fiber input). We have now clarified our rationale and the exact cited literature on line 9-14 of the paper.

(5) Regarding the statement: "As such, whenever a task activates a neocortical region, we might observe activity in the corresponding cerebellar regions regardless of whether the cerebellum is directly involved in the task or not." -- what if this is a feature, rather than a bug? That is, the organisation of the nervous system has been shaped over phylogeny such that every action, via efference copies of motor outputs, is filtered through the complex architecture of the cerebellum in order to provide a feed-forward signal to the thalamus/cortex (and other connected structures). Houk and Wise made compelling arguments in their 1995 Cerebral Cortex paper arguing that these outputs (among other systems) could act as 'forcing functions' on the kinds of dynamics that arise in the cerebral cortex. I am inclined to agree with their hypothesis, where the implication is that there are no tasks that don't (in some way) depend on cerebellar activity, albeit to a lesser or greater extent, depending on the contexts/requirements of the task. I realise that this is a somewhat philosophical point, but I do think it is important to be clear about the assumptions that form the basis of the reasoning in the paper. 

This is an interesting point. Our way of thinking about cerebellar function does indeed correspond quite well to the idea of forcing functions- the idea that cerebellar output can “steer” cortical dynamics in a particular way. However, based on patient and lesion data, it is also clear that some cortical functions rely much more critically on cerebellar input than others. We hypothesize here that cerebellar activity is higher (as compared to the neocortical activity) when the functions require cerebellar computation.

We also agree with the notion that cerebellar contribution is likely not an all-or-none issue, but rather a matter of gradation (line 324ff).

(6) Regarding the logic of expecting the cortical patterns for speed vs. force to be matched -- surely if the cerebellum was involved more in speed than force production, the feedback from the cerebellum to the cortex (via thalamus) could also contribute to the observed differences? How could the authors control for this possibility? 

Our model currently indeed does not attempt to quantify the contributions of cerebellar output to cortical activity. However, given that cerebellar output is not visible in the BOLD signal of the cerebellum (see reviewer 1, comment 1), we believe that this is a rational approach. As argued in our response to your comment 2, increased cerebellar output in the speed compared to the force condition should bring the activity relationship closer to the linear model prediction. The fact that we find increased cerebellar (as compared to neocortical) activity in the speed conditions, suggests that there is indeed task-dependent gating of cortical projections to the cerebellum.

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Author response:

eLife assessment

This valuable study reveals how a rhizobial effector protein cleaves and inhibits a key plant receptor for symbiosis signaling, while the host plant counters by phosphorylating the effector. The molecular evidence for the protein-protein interaction and modification is solid, though biological evidence directly linking effector cleavage to rhizobial infection is incomplete. With additional functional data, this work could have implications for understanding intricate plant-microbe dynamics during mutualistic interactions.

Thank you for this helpful comment. In the revised manuscript version, we will be more prudent with directly linking cleavage of Nod factor receptors by NopT and rhizobial infection.

We plan to modify the Title, the One-Sentence Summary, Abstract, and Discussion regarding this point.

Public Reviews:

Reviewer #1 (Public Review):

Bacterial effectors that interfere with the inner molecular workings of eukaryotic host cells are of great biological significance across disciplines. On the one hand they help us to understand the molecular strategies that bacteria use to manipulate host cells. On the other hand they can be used as research tools to reveal molecular details of the intricate workings of the host machinery that is relevant for the interaction/defence/symbiosis with bacteria. The authors investigate the function and biological impact of a rhizobial effector that interacts with and modifies, and curiously is modified by, legume receptors essential for symbiosis. The molecular analysis revealed a bacterial effector that cleaves a plant symbiosis signaling receptor to inhibit signaling and the host counterplay by phosphorylation via a receptor kinase. These findings have potential implications beyond bacterial interactions with plants.

Thank you for highlighting the broad significance of rhizobial effectors in understanding legume-rhizobium interactions. We fully agree with your assessment and will emphasize these points in the revised Introduction and Discussion sections of our manuscript. Specifically, we will expand our Discussion regarding the potential impact of the NopT interaction with symbiotic receptor kinases on plant immune signaling and regarding the general significance of our work.

Bao and colleagues investigated how rhizobial effector proteins can regulate the legume root nodule symbiosis. A rhizobial effector is described to directly modify symbiosis-related signaling proteins, altering the outcome of the symbiosis. Overall, the paper presents findings that will have a wide appeal beyond its primary field.

Out of 15 identified effectors from Sinorhizobium fredii, they focus on the effector NopT, which exhibits proteolytic activity and may therefore cleave specific target proteins of the host plant. They focus on two Nod factor receptors of the legume Lotus japonicus, NFR1 and NFR5, both of which were previously found to be essential for the perception of rhizobial nod factor, and the induction of symbiotic responses such as bacterial infection thread formation in root hairs and root nodule development (Madsen et al., 2003, Nature; Tirichine et al., 2003; Nature). The authors present evidence for an interaction of NopT with NFR1 and NFR5. The paper aims to characterize the biochemical and functional consequences of these interactions and the phenotype that arises when the effector is mutated.

Thank you for your positive feedback on our manuscript. In the revised Introduction and Discussion sections, we plan to better emphasize the interdisciplinary significance of our work. We will show how the knowledge gained from our study can contribute to a better understanding of microbial interactions with eukaryotic hosts in general, which may have a stimulating effect on future research in various research areas such as pathogenesis and immunity.

To ensure that the readers can easily follow the rationale behind our experiments, we will improve the Results section and provide more detailed explanations of how NopT among 15 examined effectors was selected. Additionally, we will provide more background information on NopT and the roles of NFR1 and NFR5 in symbiotic signaling in the Introduction section. As suggested, we will include the references Madsen et al. (2003) and Tirichine et al. (2003) as well as additional references on rhizobial NopT proteins into our revised manuscript version.

Evidence is presented that in vitro NopT can cleave NFR5 at its juxtamembrane region. NFR5 appears also to be cleaved in vivo. and NFR1 appears to inhibit the proteolytic activity of NopT by phosphorylating NopT. When NFR5 and NFR1 are ectopically over-expressed in leaves of the non-legume Nicotiana benthamiana, they induce cell death (Madsen et al., 2011, Plant Journal). Bao et al., found that this cell death response is inhibited by the coexpression of nopT. Mutation of nopT alters the outcome of rhizobial infection in L. japonicus. These conclusions are well supported by the data.

We appreciate that you recognize the value of our data.

The authors present evidence supporting the interaction of NopT with NFR1 and NFR5. In particular, there is solid support for cleavage of NFR5 by NopT (Figure 3) and the identification of NopT phosphorylation sites that inhibit its proteolytic activity (Figure 4C). Cleavage of NFR5 upon expression in N. benthamiana (Figure 3A) requires appropriate controls (inactive mutant versions) that have been provided, since Agrobacterium as a closely rhizobia-related bacterium, might increase defense related proteolytic activity in the plant host cells.

Thank you for recognizing the use of an inactive NopT variant in Figure 3A. In fact, increased activity of plant proteases induced by Agrobacterium is an important point that should not be neglected. We plan to mention this aspect in our revised Discussion.

In the context of your comments, we are planning to make the following improvements to the manuscript:

(1) We will add a more detailed description of the experimental conditions under which the cleavage of NFR5 by NopT was observed in vitro and in vivo.

(2) We plan to provide more comprehensive data on the phosphorylation of NopT by NFR1, including phosphorylation assays and mass spectrometry results. These additional data support the proposed mechanism by which NFR1 inhibits the proteolytic activity of NopT.

(3) We will expand the Discussion on the cell death response induced by ectopic expression of NFR1 and NFR5 in Nicotiana benthamiana. We will include more details from Madsen et al. (2011) to contextualize our findings with published literature.

We believe these additions and clarifications will enhance the clarity and impact of our findings.

Key results from N. benthamiana appear consistent with data from recombinant protein expression in bacteria. For the analysis in the host legume L. japonicus transgenic hairy roots were included. To demonstrate that the cleavage of NFR5 occurs during the interaction in plant cells the authors build largely on western blots. Regardless of whether Nicotiana leaf cells or Lotus root cells are used as the test platform, the Western blots indicate that only a small proportion of NFR5 is cleaved when co-expressed with nopT, and most of the NFR5 persists in its full-length form (Figures 3A-D). It is not quite clear how the authors explain the loss of NFR5 function (loss of cell death, impact on symbiosis), as a vast excess of the tested target remains intact. It is also not clear why a large proportion of NFR5 is unaffected by the proteolytic activity of NopT. This is particularly interesting in Nicotiana in the absence of Nod factor that could trigger NFR1 kinase activity.

Thank you for your comments regarding the cleavage of NFR5 and its functional implications. In the revised version, we will change our manuscript taking into account the following considerations:

(1) We acknowledge that the Western blots indicate only a small proportion of NFR5 is cleaved when co-expressed with NopT. It is worth noting in this context that the proteins were expressed at high levels which likely do not reflect the natural situation in L. japonicus. Low production of cleaved NFR5 in our Western blots with transformed N. benthamiana or L. japonicus cells thus may simply reflect an experimental effect due to high NFR5 protein synthesis. We suggest that the presence of high amounts of intact NFR5 does not have a significant functional impact on plant responses (cell death in N. benthamiana, rhizobial infection of L. japonicus) whereas NFR5 cleavage (or formation of NFR5 cleavage products) may be crucial for the observation of the observed phenotypic changes. The fraction of cleaved NFR5, although small, may be sufficient to disrupt crucial signaling pathways, leading to observable phenotypic changes. We will address possible differences between experimental and natural protein levels in our revised Discussion.

(2) We studied in our work three biochemical aspects of NopT: (i) physical binding of NopT to NFR1 and NFR5 (ii) proteolytical cleavage of NFR5 by NopT and (iii) phosphorylation of NopT by NFR1. These three biochemical properties appear to influence each other. Phosphorylation of NopT by NFR1 appears to reduce its proteolytic activity, thereby counteracting NFR5 degradation by NopT (NFR5 homeostasis). Moreover, as NopT is a phosphorylation substrate for NFR1, NopT probably interferes with kinase mediated downstream responses of NFR1. Thus, NFR5 cleavage activity of NopT appears to be only one feature of NopT. We plan to mention these considerations in our revised Discussion.

It is also difficult to evaluate how the ratios of cleaved and full-length protein change when different versions of NopT are present without a quantification of band strengths normalized to loading controls (Figure 3C, 3D, 3F). The same is true for the blots supporting NFR1 phosphorylation of NopT (Figure 4A).

Thank you for pointing out this aspect. Following your recommendation, we will quantify the band intensities for cleaved and full-length NFR5 in the experiments with different versions of NopT. These values will be normalized to loading controls. Similarly, the Western blots supporting NFR1 phosphorylation of NopT will be quantified. The data for normalized band intensities will be included into the revised figures. The quantifications will provide a clearer understanding of how the ratios of cleaved to full-length proteins change with different NopT variants and also will provide information to which extent NopT is phosphorylated by NFR1.

It is clear that mutation of nopT results in a quantitative infection phenotype. Nodule primordia and infection threads are still formed when L. japonicus plants are inoculated with ∆nopT mutant bacteria, but it is not clear if these primordia are infected or develop into fully functional nodules (Figure 5). A quantification of the ratio of infected and non-infected nodules and primordia would reveal whether NopT is only active at the transition from infection focus to thread or perhaps also later in the bacterial infection process of the developing root nodule.

Thank you for pointing this out. In the revised version of our manuscript, we will provide data showing that there are no obvious differences in nodule formation in plants inoculated with ∆nopT and wild-type NGR234, respectively. However, quantification of infection threads containing our GFP-labeled rhizobia in primordia and nodules would be difficult to perform due to strong autofluorescence signals in these tissues. The main goal of our study was to identify and characterize the interaction between NopT and Nod factor receptors. We therefore believe that an in-depth analysis of the bacterial infection process at later symbiotic stages is out of the scope of the present work.

Reviewer #2 (Public Review):

Summary:

This manuscript presents data demonstrating NopT's interaction with Nod Factor Receptors NFR1 and NFR5 and its impact on cell death inhibition and rhizobial infection. The identification of a truncated NopT variant in certain Sinorhizobium species adds an interesting dimension to the study. These data try to bridge the gaps between classical Nod-factor-dependent nodulation and T3SS NopT effector-dependent nodulation in legume-rhizobium symbiosis. Overall, the research provides interesting insights into the molecular mechanisms underlying symbiotic interactions between rhizobia and legumes.

Strengths:

The manuscript nicely demonstrates NopT's proteolytic cleavage of NFR5, regulated by NFR1 phosphorylation, promoting rhizobial infection in L. japonicus. Intriguingly, authors also identify a truncated NopT variant in certain Sinorhizobium species, maintaining NFR5 cleavage but lacking NFR1 interaction. These findings bridge the T3SS effector with the classical Nod-factor-dependent nodulation pathway, offering novel insights into symbiotic interactions.

We appreciate that you recognize the value of our manuscript.

Weaknesses:

(1) In the previous study, when transiently expressed NopT alone in Nicotiana tobacco plants, proteolytically active NopT elicited a rapid hypersensitive reaction. However, this phenotype was not observed when expressing the same NopT in Nicotiana benthamiana (Figure 1A). Conversely, cell death and a hypersensitive reaction were observed in Figure S8. This raises questions about the suitability of the exogenous expression system for studying NopT proteolysis specificity.

We appreciate your attention to these plant-specific differences. In view of your comments, we plan to revise the Discussion and explain the different expression systems used for studying NopT effects in planta. Previous studies showed that NopT expressed in tobacco (N. tabacum) or in specific Arabidopsis thaliana ecotypes (with PBS1/RPS5 genes) causes rapid cell death (Dai et al. 2008; Khan et al. 2022). Our data shown in Fig. S8 confirm these findings. As cell death (effector triggered immunity) is usually associated with induction of protease activities, we considered N. tabacum and A. thaliana plants as not suitable for testing NFR5 cleavage by NopT. In fact, no NopT/NFR5 experiments were performed with these plants in our study. In contrast, the expression of NopT in Nicotiana benthamiana did not lead to cell death in our experiments. Khan et al. 2022 also reported that cell death does not occur in N. benthamiana unless the cells were transformed with PBS1/RPS5 constructs. Thus, N. benthamiana is a suitable expression system to analyze NopT protease activity on co-expressed substrates. Our revision aims to better understand the advantages of the N. benthamiana expression system for studying NopT mediated proteolysis of NFR5.

(2) NFR5 Loss-of-function mutants do not produce nodules in the presence of rhizobia in lotus roots, and overexpression of NFR1 and NFR5 produces spontaneous nodules. In this regard, if the direct proteolysis target of NopT is NFR5, one could expect the NGR234's infection will not be very successful because of the Native NopT's specific proteolysis function of NFR5 and NFR1. Conversely, in Figure 5, authors observed the different results.

Our inoculation experiments clearly show that NopT of NGR234 has a negative effect on formation of infection foci (Fig. 5A) and nodule primordia (Fig. 5E). Our biochemical analysis indicates that NopT targets the NFR1/NFR5 complex, which most likely impairs activation of downstream responses such as NIN gene expression. Accordingly, NIN promoter activity was found to be higher in roots inoculated with the Δ_nopT_ mutant as compared to the NGR234 wild-type (Fig. 5B and 5D). It is therefore plausible that NopT impairs rhizobial infection of L. japonicus due to inhibition of NFR1/NFR5 functions. We agree with this Reviewer that it can be expected that “NGR234's infection will not be very successful”. Fig. 5 confirms that Δ_nopT_ mutant is indeed a better symbiont and we do not think that we obtained “unexpectedly different results”. In the revised version, we will try to formulate our discussion text better in order to avoid any misunderstandings. Furthermore, will write as figure title “NopT dampens rhizobial infection…” instead of “NopT regulates rhizobial infection…”. We are also considering changing the title of our manuscript.  

(3) In Figure 6E, the model illustrates how NopT digests NFR5 to regulate rhizobia infection. However, it raises the question of whether it is reasonable for NGR234 to produce an effector that restricts its own colonization in host plants.

We acknowledge the potential paradox of NGR234 producing an effector that appears to restrict its own colonization in host plants. In fact, depending on the host plant, most rhizobial effectors are “double-edged swords” that play either a positive or negative role in the symbiosis. In response to your comment, we will discuss the possibility that NopT may confer selective advantages in interactions between NGR234 and host plants where NopT plays a positive symbiotic role (Dai et al. 2008; Kambara et al. 2009). Inhibition of NFR1/NFR5 functions by NopT in these host plants could be a feedback response in cells in which symbiotic signaling has already started. It is tempting speculate that the interaction between NopT and Nod factor receptors reduces Nod factor perception and downstream signaling to avoid a possible overreaction of symbiotic signaling, which may result in hypernodulation or formation of empty nodules without bacteria. Furthermore, it is tempting to speculate that NopT targets not only Nod factor receptors but also other host proteins to promote symbiosis, e.g. by suppressing excessive immune responses triggered by hyperinfection of rhizobia. In our revised manuscript, we will highlight the need for further investigations to elucidate the precise mechanisms underlying the observed infection phenotype and the role of NopT in modulating symbiotic signaling pathways.  

(4) The failure to generate stable transgenic plants expressing NopT in Lotus japonicus is surprising, considering the manuscript's claim that NopT specifically proteolyzes NFR5, a major player in the response to nodule symbiosis, without being essential for plant development.

Thank you for your comments. The failure to obtain L. japonicus plants constitutively expressing NopT was indeed surprising and suggests that NopT targets not only NFR5 but also other proteins in L. japonicus. The number of NopT substrates in plants could be greater than assumed. For example, we show in our work that NopT can cleave AtLYK5 and LjLYS11. In our manuscript, we don’t provide protocols and data on our efforts to construct L. japonicus plants stably expressing NopT. Indeed, it cannot be completely ruled out that the observed failure is not due to NopT expression, but rather to other factors that influence the transformation and regeneration of explants into whole plants. Our results should therefore not be over-interpreted. We consider a discussion of our failed transformation experiments to be somewhat preliminary and not central to this manuscript. herefore, we plan to modify our Discussion and delete the sentence reporting that stable transgenic plants expressing NopT have not been successfully generated.

Reviewer #2 (Public Review):

Summary:

The goal of the paper was to trace the transitions hippocampal microglia undergo along aging. ScRNA-seq analysis allowed the authors to predict a trajectory and hypothesize about possible molecular checkpoints, which keep the pace of microglial aging. E.g. TGF1b was predicted as a molecule slowing down the microglial aging path and indeed, loss of TGF1 in microglia led to premature microglia aging, which was associated with premature loss of cognitive ability. The authors also used the parabiosis model to show how peripheral, blood-derived signals from the old organism can "push" microglia forward on the aging path.

Strengths:

A major strength and uniqueness of this work is the in-depth single-cell dataset, which may be a useful resource for the community, as well as the data showing what happens to young microglia in heterochronic parabiosis setting and upon loss of TGFb in their environment.

Weaknesses:

That said, given what we recently learned about microglia isolation for RNA-seq analysis, there is a danger that some of the observations are a result of not age, but cell stress from sample preparation (enzymatic digestion 10min at 37C; e.g. PMID: 35260865). Changes in cell state distribution along aging were made based on scRNA-seq and were not corroborated by any other method, such as imaging of cluster-specific marker expression in microglia at different ages. This analysis would allow confirming the scRNA-seq data and would also give us an idea of where the subsets are present within the hippocampus, and whether there is any interesting distribution of cell states (e.g. some are present closer to stem cells?). Since TGFb is thought to be crucial to microglia biology, it would be valuable to include more analysis of the mice with microglia-specific Tgfb deletion e.g. what was the efficiency of recombination in microglia? Did their numbers change after induction of Tgfb deletion in Cx3cr1-creERT2::Tgfb-flox mice.

Overall:

In general, I think the authors did a good job following the initial observations and devised clever ways to test the emerging hypotheses. The resulting data are an important addition to what we know about microglial aging and can be fruitfully used by other researchers, e.g. those working on microglia in a disease context.

There's a lot of really great content here. But, for readers like me (the technical/design/engineering/research side of the visualization community), I think the writing isn't landing with quite the impact that it could for a few reasons:

(1) In my interdisciplinary collaborations, I've noticed a difference in writing styles/norms between the humanities and the design/engineering disciplines. The latter tend to favor a top-down argument structure (e.g., a crisply articulated thesis that is then unpacked via clearly signposted topic sentences). I think that's because readers like me are trying to figure out how to operationalize the things we're reading/learning. So, right from the get go, we need a clearly articulate conceptual model so that, over the course of the rest of the writing, we can figure out how to integrate it with our existing mental models of practice/research.

In contrast, this piece takes a very bottom-up approach to the argument. For me, the experience of reading bottom-up writing is of assembling a mental model that feels more like a wobbly house of cards: ad hoc, duct taped together, and needing to constantly swap/rearrange it as more pieces of the conceptual contribution reveal themselves to me.

As a concrete example, for the first third of this chapter, I wasn't actually sure what I was supposed to be taking away. I almost wondered whether I should suggest titling the chapter "preface" instead of "introduction" because it opens by being focused inwardly (i.e., on the presentation of the homepage) in a way I'm more accustomed to with prefaces than introductions. Although that whole chunk of writing was very pleasant to read (which may also be a function of the fact that I had the pleasure of meeting y'all and learning about how the project came together!), I wasn't entirely sure what this chunk was hoping to do/communicate—or how it was hoping to influence my thinking.

(2) Related to the first point, while I personally find the exploration of a visualization counterhistory exciting and thought-provoking, I wonder if the writing could better motivate the goals of the counterhistory a bit more explicitly and clearly? That is, if someone isn't already bought into valuing the history of the field (or doesn't know how a counterhistory may/should affect their current practice today), how might the writing persuade them to care? Or, put another way, how can the writing speak and evangelize to an audience who is open-minded, but not yet "on side." To me, this feels like a particularly important thing for an introductory chapter to do, that seems missing in the current iteration.

(3) I'm on the fence about how central a role Tufte is given here. I think this depends on the audience you are trying to reach—I'm not sure that many (most?) visualization researchers/designers/practitioners (i.e., visualization "thought leaders") consider Tufte to play as influential a role as this chapter purports him to do. If this was the core audience, then I think the focus on Tufte could be watered down without losing much of the overall framing of "counterhistory"—because I think what this chapter describes is very much the history the field tells itself (relatively independently of Tufte, I think?).

On the other hand, if the intended audience are the folks one hop removed (i.e., people who produce/consume visualizations in their daily lives/jobs, but aren't necessarily plugged into conversations on the bleeding edge), I think Tufte serves as a useful foil. But, something about his treatment in this chapter feels a little caricatured to me (and I say this as someone relatively ambivalent about his role). I'm not quite able to put my finger on what specifically about the writing left me with that feeling, though.

(4) Starting with the "Two Stories of Data Visualization" (and particularly the subsequent chapter on "Every Datapoint is a Person"), I wondered whether the target of the book's critique is indeed visualization (i.e., the graphic representation of data) or whether it's more fundamental and broader practices of data (i.e., definition, collection, etc. more similar to the set of issues y'all discussed in Data Feminism). I really enjoyed all of the detail and discussion here—and I was convinced about the role that data played. But, I was perhaps less convinced about visualization's central/facilitating/empowering role in it. It's likely impossible to fully disentangle data from its representation (as the data table examples do a great job) but, if the book wants to maintain visualization as its target, I wonder if the writing could be refined a little to make its focus clearer/crisper?

(5) I wonder if the writing can be more explicit about its positionality? I think some of the early sections (and occasional passages throughout) set up an incorrect expectation for me of a much broader (i.e., more global) counterhistory. So, I was then surprised that this chapter maintains a relatively fixed focus on Western history. In fact, I might go further to say that the writing seems to be particularly fixated on an American point of view (e.g., I raised an eyebrow at the description of the United States as "the exemplary" colonial state; as a non-American and citizen of a former colonized nation, I would consider the British Empire to be the ultimate colonial power...). I think this focus is fine if the writing is explicit that it is primarily concerned with developing a counterhistory rooted in the West (and, at that, the United States).

Author response:

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

eLife assessment

This important study identifies differential Orsay virus infection of C. elegans when animals are fed on different bacteria. The evidence for this is however, incomplete, as experiments to control for feeding rate and bacterial pathogenicity are needed as well as direct quantification of viral load. 

We appreciate that the editors and reviewers felt that our manuscript addressed an important problem. We appreciate the constructive critiques provided by the reviewers and have worked to address all of the concerns, including a number of additional experiments as indicated below.

Public Reviews:

Reviewer #1 (Public Review):

Summary: 

This manuscript explores the importance of food type on virus infection dynamics using a nematode virus as a model system. The authors demonstrate that susceptibility to viral infection can change by several orders of magnitude based on the type of bacterial food that potential hosts consume. They go on to show that, for the bacterial food source that reduces susceptibility, the effect is modulated by quorum sensing molecules that the bacteria produce. 

Strengths: 

This manuscript shows convincingly that nematode susceptibility to viral infection changes by several orders of magnitude (i.e. doses must be increased by several orders of magnitude to infect the same fraction of the population) depending on the bacterial food source on which hosts are reared. The authors then focus on the bacteria that reduce host susceptibility to viral infection and demonstrate that certain bacterial quorum-sensing compounds are required to see this effect of reduced susceptibility. Overall, sample sizes are large, methods are generally rigorous, experiments are repeated, and patterns are clear. 

Weaknesses: 

Although the molecular correlate of reduced susceptibility is identified (i.e. quorum sensing compounds) the mechanisms underlying this effect are missing. For example, there are changes in susceptibility due to altered nutrition, host condition, the microbiome, feeding rate, mortality of infected hosts, etc. In addition, the authors focus almost entirely on the reduction in susceptibility even though I personally find the increased susceptibility generated when reared on Ochrobactrum to be much more exciting. 

I was a bit surprised that there was no data on basic factors that could have led to reductions in susceptibility. In particular, data on feeding rates and mortality rates seem really important. I would expect that feeding rates are reduced in the presence of Pseudomonas. Reduced feeding rates would translate to lower consumed doses, and so even though the same concentration of virus is on a plate, it doesn't mean that the same quantity of virus is consumed. Likewise, if Pseudomonas is causing mortality of virus-infected hosts, it could give the impression of lower infection rates. Perhaps mortality rates are too small in the experimental setup to explain this pattern, but that isn't clear in the current version of the manuscript. Is mortality greatly impacted by knocking out quorum-sensing genes? Also, the authors explored susceptibility to infection, but completely ignored variation in virus shedding. 

We have added data on feeding rates (Line numbers 141-148 and 176-182, Supplementary Figure 4). After six hours of exposure no differences in feeding rate were observed. After 24 hours minor differences emerged between O. vermis MYb71 and each Pseudomonas species, however feeding rate inversely correlated with susceptibility to Orsay virus in that O. vermis MYb71 displayed the lowest feeding rate while P. aeruginosa PA14 displayed the highest feeding rate.

We have also added data on mortality rates (Line numbers 183-200, Supplementary Figure 6). No significant mortality was observed within the 24-hour exposure period used for our Orsay infection and transmission assays. P. aeruginosa virulence is dependent upon temperature and as our assays are done at 20°C rather than 25°C this may account for reduced mortality compared to other published results. Regardless, we noted that O. vermis MYb71 killed C. elegans as quickly as P. aeruginosa PA14 under these conditions and these two bacteria led to the shortest lifespan compared to the other tested bacteria. Interestingly, P. lurida MYb11 was observed to be more virulent than P. aeruginosa PA01 under these conditions. These results suggest that there is no direct correlation between mortality and susceptibility to Orsay virus, although it does not rule out that virulence effects unique to each bacterium could contribute to alterations in host susceptibility.  

The reviewer is correct to assert that differences in viral shedding could exist. However, our susceptibility assays using exogenous Orsay virus remove this source of variation and yet we still observe the same trends such that O. vermis MYb71 promotes infection while P. lurida MYb11, P. aeruginosa PA01, and P. aeruginosa PA14 attenuate infection. Further we measured the amount of virus shed into the lawns in the presence of different bacteria and did not observe differences in shed virus that could account for the differences we observe in incidence proportion (Line numbers 241-254, Fig. 3 F). Viral stability could be an issue in both the transmission and susceptibility assays. We therefore tested viral stability in the presence of E. coli, P. lurida MYb11, P. aeruginosa PA01, and P. aeruginosa PA14 and successfully recovered virus from all lawns, suggesting virus is not rapidly degraded in the presence of any bacterium (Fig. 3D and 3E). However, we noted that the recovery of Orsay virus from lawns of E. coli OP50 and P. lurida MYb11 within 30 minutes was decreased compared to a spike-in control suggesting recovery from each lawn is not equivalent. This complicates a comparison of viral stability and shedding rates between different bacteria, but our ability to recover substantial amounts of virus in the shedding assay from the three Pseudomonas strains we examined precludes a substantial decrease in shedding rates as an explanation for the robust attenuation of Orsay virus observed in transmission assays.  

I was also curious why the authors did not further explore the mechanism behind the quorumsensing effect. Not sure whether this is possible, but would it be possible to add spent media to the infection plates where the spent media was from Pseudomonas that produce the quorum sensing compound but the plates contain OP50, Pseudomonas, or the quorum sensing knockout of Pseudomonas? That would reveal whether it is the compound itself vs. something that the compound does. 

We observed that quorum sensing mutants suppressed the attenuation of Orsay virus infection and we agree that this could be a consequence of the compounds themselves, or more likely an effect of the downstream consequences of quorum signaling. We added culture supernatant from each bacterium to lawns of E. coli OP50 to assess the effect on host susceptibility and did not observe any potent effect (Line numbers 311-318, Supplementary Figure 9). This supports an interpretation that it is not the compound itself that is responsible, however we cannot rule out that the compounds themselves may be responsible if provided at a higher concentration.

In addition, I was surprised by how much focus there was on the attenuation of infection and how little there was on the enhancement of infection. To me, enhancement seems like the more obvious thing to find a mechanism for -- is the bacteria suppressing immunity, preventing entry to gut cells, etc? 

We are also intrigued by the enhancement of infection by Ochrobactrum spp, however we chose to focus on attenuation given the availability of Pseudomonas aeruginosa genetic mutants for study. We have added data (Line numbers 371-402, Figure 7, and Supplemental Figure 12) that inform our current hypothesis regarding Ochrobactrum mediated enhancement of Orsay virus infection.

I was a bit concerned about the "arbitrary units", which were used without any effort to normalize them. David Wang and Hongbing Jiang have developed a method based on tissue culture infectious dose 50 (TCID50) that can be used to measure infectious doses in a somewhat repeatable way. Without some type of normalization, it is hard to imagine how this study could be repeated. The 24-hour time period between exposure and glowing suggests very high doses, but it is still unclear precisely how high. Also, it is clear that multiple batches of virus were used in this study, but it is entirely unclear how variable these batches were. 

We have clarified that we also measured the (TC)ID50 for every batch of virus used similar to the methods suggested by the Wang laboratory (Line numbers 107-119 and 499-506). We have added a figure showing the virus batch variability for all batches used in this study (Supp. Fig. 2). We have further clarified that the arbitrary units correspond to the actual microliters of viral filtrate used during infection and provided clear methods to replicate our viral batch production to assist with issues of reproducibility (Line numbers 107-119 and 499-506).

The authors in several places discuss high variability or low variability in incidence as though it is a feature of the virus or a feature of the host. It isn't. For infection data (or any type of binomial data) results are highly variable in the middle (close to 50% infection) and lowly variable at the ends (close to 0% or 100% infection). This is a result that is derived from a binomial distribution and it should not be taken as evidence that the bacteria or the host affect randomness. If you were to conduct dose-response experiments, on any of your bacterial food source treatments, you would find that variability is lowest at the extremely high and extremely low doses and it is most variable in the middle when you are at doses where about 50% of hosts are infected. 

Thank you for pointing this out, we have removed all reference to this throughout the manuscript.

Reviewer #2 (Public Review):

Summary and Major Findings/Strengths:

Across diverse hosts, microbiota can influence viral infection and transmission. C. elegans is naturally infected by the Orsay virus, which infects intestinal cells and is transmitted via the fecal-oral route. Previous work has demonstrated that host immune defense pathways, such as antiviral RNAi and the intracellular pathogen response (IPR), can influence host susceptibility to virus infection. However, little is known about how bacteria modulate viral transmission and host susceptibility. 

In this study, the authors investigate how diverse bacterial species influence Orsay virus transmission and host susceptibility in C. elegans. When C. elegans is grown in the presence of two Ochrobactrum species, the authors find that animals exhibit increased viral transmission, as measured by the increased proportion of newly infected worms (relative to growth on E. coli OP50). The presence of the two Ochrobactrum species also resulted in increased host susceptibility to the virus, which is reflected by the increased fraction of infected animals following exposure to the exogenous Orsay virus. In contrast, the presence of Pseudomonas lurida MYb11, as well as Pseudomonas PA01 or PA14, attenuates viral transmission and host susceptibility relative to E. coli OP50. For growth in the presence of P. aeruginosa PA01 and PA14, the attenuated transmission and susceptibility are suppressed by mutations in regulators of quorum sensing and the gacA two-component system. The authors also identify six virulence genes in P. aeruginosa PA14 that modulate host susceptibility to virus and viral transmission, albeit to a lesser extent. Based on the findings in P. aeruginosa, the authors further demonstrate that deletion of the gacA ortholog in P. lurida results in loss of the attenuation of viral transmission and host susceptibility. 

Taken together, these findings provide important insights into the species-specific effects that bacteria can have on viral infection in C. elegans. The authors also describe a role for Pseudomonas quorum sensing and virulence genes in influencing viral transmission and host susceptibility. 

Major weaknesses: 

The manuscript has several issues that need to be addressed, such as insufficient rigor of the experiments performed and questions about the reproducibility of the data presented in some places. In addition, confounding variables complicate the interpretations that can be made from the authors' findings and weaken some of the conclusions that are stated in the manuscript. 

(1) The authors sometimes use pals-5p::GFP expression to indicate infection, however, this is not necessarily an accurate measure of the infection rate. Specifically, in Figures 4-6, the authors should include measurements of viral RNA, either by FISH staining or qRT-PCR, to support the claims related to differences in infection rate. 

Following the reviewers comment we have corroborated our pals-5::GFP data using FISH staining (Line numbers 291-292 and 357-359, Figure 4D & 4E, and Figure 6C).  

(2) In several instances, the experimental setup and presentation of data lack sufficient rigor. For example, Fig 1D and Fig 2B only display data from one experimental replicate. The authors should include information from all 3 experimental replicates for more transparency. In Fig 3B, the authors should include a control that demonstrates how RNA1 levels change in the presence of E. coli OP50 for comparison with the results showing replication in the presence of PA14. In order to support the claim that "P. aeruginosa and P. lurida MYb11 do not eliminate Orsay virus infection", the authors should also measure RNA1 fold change in the presence of PA01 and P. lurida in the context of exogenous Orsay virus. Additionally, the authors should standardize the amount of bacteria added to the plate and specify how this was done in the Methods, as differing concentrations of bacteria could be the reason for species-specific effects on infection. 

All experimental replicates are now included within the supplementary information. 

We have also measured RNA1 fold change following infection in the presence of P. aeruginosa PA01 and P. lurida MYb11 (Line numbers Fig 3B and 3C) and found that these bacteria also do not eliminate Orsay virus replication. 

We thank the reviewer for their comment on controlling the amount of bacteria and have clarified our methods section to more clearly explain that we seed our plates with equivalent amounts (based on volume) of overnight bacterial culture before allowing the bacteria to grow on the plates for 48 hours.  

(3) The authors should be more careful about conclusions that are made from experiments involving PA14, which is a P. aeruginosa strain (isolated from humans), that can rapidly kill C. elegans. To eliminate confounding factors that are introduced by the pathogenicity of PA14, the authors should address how PA14 affects the health of the worms in their assays. For example, the authors should perform bead-feeding assays to demonstrate that feeding rates are unaffected when worms are grown in the presence of PA14. Because Orsay virus infection occurs through feeding, a decrease in C. elegans feeding rates can influence the outcome of viral infection. The authors should also address whether or not the presence of PA14 affects the stability of viral particles because that could be another trivial reason for the attenuation of viral infection that occurs in the presence of PA14. 

We have added data on feeding rates (Line numbers 141-148 and 176-182, Supplementary Figure 4). After six hours of exposure no differences in feeding rate were observed. After 24 hours minor differences emerged between O. vermis MYb71 and each Pseudomonas species, however feeding rate inversely correlated with susceptibility to Orsay virus in that O. vermis MYb71 displayed the lowest feeding rate while P. aeruginosa PA14 displayed the highest feeding rate.

We have also added data on mortality rates (Line numbers 183-200, Supplementary Figure 6). No significant mortality was observed within the 24-hour exposure period used for our Orsay infection and transmission assays. P. aeruginosa virulence is dependent upon temperature and as our assays are done at 20°C rather than 25°C this may account for reduced mortality compared to other published results. Regardless, we noted that O. vermis MYb71 killed C. elegans as quickly as P. aeruginosa PA14 under these conditions and these two bacteria led to the shortest lifespan compared to the other tested bacteria. Interestingly, P. lurida MYb11 was observed to be more virulent than P. aeruginosa PA01 under these conditions. These results suggest that there is no direct correlation between mortality and susceptibility to Orsay virus, although it does not rule out that virulence effects unique to each bacterium could contribute to alterations in host susceptibility.  

We tested viral stability in the presence of E. coli OP50 and Pseudomonas spp. and successfully recovered virus from all lawns, suggesting virus is not rapidly degraded in the presence of P. lurida MYb11, P. aeruginosa PA01, and P. aeruginosa PA14 (Line numbers 241-249, Fig 3D and Fig 3E). However, we noted that the recovery of Orsay virus from lawns of E. coli OP50 and P. lurida MYb11 within 30 minutes was decreased compared to a spike-in control suggesting recovery from each lawn is not equivalent. This complicates a comparison of viral stability and shedding rates between different bacteria, but our ability to recover substantial amounts of virus in the shedding assay from each Pseudomonas species precludes a substantial decrease in shedding rates as an explanation for the robust attenuation of Orsay virus observed in transmission assays.  

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors): 

Overall, I really liked this manuscript, I do think there are areas for improvement though. 

Some smaller things: 

Line 84: "can be observed spreading from a single animal" -- this isn't really great wording because the virus itself can't be observed (at least not very easily) -- even infection is hard to see. 

The wording in line 84-85 has now been adjusted to read “can spread from a single animal”.

Fig 1C: which groups are statistically significantly different from each other? 

Statistics have now been added to Figure 1C. 

Line 154: not necessary to do for this paper, but this sentence made me curious whether the effect would have been seen with mixtures of bacteria (i.e. what if 50% were OP50 and 50% were Pseudomonas?) 

This data has now been added in Line numbers 372-378, Figure 7A, and Supp. Fig. 12A and 12B.

Line 262-264: I don't find this interesting at all for the reasons mentioned earlier about binomial data being the most variable in the middle. 

These lines have been removed.

Figure 4 B: The labels for the first two tick marks on the x-axis are switched I suspect. Otherwise, the controls did not behave as expected. 

Figure 4B has been corrected.

Line 288, 297 and several other places: "Orsay Virus" should be "Orsay virus". 

We have corrected these instances.

Supplemental Figure 2: Labels in the figure legend are B and C instead of A and B. 

These labels have been adjusted for their placement within Figure 6.

Line 411: I suspect this was supposed to be 13,200 xg rather than 13.2 xg. 

This error has been corrected.

Line 416-417: This sentence is very hard to interpret. More details are needed. This is the ID50 in which host strain? Is this averaged over all batches of virus? How variable are the batches? 

This sentence (line number 114) has been amended to clarify that all ID50 values referred to here were calculated for ZD2611 populations in the presence of E. coli OP50. Further, Supplementary Figure 2 now shows all the ID50 values measured for each batch of virus used in this manuscript resulting in an average ID50 of 3.6.

Lines 467-469: Why exclude these instead of counting them as zeros in the analysis? How many plates fit this description -- were there lots or only a few over the course of all experiments? 

We have chosen to exclude these plates as these samples lost spreaders at some point during the course of the assay potentially skewing the eventual number of new infections counted depending on when the infected spreader animal crawled off the plate.  We have detailed the number of plates that fit this description in lines 559-562. 

Line 476: A critical detail that is missing here is what number of worms were counted to score infection. Please say here or in the figure legends. 

We have added the total number of worms counted and the minimum number counted per plate for each assay in the figure legends.

Line 546: Why was only a single representative experiment shown? I'm asking for a justification, not necessarily for you to show all the data. 

We chose to show a single representative experiment for two reasons:  We noted variability between susceptibility assays even when using the same batch of virus such that we could not combine experiments into a single plot as we did for transmission assays. Second, while we could normalize to a control within each experiment and expect to see similar relative differences across experiments, we believe this makes it more difficult to interpret the underlying data. For example, an increase in the infection rate of 80% compared to 10% within a population has only a single interpretation while a relative increase in the infection rate by 8x within a population could have several underlying meanings (e.g. 80% vs 10%, 64%vs 8%, 24% vs 3%). We have now included all experimental replicates in the supplementary material. 

Reviewer #2 (Recommendations For The Authors):

Minor concerns: 

(1) Lines 86-87: "utilized a collection of bacteria isolated from the environment with wild C. elegans". The authors should provide more context on the source of these bacterial strains. 

More references for the sources of these bacteria have been added to Supplementary Table 2.

(2) The presentation of data in Fig 1 could be improved. The authors should include the text "pals-5p::GFP" on the images shown in Fig 1B. The red dashed line in Fig. 1D should intersect the dose-response curve at y = 0.5. The column heading for Fig 1E states "ID50 +/- SD (a.u.)", but should read "ID50 ratio" and should not have units. It also might be more intuitive to normalize the ID50 value for O. vermis to E. coli OP50. This way, having an ID50 ratio >1 indicates decreased transmission relative to E. coli, and ID50 ratio <1 indicates increased transmission relative to E. coli. To increase the transparency and rigor of 1E, the authors should plot the ratios from all 3 experimental replicates. The authors should also briefly explain why different viral doses were used in Fig 1D and 1F. 

The text “pals-5p::GFP” has now been added to Figure 1B and throughout the text. The red dashed line in figure 1D has been corrected. Figure 1E has been adjusted to an actual figure as suggested and the y-axis label is “ID50 Ratio Compared to E. coli OP50”. The ID50 replicates have been plotted in Supplementary Figure 2. We have clarified that the doses used are the same. Briefly, the technical replicates of individual doses from Figure 1D and Supplementary Figure 3A and 3B were pooled and processed for FISH staining to provide each experimental replicate of Figure 1F. 

(3) Line 110: The claim is that Ochrobactrum and P. lurida MYb11 reduce the variability of infection levels. However, another possibility is that there's simply less dynamic range in the assay because the infection levels have been compressed to 100% and 0% under these conditions. 

This line has been removed.

(4) There are discrepancies between what is shown in Fig 2C and what is described in the text. Lines 163-164: "P. aeruginosa PA01 and P. lurida MYb11 attenuated average infection to 33% and 62% of the population respectively". In Fig 2C, the mean for PA01 is ~25% whereas the mean for P. lurida appears to be less than 62%. 

These values have been corrected.

(5) Line 196: Provide more context for why rde-1 mutants were tested. This is the first time rde-1 is mentioned in the text (i.e. why show results in rde-1 mutants when the results are in Fig 2). 

More context has been provided for why rde-1 mutants were tested (Line numbers 228-232). Briefly, using the rde-1 mutant, which has defective antiviral immunity and therefore supports higher viral replication levels than the wild-type (Félix et al. 2011), allows us to potentiate our infection assay in Figure 3B and 3C such that we maximize our chances of detecting viral replication in the presence of the Pseudomonas species, and especially P. aeruginiosa PA14, where fewer animals might be expected to get infected based upon Figure 2B and Supplementary Figure 5. 

(6) Lines 228-229: "Mutations of any the regulators of the las, rhl, or pqs quorum sensing systems suppressed the attenuation of Orsay virus infection caused by the presence of wild-type P. aeruginosa PA01". Based on this description, PA01 should have a lower fraction of GFP positive relative to the quorum sensing mutants in Fig 4B. It seems that the x-axis labels OP50 and PA01 are swapped. 

The x-axis labels of Figure 4B have been corrected. 

(7) To improve clarity, for any figures that have data showing the "fraction of individuals GFP positive", the authors should include "pals-5p::GFP" in the y-axis title and legend. 

The y-axis labels, legends, and text have been corrected throughout.  

(8) To improve overall clarity and flow, the order in which the data is presented could be reordered. In particular, Fig. 6 could be better positioned instead of being the last figure, as no further characterization is performed on the mutants, and the findings are not conserved in strains that are more relevant to the C. elegans microbiota, such as P. lurida. The overall story could be strengthened if the authors ended the manuscript with more details related to the mechanism by which regulators of quorum sensing modulate the outcome of viral infection. 

Figure 5 and Figure 6 have now been swapped.

(9) Fig 5A: Make arrow sizes consistent across diagrams (i.e. the diagram for gacA deletion). 

This figure (now Figure 6A) has been adjusted to make arrow sizes consistent across diagrams.  

(10) Lines 280-282: "These data suggest that gacA has a conserved role across distant Pseudomonas species..." Here, the authors can provide more context on how well-conserved gacA is across Pseudomonas species (i.e. phylogenetic analysis of gacA sequences across different Pseudomonas species/strains). Furthermore, the data in Fig 5 does not provide strong enough support for the conclusion that gacA has a conserved role broadly across Pseudomonas species, as the authors only assess the effects of a gacA deletion in two species, P. aeruginosa and P. lurida. 

We have adjusted lines 361-362 to “These data suggest that gacA has a conserved role between P. aeruginosa and P. lurida Myb11 in the attenuation of Orsay virus transmission and infection of C. elegans.” to reflect that we only assessed the effects of the gacA deletion in P. aeruginosa and P. lurida MYb11.

(11) The manuscript can be strengthened by performing additional experiments to elucidate the mechanism by which Pseudomonas modulates viral infection. Does the attenuation of viral transmission and host susceptibility by P. lurida and P. aeruginosa require C. elegans to be in the presence of live bacteria? For example, the authors could measure viral transmission and susceptibility of C. elegans grown on heat-killed Pseudomonas. Additionally, it would be interesting to determine if modulation of viral infection is dependent on a secreted molecule. To assess this, the authors could perform viral infections in the context of Pseudomonas culture supernatant. 

We added bacterial culture supernatant from each bacterium to lawns of E. coli OP50 to assess the effect on host susceptibility and did not observe any potent effect (Line numbers 311-318, Supplementary Figure 9). This supports an interpretation that attenuation is not mediated by a secreted molecule, however we cannot rule out that attenuation activity would become apparent if supernatant were provided at a higher concentration.

We have found substantial challenges appropriately controlling live vs. heat-killed experiments particularly with the specifics of our susceptibility experiments. With regards to the underlying question of mechanism we believe that the genetic mutants (e.g. rhlR/gacA) are equally informative and that further comparison of these mutants’ interaction with the C. elegans host as compared to wild-type may be informative. 

(12) The authors should include a discussion on the relative virulence potential of PA01, PA14, and P. lurida and the relationship between bacterial virulence potential and the outcome of viral infection. 

We have also added data on mortality rates (Line numbers 183-200, Supplementary Figure 6). No significant mortality was observed within the 24-hour exposure period used for our Orsay infection and transmission assays. P. aeruginosa virulence is dependent upon temperature and as our assays are done at 20°C rather than 25°C this may account for reduced mortality compared to other published results. Regardless, we noted that O. vermis MYb71 killed C. elegans as quickly as P. aeruginosa PA14 under these conditions and these two bacteria led to the shortest lifespan compared to the other tested bacteria. Interestingly, P. lurida MYb11 was observed to be more virulent than P. aeruginosa PA01 under these conditions. These results suggest that there is no direct correlation between mortality and susceptibility to Orsay virus, although it does not rule out that virulence effects unique to each bacterium could contribute to alterations in host susceptibility.  

(13) More information is needed on strains listed in Supplementary Table 2, particularly when there is no reference listed and the strain is "Gift of XXX lab". For example, the Troemel lab previously published about an Ochrobactrum strain in Troemel et al PLOS Biology 2008 PMID: 19071962 - is this the same strain? Please ensure that there is adequate information about each strain with as many published references as possible so that the work can be more easily reproduced. 

We have added additional information and references to the strain table in Supplementary Table 2. The strain listed as Ochrobactrum sp. has been amended to Ochrobactrum BH3 as it is the strain described in Troemel et al. 2008.

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Reply to the reviewers

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

This manuscript uses C. elegans as a model to interrogate the effects of autism-associated variants of previously unknown function in the RNA-binding protein RBM-26/RBM27.

Despite its potential impact, there are several concerns related to the technical rigor and specificity of the observed effects.

Major concerns: 1. The effects on PLM are interesting, but why was this neuron selected for study? Was this a lucky guess or are other axons also affected? It is important to clarify whether the effects of RBM-26 are specific to this neuron or act pleiotropically across many or all neurons. According to CeNGEN, rbm-26 is strongly expressed in the well-characterized neurons ASE, PVD, and HSN. Are there morphological defects in these neurons, or others? As a note, there are also functional assays for these neurons (salt sensing, touch response, and egg laying, respectively).

We have added new data to the supplemental materials showing that loss of rbm-26 function also causes the beading phenotype in the axons and dendrites of the PVD neuron (Figure S4 and lines 196-199). We have focused on the PLM neuron because our preliminary studies indicated that it had a higher penetrance of axon defects relative to the PVD neuron. Moreover, we observed expression of endogenously tagged RBM-26 in the PLM neuron (Figure 3A-C and lines 210-215).

Similarly, the choice of the MALSU homolog seemed like a shot in the dark. It is ranked 46th (out of 63 genes) for fold-enrichment following RBM-26 pull-down, and 9th for p-value. Were any of the mRNAs with greater fold-enrichment or smaller p-values examined further? It is important to determine whether many or all of these interacting genes are overexpressed in the absence of RBM-26 and whether they are also required for the phenotypic effects of RBM-26 mutants, or if the MALSU homolog is special.

We have clarified our reasoning for selecting the MALS-1 ortholog of MALSU1 for further study (see lines 283-284 and Table S2). Amongst binding partners with human orthologs, MALS-1 was by far the top ranked candidate. The adjusted p-value for MALS-1 was 0.0008. The next smallest adjusted p-value was two orders of magnitude larger (0.028 for dpy-4). Moreover, the log2fold fold enrichment for MALS-1 was 1.98, about the same as the largest (ACADS with 2.13). Nonetheless, we agree that some of the other interactors may also be of interest and have thus included them in the supplemental table S2. Although these other potential binding partners are outside the scope of this study, we expect that future studies by ourselves or others may focus on the roles of these other binding partners.

In addition to the specificity controls mentioned above, positive and negative controls are needed throughout the results. While each of these may be relatively minor by itself, as a group they raise questions about the technical rigor of the study. Briefly these include: Fig 1C. Missing loading controls and negative control (rbm-26 null allele). Additional exposures should be included to show whether RBM-26(P80L) protein or the lower band for RBM-26(L13V) are present at all, relative to the null allele.

We have added no-stain loading controls to figure 1C. We have also switched to using ECL detection, which is much more sensitive and reveals faint bands for RBM-26(P80L) and additional faint bands for RBM-26(L13V). In addition, we have included a longer exposure for the blot (Figure S1). We are unable to test the null, as we can only produce a limited number of small maternally rescued progeny, thereby precluding western blot analysis.

Fig 2. Controls to distinguish overextension of PLM axon from posterior mispositioning of ALM cell body are needed. Quantification of PLM axon lengths in microns (or normalized to body size) with standard deviation, not error of proportion, should be shown. Measurement of "beading phenotype" should be more rigorous, see for example the approach in Rawson et al. Curr. Biol. 2017 https://doi.org/10.1016/j.cub.2014.02.025 . The developmental stage examined, and the reason for choosing that stage, should be described for this and all figures.

We have added new data that shows PLM axon length relative to body length for each of the RBM-26 mutants (Figure S2 and lines 183-185). These results indicate that the PLM axon has a larger axon length to body length ration, suggesting that the PLM/ALM overlap phenotype is a result of PLM axon overextension. For most experiments, we retain penetrance, as this has been standard practice in the field and allows for a much larger sample size (see examples listed below). We have also added examples of how the beading phenotype was measured (Figure S3). Moreover, we have now analyzed this phenotype and others at multiple developmental stages (Figures 2D-H and Table S1). In general, we have conducted experiments at the L3 stage because the rbm-26(null) mutants don't survive past this stage. However, for many of our experiments we have also included additional stages as well. We have added this explanation to the methods section of phenotype analysis and also at various locations throughout the text. We have also labeled all graphs to clearly indicate the developmental stages and included.

10.1038/s41467-019-12804-3 Article by laboratory of Brock Grill

10.1371/journal.pgen.1002513 Article by laboratory of Ian Chin-Sang

doi.org/10.1073/pnas.1410263111 Article by laboratory of Chun-Liang Pan

10.1016/j.neuron.2007.07.009 Article by laboratory of Yishi Jin

doi.org/10.1523/JNEUROSCI.5536-07.2008 Article by laboratory of William Wadsworth

Fig 3. Controls without auxin and with neuronal TIR1 expression alone should be included. Controls demonstrating successful RBM-26 depletion, in larvae as well as in embryos at the time of PLM extension, should be included (weak embryonic depletion might explain why the overextension phenotype is only 14% instead of 40% as in the null). According to CeNGEN, rbm-26 expression in PLM is barely detected, thus depletion with a PLM-specific TIR1 should also be tested. To confirm the authors' identification of the cell marked "N" as the PLM cell body, co-expression of rbm-26 and a PLM-specific marker should be added. Rescue of the rbm-26 mutants with neuronal (and PLM-only) expression should be included to test sufficiency in PLM, and as a further control for potential artifacts of the AID system.

We have added new data showing that an endogenously tagged RBM-26::Scarlet protein is expressed in the PLM neuron (Figure 3A-C). Moreover, we have added rescue experiments, showing that a Pmec-7::rbm-26::scarlet transgene can rescue the beading phenotype and the PLM/ALM overlap phenotype (Figure 3 F-G). We have also added controls without auxin (Figure S7) __and without the rbm-26::scarlet::aid gene (Figure S8). We have added a new figure showing auxin-mediated depletion of RBM-26::Scarlet::AID in the PLM neuron (Figure S10)__. We examined auxin-mediated depletion at the L3 stage for consistency with our auxin-mediated phenotypic experiments. Moreover, these were done at the L3 stage for consistency with other experiments that included the rbm-26(null) mutants, which don't survive past this stage.

In general, auxin-mediated knockdown tends to be hypomorphic in neurons. This is likely due to the fact that the neuronal TIR1 driver is expressed at much lower levels relative to the other drivers. In addition, the lower penetrance observed in auxin-mediated PLM/ALM overlap phenotype could reflect the fact that this phenotype resolves by the L4 stage in the hypomorphic mutants. For example, in P80L mutants at the L3 stage we see only about a 20% penetrance of the PLM/ALM overlap phenotype (relative to about 15% in auxin-mediated knockdown).

Fig 4. More rigorous quantification of the distribution of mitochondria along the axon should be included, not only total number, and it should be clarified what region of the axon the images are taken from. Including the AID-depletion strain with and without auxin would further add to the sense of rigor. For the mitoTimer experiments, why is RBM-26(L13V) not included and why do wild-type values differ ~5-fold between experiments (despite error bars being almost non-existent)? A more rigorous approach to standardizing imaging conditions may be needed. Positive controls using compounds that affect oxidation should be included. Measurements of individual mitochondria with standard deviations should be shown, rather than aggregate averages with error of proportion.

We have changed our methodology for measuring mitochondria, so that we now report the density of mitochondria in the axon (number per 100µm), (Figure 4E-F). We agree that this method is much better than counting the total number of mitochondria per axon, as it corrects for differences in body length and axon length). We also now include data for the whole axon (Figure 4E), proximal axon (Figure 4G), and distal axon (Figure 4H). These data suggest that the mitochondrial density defects occur in the proximal axon but not in the distal axon. Using the null allele, we have also examined the timing of mitochondria defects in the axon and report that the defects begin in the L1 stage and continue throughout larval development (Figure 4F). Individual datapoints have been added for all graphs in Figure 4.

For the mitoTimer experiments (Figure 5), we have added data for L13V and have added the individual datapoints to the graph. In the prior version, the values did not differ 5-fold between experiments with the same stage, rather the different graphs were from different stages (as noted in the figure legends/main text) and the L4 stage has much more oxidation than the L2 stage. To clear this up, we have added labels to the graphs to indicate the stages for each experiment. We have also added new data, so that we now show results for the L2, L3, and L4 stages for all three rbm-26 mutants (see Figure 5C-E). We didn't test the L1 stage because the signal was not sufficient for accurate quantitation.

Fig 5. Additional positive and negative controls should be added, including additional rbm-26 alleles, the AID-tagged strain with and without auxin, and a rescued mutant.

The old Figure 5 has become Figure 6 in the new version. We have added the rbm-26(L13V) allele to each experiment, (Figure 6B-D). We have also added the loading controls for the western blot along with quantification for 3 biological replicates of the western blot analysis (Figure 6D). We agree that these additions significantly strengthen the data because they show that two independent alleles of rbm-26 cause very substantial increase in the expression of mals-1 at both the mRNA and protein levels. We did not do these experiments with the rescuing transgene or with the AID-tagged strain because these experiments are done on whole worm lysates, whereas the AID-tagged and rescuing transgene are neuron-specific.

Fig 6. Controls showing whether the Scarlet-tagged protein is functional are needed, to rule out dominant negative or toxicity-related effects.

This is Figure 7 in the new version. For this experiment, we are showing that overexpression of MALS-1 does cause defects. The idea is that excessive amounts of MALS-1 causes deleterious effects to the mitochondria. In fact, these defects could be considered as dominant negative or toxic. We considered the possibility of crossing the Pmec-7::mals-1::scarlet transgene with rbm-26; mals-1 double mutants. However, this does not seem workable, because the single copy Pmec-7::mals-1::scarlet transgene produces the phenotypes at penetrances that are similar to what we observe in rbm-26; mals-1 double mutants. We concede that the results of the overexpression experiments in Figure 7 are limited when considered in isolation. However, we think that they are meaningful when considered in combination with the results on the mals-1;rbm-26 double mutants in Figure 8.

Fig 8. Controls for other mitochondrial components need to be included. It is important to determine if the decrease in ribosomes is specific or reflects a general decrease in mitochondria. If there are fewer mitochondria as suggested in Fig. 4, then of course mitochondrial ribosomal protein levels are also reduced. Additional rbm-26 alleles should be included here as well. Is this effect dependent on the MALSU homolog?

This is Figure 8D-E in the new version. We have added new data showing that the decrease in MRPL-58 expression that is caused by the rbm-26(P80L) mutation is dependent on MALS-1. We concede that these experiments cannot be used to determine anything about the mitoribosomes per se, but rather serve as an alternative way of testing the effect of rbm-26 on mitochondria. We have revised the text accordingly (lines 355-357). Given these limitations we have elected not to try additional mitochondrial markers and have also not included additional rbm-26 alleles for this experiment.

Finally the authors should address concerns about image manipulation, which amplify the concerns about technical rigor outlined above. The image in Fig. 2A appears to have a black box placed over the lower-right portion of the field to hide some features. Black boxes also appear to have been placed over the tops of images in Fig. 4B and 4D and at the left of Fig. 6A, 6B, and 6C. While these manipulations probably do not affect the conclusions, they further undermine confidence in data integrity and experimental rigor.

We have corrected all of these image processing errors. The box in 2A was for the purpose of squaring off a corner that was clipped during image rotation. The boxes in Figures 4 and 6 (of the prior version) were added to give space for labels (without obscuring image features). We have now used alternative methods to accomplish the same goals. For example, in Figures 4-D we have placed the labels outside of the images.

Minor points. 1. C. elegans nomenclature conventions should be followed: - C. elegans gene names have three or four letters, thus the MALSU homolog cannot be named "malsu-1". Please have new gene names approved by WormBase BEFORE submitting for publication http://tazendra.caltech.edu/~azurebrd/cgi-bin/forms/gene_name.cgi

We have changed malsu-1 to mals-1. In addition, both mals-1 and mrpl-58 have now been approved by wormbase and will be listed on the website upon its next update.

• If two sequential CRISPR edits are made on the same gene then they should be listed as a compound allele, such as rbm-26(cue22cue25)

We have updated our gene names to reflect this convention.

• Genes on the same chromosome should not be separated with a semicolon, for example rbm-26(cue40) K12H4.2(syb6330)

We have updated our gene names to reflect this convention.

Describing the defects as "neurodevelopmental" is misleading in the case of axon beading or degeneration. Similarly, there is no evidence for an "axon targeting" defect as stated in the abstract.

We have revised such that instead of referring to degeneration phenotypes as neurodevelopmental, we now refer to axon degeneration phenotypes that occur during development. For example, in the abstract we now say, "These observations reveal a mechanism that regulates expression of a mitoribosomal assembly factor to protect against axon degeneration during neurodevelopment.

Regarding targeting defects, this was meant to refer to the misplacement of the PLM axon tip (which contains electrical synapses). However, our subsequent analysis has revealed that these defects are transient in P80L and L13V mutants, as they resolve by the L4 stage. The rbm-26 null axon development defects do not resolve, though these mutant die prior to the L4 stage. Given these findings, we have decided not to use the term of targeting defects. Instead, we now refer to this as an axon tiling defect or PLM/ALM overlap phenotype.

In Fig. 5A, the symbol that appears to correspond to F59C6.15 (lowest p-value) is a different size than the others and is colored as ncRNA, whereas WormBase annotates this gene as snoRNA.

This error has been corrected.

In the Introduction, the last sentences of the first two paragraphs should be varied ("However, little is known about the [...] mechanisms that protect [...] during neurodevelopment.")

This has been done.

Why is RBM-26 protein running as a doublet at both sizes?

We have improved our western blotting methodology by using 12% gel, allowing for better resolution. We have also switched from colorimetric detection to ECL detection, allowing for greater sensitivity. In our new blots, we identify 6 different RBM-26 protein bands. We don't know the reason for these bands, but speculate that they are the result of post-translational processing (148-150).

When showing the RBM-26 expression pattern (Fig. 3) please include a lower-magnification image of the entire animal.

This has been done (Figure S6)

It is confusing to refer to the RNA IP experiments as an "unbiased screen", which in C. elegans typically refers to a genetic screen.

We now refer to this as a "biochemical screen".

The relationship between axon overextension, beading, and mitochondrial localization is not clear. What causal connection between these is being proposed? The causal connections between these phenotypes, if any, should be clarified experimentally. For example, if the axon extension defects develop before mitochondrial localization defects, then it is unlikely that mitochondrial defects cause axon overextension.

We have added new data showing that the reduction in mitochondrial density within the axon begins during the L1 stage and increases throughout larval development (Figure 4F). We have also added additional data showing that the increase in mitochondrial oxidation is weak in the L2 stage and surges in the L3 stage (Figure 5C-E), coincident with the beginning of the axon degeneration phenotypes. We propose (lines 383-391) that a low level of mitochondrial defects is present in L1 larvae, giving rise to the axon tiling defects. In the L3 stage there is a surge in excessive mitochondrial oxidation, giving rise to the axon degeneration phenotypes. We have added a new section to the discussion that addresses the relationship between defects in axon development and axon degeneration (lines 375-405).

Please explain how to interpret the difference in axon beading in the two deletion alleles of the MALSU homolog (axon beading defects in tm12122 but not in syb6330). Is syb6330 not a null allele? Or are the defects in tm12122 due to other mutations in this strain background?

One likely reason for this difference is that tm12122 is predicted to cause a partial deletion of the mals-1 coding sequence, whereas the syb6330 is a full deletion. Thus, the tm12122 could be acting as a dominant negative. In fact, prior work on the MALSU1 ortholog has indicated that this protein is subject to interference by a dominant negative construct (see Rorbach et al, Nucleic Acids Res 2012). Nonetheless, we cannot rule out the possibility of a linked second mutation in tm12122. However, since we have found similar phenotypes and genetic interactions with both alleles, we can conclude that these phenotypes and interactions are due to loss of MALS-1, rather than a second mutation.

Are mitochondria reduced in number or mislocalized? If they are reduced in number, is this due to altered balance of fission/fusion?

We have adjusted our methods for quantifying mitochondria and have also analyzed the proximal vs distal axon (Figure 4). We find that the density of mitochondria is decreased in the proximal axon, but not in the distal axon. We speculate that this might reflect a higher demand on mitochondria in the proximal axon, due to a higher amount of trafficking activity in the proximal axon (lines 255-257). We propose that the loss of RBM-26 causes dysfunction in mitochondria. Since fission and fusion are mechanisms that can help to repair damaged mitochondria, it is likely that they would be involved in the phenotypes that we observe.

In Fig. 3A-D, please keep the labels in the same position in all panels and do not alter brightness settings between single-color and merged panels.

These images have been moved to the supplemental data section (Figure S5). We have adjusted the labels as suggested. We have not changed the brightness settings, as they were already the same in all panels. However, the blue signal in the merged panel does obscure some of the red signal, giving an appearance of an alteration in color balance.

The claim that rbm-26 acts cell-autonomously requires PLM-specific depletion and rescue experiments.

We have added new data indicating that a Pmec-7::rbm-26::scarlet transgene can rescue the beading phenotype (Figure 3F-G).

**Referees cross-commenting** I appreciate the use of the consultation session to resolve differences between reviewers, but in this case I fully agree with the content and tone of all the comments from the other reviewer -- I think our remarks are very well aligned!

Reviewer #1 (Significance (Required)):

The study engineers autism-associated variants in conserved residues of RBM27 into the C. elegans homolog RBM-26 and identifies neuronal phenotypes potentially relevant to autism and a potential molecular mechanism involving regulation of mitochondrial ribosome assembly.

The key claims of the study are 1} that autism-associated variants in RBM-26 decrease its protein expression; 2} that impaired RBM-26 function leads to a variety of defects in development and maintenance of a single neuron called PLM, including altered axonal localization of mitochondria; 3} that RBM-26 normally binds the mRNA for the C. elegans homolog of MALSU, a mitochondrial ribosomal assembly factor; 4} that loss of RBM-26 leads to overexpression of the MALSU homolog; and 5} that MALSU is required for some of the deleterious effects on the PLM neuron seen in RBM-26 mutants.

This study will be of interest to the autism research community because it bolsters the idea that variants in RBM27 are likely to disrupt gene function and to affect neuronal health. It will also be of interest to the broader cell biology community because it suggests an interesting potential nucleus-to-mitochondria signaling mechanism, in which a nuclear RNA-binding protein might regulate assembly of mitochondrial ribosomes.

My field of expertise is developmental biology in C. elegans.

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

Summary In this manuscript, the authors studied an ASD-associated gene, rbm-26 in neuronal morphology using the touch receptor neuron PLM in C. elegans, and found that loss-of-function rbp-27 causes overextension and the formation of bulb-like structures in the axon. Using UV-crosslinking RNA immunoprecipitation and RNA-Seq, they identify malsu-1 as a target of rbm-26. Genetic analyses suggest malsu-1 likely functions downstream of rbm-26 in controlling the PLM morphology. Major comments:

• The authors describe RBM27 is associated with ASD and ID while they only cite SFARI paper that describes a weak association of RBM27 to ASD. The appropriate referenced that show link between RBM27 and ID should be provided. The link with ID was an error. We had meant to say "ASD or other neurodevelopmental disorders." This has been corrected.

• SFARI database only has three (P79L, R190Q, G348D) mutations listed as ASD-associated. Where are other mutations L13V and R455H, particularly L13V that the authors used to generate the C. elegans mutant come from? Are they associated with intellectual disabilities? The others came from the devovo-DB. We have added a reference for this database and have also added the primary source references for each of the five de novo variants (see line 121).

• The authors should be very careful when describing 'gene X causes Y diseases'. Many (if not all) of the examples described in this manuscript are disease-associated genes without validation to be causal genes. We have revised accordingly. For example on lines 433-435, we now say," For example, mutations in the EXOSC3, EXOSC8 and EXOSC9 are thought to cause syndromes that include defects in brain development such as hypoplasia of the cerebellum and the corpus callosum". We have decided to use the phrase "thought to cause" because three of the five referenced articles on these genes use titles that indicate causation.

• The authors refer PLM axon beading and overextension phenotypes to 'axon degeneration and targeting defects'. The authors must provide additional evidence of axon degeneration (see below). Also the term 'targeting defects' is misleading as the authors did not examine if overextension of the PLM axon causes targeting defects. At least they should examine some synaptic markers. To provide more evidence of degeneration we have analyzed several additional phenotypes at multiple developmental stages (Figure 2 and Table S1). Regarding targeting defects, this was meant to refer to the misplacement of the PLM axon tip (which contains electrical synapses). However, our subsequent analysis has revealed that these defects are transient in P80L and L13V mutants, as they resolve by the L4 stage. The rbm-26 null axon development defects do not resolve, though these mutant die prior to the L4 stage. Given these findings, we have decided not to use the term of targeting defects. Instead, we now refer to this as an axon tiling defect or PLM/ALM overlap phenotype.

• Neuronal phenotypes (axon overextension and beading) should be examined at different developmental timepoints (larval, young adult, and aged animals) to test if these phenotypes are indeed degenerative instead of developmental defects. We have included new data to observe all of these phenotypes at multiple developmental time points (Figure 2 and Table S1).

• The authors use the blebbing (beading) phenotype in the axon as the sole evidence of neurodegenerative properties of the PLM neuron. A more thorough analysis of this phenotype as done by others (Pan PNAS 2006) must be provided to support the authors' claim that this phenotype represents neurodegeneration. We have included new data on multiple degenerative phenotypes in axons including: blebbing, beading, waviness and breaks (Table S1).

• The number of beads per axon should be quantified to better represent the severity of rbm-26 mutant. Individual samples should be plotted in the quantification instead of showing the percentage of animals. We have added data on the density of beads in rbm-26(null), rbm-26(P80L), and rbm-26(L13V) mutants (Figure S3). For most experiments we have decided to use penetrance to measure axon degeneration because this is a standard in the field and allows for a larger sample size. For examples please see:

10.1523/JNEUROSCI.1494-11.2012 (Toth et al, 2012)

https://doi.org/10.1016/j.cub.2014.02.025 (Rawson et al, 2014)

10.1073/pnas.1011711108 (Pan et al, 2012)

https://doi.org/10.7554/eLife.80856 (Czech et al, 2023)

https://doi.org/10.1016/j.celrep.2016.01.050 (Nichols et al, 2016)

• Based on the single gel image in Fig. 1C with no loading control, the P80L mutant appears to have no protein expression. How is the P80L viable while the null mutant is lethal? The authors should quantify the protein expression levels from multiple blots with proper loading controls. If P80L mutation is introduced into RBM-26::mScarlet strain can it cause depletion of the signal in vivo? We have added new data showing that the RBM-26::Scarlet signal is diminished by the P80L mutation in vivo (Figure 1E-F). We have also added quantification from 3 biological replicate blots (Figure 1D). Finally, we have improved the sensitivity of our blots by using ECL detection and also show various exposures to highlight the fainter bands (Figures 1C and S1). Therefore, we are now able to detect low level expression of RBM-26(P80L) mutant protein. It is likely that the low level of RBM-26(P80L) and RBM-26(L13V) seen on western blots is sufficient to prevent the lethal phenotype.

• 'Moreover, loss of either the SPTBN1 or ADD1 genes causes a neurodevelopmental syndrome that includes autism and ADHD' References are missing, and as described above, be extra careful when indicating causality. Very few genes are known to cause ASD and ADHD. We have added the citations for this work (line 81). We also note that the titles for both of the cited articles indicate causation. To be on the safe side we have revised this line to say, "Moreover, loss of either the SPTBN1 or ADD1 genes are thought to cause a neurodevelopmental syndrome that includes autism and ADHD"

• Fig. 3E F, the authors should use the strains that express TIR1 specifically in the touch receptor neurons to argue cell autonomous function of RBM-26. Alternatively, the authors may conduct PLM neuron-specific rescue experiments to test the sufficiency. We have added new data indicating that a Pmec-7::rbm-26::scarlet transgene can rescue the beading phenotype and the PLM/ALM overlap phenotype (see Figure 3F-G).

• 'Loss of RBM-26 causes mitochondria dysfunction in axons.' The authors did not examine mitochondria function in axons. They only examined the number of mitochondria, and ROS production in the soma. The authors should provide additional evidence to support the idea that elevated ROS production in the soma is due to mitochondrial dysfunction in axons. Also, the authors should use both P80L and L13V for this experiment, and indicate individual datapoint as dots. Here, they quantified at the L4 stage, which the authors should justify. We have added the L13V data to this experiment and now show the individual data points. In addition, we have now conducted this analysis at the L2, L3 and L4 stages (Figure 5C-E). We have also revised the text to indicate that loss of rbm-26 function causes mitochondrial dysfunction in the cell body which could potentially cause a reduction of mitochondria in the axon (see lines 100-101 and 268-270). We speculate that mitochondria in the axon are also dysfunctional. However, the mitoTimer signal is not bright enough in axons to allow for quantification.

• Figure 5B and C: the authors should also use L13V to quantify malsu-1 mRNA and protein level, and include quantifications in panel C (from multiple blots). This is Figure 6 in the new version. We have added new data for expression of mals-1 mRNA and protein in rbm-26(L13V) mutants (Figure 6B-D). We have also included quantifications from 3 biological replicates (Figure 6D).

• In the rbm-26 mutant, the number of mitochondria is reduced, while the amount of MALSU-1 protein is increased. If MALSU-1 is specifically localized at mitochondria in wild type, where does the excessive MALSU-1 go in the rbm-26 mutants? Quantification of MALSU-1 signal intensity should be provided. Our Pmec-7::mals-1::scarlet transgene uses the tbb-2 3'UTR and causes an overexpression phenotype. To address the question posed by the reviewer, we would need to express MALS-1 at endogenous levels. Given that endogenous levels of MALS-1 are very low, it is unlikely that we would be able to visualize its expression. Nonetheless, as a way to address this question we have attempted to create a single copy Pmec-7::mals-1::scarlet transgene that utilizes the mals-1 endogenous 3'UTR. We have tried multiple approaches for generating this construct, but all have failed, likely due to sequence complexities within the mals-1 3'UTR. While we cannot say where the extra MALS-1 protein goes, we think that it is likely overloaded into the remaining mitochondria and could also be in the cytosol as well.

• Figure 7C: malsu-1 knockout mutants exhibit PLM overextension phenotype, which is not consistent with their model. The authors should discuss this in detail. We have added a paragraph to the discussion explaining that mitochondria function could be disrupted by either MALS-1 overexpression or by MALS-1 loss of function (lines 471-480).

• 'To validate these findings, we also repeated these experiments with an independent allele of malsu-1, malsu-1(tm12122) and found similar results (Fig. 7A-C).' The malsu-1(tm12122) exhibits beading phenotype and more severe overextension phenotype which the authors must describe and discuss more carefully. One likely reason for this difference is that tm12122 is predicted to cause a partial deletion of the mals-1 coding sequence, whereas the syb6330 is a full deletion. Thus, the tm12122 could be acting as a dominant negative. In fact, prior work on the MALSU1 ortholog has indicated that this protein is subject to interference by a dominant negative construct (see Rorbach et al, Nucleic Acids Res 2012). Nonetheless, we cannot rule out the possibility of a linked second mutation in tm12122. However, since we have found similar phenotypes and genetic interactions with both alleles, we can conclude that these phenotypes and interactions are due to loss of MALS-1, rather than a second mutation (albeit at a slightly different penetrance). We have added these considerations to the results section (lines 342-244).

• Figure 8: The authors should include data from L13V, malsu-1 and rbm-26; malsu-1 mutants. Quantification from multiple blots should be provided. This is Figure 8D in the new version. We have added the malsu-1 and rbm-26;malsu-1 double mutants to this experiment. We have also added quantification from multiple biological replicate blots. As pointed out by the other reviewer, we think that this experiment does not give specific information about mitoribosomes, but is an alternative approach to looking at the reduction in mitochondria. Given this limitation and considering that we have added L13V data to the mitochondria experiment in Figure 8B, we have elected not to add additional data on L13V to the western blot experiment in Figure 8D

Minor comments: • 'Consistent with a role for mitochondria in neurodevelopmental disorders, some of these disorders include a neurodegenerative phenotype.' Why is it consistent to have neurodegenerative phenotypes if mitochondria is associated with neurodevelopmental disorders? A better explanation would help.

We have changed this sentence to, "Some neurodevelopmental syndromes feature neurodegenerative phenotypes that occur during neuronal development."

• L13V is generally more severe in axon overextension phenotype than P80L while protein level is more abundant. The authors should discuss about this. We have also added a time course for the PLM/ALM overlap phenotype mutants (Figure 2D). This new data shows that the PLM/ALM overlap is quite similar overall between the P80L and L13V mutants. Both of these mutations cause an increase in PLM/ALM overlap in early larval development that is resolved by the L4 stage. The P80L phenotype resolves slightly sooner for reasons that are unknown. This could reflect differences in expression within the PLM that are not reflected in the whole worm lysate. This could also be due to a slight difference in the genetic background or other stochastic factors. The key point is that these two independent alleles cause similar phenotype overall, indicating that this phenotype is the result of loss in RBM-26 function.

• Fig. 2E, F: 'Beading refers to focal enlargement or bubble-like lesions which were at least twice the diameter of the axon in size.' How are the diameters of axons measured? A more detailed quantification method, and examples of measurement should be provided. We have added example measurements to the supplemental section (Figure S3). Additional detail on the measurements are in the Methods section (lines 517-518).

• Figure 3: The authors should also include low-magnification images to show where RBM-26 is expressed. The current image does now allow identifying cells. The transgene that labels the nuclei of hypodermis should be indicated in the manuscript. Specifically, the expression of the RBM-26 in the PLM should be shown. We have added a low magnification image (Figure S6) and have also added images of endogenously tagged RBM-26:Scarlet in the PLM (Figure 3A-C). The transgenic label for the hypodermis has been added to the legend of Figure S5.

• Figure 3: 'Tissue specific degradation of RBM-26::SCARLET::AID was achieved due to cell-type specific TIR-1 driver lines (see methods for details).' This information is not provided in the method section. This information has been added to methods section, "Auxin proteindegredation"

• Fig. 4 E. Values from individual samples should be indicated as dots. Representative images of P80L and L13V should be included. Conduct quantifications at adult stage as the authors use in other quantifications, or justify use of specific developmental stage (L3) they used. Figure 4 has become Figures 4 and 5 in the revised version. We have updated the graphs to include dots for individual data points. We have added quantifications of the mitoTImer experiments for the L2, L3 and L4 stages (Figure 5C-E). We note that our other experiments were done at the L1, L2, L3 and L4 and adult stages. The mitoTimer signal is not sufficient at the L1 stage for quantification. At the adult stage, the red signal becomes saturated. We have added representative images for mitoTimer in P80L and L13V mutants (Figure S9).

• The genes malsu-1 and mrpl-58 are not listed on wormbase. If the authors would like to designate names to these gene, they should clearly indicate that along with the sequence name. We have changed malsu-1 to mals-1. In addition, both mals-1 and mrpl-58 have now been approved by wormbase and will be listed on the website upon its next update.

• The authors found that MRPL-58 amount is reduced in rbm-26 mutants (which require additional verifications). This can be explained by the fact that axonal mitochondria number is reduced in the rbm-26 mutants. How did the authors confirm that the reduction in MRPL-58 level is due to the disruption of mitoribosome assembly? This is Figure 8D-E in the new version. We have added new data showing that the decrease in MRPL-58 expression that is caused by the rbm-26(P80L) mutation is dependent on MALS-1. We concede that these experiments cannot be used to determine anything about the mitoribosomes per se, but rather serve as an alternative way of testing the effect of rbm-26 on mitochondria. We have revised the text accordingly (lines 355-357).

• 'MALSU-1 is a mitoribosomal assembly factor that functions as part of the MALSU1:LOR8F8:mtACP anti-association module [37-39].' I don't think these are known for C. elegans MALSU-1. We have revised to, "MALS-1 is an ortholog of the MALSU1 mitoribosomal assembly factor that functions as part of the MALSU1:LOR8F8:mtACP anti-association module"

• 'Moreover, our results also suggest that disruption of this process can give rise to neurodevelopmental disorders.' I feel this is a quite a bit of stretch.

This has been replaced with, "Therefore, we speculate that human RBM26/27 could function with the RNA exosome complex to protect against neurodevelopmental defects and axon degeneration in infants." (lines 371-373)

**Referees cross-commenting** Yes, many of our comments overlap, and I fully agree with all comments from the other reviewer too. Reviewer #2 (Significance (Required)):

I found the manuscript interesting particularly the use of innovative techniques in identifying the target of RBM-26, The genetic analyses of rbm-26 and malsu-1 generally support the authors main conclusions that rbm-26 inhibits malsu-1 and be of potential interest to basic neuroscientists and cell biologists. However, the current manuscript looked premature which made my reading experience less pleasant. The phenotypic analyses is superficial compared to works similar to this work, which are insufficient to support the authors' claim of 'axon degeneration and targeting defects'. A number of issues listed above should be addressed before this manuscript is published. The reviewer's expertise: neurodevelopment in model organisms.

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Referee #2

Evidence, reproducibility and clarity

Summary

In this manuscript, the authors studied an ASD-associated gene, rbm-26 in neuronal morphology using the touch receptor neuron PLM in C. elegans, and found that loss-of-function rbp-27 causes overextension and the formation of bulb-like structures in the axon. Using UV-crosslinking RNA immunoprecipitation and RNA-Seq, they identify malsu-1 as a target of rbm-26. Genetic analyses suggest malsu-1 likely functions downstream of rbm-26 in controlling the PLM morphology.

Major comments:

• The authors describe RBM27 is associated with ASD and ID while they only cite SFARI paper that describes a weak association of RBM27 to ASD. The appropriate referenced that show link between RBM27 and ID should be provided.
• SFARI database only has three (P79L, R190Q, G348D) mutations listed as ASD-associated. Where are other mutations L13V and R455H, particularly L13V that the authors used to generate the C. elegans mutant come from? Are they associated with intellectual disabilities?
• The authors should be very careful when describing 'gene X causes Y diseases'. Many (if not all) of the examples described in this manuscript are disease-associated genes without validation to be causal genes.
• The authors refer PLM axon beading and overextension phenotypes to 'axon degeneration and targeting defects'. The authors must provide additional evidence of axon degeneration (see below). Also the term 'targeting defects' is misleading as the authors did not examine if overextension of the PLM axon causes targeting defects. At least they should examine some synaptic markers.
• Neuronal phenotypes (axon overextension and beading) should be examined at different developmental timepoints (larval, young adult, and aged animals) to test if these phenotypes are indeed degenerative instead of developmental defects.
• The authors use the blebbing (beading) phenotype in the axon as the sole evidence of neurodegenerative properties of the PLM neuron. A more thorough analysis of this phenotype as done by others (Pan PNAS 2006) must be provided to support the authors' claim that this phenotype represents neurodegeneration.
• The number of beads per axon should be quantified to better represent the severity of rbm-26 mutant. Individual samples should be plotted in the quantification instead of showing the percentage of animals.
• Based on the single gel image in Fig. 1C with no loading control, the P80L mutant appears to have no protein expression. How is the P80L viable while the null mutant is lethal? The authors should quantify the protein expression levels from multiple blots with proper loading controls. If P80L mutation is introduced into RBM-26::mScarlet strain can it cause depletion of the signal in vivo?
• 'Moreover, loss of either the SPTBN1 or ADD1 genes causes a neurodevelopmental syndrome that includes autism and ADHD' References are missing, and as described above, be extra careful when indicating causality. Very few genes are known to cause ASD and ADHD.
• Fig. 3E F, the authors should use the strains that express TIR1 specifically in the touch receptor neurons to argue cell autonomous function of RBM-26. Alternatively, the authors may conduct PLM neuron-specific rescue experiments to test the sufficiency.
• 'Loss of RBM-26 causes mitochondria dysfunction in axons.' The authors did not examine mitochondria function in axons. They only examined the number of mitochondria, and ROS production in the soma. The authors should provide additional evidence to support the idea that elevated ROS production in the soma is due to mitochondrial dysfunction in axons. Also, the authors should use both P80L and L13V for this experiment, and indicate individual datapoint as dots. Here, they quantified at the L4 stage, which the authors should justify.
• Figure 5B and C: the authors should also use L13V to quantify malsu-1 mRNA and protein level, and include quantifications in panel C (from multiple blots).
• In the rbm-26 mutant, the number of mitochondria is reduced, while the amount of MALSU-1 protein is increased. If MALSU-1 is specifically localized at mitochondria in wild type, where does the excessive MALSU-1 go in the rbm-26 mutants? Quantification of MALSU-1 signal intensity should be provided.
• Figure 7C: malsu-1 knockout mutants exhibit PLM overextension phenotype, which is not consistent with their model. The authors should discuss this in detail.
• 'To validate these findings, we also repeated these experiments with an independent allele of malsu-1, malsu-1(tm12122) and found similar results (Fig. 7A-C).' The malsu-1(tm12122) exhibits beading phenotype and more severe overextension phenotype which the authors must describe and discuss more carefully.
• Figure 8: The authors should include data from L13V, malsu-1 and rbm-26; malsu-1 mutants. Quantification from multiple blots should be provided.

Minor comments:

• 'Consistent with a role for mitochondria in neurodevelopmental disorders, some of these disorders include a neurodegenerative phenotype.' Why is it consistent to have neurodegenerative phenotypes if mitochondria is associated with neurodevelopmental disorders? A better explanation would help.
• L13V is generally more severe in axon overextension phenotype than P80L while protein level is more abundant. The authors should discuss about this.
• Fig. 2E, F: 'Beading refers to focal enlargement or bubble-like lesions which were at least twice the diameter of the axon in size.' How are the diameters of axons measured? A more detailed quantification method, and examples of measurement should be provided.
• Figure 3: The authors should also include low-magnification images to show where RBM-26 is expressed. The current image does now allow identifying cells. The transgene that labels the nuclei of hypodermis should be indicated in the manuscript. Specifically, the expression of the RBM-26 in the PLM should be shown.
• Figure 3: 'Tissue specific degradation of RBM-26::SCARLET::AID was achieved due to cell-type specific TIR-1 driver lines (see methods for details).' This information is not provided in the method section.
• Fig. 4 E. Values from individual samples should be indicated as dots. Representative images of P80L and L13V should be included. Conduct quantifications at adult stage as the authors use in other quantifications, or justify use of specific developmental stage (L3) they used.
• The genes malsu-1 and mrpl-58 are not listed on wormbase. If the authors would like to designate names to these gene, they should clearly indicate that along with the sequence name.
• The authors found that MRPL-58 amount is reduced in rbm-26 mutants (which require additional verifications). This can be explained by the fact that axonal mitochondria number is reduced in the rbm-26 mutants. How did the authors confirm that the reduction in MRPL-58 level is due to the disruption of mitoribosome assembly?
• 'MALSU-1 is a mitoribosomal assembly factor that functions as part of the MALSU1:LOR8F8:mtACP anti-association module [37-39].' I don't think these are known for C. elegans MALSU-1.
• 'Moreover, our results also suggest that disruption of this process can give rise to neurodevelopmental disorders.' I feel this is a quite a bit of stretch.

Referees cross-commenting Yes, many of our comments overlap, and I fully agree with all comments from the other reviewer too.

Significance

I found the manuscript interesting particularly the use of innovative techniques in identifying the target of RBM-26, The genetic analyses of rbm-26 and malsu-1 generally support the authors main conclusions that rbm-26 inhibits malsu-1 and be of potential interest to basic neuroscientists and cell biologists. However, the current manuscript looked premature which made my reading experience less pleasant. The phenotypic analyses is superficial compared to works similar to this work, which are insufficient to support the authors' claim of 'axon degeneration and targeting defects'. A number of issues listed above should be addressed before this manuscript is published.

The reviewer's expertise: neurodevelopment in model organisms.

Author response

The following is the authors’ response to the previous reviews

eLife assessment 

This work is an attempt to establish conditions that accurately and efficiently mimic a drought response in Arabidopsis grown on defined agar-solidified media - an admirable goal as a reliable experimental system is key to conducting successful low water potential experiments and would enable high-throughput genetic screening (and GWAS) to assess the impacts of environmental perturbations on various genetic backgrounds. The authors compare transcriptome patterns of plant subjected to water limitation imposed with different experimental systems. The work is valuable in that it lays out the challenges of such an endeavor and points out shortcomings of previous attempts. There was concern, however, that a purely gene expression-based approach may not provide sufficient physiologically relevant information about plant responses to drought, and therefore, despite improvements from a previous version, the new methodology championed by this work remains inadequate.   

Molecular biologists who study drought stress must make choices about which assays to use in their investigation. Serious resources and effort are put into their endeavor, and choice of assay matters. Our manuscript’s goal was largely practical: to guide molecular biologists employing transcriptomics in their choice of drought stress assay, and thus help ensure their work will discover transcriptional signatures of importance, and not those that may be an artifact from lowering water potential using chemical agents on agar plates.  

We examine how different approaches of reducing water potential impact the Arabidopsis root and shoot transcriptome. Our manuscript shows that each method of reducing water potential has a different effect on Arabidopsis root transcriptome responses. We acknowledge that drought stress induces a complex physiological response, and can vary depending on the method used. However, by comparing across assays, we find instances where a gene is downregulated by low water potential in one assay, and upregulated by low water potential in another assay. We feel it is only natural to question why this could be, and to hypothesize that it may be caused by secondary effects caused by the way low water potential is imposed.  We note that comparative transcriptomics has been a standard approach for decades. We take it as the reviewer’s opinion that it may not be insightful, but it does not factually impact our findings. 

Reviewer #2 (Public Review): 

This manuscript purports to develop a new system to study low water potential (drought) stress responses in agar plates. They make numerous problematic comparisons among transcriptome datasets, particularly to transcriptome data from a vermiculite drying experiment which they inappropriately present as representing an authentic "drought response" to the exclusion of all other data. For some reason, which the reviewer cannot fully understand, the authors seem intent on asserting the superiority of their experimental system to all others. They do not succeed in this and such an effort is ultimately a disservice to the field of drought research as a whole. 

While they devote considerable effort in comparing transcriptome data among various experimental systems, the potentially more informative experiment at the end of the manuscript of testing growth responses of a number of Arabidopsis accessions is only done for their "LW" system. The focus of this manuscript on transcriptome data to the almost complete exclusion of other types of data which is a symptom of a broader over-emphasis on transcriptome that unfortunately is quite prevalent in plant science now. It is worth reminding that for protein coding genes, which constitute the vast majority of genes, transcriptome data is a proxy measurement. The really important thing is protein amount, and even more so protein activity/function, which we know has an imperfect, at best, correlation with transcript level. We measure transcriptomes because we can, not because it is inherently the most informative thing to do. The author's quixotic quest to see if the transcriptomes of different stress treatments match is of limited value and further diminished by their misleading presentation of one particular transcriptome data set (from their vermiculite drying experiments) as somehow a special data set that everything else must be evaluated against. This study sheds no new light on how to do relevant drought (low water potential) experiments in the lab. 

Although the reviewer acknowledges that the authors have made some effort to respond to previous comments, the fundamental flaws remain and the present version of this study is little improved from the first submission. 

One challenge faced by the drought community is establishing consensus regarding the definition of drought itself. According to the criteria followed by the reviewer, any method leading to a reduction in water potential qualifies as drought stress. However, the findings presented in this manuscript demonstrate that transcriptional responses in roots vary considerably across five different methods of reducing water potential. This indicates that beyond responding to a change in water potential itself, root transcriptomes will also respond to the specific way low water potential is introduced. We believe this variability is of interest to the drought research community. 

Of the five methods we explore, we hold the view that the gene expression changes induced by vermiculite drying as the most analogous to the expression signatures Arabidopsis would exhibit in response to low water potential in the natural environment. In contrast, we posit that Arabidopsis grown on agar plates - where the root system is exposed to air and light, and where water potential is lowered using chemical agents - may contain gene expression signatures plant molecular biologists may not find particularly relevant. However, we acknowledge that this is our opinion, and will make this more explicit on our revised text. 

More broadly, we believe that the reviewer’s observation regarding the ‘over-emphasis’ on transcriptomics that is prevalent within the plant science community justifies, rather than diminishes, the work presented here. If transcriptomics is a commonly employed method, then we anticipate that the outcomes of this study will hold value for a broad audience. Such researchers are likely not only using transcriptomics as a proxy measure for protein abundance, as the reviewer suggests, but also because it is one of the more straightforward genomic techniques biologists can use to identify candidate genes that may be chosen for further scrutiny. 

Reviewer #3 (Public Review): 

Comments on revised version: 

Specific previous criticisms that were addressed are: 

(1) that gene expression changes were only compared between the highest dose of each stress assay. In the revised version, the authors changed their framework and are now using linear modelling to detect genes that display a dose response to each specific treatment. I agree that this might be a more robust approach to selecting genes that are specific to a certain treatment. 

(2) that concentrations of PEG, mannitol, NaCl, and the "low water" agar which were chosen are not comparable in regards to their specific osmotic component. I appreciate that the authors measured the osmotic potential of each treatment. It revealed that both PEG and NaCl at their highest concentration had a much more negative osmotic potential compared to the other treatment. The authors claim that using ANCOVA they did not detect any significant differences between the treatments (lines 113, 114). I do believe that ANCOVA is not the appropriate test in this case. ANCOVA has an assumption of linearity, while the dose response between concentration and osmotic potential is non-linear. This is particularly evident for PEG (Steuter AA. Water potential of aqueous polyethylene glycol. Plant Physiol. 1981 Jan;67(1):64-7. doi: 10.1104/pp.67.1.64.). Since the treatments are not the same at the highest level, I think this could have effects on the validity of comparisons by linear model. One approach could be to remove the treatment level with the highest concentration and compare the results or adjust the treatments to the same osmolarity. 

(3) that only two biological replicates were collected for RNA sequencing which makes it impossible to know how much variance exists between samples. The authors added a third replicate in the revised version for most treatments. However, some treatments still have only two replicates, which cannot be easily seen from the text or the figure. I would prefer that those differences are pointed out. 

(4) that the original manuscript did not explore what effect the increase of agar and nutrient concentration in the "low water" agar had on water potentials. The authors conducted additional experiments showing that changes in water potential were exclusively caused by changes in the nutrient concentration (Figure 2-figure supplement 5; lines 222-224). However, the increase in agar strength had also some effect on gene expression. While this is not further discussed in the text, I believe this effect of agar on gene expression could be similar to root responses to soil compaction. 

(5) That the lower volume of media in the "low water" agar could have an effect on plants. The authors compared these effects in Figure 2-figure supplement 7. They claim that "different volumes of LW agar media do not play a significant part in modulating gene expression". While I can see that they detected 313 overlapping DEGs, there were still 146 and 412 non-overlapping DEGs. The heatmap in subpanel E also shows that there were differences in particular in the up-regulated genes. My conclusion would be that the change in volume does play a role and this should be a consideration in the manuscript. 

We thank the reviewer for their suggestions. We plan to resubmit the manuscript reflecting the requested changes. Specifically, we will: 

-       We will detail more thoroughly the effects of agar volume on gene expression changes elicited by LW agar treatment. 

-       We will investigate whether the tensile stress introduced by hard agar is similar to soil compaction by an analysis with existing literature. 

-       Assess more rigorously the suitability of the ANCOVA model for assessing water potential changes of different media types.

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Reply to the reviewers

Reviewer 1

The paper is overall convincing. However, a little more attention to data presentation and possibly the addition of at least another technique (see below) would greatly strengthen the findings.

As we hope to demonstrate below, we have taken steps to improve our manuscript on both fronts (data presentation and experimental evidence).

The absence of statistics catches immediately the eye. I am sure that the shown differences are statistically significant (thanks to the number of analyzed cells), but reporting the result of some statistical test would help the reader in identify the relevant data in a plot. This is somehow necessary considering that sometimes in the text something is deemed to be "significant" or "not significant", and I felt that I really needed that when looking at the plot in Fig. 3D.

To facilitate the interpretation of figures that contain data from multiple strains (such as the one mentioned by the reviewer), we have carried out a nonparametric single-step multiple comparison test (Games-Howell) to identify mutants whose means differ significantly from each other. To avoid overcrowding the figures, we have graphically summarized the p-values of all pairwise comparisons in a small matrix within the corresponding panel, and provided 99% confidence intervals and p-values of all differences in the Supplement.

Related to the previous point: for every N/C distribution analysis, a number of analyzed cells is reported. By the way it is written, it seems that the replication relies solely by the cells in that specific population, i.e.: each cell is treated as a replicate. At least I could not find if that is not the case in the legends or in the methods. I wonder what the results would be (and their significance) if each replicate would be a new assay on another population.

Cell populations exhibit significant variability in their phenotypic characteristics. Consequently, the quantification of a specific feature (e.g., the Sfp1 nuclear/cytoplasmic ratio) across a sample of cells from a given population results in a distribution rather than a single fixed value. For each quantification, we report the number of cells that were used to construct the corresponding distribution, i.e. the sample size. To compare samples from different populations (e.g., different Sfp1 mutant strains), we run them in parallel during microscopy experiments and compare their means, as described above. Throughout our study, we have tried to ensure that we quantify a sufficiently large number of cells to overcome cell-to-cell variability and enhance the reliability of our results.

In this context, the question of the reviewer is not entirely clear to us, as individual measurements of a sample are not replicates. However, one can replicate the entire experiment on a different day by re-growing the different strains, running microscopy, quantifying the new movies etc. In this sense, the experiments shown in the manuscript consist of single replicates, i.e. experiments that were carried out on the same day, with all the relevant mutants and controls quantified together. However, we have monitored many of our mutants multiple times over the course of our work. For example, Fig. 1 below shows replicates of the Sfp1 N/C ratio distributions at steady-state in the analog-sensitive (A) and wild-type (B) background, which were quantified several times across various experiments. While day-to-day variability in the empirical distributions of the same mutant exists to a small extent, it is quite small.

The scale of x axes in N/C ratio plots. Besides not being consistent throughout the figures, it originates from 1, visually enhancing the differences.

We believe the reviewer was referring to the y-axes, as the x-axes represent time. Summarizing the N/C ratio dynamics of different Sfp1 mutants has been challenging. First, the average N/C ratios at steady-state vary considerably across different mutants, as shown in the panels that summarize steady-state N/C ratios. To compare the magnitude and features of their responses, normalization is necessary. We chose to normalize the time series of each mutant to have a mean of 1 prior to the onset of a perturbation. This allows the normalized time series to represent the percentage-wise changes in the Sfp1 N/C ratio upon perturbation.

Using a common y-axis scale for all plots of N/C ratio dynamics not ideal, as some responses are subtler than others. Additionally, we do not believe that N/C dynamics across different figures need to (or should) be compared to each other. However, within a figure, panels that require comparison are placed in the same row and share the same y-axis scale. We believe that this approach optimizes data visualization and facilitates important visual comparisons.

Related to the previous point: it is evident from the plots that the N/C ratio is always positive, even in the most deficient of the analyzed mutants. This implies that a relevant fraction of Sfp1 is still nuclear. I thus wonder what the impact of these mutations would be on the actual function of Sfp1. For this reason, I feel that qPCR evaluation of transcripts of Sfp1 target genes is particularly needed. Since lack of Sfp1 is known to yield some of the smallest cells possible, it would also be cool to have an estimate of the size of mutants where Sfp1 is less nuclear. These analyses could confer phenotypical relevance to the data, but would also help in assessing a currently unexplored possibility, that phosphorylation events by PKA influence Sfp1 function besides its localization, i.e.: the still somehow nuclear fraction is not as functional as wt Sfp1 in promoting transcription.

It is indeed the case that the recorded N/C ratios are larger than 1 in all strains that we have monitored. We have never observed an N/C ratio smaller than 1 using widefield microscopy for two main reasons: first, out-of-focus light from the cytosol above and below the nucleus is added to the nuclear signal, causing the nuclear signal to always be non-zero, even for predominantly cytosolic proteins. Second, both in- and out of focus vacuoles are devoid of the fluorescent protein fusions that we quantify, which reduces the average brightness of the cytosol. For these reasons, even when a protein is largely cytosolic, the average N/C ratio over a cell population is no lower than around 1.5. Keeping these points in mind, one can observe that our most delocalized Sfp1 mutants have an N/C ratio that is around 1.6-1.7, which is very close to the lower limit. This means that these Sfp1 mutants are largely cytosolic, and the nuclear fraction (if non-zero) is quite small.

We agree that assessing the phenotypic relevance of Sfp1 mutations is of interest. However, this was impossible with our original strains, as we introduced each Sfp1 mutant as an extra copy in the HO locus while leaving the endogenous Sfp1 locus intact. This was done in order to avoid any phenotypic changes that might result from changes in Sfp1 activity.

To address the suggestion of the reviewer, we therefore deleted the endogenous Sfp1 copy in strains carrying sfp1PKA2A, sfp1PKA2D and sfp113A, leaving only the mutated Sfp1 copy at the HO locus. Surprisingly, the growth rate and drug sensitivity (determined by halo assays) of these single-copy mutants did not differ much in comparison to the mutants carrying the functional Sfp1 copy and from the wild-type (Supp. Figs. 4J and 7). This observation aligns with findings for the single-copy sfp1-1 mutant in [Lempiäinen et al. 2009], which corresponds to sfp1TOR7A in our work. [Lempiäinen et al. 2009] had suggested that Sch9 compensates for the loss of Sfp1 activity via a feedback mechanism, which could explain our results as well. If this is the case, acute depletion of wild-type Sfp1 could unveil transient changes in cell growth, before the compensatory effect of Sch9 was established. Unfortunately, we were unable to efficiently degrade wild-type Sfp1 carrying a C-terminal auxin-inducible degron. Instead, we followed the same approach with [Lempiäinen et al. 2009] and deleted SCH9.

As we describe in the last section of Results, the difference was dramatic for sfp113A __mutants, which were extremely slow-growing in the absence of Sch9 (doubling time was around 4 hours, but it was hard to estimate because we could not grow the cells consistently). Interestingly, SCH9 deletion had a negative impact on sfp1__PKA2D __but not sfp1__PKA2A __cells (__Supp. Fig. 7). Overall, these results demonstrate that Sch9 can compensate for loss of Sfp1 activity, which makes it challenging to study the impact of Sfp1 mutations on cellular phenotypes.

To further understand to what extent Sch9 compensates for loss of Sfp1 phosphorylation, we carried out RNA-seq on WT and cells carrying a single copy of sfp113A (with the endogenous SFP1 copy removed). Despite the fact that sfp113A __grow as well as WT, RNA-seq picked up several differentially expressed genes related to amino acid biosynthesis. This surprising finding is presented in the last section of Results, and in __Supplementary Figures 8, 9 and 10. We explore the relevance of these results and their connection with past literature on Sfp1 and Sch9 in the Discussion section.

I found some typos here and there, and it would greatly help to report them if in the manuscript line numbers were included.

We apologize for the typos. We have tried to eliminate them, and we have also added line numbers to the manuscript.

Reviewer 2

There is no biochemical evidence presented that the putative PKA sites (S105 and S136) are genuinely phosphorylated by PKA. The fact that they match the PKA consensus motif, alone, does not guarantee this. In order to claim that they are looking at the effect of PKA by mutagenizing these residues, the authors have to demonstrate the PKA-dependency of S105 and S136 phosphorylation by, for example, mass spec experiments or western blotting with phospho-specific antibodies (Cell Signaling Technology #9624 for example). Also, does the band-shift caused by PKA inhibition (Fig 3C) is canceled by the S105A/S136A mutation?

We took several actions to demonstrate that the putative PKA sites are indeed phosphorylated by PKA. We first tried to detect Sfp1 phosphorylation using the antibody mentioned by the reviewer, but failed as the sensitivity of this antibody appears to be quite low. On the other hand, mass spectrometry did not produce the right fragments to detect the sites of interest. We therefore resorted to an in vitro kinase assay using [γ-32P]ATP together with purified PKA and Sfp1. Unfortunately, bacterial overexpression of MBP-tagged Tpk1, Tpk2 and Tpk3 (the catalytic subunits of PKA) was quite challenging and we were unable to produce soluble protein. We therefore resorted to commercially available bovine PKA (bPKA, PKA catalytic subunit, Sigma-Aldrich 539576), which shows high homology to the yeast Tpk kinases [Toda et al. 1987]. Moreover 87% of bPKA substrates have been shown to also be Tpk1 substrates [Ptacek et al. 2005], and bPKA has been used to identify new Tpk substrates in budding yeast [Budovskaya et al. 2005__]. As we show in the revised manuscript, bovine PKA does phosphorylate Sfp1. Moreover, phosphorylation is reduced by 50% in the double S105A, S136A mutant (Fig.1F), and becomes undetectable in the 13A mutant__ (Supp Fig. 6). Together with the rapid response of Sfp1 localization to acute PKA inhibition which we had already reported, we believe that these results provide strong evidence that Sfp1 is a direct PKA substrate, and that the two phosphosites that we identified are functional.

As the above in vivo experiments do not exclude S105/S136 phosphorylation by other kinases downstream of PKA, in order to claim the direct phosphorylation, the authors need in vitro PKA kinase assay. These biochemical experiments are not trivial, but I think absolutely necessary for this story.

One cannot exclude that S105/S136 are also phosphorylated by other kinases of the AGC family (note that [Lempiäinen et al. 2009] has already excluded Sch9). However, as we hope to have shown, PKA indeed phosphorylates Sfp1. Examining if other kinases besides PKA and TORC1 target Sfp1 is a very interesting question that should be addressed in future work.

The authors only look at the localization of Sfp1. To assess its functionality and so physiological impact, it would be informative to measure the mRNA level of target ribosomal genes in various Sfp1 mutants they created.

As we described in our response to Reviewer 1 above, we did perform RNA-seq on WT and cells carrying a single copy of sfp113A. We observed a notable absence of differentially expressed ribosomal genes and ribosome-related categories in the GO analysis (Supp. Figs. 8, 9 and 10). Together with our observations on SCH9 deletion (Supp. Fig. 7), these results suggest that Sch9 can largely compensate for the loss of Sfp1 activity. On the other hand, the emergence of differentially expressed amino acid biosynthesis genes is a finding that merits further investigation, as it connects with previous observations made with Sch9 deletion mutants and the [ISP+] prion form of Sfp1 (cf. Discussion).

In the experiments using analog-sensitive PKA (Fig 1D and E for example), they directly compare wildtype-PKA versus analog sensitive-PKA, or with 1-NM-PP1 versus without 1-NM-PP1. This makes interpretation difficult, particularly because 1-NM-PP1 itself has a significant impact even in the wild PKA strain. The real question is the difference between wild-type Sfp1 versus mutant Sfp1. In the current form, they compare Fig 1D versus 1E, these two do not look like a single, side-by-side experiment. They should compare wild-type Sfp1 versus mutant Sfp1 side-by-side.

Figure 1D shows that 1-NM-PP1 has a transient off-target effect on Sfp1 localization in WT cells, which could also affect Sfp1 mutants. This observation prompted us to use wild-type PKA as a control when testing the effect of 1-NM-PP1 on sfp1PKA2D in cells carrying PKAas (Figure 1E). As Fig. 1E shows, the effect of 1-NM-PP1 on sfp1PKA2D localization in PKAas cells is quite similar to the off-target effect in cells carrying sfp1__PKA2D __and wild-type PKA. This behavior of sfp1__PKA2D __is clearly different from the response of wild-type Sfp1 to PKAas inhibition, which results in sustained delocalization. We have made the latter observation repeatedly, both in this study and our previously published work [Guerra et al. 2021].

In Figure 3, the argument around the additive effects of PKA and TORC1 is confusing. The authors say they are additive referring Figure 3E, but say they are not additive referring Figure 3B. Which is true? In fact, Figure 3B appears to show an additive effect as well.

We did not use the word "additive" in the text, because we find it difficult to interpret. Instead, we state that PKA and TORC1 appear to control Sfp1 phosphorylation independently of each other. PKA and TORC1 phosphorylation converges to the same response, affecting Sfp1 localization. It appears that loss of either kinase delocalizes Sfp1, while loss of both kinases may only have a small additional effect.

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Reply to the reviewers

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

The nucleus is recognised as a core component of mechanotransduction with many mechano-sensitive proteins shuttling between the nucleus and cytoplasm in response to mechanical stimuli. In this work, Granero-Moya et al characterise a live florescent marker of nucleocytoplasmic transport (NCT) and how it responds to a variety of cues. This work follows on from the authors previous study (Andreu 2022) where they examined the response of passive and active NCT to mechanical signalling using a series of artificial constructs. One of these constructs (here named Sencyt) showed a differential localisation depending on substrate stiffness, accumulating in the nucleus on stiffer substrates (which the authors previously showed was due to differences in mechano-sensitivity of passive versus facilitated NCT). Here the authors use Sencyt as a tool to probe how different cues affect NCT and thus nuclear force-sensing in two different cell lines (one epithelial, one mesenchymal). *

They have established a 3D image segmentation pipeline to measure both the nuclear/cytoplasmic ratio of Sencyt and 3D nuclear shape parameters. As a proof-of -principle, they show that hypoosmotic shock (which inflates the nucleus and would be expected to increase nuclear tension) and hyper-osmotic shock (which shrinks and deforms the nucleus) alter Sencyt nuclear-cytoplasmic ration as expected. They then show that inhibiting acto-myosin, which would be expected to block force transduction to the nucleus, reduces NCT, although interestingly this is without any changes to nuclear morphology. They then examine how cell density affects NCT and show that Sencyt localisation correlates only weakly with density but much more strongly with nuclear deformation (especially as measured by solidity). This is surprising considering that mechano-sensitive transcription factors such as YAP have been shown to exit the nucleus at high cell densities. Therefore, the authors directly compare Sencyt and Yap nucleo/cytoplasmic localisation and show that Sencyt behaves differently to YAP with YAP localisation correlating strongly with cell density. This reveals an added layer of complexity in YAP regulation beyond pure changes to NCT.* Major points *

The data presented throughout this work are high quality and rigorous. The controls used are appropriate (including the use of a freely diffusing mCherry to illustrate the specificity of the Sencyt probe in osmotic shock experiments - figure S2). Experiments are properly replicated and the statistical analysis is appropriate. The data are beautifully presented in figures and the manuscript is well written and very clear. Overall this is a high quality work.

We thank the reviewer for the positive assessment of the manuscript.

* The discussion is careful and the conclusions are supported by the data. My only small concern is that the authors place too much emphasis on how this work is in 'multicellular systems' as opposed to their previous work in single cells (for example "Here, we demonstrate that mechanics also plays a role in multicellular systems, in response to both hypo and hyper-osmotic shocks, and to cell contractility. L212). Cell density is only controlled in figures 3 and 4 and in some of the earlier experiments, cells look quite sparse (eg Figure 2). It's also debatable how far a monolayer of cancer cells, which lack contact inhibition of growth, is a multicellular system. Furthermore, the authors don't specifically look at cell/cell adhesion or observe major differences between the epithelial or mesenchymal lines. For this reason, the authors should tone down this discussion before publication. *

• *

We agree with the reviewer that properly assessing cell-cell adhesion is important in the context of the work. To this end, we have stained for E-cadherin in both cell lines. As expected and as described previously, the results confirm that MCF7 cells do have clear cadherin-mediated cell-cell adhesions, with a cadherin staining localized specifically in cell-cell junctions. Also as expected, C26 cells show much lower cadherin expression, without a clear pattern. Further confirming this difference, MCF7 cells show clearly distinct actin organizations in their apical and basal sides, whereas C26 cells do not. Thus, we believe that the two cell models do represent a reasonable assessment of epithelial versus mesenchymal phenotypes, in a multicellular context. The data are presented in new supplementary fig. 1, and discussed in page 3 of the manuscript (first paragraph). We have also included a paragraph in the discussion to comment on the differences between cell types (page 7, 2nd paragraph).

* Optional experimental suggestions: For me, the most compelling finding is that nuclear deformation has a greater correlation with NCT than cell density and that this is different from the behaviour of YAP. To cement the importance of nuclear deformation, the authors could induce deformation in single cells, for example by culture on very thin micropatterned lines and assess the localisation of Sencyt and YAP. It would also be interesting to assess the role of force transduction in this context or in different densities by removing actin, which affects NCT without inducing nuclear shape changes. These functional experiments would allow the authors to draw stronger conclusions about the role of nuclear shape and deformation but they aren't necessary for publication. *

• *

This is a very interesting suggestion. Following the reviewer's advice, we have now carried out experiments in which we have seeded cells on micropatterns of different sizes, and measured both sencyt and YAP ratios. In C26 cells, we have found as expected that increasing spreading leads to progressive nuclear deformation (as measured through nuclear solidity) and progressive increase in both sencyt and YAP ratios. Interestingly, cell spreading in MCF7 did not affect nuclear solidity, sencyt ratios, or YAP ratios. This further confirms the relationship between nuclear deformation and nucleocytoplasmic transport, and shows as well that different cell lines have different sensitivities. The lack of response of MCF7 cells is consistent with the lower sencyt response, and lower sencyt/nuclear shape correlation measured in fig. 4. It suggests that MCF7 cells may have mechanisms to shield the nucleus from deformation, something which we have reported in a different context (Kechagia et al., Nat. Mater. 2023). The new results are reported in new fig. 3, and supplementary fig. 8, and discussed in pages 5 (1st paragraph) and 6 (1st paragraph) of the manuscript results.

• *

Minor points

* - I'd like to see better examples of 3D reconstructions of nuclei (ie fig 1C but bigger) in different conditions. This is especially important in figure 3 where it would be helpful to see examples of nuclei with high or low solidity. The differences in oblateness are clear to see from the images in 3a and 3f but solidity could be better illustrated. *

• *

We have now added 3D reconstructions as requested, which illustrate the nuclear shape changes that take place. This is shown in figs. 1, 4 (which corresponds to figure 3 in the previous version of the manuscript), s3, and s7.

*

• Where Sencyt index is plotted, it would be clearer to add labels to at least figure 1 which indicate whether it is more cytoplasmic or nuclear. *

• *

We have done this as requested in figure 1.

* Reviewer #1 (Significance (Required)): *

* In this work, Granero-Moya et al characterise a new tool for measuring NCT and show that it is mechanically regulated. Given the importance of NCT in mechano-transduction, this tool will be a great asset to the mechano-biology community and will likely be adopted by multiple groups in the future. The findings about the effects of cell density on NCT and differences from YAP are interesting but could be further fleshed out. This work is likely to be of greatest interest to a specialised audience working in the fields of mechano-biology and nuclear transport. *

• *

We thank the reviewer for the positive assessment.

* *

• *

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

* The study conducted by Granero Moya and colleagues describes the application of a synthetic protein which is observed to enter the nucleus in response to mechanical strains, rather than being influenced by cell density. However, the novelty of this work is minimal since the conceptual framework and the utilization of this identical or similar tool have been previously reported by the same team in earlier publications. *

• *

We respectfully disagree with the assessment of the reviewer. Please see below for a detailed response regarding novelty.

• *

*In their experiments, they employ this GFP-based sensor, referred to as Sencyt, in cells subjected to osmotic shocks. These shocks are highly stressful and impact a range of cellular processes, including stress response pathways MAPK and others; Osmoregulatory pathways; cell cycle regulations, autophagy and death pathway; ion channel regulations and others. The second findings are on cells treated with a combo of drugs affecting the actin cytoskeleton. The justification for using a combination of two specific drugs remains unclear, as the study does not adequately explain the rationale behind this choice. Additionally, there is a lack of information regarding the full range of targets these drugs affect. This raises questions about the comprehensiveness and applicability of the findings, as understanding the complete scope of the drugs' targets is crucial for interpreting the results within a minimal frame of physiological context. *

• *

The two drugs used are paranitroblebbistatin (a photostable version of blebbistatin) and Ck666. We apologize for not explaining in more detail the action of these drugs, both of which have been characterized and used extensively in the literature. Paranitroblebbistatin binds to myosin, preventing its ATPase activity and therefore impairing actomyosin contractility (https://doi.org/10.1002/anie.201403540). It acts on different myosin isoforms, including non-muscle myosin II, the main type of myosin responsible for actomyosin contractility in non-muscle cells. CK666 binds to and inhibits arp2/3, a protein responsible for nucleating branched actin (https://doi.org/10.1016/j.chembiol.2013.03.019). This impairs lamellipodial formation and therefore cell spreading (see for instance https://doi.org/10.1371/journal.pone.0100943).

The rationale for using both drugs in combination was explained in page 4 of the manuscript. In our previous work, we determined that myosin inhibition with blebbistatin is not sufficient to inhibit nuclear mechanotransduction. Indeed, in an epithelial context, we observed that due to reduced contractility, blebbistatin-treated epithelial cells in fact spread more on their substrate. This leads to more deformed (flattened) nuclei, leading to the counterintuitive result that YAP nuclear localization increases rather than decreases. If cell spreading is impaired by interfering with branched actin nucleation, then this spreading is prevented, and the combination of drugs leads to reduced nuclear deformation, and reduced YAP nuclear localization (see supplementary fig. 7 in Kechagia et al, Nat. Mater. 2023, https://doi.org/10.1038/s41563-023-01657-3). Similar results had been published previously by the group of Clare Waterman (https://doi.org/10.1074/jbc.M115.708313).

Thus, the combination of drugs was designed to ensure that we were impairing nuclear mechanotransduction. Of course, we agree with the reviewer that all perturbations have potential side effects. Osmotic shocks will affect a range of cellular processes (as mentioned in the discussion of the manuscript), and any drug treatment can potentially have off-target effects. However, the fact that two orthogonal perturbations with different potential side effects (osmotic shocks versus actomyosin-targeting drugs) lead to the same effects in sencyt strongly suggests that the effect is mediated by mechanics, and not other factors. To reinforce this, we have now added an additional mechanical manipulation: seeding cells on micropatterned islands of different sizes. As spreading increases, cells are known to increase actomyosin contractility, and nuclear deformation (https://doi.org/10.1529/biophysj.107.116863, https://doi.org/10.1073/pnas.0235407100, https://www.nature.com/articles/ncomms1668, https://doi.org/10.1073/pnas.1902035116). As expected, nuclear solidity, sencyt ratios, and Yap ratios all increased with cell spreading. Interestingly, this occurred only for C26 and not MCF7 cells, where no changes were measured in solidity, sencyt, or YAP. The lack of response of MCF7 cells is consistent with the lower sencyt response, and lower sencyt/nuclear shape correlation measured in fig. 4. It suggests that MCF7 cells may have mechanisms to shield the nucleus from deformation, something which we have reported in a different context (Kechagia et al., Nat. Mater. 2023).

The new results are shown in figs. 3 and s8. We have also expanded the explanation of drug treatments in page 4 (3rd paragraph).

* The novelty is on the specificity of this synthetic fusion protein for these manipulations and not on cell density. Yet, the reasons behind this selective response remain unexplained, potentially attributable to the unique characteristics or sensitivity thresholds of their synthetic probe. As comparison, YAP localization and this is sensitive to both inputs, but this is also already published (fig4). The focus is anyway on Sencyt for which they offer simple observations and quantifications. *

• *

The main novelty of the work lies in the characterization of the role of nucleocytoplasmic transport in mechanotransduction, in the context of multicellular systems. We and others had shown that nucleocytoplasmic transport responds to mechanical force in the context of single cells (see for instance Andreu et al. 2022 from our group, but also https://doi.org/10.1126/science.abd9776 from the Martin Beck group). However, to what extent this applies to multicellular systems was unknown. It is true that in multicellular systems, the response of YAP and other mechanosensitive transcription factors has been characterized (such as in our Elosegui-Artola 2017 paper, mostly done at the single cell level but including one figure panel on epithelial cell monolayers). The reviewer argues here and in the consultation comments with other reviewers (see below) that this demonstrated the role of nucleocytoplasmic transport in multicellular systems. However, we respectfully disagree. As also noted by reviewer 3 in the consultation, the response of YAP, and of any transcription factor, may include effects on nucleocytoplasmic transport, but will also likely include effects caused by the complex biochemical signalling pathways that regulate them. Disentangling such effects requires a sensor that only responds to nucleocytoplasmic transport, and this is precisely what Sencyt provides.

The reviewer also states that our manuscript does not explain why sencyt responds to mechanics and not cell density. We disagree: sencyt responds to mechanics for the reasons explained in our previous work (Andreu et al., Nat. Cell Biol. 2022), and there is no reason to expect a specific response to cell density. In this regard, we don't think there are any sensitivity thresholds to detect cell density, as the probe is not designed to sense this parameter in the first place. The fact that YAP responds to both mechanics and cell density shows that the response to density cannot be merely explained by mechanics, and is rather due to signalling through other means. Of course, we agree that we do not explain the mechanism by which YAP senses cell density, but we think this lies clearly out of the scope of our manuscript.

In terms of novelty, our work also characterizes a tool to assess nucleocytoplasmic transport live in cells. We agree with the reviewer that the specific construct had been reported in our previous paper, but it had not been characterized in detail. This is done here, enabling its use by the community as a tool to measure nucleocytoplasmic transport in any context, be it related to mechanics or not.

• *

When reviewing the figures presented, I find it challenging to detected marked differences, despite their quantitative data suggesting otherwise.

• *

We assume here that the reviewer refers to differences in sencyt nuclear localization, that is, the sencyt index. We have now checked the example images showing changes in sencyt index, in figures 1 and 2. In figure 1, the example cells under hypo-osmotic shocks increase their sencyt index from 1.2 to 1.45 (C26). In figure 1, the example cells under hyper-osmotic shocks decrease their sencyt index from 0.9 to 0.3 (MCF7) and from 1.4 to 0.5 (C26). In figure 2, the example cells increase their sencyt index upon drug washout from 0.2 to 1.4 (MCF7) and from 0 to 0.9 (C26). Of course, these individual values don't reflect exactly average values, but they do reflect the reported average trends and their magnitudes faithfully. Here we note that even though sencyt changes with the different treatments, it is always more nuclear than cytosolic (sencyt index >0, as it has an NLS). Thus, to the naked eye, sencyt always seems to show a "bright" nucleus, and it is hard to intuitively see changes in its localization. Further, we also note that osmotic shocks lead to overall changes in fluorescence levels due to volume changes (as GFP molecules get diluted or concentrated in hypo or hyper osmotic shocks, respectively). This does not affect ratiometric quantifications as assessed with our mcherry control, but means that changes in ratios are hard to see by eye. To help in this visualization, we have now changed the images from green to grayscale, which is better perceived by the human eye. We have also specified the issue of fluorescence intensity changes in the legend of the figure.

In addition to this, we have seen that there is indeed a case in which examples were not following average trends. In the case of hypo-osmotic shocks in figure 1, example MCF7 cells were barely changing their sencyt index with treatment. We apologize for choosing this non-representative image for the figure, we have now changed the figure to show more representative cells.

• Furthermore, the study attempts to correlate the behavior of Sencyt with the nuclear geometric parameter of solidity, a connection that seems to lack a clear basis in cell biology and could potentially lead to misconceptions. *

• *

Mechanical effects on nucleocytoplasmic transport are mediated by mechanical tension application to nuclear pores, which are embedded in the nuclear membrane (nuclear envelope). Whereas nuclear envelope tension is very challenging to measure directly, it can be indirectly related to nuclear shape. Indeed, a tense membrane will tend to even out membrane irregularities and appear rounded, whereas a membrane under low tension will tend to show wrinkles. Nuclear solidity is a geometric parameter that compares actual nuclear volume to the volume of the convex hull (intuitively, the volume of the smallest wrinkle-free object containing all of the nucleus). Thus, it is the geometric parameter that best reflects the presence of wrinkles, folds or irregularities, and as such the one that should best correlate to membrane tension. Of course, this correlation is not perfect, and there could be many situations in which changes in membrane tension may not directly affect nuclear solidity. But we do believe that solidity is the geometrical parameter that should best reflect membrane tension, and this is why we focus on it. Consistent with our hypothesis, solidity is the geometrical parameter that best correlates with sencyt. To further clarify this, we now explain this rationale in detail in page 4 of the manuscript (1st paragraph).

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

* In sum, I think the MS is of interest for a very specialistic audience. There are no clear interpretations. The work is done in one or two cellular model systems in vitro; and the general significance of these observations is of very limited impact and no novelty. *

We strongly disagree. The study is done on two cellular models, one with epithelial and the other with mesenchymal phenotype, and thus highly relevant for multicellular systems. Following suggestions by reviewers 1 and 2, we have now characterized the epithelial/mesenchymal behaviour of the cell types in detail (see supp. fig. 1). The results are novel in that they demonstrate the role of nucleocytoplasmic transport in multicellular systems, something which as argued above had not been done before. The difference with YAP, and the disentanglement between transport and signalling, is also novel. Finally, we believe the manuscript will be impactful because of this novelty, but also because of the availability of sencyt as a tool for the community. In fact, since placing this manuscript in biorxiv, we have received many requests (directly and through addgene) to share sencyt, which is currently being used in several labs across the world.

• *

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

• *

In this very well-written manuscript, Pere Roca-Cusachs and colleagues investigated the response of nucleocytoplasmic transport (NCT) to mechanical stress and tested whether this response is similar in epithelial and mesenchymal cells using a combination of quantitative approaches. This study builds upon their earlier findings, which elegantly demonstrated that NCT is sensitive to mechanical forces transmitted to the nuclear membrane. Using a similar approach to their recent work, they quantitatively analyzed NCT and compared the two cell types using various treatments that impact nuclear membrane tension. The study is straightforward and experimentally sound, with an adequate number of replicates and independent experiments. While one might consider the limitations given their previous work, none have demonstrated that NCT is mechanosensitive in epithelial cells. Additionally, they provide a simple approach to measure NCT, which should be of interest in the field. However, it is unclear how the authors defined the epithelial phenotype in this work and whether they solely based this characterization on the tissue/cell's origin. Epithelia can be defined ultrastructurally with reference to their apico-basal polarity and specific cell-cell junctions (Alberts et al., 1994; Davies and Garrods, 1997). Changing cell density should affect cell/cell adhesion, but the authors provide no evidence that the cells tested in the study are attached to their neighbors on all sides and form an epithelium. While I recognize that the objective of this study is not to mimic the in vivo behavior of epithelial tissue, the authors should at least ensure that cells form a monolayer by quantitatively assessing cell-cell junctions (or they should adjust their conclusions adequately). This control is specifically important for Figure 3 and 4, whose objective is to test the impact of cell/cell contacts. But it would also be important to provide this essential control for Figure 1 and 2, as it is unclear from the images provided if MCF7 cells are forming an epithelium (and form cell/cell junctions).

• *

We thank the reviewer for the positive assessment of our work. We fully agree with the reviewer that properly assessing cell-cell adhesion is important in the context of the work. To this end, we have stained for E-cadherin in both cell lines. As expected and as described previously, the results confirm that MCF7 cells do have clear cadherin-mediated cell-cell adhesions, with a cadherin staining localized specifically in cell-cell junctions. Also as expected, C26 cells show much lower cadherin expression, without a clear pattern. Further confirming this difference, MCF7 cells (but not C26 cells) show a clear apico-basal polarization, with distinct actin organizations in their apical and basal sides. Thus, we believe that the two cell models do represent a reasonable assessment of epithelial versus mesenchymal phenotypes, in a multicellular context. The data are presented in new supplementary fig. 1. We have also included a paragraph in the discussion to comment on the differences between cell types (page 7, 2nd paragraph).

• Reviewer #3 (Significance (Required)): *

• *

The mechanosensitivity of NCT is an important question central to many aspects of cell biology. One might consider the impact of the proposed work limited, given their previous research. However, none have demonstrated that NCT is mechanosensitive in epithelial cells, making it a crucial question that needs to be addressed. Additionally, they provide a simple approach to measure NCT, which should be of interest to a broad audience.

We thank again the reviewer for this positive assessment.

• *

*Referees cross-commenting *

* Here comments from all 3 reviewers are reported *

* Reviewer 1: *

* I disagree with R2's comment that there is 'no novelty' here. Although this work is going to be of greater interest to a specialised rather than general audience, it characterises in depth a simple tool to measure NCT which will be useful for mechanobiology field. Also, using 'two cellular model systems in vitro' is very standard in the field when assessing subcellular processes like NCT. Using this approach in vivo would be very interesting but challenging and would be an entirely different study . *

• *

*I agree with R2's comments that the authors should better justify their combination of two actin inhibitors and R3s point on better assessing cell/cell junctions. *

• *

We thank the reviewer for these comments. Both issues have been addressed, as described in the response to reviewers above.

* Reviewer 2 *

* About Reviewer 3's comments, I believe it's a stretch to highlight the strength and novelty based on "NCT's mechanosensitivity in epithelial cells has not been demonstrated,". There are thousands of papers on the Hippo pathway, that is known to be mechanosensitive, on the regulation of YAP, that enters in the nucleus in Hippo inhibited conditions and exits to the cytoplasm in Hippo induced cells, including downstream of mechanical signals. The phenomenon of nuclear-cytoplasmic shuttling being a common event from neurons to endothelial and multiple types of epithelial, immune, and fibroblast cells is already established through NCT of this and other endogenous proteins. This is simply an accepted fact. Then, The Nature cell Biology 2022 was offering a very general claim. No warning that conclusions could have been cell type specific. In the Artola 2017 Cell paper they also showed NCT in mammary epithelial cells. We should definitively conclude that NCT's mechanosensitivity in epithelial cells has been well demonstrated. *

• *

We disagree with this assessment, for the same reasons also exposed by reviewer 3 below. Previous work on YAP and other transcription factors cannot be seen as a demonstration of the role of nucleocytoplasmic transport per se. The localization of any transcription factor is highly regulated by complex signalling pathways, and can be affected by many factors. One of them is nucleocytoplasmic transport, but signalling events (for instance through phosphorylation) could change localization by promoting binding to cytosolic or nuclear binding partners, by promoting protein degradation, by masking nuclear localization signals, and others. To isolate the role of nucleocytoplasmic transport, a probe sensitive only to this factor should be designed. This is exactly what sencyt provides. In fact, this has allowed us to answer an important open question: is the sensitivity of YAP to cell density mediated by mechanics and nucleocytoplasmic transport, or is it mediated by some other factor? Our results suggest that some other factor, likely mediated by the Hippo pathway and not necessarily mechanotransduction, explains this sensing of cell density. This is a novel finding, which was not provided in either our Elosegui-Artola 2017 paper or our Andreu 2022 paper.

* About Reviewer 1: I find it challenging to grasp the point made in the comment. On novelty, in their previous study in NBC 2022 Syncet was already shown to undergo NCT. The reviewer states that the study presents "a simple tool to measure nuclear-cytoplasmic transport (NCT) beneficial for the mechanobiology field, and evidence that this demonstrates a novel layer of regulation in hippo signaling (also because this is observational and not a mechanistic study). The tool in question is far from simple. Its application requires transfection into cell cultures, conducting live imaging, etc. If one aims to measure NCT of endogenous proteins, straightforward immunofluorescence or live imaging of endogenous proteins (like GFP-tagged YAP, Twist, Smads, etc.) using the same experimental setup should suffice to demonstrate relevance, without necessitating any additional experiments. What then, is the unique benefit of this proposed tool? Given it's an artificial construct combining NLS-GFP with a bacterial protein, questions arise about the effects of the forced nuclear localization signal (NLS) or the bacterial component. It is an empirical artificial construct and there is no mechanism to explain its behavior.The comparison of Syncet with YAP seems to me questionable and of limited utility. *

As also noted by reviewer 3 below, the use of genetically encoded fluorescent sensors that require transfection is by now absolutely standard in biology, and cannot be considered to be "far from simple". And as stated above, imaging of endogenous transcription factors (which also requires transfection if it is done live) does not isolate the role of nucleocytoplasmic transport. We also disagree that "there is no mechanism to explain its behaviour". Sencyt was developed in our previous andreu et al 2022 paper, where the mechanism is explained in detail.

• *

*It's unsurprising that an artificial construct only mirrors some aspects of what is considered a genuine mechanosensitive protein. The utility of a synthetic tool lies in its ability to replicate actual phenomena, not in what it fails to do. In comparison to their NBC 2022 study, this manuscript focuses on what their reporter fails to detect. *

We disagree that a synthetic tool is only useful if it replicates the behaviour of endogenous proteins. A synthetic tool, precisely due to its engineered, artificial nature, can be made to respond only to specific factors (in this case, nucleocytoplasmic transport). This can then be used to disentangle the role of such specific factors, as done here.

The osmotic shock was the assay in their 2017 Cell paper. Here they demonstrate that a combination of Blebbistatin+CK (an unclear choice of drugs) is ineffective, as is cell density. Are there other specific peculiarities associated with this construct?

Here, we note that our osmotic shock experiments in our 2017 paper were done for YAP (not nucleocytoplasmic transport in general). Regarding the choice of drugs, please refer to our answer to the reviewer comments above for a full explanation. Also, we want to clarify that this combination is not ineffective, as it leads to clear changes in sencyt. * *

* My other concern is on the minor quantitative changes reported, which seem inconsistent with the provided representative images, where significant differences are difficult to appreciate. For instance, the claim that the transfected sensor differs from an endogenous NCT protein, YAP, after cell density treatment, is hard to detect in their images. In Figure 4, comparing YAP and Syncet in C26 cells, YAP appears uniformly nuclear at high cell density, potentially more nuclear than the synthetic sensor, which is not coherent with their claim.*

• *

Regarding the concern of the minor changes seen in images, please refer to our full response to the reviewer comments above. Regarding the comparison between sencyt and YAP, we want to clarify that in our manuscript we do not compare the absolute values of nuclear localization between YAP and sencyt. As the reviewer notes, these are two different proteins, so which one is more nuclear does not really provide useful information. So whether YAP is more or less nuclear than sencyt is unrelated to (not incoherent with) our claim. What we state in figure 4 is that YAP responds to cell density, whereas sencyt does not. This is clear from the quantifications and also from the images.

• *

• From the Hippo perspective, there is really an unusual amount of nuclear YAP left in their cells. This should be almost completely cytoplasmic from prior contact inhibition studies in the Hippo field. Syncet could be simply less sensitive than YAP in these borderline conditions. Although there's a more noticeable cytoplasmic noise in dense cells with YAP compared to Syncet, this could be attributed to several factors, including differences in protein degradation rates, which I suspect to be quicker for a synthetic protein. From a technical perspective it is complex to get strong conclusions after comparing something so unrelated with each other. One is a live GFP detection and the other is a staining by immunofluorescence. the nature of the background is also different and so conclusions from comparisons between unrelated systems is not justified. *

• *

In conditions of high density, average YAP ratios are close to one (zero in logarithmic scale, as reported in the figures) for MCF10A cells, so there is no nuclear localization. This is similar to what we and others have previously reported in similar conditions (Elosegui Artola et al 2017, Kechagia et al. 2023, for example). In C26 cells, YAP levels at high density are a bit higher. This is likely due to their mesenchymal nature, and therefore diminished cell-cell contact inhibition (as assessed in detail in this revision). This in fact further suggests that the response of YAP to cell-cell contacts is different from a mere mechanical factor, supporting our hypothesis. Regarding the issue of noise, background noise is removed from quantifications, and potential noise coming from non-specificities or autofluorescence is also cancelled by the fact that we compute fluorescence ratios between nucleus and cytoplasm (and not absolute values). Thus, we don't think noise is an issue. Further, we note again that we do not directly compare values between sencyt and yap.

* This suggests caution on what is heralded as the main claim here put forward. *

* Reviewer 1: *

*I do have some sympathy with R2s comments in the consultation. I agree that showing that NCT is mechanosensitive in an epithelium is not new. I also agree that sometimes it is difficult to see the quantitative differences by eye. This second point could be addressed by including more details of the segmentation and analysis in the supplemental material (along with some example images). *

• *

We thank the reviewer for the suggestions. Regarding the novelty, please see above for a detailed discussion, and also the comments of reviewer 3 below (previous work studied not NCT but transcription factors, affected by many parameters). Regarding quantitative differences, we have now addressed this issue by showing images in grayscale rather than green, and also by replacing one example cell in figure 1 which indeed did not reflect the average measured trends. We now also show examples of 3D rendered images of the nuclei in different conditions. We have also gone through the methods and clarified in detail how ratios are calculated, the segmentation procedure is also explained in detail.

* Regarding novelty, I would be interested to know if R2 thinks that there are experiments that the authors could do to improve the work. Or do they need to simply tone down their claims? It's perfectly acceptable to publish a well characterised tool with a series of observations and it's beneficial to the community to do so.*

• Reviewer 3 *

* Thanks to Reviewers #1 and #2 for using this consultation option; I truly appreciate their feedback on my comments and find it extremely valuable. I agree with Reviewer #1 that the method proposed here is relatively simple. Transfecting cells and conducting live fluorescent imaging can hardly be considered difficult. I believe the construct used/designed by the authors is the main advantage as it provides a specific way to quantitatively assess NCT and not limit the analysis to a single nuclear protein (such as YAP). Reviewer #2 suggests using immunofluorescence staining of YAP or live imaging of fusion fluorescent protein (following transfection) to analyze NCT, but this approach would yield a readout not only based on NCT but also on the many other interacting partners/mechanisms that regulate the candidate localization, resulting in an unspecific readout (and similar transfection/live imaging set-up). *

• *

We thank the reviewer for this comment, we fully agree and have elaborated on this in our responses above.

* Regarding the impact of the study, I agree that it is certainly not as impactful as previous publications on this topic. Although I find reviewer#2 argument on Yap irrelevant, as YAP is not the main focus of this paper. Some experiments have been done with cells of epithelial origin, but NCT mechanosensitivity has not been clearly tested in epithelial monolayer, which is the main claim of the proposed study here. The 2017 Cell paper focused on YAP transport into the nucleus (and not NCT in general) and they showed a correlation between YAP nuclear localization and traction force in MCF10A. I am not sure if one would say that "NCT mechanosensitivity has been well demonstrated in epithelial cells" based on this single panel. The impact of the proposed study is certainly not outstanding but offering a thorough analysis in epithelial cells (as monolayers and not as individual cells) and presenting a well-defined experimental approach should be of interest in the field. I agree with comments from reviewer#2 that some reported effects in graph are unclear on main images. More experimental details should hopefully clarify this aspect.*

• *

We fully agree with the reviewer. Regarding quantitative differences, we have now addressed this issue by showing images in grayscale rather than green, and also by replacing one example cell in figure 1 which indeed did not reflect the average measured trends.

Author response:

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

(1) The authors should show i) whether the variants exhibit the same surface expression as wildtype and ii) whether changes of surface expression (e.g. wt transporter expressed low and high) alters growth rates under conditions where growth depends on amino acid uptake. The authors say that the uptake of radioactive substrate and the overall fitness coincide (Figures 5 and 6), but it would be good to quantify the correlation, perhaps by using a scatterplot and linear regression.

We thank the reviewer for the questions and proposals. The comparison of the surface expression between the transporter-expressing variants was added to the manuscript (Figure 3- Figure supplement 1 and 2). In the case of the AGP1 variants it was calculated that surface expression between the evolved mutants and the wild-type is similar, indicating that the transporter overexpression has no impact on the growth rate per se. The same analysis for the PUT4 variants showed significant difference, with the PUT4-S variant seemingly expressed more than the wild-type. However, that does not seem to affect the uptake effect of the mutation in the cases of the original substrates of Ala, Gly and GABA, since in those cases the transporter activity for the evolved variant is substantially decreased (Figure 5). Thus, the variation on the surface expression between the mutant and the wild-type, which could be attributed to the small sample size and the inherent limitations of the analysis (imaging of a culture with cells in different planes), is not expected to interfere with the reported results.

Additionally, a scatterplot accompanied with a linear regression curve describing the connection between the overall fitness and uptake of 2 mM radioactive substrates was added to the manuscript, as advised (Figure 5- Figure supplement 2). In both cases of 2 mM Phe or Glu, the regression model explains 60-70% of the variation observed in the uptake rate of the amino acids by the different variants if changes in the uptake rate are dependent on changes in the fitness.

(2) The authors should further investigate to what extent the (over)expression of wildtype versus variant transporters impacts growth rates. I would recommend such experiments being done under conditions where nitrogen uptake does not depend on amino acid uptake. I could imagine that some of the fitness data are confounded by the general effects of mutations on growth rates. More concretely, I could imagine that overexpression of e.g. the AGP1-G variant is less of a burden for the yeast cells and would allow to grow them better in general. This could explain why its overall fitness is close to wt, whereas other variants exhibit diminished fitness (Fig. 4A).

The growth curves of all transporter variant cultures in the absence of selection for amino acid uptake have been presented in Figure 4 - Supplement figure 1. As proposed, the growth rates of the variants in medium with ammonium as nitrogen source were calculated and presented in Figure 3- Supplement figure 1 and 2. For both cases of AGP1 and PUT4 expressing variants, statistical analysis showed no significant difference between the mutants and the wild-type.

(3) It is quite remarkable that the PUT4-S variant has such a dramatically enlarged substrate spectrum. In addition, the fitness losses for Alanine and GABA are rather small. This striking finding asks the question of why yeast has not evolved this much better/more efficient variant in the first place?

We thank the reviewer for this very good question. We now included an explanation in the Discussion, but to give a short answer here: One should keep in mind that we used a 10-gene deletion strain to select for given mutants. Wild-type cells have a wide spectrum of substrates through the use of many amino acid transporters, and their regulation is intricately tuned to achieve optimum transport under any environmental circumstance. Broadening the spectrum of a single transporter thus would not lead to increased fitness. On the contrary, it would probably throw off this fine balance.

(4) It would be generally interesting which types of selections (transporter/amino acid combinations) were tried (maybe as part of the methods section). I could imagine that the examples that are shown in the paper are the "tip of the iceberg", and that many other trials may have failed either because the cultures died, or the identified clones would grow faster due to mutations outside of the plasmid. It would be helpful for researchers planning such experiments in the future to be made aware of potential stepping stones.

The issues raised here are spot-on, as we actually did test the evolution of PUT4 towards transport of other amino acids than the two mentioned in the report. Aside from the successful Asp and Glu, we ran parallel cultures selecting for transport of Gln, Thr, Trp, Tyr, and Cit. Neither of these evolution regimes led to increased growth phenotypes that were linked to the evolved gene, and we did not investigate these cultures further. At this point, we cannot fully explain this result, which is why we decided to omit it from the report. The L207S variant of PUT4 was later shown to indeed support growth on Gln, Thr, and Cit. Therefore, we speculate that the reason for not evolving this mutant in the respective evolution cultures was that the fitness gain in these amino acids was not large enough to be sufficiently enriched in the course of the evolution trial. Given that the Δ10AA strain still harbors nine amino acid transporter genes in its genome, it is conceivable that upregulation of some of these genes causes growth in some amino acids, prohibiting the selection of mutations in PUT4 (e.g., by mutations outside the plasmid, as the reviewer aptly suggested). We deemed these (negative) results not appropriate for the manuscript, as our main focus was characterizing the fitness effects of single mutations, not the laboratory evolution process of obtaining the mutants.

(5) The authors took a genetic gain-of-function approach based on random mutagenesis of the transporter. In such approaches, it is difficult to know which mutation space is finally covered/tested, and information that can be gained from loss-of-function analyses is missed. Accordingly, the outcome is somewhat anecdotal. To provide an idea of the mutational landscape accessible, the authors could perform NGS of cultures without any selective pressure, and report the distribution of missense variants in the population.

We very much appreciate the interest in the details of the mutagenesis. Based on the information given in the original OrthoRep publications (e.g., Ravikumar et al., DOI: 10.1016/j.cell.2018.10.021; mutation rate approx. 10-5 per generation and nucleotide), we calculated the expected number of mutations per passage in our experiments. For AGP1, it is about 5000 mutational events per passage (10 mL culture volume and 1:200 dilution), and for PUT4, it is about 1000 mutational events per passage (2 mL culture volume and 1:100 dilution). At a gene length of about 2000 bp, we expect to cover most single mutations already in the first or second passage (in the absence of selection). This is reflected in the result that the strongly beneficial mutation L207S in PUT4 was recovered in every selection on Asp or Glu we tested. We included this information in the Methods section.

That said, the present study was consciously designed to research gain-of-function mutations, as we wanted to know if and how membrane transporters can evolve new substrate specificities without losing the original functions. Our approach was chosen to reflect as close as possible a natural scenario where a microorganism encounters a new ecological niche (a new nutrient to be transported). At the same time, we included selective pressure to keep the capacity to thrive in the original niche (to assimilate an ancestral nutrient). This approach is designed to specifically select against any loss-of-function mutations, which is in line with most modern theories about evolution of protein function (excellently reviewed in Soskine and Tawfik, DOI: 10.1038/nrg2808). We find that this approach gives a good idea how transporters could evolve new functions in a natural setting. By engineering single mutations in the wild-type background of the transporters, we show the fitness effects of different single mutations - this finding thus does not depend on the mutational landscape that is covered in the experiment.

(6) The authors do not discuss the impact of these mutations on transport rates/kinetics, which are known to play a role in substrate selection in solute carriers (https://www.nature.com/articles/s41467-023-39711-y). Do the authors think ligand binding/recognition is more important than kinetic selection in the evolution of function?

Indeed, the observed phenotypes can stem from both changes in transport rate and changes in substrate binding. In our opinion, both are perfectly possible explanations for the behavior of evolved transporter variants. We are not discussing this in the manuscript as the weak transport of the novel substrates in the wild-type transporters did not allow us to unambiguously assign one or the other. Yet, we can lend minor circumstantial evidence pointing towards substrate affinity being the more important factor in evolving a new activity in transporters: Overall transport rate (for original substrates) declined in most evolved transporters. Therefore, it is a bit less likely that improved transport rate allowed novel substrates to be used as a nutrient. However, this is not to say that both processes can occur (even side by side).

(7) Ultimately, what are the selective pressures that drive transporter function? The authors pose this question but don't fully develop the idea. Would promiscuous variants still be selected for if the limiting nitrogen source was taken up by the cell via a different pathway (i.e. ammonium or perhaps arginine)?

Evolution and regulation of transporters is a very complex system, and we simplify this system in our single-transporter/single-amino acid approach. In nature, the selective forces are assumed to be much smaller than in our system, and multiple selective pressures might occur at the same time (maybe even in opposite directions). Therefore, such predictions are beyond the scope of the present study. To put it shortly, yeasts (and other organisms) have evolved the capacity to transport all natural amino acids. Yet, to actually allow fine-tuned regulation of transport of each individual amino acid, narrow- and broad-range transporters have evolved, including a lot of redundancy. This means that the question posed cannot be answered by yes or no, but by “it depends”.

(8) Amino acids are a special class of metabolites, in that they all have the same basic structure. Thus, transport systems really only need to recognize the amino and carboxyl groups with high fidelity, and can modulate the side chain binding site to increase specificity. This was demonstrated in a bacterial APC transporter (https://www.nature.com/articles/s41467-018-03066-6#Sec2). Is this why the APC fold is largely responsible for AA uptake in biology?

Indeed, typically, APC-type amino acid transporters bind the amino and carboxyl groups in the same position by backbone interactions. Therefore, this might be an ancestral feature of the APC superfamily and explain why this group represents the main group of amino acid transporters.

(9) There isn't much discussion on the location of the mutations with respect to binding site vs. gating helices. Are there hotspots of mutations within the APC, and areas where variation is poorly tolerated? It would be helpful to briefly review what is known about mutations that change amino acid specificity in the APC family. My impression is that other studies applying rational mutagenesis have also shown that single-site mutations in the binding pocket alter substrate specificity - are these analogous to the L207 in PUT4? PUT4: I64T comes up in 3 of 5 selections. Did the authors consider a closer analysis of this mutation, and if not, why?

We agree that it would be helpful to determine hotspots of mutations in APC transporters that lead to changes in selectivity. However, we feel that the current literature does not lend enough data to support an extended analysis of such hotspots. Conversely, the natural sequences of APC transporters are not similar enough to determine which residues are responsible for a certain selectivity profile. There are however some studies on site-directed mutagenesis, as mentioned by the reviewer. A short summary of those is discussed in the revised paper. Interpretation of the previous studies under the light of our results suggests that the evolutionary evolved sites derived in our work play a significant role in substrate selectivity and transporter function within the superfamily of the APC transporters.

As to the question why we did not include the I64T mutation in our experiments: this mutation lies within the poorly defined N-terminus of the protein, which is not part of the transmembrane core. We therefore deemed this residue as probably not connected to the specificity of the protein; it might be related to the protein’s stability in the cell, as the termini of transporters are known to be important for post-translational regulation, especially vacuolar degradation.

(10) What do we learn about the APC fold that informs our understanding of where substrate specificity arises in this fold? Do the authors think all SLC folds are equally capable of adaption, or are some more evolutionary-ready than others? An evolutionary analysis of these transporters to gain insights into whether the identified substitutions also occurred during natural evolution under real-life conditions would further strengthen the manuscript. Could the authors provide a sense of how similar the 18 yeast amino acid transporters are, such as sequence alignments or a matrix of pairwise sequence identity/similarity? Are they very diverged, or is the complement of amino acid substrates covered by a rather conserved suite of transporters?

We do not want to make bold statements about adaptive evolution in other SLC folds, but we consider it not unlikely that a similar approach will lead to similar conclusions in other transporters.<br /> As advised, a pairwise identity matrix was added to the manuscript (Figure 1–figure supplement 2).

As to the proposed analysis focusing on natural occurrence of the mutations we found: we have indeed looked into this, but have not found evidence of such mutations. This is actually expected, as our selection regime puts “unnatural” selective pressures on a single transporter in isolation, which in reality co-evolved with a whole suite of other transporters that already have the capacity to transport all amino acids. Therefore, it is unlikely that the same mutations would happen in a natural setting. Our study is designed to capture evolution where a completely novel substrate is encountered, for which no transport mechanism has evolved yet.

(11) Throughout: some of the bar graphs show individual data points, but others do not (Figure 3, Figure 5). These should be shown for all experiments.

We thank the reviewer for the comment. In the revised version of the manuscript, we included individual data points in all bar graphs.

(12) For bar graphs in which no indication of significance is shown, does this mean that p>0.05? Comparisons that are not significant (p>0.05) should be indicated as such.

We thank the reviewer for the comment. In the revised version of the manuscript, we indicated in the legends that in cases of no significant difference (p > 0.05) between the wild-type and the evolved variants, no asterisks are shown.

(13) Figure 5, Figure 6: Are the three confocal images just three different fields of view? It might be useful to include a zoom-in on a single representative cell, as it is hard for the reader to see to evaluate the membrane localization.

In the revised version of the manuscript, we clarified that the three confocal images represent three different cultures, as each variant was tested in triplicates. We also included a zoom-in of a representative cell, as suggested.

(14) In the main text, page 9, the conditions used for each experimental evolution are not clear ("nitrogen limiting mixture of amino acids (1 mM final concentration)". I think this is an important detail, since the mixtures are quite different for the more promiscuous vs. the more selective transporter, and it would be helpful if this was described more clearly in the main text.

We thank the reviewer for the comment. We have included further clarification in the revised manuscript.

(15) Figure 1-Supplement 1 and Figure 4 Supplement 4 - can't read the figure labels. Try labeling columns and rows rather than individual plots.

We have taken the proposal into account and revised the proposed Figures accordingly.

(16) Page 9: "The transporter gene was sequenced and re-introduced into Delta-10AA cells." Was the plasmid isolated, sequenced, and re-introduced, or was the gene cut-and-pasted into a new vector backbone?

In the revised manuscript we have clarified that the gene was sequenced and then cloned into the expression vector and re-introduced into naïve Δ10AA cells.

Author response:

Reviewer #1 (Public Review):

The authors report a high-quality genome assembly for a member of Xenacoelomorpha, a taxon that is at the center of the last remaining great controversies in animal evolution. The taxon and the species in question have "jumped around" the animal tree of life over the past 25 years, and seemed to have found their place as a sister-group to all remaining bilaterians. This hypothesis posits that the earliest split within Bilateria includes Xenacoelomorpha on the one hand and a clade known as Nephrozoa (Protostomia + Deuterostomia) on the other, and is thus referred to as the Nephrozoa hypothesis. Nephrozoa is supported by phylogenomic evidence, by a number of synapomorphic morphological characters in the Nephrozoa (namely, the presence of nephridia) and lack of some key bilaterian characters in Xenacoelomorpha, and by the presence of unique miRNAs in Nephrozoa.

The Nephrozoa hypothesis has been challenged several times by the authors' groups who alternatively suggest placing Xenacoelomorpha within Deuterostomia as a sister group to a clade known as Ambulacraria. This hypothesis (the Xenambulacraria hypothesis) is supported by alternative phylogenomic datasets and by the shared presence of a number of unique molecular signatures. In this contribution, the authors aim to strengthen their case by providing full genome data for Xenoturbella bocki.

The actual sequencing and analysis are technically and methodologically excellent. Some of the analyses were done several years ago using approaches that may now seem obsolete, but there is no reason not to include them. As a detailed report of a newly sequenced genome, the manuscript meets the highest standards.

The authors emphasize a number of key findings. One is the fact that the genome is not as simple as one might expect from a "basal" taxon, and is on par with other bilaterian genomes and even more complex than the genome of secondarily simplified bilaterians. There is an implicit expectation here that the sister group to all Bilateria would represent the primitive state. This is of course not true, and the authors are aware of this, but it sometimes feels as though they are using this implicit assumption as a straw dog argument to say that since the genome is not as simple as expected, X. bocki must be nested within Bilateria. The authors get around this by acknowledging that their finding is consistent with a "weak version of the Nephrozoa hypothesis", which is essentially the Nephrozoa phylogenetic hypothesis without implicit assumptions of simplicity.

We were NOT suggesting that Xenacoels are ‘basal’ though others have certainly done so. We were testing, instead, whether their supposed simplicity is reflected in the compostion of the genome.

Another finding is a refutation of the miRNA data supporting Nephrozoa. This is an important finding although it is somewhat flogging a dead horse, since there is already a fair amount of skepticism about the validity of the miRNA data (now over 20 years old) for higher-level phylogenetics.

The missing bilaterian microRNAs was one of the early pieces of evidence excluding the Xenacoelomorpha from Nephrozoa. Our new data are an important refutation of this source of evidence and add to the picture that this phylum is not lacking characters of Bilateria as had been suggested (missing micro RNAs Hox genes explicitly interpreted in this way).

The finding that the authors feel is most important is gene presence-absence data that recovers a topology in which X. bocki is sister to Abulacraria. The problem is that the same tree does not support the monophyly of Xenacoelomorpha. This may be an artifact of fast evolving acoel genomes, as the authors suggest, but it still raises questions about the robustness of the data.

In sum, the authors' results and analyses leave an open window for the Xenambulacraria hypothesis, but do not refute the Nephrozoa hypothesis. The manuscript is a valuable contribution to the debate but does not go a significant way towards its resolution.

The manuscript has gone through several rounds of review and revision on a preprint server and is thus fairly clear of typos, inconsistencies and lack of clarity. The authors are honest and open in their interpretation of the results and their strengths.

We thank the reviewer for their assessment of our manuscript. We have responded to some of the points they make above. As there were no specific points to edit or change raised by reviewer 1, we are replying in detail only to reviewer 2. We like to note that we have modified the text and thus focus of our manuscript in accordance to with what we think reviewer 1 is suggesting in the last two paragraphs of their review.

Reviewer #2 (Public Review):

The manuscript describes the genome assembly and analysis of Xenoturbella bocki, a worm that bears many morphological features ascribed to basal bilateria. The authors aim to analyse this genome in an attempt to determine the phylogenetic position of X. bocki as a representative of Xenacoelomorpha and its associated acoelomorphs. In doing so, they want to inform the debate as to whether xenacoelomorph belong among, or is in fact paraphyletic to all bilaterians.

This paper presents a high-quality assembly of the X. bocki genome. By virtue of the phylogenetic position of this species, this genome has considerable scientific interest. This assembly appears to be highly complete and is a strength of the paper. The further characterisation of the genome is well executed and presented. Solid results from this paper include a comprehensive description of the Hox genes, miRNA and neruopeptide repertoire, as well as a description of the linkage group and how they relate to the ancestral linkage groups.

Where this paper is weaker is that for the central claims and questions of this paper, i.e,. the question of the phylogenetic position of xenacoelomorph and whether X. bocki is a slowly evolving, but otherwise representative member of this clade, remains insufficiently resolved.

The authors have achieved the goal of describing the X. bocki genome very well. By contrast, it is unclear, based on the presented evidence, whether xenacoelomorph is truly a monophyletic group. The balance of the evidence seems to suggest that the X. bocki genome belongs within the bilateria group. However, it is unclear as to what is driving the position of the other acoels. Assuming that X. bocki and the other two species in that group are monophyletic, then the evidence will favour the authors' conclusion (but without clearly rejecting the alternatives).

This paper will likely further animate the debate regarding this basal species, and also questions related to the ancestral characters of bilateria as a whole. In particular the results from the HOX and paraHOX clusters, may provide an interesting counterpoint to the previous results based on the acoels.

We thank the Reviewer for their extended comments on our manuscript. We would firstly like to point out that our work was not aiming to resolve the phylogenetic position of X. bocki. We discussed this question at length, as it was and is a major and important question in evolutionary biology, however we think that we had phrased any conclusions in this regard very cautiously as we are well aware of limitations in our data to resolve the conundrum.

In this revision we have further modified our text, specifically in the Introduction and Abstract, to make it clear that we are contributing to the understanding of the evolution and biology of a fascinating organism that cannot easily be cultured in the laboratory.

In addition, we have supplied more explanation on why Xenacoelomorpha are generally seen as a monophyletic group and which lines of evidence point to this. Again, it should be noted here that colleagues who regard the Nephrozoa hypothesis as true, do not doubt the monophyly of Xenacoelomorpha.

The disorderly environment suggests a lack of organization and may impact the learning atmosphere negatively. While reading "Hamlet" provides valuable literary exposure, the lack of student input or discussion beyond teacher-directed notes may limit critical thinking and analysis.

In the letter-writing process with students, Rashida Registe expresses her motivation for the project. She sees it as an opportunity not only to enhance her writing skills but also to amplify the voices of students, which she feels may not always be fully heard even through dialogue. Rashida believes that by sharing their personal experiences through the journal, they can make a difference and touch the hearts of those willing to support them.

Author response:

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

eLife assessment

This study assesses homeostatic plasticity mechanisms driven by inhibitory GABAergic synapses in cultured cortical neurons. The authors report that up- or down-regulation of GABAergic synaptic strength, rather than excitatory glutamatergic synaptic strength, is critical for homeostatic regulation of neuronal firing rates. The reviewers noted that the findings are potentially important, but they also raised questions. In particular, the evidence supporting the findings is currently incomplete and demonstration of independent regulation of mEPSCs and mIPSCs is a necessary experiment to support the major claims of the study. 

We appreciate the detailed, thoughtful assessment of our paper by the reviewers and editors and now submit a revised version that addresses the reviewers’ comments as detailed below in response to each concern. We include a more open discussion of alternative possibilities and have added experiments demonstrating that AMPAergic scaling in our mouse cortical cultures is triggered differently than GABAergic scaling. We treated the cultured neurons exactly as described for triggering GABAergic scaling (20µM CNQX for 24 hours), however this did not trigger AMPAergic upscaling (new Figure 7), even though it did reduce spiking/bursting activity. Below we explain the result further, but ultimately this does demonstrate independent regulation of mEPSCs and mIPSCs as requested by the editor/reviewer (spike reductions induced by CNQX reduced mIPSC amplitude, but had no effect on mEPSC amplitude).

Reviewer #1 (Public Review):

While the paper is ambitious in its rhetorical scope and certainly presents intriguing findings, there are several serious concerns that need to be addressed to substantiate the interpretations of the data. For example, the CTZ data do not support the interpretations and conclusions drawn by the authors. Summarily, the authors argue that GABAergic scaling is measuring spiking (at the time scale of the homeostatic response, which they suggest is a key feature of a homeostat) yet their data in figure 5B show more convincingly that CTZ does not influence spiking levels - only one out of four time points is marginally significant (also, I suspect that the bootstrapping method mentioned in line 454-459 was conducted as a pairwise comparison of distributions. There is no mention of multiple comparisons corrections, and I have to assume that the significance at 3h would disappear with correction).

We certainly understand the criticism here (similar to reviewer 2’s third point). We now discuss these complications in a more detailed description in the manuscript (CTZ section of results and at end of the discussion). First, we are presenting our entire dataset to be as transparent as possible. Unlike most synaptic scaling studies (including our own) that apply drugs to alter activity and assess mPSC amplitude at the final time point, here we are actually showing CTZ’s effect on spiking activity within the culture over time. This is critical because it has informed us of the drug’s true effect on spiking, the variability that is associated with these perturbations, and the ability and timing of the cultured network to homeostatically recover initial levels. This was important because it revealed that the drugs do not always influence activity in the way we assume, and this provides greater context to our results. Second, we are showing all of our data, and presenting it using estimation statistics which go beyond the dichotomy of a simple p value yes or no (Ho J, Tumkaya T, Aryal S, Choi H, Claridge-Chang A. 2019. Moving beyond P values: data analysis with estimation graphics. Nat Methods 16: 565-66). Estimation statistics have become a more standard statistical approach in the last 15 years and is the preferred method for the Society for Neuroscience’s eNeuro Journal. This method shows the effect size and the confidence interval of the distribution. For the 3 hr time point in Fig. 5B the CTZ/ethanol vs. ethanol data points exhibit very little overlap and the effect size demonstrates a near doubling of spike frequency, and the confidence interval shows a clear separation from 0. This was a pairwise comparison as we compared values at each time point after the addition of ethanol or ethanol/CTZ. Third, the plots illustrate an upward trend in spike frequency at 1 and 6 hrs, but that there is also clear variability. It is important to note that these are multiunit recordings and not purely excitatory principal neurons that we target for mPSC recordings. This complication along with the variability inherent in these cultures could make simple comparisons difficult to interpret and we now discuss this (end of discussion). Regardless, we do see some increase in spiking with CTZ and we clearly see increases in mIPSC amplitude, thus providing some support for the idea that spiking could be a critical player in terms of GABAergic scaling, particularly when put in the context of all of our findings. Future work will be necessary to determine how alterations in spiking lead to changes in mIPSC amplitude and we now discuss this (2nd to last paragraph in discussion).

Then, the fact that TTX applied on top of CTZ drives an increase in mIPSC amplitude is interpreted as a conclusive demonstration that GABAergic scaling is sensing spiking. It is inevitable, however, that TTX will also severely reduce AMAP-R activation - a very plausible alternative explanation is that the augmentation of AMPAR activation caused by CTZ is not sufficient to overcome the dramatic impact of TTX. All together, these data do not provide substantial evidence for the conclusion drawn by the authors. 

We believe that the most parsimonious explanation for our results is that spiking activity, not AMPAR activation, triggers GABAergic downscaling. GABAergic scaling is no different when comparing 24hr TTX treatment vs TTX+CTZ, and optogenetic restoration of spiking activity while continuing to block AMPAR activation was able to restore GABAergic mPSC amplitudes to control levels. It is important to emphasize that our results with TTX vs. TTX+CTZ are different for GABAergic scaling (no difference in this study) and AMPAergic scaling (CTZ diminished upward scaling in previous study – Fong et al., 2015 - PMID: 25751516) suggesting different triggers for the two forms of scaling. While we strongly believe we have demonstrated that GABAergic downscaling is dependent on spiking (not AMPAergic transmission), we now acknowledge that we cannot rule out the possibility that upward GABAergic scaling may be influenced by AMPAR activation (2nd paragraph discussion), although we have no evidence in support of this.

Specific points:

- The logic of the basis for the argument is somewhat flawed: A homeostat does not require a multiplicative mechanism, nor does it even need to be synaptic. Membrane excitability is a locus of homeostatic regulation of firing, for example. In addition, synapse-specific modulation can also be homeostatic. The only requirement of the homeostat is that its deployment subserves the stabilization of a biological parameter (e.g., firing rate). 

We largely agree with the reviewer and should not have implied that this was a necessary requirement for a spike rate homeostat. What we should have said was that historically this definition has been applied to AMPAergic scaling, which is thought to be a spike rate homeostat. We have now corrected this (introduction and discussion).

- Line 63 parenthetically references an important, but contradictory study as a brief "however". Given the tone of the writing, it would be more balanced to give this study at least a full sentence of exposition. 

Agreed, and we have now done this.

- The authors state (line 11) that expression of a hyperpolarizing conductance did not trigger scaling. More recent work ('Homeostatic synaptic scaling establishes the specificity of an associative memory') does this via expression of DREADDs and finds robust scaling.

The purpose of citing this study was to argue that the spike rate homeostat hypothesis doesn’t make sense for AMPAergic scaling based on a study that hyperpolarized an individual cell while leaving the rest of the network unaltered and therefore leaving network activity and neurotransmission largely normal. In this previous study scaling was not triggered, suggesting reduced spike rate within an individual cell was insufficient to trigger scaling in that cell. The more recent study mentioned by the reviewer achieved scaling by hyperpolarizing a majority of cells in the network. Importantly, this approach alters neurotransmission throughout the network, making it challenging to isolate the specific contributions of spiking vs. receptor activation. Unlike the previous study, which focused on the impact within individual cells, this newer study involves global alterations in network activity, complicating the interpretation of the role of spiking versus receptor activation in triggering scaling.

- Supplemental figure 1 looks largely linear to me? Out of curiosity, wouldn't you expect the left end to be aberrant because scaling up should theoretically increase the strength of some synapses that would have been previously below threshold for detection?

We agree that the scaling ratio plot is largely linear. To be clear, the linearity of the ratio plot was not our point here, rather that there was a positive slope meaning ratios (CNQX mEPSC amplitudes/control mEPSC amplitudes) got bigger for the larger CNQX-treated mEPSCs. Alternatively, a multiplicative relationship where mEPSCs are all increased by a single factor (e.g. 2X) would be a flat line with 0 slope at the multiplicative value (e.g. 2). In terms of the left side of the plot, we do see values that rise abruptly from 1 - this was partially obstructed by the Y axis in this figure and we have adjusted this. This left part of the plot is likely due the CNQX-induced increases in mEPSC amplitudes of mini’s that where below our detection threshold of 5pA, as suggested by the reviewer. Therefore, mini’s that were 4pAs could now be 5pAs after CNQX treatment and these are then divided by the smallest control mEPSCs which are 5 pAs (ratio of 1). We tried to do a better job describing this in the resubmission (1st paragraph of results).

- Given that figure 2B also shows warping at the tail ends of similar distributions, how is this to be interpreted? 

The left side of the ratio plot shows evidence consistent with the idea that mIPSCs are dropping into the noise after CNQX treatment (smallest GABA mIPSCs that don’t fall into noise are 5pA and this is divided by the smallest control GABA mPSCs of 5pPA and therefore the ratio is 1). The rest of the distribution will then approach the scaling factor (50% in this case). On the right side of the ratio plot the values appear to slightly increase. We are not sure why this is happening, but it maybe that a small percentage of mIPSCs are not purely multiplicative at 0.5, however the biggest mPSCs can vary to a great degree from one cell to the next and in other cases we do not see this (Figure 4B, Figure 5E). We tried to do a better job describing this in the resubmission (results describing Figure 2).

- The readability of the figures is poor. Some of them have inconsistent boundary boxes, bizarre axes, text that appears skewed as if the figures were quickly thrown together and stretched to fit. 

We have adjusted the figures to be more consistent throughout the manuscript.

- I'm concerned about the optogenetic restoration of activity experiment. Cortical pyramidal neuron mean firing rates are log normally distributed and span multiple orders of magnitude. The stimulation experiments can only address the total firing at a network-level - given than a network level "mean" is meaningless in a lognormal distribution, how are we to think about the effect of this manipulation when it comes to individual neurons homeostatically stabilizing their own activities? In essence, the argument is made at the single-neuron level, but the experiment is conducted with a network-level resolution. 

As described above, we do not have the capacity to know what the actual firing rate of a particular neuron was before and after perturbing the system, and certainly not for the specific cells we recorded from to obtain mPSC amplitudes, and so we cannot say that we have perfectly restored the original firing rates of neurons. However, there is reason to believe that this is achieved to some extent. Our optogenetic stimulation is only 50-100 ms long activating a subset of neurons. This is sufficient to provide a synaptic barrage that then triggers a full blown network burst where the majority of spikes occur, but this is after the light is off. In other words, the optogenetic light pulse only initiates what becomes a relatively normal network burst that fortunately allows the individual cells to express their relatively normal (pre-drug) activity pattern. In our previous study using optogenetic activity restoration (Fong et al., 2015) we were able to show that this was the case for individual units - the spiking of an individual unit during a burst is similar before and after CNQX/optogenetic stimulation (see Figure 4b and Suppl. Fig 4 in Fong et al. 2015). We are not claiming that we have restored spiking to exactly the pre-drug state, but bring it back toward those levels and we see this is associated with a return of the mIPSC amplitude to near control levels. We now include a brief description of this in the manuscript (results describing Figure 3).

- Line 198-99: multiplicativity is not a requirement of a homeostatic mechanism.

- Line 264-265 - again, neither multiplicativity and synaptic mechanisms are fundamentally any more necessary for a homeostatic locus than anything else that can modulate firing rate in via negative feedback. 

As mentioned above, the multiplicative nature of scaling has been a historical proposal for AMPAergic scaling and we have now found such a relationship for GABAergic scaling. This is important for understanding how this plasticity works, but we agree that it is not necessary for a homeostat and we have adjusted the manuscript accordingly.

- 277: do you mean AMPAR? 

We were not clear enough here. We actually do mean GABAR. The idea was that CTZ increases network activity and thus increases both AMPAergic and GABAergic transmission. We have rewritten this part of the discussion to avoid any confusion (2nd paragraph discussion).

- Example: Figure 1A is frustratingly unreadable. The axes on the raster insets are microscopic, the arrows are strangely large, and it seems unnecessary to fill so much realestate with 4 rasters. Only one is necessary to show the concept of a network burst. The effect of time+CNQX on the frequency of burst is shown in B and C.

- Example: Figure 2 appears warped and hastily assembled. Statistical indications are shown within and outside of bounding boxes. Axes are not aligned. Labels are not aligned. Font sizes are not equal on equivalent axes. 

These figures were generated by the estimation statistics website and text may have been resized inappropriately. We have tried to adjust this and now have attempted to standardize the axes text to the best of our ability.

- The discussion should include mention of the limitations and/or constraints of drawing general conclusions from cell culture. 

We have added this consideration at the end of the discussion. Further, this is why we cited studies that argue GABAergic neurons have a particularly important role in homeostatic regulation of firing following sensory deprivations in vivo.

- The discussion should include mention of the role of developmental age in the expression of specific mechanisms. It is highly likely that what is studied at ~P14 is specific to early postnatal development. 

We now discuss caveats of cortical cultures at the end of the discussion.

It is essential to ensure that the data presented in the paper adequately supports the conclusions drawn. A more cautious approach in interpreting the results may lead to a stronger argument and a more robust understanding of the underlying mechanisms at play. 

We have broadened our discussion of alternative interpretations throughout the manuscript.

Reviewer #1 (Recommendations For The Authors):

While I am hesitant to judge a paper based on its tone, I would personally recommend revision of some of the subjective words and statements, as the manuscript undermines its own effectiveness by making unnecessarily strong statements. The text repeatedly paints an "either A or B" picture, and if there's any general lesson in biology, it's that it's always A and B. Global, multiplicative glutamatergic scaling could quite conceivably occur alongside GABAergic scaling, as well as synapse-specific homeostatic modifications. It seems that it would be wise to acknowledge that, while the data presented here point in one direction, in vivo results in an adult brain (for example) might present an entirely different set of patterns. This will not only enhance the readability of the paper but also ensure that the scientific community can engage with the work in a constructive and collaborative manner. Again, I present this as only a constructive and supportive suggestion. I am a big fan of work from this laboratory, and I would love to see this paper in an improved form - it's an important set of ideas and I do believe that these data are rigorously collected. 

We have attempted to provide a more comprehensive interpretation of our results. We agree that a homeostat can come in many flavors, but do believe that GABAergic scaling is strong candidate, whereas AMPAergic scaling does not currently fit such a role. We do now discuss caveats with our work and are open to other interpretations that need to be flushed out in future work.

Reviewer #2 (Public Review):

Major points:

(1) The reason why CNQX does not completely eliminate spiking is unclear (Fig. 1). What is the circuit mechanism by which spiking continues, although at lower frequency, in the absence of AMPA-mediated transmission and what the mechanism by which spiking frequency grows back after 24h (still in the absence of AMPA transmission)?

Is it possible that NMDA-mediated transmission takes over and triggers a different type of network plasticity?

The bursting in AMPAR blockade is due to the remaining NMDA receptor-mediated transmission. We showed this in our previous study in Suppl. Figure 2 and 6 of Fong et al., 2015 (PMID: 25751516). Our ability to optically induce normal looking bursts of spikes was also dependent NMDAR activation (Fong et al 2015 and Figure 6 Newman et al., 2015 - PMID: 26140329). Further, in Dr Fong’s PhD dissertation it was shown that the bursting activity was abolished when AMPA and NMDA receptors were both blocked. There are likely many factors that contribute to the recovery of activity, and certainly one of them is likely to be the weakening of inhibitory GABAergic currents as we had mentioned. We have now added the point about NMDARs mediating the remaining bursts in the manuscript (results associated with Figure 1). We are not clear on what the reviewer has in mind in terms of “NMDA-mediated transmission takes over and triggers a different kind of network plasticity”, but we do discuss the possibility that spiking triggers GABAergic scaling through its effect on NMDAergic transmission, which we cannot rule out, but also have no evidence in support of this idea (3rd and 5th paragraph of discussion). We do plan on addressing this in a future work.

(2) A possible activation of NMDARs should be considered. One would think that experiments involving chronic glutamatergic blockade could have been conducted in the presence of NMDAR blockers. Why this was not the case?

Unfortunately, it was not possible to optogenetically restore normal bursting in the presence of NMDAR blockade (even when AMPAergic transmission was intact), as NMDARs appeared to be critical for the optical restoration of the normal duration and form of the burst in rat cortical cultures (see Suppl. Figure 6 Fong et al., 2015 Nat Comm and Figure 6 Newman et al., 2015). Even high concentrations of CNQX (40µM) prevented us from restoring spiking in mouse cultures in the current study, which is why we moved to 20µM CNQX for this study. The reviewer raises an excellent point about a possible NMDAR contribution to altered synaptic strength, however. It is likely that NMDAR signaling is reduced in the presence of CNQX since burst frequency was dramatically reduced along with AMPAR-mediated depolarizations. We cannot rule out the possibility that NMDAR signaling could contribute to the alterations in GABAergic mIPSCs and discuss this in the resubmission (3rd and 5th paragraph of the discussion). We had not considered this previously because prior work suggested that 24/48 hour block NMDARs (APV) did not trigger AMPAergic scaling in cortical or hippocampal cultures (see Figure 1 Turrigiano et al., 1998 Nature and Suppl. Figure 4 Sutton et al., 2006 Cell), moreover, our previous study showed that restoring NMDAergic transmission ontogenetically, at least to some extent, had no influence on AMPAergic scaling (Fong et al., 2015).

Also, experiments with global ChR2 stimulation with coincident pre and postsynaptic firing might also activate NMDARs and result in additional effects that should be taken into consideration for the global scaling mechanism.

To be clear, our optical stimulation was of short duration (duration 50-100 ms) and was turned off before the vast majority of spiking that occurred in the bursts. So the light flash was a trigger that allowed a relatively normal looking burst to occur after the light was off (see lower panel of Figure 3B optogenetic stimulation – short duration only at onset of burst – we now make this clearer in resubmission). Therefore, we were unlikely to trigger significant synchronous activation that does not normally occur in network bursts.

(3) Cultures exposed to CTZ to enhance AMPA receptors generated variable results (Fig. 5), somewhat increasing spiking activity in a non-significant manner but, at the same time, strengthening mIPSC amplitude. This result seems to suggest that spiking might be involved in GABAergic scaling, but it does not seem to prove it. Then, addition of TTX that blocked spiking reduced mIPSC amplitude. It was concluded here that the ability of CTZ to enhance GABAergic currents was primarily due to spiking, rather than the increase in AMPA-mediated currents. However, in addition to blocking action potentials, TTX would also prevent activation of AMPARs in the presence of CTZ due to the lack of glutamatergic release. Therefore, under these conditions, an effect of glutamatergic activation on GABAergic scaling cannot be ruled out.

These concerns were very similar to reviewer 1’s first comments (see above). To be clear we are going a step beyond most scaling studies by assessing MEA-wide firing rate, but this still provides an incomplete picture of the particular cells that we target for patch recordings in terms of their firing before and after a drug. Further, we see considerable variability in effect on firing rate from culture to culture, which we now discuss in the resubmission (final paragraph discussion). The fact that mIPSCs are no different after TTX treatment vs CTZ+TTX treatment suggests that AMPAergic transmission is not so influential on GABAergic downscaling. While the CTZ results are not conclusive by themselves, taken together with the optogenetic results, where restoration of spiking in AMPAR blockade reverses scaling, is most consistent with idea that GABAergic scaling is triggered by spiking rather than AMPAR activation and places GABAergic scaling as a strong candidate as spike rate homeostat. Although we do feel that we have demonstrated that downward GABAergic scaling is dependent on spiking, we cannot rule out the possibility that upward GABAergic scaling could be influenced by AMPAR activation to some extent. We now acknowledge this possibility (2nd paragraph discussion).

(4) The sample size is not mentioned in any figure. How many cells/culture dishes were used in each condition?

The individual dots represent either individual cells for mIPSC amplitude or individual cultures in MEA experiments. Number of cultures and cells are now stated in the figure legends.

(5) Cortical cultures may typically contain about 5-10% GABAergic interneurons and 90-95 % pyramidal cells. One would think that scaling mechanisms occurring in pyramidal cells and interneurons could be distinct, with different impact on the network. Although for whole-cell recordings the authors selected pyramidal looking cells, which might bias recordings towards excitatory neurons, naked eye selection of recording cells is quite difficult in primary cultures. Some of the variability in mIPSC amplitude values (Fig. 2A for example) might be attributed to the cell type? One could use cultures where interneurons are fluorescently labeled to obtain an accurate representation. The issue of the possible differential effects of scaling in pyramidal cells vs. interneurons and the consequences in the network should be discussed.

We now include this discussion in the resubmission (final paragraph discussion). Briefly, we chose large cells, which will be predominantly glutamatergic neurons as suggested by the reviewer. Ultimately, even among glutamatergic principal cells there may be variability in the response to drug application. All of these issues could contribute to variability and we have expanded our description of the variability in our results, including that based on cellular heterogeneity. 

Reviewer #2 (Recommendations For The Authors):

Minor comments –

Fig S3: Please quantify changes in frequency

We have done this (Supplemental Figure 5).

Fig 2: please choose colors with higher contrast for CNQX/TTX

We have done this.

Fig. 3C: Why doesn't CNQX+PhotoStim reach control levels of bursting at 2h?

The program was designed to follow and maintain total spike frequency and so it does a better job at this than maintaining burst frequency.

Fig. 5A: please include a comparison between control and Ethanol

We now do this in Figure 5C. Both around 26pAs.

Fig. 5C: where is the Etoh condition?

We have made this figure more clear in terms of controls (Figure 5C & D).

Reviewer #3 (Public Review):

This paper concerns whether scaling (or homeostatic synaptic plasticity; HSP) occurs similarly at GABA and Glu synapses and comes to the surprising conclusion that these are regulated separately. This is surprising because these were thought to be co-regulated during HSP and in fact, the major mechanisms thought to underlie downscaling (TTX or CNQX driven), retinoic acid and TNF, have been shown to regulate both GABARs and AMPARs directly. (As a side note, it is unclear that the manipulations used in Josesph and Turrigiano represent HSP, and so might not be relevant). Thus the main result, that GABA HSP is dissociable from Glu HSP, is novel and exciting. This suggests either different mechanisms underlie the two processes, or that under certain conditions, another mechanism is engaged that scales one type of synapse and not the other.

However, strong claims require strong evidence, and the results presented here only address GABA HSP, relying on previous work from this lab on Glu HSP (Fong, et al., 2015). But the previous experiments were done in rat cultures, while these experiments are done in mice and at somewhat different ages (DIV). Even identical culture systems can drift over time (possibly due to changes in the components of B27 or other media and supplements). Therefore it is necessary to demonstrate in the same system the dissociation. To be convincing, they need to show the mEPSCs for Fig 4, clearly showing the dissociation. Doing the same for Fig 5 would be great, but I think Fig 4 is the key.

We understand the concern of the reviewer as we do see significant variability within our cultures and they were plated in different places, by different people, in different species (rat vs mouse). Therefore, we have attempted to redo the study on AMPAergic scaling on these mouse cortical neurons. Surprisingly, we found that 20µM CNQX did not trigger AMPAergic upscaling (new Figure 7), even though it did reduce spiking activity and was able to produce GABAergic downscaling. We did not carry out the optogenetic restoration of activity, because we did not trigger upscaling. The result does however, show that the reductions in spiking/bursting that trigger GABAergic downscaling, did not trigger AMPAergic upscaling and therefore dissociate the 2 forms of scaling in these mouse cultures. We do not know why 20 µM CNQX did not trigger scaling in these cultures since it does reduce spiking and AMPAR activation. In the Fong study we used 40µM CNQX because intracellular recordings from rat cortical neurons suggested this was required to completely block AMPAergic currents. Our initial studies in the current manuscript examining GABAergic scaling in mouse cortical cultures used 40µM CNQX, however, this concentration of CNQX prevented us from restoring spiking through optogenetic activation, so we reduced our concentration to 20µM CNQX, which did trigger GABAergic downscaling and allowed the restoration of spiking. We now show and discuss this result (Figure 7 and 3rd paragraph discussion).

The paper also suggests that only receptor function or spiking could control HSP, and therefore if it is not receptor function then it must be spiking. This seems like a false dichotomy; there are of course other options. Details in the data may suggest that spiking is not the (or the only) homeostat, as TTX and CNQX causes identical changes in mIPSC amplitude but have different effects on spiking. Further, in Fig 5, CTZ had a minimal effect on spiking but a large effect on mIPSCs. Similar issues appear in Fig 6, where the induction of increased spiking is highly variable, with many cells showing control levels or lower spiking rates. Yet the synaptic changes are robust, across all cells. Overall, this is not persuasive that spiking is necessarily the homeostat for GABA synapses.

Together our results argue against AMPAR or GABAR activation as a trigger for GABAergic scaling and that this is different than our results for AMPAergic scaling. These points alone are important to recognize. While changes in spiking do not perfectly follow the changes in GABAergic scaling they do always trend in the right direction. As mentioned above, total spiking activity is only one measure of spiking. It is possible that these drugs alter the pattern of spiking that translates into an altered calcium transients which may be important for triggering the plasticity. Further, we acknowledge that we cannot rule out a role for NMDARs contributing to GABAergic scaling (3rd and 5th paragraph of discussion). Based on the variability that we observe and the nature of our MEA recordings we cannot precisely determine how the total activity or pattern of activity changes with drug application in the specific cells that we target for whole cell recordings, and this is now discussed (final paragraph of discussion). Again, it is important to note that we are going a step beyond most homeostatic plasticity studies that add a drug and simply assume it is having an effect on spiking (e.g. CNQX was initially thought to completely abolish spiking, but clearly does not). However, we believe that the most parsimonious explanation of our results supports our proposal that GABAergic scaling is a strong candidate as a spike rate homeostat. Regardless, in the resubmission we have included a broader discussion about these possibilities, and recognize that we cannot rule out the possibility that AMPAergic transmission could contribute to upward GABAergic scaling (2nd paragraph discussion).

The paper also suggests that the timing of the GABA changes coincides with the spiking changes, but while they have the time course of the spiking changes and recovery, they only have the 24h time point for synaptic changes. It is impossible to conclude how the time courses align without more data.

We can only say that by the 24 hour CNQX time point, when overall spiking is recovered in some but not all cultures and bursts have not recovered, that GABAergic scaling has already occurred. We now state this more clearly in the resubmission (near the end of the 2nd paragraph of the discussion).

Reviewer #3 (Recommendations For The Authors):

The statistics are inadequately described. The full information including actual p values should be given, particularly for the non-significant trends reported.

We have done this in Figure legends.

The abstract and introduction give the impression that GABA and Glu HSP are independent, though most work links them as occurring simultaneously and in a coordinated fashion to achieve homeostasis.

While it is true that many studies have triggered both forms of scaling with activity or transmission blockade, these studies have not addressed whether these forms of scaling are actually triggered in the same way mechanistically, except potentially for the one study that we mentioned (Joseph et al.,). Our results suggest they are independent. We now do mention the idea that these two forms of scaling have been assumed to be commonly triggered (3rd paragraph introduction).

The data in Fig 6 is presented as if BIC treatment is a novel result, although BIC/Gabazine/PTX have been used to induce down-scaling in many previous papers. While it's good to have the results, they should be put in proper context. As suggested in the paper, testing if decreased GABAR function would lead to upscaling does not make sense given all the previous data. 

Figure 6 shows GABAergic upscaling in response to GABAR block (bicuculline), but we are aware of only two other studies that looked at GABAergic scaling after treating with a GABAR blocker and they found upscaling but this was in hippocampal cultures, not cortical cultures (Peng et al., 2010 - PMID: 21123568, Pribiag et al., 2014 - PMID: 24753587). We now mention this in the results section describing Figure 6. While many studies have blocked GABARs and find AMPAergic downscaling, we are addressing the triggers for GABAergic scaling in Figure 6.

Is Fig S4B mislabeled? The title says spike rate but the graph axis says burst frequency.

The reviewer is correct and we have now adjusted this.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

Protein conformational changes are often critical to protein function, but obtaining structural information about conformational ensembles is a challenge. Over a number of years, the authors of the current manuscript have developed and improved an algorithm, qFit protein, that models multiple conformations into high resolution electron density maps in an automated way. The current manuscript describes the latest improvements to the program, and analyzes the performance of qFit protein in a number of test cases, including classical statistical metrics of data fit like Rfree and the gap between Rwork and Rfree, model geometry, and global and case-by-case assessment of qFit performance at different data resolution cutoffs. The authors have also updated qFit to handle cryo-EM datasets, although the analysis of its performance is more limited due to a limited number of high-resolution test cases and less standardization of deposited/processed data.

Strengths:

The strengths of the manuscript are the careful and extensive analysis of qFit's performance over a variety of metrics and a diversity of test cases, as well as the careful discussion of the limitations of qFit. This manuscript also serves as a very useful guide for users in evaluating if and when qFit should be applied during structural refinement.

Reviewer #2 (Public Review):

Summary

The manuscript by Wankowicz et al. describes updates to qFit, an algorithm for the characterization of conformational heterogeneity of protein molecules based on X-ray diffraction of Cryo-EM data. The work provides a clear description of the algorithm used by qFit. The authors then proceed to validate the performance of qFit by comparing it to deposited X-ray entries in the PDB in the 1.2-1.5 Å resolution range as quantified by Rfree, Rwork-Rfree, detailed examination of the conformations introduced by qFit, and performance on stereochemical measures (MolProbity scores). To examine the effect of experimental resolution of X-ray diffraction data, they start from an ultra high-resolution structure (SARS-CoV2 Nsp3 macrodomain) to determine how the loss of resolution (introduced artificially) degrades the ability of qFit to correctly infer the nature and presence of alternate conformations. The authors observe a gradual loss of ability to correctly infer alternate conformations as resolution degrades past 2 Å. The authors repeat this analysis for a larger set of entries in a more automated fashion and again observe that qFit works well for structures with resolutions better than 2 Å, with a rapid loss of accuracy at lower resolution. Finally, the authors examine the performance of qFit on cryo-EM data. Despite a few prominent examples, the authors find only a handful (8) of datasets for which they can confirm a resolution better than 2.0 Å. The performance of qFit on these maps is encouraging and will be of much interest because cryo-EM maps will, presumably, continue to improve and because of the rapid increase in the availability of such data for many supramolecular biological assemblies. As the authors note, practices in cryo-EM analysis are far from uniform, hampering the development and assessment of tools like qFit.

Strengths

qFit improves the quality of refined structures at resolutions better than 2.0 A, in terms of reflecting true conformational heterogeneity and geometry. The algorithm is well designed and does not introduce spurious or unnecessary conformational heterogeneity. I was able to install and run the program without a problem within a computing cluster environment. The paper is well written and the validation thorough.

I found the section on cryo-EM particularly enlightening, both because it demonstrates the potential for discovery of conformational heterogeneity from such data by qFit, and because it clearly explains the hurdles towards this becoming common practice, including lack of uniformity in reporting resolution, and differences in map and solvent treatment.

Weaknesses

The authors begin the results section by claiming that they made "substantial improvement" relative to the previous iteration of qFit, "both algorithmically (e.g., scoring is improved by BIC, sampling of B factors is now included) and computationally (improving the efficiency and reliability of the code)" (bottom of page 3). However, the paper does not provide a comparison to previous iterations of the software or quantitation of the effects of these specific improvements, such as whether scoring is improved by the BIC, how the application of BIC has changed since the previous paper, whether sampling of B factors helps, and whether the code faster. It would help the reader to understand what, if any, the significance of each of these improvements was.

Indeed, it is difficult (embarrassingly) to benchmark against our past work due to the dependencies on different python packages and the lack of software engineering. With the infrastructure we’ve laid down with this paper, made possible by an EOSS grant from CZI, that will not be a problem going forward. Not only is the code more reliable and standardized, but we have developed several scientific test sets that can be used as a basis for broad comparisons to judge whether improvements are substantial. We’ve also changed with “substantial improvement” to “several modifications”  to indicate the lack of comparison to past versions.

The exclusion of structures containing ligands and multichain protein models in the validation of qFit was puzzling since both are very common in the PDB. This may convey the impression that qFit cannot handle such use cases. (Although it seems that qFit has an algorithm dedicated to modeling ligand heterogeneity and seems to be able to handle multiple chains). The paper would be more effective if it explained how a user of the software would handle scenarios with ligands and multiple chains, and why these would be excluded from analysis here.

qFit can indeed handle both. We left out multiple chains for simplicity in constructing a dataset enriched for small proteins while still covering diversity to speed the ability to rapidly iterate and test our approaches. Improvements to qFit ligand handling will be discussed in a forthcoming work as we face similar technical debt to what we saw in proteins and are undergoing a process of introducing “several modifications” that we hope will lead to “substantial improvement” - but at the very least will accelerate further development.

It would be helpful to add some guidance on how/whether qFit models can be further refined afterwards in Coot, Phenix, ..., or whether these models are strictly intended as the terminal step in refinement.

We added to the abstract:

“Importantly, unlike ensemble models, the multiconformer models produced by qFit can be manually modified in most major model building software (e.g. Coot)  and fit can be further improved by refinement using standard pipelines (e.g. Phenix, Refmac, Buster).”

and introduction:

“Multiconformer models are notably easier to modify and more interpretable in software like Coot12 unlike ensemble methods that generate multiple complete protein copies(Burnley et al. 2012; Ploscariu et al. 2021; Temple Burling and Brünger 1994).”

and results:

“This model can then be examined and edited in Coot12 or other visualization software, and further refined using software such as phenix.refine, refmac, or buster as the modeler sees fit.”

and discussion

“qFit is compatible with manual modification and further refinement as long as the subsequent software uses the PDB standard altloc column, as is common in most popular modeling and refinement programs. The models can therefore generally also be deposited in the PDB using the standard deposition and validation process.”

Appraisal & Discussion

Overall, the authors convincingly demonstrate that qFit provides a reliable means to detect and model conformational heterogeneity within high-resolution X-ray diffraction datasets and (based on a smaller sample) in cryo-EM density maps. This represents the state of the art in the field and will be of interest to any structural biologist or biochemist seeking to attain an understanding of the structural basis of the function of their system of interest, including potential allosteric mechanisms-an area where there are still few good solutions. That is, I expect qFit to find widespread use.

Reviewer #3 (Public Review):

Summary:

The authors address a very important issue of going beyond a single-copy model obtained by the two principal experimental methods of structural biology, macromolecular crystallography and cryo electron microscopy (cryo-EM). Such multiconformer model is based on the fact that experimental data from both these methods represent a space- and time-average of a huge number of the molecules in a sample, or even in several samples, and that the respective distributions can be multimodal. Different from structure prediction methods, this approach is strongly based on high-resolution experimental information and requires validated single-copy high-quality models as input. Overall, the results support the authors' conclusions.

In fact, the method addresses two problems which could be considered separately:

- An automation of construction of multiple conformations when they can be identified visually;

- A determination of multiple conformations when their visual identification is difficult or impossible.

We often think about this problem similarly to the reviewer. However, in building qFit, we do not want to separate these problems - but rather use the first category (obvious visual identification) to build an approach that can accomplish part of the second category (difficult to visualize) without building “impossible”/nonexistent conformations - with a consistent approach/bias.

The first one is a known problem, when missing alternative conformations may cost a few percent in R-factors. While these conformations are relatively easy to detect and build manually, the current procedure may save significant time being quite efficient, as the test results show.

We agree with the reviewers' assessment here. The “floor” in terms of impact is automating a tedious part of high resolution model building and improving model quality.

The second problem is important from the physical point of view and has been addressed first by Burling & Brunger (1994; https://doi.org/10.1002/ijch.199400022). The new procedure deals with a second-order variation in the R-factors, of about 1% or less, like placing riding hydrogen atoms, modeling density deformation or variation of the bulk solvent. In such situations, it is hard to justify model improvement. Keeping Rfree values or their marginal decreasing can be considered as a sign that the model is not overfitted data but hardly as a strong argument in favor of the model.

We agree with the overall sentiment of this comment. What is a significant variation in R-free is an important question that we have looked at previously (http://dx.doi.org/10.1101/448795) and others have suggested an R-sleep for further cross validation (https://pubmed.ncbi.nlm.nih.gov/17704561/). For these reasons it is important to get at the significance of the changes to model types from large and diverse test sets, as we have here and in other works, and from careful examination of the biological significance of alternative conformations with experiments designed to test their importance in mechanism.

In general, overall targets are less appropriate for this kind of problem and local characteristics may be better indicators. Improvement of the model geometry is a good choice. Indeed, yet Cruickshank (1956; https://doi.org/10.1107/S0365110X56002059) showed that averaged density images may lead to a shortening of covalent bonds when interpreting such maps by a single model. However, a total absence of geometric outliers is not necessarily required for the structures solved at a high resolution where diffraction data should have more freedom to place the atoms where the experiments "see" them.

Again, we agree—geometric outliers should not be completely absent, but it is comforting when they and model/experiment agreement both improve.

The key local characteristic for multi conformer models is a closeness of the model map to the experimental one. Actually, the procedure uses a kind of such measure, the Bayesian information criteria (BIC). Unfortunately, there is no information about how sharply it identifies the best model, how much it changes between the initial and final models; in overall there is not any feeling about its values. The Q-score (page 17) can be a tool for the first problem where the multiple conformations are clearly separated and not for the second problem where the contributions from neighboring conformations are merged. In addition to BIC or to even more conventional target functions such as LS or local map correlation, the extreme and mean values of the local difference maps may help to validate the models.

We agree with the reviewer that the problem of “best” model determination is poorly posed here. We have been thinking a lot about htis in the context of Bayesian methods (see: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9278553/); however, a major stumbling block is in how variable representations of alternative conformations (and compositions) are handled. The answers are more (but by no means simply) straightforward for ensemble representations where the entire system is constantly represented but with multiple copies.

This method with its results is a strong argument for a need in experimental data and information they contain, differently from a pure structure prediction. At the same time, absence of strong density-based proofs may limit its impact.

We agree - indeed we think it will be difficult to further improve structure prediction methods without much more interaction with the experimental data.

Strengths:

Addressing an important problem and automatization of model construction for alternative conformations using high-resolution experimental data.

Weaknesses:

An insufficient validation of the models when no discrete alternative conformations are visible and essentially missing local real-space validation indicators.

While not perfect real space indicators, local real-space validation is implicit in the MIQP selection step and explicit when we do employ Q-score metrics.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

A point of clarification: I don't understand why waters seem to be handled differently in for cryo-EM and crystallography datasets. I am interested about the statement on page 19 that the Molprobity Clashscore gets worse for cryo-EM datasets, primarily due to clashes with waters. But the qFit algorithm includes a round of refinement to optimize placement of ordered waters, and the clashscore improves for the qFit refinement in crystallography test cases. Why/how is this different for cryo-EM?

We agree that this was not an appropriate point. We believe that the high clash score is coming from side chains being incorrectly modeled. We have updated this in the manuscript and it will be a focus of future improvements.

Reviewer #2 (Recommendations For The Authors):

- It would be instructive to the reader to explain how qFit handles the chromophore in the PYP (1OTA) example. To this end, it would be helpful to include deposition of the multiconformer model of PYP. This might also be a suitable occasion for discussion of potential hurdles in the deposition of multiconformer models in the PDB (if any!). Such concerns may be real concerns causing hesitation among potential users.

Thank you for this comment. qFit does not alter the position or connectivity of any HETATM records (like the chromophore in this structure). Handling covalent modifications like this is an area of future development.

Regarding deposition, we have noted above that the discussion now includes:

“qFit is compatible with manual modification and further refinement as long as the subsequent software uses the PDB standard altloc column, as is common in most popular modeling and refinement programs. The models can therefore, generally also be deposited in the PDB using the standard deposition and validation process.”

Finally, we have placed all PDBs in a Zenodo deposition (XXX) and have included that language in the manuscript. It is currently under a separate data availability section (page XXX). We will defer to the editor as to the best header that should go under.

- It may be advisable to take the description of true/false pos/negatives out of the caption of Figure 4, and include it in a box or so, since these terms are important in the main text too, and the caption becomes very cluttered.

We think adding the description of true/false pos/negatives to the Figure panel would make it very cluttered and wordy. We would like to retain this description within the caption. We have also briefly described each in the main text.

- page 21, line 4: some issue with citation formatting.

We have updated these citations.

- page 25, second paragraph: cardinality is the number of members of a set. Perhaps "minimal occupancy" is more appropriate.

Thank you for pointing this out. This was a mistake and should have been called the occupancy threshold.

- page 26: it's - its

Thank you, we have made this change. 

- Font sizes in Supplementary Figures 5-7 are too small to be readable.

We agree and will make this change. 

Reviewer #3 (Recommendations For The Authors):

General remarks

(1) As I understand, the procedure starts from shifting residues one by one (page 4; A.1). Then, geometry reconstruction (e.g., B1) may be difficult in some cases joining back the shifted residues. It seems that such backbone perturbation can be done more efficiently by shifting groups of residues ("potential coupled motions") as mentioned at the bottom of page 9. Did I miss its description?

We would describe the algorithm as sampling (which includes minimal shifts) in the backbone residues to ensure we can link neighboring residues. We agree that future iterations of qFit should include more effective backbone sampling by exploring motion along the Cβ-Cα, C-N, and (Cβ-Cα × C-N) bonds and exploring correlated backbone movements.

(2) While the paper is well split in clear parts, some of them seem to be not at their right/optimal place and better can be moved to "Methods" (detailed "Overview of the qFit protein algorithm" as a whole) or to "Data" missed now (Two first paragraphs of "qFit improves overall fit...", page 8, and "Generating the qFit test set", page 22, and "Generating synthetic data ..." at page 26; description of the test data set), At my personal taste, description of tests with simulated data (page 15) would be better before that of tests with real data.

Thank you for this comment, but we stand by our original decision to keep the general flow of the paper as it was submitted.

(3) I wonder if the term "quadratic programming" (e.g., A3, page 5) is appropriate. It supposes optimization of a quadratic function of the independent parameters and not of "some" parameters. This is like the crystallographic LS which is not a quadratic function of atomic coordinates, and I think this is a similar case here. Whatever the answer on this remark is, an example of the function and its parameters is certainly missed.

We think that the term quadratic programming is appropriate. We fit a function with a loss function (observed density - calculated density), while satisfying the independent parameters. We fit the coefficients minimizing a quadratic loss. We agree that the quadratic function is missing from the paper, and we have now included it in the Methods section.

Technical remarks to be answered by the authors :

(1) Page 1, Abstract, line 3. The ensemble modeling is not the only existing frontier, and saying "one of the frontiers" may be better. Also, this phrase gives a confusing impression that the authors aim to predict the ensemble models while they do it with experimental data.

We agree with this statement and have re-worded the abstract to reflect this.

(2) Page 2. Burling & Brunger (1994) should be cited as predecessors. On the contrary, an excellent paper by Pearce & Gros (2021) is not relevant here.

While we agree that we should mention the Burling & Brunger paper and the Pearce & Gros (2021) should not be removed as it is not discussing the method of ensemble refinement.

(3) Page 2, bottom. "Further, when compared to ..." The preference to such approach sounds too much affirmative.

We have amended this sentence to state:

“Multiconformer models are notably easier to modify and more interpretable in software like Coot(Emsley et al. 2010) unlike ensemble methods that generate multiple complete protein copies(Burnley et al. 2012; Ploscariu et al. 2021; Temple Burling and Brünger 1994).”

“The point we were trying to make in this sentence was that ensemble-based models are much harder to manually manipulate in Coot or other similar software compared to multiconformer models. We think that the new version of this sentence states this point more clearly.”

(4) Page 2, last paragraph. I do not see an obvious relation of references 15-17 to the phrase they are associated with.

We disagree with this statement, and think that these references are appropriate.

“Multiconformer models are notably easier to modify and more interpretable in software like Coot12 unlike ensemble methods that generate multiple complete protein copies(Burnley et al. 2012; Ploscariu et al. 2021; Temple Burling and Brünger 1994).”

(5) Page 3, paragraph 2. Cryo-EM maps should be also "high-resolution"; it does not read like this from the phrase.

We agree that high-resolution should be added, and the sentence now states:

“However, many factors make manually creating multiconformer models difficult and time-consuming. Interpreting weak density is complicated by noise arising from many sources, including crystal imperfections, radiation damage, and poor modeling in X-ray crystallography, and errors in particle alignment and classification, poor modeling of beam induced motion, and imperfect detector Detector Quantum Efficiency (DQE) in high-resolution cryo-EM.”

(6) Page 3, last paragraph before "results". The words "... in both individual cases and large structural bioinformatic projects" do not have much meaning, except introducing a self-reference. Also, repeating "better than 2 A" looks not necessary.

We agree that this was unnecessary and have simplified the last sentence to state:

“With the improvements in model quality outlined here, qFit can now be increasingly used for finalizing high-resolution models to derive ensemble-function insights.”

(7) Page 3. "Results". Could "experimental" be replaced by a synonym, like "trial", to avoid confusing with the meaning "using experimental data"?

We have replaced experimental with exploratory to describe the use of qFit on CryoEM data. The statement now reads:

“For cryo-EM modeling applications, equivalent metrics of map and model quality are still developing, rendering the use of qFit for cryo-EM more exploratory.”

(8) Page 4, A.1. Should it be "steps +/- 0.1" and "coordinate" be "coordinate axis"? One can modify coordinates and not shift them. I do not understand how, with the given steps, the authors calculated the number of combinations ("from 9 to 81"). Could a long "Alternatively, ...absent" be reduced simply to "Otherwise"?

We have simplified and clarified the sentence on the sampling of backbone coordinates to state:

“If anisotropic B-factors are absent, the translation of coordinates occurs in the X, Y, and Z directions. Each translation takes place in steps of 0.1 along each coordinate axis, extending to 0.3 Å, resulting in 9 (if isotropic) or to 81 (if anisotropic) distinct backbone conformations for further analysis.”

(9) Page 6, B.1, line 2. Word "linearly" is meaningless here.

We have modified this to read:

“Moving from N- to C- terminus along the protein,”

(10) Page 9, line 2. It should be explained which data set is considered as the test set to calculate Rfree.

We think this is clear and would be repetitive if we duplicated it.

(11) Page 9, line 7. It should be "a valuable metric" and not "an"

We agree and have updated the sentence to read:

“Rfree is a valuable metric for monitoring overfitting, which is an important concern when increasing model parameters as is done in multiconformer modeling.”

(12) Page 10, paragraph 3. "... as a string (Methods)". I did not find any other mention of this term "string", including in "Methods" where it supposed to be explained. Either this should be explained (and an example is given?), or be avoided.

We agree that string is not necessary (discussing the programmatic datatype). We have removed this from the sentence. It now reads:

“To quantify how often qFit models new rotameric states, we analyzed the qFit models with phenix.rotalyze, which outputs the rotamer state for each conformer (Methods).”

(13) Page10, lines 3-4 from bottom. Are these two alternative conformations justified?

We are unsure what this is referring to.

(14) Page 12, Fig. 2A. In comparison with Supplement Fig 2C, the direction of axes is changed. Could they be similar in both Figures?

We have updated Supplementary Figure 2C to have the same direction of axes as Figure 2A.

(15) Page 15, section's title. Choose a single verb in "demonstrate indicate".

We have amended the title of this section to be:

“Simulated data demonstrate qFit is appropriate for high-resolution data.”

(16) Page 15, paragraph 2. "Structure factors from 0.8 to 3.0 A resolution" does not mean what the author wanted apparently to tell: "(complete?) data sets with the high-resolution limit which varied from 0.8 to 3.0 A ...". Also, a phrase of "random noise increasing" is not illustrated by Figs.5 as it is referred to.

We have edited this sentence to now read:

“To create the dataset for resolution dependence, we used the ground truth 7KR0 model, including all alternative conformations, and generated artificial structure factors with a high resolution limit ranging from  0.8 to 3.0 Å resolution (in increments of 0.1 Å).”

(17) Page 15, last paragraph is written in a rather formal and confusing way while a clearer description is given in the figure legend and repeated once more in Methods. I would suggest to remove this paragraph.

We agree that this is confusing. Instead of create a true positive/false positive/true negative/false negative matrix, we have just called things as they are, multiconformer or single conformer and match or no match. We have edited the language the in the manuscript and figure legends to reflect these changes.

(18) Page 16. Last two paragraphs start talking about a new story and it would help to separate them somehow from the previous ones (sub-title?).

We agree that this could use a subtitle. We have included the following subtitle above this section:

“Simulated multiconformer data illustrate the convergence of qFit.”

(19) Page 20. "or static" and "we determined that" seem to be not necessary.

We have removed static and only used single conformer models. However, as one of the main conclusions of this paper is determining that qFit can pick up on alternative conformers that were modeled manually, we have decided to the keep the “we determined that”.

(20) Page 21, first paragraph. "Data" are plural; it should be "show" and "require"

We have made these edits. The sentence now reads:

“However, our data here shows that not only does qFit need a high-resolution map to be able to detect signal from noise, it also requires a very well-modeled structure as input.”

(21) Page 21, References should be indicated as [41-45], [35,46-48], [55-57]. A similar remark to [58-63] at page 22.

We have fixed the reference layout to reflect this change.

(22) Page 21, last paragraph. "Further reduce R-factors" (moreover repeated twice) is not correct neither by "further", since here it is rather marginal, nor as a goal; the variations of R-factors are not much significant. A more general statement like "improving fit to experimental data" (keeping in mind density maps) may be safer.

We agree with the duplicative nature of these statements. We have amended the sentence to now read:

“Automated detection and refinement of partial-occupancy waters should help improve fit to experimental data further reduce Rfree15 and provide additional insights into hydrogen-bond patterns and the influence of solvent on alternative conformations.”

(23) Page 22. Sub-sections of "Methods" are given in a little bit random order; "Parallelization of large maps" in the middle of the text is an example. Put them in a better order may help.

We have moved some section of the Methods around and made better headings by using an underscore to highlight the subsections (Generating and running the qFit test set, qFit improved features, Analysis metrics, Generating synthetic data for resolution dependence).

(24) Page 24. Non-convex solution is a strange term. There exist non-convex problems and functions and not solutions.

We agree and we have changed the language to reflect that we present the algorithm with non-convex problems which it cannot solve.

(25) Page 26, "Metrics". It is worthy to describe explicitly the metrics and not (only) the references to the scripts.

For all metrics, we describe a sentence or two on what each metric describes. As these metrics are well known in the structural biology field, we do not feel that we need to elaborate on them more.

(26) Page 26. Multiplying B by occupancy does not have much sense. A better option would be to refer to the density value in the atomic center as occ*(4*pi/B)^1.5 which gives a relation between these two entities.

We agree and have update the B-factor figures and metrics to reflect this.

(27) Page 40, suppl. Fig. 5. Due to the color choice, it is difficult to distinguish the green and blue curves in the diagram.

We have amended this with the colors of the curves have been switched.

(28) Page 42, Suppl. Fig. 7. (A) How the width of shaded regions is defined? (B) What the blue regions stand for? Input Rfree range goes up to 0.26 and not to 0.25; there is a point at the right bound. (C) Bounds for the "orange" occupancy are inversed in the legend.

(A) The width of the shaded region denotes the standard deviations among the values at every resolution. We have made this clearer in the caption

(B) The blue region denotes the confidence interval for the regression estimate. Size of the confidence interval was set to 95%. We have made this clearer in the caption

(C) This has been fixed now

The maximum R-free value is 0.2543, which we rounded down to 0.25.

(29) Page 43. Letters E-H in the legend are erroneously substituted by B-E.

We apologize for this mistake. It is now corrected.

Author response:

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

eLife assessment

The study makes a valuable empirical contribution to our understanding of visual processing in primates and deep neural networks, with a specific focus on the concept of factorization. The analyses provide solid evidence that high factorization scores are correlated with neural predictivity, yet more evidence would be needed to show that neural responses show factorization. Consequently, while several aspects require further clarification, in its current form this work is interesting to systems neuroscientists studying vision and could inspire further research that ultimately may lead to better models of or a better understanding of the brain.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The paper investigates visual processing in primates and deep neural networks (DNNs), focusing on factorization in the encoding of scene parameters. It challenges the conventional view that object classification is the primary function of the ventral visual stream, suggesting instead that the visual system employs a nuanced strategy involving both factorization and invariance. The study also presents empirical findings suggesting a correlation between high factorization scores and good neural predictivity.

Strengths:

(1) Novel Perspective: The paper introduces a fresh viewpoint on visual processing by emphasizing the factorization of non-class information.

(2) Methodology: The use of diverse datasets from primates and humans, alongside various computational models, strengthens the validity of the findings.

(3) Detailed Analysis: The paper suggests metrics for factorization and invariance, contributing to a future understanding & measurements of these concepts.

Weaknesses:

(1) Vagueness (Perceptual or Neural Invariance?): The paper uses the term 'invariance', typically referring to perceptual stability despite stimulus variability [1], as the complete discarding of nuisance information in neural activity. This oversimplification overlooks the nuanced distinction between perceptual invariance (e.g., invariant object recognition) and neural invariance (e.g., no change in neural activity). It seems that by 'invariance' the authors mean 'neural' invariance (rather than 'perceptual' invariance) in this paper, which is vague. The paper could benefit from changing what is called 'invariance' in the paper to 'neural invariance' and distinguish it from 'perceptual invariance,' to avoid potential confusion for future readers. The assignment of 'compact' representation to 'invariance' in Figure 1A is misleading (although it can be addressed by the clarification on the term invariance). [1] DiCarlo JJ, Cox DD. Untangling invariant object recognition. Trends in cognitive sciences. 2007 Aug 1;11(8):333-41.

Thanks for pointing out this ambiguity. In our Introduction we now explicitly clarify that we use “invariance” to refer to neural, rather than perceptual invariance, and we point out that both factorization and (neural) invariance may be useful for obtaining behavioral/perceptual invariance.

(2) Details on Metrics: The paper's explanation of factorization as encoding variance independently or uncorrelatedly needs more justification and elaboration. The definition of 'factorization' in Figure 1B seems to be potentially misleading, as the metric for factorization in the paper seems to be defined regardless of class information (can be defined within a single class). Does the factorization metric as defined in the paper (orthogonality of different sources of variation) warrant that responses for different object classes are aligned/parallel like in 1B (middle)? More clarification around this point could make the paper much richer and more interesting.

Our factorization metric measures the degree to which two sets of scene variables are factorized from one another. In the example of Fig. 1B, we apply this definition to the case of factorization of class vs. non-class information. Elsewhere in the paper we measure factorization of several other quantities unrelated to class, specifically camera viewpoint, lighting conditions, background content, and object pose. In our revised manuscript we have clarified the exposition surrounding Fig. 1B to make it clear that factorization, as we define it, can be applied to other quantities as well and that responses do not need to be aligned/parallel but simply live in a different set of dimensions whether linearly or nonlinearly arranged. Thanks for raising the need to clarify this point.

(3) Factorization vs. Invariance: Is it fair to present invariance vs. factorization as mutually exclusive options in representational hypothesis space? Perhaps a more fair comparison would be factorization vs. object recognition, as it is possible to have different levels of neural variability (or neural invariance) underlying both factorization and object recognition tasks.

We do not mean to imply that factorization and invariance are mutually exclusive, or that they fully characterize the space of possible representations. However, they are qualitatively distinct strategies for achieving behavioral capabilities like object recognition. In the revised manuscript we also include a comparison to object classification performance (Figures 5C & S4, black x’s) as a predictor of brain-like representations, alongside the results for factorization and invariance.

In our revised Introduction and beginning of the Results section, we make it more clear that factorization and invariance are not mutually exclusive – indeed, our results show that both factorization and invariance for some scene variables like lighting and background identity are signatures of brain-like representations. Our study focuses on factorization because we believe its importance has not been studied or highlighted to the degree that invariance to “nuisance” parameters has in concert with selectivity to object identity in individual neuron tuning functions. Moreover, the loss functions used for supervised training functions of neural networks for image classification would seem to encourage invariance as a representational strategy. Thus, the finding that factorization of scene parameters is an equally good if not better predictor of brain-like representations may motivate new objective functions for neural network training.

(4) Potential Confounding Factors in Empirical Findings: The correlation observed in Figure 3 between factorization and neural predictivity might be influenced by data dimensionality, rather than factorization per se [2]. Incorporating discussions around this recent finding could strengthen the paper.

[2] Elmoznino E, Bonner MF. High-performing neural network models of the visual cortex benefit from high latent dimensionality. bioRxiv. 2022 Jul 13:2022-07.

We thank the Reviewer for pointing out this important, potential confound and the need for a direct quantification. We have now included an analysis computing how well dimensionality (measured using the participation ratio metric for natural images, as was done in [2] Elmoznino& Bonner bioRxiv. 2022) can account for model goodness-of-fit (additional pink bars in Figure 6). Factorization of scene parameters appears to add more predictive power than dimensionality on average (Figure 6, light shaded bars), and critically, factorization+classification jointly predict goodness-of-fit significantly better than dimensionality+classification for V4 and IT/HVC brain areas (Figure 6, dark shaded bars). Indeed, dimensionality+classification is only slightly more predictive than classification alone for V4 and IT/HVC indicating some redundancy in those measures with respect to neural predictivity of models (Figure 6, compare dark shaded pink bar to dashed line).

That said, high-dimensional representations can, in principle, better support factorization, and thus we do not regard these two representational strategies necessarily in competition. Rather, our results suggest (consistent with [2]) that dimensionality is predictive of brain-like representation to some degree, such that some (but not all) of factorization’s predictive power may indeed owe to a partial correlation with dimensionality. We elaborate in the Discussion where this point comes up and now refer to the updated Figure 6 that shows the control for dimensionality.

Conclusion:

The paper offers insightful empirical research with useful implications for understanding visual processing in primates and DNNs. The paper would benefit from a more nuanced discussion of perceptual and neural invariance, as well as a deeper discussion of the coexistence of factorization, recognition, and invariance in neural representation geometry. Additionally, addressing the potential confounding factors in the empirical findings on the correlation between factorization and neural predictivity would strengthen the paper's conclusions.

Taken together, we hope that the changes described above address the distinction between neural and perceptual invariance, provide a more balanced understanding of the contributions of factorization, invariance, and local representational geometry, and rule against dimensionality for natural images as contributing to the main finding of the benefits from factorization of scene parameters.

Reviewer #2 (Public Review):

Summary:

The dominant paradigm in the past decade for modeling the ventral visual stream's response to images has been to train deep neural networks on object classification tasks and regress neural responses from units of these networks. While object classification performance is correlated to the variance explained in the neural data, this approach has recently hit a plateau of variance explained, beyond which increases in classification performance do not yield improvements in neural predictivity. This suggests that classification performance may not be a sufficient objective for building better models of the ventral stream. Lindsey & Issa study the role of factorization in predicting neural responses to images, where factorization is the degree to which variables such as object pose and lighting are represented independently in orthogonal subspaces. They propose factorization as a candidate objective for breaking through the plateau suffered by models trained only on object classification.

They claim that (i) maintaining these non-class variables in a factorized manner yields better neural predictivity than ignoring non-class information entirely, and (ii) factorization may be a representational strategy used by the brain.

The first of these claims is supported by their data. The second claim does not seem well-supported, and the usefulness of their observations is not entirely clear.

Strengths:

This paper challenges the dominant approach to modeling neural responses in the ventral stream, which itself is valuable for diversifying the space of ideas.

This paper uses a wide variety of datasets, spanning multiple brain areas and species. The results are consistent across the datasets, which is a great sign of robustness.

The paper uses a large set of models from many prior works. This is impressively thorough and rigorous.

The authors are very transparent, particularly in the supplementary material, showing results on all datasets. This is excellent practice.

Weaknesses:

(1) The primary weakness of this paper is a lack of clarity about what exactly is the contribution. I see two main interpretations: (1-A) As introducing a heuristic for predicting neural responses that improve over-classification accuracy, and (1-B) as a model of the brain's representational strategy. These two interpretations are distinct goals, each of which is valuable. However, I don't think the paper in its current form supports either of them very well:

(1-A) Heuristic for neural predictivity. The claim here is that by optimizing for factorization, we could improve models' neural predictivity to break through the current predictivity plateau. To frame the paper in this way, the key contribution should be a new heuristic that correlates with neural predictivity better than classification accuracy. The paper currently does not do this. The main piece of evidence that factorization may yield a more useful heuristic than classification accuracy alone comes from Figure 5. However, in Figure 5 it seems that factorization along some factors is more useful than others, and different linear combinations of factorization and classification may be best for different data. There is no single heuristic presented and defended. If the authors want to frame this paper as a new heuristic for neural predictivity, I recommend the authors present and defend a specific heuristic that others can use, e.g. [K * factorization_of_pose + classification] for some constant K, and show that (i) this correlates with neural predictivity better than classification alone, and (ii) this can be used to build models with higher neural predictivity. For (ii), they could fine-tune a state-of-the-art model to improve this heuristic and show that doing so achieves a new state-of-the-art neural predictivity. That would be convincing evidence that their contribution is useful.

Our paper does not make any strong claim regarding the Reviewer’s point 1-A (on heuristics for neural predictivity). In the Discussion, last paragraph, we better specify that our work is merely suggestive of claim 1-A about heuristics for more neurally predictive, more brainlike models. We believe that our paper supports the Reviewer’s point 1-B (on brain representation) as we discuss below.

We leave it to future work to determine if factorization could help optimize models to be more brainlike. This treatment may require exploration of novel model architectures and loss functions, and potentially also more thorough neural datasets that systematically vary many different forms of visual information for validating any new models.

(1-B) Model of representation in the brain. The claim here is that factorization is a general principle of representation in the brain. However, neural predictivity is not a suitable metric for this, because (i) neural predictivity allows arbitrary linear decoders, hence is invariant to the orthogonality requirement of factorization, and (ii) neural predictivity does not match the network representation to the brain representation. A better metric is representational dissimilarity matrices. However, the RDM results in Figure S4 actually seem to show that factorization does not do a very good job of predicting neural similarity (though the comparison to classification accuracy is not shown), which suggests that factorization may not be a general principle of the brain. If the authors want to frame the paper in terms of discovering a general principle of the brain, I suggest they use a metric (or suite of metrics) of brain similarity that is sensitive to the desiderata of factorization, e.g. doesn't apply arbitrary linear transformations, and compare to classification accuracy in addition to invariance.

We agree with the Reviewer about the shortcomings of neural predictivity for comparing representational geometries, and in our revised manuscript we have provided a more comprehensive set of results that includes RDM predictivity in new Figures 6 & 7, alongside the results for neural fit predictivity. In addition, as suggested we added classification accuracy predictivity in Figures 5C & S4 (black x’s) for visual comparison to factorization/invariance. In Figure S4 on RDMs, it is apparent how factorization is at least as good a predictor as classification on all V4 & IT datasets from both monkeys and humans (compared x’s to filled circles in Figure S4; note that some of the points from the original Figure S4 changed as we discovered a bug in the code that specifically affected the RDM analysis for a few of the datasets).

We find that the newly included RDM analyses in Figures 6 & 7 are consistent with the conclusions of the neural fit regression analyses: that the correlation of factorization metrics with RDM matches are strong, comparable in magnitude to that of classification accuracy (Figure 6, 3rd & 4th columns, compare black dashed line to faded colored bars) and are not fully accounted for by the model’s classification accuracy alone (Figure 6, 3rd & 4th columns, higher unfaded bars for classification combined with factorization, and see corresponding example scatters in Figure 7 middle/bottom rows).

It is encouraging that the added benefit of factorization for RDM predictivity accounting for classification performance is at least as good as the improvement seen for neural fit predictivity (Figure 6, 1st & 2nd columns for encoding fits versus 3rd & 4th columns for RDM correlations).

(2) I think the comparison to invariance, which is pervasive throughout the paper, is not very informative. First, it is not surprising that invariance is more weakly correlated with neural predictivity than factorization, because invariant representations lose information compared to factorized representations. Second, there has long been extensive evidence that responses throughout the ventral stream are not invariant to the factors the authors consider, so we already knew that invariance is not a good characterization of ventral stream data.

While we appreciate the Reviewer’s intuition that highly invariant representations are not strongly supported in the high-level visual cortex, we nevertheless thought it was valuable to put this intuition to a quantitative, detailed test. As a result, we uncovered effects that were not obvious a priori, at least to us – for example, that invariance for some scene parameters (camera view, object pose) is negatively correlated with neural predictions while invariance to others (background, lighting) is positively correlated. Thus, our work exercises the details of invariance for different types of information.

(3) The formalization of the factorization metric is not particularly elegant, because it relies on computing top K principal components for the other-parameter space, where K is arbitrarily chosen as 10. While the authors do show that in their datasets the results are not very sensitive to K (Figure S5), that is not guaranteed to be the case in general. I suggest the authors try to come up with a formalization that doesn't have arbitrary constants. For example, one possibility that comes to mind is E[delta_a x delta_b], where 'x' is the normalized cross product, delta_a, and delta_b are deltas in representation space induced by perturbations of factors a and b, and the expectation is taken over all base points and deltas. This is just the first thing that comes to mind, and I'm sure the authors can come up with something better. The literature on disentangling metrics in machine learning may be useful for ideas on measuring factorization.

Thanks to the Reviewer for raising this point. First, we wish to clarify a potential misunderstanding of the factorization metric: the number K of principal components we choose is not an arbitrary constant, but rather calibrated to capture a certain fraction of variance, set to 90% by default in our analyses. While this variance threshold is indeed an arbitrary hyperparameter, it has a more intuitive interpretation than the number of principal components.

Nonetheless, the Reviewer’s comment did inspire us to consider another metric for factorization that does not depend on any arbitrary parameters. In the revised version, we now include a covariance matrix based metric which simply measures the elementwise correlation of the covariance matrices induced by varying the scene parameter of interest and the covariance matrix induced by varying the other parameters (and then subtracts this quantity from 1).

Correspondingly, we now present results for both the new covariance based measure and the original PCA based one in Figures 5C, 6, and 7. The main findings remain largely the same when using the covariance based metric, and the covariance based metric (Figure 5C, compare light shaded to dark shaded filled circles; Figure 6, compare top row to bottom row; Figure 7, compare middle rows to bottom rows).

Ultimately, we believe these two metrics are complementary and somewhat analogous to two metrics commonly used for measuring dimensionality (the number of components needed to explain a certain fraction of the variance, analogous to our original PCA based definition; the participation ratio, analogous to our covariance based definition). We have added the formula for the covariance based factorization metric along with a brief description to the Methods.

(4) The authors defined the term "factorization" according to their metric. I think introducing this new term is not necessary and can be confusing because the term "factorization" is vague and used by different researchers in different ways. Perhaps a better term is "orthogonality", because that is clear and seems to be what the authors' metric is measuring.

We agree with the Reviewer that factorization has become an overloaded term. At the same time, we think that in this context, the connotation of the term factorization effectively conveys the notion of separating out different latent sources of variance (factors) such that they can be encoded in orthogonal subspaces.

To aid clarity, we now mention in the Introduction that factorization defined here is meant to measure orthogonalization of scene factors. Additionally, in the Discussion section, we now go into more detail comparing our metric to others previously used in the literature, including orthogonality, to help put it in context.

(5) One general weakness of the factorization paradigm is the reliance on a choice of factors. This is a subjective choice and becomes an issue as you scale to more complex images where the choice of factors is not obvious. While this choice of factors cannot be avoided, I suggest the authors add two things: First, an analysis of how sensitive the results are to the choice of factors (e.g. transform the basis set of factors and re-run the metric); second, include some discussion about how factors may be chosen in general (e.g. based on temporal statistics of the world, independent components analysis, or something else).

The Reviewer raises a very reasonable point about the limitation of this work. While we limited our analysis to generative scene factors that we know about and that could be manipulated, there are many potential factors to consider. It is not clear to us exactly how to implement the Reviewer’s suggestion of transforming the basis set of factors, as the factors we consider are highly nonlinear in the input space. Ultimately, we believe that finding unsupervised methods to characterize the “true” set of factors that is most useful for understanding visual representations is an important subject for future work, but outside the scope of this particular study. We have added a comment to this effect in the Discussion.

Reviewer #3 (Public Review):

Summary:

Object classification serves as a vital normative principle in both the study of the primate ventral visual stream and deep learning. Different models exhibit varying classification performances and organize information differently. Consequently, a thriving research area in computational neuroscience involves identifying meaningful properties of neural representations that act as bridges connecting performance and neural implementation. In the work of Lindsey and Issa, the concept of factorization is explored, which has strong connections with emerging concepts like disentanglement [1,2,3] and abstraction [4,5]. Their primary contributions encompass two facets: (1) The proposition of a straightforward method for quantifying the degree of factorization in visual representations. (2) A comprehensive examination of this quantification through correlation analysis across deep learning models.

To elaborate, their methodology, inspired by prior studies [6], employs visual inputs featuring a foreground object superimposed onto natural backgrounds. Four types of scene variables, such as object pose, are manipulated to induce variations. To assess the level of factorization within a model, they systematically alter one of the scene variables of interest and estimate the proportion of encoding variances attributable to the parameter under consideration.

The central assertion of this research is that factorization represents a normative principle governing biological visual representation. The authors substantiate this claim by demonstrating an increase in factorization from macaque V4 to IT, supported by evidence from correlated analyses revealing a positive correlation between factorization and decoding performance. Furthermore, they advocate for the inclusion of factorization as part of the objective function for training artificial neural networks. To validate this proposal, the authors systematically conduct correlation analyses across a wide spectrum of deep neural networks and datasets sourced from human and monkey subjects. Specifically, their findings indicate that the degree of factorization in a deep model positively correlates with its predictability concerning neural data (i.e., goodness of fit).

Strengths:

The primary strength of this paper is the authors' efforts in systematically conducting analysis across different organisms and recording methods. Also, the definition of factorization is simple and intuitive to understand.

Weaknesses:

This work exhibits two primary weaknesses that warrant attention: (i) the definition of factorization and its comparison to previous, relevant definitions, and (ii) the chosen analysis method.

Firstly, the definition of factorization presented in this paper is founded upon the variances of representations under different stimuli variations. However, this definition can be seen as a structural assumption rather than capturing the effective geometric properties pertinent to computation. More precisely, the definition here is primarily statistical in nature, whereas previous methodologies incorporate computational aspects such as deviation from ideal regressors [1], symmetry transformations [3], generalization [5], among others. It would greatly enhance the paper's depth and clarity if the authors devoted a section to comparing their approach with previous methodologies [1,2,3,4,5], elucidating any novel insights and advantages stemming from this new definition.

[1] Eastwood, Cian, and Christopher KI Williams. "A framework for the quantitative evaluation of disentangled representations." International conference on learning representations. 2018.

[2] Kim, Hyunjik, and Andriy Mnih. "Disentangling by factorising." International Conference on Machine Learning. PMLR, 2018.

[3] Higgins, Irina, et al. "Towards a definition of disentangled representations." arXiv preprint arXiv:1812.02230 (2018).

[4] Bernardi, Silvia, et al. "The geometry of abstraction in the hippocampus and prefrontal cortex." Cell 183.4 (2020): 954-967.

[5] Johnston, W. Jeffrey, and Stefano Fusi. "Abstract representations emerge naturally in neural networks trained to perform multiple tasks." Nature Communications 14.1 (2023): 1040.

Thanks to the Reviewer for this suggestion. We agree that our initial submission did not sufficiently contextualize our definition of factorization with respect to other related notions in the literature. We have added additional discussion of these points to the Discussion section in the revised manuscript and have included therein the citations provided by the Reviewer (please see the third paragraph of Discussion).

Secondly, in order to establish a meaningful connection between factorization and computation, the authors rely on a straightforward synthetic model (Figure 1c) and employ multiple correlation analyses to investigate relationships between the degree of factorization, decoding performance, and goodness of fit. Nevertheless, the results derived from the synthetic model are limited to the low training-sample regime. It remains unclear whether the biological datasets under consideration fall within this low training-sample regime or not.

We agree that our model in Figure 1C is very simple and does not fully capture the complex interactions between task performance and features of representational geometry, like factorization. We intend it only as a proof of concept to illustrate how factorized representations can be beneficial for some downstream task use cases. While the benefits of factorized representations disappear for large numbers of samples in this simulation, we believe this is primarily a consequence of the simplicity and low dimensionality of the simulation. Real-world visual information is complex and high-dimensional, and as such the relevant sample size regime in which factorization offers tasks benefits may be much greater. As a first step toward this real-world setting, Figure 2 shows how decreasing the amount of factorization in neural population data in macaque V4/IT can have an effect on object identity decoding.

Recommendations for the authors

Reviewer #1 (Recommendations For The Authors):

Missing citations: The paper could benefit from discussions & references to related papers, such as:

Higgins I, Chang L, Langston V, Hassabis D, Summerfield C, Tsao D, Botvinick M. Unsupervised deep learning identifies semantic disentanglement in single inferotemporal face patch neurons. Nature communications. 2021 Nov 9;12(1):6456.

We have added additional discussion of related work, including the suggested reference and others on disentanglement, to the Discussion section in the revised manuscript.

Reviewer #2 (Recommendations For The Authors):

Here are several small recommendations for the authors, all much more minor than those in the public review:

I suggest more use of equations in methods sections about Figure 1C and macaque neural data analysis.

Thanks for this suggestion. We have added new Equation 1 for the method transforming neural data to reduce factorization of a variable while preserving other firing rate statistics.

In Figure 1-C, the methods indicate that Gaussian noise was added. This is a very important detail, and complexifies the interpretation of the figure because it adds an assumption about the structure of noise. In other words, if I understand correctly, the correct interpretation of Figure 1C is "assuming i.i.d. noise, decoding accuracy improves with factorization." The i.i.d. noise is a big assumption, and it is debated how well the brain satisfies this assumption. I suggest you either omit noise for this figure or clearly state in the main text (e.g. caption) that the figure must be interpreted under an i.i.d. noise assumption.

We have added an explicit statement of the i.i.d. noise assumption to the Figure 1C legend.

For Figure 2B, I suggest labeling the x-axis clearly below the axis on both panels. Currently, it is difficult to read, particularly in print.

We have made the x-axis labels more clear and included on both panels.

Figure 3A is difficult to read because of the very small task. I suggest avoiding such small fonts.

We agree that Figure 3A is difficult to read. We have broken out Figure 3 into two new Figures 3 & 4 to increase clarity and sizing of text in Figure 3A.

Reviewer #3 (Recommendations For The Authors):

To strengthen this work, it is advisable to incorporate more comprehensive comparisons with previous research, particularly within the machine learning (ML) community. For instance, it would be beneficial to explore and reference works focusing on disentanglement [1,2,3]. This would provide valuable context and facilitate a more robust understanding of the contributions and novel insights presented in the current study.

We have added additional discussion of related work and other notions similar to factorization to the Discussion section in the revised manuscript.

Additionally, improving the quality of the figures is crucial to enhance the clarity of the findings:

• Figure 2: The caption of subfigure B could be revised for greater clarity.

Thank you, we have substantially clarified this figure caption.

• Figure 3: Consider a more equitable approach for computing the correlation coefficient, such as calculating it separately for different types of models. In the case of supervised models, it appears that the correlation between invariance and goodness of fit may not be negligible across various scene parameters.

We appreciate the suggestion, but we are not confident in our ability to conclude much from analyses restricted to particular model classes, given the relatively small N and the fact that the different model classes themselves are an important source of variance in our data.

• Figure 4: To enhance the interpretability of subfigures A and B, it may be beneficial to include p-values (indicating confidence levels).

As we supply bootstrapped confidence intervals for our results, which provide at least as much information as p-values, and most of the effects of interest are fairly stark when comparing invariance to factorization, p-values were not needed to support our points. We added a sentence to the legend of new Figure 5 (previously Figure 4) indicating that error bars reflect standard deviations over bootstrap resampling of the models.

• Figure 5: For subfigure B, it could be advantageous to plot the results solely for factorization, allowing for a clear assessment of whether the high correlation observed in Classification+Factorization arises from the combined effects of both factors or predominantly from factorization alone.

First, we clarify/note that the scatters solely for factorization that the Reviewer seeks are already presented earlier in the manuscript across all conditions in Figures 4A,B and Figure S2.

While we could also include these in new Figure 7 (previously Figure 5B) as the Reviewer suggests, we believe it would distract from the message of that figure at the end of the manuscript – which is that factorization is useful as a supplement to classification in predictive matches to neural data. Nonetheless, new Figure 6 (old Figure 5A) provides a summary quantification of the information that the reviewer requests (Fig. 6, faded colored bars reflect the contribution of factorization alone).

Author response:

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

eLife assessment

The study elucidates a detailed molecular mechanism of the initial stages of transport in a medically relevant GABA neurotransmitter transporter GAT1 and thus generates useful new insights for this protein family. In particular, it presents convincing evidence for the presence of a "staging binding site" that locally concentrates Na+ ions to increase transport activity, whilst solid evidence for how Na+ binding affects the larger scale dynamics.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The manuscript authored by Stockner and colleagues delves into the molecular simulations of Na+ binding pathway and the ionic interactions at the two known sodium binding sites site 1 and site 2. They further identify a patch of two acidic residues in TM6 that seemingly populate the Na+ ions prior to entry into the vestibule. These results highlight the importance of studying the ion-entry pathways through computational approaches and the authors also validate some of their findings through experimental work. They observe that sodium site 1 binding is stabilized by the presence of the substrate in the S1 site and this is particularly vital as the GABA carboxylate is involved in coordinating the Na+ ion unlike other monoamine transporters and binding of sodium to the Na2 site stabilizes the conformation of the GAT1 by reducing flexibility among the helical bundles involved in alternating access.

Strengths:

The study displays results that are generally consistent with available information from experiments on SLC6 transporters particularly GAT1 and puts forth the importance of this added patch of residues in the extracellular vestibule that could be of importance to the ion permeation in SLC6 transporters. This is a nicely performed study and could be improved if the authors could comment on and fix the following queries.

We thank our reviewer for the overall positive evaluation.

Weaknesses:

(1) How conserved are the residue pair of D281-E283 in other SLC6 transporters. The authors commented on the presence of these residues in SERT but it would be nice to know how widespread these residues are in other SLC6 transporters like NET, GlyT, and DAT.

We have created a sequence alignment of the entire human SLC6 family (Supplementary Figure 1) and found that E283 is polar or charged in all SLC6 transporters. D281 shows a higher level of conservation across the family compared to E283. D281 is negatively charged in approximately 50% of the SLC6 family members, an aspartate in all GABA transporters and a glutamate in all monoamine transporters.

(2) Further, one would like to see the effect of individual mutations D281A and E283A on transport, surface expression, and EC50 of Na+ to gauge the effect on transport.

We have carried out experiments to investigate the effects of the individual mutations. The results revealed intermediate effects between WT and the double mutant (D281A-E283A) and showed that the effects mostly align with the degree of conservation, as a neutralisation of D281 by alanine has a stronger effect than the E283A mutant. Both single mutants had minimal effects on the sodium dependence of uptake, D281A had a stronger effect on expression, Km and Vmax as compared to E283. Only D281A reduced surface expression, while E283A expresses to a similar level as wild type GAT1.

(3) A clear figure of the S1 site where Na+ tends to stay prior to Na1 site interactions needs to be provided with a clear figure. Further, it is not entirely clear how access to S1 is altered if the transporter is in an outwardoccluded conformation if F294 is blocking solvent access. Please comment.

We have modified the structural images in Figure 1, 5, 6 and 7 to improve their comprehensibility. We have also added a comment on the role of F294 as part of the outer hydrophobic gate to the discussion. In short, F294 does not occlude the passage to the S1 as long as GAT1 is outward open, and we find that GAT1 is outward open in all sodium binding simulations.

(4) The p-value of the EC50 differences between GAT1WT and GAT1double mutant need to be mentioned. The difference in sodium dependence EC50 seems less than twofold, and it would be useful to mention how critical the role of the recruitment site is. Since the transport is not affected the site could play a transient role in attracting ions.

We have added p-values or standard deviation to our data.

(5) It would be very nice to know how K+ ions are attracted by this recruitment site. This could further act as a control simulation to test the preference for Na+ ions among SLC6 members.

We think that attraction of potassium to the recruitment site is not of relevance, as the residues are at the extracellular side and exposed to bulk, where the concentration of sodium is high (typically 130-150 mM), while the concentration of potassium is very small (3-5 mM). Exploring sodium binding by simulations for all SLC6 members could be interesting, but clearly outside the scope of this manuscript.

(6) Some of the important figures are not very clear. For instance, there should be a zoomed-in view of the recruitment site. The current one in Fig. 1b and 1c could be made clearer. Similarly as mentioned earlier the Na residence at the S1 site away from the Na1 and Na2 sites needs to be shown with greater clarity by putting side chain information in Fig. 6d.

We have modified the structural images in Figure 1, 5, 6 and 7 to improve their comprehensibility.

(7) The structural features that comprise the two principal components PC1 and PC2 should be described in greater detail.

We have modified Figure 6 and added images that show the motions along PC1 and PC2. In addition, these are now better explained in the text.

Reviewer #2 (Public Review):

Summary:

Starting from an AlphaFold2 model of the outward-facing conformation of the GAT1 transporter, the authors primarily use state-of-the-art MD simulations to dissect the role of the two Na+ ions that are known to be cotransported with the substrate, GABA (and a co-transported Cl- ion). The simulations indicated that Na+ binding to OF GAT depends on the electrostatic environment. The authors identify an extracellular recruiting site including residues D281 and E283 which they hypothesized to increase transport by locally increasing the available Na+ concentration and thus increasing binding of Na+ to the canonical binding sites NA1 and NA2. The charge-neutralizing double mutant D281A-E283A showed decreased binding in simulations. The authors performed GABA uptake experiments and whole-cell patch clamp experiments that taken together validated the hypothesis that the Na+ staging site is important for transport due to its role in pulling in Na+.

Detailed analysis of the MD simulations indicated that Na+ binding to NA2 has multiple structural effects: The binding site becomes more compact (reminiscent of induced fit binding) and there is some evidence that it stabilizes the outward-facing conformation.

Binding to NA1 appears to require the presence of the substrate, GABA, whose carboxylate moiety participates in Na+ binding; thus the simulations predict cooperativity between binding of GABA and Na+ binding to NA1.

Strengths:

-  MD simulations were used to propose a hypothesis (the existence of the staging Na+ site) and then tested with a mutant in simulations AND in experiments. This is an excellent use of simulations in combination with experiments.

-  A large number of repeat MD simulations are generally able to provide a consistent picture of Na+ binding. Simulations are performed according to current best practices and different analyses illuminate the details of the molecular process from different angles.

-  The role of GABA in cooperatively stabilizing Na+ binding to the NA1 site looks convincing and intriguing.

We thank the review for the very supportive assessment.

Weaknesses:

-  Assessing the effects of Na+ binding on the large-scale motions of the transporter is more speculative because the PCA does not clearly cover all of the conformational space and the use of an AlphaFold2 model may have introduced structural inconsistencies. For example, it is not clear if movements of the inner gate are due to an AF2 model that's not well packed or really a feature of the open outward conformation.

The long range effect of sodium binding to GAT1 and destabilisation of the inner gate has, based on our data, a causal effect. PCA separates conformational motions into degrees of freedom and sorts them according to the largest motions. Motions of TM5a were among the 2 largest motions, which suggests that these are relevant motions. To directly quantify their behaviour, we measured informative distances at the inner gate of GAT1, as shown in Figure 6i,j,k and separated data according to the presence of sodium in NA2.

For the following reasons we exclude that the results are a consequence of structural inconsistencies introduced by AlphaFold2 and therefore not reflecting functionally relevant effects:

(1) If depending on the model instead of sodium binding, the effects should not be correlated with the presence of sodium in the NA2 binding site.

(2)  We carried out new simulations starting from the occluded GAT1 structure (Figure 6j,k). The data shows that in the occluded state the distance across the inner vestibule and the length of TM5a differ, consistent with our interpretation of the data. As sodium binding fixes GAT1 outwardfacing, as it also occurs in other SLC6 family members (Szöllősi and Stockner, 2022), the distances of the outward-open GAT1 are at the short extreme of the scale, distances of the inward-open state of the cryo-EM structure(s) are at the other extreme, while the occluded conformation of GAT1 shows intermediate values.

(3)  We have observed the same property in SERT, for which we used experimental structures as starting structure (Gradisch et al., 2024), suggesting that this could be a generally mechanism.

(4)  All available structures from the entire SLC6 family are consistent with structural effects of TM5a in response to bundle domain motions and therefore to binding of sodium to NA2 as it stabilized the outward-open state as well as transition to the inward facing conformation.

- Quantitative analyses are difficult with the existing data; for example, the tICA "free energy" landscape is probably not converged because unbinding events haven't been observed.

Simulations can always be too short and therefore not fully describe the complete underlying conformational ensemble. We added a statement in the discussion indicating this shortcoming. With respect to the tICA analysis in our manuscript, the tICA approach does, by design, not need long simulations that capture the full binding and unbinding in multiple instances to construct a correct free energy landscape. Instead, the tICA method builds on Markov chain dependencies and relies only on the convergence of transitions between hundreds of conformational microstates and the fluxes between them. The free energy profile derived for the S1, including NA1, TMP and NA2 and up to the salt bridge of the outer gate is well converged and we observed many transitions. In contrast, the entry from the recruitment side to the S1 has most likely a too low density of microstate and a too small number of transition to be considered converged with respect to quantifying the free energy of binding from bulk. We now explain this shortcoming.

Recommendations for the authors:

Reviewer #1 (Recommendations for The Authors):

Authors should furnish p-values in the figure legends for experimental results.

We have added the p-values to text and figure legends.

Reviewer #2 (Recommendations For The Authors):

-  Deposit simulation data in a public repository (input files, trajectories (possibly subsampled)).

We deposited the data to Zenodo and provided the DOI: 10.5281/zenodo.10686813 to the data. As we were unable to upload the trajectories to zenodo, we deposited the starting and the end structures of the simulations.

-  Please include a short discussion of the reliability of using an AF2 model instead of experimental structures. What is expected to be correct/which parts of the structure are potentially incorrect? What makes you think that the AF2 model is a good model of the OF conformation of GAT1?

Unfortunately, an outward-facing structure of GAT1 is not available. We have initially worked with an outward-open homology model of GAT1 based on SERT (build with MODELLER), but the structural differences between SERT and GAT1 are sufficiently large that these models did not behave well in simulations and too frequently could not maintain a sealed inner gate, also forming a channel. In contrast to the SERT-based GAT1 model, the AlphaFold2 model of GAT1 behaved as expected and consistent with the behaviour of SERT in simulations and with general knowledge of protein dynamics from literature. Based on structural analysis of our simulations and on the comparison to SERT we could not identify a region of GAT1 which would be potentially behave incorrect or unexpectedly. We added a statement to the discussion on this potential limitation of the use of homology models.

-  Fig 1a: Na+ densities are not very clear (both due to small size and the transparency). I have a hard time seeing where bulk, 2*bulk regions are --- are you showing "onion shells" of density? Perhaps investigate presenting as cuts through the full density?

I like the labelling in terms of absolute density and multiples of bulk.

We have created new images to improve the visualisation of data. The data are shown as onion shells (isosurface), with the shells at the indicated densities. This is now clearly stated. Transparency is needed, otherwise e.g. the inner onion shells would not be visible. The cut-through is intuitive, but we could not find a useful plain, as the densities are too extensively distributed in 3D and not on a single plain.

-  Fig 1h-k: would be clearer if "recruitment site" (TMP?) was indicated in the figure.

We have created a new image for the recruiting site (Figure 1b,c) and temporary site (Figure 1g) and indicated these two sites as appropriate.

-  Show time series of Na+ binding with a suitable order parameter (z or distances to NA1 and NA2?) to show how ions bind spontaneously. Mark the different sites. Mark pre- and post-binding parts of trajectories.

We have added time series for every simulation that shows sodium binding to the NA1 or NA2 to the supplementary information Figure 2a,b,c. These quantify the distances to the recruiting site, the temporary site and the respective sodium binding site.

-  PCA - how much of the total variance was captured by PC1 and PC2?

The variance captured by the PCs are shown as eigenvalues in supplementary information Figure 4. PC1 captures about 19% of the variance, PC2 8%.

-  "We found that the inner hydrophobic gate is dynamic in the absence of Na2" -- is this instability due to the AF2 model or likely realistic? E.g. was similar behaviour ever observed in simulations of the occluded state?

In simulations of the occluded state we do not see such instabilities as observed in the outward-open state in the absence of sodium (Figure 6). As these larger scale fluctuations are not randomly distributed across all simulations starting from the AlphaFold2 models, but confined to the systems without sodium, it is unlikely an effect of the AlphaFold2 model.

Please note, we have seen comparable behaviour in simulations of SERT starting from experimental structures (Gradisch et al., 2024), therefore suggesting a more general mechanism.

-  Cooperativity between GABA-binding and Na+ binding to NA1: How would this lead to an experimentally measurable signature, i.e., which experiments could validate this interesting prediction?

Direct detection of cooperativity is difficult to separate from other effects in experiments, as sodium binding and transport involves NA1 and NA2, NA2 has a higher affinity according to our data, while mutations will not only affect cooperativity, but will also have other effects.

Conformational changes can also complicate experimental detection, as NA2 stabilises the outward-open conformation, while NA1+GABA binding triggers the transition to the inward-open state. To quantify cooperativity, it would be important to isolate the cooperative from all other effects, which is a challenge. Support for cooperativity has been found by (Zhou, Zomot and Kanner, 2006; Meinild and Forster, 2012) using this route. In the first paper the authors make use of lithium that only binds to the NA2, even though lithium is not only a mere NA2 selective ligand and otherwise identical to sodium. By comparing two GABA concentrates the authors showed that the sodium dependence of GABA transport is left shifted at higher GABA concentrations, which is not the case in the absence of lithium. This data is indirect, but consistent with cooperativity between GABA and NA1-bound sodium, as GABA transport mainly reflects binding of sodium to NA1. Similar approaches could be further explored, for example by varying the GABA concentration instead of sodium. Other options could be to create an outward-facing and conformationally locked GAT1 and to measure the cooperativity of sodium and GABA binding using for example the scintillation proximity assay. Most likely the assay would also need a way to be NA2 binding independent. We are not aware of such a GABA transporter system.

-  There are some instances of [SI Figure] or [citation needed] that should be cleaned up.

We have corrected these instances.

References

Gradisch, R. et al. (2024) ‘Ligand coupling mechanism of the human serotonin transporter differentiates substrates from inhibitors’, Nature Communications, 15(1), p. 417. Available at: https://doi.org/10.1038/s41467-023-44637-6.

Meinild, A.-K. and Forster, I.C. (2012) ‘Using lithium to probe sequential cation interactions with GAT1’, American Journal of Physiology. Cell Physiology, 302(11), pp. C1661-1675. Available at: https://doi.org/10.1152/ajpcell.00446.2011.

Szöllősi, D. and Stockner, T. (2022) ‘Sodium Binding Stabilizes the Outward-Open State of SERT by Limiting Bundle Domain Motions’, Cells, 11(2), p. 255. Available at: https://doi.org/10.3390/cells11020255.

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Author response:

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

Response to reviewer #1:

We thank the reviewer for the further recommendations for improving our presentation. We would like to carefully address the remaining concerns of the reviewer.

(1) I realize now that I didn't make my point clear enough, which was that as far as I know there is no reason to believe that an oscillatory state cannot be induced with synaptic depression as with spike frequency adaptation when used in the context of the author's model. I'm fine with how the authors have distinguished their model from R&T 2015, but I think the more interesting question is whether there is any reason to believe that STD is not equally capable of doing all the things mentioned in this paper as SFA, and if not why not. I would like the authors to go out on a limb and address this, if only with a few sentences in the discussion. 

Thank you for pointing this out again. In response to your query regarding the comparison between STD and SFA in generating bump sweeps, we have done simulations based on STD. The results showed that both STD and SFA are capable of inducing bi-directional sweeps. However, (based on our simulations) only SFA can produce uni-directional sweeps. The absence of uni-directional sweeps based on STD may be due to the subtle yet important differences between the two mechanisms. Specifically, STD modulates the neural activity by weakening the recurrent connections, which theoretically can only inhibit recurrent inputs, while SFA can attenuate all forms of excitatory inputs, including external inputs. However, since we did not exhaustively explore the entire parameter space, we cannot conclude that STD is incapable of producing uni-directional sweeps. Future simulations are required.

According to the Reviewer’s suggestion, we added few sentences to discuss the distinctions between STD and SFA in generating theta sweeps in the CANN in line 432 to 440 in the Discussion session:

“Based on our simulation, both STD and SFA show the ability to produce bi-directional sweeps within a CANN model, with the SFA uniquely enabling uni-directional sweeps in the absence of external theta inputs. This difference might be due to the lack of exhaustively exploration of the entire parameter space. However, it might also attribute to the subtle yet important theoretical distinctions between STD and SFA. Specifically, STD attenuates the neural activity through a reduction in recurrent connection strength, whereas SFA provides inhibitory input directly to the neurons, potentially impacting all excitatory inputs. These differences might explain the diverse dynamical behaviors observed in our simulations. Future experiments could clarify these distinctions by monitoring changes in synaptic strength and inhibitory channel activation during theta sweeps.”

(2) I appreciate the inclusion of the experimental data in Fig 6a (though I don't find the left-most panel very useful). I also understand what the authors are trying to convey with plots in 6c and 6c. However, I don't find the text that was added above very helpful at all. I was hoping for a simpler demonstration of the effect, by plotting a series of sequential sweeps (cell index vs time, with color indicating firing rate, as in Fig 2d) in the case of both the slow speed and fast speed regimes. Here, vertical lines could mark the individual theta cycles and the firing of individual cells, showing the constancy of the former but change of the latter. 

Thank you for your constructive feedback. It seems there might be a misunderstanding in our previous explanation, for which we apologize. The phenomenon we want to elucidate is not an increase in the theta frequency as detected in LFPs, but rather the slope of phase precession with respect to the animal's movement speed. Due to phase precession, the oscillations of place cells as the animal traverses the field is higher than the theta frequency. A plot as Fig 2.d will not make this point clearer, since it shows the baseline theta frequency (i.e., theta sweeps as we claimed previously). A straightforward way of thinking this point is as we added previously: “…The faster the animal runs, the faster the extra half cycle can be accomplished. Consequently, the firing frequency will increase more (a steeper slope in Fig. 6c red dots) than the baseline frequency”. We hope this clarification addresses the concerns raised.

(3) This is still confusing to me. I just don't understand how the *phase* of the oscillating activity bump has anything to do with the movement of the animal. I would like to see a plot of the sweeps (again, cell index vs time, with color indicating the firing rate) before and after inactivation for short and long duration inactivation. Perhaps I am not understanding or appreciating how the bump recovers after inactivation and how this is related to the motion of the animal. 

Thank you for pointing this out again. The activity bump will naturally pop out at the input location (which moves forward than before) after we remove the inactivation and then starts to sweep again as before the inactivation. Single cell phase precession and populational theta sweeps are actually the two sides of the same coin (if all cells start at roughly the same phase in theta cycles). If the reviewer accept this, then at the new location, the activity bump sweeps again (around the new location), and therefore phase precession starts again at a further phase, since phase codes the position as the animal traverses the place field.

(4) I am glad the authors are spending more time discussing this phenomenon, but I am unsure of their explanation: for a sweep moving at constant speed, neurons all along the path will be equally affected (inhibited), so where does the bias for suppressing the "end" neurons come from? 

While it may appear that neurons along the path are equally inhibited as the bump sweeps over them, our model incorporates external inputs with Gaussian profiles. These inputs bias neurons closer to the input location, resulting in fewer activations in neurons further away from the input position.

(5) Here I was hoping that the authors might comment on what they suspect happens when the animal starts (or stops) moving, and how the network shifts from tracking regime to oscillatory regime (or vice versa), as is typically seen in experimental data (see for example, Kay et al., 2020, fig 4b,c). My apologies for not making this point clearer. 

Thank you for pointing this out. In our model, we observed that when the animal stops, the network continues to generate theta oscillations near the input location, albeit with reduced amplitude (so the network dynamics looks like in the tracking regime). However, we hypothesize that when the animal pauses its movement for enough time (immobile but awake states), sensory input into the hippocampus also decreases, which is similar to removing external inputs in our model. In this case, the activity bump spontaneously moves away, resembling the phenomenon of replay (see also Romani & Tsodyks 2015).

Regarding the experimental data (Kay et al.), it indeed appears that theta sweeps decoded from neural activity become less pronounced when the mouse moves at slower speeds. This observation could potentially correspond to a decrease in the amplitude of bump oscillations when external inputs associated with movement are halted but not entirely removed in our model. However, in experiments, when the mouse's movement slows down, hippocampal activity no longer oscillates at theta frequency, making it challenging to decode theta sweeps.

We appreciate your clarification on this point and recognize the importance of further investigating how our model can accurately replicate the transition between tracking and oscillatory regimes observed in experimental data.

Reviewer #3 (Public Review):

This manuscript examines the impact of congenital visual deprivation on the excitatory/inhibitory (E/I) ratio in the visual cortex using Magnetic Resonance Spectroscopy (MRS) and electroencephalography (EEG) in individuals whose sight was restored. Ten individuals with reversed congenital cataracts were compared to age-matched, normally sighted controls, assessing the cortical E/I balance and its interrelationship to visual acuity. The study reveals that the Glx/GABA ratio in the visual cortex and the intercept and aperiodic signal are significantly altered in those with a history of early visual deprivation, suggesting persistent neurophysiological changes despite visual restoration.

My expertise is in EEG (particularly in the decomposition of periodic and aperiodic activity) and statistical methods. I have several major concerns in terms of methodological and statistical approaches along with the (over)interpretation of the results. These major concerns are detailed below.

(1) Variability in visual deprivation:

- The document states a large variability in the duration of visual deprivation (probably also the age at restoration), with significant implications for the sensitivity period's impact on visual circuit development. The variability and its potential effects on the outcomes need thorough exploration and discussion.

(2) Sample size:

- The small sample size is a major concern as it may not provide sufficient power to detect subtle effects and/or overestimate significant effects, which then tend not to generalize to new data. One of the biggest drivers of the replication crisis in neuroscience.

- The main problem with the correlation analyses between MRS and EEG measures is that the sample size is simply too small to conduct such an analysis. Moreover, it is unclear from the methods section that this analysis was only conducted in the patient group (which the reviewer assumed from the plots), and not explained why this was done only in the patient group. I would highly recommend removing these correlation analyses.

(3) Statistical concerns:

- The statistical analyses, particularly the correlations drawn from a small sample, may not provide reliable estimates (see https://www.sciencedirect.com/science/article/pii/S0092656613000858, which clearly describes this problem).

- Statistical analyses for the MRS: The authors should consider some additional permutation statistics, which are more suitable for small sample sizes. The current statistical model (2x2) design ANOVA is not ideal for such small sample sizes. Moreover, it is unclear why the condition (EO & EC) was chosen as a predictor and not the brain region (visual & frontal) or neurochemicals. Finally, the authors did not provide any information on the alpha level nor any information on correction for multiple comparisons (in the methods section). Finally, even if the groups are matched w.r.t. age, the time between surgery and measurement, the duration of visual deprivation, (and sex?), these should be included as covariates as it has been shown that these are highly related to the measurements of interest (especially for the EEG measurements) and the age range of the current study is large.

- EEG statistical analyses: The same critique as for the MRS statistical analyses applies to the EEG analysis. In addition: was the 2x3 ANOVA conducted for EO and EC independently? This seems to be inconsistent with the approach in the MRS analyses, in which the authors chose EO & EC as predictors in their 2x2 ANOVA.

- Figure 4: The authors report a p-value of >0.999 with a correlation coefficient of -0.42 with a sample size of 10 subjects. This can't be correct (it should be around: p = 0.22). All statistical analyses should be checked.

- Figure 2c. Eyes closed condition: The highest score of the *Glx/GABA ratio seems to be ~3.6. In subplot 2a, there seem to be 3 subjects that show a Glx/GABA ratio score > 3.6. How can this be explained? There is also a discrepancy for the eyes-closed condition.

(4) Interpretation of aperiodic signal:

- Several recent papers demonstrated that the aperiodic signal measured in EEG or ECoG is related to various important aspects such as age, skull thickness, electrode impedance, as well as cognition. Thus, currently, very little is known about the underlying effects which influence the aperiodic intercept and slope. The entire interpretation of the aperiodic slope as a proxy for E/I is based on a computational model and simulation (as described in the Gao et al. paper).

- Especially the aperiodic intercept is a very sensitive measure to many influences (e.g. skull thickness, electrode impedance...). As crucial results (correlation aperiodic intercept and MRS measures) are facing this problem, this needs to be reevaluated. It is safer to make statements on the aperiodic slope than intercept. In theory, some of the potentially confounding measures are available to the authors (e.g. skull thickness can be computed from T1w images; electrode impedances are usually acquired alongside the EEG data) and could be therefore controlled.

- The authors wrote: "Higher frequencies (such as 20-40 Hz) have been predominantly associated with local circuit activity and feedforward signaling (Bastos et al., 2018; Van Kerkoerle et al., 2014); the increased 20-40 Hz slope may therefore signal increased spontaneous spiking activity in local networks. We speculate that the steeper slope of the aperiodic activity for the lower frequency range (1-20 Hz) in CC individuals reflects the concomitant increase in inhibition." The authors confuse the interpretation of periodic and aperiodic signals. This section refers to the interpretation of the periodic signal (higher frequencies). This interpretation can not simply be translated to the aperiodic signal (slope).

- The authors further wrote: We used the slope of the aperiodic (1/f) component of the EEG spectrum as an estimate of E/I ratio (Gao et al., 2017; Medel et al., 2020; Muthukumaraswamy & Liley, 2018). This is a highly speculative interpretation with very little empirical evidence. These papers were conducted with ECoG data (mostly in animals) and mostly under anesthesia. Thus, these studies only allow an indirect interpretation by what the 1/f slope in EEG measurements is actually influenced.

(5) Problems with EEG preprocessing and analysis:

- It seems that the authors did not identify bad channels nor address the line noise issue (even a problem if a low pass filter of below-the-line noise was applied).

- What was the percentage of segments that needed to be rejected due to the 120μV criteria? This should be reported specifically for EO & EC and controls and patients.

- The authors downsampled the data to 60Hz to "to match the stimulation rate". What is the intention of this? Because the subsequent spectral analyses are conflated by this choice (see Nyquist theorem).

- "Subsequently, baseline removal was conducted by subtracting the mean activity across the length of an epoch from every data point." The actual baseline time segment should be specified.

- "We excluded the alpha range (8-14 Hz) for this fit to avoid biasing the results due to documented differences in alpha activity between CC and SC individuals (Bottari et al., 2016; Ossandón et al., 2023; Pant et al., 2023)." This does not really make sense, as the FOOOF algorithm first fits the 1/f slope, for which the alpha activity is not relevant.

- The model fits of the 1/f fitting for EO, EC, and both participant groups should be reported.

(6) Validity of GABA measurements and results:

- According the a newer study by the authors of the Gannet toolbox (https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/abs/10.1002/nbm.5076), the reliability and reproducibility of the gamma-aminobutyric acid (GABA) measurement can vary significantly depending on acquisition and modeling parameter. Thus, did the author address these challenges? Furthermore, the authors wrote: "We confirmed the within-subject stability of metabolite quantification by testing a subset of the sighted controls (n=6) 2-4 weeks apart. Looking at the supplementary Figure 5 (which would be rather plotted as ICC or Blant-Altman plots), the within-subject stability compared to between-subject variability seems not to be great. Furthermore, I don't think such a small sample size qualifies for a rigorous assessment of stability.

- "Why might an enhanced inhibitory drive, as indicated by the lower Glx/GABA ratio" Is this interpretation really warranted, as the results of the group differences in the Glx/GABA ratio seem to be rather driven by a decreased Glx concentration in CC rather than an increased GABA (see Figure 2).

- Glx concentration predicted the aperiodic intercept in CC individuals' visual cortices during ambient and flickering visual stimulation. Why specifically investigate the Glx concentration, when the paper is about E/I ratio?

(7) Interpretation of the correlation between MRS measurements and EEG aperiodic signal:

- The authors wrote: "The intercept of the aperiodic activity was highly correlated with the Glx concentration during rest with eyes open and during flickering stimulation (also see Supplementary Material S11). Based on the assumption that the aperiodic intercept reflects broadband firing (Manning et al., 2009; Winawer et al., 2013), this suggests that the Glx concentration might be related to broadband firing in CC individuals during active and passive visual stimulation." These results should not be interpreted (or with very caution) for several reasons (see also problem with influences on aperiodic intercept and small sample size). This is a result of the exploratory analyses of correlating every EEG parameter with every MRS parameter. This requires well-powered replication before any interpretation can be provided. Furthermore and importantly: why should this be specifically only in CC patients, but not in the SC control group?

(8) Language and presentation:

- The manuscript requires language improvements and correction of numerous typos. Over-simplifications and unclear statements are present, which could mislead or confuse readers (see also interpretation of aperiodic signal).

- The authors state that "Together, the present results provide strong evidence for experience-dependent development of the E/I ratio in the human visual cortex, with consequences for behavior." The results of the study do not provide any strong evidence, because of the small sample size and exploratory analyses approach and not accounting for possible confounding factors.

- "Our results imply a change in neurotransmitter concentrations as a consequence of *restoring* vision following congenital blindness." This is a speculative statement to infer a causal relationship on cross-sectional data.

- In the limitation section, the authors wrote: "The sample size of the present study is relatively high for the rare population , but undoubtedly, overall, rather small." This sentence should be rewritten, as the study is plein underpowered. The further justification "We nevertheless think that our results are valid. Our findings neurochemically (Glx andGABA+ concentration), and anatomically (visual cortex) specific. The MRS parameters varied with parameters of the aperiodic EEG activity and visual acuity. The group differences for the EEG assessments corresponded to those of a larger sample of CC individuals (n=38) (Ossandón et al., 2023), and effects of chronological age were as expected from the literature." These statements do not provide any validation or justification of small samples. Furthermore, the current data set is a subset of an earlier published paper by the same authors "The EEG data sets reported here were part of data published earlier (Ossandón et al., 2023; Pant et al., 2023)." Thus, the statement "The group differences for the EEG assessments corresponded to those of a larger sample of CC individuals (n=38) " is a circular argument and should be avoided.

Author response:

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

We appreciate the reviewers for their insightful comments, which have helped to improve the manuscript. We provide specific examples and a point-by-point response to all comments, below. Based on the Reviewers’ comments, we revised our manuscript, adding considerable amount of new data (found in Fig. 1A,B, 4E-G, 7C,D, 8C,E, S1B,C, S2C-G, S4C, and Video 1). In the main manuscript text, blue fonts indicate added or revised texts. An additional author (Lauren N. Juga) is added for the newly generated data in the revised manuscript.

Reviewer #1: 

Sekulovski et al present an interesting and timely manuscript describing the temporal transition from epiblast to amnion. The manuscript builds on their previous work describing this process using stem cell models. 

They suggest a multi-step process initiated by BMP induction of GATA3, followed by expression of TFAP2A, followed by ISL1/HAND1 in parallel with loss of pluripotency markers. This transition was reproduced through IF analysis of CS6/7 NHP embryo. 

There are significant similarities in the expression of trophectoderm and the amnion. There are also ample manuscripts showing trophoblast induction following BMP stimulation of primed pluripotent stem cells. The authors should ensure that the amnion indeed is only amnion and not trophectoderm (or the amount of contribution to trophectoderm). As an extension, does the amnion character remain after the 48h BMP4 treatment, and is a trophectoderm-like state adopted as suggested by Ohgushi et al 2022?  

Thank you for this insightful comment. As pointed out, Ohgushi et al. showed that, in their culture method, amnion is first induced, and extended culturing leads to the formation of trophectoderm-like cells (Ohgushi et al., 2022).

Importantly, we would like to note that our culture system differs substantially from that of Ohgushi et al. in several respects. First our system uses a 3D culture method while Ohgushi et al. employ 2D hPSC monolayers. Second, the two systems are chemically quite distinct. In our Glass-3D+BMP protocol, cells are cultured in mTeSR media (which contains FGF2 and TGFb1) for two days, by which time they generate 3D pluripotent cysts. BMP is then added to the culture medium for 24 hours, followed by another 24 hours without BMP4. In stark contrast, Ohgushi et al. employ A83-01, an Activin/Nodal signaling inhibitor, and PD173074, an FGF signaling inhibitor (a protocol which they call AP). This treatment leads to spontaneous activation of BMP signaling, but it also clearly inhibits Activin/Nodal and FGF signaling pathways, which remain active in our system. As a result of these distinct chemical as well as geometrical culturing protocols, their system produces amnion and trophectoderm, while our system produces exclusively amnion.

Further analysis of gene expression data provides additional data supporting our contention that our system produces amnion. Though the gene expression profiles of amnion and trophectoderm are quite similar, specific markers of trophectoderm have been identified including GCM1, PSG1, PSG4 and CGB (Blakeley et al., 2015; Meistermann et al., 2021; Ohgushi et al., 2022; Okae et al., 2018; Petropoulos et al., 2016; Yabe et al., 2016). Importantly, while all of these markers are abundantly expressed in the Ohgushi et al. system, bulk RNA sequencing analysis of our Glass-3D+BMP hPSC-amnion cells reveals that none of these markers are detectable. Indeed, SDC1, a marker that Ohgushi et al. claim distinguishes trophoblast from amnion actually decreases (more than 8-fold) as pluripotent cysts transition to amnion in Glass3D+BMP. Finally, Ohgushi et al. report that ISL1, a key marker of specified amnion population, is initially increased in their system, but is reduced to a basal level overtime. In contrast, in Glass3D+BMP hPSC-amnion, ISL1 expression continuously increases with time, and ISL1 protein expression is seen uniformly throughout the amnion cysts. This uniform expression is also seen in CS6/7 cynomolgus macaque amnion. Together, these results support out conclusion that the Glass-3D+BMP system leads to the formation of amniotic cells, and not trophectoderm cells.

The functional data does not support a direct function of GATA3 prior to TFAP2A and the authors suggest compensatory mechanisms from other GATAs. If so, which GATAs are expressed in this system, with and without GATA3 targeting? Would it not be equally likely that the other early genes could be the key drivers of amnion initiation, such as ID2? 

We appreciate this helpful comment. We agree that our data do not provide sufficient evidence for the role of GATA3 in early amniogenesis. We also agree that other early genes could be key drivers, and apologize for including our speculation that focuses only on GATA2. GATA2 was selected because, among the other GATAs, GATA2 and GATA3 are the only abundantly expressed GATA factors. This point suggesting a potentially redundant role of GATA2 is now removed from the manuscript (Line#355 of the original manuscript).

The targeting of TFAP2A displays a very interesting phenotype which suggests that amnion and streak share an initial trajectory but where TFAP2A is necessary to adopt amnion fate. It would again be important to ensure that this alternative fate is indeed in streak and not misannotated alternative lineages, including trophoblast. 

Is TBXT induced in this setting as well as in the wt situation during amnion induction? This should be displayed as in Figure 3D and would be nice to be complimented by NHP IF analysis.

We will address these two closely related comments together.

TFAP2A-KO cysts contain ISL1+ squamous cells as well as SOX2+ pluripotent cells, suggesting that, while the initial focal amniogenesis is seen, subsequent spreading event is not seen. Interestingly, our new data show that TFAP2A-KO cysts display cells with high TBXT expression (Fig. 8E, Line#373-374). This result suggests that, in the absence of TFAP2A, once amnion lineage progression is halted, more primitive streak-like (TBXThigh) lineage emerges. It is important to note that TBXT expression is not seen in the trophectoderm population of cynomolgus macaque peri-gastrula (Sasaki et al., 2016; Yang et al., 2021).

As suggested, we now include a TBXT expression time course during hPSC-amnion formation in Fig. S2D of the revised manuscript. These data show weak TBXT expression (transcripts) starting at the 24-hr timepoint. However, a clear TBXT protein signal could not be detected using IF (Fig. S2C), likely because TBXT expression is very low (Line#264-265). While statistically significant compared to the 12-hr timepoint, TBXT expression is 31 FPKM +/- 0.8 (standard deviation) at 24-hr and 48 FPKM +/- 6 at 48-hr. These are low expression values compared to, for example, TFAP2A, which displays 572 FPKM +/- 23 at 12-hr and 1169 FPKM +/- 27 at 24-hr, at which TFAP2A is readily detected using IF. While weak nuclear TFAP2A is seen using IF at 6hr (187 FPKM +/- 7), no clear TFAP2A is detected at 3-hr (74 FPKM +/- 7). Another example is ISL1, which displays 758 FPKM +/- 55 at 24-hr and 1505 FPKM +/- 26 at 48-hr, when ISL can be detected using IF. Importantly, we were not able to detect ISL1 protein expression using IF at

12-hr, at which its expression level is 12 FPKM +/-18. Lastly, we now show that, in the cynomolgus macaque peri-gastrula, while pSMAD1/5+ primitive streak-derived disseminating cells show abundant TBXT expression, no clear TBXT expression is seen in the amnion territory (Fig. S2G, Line#291-293). 

Together, these results show that while a TBXTlow state clearly emerges during hPSC-amnion development, in wild-type hPSC cultured in Glass-3D+BMP, TBXT levels remain low throughout amnion differentiation. However, in the absence of TFAP2A, a TBXThigh state is seen, suggesting that TFAP2A is critical for suppressing this TBXThigh state in fate spreading cells, perhaps by preventing BMP responding cells from acquiring embryonic lineages (e.g., mesodermal and/or primordial germ cells).

The authors should address why they get different results from Castillo-Venzor et al 2023 DOI: 10.26508/lsa.202201706  

Thank you very much for this helpful suggestion, and we now include a section detailing this in the Discussion (Line#410-432). In short, we propose several possibilities. First, culturing conditions are highly distinct. Castillo-Venzor et al. (Castillo-Venzor et al., 2023) utilize initial “pre-mesoderm” conditioning by Activin and CHIR, followed by treating floating embryoid bodies with a growth factor cocktail (BMP, SCF, EGF and LIF). In contrast, our system (Glass-3D+BMP) employs BMP stimulation of pluripotent cysts. Thus, we suspect that, in the PGCLC differentiation condition, cells are conditioned to the pre-mesodermal lineage. Moreover, we propose that amnion fate spreading may not be present in the PGCLC system, perhaps due to differences in geometry (aggregates versus cysts), or due to differing lineage commitment programs. That is, while initial amniogenesis is seen in the PGCLC system, most cells may already be committed to the PGC-like or mesodermal lineages by the time amnion fate spreading can occur. Alternatively, because several cell types (PGC-like, mesodermal and amniotic) co-exist in the culture by Castillo-Venzor et al., PGC-like and/or mesodermal cells may compensate for the loss of TFAP2A.

Reviewer #2: 

In this study, Sekulovski and colleagues report refinements to an in vitro model of human amnion formation. Working with 3D cultures and BMP4 to induce differentiation, the authors chart the time course of amnion induction in human pluripotent stem cells in their system using immunofluorescence and RNA-seq. They carry out validation through comparison of their data to existing embryo datasets, and through immunostaining of post-implantation marmoset embryos. Functional experiments show that the transcription factor TFAP2C drives the amnion differentiation program once it has been initiated. 

There is currently great interest in the development of in vitro models of human embryonic development. While it is known that the amnion plays an important structural supporting role for the embryo, its other functions, such as morphogen production and differentiation potential, are not fully understood. Since a number of aspects of amnion development are specific to primates, models of amniogenesis will be valuable for the study of human development. Advantages of this model include its efficiency and the purity of the cell populations produced, a significant degree of synchrony in the differentiation process, benchmarking with single-cell data and immunocytochemistry from primate embryos, and identification of key markers of specific phases of differentiation. Weaknesses are the absence of other embryonic tissues in the model, and overinterpretation of certain findings, in particular relating bulk RNA-seq results to scRNA-seq data from published analyses of primate embryos and results from limited (though high quality) embryo immunostainings.  

We are happy that Reviewer #2 agrees that our Glass-3D+BMP model is important for investigating additional roles of amniogenesis, as well as roles of amnion as a signaling hub, due to the purity of the amniotic cell population, and a high degree of synchrony of differentiation.

We respectfully disagree that the absence of other embryonic tissues in the model is a weakness: rather, we believe it is a strength because this single lineage amnion model allows us to directly (and independently) investigate mechanisms underlying amnion lineage progression. For example, as noted above in our response to Reviewer #1, use of our hPSCamnion model allowed us to see a very specific and interesting phenotype in the absence of TFAP2A (reduced amnion formation and emergence of an alternative lineage), though previous findings by Castilllo-Venzor et al. concluded that amniogenesis is not affected by loss of TFAP2A. We noted that the culture method used by Castillo-Venzor et al. contains several cell types (amniotic, mesodermal and PGC-like), and that amniogenesis may be intact in that model due to compensation by the presence of these other cell types. That is, while cell-cell interactions can indeed be gleaned in culture systems with several cell types, the presence of multiple cell types and their additional signaling inputs can also confound some aspects of mechanistic investigations. We now include a paragraph in the Discussion of the revised manuscript (Line#410-432), in which we detail these ideas, and suggest that, because of the cell purity, our Glass-3D+BMP model enables robust mechanistic examinations, specifically during amnion formation.

We address Reviewer #2’s point about bulk vs. single cell transcriptomic similarity analysis in Reviewer’s specific point #4 below. We do, however, want to note here that we have performed the same analysis using a 14-day old cynomolgus macaque peri-gastrula single cell RNA sequencing dataset generated by Yang et al. (Yang et al., 2021), and obtained a lineage trajectory (Fig. 4F, Line#265-268) similar to that seen when the Tyser et al. dataset (Tyser et al., 2021) was used (Fig. 4C).

Importantly, while cynomolgus macaque early embryo samples are limited, we now include additional staining (Fig. S2G). 

Reviewer #2 (Recommendations For The Authors): 

Provide more confirmation of key findings in more than one stem cell line. 

We now confirm key findings in the H7 human embryonic stem cell line (Fig. S1C).

Provide stronger evidence e.g. scRNA-seq to support the existence of intermediate cells or tone down the conclusions.  

We agree that this is a very important point. In our recent study (Sekulovski et al., 2023), we performed single cell RNA sequencing of Gel-3D, another hPSC-amnion model. In this study, we comprehensively described the transcriptome associated with the “intermediate” cell types, as well as CLDN10 as a marker of these cell types. Moreover, we now include additional data showing the molecular characteristics of the TBXTlow intermediate cells during amniogenesis in hPSC-amnion (Fig. S2C, S2D) and d14 cynomolgus macaque peri-gastrula (Fig 4G, replot of single cell RNAseq by (Yang et al., 2021), Line#264-268).

Provide more data on the expression of DLX5 in the model. 

We now provide a DLX5 staining time course in Fig. 7C. We find that, similar to ISL1, prominent DLX5 staining is seen in the focal cells at 24-hr post-BMP. Interestingly, at 48-hr, while some cells show high levels of DLX5, some cells show low DLX5 levels; this is of an interest for future investigations.

(1) L159 - the authors should repeat more of the key results in at least one other hPSC line, to ensure reproducibility of the method. Figure S1 contains minimal information (one timepoint, three genes, one biological replicate) on a single different hPSC line. 

We now include additional validation analysis using the H7 human ESC line (Fig. S1).

(2) Figure 1- it is a little difficult to appreciate cyst formation from images taken at one level in the stack, can the authors perhaps show a 3D rendering or video to display morphogenesis better? 

We now provide all optical sections of cysts shown in Movie 1.

(3) Figure 1-did the authors carry out podocalyxin staining? This is a standard marker for lumenogenesis.  

We now provide PODXL staining (Fig. 1A,1B).

(4) L248 onwards and Figure 4-I am a little skeptical concerning conclusions drawn from an overlay of bulk RNA-seq onto scRNA-seq UMAP plots. I think the authors need to provide some strong justification for this approach. I would be particularly careful about concluding that cells depicted in Fig 4D represent an intermediate close to primitive streak and even more careful about claiming any lineage relationship between T-positive "primitive streak like intermediates" and the trajectory of cells in the model. UMAP is a dimension-reduction technique for the visualization of clusters in high-dimensional data. It is not a lineage-tracing methodology. It would have been preferable for the authors to present their own scRNA-seq data from the model.  

We are sorry that it was not clear that our approach to find similarity between bulk and single cell RNA-seq data is largely based on a published work (Granja et al., Nature Biotechnology 2019, (Granja et al., 2019)) named projectLSI. Please refer to our Methods section for details of the implementation and how we modified it for better visualization (addressed in Line#667-676 of the original manuscript, now in Line#718-730). The performance of projectLSI was extensively evaluated in the original article. Furthermore, as pointed out, UMAP is indeed a dimension reduction method that has been widely used in single cell RNA-seq research. In addition to visualizing clusters, trajectory analysis, such as RNA-velocity (which is used in this study), is another successful and widely adapted application of UMAP to gauge fate progression. Therefore, we believe that UMAP can be effectively used as a lineage prediction methodology, and that our use of bulk to single cell transcriptomic similarity analysis leveraging projectLSI is well justified at conceptual and technical levels.

As illustrated in Fig. 5A, we performed RNA-velocity analysis of the Tyser et al. dataset, and our result clearly predicts a differentiation trajectory from Epiblast, a part of the TBXTlow population shown in Fig. 4D, and, then, to Ectoderm/Amnion cells. Consistent with this bioinformatic result, we now show that some cells show some but weak TBXT expression (at the transcript level) at the 24-hr post-BMP timepoint in control hPSC-amnion (Fig. S2D, Line#264-265). Importantly, our conclusion is drawn from a trajectory based on our time course (0, 0.5, 1, 3, 6, 12, 24, and 48 hours post-BMP treatment) which shows a clear transition from epiblast cells to TBXTlow and then finally to the ectoderm/amnion population. Moreover, using the transcriptomic similarity analysis, we found that the loss of TFAP2A leads to emergence of more primitive streak-like transcriptional characteristics (Fig. 8D). Indeed, using IF, we now show that several fate spreading cells in the TFAP2A-KO cysts are TBXThigh (Fig. 8E, Line#373-374). Thus, the new data provide additional evidence for the successful implementation of this bulk/single cell transcriptomic similarity analysis.

Together, our bioinformatic and localization analyses show that the Glass-3D+BMP system recapitulates the trajectory found in our Tyser et al. RNA-velocity analysis, further supporting the validity of this differentiation trajectory. To avoid confusion, however, we now omit the “primitive streak-like” phrase when describing the TBXTlow cells because, while they may show some TBXT expression, they are likely intermediate fate transitioning cells. Indeed, a recent study by Ton et al. (Ton et al., 2023) showed that the Tyser et al. Primitive Streak cells consist of a mix of several lineage progressing cells (e.g., Epiblast, Non-neural ectoderm, Anterior or caudal primitive streak, PGC). Therefore, these cells are now specifically described as “TBXTlow” state; TBXThigh cells are described as primitive streak-like state.

(5) L276 Tyser data do come from a primate model; the authors mean NHP.  

We now specifically state that the validation is performed in a non-human primate model (Line#280).

(6) Figure 5-though the immunostaining of the CS6/7 monkey embryos is excellent, the authors should not overinterpret these images. What is shown is not a time course, and one can only infer that a particular pattern of gene expression exists in a spatial sense from these images. In the model (Figure 2), the epiblast markers gradually fade and overlap for a time with emergent amnion markers, but in Figure 5 the transition between epiblast and amnion in the embryo seems pretty sharp, at least in terms of gene expression. There may be a few cells in D that show overlap of SOX2 and TFAP2A, but if the authors want to claim that a transition zone exists, they need to produce stronger evidence. Figure 7 is more convincing but see the next point. 

Thank you for this insightful comment. We now address the nature of the transitioning boundary cell population extensively in our other recent study (Sekulovski et al., 2023).

(7) Figure 7 further confuses the issue. A zone at either end of the epiblast is clearly positive for Sox2 and the two amnion markers, clearer than in Figure 5, but why does the marker DLX5 overlap with SOX2 in the embryo (7d) but not the model (7C)? Arguments regarding intermediate cell populations would be greatly strengthened by scRNA-seq data on the model system. 

In our original manuscript, our DLX5 staining was performed at 48-hr post-BMP, at which SOX2 expression is absent in all cells. Our new analysis at the 24-hr timepoint now shows that DLX5 is expressed in SOX2+ cells (this is now presented in Fig. 7C).

As stated in the point #6, our recent study comprehensively describes the transcriptomic and spatial characteristics of the transitioning boundary cell population (Sekulovski et al., 2023).

(8) L357 TFAP2C KO does not resemble intermediate cysts in Figure 2. In Figure 2, both SOX2 and amnion markers are co-expressed in the same cells. In 8C, SOX2 and ISL1 are mutually exclusive.  

We agree with this comment, and now removed this statement pointing out the resemblance (Line#359 of the original manuscript).

(9) Figure 8d-the same caveats noted above regarding the interpretation of superposition of bulk RNA-seq data with scRNA-seq UMAP analysis apply here.  

Please refer to our explanation in point#4.

Reviewer #3: 

In this work, the authors tried to profile time-dependent changes in gene and protein expression during BMP-induced amnion differentiation from hPSCs. The authors depicted a GATA3 - TFAP2A - ISL1/HAND1 order of amniotic gene activation, which provides a more detailed temporary trajectory of amnion differentiation compared to previous works. As a primary goal of this study, the above temporal gene/protein activation order is amply supported by experimental data. However, the mechanistic insights on amniotic fate decision, as well as the transcriptomic analysis comparing amnion-like cells from this work and other works remain limited. While this work allows us to see more details of amnion differentiation and understand how different transcription factors were turned on in a sequence and might be useful for benchmarking the identity of amnion in ex utero cultured human embryos/embryoids, it provides limited insights on how amnion cells might diverge from primitive streak / mesoderm-like cells, despite some transcriptional similarity they shared, during early development.  

We are happy that Reviewer #3 appreciates that our model can be used effectively to identify previously unrecognized amniotic gene activation cascade, providing a comprehensive timecourse transcriptomic resource.

As detailed below, we address specific concerns raised by Reviewer #3. We now provide additional mechanistic insights into amnion fate progression, and include additional transcriptomic comparisons with a cynomolgus macaque single cell RNA sequencing dataset.

Reviewer #3 (Recommendations For The Authors): 

(1) The authors generated KO cell lines lacking GATA3 and TFAP2A, respectively. Their results showed some disrupted amnion differentiation only in TFAP2A-KO. Therefore, these data do not provide sufficient evidence to support whether these transcription factors are crucial for amnion fate specification. Perhaps an experiment could be done with overexpression of these markers and testing if they could force hPSC to adopt amnion-like fate.  

Thank you for this insightful comment. We generated cell lines that enable us to inducibly express GATA3 or TFAP2A, and the transgene expression was induced at d2 (when BMP treatment is normally initiated) until d4. However, this inducible expression did not lead to amniogenesis, and cysts maintained pluripotency. Due to the uninterpretable nature, these results are not included in the revised manuscript.

As detailed extensively in the manuscript, within each cyst, amniogenesis is initially seen focally, then spreads laterally resulting in fully squamous amnion cysts. This is also seen in our previously published Gel-3D amnion model (extensively described in (Shao et al., 2017)). In the absence of TFAP2A, we showed that the focal amniogenesis is observed, but spreading is not seen, suggesting that TFAP2A controls amnion fate progression. Therefore, while TFAP2A is not critical for the amnion fate specification in the focal cells, our results show that TFAP2A indeed helps to promote amniotic specification of cells neighboring the focal amniotic cells. Moreover, in the revised manuscript, we now show that TFAP2A transgene expression in the TFAP2A-KO background restores formation of fully squamous hPSC-amnion, further establishing the role of TFAP2A in amnion fate progression (Fig. 8C of the revised manuscript, Line#362-364).

(2) The transcriptomic analysis made by the authors provides some comparison between BMPinduced amnion-like cells in vitro and the amnion-like cells from CS7 human embryo in vivo. However, the data set from the human embryo contains only a limited number of cells, and might not provide a sufficient base for decisive assessment of the true identity of amnion-like cells obtained in vitro. It might help if the authors could integrate their bulk sequencing data with other primate embryo data sets.  

Thank you for this helpful comment. We have now performed our transcriptional similarity analysis using early (day 14) cynomolgus macaque embryo datasets generated in a study by (Yang et al., 2021), and found that the bulk time-course transcriptome of our hPSC-amnion model overlaps with the cynomolgus macaque amniotic lineage progression (Fig. 4F, Line#265268). We also now provide the expression of key markers within the Yang et al. dataset (GATA3, TFAP2A, ISL1, TBXT, DLX5, Fig. 4G, S2F).

(3) Following the point above, the authors used transcriptomic analysis to identify several intermediate states of cells during amnion differentiation and claimed that there is a primitivestreak-like intermediate. However, this might be an overstatement. During stem cell culture and differentiation, intermediate states showing a mixture of biomarkers are very common and do not imply that such intermediates have any biological meaning. However, stating that amnion differentiation passes through primitive streak-like intermediates, might imply a certain connection between these two lineages, for which there is a lack of solid support. Instead, a more interesting question might be how amnion and primitive streak differentiation, despite some transcriptomic similarity, diverge from each other during early development. What factors make this difference? The authors might further analyze RNA-seq data to provide some insights.  

Thank you very much for the insightful comments. 

We understand Reviewer #3’s concern that the intermediate state that we see may not recapitulate a primitive streak-like state. However, in our original manuscript, we described these cells as “Primitive Streak-like” because those cells were annotated as Primitive Streak in the dataset by Tyser et al. Interestingly, a recent study by Ton et al. showed that the Tyser et al. Primitive Streak cells actually consist of a mixture of different cell lineages (e.g., Epiblast, Nonneural ectoderm, Anterior or caudal primitive streak, PGC (Ton et al., 2023)). Therefore, we agree that it was an overstatement to call them “Primitive Streak-like”, and, to avoid confusions, we now label the TBXTlow sub-population found in the Tyser et al. Primitive Streak population as “TBXTlow state” throughout the manuscript.

Our data indicate that TFAP2A may play a role in controlling the lineage decision between amnion and primitive streak cells that abundantly express TBXT (TBXThigh). In the original manuscript, we included data showing that 48-hr TFAP2A-KO cysts show transcriptomic characteristics similar to some Primitive Streak cells (Fig. 8D). Intriguingly, our new data show that, in the absence of TFAP2A, some TBXThigh cells are indeed seen (Fig. 8E, Line#373-374). These results provide a body of evidence for the role of TFAP2A in promoting the amniotic lineage, perhaps by suppressing the TBXThigh state. This point is now addressed in the Discussion (Line#401-409).

Additional new data:

Using Western blot, we now show that GATA3 is absent in the GATA3-KO lines (Fig. S4C). We noticed that this was lacking in the original manuscript.

We now show that an inducible expression of TFAP2A in the TFAP2A-KO cysts leads to controllike cysts (Fig. 8C, Line#362-364).

Additional changes:

Typos were fixed in Fig. 5I – “boundary” and “disseminating” were not spelled correctly.

Line#350 – we originally noted “GATA3 expression precedes TFAP2A expression by approximately 12 hours”. This was incorrect, and is changed to 9 hours in the revised manuscript. We apologize for this mistake.

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