Author Response

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

Reviewer #1 (Public Review):

Meta-cognition, and difficulty judgments specifically, is an important part of daily decision-making. When facing two competing tasks, individuals often need to make quick judgments on which task they should approach (whether their goal is to complete an easy or a difficult task).

In the study, subjects face two perceptual tasks on the same screen. Each task is a cloud of dots with a dominating color (yellow or blue), with a varying degree of domination - so each cloud (as a representation of a task where the subject has to judge which color is dominant) can be seen an easy or a difficult task. Observing both, the subject has to decide which one is easier.

It is well-known that choices and response times in each separate task can be described by a driftdiffusion model, where the decision maker accumulates evidence toward one of the decisions (”blue” or ”yellow”) over time, making a choice when the accumulated evidence reaches a predetermined bound. However, we do not know what happens when an individual has to make two such judgments at the same time, without actually making a choice, but simply deciding which task would have stronger evidence toward one of the options (so would be easier to solve).

It is clear that the degree of color dominance (”color strength” in the study’s terms) of both clouds should affect the decision on which task is easier, as well as the total decision time. Experiment 1 clearly shows that color strength has a simple cumulative effect on choice: cloud 1 is more likely to be chosen if it is easier and cloud 2 is harder. Response times, however, show a more complex interactive pattern: when cloud 2 is hard, easier cloud 1 produces faster decisions. When cloud 2 is easy, easier cloud 1 produces slower decisions.

The study explores several models that explain this effect. The best-fitting model (the Difference model is the paper’s terminology) assumes that the decision-maker accumulates evidence in both clouds simultaneously and makes a difficulty judgment as soon as the difference between the values of these decision variables reaches a certain threshold. Another potential model that provides a slightly worse fit to the data is a two-step model. First, the decision maker evaluates the dominant color of each cloud, then judges the difficulty based on this information.

Thank you for a very good summary of our work.

Importantly, the study explores an optimal model based on the Markov decision processes approach. This model shows a very similar qualitative pattern in RT predictions but is too complex to fit to the real data. It is hard to judge from the results of the study how the models identified above are specifically related to the optimal model - possibly, the fact that simple approaches such as the Difference model fit the data best could suggest the existence of some cognitive constraints that play a role in difficulty judgments.

The reviewer asks “how the models identified above are specifically related to the optimal model”. We did fit the four models to simulations of the optimal model and found that the Difference model was the closest. However, we did not fit the parameters of the optimal model to the data (no easy feat given the complexity of the model) as the experiment was not designed to incentivize maximization of the reward rate and fitting would have been computationally laborious. We therefore focused on the qualitative features of the optimal model and how they compare to our models. We now also include the optimal model for the known color dominance RT experiment (line 420). We have also added a new paragraph in the Discussion on the optimal model at line 503 comparing it qualitatively to the Difference model.

The Difference model produces a well-defined qualitative prediction: if the dominant color of both clouds is known to the decision maker, the overall RT effect (hard-hard trials are slower than easyeasy trials) should disappear. Essentially, that turns the model into the second stage of the twostage model, where the decision maker learns the dominant colors first. The data from Experiment 2 impressively confirms that prediction and provides a good demonstration of how the model can explain the data out-of-sample with a predicted change in context.

Overall, the study provides a very coherent and clean set of predictions and analyses that advance our understanding of meta-cognition. The field would benefit from further exploration of differences between the models presented and new competing predictions (for instance, exploring how the sequential presentation of stimuli or attentional behavior can impact such judgments). Finally, the study provides a solid foundation for future neuroimaging investigations.

Thank you for your positive comments and suggestions.

Reviewer #2 (Public Review):

Starting from the observation that difficulty estimation lies at the core of human cognition, the authors acknowledge that despite extensive work focusing on the computational mechanisms of decision-making, little is known about how subjective judgments of task difficulty are made. Instantiating the question with a perceptual decision-making task, the authors found that how humans pick the easiest of two stimuli, and how quickly these difficulty judgments are made, are best described by a simple evidence accumulation model. In this model, perceptual evidence of concurrent stimuli is accumulated and difficulty is determined by the difference between the absolute values of decision variables corresponding to each stimulus, combined with a threshold crossing mechanism. Altogether, these results strengthen the success of evidence accumulation models, and more broadly sequential sampling models, in describing human decision-making, now extending it to judgments of difficulty.

The manuscript addresses a timely question and is very well written, with its goals, methods and findings clearly explained and directly relating to each other. The authors are specialists in evidence accumulation tasks and models. Their modelling of human behaviour within this framework is state-of-the-art. In particular, their model comparison is guided by qualitative signatures which are diagnostic to tease apart the different models (e.g., the RT criss-cross pattern). Human behaviour is then inspected for these signatures, instead of relying exclusively on quantitative comparison of goodness-of-fit metrics. This work will likely have a wide impact in the field of decisionmaking, and this across species. It will echo in particular with many other studies relying on the similar theoretical account of behaviour (evidence accumulation).

Thank you for these generous comments.

A few points nevertheless came to my attention while reading the manuscript, which the authors might find useful to answer or address in a new version of their manuscript.

1) The authors acknowledge that difficulty estimation occurs notably before exploration (e.g., attempting a new recipe) or learning (e.g., learning a new musical piece) situations. Motivated by the fact that naturalistic tasks make difficult the identification of the inference process underlying difficulty judgments, the authors instead chose a simple perceptual decision-making task to address their question. While I generally agree with the authors’s general diagnostic, I am nevertheless concerned so as to whether the task really captures the cognitive process of interest as described in the introduction. As coined by the authors themselves, the main function of prospective difficulty judgment is to select a task which will then ultimately be performed, or reject one which won’t. However, in the task presented here, participants are asked to produce difficulty judgments without those judgements actually impacting the future in the task. A feature thus key to difficulty judgments thus seems lacking from the task. Furthermore, the trial-by-trial feedback provided to participants also likely differ from difficulty judgments made in real world. This comment is probably difficult to address but it might generally be useful to discuss the limitations of the task, in particular in probing the desired cognitive process as described in introduction. Currently, no limitations are discussed.

We have added a Limitations paragraph to the Discussion and one item we deal with is the generalization of the model to more complex tasks (line 539).

2) The authors take their findings as the general indication that humans rely on accumulation evidence mechanisms to probe the difficulty of perceptual decisions. I would probably have been slightly more cautious in excluding alternative explanations. First, only accumulation models are compared. It is thus simply not possible to reach a different conclusion. Second, even though it is particularly compelling to see untested predictions from the winning model in experiment #1 to be directly tested, and validated in a second experiment, that second experiment presents data from only 3 participants (1 of which has slightly different behaviour than the 2 others), thereby limiting the generality of the findings. Third, the winning model in experiment #1 (difference model) is the preferred model on 12 participants, out of the 20 tested ones. Fourth, the raw BIC values are compared against each other in absolute terms without relying on significance testing of the differences in model frequency within the sample of participants (e.g., using exceedance probabilities; see Stephan et al., 2009 and Rigoux et al., 2014). Based on these different observations, I would thus have interpreted the results of the study with a bit more caution and avoided concluding too widely about the generality of the findings.

Thank you for these suggestions.

i) We have now make it clear in the Results (line 126) that all four models we examine are accumu-lation models. In addition, we have added a paragraph on Limitations (line 530) in the Discussion where we explain why we only consider accumulation models and acknowledge that there are other non-accumulation models.

ii) Each of three participants in Experiment 2 performed 18 sessions making it a large and valuabledataset necessary to test our hypothesis. We have now included a mention of the the small number of participants in Experiment 2 in a Limitations paragraph in the Discussion (line 539).

iii) As suggested, we have now calculated exceedance probabilities for the 4 models which gives[0,0.97,0.03,0]. This shows that there is a 0.97 probability of the Difference model being the most frequent and only a 0.03 probability of the two-step model. We have included this in the results on line 237.

3) Deriving and describing the optimal model of the task was particularly appreciated. It was however a bit disappointing not to see how well the optimal model explains participants behaviour and whether it does so better than the other considered models. Also, it would have been helpful to see how close each of the 4 models compared in Figures 2 & 3 get to the optimal solution. Note however that neither of these comments are needed to support the authors’ claims.

The reviewer asks how close each of the four models is to the optimal solution. We did fit the four models to simulations of the optimal model and found that the Difference model was the closest. However, we did not fit the parameters of the optimal model to the data (no easy feat given the complexity of the model) as the experiment was not designed to incentivize maximization of the reward rate and fitting would have been computationally laborious. We therefore focused on the qualitative features of the optimal model and how they compare to our models. We now also include the optimal model for the known color dominance RT experiment (line 420). We have also added a new paragraph in the Discussion on the optimal model at line 503 comparing it qualitatively to the Difference model.

4) The authors compared the difficulty vs. color judgment conditions to conclude that the accumulation process subtending difficulty judgements is partly distinct from the accumulation process leading to perceptual decisions themselves. To do so, they directly compared reaction times obtained in these two conditions (e.g. ”in other cases, the two perceptual decisions are almost certainly completed before the difficulty decision”). However, I find it difficult to directly compare the ’color’ and ’difficulty’ conditions as the latter entails a single stimulus while the former comprises two stimuli. Any reaction-time difference between conditions could thus I believe only follow from asymmetric perceptual/cognitive load between conditions (at least in the sense RT-color < RT-difficulty). One alternative could have been to present two stimuli in the ’color’ condition as well, and asking participants to judge both (or probe which to judge later in the trial). Implementing this now would however require to run a whole new experiment which is likely too demanding. Perhaps the authors could instead also acknowledge that this a critical difference between their conditions, which makes direct comparison difficult.

We feel we can rule out that participants make color decisions (as in the color task) to make difficulty decisions. For example, making a color choice for 0% color strength takes longer than a difficulty choice for 0:52% color strengths. Thus, the difficulty judgment does not require completion of the color decisions. Therefore, average reaction time for a single color patch (C𝑆1) can be longer than the reaction time for the difficulty task which contains the same coherence (C𝑆1) for one of the patches. This is true despite the difficulty decision requiring monitoring of two patches (which might be expected to be slower than monitoring one patch). We have added this in to the Discussion at line 449.

Reviewer #3 (Public Review):

The manuscript presents novel findings regarding the metacognitive judgment of difficulty of perceptual decisions. In the main task, subjects accumulated evidence over time about two patches of random dot motion, and were asked to report for which patch it would be easier to make a decision about its dominant color, while not explicitly making such decision(s). Using 4 models of difficulty decisions, the authors demonstrate that the reaction time of these decisions are not solely governed by the difference in difficulties between patches (i.e., difference in stimulus strength), but (also) by the difference in absolute accumulated evidence for color judgment of the two stimuli. In an additional experiment, the authors eliminated part of the uncertainty by informing participants about the dominant color of the two stimuli. In this case, reaction times were faster compared to the original task, and only depended on the difference between stimulus strength.

Overall, the paper is very well written, figures and illustrations clearly and adequately accompanied the text, and the method and modeling are rigor.

The weakness of the paper is that it does not provide sufficient evidence to rule out the possibility that judging the difficulty of a decision may actually be comparing between levels of confidence about the dominant color of each stimulus. One may claim that an observer makes an implicit color decision about each stimulus, and then compares the confidence levels about the correctness of the decisions. This concern is reflected in the paper in several ways:

We tested a Difference in confidence model (line 315) in the orginal paper and showed it was inferior to the Difference model. We did this for experiment 2, RT task so that we could fit the unknown color condition and try to predict the known color condition. To emphasize this model (which we think the reviewer may have missed) we have moved the supplementary figure to the main results (now Fig. 6) as we think it is very cool that we were able to discard the confidence model.

When comparing the confidence model to the Difference we found the difference model was pre-Δ ferred with BIC of 38, 56, 47. We are unsure why the reviewer feels this “does not provide sufficient evidence to rule out the possibility that judging the difficulty of a decision may actually be comparing between levels of confidence about the dominant color of each stimulus.” We regard this as strong evidence.

1) It is not clear what were the actual instructors to the participants, as two different phrasings appear in the methods: one instructs participants to indicate which stimulus is the easier one and the other instructs them to indicate the patch with the stronger color dominance. If both instructions are the same, it can be assumed that knowing the dominant color of each patch is in fact solving the task, and no judgment of difficulty needs to be made (perhaps a confidence estimation). Since this is not a classical perceptual task where subjects need to address a certain feature of the stimuli, but rather to judge their difficulties, it is important to make it clear.

We now include the precise words used to instruct the participant (line 604): “Your task is to judge which patch has a stronger majority of yellow or blue dots. In other words: For which patch do you find it easier to decide what the dominant color is? It does not matter what the dominant color of the easier patch is (i.e., whether it is yellow or blue). All that matters is whether the left or right patch is easier to decide”.

Knowing both colors or the dominant color is not sufficient to solve the task. Knowing both are yellow does not tell you which has more yellow which is what you need to estimate to solve the task. Again, we tested a confidence model in the original version of the paper and showed it was a poor model compared to the Difference model.

2) Two step model: two issues are a bit puzzling in this model. First, if an observer reaches a decision about the dominant color of each patch, does it mean one has made a color decision about the patches? If so, why should more evidence be accumulated? This may also support the possibility that this is a ”post decision” confidence judgment rather than a ”pre decision” difficulty judgment. Second, the authors assume the time it takes to reach a decision about the dominant color for both patches are equal, i.e., the boundaries for the ”mini decision” are symmetrical. However, it would make sense to assume that patches with lower strength would require a longer time to reach the boundaries.

In the Two-step model we assume a mini decision is made for the color of each stimulus. However, the assumption is that this is made with a low bound so it is not a full decision as in a typical color decision. Again estimating the colors from the mini decision does not tell you which is easier so you need to accumulate more evidence to make this judgment. In fact the Race model is a version of the two step in which no further accumulation is made after the initial decision and this model fits poorly (we now explain this on line 185). We assume for simplicity that the first stimulus to cross a bound triggers both mini color decisions. So although the bounds are equal the one with stronger color dominance is more likely to hit the bound first.

We have already addressed this concern about the comparison with confidence above.

3) Experiment 2: the modification of the Difference model to fit the known condition (Figure 5b),can also be conceptualized as the two-step model, excluding the ”mini” color decision time. These two models (Difference model with known color; two-step model) only differ from each other in a way that in the former the color is known in advance, and in the second, the subject has to infer it. One may wonder if the difference in patterns between the two (Figure 3C vs. Figure 6B) is only due to the inaccuracies of inferring the dominant color in the two-step model.

In Experiment 2 the participant is explicitly informed as to the color dominance of both stimuli. Therefore, assuming the two-step model skips the first step and uses this explicit information in the second step, the difference and two-step model are identical for modeling Experiment 2. We explain this now on line 277.

As the reviewer suggests, differences in predictions between the Difference and Two-step arise from trials in which there is a mismatch between the inferred dominant colors from the two-step model and the color associated with the final DVs in the Difference model. We now explain this on line 187. We do not see this as a problem of any sort but just defines the difference between the models. Note that the new exceedance analysis now strongly supports the Difference model as the most common model among the participants.

An additional concern is about the controlled duration task: Why were these specific durations chosen (0.1-1.65 sec; only a single duration was larger than 1sec), given the much longer reaction times in the main task (Experiment 1), which were all larger on average than 1sec? This seems a bit like an odd choice. Additionally, difficulty decision accuracies in this version of the task differ between known and unknown conditions (Figure 7), while in the reaction time version of the same task there were no detectable differences in performance between known and unknown conditions (Figure 6C), just in the reaction times. This discrepancy is not sufficiently explained in the manuscript. Could this be explained by the short trial durations?

The reviewer asks about the choice of stimulus durations in Experiment 2. First, RTs in Experiment 1 do not only reflect the time needed to make decisions but also contain non-decision times (0.23-0.47 s). So to compare decision time in RT and controlled duration experiment one must subtract the non-decision time from the RTs (the non-decision time is not relevant to the controlled duration experiment). Second, the model specifically predicts that differences in performance between the known and unknown color dominance conditions are largest for short duration stimulus presentation trials (see Fig. 7). We explain this on line 346. For long durations, performance pretty much plateaus, and many decisions have already terminated (Kiani 2008). We sample stimulus durations from a discrete truncated exponential distribution to get roughly equal changes in accuracy between consecutive durations (which we now explain at line 345).

Group consensus review

The reviewers have discussed with each other, and they have discussed a series of revisions which, if carried out, would make their evaluation of your paper even more positive. I outline them below in case you would be interested in revising your paper based on these reviews. You will see below that the reviewers share overall a quite positive evaluation of your study. All three limitations described in the Public Reviews could be addressed explicitly in the discussion which for the moment is limited to description and generalization of findings.

1) The model selection procedure should be amended and strengthened to provide clearer results. As noted by one of the reviewers during the consultation session, ”the Difference model just barely wins over the two-step model, and the two-step model might produce the same prediction for the next experiment.” You will also see below that Reviewer #2 provides guidance to improve the model selection process: ”[...] the second experiment presents data from only 3 participants (1 of which has slightly different behaviour than the 2 others), thereby limiting the generality of the findings. Third, the winning model in experiment #1 (difference model) is the preferred model on 12 participants, out of the 20 tested ones. Fourth, the raw BIC values are compared against each other in absolute terms without relying on significance testing of the differences in model frequency within the sample of participants (e.g., using exceedance probabilities; see Stephan et al., 2009 and Rigoux et al., 2014).” Altogether, model selection appears currently to be the ’weakest’ part of the paper (Difference model vs. Two-step model, model comparison, how to better incorporate the optional model with the other parts). It would be great if you would improve this section of the Results.

Thank you for these suggestions.

i) We have now make it clear in the Results (line 126) that all four models we examine are accumu-lation models. In addition, we have added a paragraph on Limitations (line 530) in the Discussion where we explain why we only consider accumulation models and acknowledge that there are other non-accumulation models.

ii) Each of three participants in Experiment 2 performed 18 session making it a large and valuabledataset necessary to test our hypothesis. We have now included a mention of the the small number of participants in Experiment 2 in a Limitations paragraph in the Discussion (line 539).

iii) We have now calculated exceedance probabilities for the 4 models which gave [0,0.97,0.03,0]. This shows that there is a 0.97 probability of the Difference model being the most frequent and only a 0.03 probability of the two-step model. We have included this in the results on line 237.

2) All reviewers have noted that the relation of the optimal model with the human data and theother models should be clarified and discussed in a revised version of the manuscript. You will find their specific comments in their individual reviews, appended below.

We now include the optimal model for the known color dominance RT experiment (line 420). We have also added a new paragraph in the Discussion on the optimal model at line 503 comparing it to the Difference model.

3) Finally, the exclusion strategy is also unclear at the moment and should be clarified and discussed explicitly somewhere in a revised version of the manuscript. Reviewers were wondering why so many participants were excluded from Experiment 1, and only 3 participants were included in Experiment 2. This should also be clarified better in the manuscript.

We have clarified the exclusion criteria in the Methods at line 651 as a new subsection.

The data quality problem with MTurk is well documented (Chmielewski, M & Kucker SC. 2020. An MTurk Crisis? Shifts in Data Quality and the Impact on Study Results. Social Psychological and Personality Science, 11, 464-473). Given that this was an online experiment on MTurk, it is hard to know exactly why some participants showed low accuracy, but it’s likely that some may have misunderstood the instructions in the difficulty task or they may have been unmotivated to do well in this highly repetitive task. Either reason would be problematic for our model comparisons that are based on choice-RT patterns. Note that the cut-offs we chose for inclusion were purely based on accuracy, whereas the modeling approach considered RTs, which importantly were not used as a inclusion criterion (see revised methods). Moreover, accuracy cut-offs were fairly lenient and mainly aimed to exclude participants who appeared to be guessing/misunderstood instructions (for reference: mean sensitivity of participants who were included was 2x higher than the cut-offs we used).

Each of three participants in Experiment 2 performed 18 session making it a large and valuable dataset necessary to test our hypothesis. We have now included a mention of the the small number of participants in Experiment 2 in a Limitations paragraph in the Discussion (line 539).

Reviewer #1 (Recommendations For The Authors):

Thank you for an excellent paper, I enjoyed reading it a lot. I have a few questions that could potentially clarify some aspects for the reader.

(1) It seems from the model fit plots (Figure 3) that the RT predictions of the model tend to overshoot in cases where one of the clouds is very easy. Could you include potential interpretations of this effect?

We assume the reviewer is examining the Difference Model (i.e. the preferred model) panel when commenting on the overshoot. It is true the predictions for the highest coherence (bottom purple line) for RT is above the data but it is barely outside the data errorbars of 1 s.e. To be honest we regard this as a pretty good fit and would not want to over-interpret this small mismatch.

(2) On page 4, around line 121, the study discusses the ”criss-crossing” effect in the RT data. You mention that the fact that RTs are long in hard-hard trials compared to easy-easy trials could be important here: ”These tendencies lead to a criss-cross pattern..”. It is confusing since, for instance, the race model does not have a criss-cross, but still exhibits the overall effect. I was intrigued bythe criss-crossing, and after some quick simulations, I found that the equation RT2 ∗ = 2 − 2 ∗ Cs12 − Cs22 + 6 ∗ (Cs1 ∗ Cs2)2 can (very roughly) replicate Figure 1d (bottom panel), so it seems that the criss-crossing effect must be produced by some interactive effect of color strengths on RTs. I wonder if you could provide a better explanation of how this interactive effect is generated by the model, given that it is the main interesting finding in the data. I believe at this point the intuition is not well-outlined.

The criss cross arises through an interaction of the coherences as the reviewer suspects. That is, for the Difference model the RT related to abs(|Coh1|- |Coh2|). If we replace the first abs with a square we get

|coh1|2 + |coh2|2 − 2|coh1||coh2|

The larger this is, the smaller the RT so

RT = constant − coh12 − coh22 + 2|coh1||coh2|

which is very similar to the formula the reviewer mentions.

We now supply an intuition as to why the criss-cross arises in the Difference model (line 167). We do not get a criss-cross in the race model, because there the RT is determined by the Race that that reaches a bound first. Because the races are independent, RTs will be fastest when coherence is high for either stimuli.

(3) Am I wrong in my intuition that the two-step model would produce very similar predictions as the Difference model for Experiment 2? It would be great to discuss that either way since the twostep model seems to produce very close quantitative and pretty much the same qualitative fit to the data of Experiment 1.

In Experiment 2 the participant is explicitly informed about the color dominance of both stimuli. Therefore, assuming the two-step model skips the first step and uses this explicit information in the second step, the difference and two-step model are identical for modeling Experiment 2. We explain this now on line 277.

(4) The inclusion of the optimal model is great. It would be beneficial to provide some more connections to the rest of the paper here. Would this model produce similar predictions for Experiment 2, for instance?

We now include the optimal model for the known color dominance RT experiment (line 420). We have also added a new paragraph in the Discussion on the optimal model at line 503 comparing it to the Difference model.

(5) In the Methods, it is quite striking that out of 51 original participants, most were excluded and only 20 were studied. It is not easy to trace through this section why and how and who was excluded, so it would be great if this information was organized and presented more clearly.

We have clarified this in the Methods at line 651 as a new subsection in the Methods. We also explain that exclusion was not made on RT data which is our main focus in the models.

Reviewer #2 (Recommendations For The Authors):

• As detailed in the ’public review’, a more cautious discussion, notably delineating the limitations of the study would be appreciated.

• In their models, the authors assume that participants sequentially allocate attention between the two stimuli, alternating between them. Did the authors test this assumption and did they consider the possibility that participants could sample from both stimuli in parallel? In particular, does the conclusion of the model comparison also holds under this parallel processing assumption?

Our results are not affected by whether participants sample the stimulus sequentially through alternation or in a parallel manner (Kang et al., 2021). What does change is the parameters of the model (but not their predictions/fits). In the parallel model, information is acquired at twice the rate of the serial model. We can, therefore, obtain the parameters of parallel models (that has serial and parallel models): 𝜅𝑝 = 𝜅𝑠/√2, 𝑢𝑝 = 𝑢𝑠√2, 𝑎𝑝 = 𝑎𝑠/2 and 𝑑𝑝 = 2𝑑𝑠 (Eq. 2). We now explain𝑠 𝑝 identical predictions to the serial model) directly from the parameters of the current sequential models simply by adjusting the parameters that depend on the time scale (subscripts and for this on line 518.

• I found the small paragraph corresponding to lines 193-196 particularly difficult to understand. If the authors could think of a better way to phrase their claim, it would probably help.

We have rewritten this paragraph at line 211

• I found a type on line 122: ”wheres” instead of ”whereas”.

Corrected

• I found a type on line 181: ”or” instead of ”of”.

Yes corrected

• Figure #2 is extremely useful in understanding the models and their differences, make sure it remains after addressing the reviews!

Thank you, this figure is retained.

Reviewer #3 (Recommendations For The Authors):

All comments are detailed in the public review, with some clarifications here:

1) The confusing instructions to the participants are detailed here: under ”overview of experimental tasks” in the methods it says: ”They were instructed... to indicate whether the left or right stimulus was the easier one” (line 520), and below it ”they were required to indicate which patch had the stronger color dominance...” (line 524).

We have clarified the instructions by providing the actual text displayed to participants in the methods and have ensured consistency in the method to talk about judging the easier stimulus (line 604).

The instructions were “Your task is to judge which patch has a stronger majority of yellow or blue dots. In other words: For which patch do you find it easier to decide what the dominant color is? It does not matter what the dominant color of the easier patch is (i.e., whether it is yellow or blue). All that matters is whether the left or right patch is easier to decide”.

2) Minor comments: Line 76: ”that” should be ”than”.

Thanks, corrected

Line 574: ”variable duration task” means ”controlled duration task”?

Yes, corrected

Line 151: ”or” should be ”of”.

Corrected

Reviewer #1 (Public Review):

Meta-cognition, and difficulty judgments specifically, is an important part of daily decision-making. When facing two competing tasks, individuals often need to make quick judgments on which task they should approach (whether their goal is to complete an easy or a difficult task).

In the study, subjects face two perceptual tasks on the same screen. Each task is a cloud of dots with a dominating color (yellow or blue), with a varying degree of domination - so each cloud (as a representation of a task where the subject has to judge which color is dominant) can be seen an easy or a difficult task. Observing both, the subject has to decide which one is easier.

It is well-known that choices and response times in each separate task can be described by a drift-diffusion model, where the decision maker accumulates evidence toward one of the decisions ("blue" or "yellow") over time, making a choice when the accumulated evidence reaches a predetermined bound. However, we do not know what happens when an individual has to make two such judgments at the same time, without actually making a choice, but simply deciding which task would have stronger evidence toward one of the options (so would be easier to solve).

It is clear that the degree of color dominance ("color strength" in the study's terms) of both clouds should affect the decision on which task is easier, as well as the total decision time. Experiment 1 clearly shows that color strength has a simple cumulative effect on choice: cloud 1 is more likely to be chosen if it is easier and cloud 2 is harder. Response times, however, show a more complex interactive pattern: when cloud 2 is hard, easier cloud 1 produces faster decisions. When cloud 2 is easy, easier cloud 1 produces slower decisions.

The study explores several models that explain this effect. The best-fitting model (the Difference model is the paper's terminology) assumes that the decision-maker accumulates evidence in both clouds simultaneously and makes a difficulty judgment as soon as the difference between the values of these decision variables reaches a certain threshold. Another potential model that provides a slightly worse fit to the data is a two-step model. First, the decision maker evaluates the dominant color of each cloud, then judges the difficulty based on this information.

Importantly, the study explores an optimal model based on the Markov decision processes approach. This model shows a very similar qualitative pattern in RT predictions but is too complex to fit to the real data. Possibly, the fact that simple approaches such as the Difference model fit the data best could suggest the existence of some cognitive constraints that play a role in difficulty judgments and could be explored in future research.

The Difference model produces a well-defined qualitative prediction: if the dominant color of both clouds is known to the decision maker, the overall RT effect (hard-hard trials are slower than easy-easy trials) should disappear. Essentially, that turns the model into the second stage of the two-stage model, where the decision maker learns the dominant colors first. The data from Experiment 2 impressively confirms that prediction and provides a good demonstration of how the model can explain the data out-of-sample with a predicted change in context.

Overall, the study provides a very coherent and clean set of predictions and analyses that advance our understanding of meta-cognition. The field would benefit from further exploration of differences between the models presented and new competing predictions (for instance, exploring how the sequential presentation of stimuli or attentional behavior can impact such judgments). Finally, the study provides a solid foundation for future neuroimaging investigations.

Reviewer #2 (Public Review):

Starting from the observation that difficulty estimation lies at the core of human cognition, the authors acknowledge that despite extensive work focusing on the computational mechanisms of decision-making, little is known about how subjective judgments of task difficulty are made. Instantiating the question with a perceptual decision-making task, the authors found that how humans pick the easiest of two stimuli, and how quickly these difficulty judgments are made, are best described by a simple evidence accumulation model. In this model, perceptual evidence of concurrent stimuli is accumulated and difficulty is determined by the difference between the absolute values of decision variables corresponding to each stimulus, combined with a threshold crossing mechanism. Altogether, these results strengthen the success of evidence accumulation models in describing human decision-making, now extending it to judgments of difficulty.

The manuscript addresses a timely question and is very well written, with its goals, methods and findings clearly explained and directly relating to each other. The authors are specialists of evidence accumulation tasks and models. Their modelling of human behaviour within this framework is state-of-the-art. In particular, their model comparison is guided by qualitative signatures which are diagnostic to tease apart different models (e.g., the RT criss-cross pattern). Human behaviour is then inspected for these signatures, instead of relying exclusively on quantitative comparison of goodness-of-fit metrics.

The study has potential limitations well flagged by the authors after the revision process. The main limitation pertains to the (dis)similarity between the behavioural task used in the study and difficulty judgments people actually do in real world (and which are well illustrated in the introduction). First, difficulty judgments made in the task never impact the participant (a new trial simply follows) while difficulty judgments in the wild often determine whether to pursue or quit the corresponding task, which can have consequences years after the difficulty estimation (e.g., deciding to engage in a particular academic path as a function of the estimated difficulty). Second, while trial-by-trial feedback is delivered in the task, difficulty estimation in the wild has to be made with partial information and feedback is either absent or delayed. How much these differences are key in providing an accurate computational description of human difficulty judgments will likely require further research.

Another limitation is the absence of models based on computational principles other than evidence accumulation. Although there are good reasons to favour evidence accumulation models in these settings (as mentioned by the authors in their manuscript), showing that evidence accumulation models would have won against competitors would have further strengthened the authors' claim that difficulty judgment about perceptual information are firmly anchored in the principles of evidence accumulation.

These limitations should not distract the reader from the impact of the present work, which will likely be wide, spanning the whole field of decision-making, and this across species. It will echo in particular with the many other seminal studies that have relied on a similar theoretical account of behaviour and brain activity (evidence accumulation). In addition, this study will hopefully inspire novel task designs aiming at addressing difficulty judgment estimations in controlled lab experiments, possibly with features closer to real world difficulty estimation (e.g., long-term consequences of difficulty estimation and absence of feedback).

Reviewer #3 (Public Review):

The manuscript presents novel findings regarding the judgment of difficulty of perceptual decisions. In the main task (Experiment 1), participants accumulated evidence over time about two tasks, patches of random dot motion, and were asked to report for which patch it would be easier to make a decision about its dominant color, while not explicitly making such decision(s). By fitting several alternative models, authors demonstrated that while accuracy changes as a function of the difference between stimulus strengths, reaction times of such decisions are not solely governed by the difference in stimulus strength, but (also) by the difference in absolute accumulated evidence for color judgment of the two stimuli ('Difference model'). Predictions from the best fitted model were then tested with a new set of conditions and participants (Experiment 2). Here, authors eliminated part of the uncertainty by informing participants about the dominant color of the two stimuli ('known color' condition) and showing that reaction times were faster compared to the 'unknown color' task, and only depended on the difference between stimulus strengths.

The paper deals with a valuable question about a metacognitive aspect of perceptual decision making, which was only sparsely addressed before. The paper is very well written, figures and illustrations clearly accompanied the text, and methods and modeling are rigor. The authors also address the concern that a difficulty judgment might be a confidence estimation, another metacognitive judgment of perceptual decisions, by fitting a Confidence model to the 'known color' condition in Experiment 2 and showing that this model performs worse compared to the Difference model. This is an important control analysis, given the possibility that humans might make an implicit decision about the dominant color of each patch, and then report their level of confidence.

This work is likely to be of great interest in the field of behavioral modeling of perceptual decision making, and might encourage further investigations of how judging the difficulty of a task affects subsequent decisions about the same task.

eLife assessment

This important and compelling study investigates the problem of intracellular acidification induced by commonly-used optogenetic stimulating opsins. The low proton permeability of two high-performance opsins is shown to reduce photostimulated acidification. The findings may be of broad interest in the fields of neuroscience research and optogenetic therapies.

Reviewer #1 (Public Review):

In this manuscript, "Diminishing neuronal acidification by channelrhodopsins with low proton conduction" by Hayward and colleagues, the authors report on the properties of novel optogenetic tools, PsCatCh2.0 and ChR2-3M, that minimize photo-induced acidification. The authors point out that acidification is an undesirable side-effect of many optogenetic approaches that could be minimized using the new tools. ChRs are known to acidify cells, while Arch are known to alkalize cells. This becomes particularly important when optical stimulation is prolonged and pH changes can become significant. pH is known to affect neuronal excitability, vesicular release, and more. To develop novel optogenetic tools with minimal proton conductances, the authors combined channelrhodopsin stimulation with a red-shifted pH sensor to measure pH during optogenetic stimulation. The authors report that optogenetic activation of CheRiff caused slow cellular acidification. 150 seconds of illumination caused a 3-fold increase in protons or approximately a 0.6 unit pH change that returned to baseline very slowly. They also found that pH changes occurred more rapidly, and recovered more rapidly, in dendrites. The authors go on to robustly characterize PsCatCh2.0 and ChR2-3M in terms of their proton conductances, photocurrent, kinetics, and more. They convincingly show that these constructs induced reduced acidification while maintaining robust photocurrents. In sum, this manuscript shows important findings that convincingly characterizes 2 optogenetic tools that have reduced pH artifacts that may be of broad interest to the field of neuroscience research and optogenetic therapies.

Reviewer #2 (Public Review):

In this paper, the authors utilize optogenetic stimulation and imaging techniques with fluorescent reporters for pH and membrane voltage to examine the extent of intracellular acidification produced by different ion-conducting opsins. The commonly used opsin CheRiff is found to conduct enough protons to alter intracellular pH in soma and dendrites of targeted neurons and in monolayers of HEK293T cells, whereas opsins ChR2-3M and PsCatCh2.0 are shown to produce negligible changes in intracellular pH as their photocurrents are mostly carried by metal cations. The conclusion that ChR2-3M and PsCatCh2.0 are more suited than proton conducting opsins for optogenetic applications is well supported by the data.

eLife assessment

This study presents important findings indicating that gradients of functional connectivity are present in the human foetal brain, and that these gradients develop further during gestation, particularly in multisensory brain regions. The study uses state-of-the-art connectomic mapping techniques. However, recent findings suggest that such gradients may reflect confounds within the analysis technique more than underlying brain functions. The evidence for the authors' claims therefore currently appears inadequate as it does not account for these potential confounds.

Reviewer #1 (Public Review):

Summary:

The work studies functional connectivity gradients using advanced resting-state analyses in fetuses and sheds light on pre-existing functional topographies and their continued development during the third trimester of gestation.

Strengths:

The work is novel, and applies state of the art connectomic mapping techniques to study fetal brain organization. The work capitalizes on the existence of large, open access datasets, and shows interesting and impactful findings on the presence of functional topographies from 25GW onwards.

Weaknesses:

To better understand underlying factors in cortical functional organization, the authors could add additional exploratory analyses to assess the role of cortical microstructure/myelin and thalamic connectivity.

Reviewer #2 (Public Review):

In this study, Moore et al. utilise resting-state fMRI data from the Developing Human Connectome Project, applying a recently developed technique ("connectopic mapping") to identify gradients of functional connectivity within resting-state networks in the human foetal brain. Whilst such gradients have previously been identified in adults, this is the first study to explore the topographic organisation of functional connectivity in the foetal brain. Furthermore, the authors describe localised changes within these gradients over the course of gestation, particularly in brain regions implicated in multisensory processing. Together, these results imply that topographic gradients of brain function are present within the developing foetal brain, and continue to develop through gestation. However, the study does not consider critical confounds inherent in the connectopic mapping technique, and as such I do not believe that the data as presented are sufficient to support the conclusions.

Recent evidence (Watson & Andrews, 2023, Neuroimage) has indicated that the connectopic mapping technique employed here can be substantially confounded by spatial autocorrelations present within the data (for instance, occurring naturally due to the inherent smoothness of the BOLD response, and/or introduced artificially during standard data pre-processing steps such as spatial smoothing or interpolation between co-ordinate spaces). These confounds allow connectopic gradients to be obtained even from random data, and which appear highly similar to those obtained from real data, suggesting that these gradients are strongly influenced by such confounds. Consequently, the resulting gradients may be an inevitability of the way the connectopic mapping technique works, rather than reflecting underlying brain functions per se.

In the current study, all of the gradients flow smoothly and continuously along a single axis within every network region, typically oriented relative to the long axis of the region. To put it another way - the connectopic mapping gives fundamentally the same answer in every network region. Such an organisation does feel a bit biologically implausible, and could be more consistent with the gradients representing an inevitable solution of the analysis technique, rather than necessarily reflecting brain function. Indeed, in some cases the gradients do not correspond well to known organisational principles of the regions. For instance, the primary gradient in the principal visual network flows smoothly along a superior to inferior axis, which the authors suggest corresponds to retinotopic polar angle maps - however, polar angle maps would be expected to reverse direction between each visual region, yet such reversals are not present in this connectopic map. The authors note that the foetal gradients appear highly similar to those previously obtained within similar regions in adult participants - this could be indicative of a consistent organisation across development, but would also be consistent with the same confound affecting foetal and adult participants. The reported changes in the gradients across gestation could reflect changes in the extent of these spatial autocorrelations or in the shape of the regions of interest (perhaps in turn resulting from changes in the underlying brain geometry) rather than necessarily reflecting development of brain function or specialisation. None of this precludes the possibility that these connectopic gradients may (at least partially) also reflect genuine brain functions, but it does obfuscate the extent to which they do so. It would be useful for the authors to give some consideration to this issue.

On a different note, could the authors comment on their reason for studying these gradients at the network level. The authors argue (and I agree) that brain function is likely to be organised topographically, rather than split into discrete parcellated regions. Nevertheless, the brain networks the authors choose to use are themselves discrete regions of interest (albeit fairly large ones). Other groups (e.g., Margulies et al, 2016, PNAS) have described coarser-scale connectopic gradients spanning the whole brain. Is there a reason that the authors have chosen to extract network-level gradients, rather than say coarser-scale whole-brain gradients? Have the authors considered examining how whole-brain gradients change over gestation?

Lastly, the correlated changes between gradients and gestation week appear to occur within small localised clusters. Does this reflect local perturbations of the gradient, or is there perhaps a wider change in the gradient as a whole and these clusters reflect extreme points within this that have changed the most (for instance corresponding to an expansion/contraction of the gradient)?

eLife assessment

This important study uses a combination of computational modeling and glutamate imaging to show how a particular synaptic organization referred to as space-time wiring has a limited contribution to a dendritic computation that occurs in the retina. The evidence supporting the claims of the authors is convincing, although the findings are largely confirmatory of previous modeling and experimental results. The work will be of interest to retinal neurobiologists and neurophysiologists interested in dendritic computations.

Reviewer #1 (Public Review):

Summary: Direction selectivity (DS) in the visual system is first observed in the radiating dendrites of starburst amacrine cells (SACs). Studies over the last two decades have aimed to understand the mechanisms that underlie these unique properties. Most recently, a 'space-time' model has garnered special attention. This model is based on two fundamental features of the circuit. First, distinct anatomical types of bipolar cells (BCs) are connected to proximal/distal regions of each of the SAC dendritic sectors (Kim et al., 2014). Second, that input across the length of the starburst is kinetically diverse, a hypothesis that has been only recently demonstrated experimentally using iGluSnFR imaging (Srivastava et al., 2022). However, the stark kinetic distinctions, i.e., the sustained/transient nature of BC input to SACs dendrites appear to be present mainly in responses to stationary stimuli. When BC receptive field properties are probed using white noise stimuli, the kinetic differences between BCs are relatively subtle or nonexistent (Gaynes et al., 2022; Strauss et al., 2022, Srivastava et al., 2022). Thus, if and how BCs contribute to direction selectivity driven by moving spots that are commonly used to probe the circuit remains to be clarified. To address this issue, Gaynes et al., combine evolutionary computational modeling (Ankri et al., 2020) with two-photon iGluSnFR imaging to address to what degree BCs contribute to the generation of direction selectivity in the starburst dendrites in response to stimuli that are commonly used experimentally.

Strengths:

Combining theoretical models and iGluSnFR imaging is a powerful approach as it first provides a basic intuition on what is required for the generation of robust DS, and then tests the extent to which the experimentally measured BC output meets these requirements.

The conclusion of this study builds on the previous literature and comprehensively considers the diverse BC receptive field properties that may contribute to DS (e.g. size, lag, rise time, decay time).

By 'evolving' bipolar inputs to produce robust DS in a model network, these authors provide a sound framework for understanding which kinetic properties could potentially be important for driving downstream DS. They suggest that response delay/decay kinetics, rather than the center/surround dynamics are likely to be most relevant (albeit the latter could generate asymmetric responses to radiating/looming stimuli).

Weaknesses: Finally, these authors report that the experimentally measured BC responses are far from optimal for generating DS. Thus, the BC-based DS mechanism does not appear to explain the robust DS observed experimentally (even with mutual inhibition blocked). Nevertheless, I feel the comprehensive description of BC kinetics and the solid assessment of the extent to which they may shape DS in SAC dendrites, is a significant advancement in the field.

Reviewer #2 (Public Review):

Summary:<br /> In this study, the authors sought to understand how the receptive fields of bipolar cells contribute to direction selectivity in starburst amacrine cell (SAC) dendrites, their post synaptic partners. In previous literature, this contribution is primarily conceptualized as the 'space-time wiring model', whereby bipolar cells with slow-release kinetics synapse onto proximal dendrites while bipolar cells with faster kinetics synapse more distally, leading to maximal summation of the slow proximal and fast distal depolarizations in response to motion away from the soma. The space-time wiring contribution to SAC direction selectivity has been extensively tested in previous literature using connectomic, functional, and modeling approaches. However, the authors argue that previous functional studies of bipolar cell kinetics have focused on static stimuli, which may not accurately represent the spatiotemporal properties of the bipolar cell receptive field in response to movement. Moreover, this group and others have recently shown that bipolar cell signal processing can change directionally when visual stimuli starts within the receptive field rather than passing through it, complicating the interpretation of moving stimuli that start within a bipolar cell of interest's receptive field (e.g. stimulating only one branch of a SAC or expanding/contracting rings). Thus, the authors choose to focus on modeling and functionally mapping bipolar cell kinetics in response to moving stimuli across the entire SAC dendritic field.

General Comments<br /> There have been several studies that have addressed the contribution of space-time wiring to SAC process direction selectivity. The impact of this project is to show that this contribution is limited. First, the optimal solution obtained by the evolutionary algorithm to generate DS processes is slow proximal and fast distal inputs - exactly what is predicted by space-time wiring, which is exactly what is required of the HRC model. Hence, this result seems expected and it's not clear what the alternative hypothesis is. Second, the experimental results based on glutamate imaging to assess the kinetics of glutamate release under conditions of visual stimulation across a large region of retina confirm previous observations but were important to test. Third, by combining their model model with this experiment data, they conclude that even the optimal space-time wiring is not sufficient to explain the SAC process DS. The results of this approach might be more impactful if the authors come to some conclusion as to what factors do determine the direction selectivity of the SAC process since they have argued that all the current models are not sufficient.

Reviewer #3 (Public Review):

Gaynes et al. investigated the presynaptic and postsynaptic mechanisms of starburst amacrine cell (SAC) direction selectivity in the mouse retina by computational modeling and glutamate sensitivity (iGluSnFR) imaging methods. Using the SAC computational simulation, the authors initially tested bipolar cell contributions (space-time wiring model, presynaptic effect) and SAC axial resistance contributions (postsynaptic effect) to the SAC DS. Then, the authors conducted two-photon iGluSnFR imaging from SACs to examine the presynaptic glutamate release, and found seven clusters of ON-responding and six clusters of OFF-responding bipolar cells. They were categorized based on their response kinetics: delay, onset phase, decay time, and others. Finally, the authors generated a model consisting of multiple clusters of bipolar cells on proximal and distal SAC dendrites. When the SAC DS was measured using this model, they found that the space-time wiring model accounted for only a fraction of SAC DS.

The article has many interesting findings, and the data presentation is superb. Strengths and weaknesses are summarized below.

Major Strengths:<br /> • The authors utilized solid technology to conduct computational modeling with Neuron software and a machine-learning approach based on evolutionary algorithms. Results are effectively and thoroughly presented.

• The space-time wiring model was evaluated by changing bipolar cell response properties in the proximal and distal SAC dendrites. Many response parameters in bipolar cells are compared, and DSI was compared in Figure 3.

• Two-photon microscopy was used to measure the bipolar cell glutamate outputs onto SACs by conducting iGluSnFR imaging. All the data sets, including images and transients, are elegantly presented. The authors analyzed the response based on various parameters, which generated more than several response clusters. The clustering is convincing.

Major Weaknesses:<br /> • In Figure 9, the authors generated the bipolar cell cluster alignment based on the space-time wiring model. The space-time wiring model has been proposed based on the EM study that distinct types of bipolar cells synapse on distinct parts of SAC dendrites (Green et al 2016, Kim et al 2014). While this is one of the representative Reicardt models, it is not fully agreed upon in the field (see Stincic et al 2016). While the authors' approach of testing the space-time wiring model and conclusions is interesting and appreciated, the authors could address more issues: mainly two clusters were used to generate the model, but more numbers of clusters should be applied. Although the location of each cluster on the SAC dendrites is unknown, the authors should know the populations of clusters by iGluSnFR experiments. Furthermore, the authors could provide more suggestive mechanisms after declining postsynaptic factors and the space-time wiring model.<br /> • The computational modeling demonstrates intriguing results: SAC dendritic morphology produces dendritic isolation, and a massive input overcomes the dendritic isolation (Figure 1). This modeling seems to be generated by basic dendritic cable properties. However, it has been reported that SAC dendrites express Kv3 and voltage-gated Ca channels. It seems to be that these channels are not incorporated in this model.

• In Figure 5B, representative traces are shown responding to moving bars in horizontal directions. These did not show different responses to two directional stimuli. It is unclear whether directional preference was not detected, which was shown by Yonehara's group recently (Matsumoto et al 2021). Or that was not investigated as described in the Discussion.

• The authors found seven ON clusters and six OFF clusters, which are supposed to be bipolar cell terminals. However, bipolar cells reported to provide synaptic inputs are T-7, T-6, and multiple T-5s for ON SACs and T-1, T-2, and T-3s for OFF SACs. The number of types is less than the number of clusters. Potentially, clusters might belong to glutamatergic amacrine cells. These points are not fully discussed.

Reviewer #1 (Public Review):

Guglielmo et al. characterized addiction-like behaviors in more than 500 outbred heterogeneous stock (HS) rats using extended access to cocaine self-administration (6 h/daily) and analyzed individual differences in escalation of intake, progressive-ratio (PR) responding, continued use despite adverse consequence (contingent foot shocks), and irritability-like behavior during withdrawal. By principal component analysis, they found that escalation of intake, progressive ratio responding, and continued use despite adverse consequences loaded onto the same factor, whereas irritability-like behaviors loaded onto a separate factor. Characterization of rats in four categories of resilient, mild, moderate, and severe addiction-like phenotypes showed that females had higher addiction-like behaviors, particularly due to a lower number of resilient individuals, than males. The authors suggest that escalation of intake, continued use despite adverse consequences, and progressive ratio responding are highly correlated measures of the same psychological construct and that a significant proportion of males, but not females may be resilient to addiction-like behaviors. The amount of work in this study is impressive, and the results are interesting. However, there are several issues that need to be addressed to improve their manuscript. In particular, the language should be toned down and the statistical analysis approach could be improved.

Strengths: Large dataset. Males and females included.

Weaknesses: Language and statistical analysis can be improved.

Reviewer #2 (Public Review):

Summary:<br /> In this paper by de Guglielmo and colleagues, the authors were interested in analyzing addiction-like behaviors using a very large number of heterogeneous outbred rats in order to determine the relationships among these behaviors. The paper used both males and females on the order of hundreds of rats, allowing for detailed and complex statistical analyses of the behaviors. The rats underwent cocaine self-administration, first via 2-hour access and then via 6-hour access. The rats also underwent a test of punishment resistance in which footshocks were administered a portion of the time a lever was pressed. The authors also conducted a progressive ratio test to determine the break point for "giving up" pressing the lever and a bottle-brush test to determine the rats' "irritability". Ultimately, principal component analysis revealed that escalation of intake during 6-hour access, punishment resistance, and breakpoint all loaded onto the same principal component. Moreover, the authors also identified a subgroup of "resilient" rats that qualitatively differed from the "vulnerable" rats and also identified sex differences in their work.

Strengths:<br /> The use of heterogeneous rats and the use of so many rats are major strengths of this paper. Moreover, the statistical analyses are particular strengths as they enabled the identification of the three measures as likely reflecting a single underlying construct. The behavioral methods themselves are also strong, as the authors used behavioral measures commonly used in the field that will enable comparison with the field at large. In general, the results support most of the conclusions and provide a wealth of data to the field.

Weaknesses:<br /> Because the authors used so many rats (~600), it is not clear how strong the effects are. That is, a large n makes it easy to identify small effect sizes, but no effect sizes are presented regarding the findings.

The Discussion includes parts that argue that the extended access model is a better model of addiction than short access and suggests that this paper provides support for that. However, there were no rats given short-access for the same period of time as the rats in this paper - i.e., no comparison group. Rather, the only comparison that can be made is as the rats transition from short to long access. The data in Figure 1B appear to show that the rats continue their increase in cocaine intake when they transition from short access to long access. The authors do not provide any statistical analyses about this escalation of intake during short access. However, they claim that "measures related to short-term cocaine intake" were orthogonal to those collected during longer access periods, yet it is not clear to me what measures those are. Nonetheless, as indicated in Figure 1H, it appears that the rats consistently shift from PC1 to PC2 across self-administration, regardless of whether they are in the short or long access period. That is, the long-access measures appear to simply be a continuation of the pattern begun during short access. As a result, notwithstanding the lack of a true short-access control group, it is difficult to see how the authors can draw conclusions about short vs. long access in this paper.

Moreover, as illustrated in Figure 3A, the resilient vs. vulnerable subtypes are apparent during short access self-administration (i.e., they do not require long-access self-administration to develop or be revealed). This suggests, if anything, that short access would be sufficient for identifying such groups. Similarly, Figure 5 shows that short access would be sufficient to identify the "low" vulnerability quartile vs. the other three groups.

During the discussion, the authors briefly discuss gender differences with regard to cocaine use disorder, with the authors trying to claim that women may be more vulnerable to cocaine use disorder. However, the two papers cited do not support that, as they are papers with rodents. A recent comprehensive review on humans with regard to cocaine craving and relapse noted no reliable gender differences (Nicolas et al., 2022, Pharmacological Reviews) and, as the authors themselves noted, men suffer from cocaine use disorder at higher rates than women.

The authors noted that the rats received 0.5 mg/kg/infusion of cocaine but provided no explanation for how this dosing was maintained (or whether it was maintained) across the length of the study. Considering that rats, especially males, increase in size quite a bit during this stage, this could affect measures like intake as well as skew sex difference results. Likewise, the data are presented strictly in the number of cocaine infusions, which does not allow for consideration of body weight.

In the Introduction, the authors make a number of arguments in the second paragraph that have no citations and, therefore, are unsupported.

Reviewer #3 (Public Review):

Summary: The manuscript by de Guglielmo et al. presents data demonstrating that escalation of drug intake, increased motivation for drug under a progressive ratio, and drug-seeking despite adverse consequence can be explained by the same construct, while irritability-like behavior during withdrawal is statistically unrelated to an addiction-like phenotype.

Strengths: It is commendable that the authors used large cohorts of heterogenous male and female rats to mitigate common preclinical limitations that can hinder the translational relevance of research findings. The overall question is important and the authors provide a large amount of data to support their claim.

Weaknesses: However, there are a number of factors - such as behavioral rate - that are not considered and likely co-vary with other measures. This is critical as previous work has shown that rate of behavior in reinforcement tasks is a large determinant of sensitivity to both drug effects on that behavior and punishers. This is not considered and but additional information and tempering the interpretation of the data would further strengthen the manuscript.

eLife assessment

The finding that Fusicoccin promotes locomotor recovery after spinal cord injury is useful, and the idea of harnessing small molecules that may affect protein-protein interactions to promote axon regeneration is interesting and worthy of study. However, the main methods, data, and analyses are inadequate to support the primary claim of the manuscript that a 14-3-3-Spastin complex is necessary for the observed Fusicoccin effects.

Reviewer #1 (Public Review):

The present work establishes 14-3-3 proteins as binding partners of Spastin and suggests that this binding is positively regulated by phosphorylation of Spastin. The authors show evidence that 14-3-3 - Spastin binding prevents Spastin ubiquitination and final proteasomal degradation, thus increasing the availability of Spastin. The authors measured microtubule severing activity in cell lines and axon regeneration and outgrowth as a prompt to Spastin activity. By using drugs and peptides that separately inhibit 14-3-3 binding or Spastin activity, they show that both proteins are necessary for axon regeneration in cell culture and in vivo models in rats.

The following is an account of the major strengths and weaknesses of the methods and results.

Major strengths<br /> -The authors performed pulldown assays on spinal cord lysates using GST-spastin, then analyzed pulldowns via mass spectrometry and found 3 peptides common to various forms of 14-3-3 proteins. In co-expression experiments in cell lines, recombinant Spastin co-precipitated with all 6 forms of 14-3-3 tested.<br /> -By protein truncation experiments they found that the Microtubule Binding Domain of Spastin contained the binding capability to 14-3-3. This domain contained a putative phosphorylation site, and substitutions that cannot be phosphorylated cannot bind to Spastin.<br /> -Spastin overexpression increased neurite growth and branching, and so did the phospho null spastin. On the other hand, the phospho mimetic prevents all kinds of neurite development.<br /> -Overexpression of GFP-Spastin shows a turn-over of about 12 hours when protein synthesis is inhibited by cycloheximide. When 14-3-3 is co-overexpressed, GFP-Spastin does not show a decrease by 12 hours. When S233A is expressed, a turn-over of 9 hours is observed, indicating that the ability to be phosphorylated increases the stability of the protein.<br /> -In support of that notion, the phospho-mimetic S233D makes it more stable, lasting as much as the over-expression of 14-3-3.<br /> -Authors show that Spastin can be ubiquitinated, and that in the presence of ubiquitin, Spastin-MT severing activity is inhibited.<br /> -By combining FCA with Spastazoline, the authors claim that FCA increased regeneration is due to increased Spastin Activity in various models of neurite outgrowth and regeneration in cell culture and in vivo, the authors show impressive results on the positive effect of FCA in regeneration, and that this is abolished when Spastin is inhibited.

Major weaknesses<br /> -However convincing the pull-downs of the expressed proteins, the evidence would be stronger if a co-immunoprecipitation of the endogenous proteins were included.<br /> -To better establish the impact of Spastin phosphorylation in the interaction, there is no indication that the phosphomimetic (S233D) can better bind Spastin, and this result is contradicting to the conclusion of the authors that Spastin-14-3-3 interaction is necessary for (or increases) Spastin function<br /> -To fully support the authors' suggestion that 14-3-3 and Spastin work in the same pathway to promote regeneration, I believe that some key observations are missing.<br /> 1-There is no evidence showing that 14-3-3 overexpression increases the total levels of Spastin, not only its turnover.<br /> 2- There is no indication that increasing the ubiquitination of Spastin decreases its levels. To suggest that proteasomal activity is affecting the levels of a protein, one would expect that proteasomal inhibition (with bortezomib or epoxomycin), would increase its levels.<br /> 3- Authors show that S233D increases MT severing activity, and explain that it is related to increased binding to 14-3-3. An alternative explanation is that phosphorylation at S233 by itself could increase MT severing activity. The authors could test if purified Spastin S233D alone could have more potent enzymatic activity.<br /> -Finally, I consider that there are simpler explanations for the combined effect of FC-A and spastazoline. FC-A mechanism of action can be very broad, since it will increase the binding of all 14-3-3 proteins with presumably all their substrates, hence the pathways affected can rise to the hundreds. The fact that spastazoline abolishes FC-A effect, may not be because of their direct interaction, but because Spastin is a necessary component of the execution of the regeneration machinery further downstream, in line with the fact that spastizoline alone prevented outgrowth and regeneration, and in agreement with previous work showing that normal Spastin activity is necessary for regeneration.

In summary, the evidence of the interaction of 14-3-3 and Spastin is solid, but it is weak with respect to showing evidence for the binding of endogenous proteins in neurons. Another strength of the manuscript is the important recovery of function after spinal cord injury after stimulation of 14-3-3 interactions. Although it is experimentally difficult to demonstrate that the effect of FC-A is due to the prevention of Spastin ubiquitination, the effect itself is very robust and remarkable in vivo.

Reviewer #2 (Public Review):

Summary:<br /> The idea of harnessing small molecules that may affect protein-protein interactions to promote axon regeneration is interesting and worthy of study. In this manuscript, Liu et al. explore a 14-3-3-Spastin complex and its role in axon regeneration.

Strengths:<br /> Some of the effects of FC-A on locomotor recovery after spinal cord contusion look interesting.

Weaknesses:<br /> The manuscript falls short of establishing that a 14-3-3-Spastin complex is important for any FC-A-dependent effects and there are several issues with data quality that make it difficult to interpret the results. Importantly, the effects of the Spastin inhibitor have a major impact on neurite outgrowth suggesting that cells simply cannot grow in the presence of the inhibitor and raising serious questions about any selectivity for FC-A - dependent growth. Aspects of the histology following spinal cord injury were not convincing.

Reviewer #3 (Public Review):

Summary:<br /> The current manuscript claims that 14-3-3 interacts with Spastin and that the 14-3-3/spastin interaction is important to regulate axon regeneration after spinal cord injury.

Strengths:<br /> In its present form, this reviewer identified no clear strengths for this manuscript.

Weaknesses:<br /> In general, most of the figures lack sufficient quality to allow analyses and support the author's claims (detailed below). The legends also fail to provide enough information on the figures which makes it hard to interpret some of them. Most of the quantifications were done based on pseudo-replication. The number of independent experiments (that should be defined as n) is not shown. The overall quality of the written text is also low and typos are too many to list. The original nature of the spinal cord injury-related experiments is unclear as the role of 14-3-3 (and Spastin) in axon regeneration has been extensively explored in the past.

eLife assessment

Franke et al. explore and characterize the color response properties in the mouse primary visual cortex, revealing specific color opponent encoding strategies across the visual field. The data is solid; however, the evidence supporting some conclusions and details about some procedures are incomplete. In its current form, the paper makes a useful contribution to how color is coded in mouse V1. Significance would be enhanced with some additional analyses and resolution of some technical issues.

Reviewer #1 (Public Review):

Summary: In this study, Franke et al. explore and characterize the color response properties across the primary visual cortex, revealing specific color opponent encoding strategies across the visual field. The authors use awake-behaving 2P imaging to define the spectral response properties of visual interneurons in Layer 2/3. They find that opponent responses are more prominent at photopic light levels, and diversity in color opponent responses exists across the visual science, with green ON/ UV OFF responses being stronger represented in the upper visual field. This is argued to be relevant for detecting certain features that are more salient when the chromatic space is used, possibly due to noise reductions.

Strengths: The work is well crafted and written and provides a thorough characterization that reveals an uncharacterized diversity of visual properties in V1. I find this characterization important because it reveals how strongly chromatic information can modulate the response properties in V1. In the upper visual field, 25% of the cells differentially relay chromatic information, and one may wonder how this information will be integrated and subsequently used to aid vision beyond the detection of color per see. I personally like the last paragraph of the discussion that highlights this fact.

Weaknesses:

One major point highlighted in this paper is the fact that Green ON/UV OFF responses are not generated in the retina. But glancing through the literature, I saw this is not necessarily true. Fig 1. of Joesch & Meister, a paper cited, shows this can be the case. Thus, I would not emphasize that this wasn't present in the retina. This is a minor point, but even if the retina could not generate these signals, I would be surprised if the diversity of responses would only arise through feed-forward excitation, given the intricacies of cortical connectivity. Thus, I would argue that the argument holds for most of the responses seen in V1; they need to be further processed by cortical circuitries. This takes me to my second point, defining center and surround. The center spot is 37.5 deg of visual angle, more than 1 mm of the retinal surface. That means that all retinal cells, at least half and most likely all of their surrounds will also be activated. Although 37.5 deg is roughly the receptive field size previously determined for V1 neurons, the one-to-one comparison with retinal recording, particularly with their center/surround properties, is difficult. This should be discussed. I assume that the authors tried a similar approach with sparse or dense checker white noise stimuli. If so, it would be interesting if there were better ways of defining the properties of V1 neurons on their complex/simple receptive field properties to define how much of their responses are due to an activation of the true "center" or a coactivation of the surround. Interestingly, at least some of the cells (Fig. 1d, cells 2 and 5) don't have a surround. Could it be that in these cases, the "center" and "surround" are being excited together? How different would the overall statistics change if one used a full-filed flicker stimulus instead of a center/surround stimulus? How stable are the results if the center/surround flicker stimulus is shifted? These results won't change the fact that chromatic coding is present in the VC and that there are clear differences depending on their position, but it might change the interpretation. Thus, I would encourage you to test these differences and discuss them.

Reviewer #2 (Public Review):

Summary: Franke et al. characterize the representation of color in the primary visual cortex of mice and how it changes across the visual field, with a particular focus on how this may influence the ability to detect aerial predators. Using calcium imaging in awake, head-fixed mice, they characterize the properties of V1 neurons (layer 2/3) using a large center-surround stimulation where green and ultra-violet were presented in random combinations. Using a clustering approach, a set of functional cell-types were identified based on their preference to different combinations of green and UV in their center and surround. These functional types were demonstrated to have varying spatial distributions in V1, including one neuronal type (Green-ON/UV-OFF) that was much more prominent in the posterior V1 (i.e. upper visual field). Modelling work suggests that these neurons likely support the detection of predator-like objects in the sky.

Strengths:<br /> The large-scale single-cell resolution imaging used in this work allows the authors to map the responses of individual neurons across large regions of the visual cortex. Combining this large dataset with clustering analysis enabled the authors to group V1 neurons into distinct functional cell types and demonstrate their relative distribution in the upper and lower visual fields. Modelling work demonstrated the different capacity of each functional type to detect objects in the sky, providing insight into the ethological relevance of color opponent neurons in V1.

Weaknesses:<br /> While the study presents solid evidence a few weaknesses exist, including the size of the dataset, clarity regarding details of data included in each step of the analysis and discussion of caveats of the work. The results presented here are based on recordings of 3 mice. While the number of neurons recorded is reasonably large (n > 3000) an analysis that tests for consistency across animals is missing. Related to this, it is unclear how many neurons at each stage of the analysis come from the 3 different mice (except for Suppl. Fig 4). Finally, the paper would greatly benefit from a more in depth discussion of the caveats related to the conclusion drawn at each stage of the analysis. This is particularly relevant regarding the caveats related to using spike triggered averages to assess the response preferences of ON-OFF neurons, and the conclusions drawn about the contribution of retinal color opponency.

The authors provide solid evidence to support an asymmetric distribution of color opponent cells in V1 and a reduced color contrast representation in lower light levels. Some statements would benefit from more direct evidence such as the integration of upstream visual signals for color opponency in V1.

Overall, this study will be a valuable resource for researchers studying color vision, cortical processing, and the processing of ethologically relevant information. It provides a useful basis for future work on the origin of color opponency in V1 and its ethological relevance.

Reviewer #3 (Public Review):

This paper studies chromatic coding in mouse primary visual cortex. Calcium responses of a large collection of cells are measured in response to a simple spot stimulus. These responses are used to estimate chromatic tuning properties - specifically sensitivity to UV and green stimuli presented in a large central spot or a larger still surrounding region. Cells are divided based on their responses to these stimuli into luminance or chromatic sensitive groups. Several technical concerns limit how clearly the data support the conclusions. If these issues can be fixed, the paper would make a valuable contribution to how color is coded in mouse V1.

Analysis<br /> The central tool used to analyze the data is a "spike triggered average" of the responses to randomly varying stimuli. There are several steps in this analysis that are not documented, and hence evaluating how well it works is difficult. Central to this is that the paper does not measure spikes. Instead, measured calcium traces are converted to estimated spike rates, which are then used to estimate STAs. There are no raw calcium traces shown, and the approach to estimate spike rates is not described in any detail. Confirming the accuracy of these steps is essential for a reader to be able to evaluate the paper. Further, it is not clear why the linear filters connecting the recorded calcium traces and the stimulus cannot be estimated directly, without the intermediate step of estimating spike rates.

A further issue about the STAs is that the inclusion criterion (correlation of predicted vs measured responses of 0.25) is pretty forgiving. It would be helpful to see a distribution of those correlation values, and some control analyses to check whether the STA is providing a sufficiently accurate measure to support the results (e.g. do the central results hold for the cells with the highest correlations).

Limitations of stimulus choice<br /> The paper relies on responses to a large (37.5 degree diameter) modulated spot and surrounding region. This spot is considerably larger than the receptive fields of both V1 cells and retinal ganglion cells. As a result, the spot itself is very likely to strongly activate both center and surround mechanisms, and responses of cells are likely to depend on where the receptive fields are located within the spot (and, e.g., how much of the true neural surround samples the center spot vs the surround region). The impact of these issues on the conclusions is considered briefly at the start of the results but needs to be evaluated in considerably more detail. This is particularly true for retinal ganglion cells given the size of their receptive fields (see also next point).

Comparison with retina<br /> A key conclusion of the paper is that the chromatic tuning in V1 is not inherited from retinal ganglion cells. This conclusion comes from comparing chromatic tuning in a previously-collected data set from retina with the present results. But the retina recordings were made using a considerably smaller spot, and hence it is not clear that the comparison made in the paper is accurate. This issue may be handled by the analysis presented in the paper, but if so it needs to be described more clearly.<br /> The paper from which the retina data is taken argues that rod-cone chromatic opponency originates largely in the outer retina. This mechanism would be expected to be shared across retinal outputs. Thus it is not clear how the Green-On/UV-Off vs Green-Off/UV-On asymmetry could originate. This should be discussed.

Residual chromatic cells at low mesopic light levels<br /> The presence of chromatically tuned cells at the lowest light level probed is surprising. The authors describe these conditions as rod-dominated, in which case chromatic tuning should not be possible. This again is discussed only briefly. It either reflects the presence of an unexpected pathway that amplifies weak cone signals under low mesopic conditions such that they can create spectral opponency or something amiss in the calibrations or analysis. Data collected at still lower light levels would help resolve this.

eLife assessment

This study presents valuable findings on the roles of the axon growth regulator Sema7a in the formation of peripheral sensory circuits in the lateral line system of zebrafish. The evidence supporting the claims of the authors is solid, although further work directly testing the roles of different sema7a isoforms would strengthen the analysis. The work will be of interest to developmental neuroscientists studying circuit formation.

Reviewer #1 (Public Review):

In this work, Dasguta et al. have dissected the role of Sema7a in fine tuning of a sensory microcircuit in the posterior lateral line organ of zebrafish. They attempt to also outline the different roles of a secreted verses membrane-bound form of Sema7a in this process. Using genetic perturbations and axonal network analysis, the authors show that loss of both Sema7a isoforms causes abnormal axon terminal structure with more bare terminals and fewer loops in contact with presynaptic sensory hair cells. Further, they show that loss of Sema7a causes decreased number and size of both the pre- and post-synapse. Finally, they show that overexpression of the secreted form of Sema7a specifically can elicit axon terminal outgrowth to an ectopic Sema7a expressing cell. Together, the analysis of Sema7a loss of function and overexpression on axon arbor structure is fairly thorough and revealed a novel role for Sema7a in axon terminal structure. However, the connection between different isoforms of Sema7a and the axon arborization needs to be substantiated. Furthermore, an autocrine role for Sema7a on the presynaptic cell is not ruled out as a contributing factor to the synaptic and axon structure phenotypes. Finally, critical controls are absent from the overexpression paradigm. These issues weaken the claims made by the authors including the statement that they have identified differential roles for the GPI-anchored verses secreted forms of Sema7a on synapse formation and as a chemoattractant for axon arborization respectively. The manuscript itself would benefit from the inclusion of details in the text to help the reader interpret the figures, tools, data, and analysis.

Reviewer #2 (Public Review):

In this work, Dasgupta et al. investigates the role of Sema7a in the formation of peripheral sensory circuit in the lateral line system of zebrafish. They show that Sema7a protein is present during neuromast maturation and localized, in part, to the base of hair cells (HCs). This would be consistent with pre-synaptic Sema7a mediating formation and/or stabilization of the synapse. They use sema7a loss-of-function strain to show that lateral line sensory terminals display abnormal arborization. They provide highly quantitative analysis of the lateral line terminal arborization to show that a number of specific topological parameters are affected in mutants. Next, they ectopically express a secreted form of Sema7a to show that lateral line terminals can be ectopically attracted to the source. Finally, they also demonstrate that the synaptic assembly is impaired in the sema7a mutant. Overall, the data are of high quality and properly controlled. The availability of Sema7a antibody is a big plus, as it allows to address the endogenous protein localization as well to show the signal absence in the sema7a mutant. The quantification of the arbor topology should be useful to people in the field who are looking at the lateral line as well as other axonal terminals. I think some results are overinterpreted though. The authors state: "Our findings demonstrate that Sema7A functions both as a juxtracrine and as a secreted cue to pattern neural circuitry during sensory organ development." However, they have not actually demonstrated which isoform functions in HCs (also see comments below). In addition, they have to be careful in interpreting their topology analysis, as they cannot separate individual axons. Thus, such analysis can generate artifacts. They can perform additional experiments to address these issues or adjust their interpretations.

Reviewer #3 (Public Review):

Summary:

This study demonstrates that the axon guidance molecule Sema7a patterns the innervation of hair cells in the neuromasts of the zebrafish lateral line, as revealed by quantifying gain- and loss-of function effects on the three-dimensional topology of sensory axon arbors over developmental time. Alternative splicing can produce either a diffusible or membrane-bound form of Sema7a, which is increasingly localized to the basolateral pole of hair cells as they develop (Figure 1). In sema7a mutant zebrafish, sensory axon arbors still grow to the neuromast, but they do not form the same arborization patterns as in controls, with many arbors overextending, curving less, and forming fewer loops even as they lengthen (Figure 2,3). These phenotypes only become significant later in development, indicating that Sema7a functions to pattern local microcircuitry, not the gross wiring pattern. Further, upon ectopic expression of the diffusible form of Sema7a, sensory axons grow towards the Sema7a source (Figure 4). The data also show changes in the synapses that form when mutant terminals contact hair cells, evidenced by significantly smaller pre- and post-synaptic punctae (Figure 5). Finally, by replotting single cell RNA-sequencing data (Figure 6), the authors show that several other potential cues are also produced by hair cells and might explain why the sema7a phenotype does not reflect a change in growth towards the neuromast. In summary, the data strongly indicate that Sema7a plays a role in shaping connectivity within the neuromast.

Strengths:

The main strength of this study is the sophisticated analysis that was used to demonstrate fine-level effects on connectivity. Rather than asking "did the axon reach its target?", the authors asked "how does the axon behave within the target?". This type of deep analysis is much more powerful than what is typical for the field and should be done more often. The breadth of analysis is also impressive, in that axon arborization patterns and synaptic connectivity were examined at 3 stages of development and in three-dimensions.

Weaknesses:

The main weakness is that the data do not cleanly distinguish between activities for the secreted and membrane-bound forms of Sema7a, which the authors speculate may influence axon growth and synapse formation respectively. The authors do not overstate the claims, but it would have been nice to see some additional experimentation along these lines, such as the effects of overexpressing the membrane-bound form, some analysis of the distance over which the "diffusible" form of Sema7a might act (many secreted ligands are not in fact all that diffusible), or some live-imaging of axons before they reach the target (predicted to be the same in control and mutants) and then within the target (predicted to be different). Clearly, although the gain-of-function studies show that Sema7a can act at a distance, other cues are sufficient. Although the lack of a phenotype could be due to compensation, it is also possible that Sema7a does not actually act in a diffusible manner within its natural context.

Overall, the data support the authors' carefully worded conclusions. While certain ideas are put forward as possibilities, the authors recognize that more work is needed. The main shortcoming is that the study does not actually distinguish between the effects of the two forms of Sema7a, which are predicted but not actually shown to be either diffusible or membrane linked (the membrane linkage can be cleaved). Although the study starts by presenting the splice forms, there is no description of when and where each splice form is transcribed. Additionally, since the mutants are predicted to disrupt both forms, it is a bit difficult to disentangle the synaptic phenotype from the earlier changes in circuit topology - perhaps the change at the level of the synapse is secondary to the change in topology. Further, the authors do not provide any data supporting the idea that the membrane bound form of Sema7a acts only locally. Without these kinds of data, the authors are unable to attribute activities to either form.

The main impact on the field will be the nature of the analysis. The field of axon guidance benefits from this kind of robust quantification of growing axon trajectories, versus their ability to actually reach a target. This study highlights the value of more careful analysis and as a result, makes the point that circuit assembly is not just a matter of painting out paths using chemoattractants and repellants, but is also about how axons respond to local cues. The study also points to the likely importance of alternative splice forms and to the complex functions that can be achieved using different forms of the same ligand.

Reviewer #4 (Public Review):

Summary:<br /> The work by Dasgupta et al identifies Sema7a as a novel guidance molecule in hair cell sensory systems. The authors use the both genetic and imaging power of the zebrafish lateral-line system for their research. Based on expression data and immunohistochemistry experiments, the authors demonstrate that Sema7a is present in lateral line hair cells. The authors then examine a sema7a mutant. In this mutant, Sema7a proteins levels are nearly eliminated. Importantly, the authors show that when Sema7a is absent, afferent terminals show aberrant projections and fewer contacts with hair cells. Lastly the authors show that ectopic expression of the secreted form of Sema7a is sufficient to recruit aberrant terminals to non-hair cell targets. The sema7a innervation defects are well quantified. Overall, the paper is extremely well written and easy to follow.

Strengths:<br /> 1. The axon guidance phenotypes in sema7a mutants are novel, striking and thoroughly quantified.<br /> 2. By combining both loss of function sema7a mutants and ectopic expression of the secreted form of Sema7a the authors demonstrate the Sema7a is both necessary and sufficient to guide sensory axons

Weaknesses:<br /> 1. Control. There should be an uninjected heatshock control to ensure that heatshock itself does not cause sensory afferents to form aberrant arbors. This control would help support the hypothesis that exogenously expressed Sema7a (via a heatshock driven promoter) is sufficient to attract afferent arbors.<br /> 2. Synapse labeling. The numbers obtained for postsynaptic labeling in controls do not match up with the published literature - they are quite low. Although there are clear differences in postsynaptic counts between sema7a mutants and controls, it is worrying that the numbers are so low in controls. In addition, the authors do not stain for complete synapses (pre- and post-synapses together). This staining is critical to understand how Sema7a impacts synapse formation.<br /> 3. Hair cell counts. The authors need to provide quantification of hair cell counts per neuromast in mutant and control animals. If the counts are different, certain quantification may need to be normalized.<br /> 4. Developmental delay. It is possible that loss of Sema7a simply delays development. The latest stage examined was 4 dpf, an age that is not quite mature in control animals. The authors could look at a later age, such as 6 dpf to see if the phenotypes persist or recover.

eLife assessment

This valuable study has the potential to shed mechanistic light on how attention mechanisms that influence competition between multiple visual stimuli are modulated by the relative neural similarity of these stimuli. The study implements an interesting experimental design that provides relevant data, especially for future modeling efforts. However, the presented evidence is considered incomplete due to some features of the design and model, as well as certain analysis choices.

Reviewer #1 (Public Review):

Summary:<br /> The authors report an fMRI investigation of the neural mechanisms by which selective attention allows capacity-limited perceptual systems to preferentially represent task-relevant visual stimuli. Specifically, they examine competitive interactions between two simultaneously-presented items from different categories, to reveal how task-directed attention to one of them modulates the activity of brain regions that respond to both. The specific hypothesis is that attention will bias responses to be more like those elicited by the relevant object presented on its own, and further that this modulation will be stronger for more dissimilar stimulus pairs. This pattern was confirmed in univariate analyses that measured the mass response of a priori regions of interest, as well as multivariate analyses that considered the patterns of evoked activity within the same regions. The authors follow these neuroimaging results with a simulation study that favours a "tuning" mechanism of attention (enhanced responses to highly effective stimuli, and suppression for ineffective stimuli) to explain this pattern.

Strengths:<br /> The manuscript clearly articulates a core issue in the cognitive neuroscience of attention, namely the need to understand how limited perceptual systems cope with complex environments in the service of the observer's goals. The use of a priori regions of interest, and the inclusion of both univariate and multivariate analyses as well as a simple model, are further strengths. The authors carefully derive clear indices of attentional effects (for both univariate and multivariate analyses) which makes explication of their findings easy to follow.

Weaknesses:<br /> There are some relatively minor weaknesses in presentation, where the motivation behind some of the procedural decisions could be clearer. There are some apparently paradoxical findings reported -- namely, cases in which the univariate response to pairs of stimuli is greater than to the preferred stimulus alone -- that are not addressed. It is possible that some of the main findings may be attributable to range effects: notwithstanding the paradox just noted, it seems that a floor effect should minimise the range of possible attentional modulation of the responses to two highly similar stimuli. One possible limitation of the modelled results is that they do not reveal any attentional modulation at all under the assumptions of the gain model, for any pair of conditions, implying that as implemented the model may not be correctly capturing the assumptions of that hypothesis.

Reviewer #2 (Public Review):

Summary:<br /> In an fMRI study requiring participants to attend to one or another object category, either when the object was presented in isolation or with another object superimposed, the authors compared measured univariate and multivariate activation from object-selective and early visual cortex to predictions derived from response gain and tuning sharpening models. They observed a consistent result across higher-level visual cortex that more-divergent responses to isolated stimuli from category pairs predicted a greater modulation by attention when attending to a single stimulus from the category pair presented simultaneously, and argue via simulations that this must be explained by tuning sharpening for object categories.

Strengths:<br /> - Interesting experiment design & approach - testing how category similarity impacts neural modulations induced by attention is an important question, and the experimental approach is principled and clever.

- Examination of both univariate and multivariate signals is an important analysis strategy.

- The acquired dataset will be useful for future modeling studies.

Weaknesses:<br /> - The experimental design does not allow for a neutral 'baseline' estimate of neural responses to stimulus categories absent attention (e.g., attend fixation), nor of the combination of the stimulus categories. This seems critical for interpreting results (e.g., how should readers understand univariate results like that plotted in Fig. 4C-D, where the univariate response is greater for 2 stimuli than one, but the analyses are based on a shift between each extreme activation level?).

- Related, simulations assume there exists some non-attended baseline state of each individual object representation, yet this isn't measured, and the way it's inferred to drive the simulations isn't clearly described.

- Some of the simulation results seem to be algebraic (univariate; Fig. 7; multivariate, gain model; Fig. 8).

- Cross-validation does not seem to be employed - strong/weak categories seem to be assigned based on the same data used for computing DVs of interest - to minimize the potential for circularity in analyses, it would be better to define preferred categories using separate data from that used to quantify - perhaps using a cross-validation scheme? This appears to be implemented in Reddy et al. (2009), a paper implementing a similar multivariate method and cited by the authors (their ref 6).

- Multivariate distance metric - why is correlation/cosine similarity used instead of something like Euclidean or Mahalanobis distance? Correlation/cosine similarity is scale-invariant, so changes in the magnitude of the vector would not change distance, despite this likely being an important data attribute to consider.

- Details about simulations implemented (and their algebraic results in some cases) make it challenging to interpret or understand these results. E.g., the noise properties of the simulated data aren't disclosed, nor are precise (or approximate) values used for simulating attentional modulations.

- Eye movements do not seem to be controlled nor measured. Could it be possible that some stimulus pairs result in more discriminable patterns of eye movements? Could this be ruled out by some aspect of the results?

- A central, and untested/verified, assumption is that the multivariate activation pattern associated with 2 overlapping stimuli (with one attended) can be modeled as a weighted combination of the activation pattern associated with the individual stimuli. There are hints in the univariate data (e.g., Fig. 4C; 4D) that this might not be justified, which somewhat calls into question the interpretability of the multivariate results.

- Throughout the manuscript, the authors consistently refer to "tuning sharpening", an idea that's almost always used to reference changes in the width of tuning curves for specific feature dimensions (e.g., motion direction; hue; orientation; spatial position). Here, the authors are assaying tuning to the category (across exemplars of the category). The link between these concepts could be strengthened to improve the clarity of the manuscript.

eLife assessment

This study provides a valuable starting point for unraveling the molecular basis of the pathological phenotypes of the repeat expansion in the gene associated with open reading frame 72 in human chromosome 9. The coarse-grained simulation method used by the authors goes beyond the state of the art, investigating a compelling number of binding partners. The evidence supporting the claims of the authors is solid, although validation of the results is needed to further strengthen the major conclusions of the work. The work will be of broad interest to biophysicists and biochemists.

Reviewer #1 (Public Review):

Jafarinia et al. have made an interesting contribution to unravelling the molecular mechanisms underlying pathological phenotypes of repeat expansion of the C9orf72 gene. The repeat expression leads to the expression of polyPR proteins. Using coarse-grained molecular dynamics simulations, the authors identify putative binding partners involved in nucleocytoplasmic transport (NCT), and that conjecture that polyPR affects essential processes by binding to NCT-related proteins. The results are well-reported, but only putative, and need experimental support to be more conclusive. Also, a comparison with results from all-atom MD simulations in explicit water could help verify the results. But even without these, the work is very useful as a first step to unravel the role of polyPR and related peptides.

Reviewer #2 (Public Review):

This study used coarse-grained molecular dynamics simulation to explain how the binding of polyPR might interfere with distinct stages of the transport cycle. This finding shows that the interaction between polyPR and transport components is driven by electrostatic interactions and is correlated with the salt concentration and the length of polyPR, providing an important basis for subsequent exploration of the impact of C9orf72 R-DPRs on NCT disruption.

Reviewer #3 (Public Review):

Onck and co-workers present in this work the identification of binding partners and sites of polyPR on various nuclear transport components and elucidate how polyPR might potentially influence the transport process. It's interesting to note that some interaction sites on transport components also serve as their inherent/functional binding sites. The difference in the effects between short polyPR (PR7) and long polyPR (PR50) is also evident, although the authors might need to clarify the mechanisms better. Overall, the manuscript is well organized and concisely written, and it would greatly enhance our understanding of the toxicity induced by polyPR. In general, the 1-bead per atom force field model used in the study is well-tuned for studying the interactions between polyPR and proteins, as the essential cation-pi interactions (between Arg and Phe/Tyr/Trp) were included using an 8-6 LJ model.

eLife assessment

This paper presents data suggesting the novel finding that stimulating the senses can open the normal barrier to the brain and lead to changes in the brain. However, the paper was unclear in methods and data, which made the strength of evidence for the conclusions seem incomplete. However, the reviewers considered the potential significance of the study to be important.

Reviewer #1 (Public Review):

The goal of the current study was to evaluate the effect of neuronal activity on blood-brain barrier permeability in the healthy brain, and to determine whether changes in BBB dynamics play a role in cortical plasticity. The authors used a variety of well-validated approaches to first demonstrate that limb stimulation increases BBB permeability. Using in vivo-electrophysiology and pharmacological approaches, the authors demonstrate that albumin is sufficient to induce cortical potentiation and that BBB transporters are necessary for stimulus-induced potentiation. The authors include a transcriptional analysis and differential expression of genes associated with plasticity, TGF-beta signaling, and extracellular matrix were observed following stimulation. Overall, the results obtained in rodents are compelling and support the authors' conclusions that neuronal activity modulates the BBB in the healthy brain and that mechanisms downstream of BBB permeability changes play a role in stimulus-evoked plasticity. These findings were further supported with fMRI and BBB permeability measurements performed in healthy human subjects performing a simple sensorimotor task. While there are many strengths in this study, there is literature to suggest that there are sex differences in BBB dysfunction in pathophysiological conditions. The authors only used males in this study and do not discuss whether they would also expect to sex differences in stimulation-evoked BBB changes in the healthy brain. Another minor limitation is the authors did not address the potential impact of anesthesia which can impact neurovascular coupling in rodent studies. The authors could have also better integrated the RNAseq findings into mechanistic experiments, including testing whether the upregulation of OAT3 plays a role in cortical plasticity observed following stimulation. Overall, this study provides novel insights into how neurovascular coupling, BBB permeability, and plasticity interact in the healthy brain.

Reviewer #2 (Public Review):

Summary:<br /> This study builds upon previous work that demonstrated that brain injury results in leakage of albumin across the blood-brain barrier, resulting in activation of TGF-beta in astrocytes. Consequently, this leads to decreased glutamate uptake, reduced buffering of extracellular potassium, and hyperexcitability. This study asks whether such a process can play a physiological role in cortical plasticity. They first show that stimulation of a forelimb for 30 minutes in a rat results in leakage of the blood-brain barrier and extravasation of albumin on the contralateral but not ipsilateral cortex. The authors propose that the leakage is dependent upon neuronal excitability and is associated with an enhancement of excitatory transmission. Inhibiting the transport of albumin or the activation of TGF-beta prevents the enhancement of excitatory transmission. In addition, gene expression associated with TGF-beta activation, synaptic plasticity, and extracellular matrix are enhanced on the "stimulated" hemisphere. That this may translate to humans is demonstrated by a breakdown in the blood-brain barrier following activation of brain areas through a motor task.

Strengths:<br /> This study is novel and the results are potentially important as they demonstrate an unexpected breakdown of the blood-brain barrier with physiological activity and this may serve a physiological purpose, affecting synaptic plasticity.

The strengths of the study are:<br /> 1) The use of an in vivo model with multiple methods to investigate the blood-brain barrier response to a forelimb stimulation.<br /> 2) The determination of a potential functional role for the observed leakage of the blood-brain barrier from both a genetic and electrophysiological viewpoint.<br /> 3) The demonstration that inhibiting different points in the putative pathway from activation of the cortex to transport of albumin and activation of the TGF-beta pathway, the effect on synaptic enhancement could be prevented.<br /> 4) Preliminary experiments demonstrating a similar observation of activity-dependent breakdown of the blood-brain barrier in humans.

Weaknesses:<br /> There are both conceptual and experimental weaknesses.

1) The stimulation is in an animal anesthetized with ketamine, which can affect critical receptors (ie NMDA receptors) in synaptic plasticity.

2) The stimulation protocol is prolonged and it would be helpful to know if briefer stimulations have the same effect or if longer stimulations have a greater effect ie does the leakage give a "readout" of the stimulation intensity/length.

3) For some of the experiments (see below), the numbers of animals are low and the statistical tests used may not be the most appropriate, making the results less clear cut.

4) The experimental paradigms are not entirely clear, especially the length of time of drug application and the authors seem to try to detect enhancement of a blocked SEP.

4) It is not clear how long the enhancement lasts. There is a remark that it lasts longer than 5 hours but there is no presentation of data to support this.

5) It is not clear if this enhancement of synaptic transmission has any physiological role.

6) The spatial and temporal specificity of this effect is unclear (other than hemispheric in rats) and even less clear in humans.

7) It is not clear to what extent the experimenters and those doing the analysis were blinded to group. If neither were blind to group, then considerable biases could be introduced.

8) The experimenters rightly use separate controls for most of the experiments but this is not always the case, also raising the possibility that the application of drugs was not done randomly or interleaved, but possibly performed in blocks of animals, which can also affect results.

9) Methyl-beta-cyclodextrin clears cholesterol so the effect on albumin transport is not specific, it could be mediating its effect through some other pathway.

10) Since the breakdown of the blood-brain barrier can be inhibited by a TGF-beta inhibitor, then this implies that TGF-beta is necessary for the breakdown of the blood-brain barrier. This does not sit well with the hypothesis that TGF-beta activation depends upon blood-brain barrier leakage.

Reviewer #3 (Public Review):

Summary:<br /> This study used prolonged stimulation of a limb to examine possible plasticity in somatosensory evoked potentials induced by the stimulation. They also studied the extent that the blood-brain barrier (BBB) was opened by prolonged stimulation and whether that played a role in the plasticity. They found that there was potentiation of the amplitude and area under the curve of the evoked potential after prolonged stimulation and this was long-lasting (>5 hrs). They also implicated extravasation of serum albumin, caveolae-mediated transcytosis, and TGFb signalling, as well as neuronal activity and upregulation of PSD95. Transcriptomics was done and implicated plasticity-related genes in the changes after prolonged stimulation, but not proteins associated with the BBB or inflammation. Next, they address the application to humans using a squeeze ball task. They imaged the brain and suggested that the hand activity led to an increased permeability of the vessels, suggesting modulation of the BBB.

Strengths:<br /> The strengths of the paper are the novelty of the idea that stimulation of the limb can induce cortical plasticity in a normal condition, and it involves the opening of the BBB with albumin entry. In addition, there are many datasets and both rat and human data.

Weaknesses:<br /> The conclusions are not compelling however because of a lack of explanation of methods and quantification. It also is not clear whether the prolonged stimulation in the rat was normal conditions. To their credit, the authors recorded the neuronal activity during stimulation, but it seemed excessive excitation. Since seizures open the BBB this result calls into question one of the conclusions. that the results reflect a normal brain. The authors could either conduct studies with stimulation that is more physiological or discuss the caveats of using a supraphysiological stimulus to infer healthy brain function.

eLife assessment

This study suggests that PAK3 may play a critical role in cognitive function after cranial irradiation, highlighting a potential therapeutic target to mitigate the adverse effects of radiotherapy. The main finding is of significance, and the evidence is compelling to support their major conclusion. This is an important discovery providing novel insight into the molecular mechanism of how PAK3/LIMK/Cofilin signaling modulates synaptic spine morphology in response to irradiation.

Reviewer #1 (Public Review):

Summary:

Exposure to cranial irradiation (IR) leads to cognitive deficits in the survivors of brain cancer. IR upregulates miR-206-3p, which in turn reduces the PAK3-LIMK1 axis leading to the loss of F and G-actin ratio and, thereby, mature dendritic spine loss. Silencing miR-206-3p reverses these degenerative consequences.

Strengths:<br /> The authors show compelling data indicating a clear correlation between PAK3 knockdown and the loss of mature dendritic spine density. In contrast, overexpression of PAK3 in the irradiated neurons restored mature spine types and recovered the F/G ratio. These in vitro results support the authors' hypotheses that PAK3 and LIMK1-mediated downstream signaling impact neuronal structure and reorganization in vitro. These data were supported by similar experiments using differentiated human neurons. Importantly, silencing miR-206-30 using antagonist miR also reverses IR-induced downregulation of the PAK3-LIMK1 axis, preventing spine loss and cognitive deficits.

Weaknesses:

All the miR-206-3p data are presented from in vitro cortical neurons or human stem cell-derived neuron cultures. This data (IR-induced elevation of miR-206-3p) should also be confirmed in vivo using an irradiated mouse brain to correlate the cognitive dysfunction timepoint.

Antago-miR-206-3p reversed Ir-induced upregulation of miR-206 (in vitro), and prevent reductions in PAK3 and downstream markers. Importantly, it reversed cognitive deficits induced by IR. This data should be supported by in vivo staining for important dendritic markers, including cofillin, p-cofilin, PSD-95, F- and G-actin within the hippocampal and PFC regions.

Other neuronal and non-neuronal targets of miR-206-3p should be discussed and looked into as a downstream impact of IR-induced functional and physiological impairments in the brain.

Reviewer #2 (Public Review):

Summary:<br /> The paper entitled "PAK3 downregulation induces cognitive 1 impairment following cranial irradiation" by Lee et al. aimed at investigating the functional impact of cranial irradiation in mouse and propose PAK3 as molecular element involved in radiation-induced cognitive decrement. The results provided in this paper are problematic as both the irradiation paradigm (5X2 Gy) as well as the timing of investigation (3 to 8 days post-IR) are completely irrelevant to investigate radiation induced neurocognitive impairment. This testifies to the team's lack of knowledge in radiobiology/radiotherapy and the methodology to explore radiation induced neurocognitive damages. It precludes any further relevance of the molecular results.

Weaknesses:<br /> First and according to the BED equation a single dose of 10 Gy cannot not be approximated by 5 fractions of 2 Gy, as fractionation is known to decrease normal tissue toxicity. Note that in radiobiology/radio-oncology, the BED stands for "Biologically Effective Dose." This equation is used to compare the effects of different radiation treatments on biological tissues, taking into account the dose, fractionation, and the overall biological response of the tissue to radiation.<br /> The BED equation is commonly used to calculate the equivalent dose of a fractionated radiation treatment, which is the dose that would produce the same biological effect as a single, higher dose delivered in a single fraction.<br /> The general formula for BED is:BED = D * (1 + d / α/β)<br /> D is the total physical dose of radiation delivered in Grays (Gy)<br /> d is the dose per fraction in Gy<br /> α/β is the tissue-specific ratio of the linear (α) and quadratic (β) components of the radiation response. It is measured in Gy and describes how the tissue responds to different fractionation schedules (usually equal to 3 for the normal brain).<br /> Please refers to radiobiology/radiotherapy textbooks by Hall or Joiner.

Second, the brain is a late responding organ. GBM patients treated with 60 Gy exhibit progressive and debilitating impairments in memory, attention and executive function several month post-irradiation. In mice, neurocognitive decrements after a single dose of 10 Gy delivered to the whole brain does occur at late time point, usually > 2 months post-exposure. Multiple publications such as the one by Limoli C lab, Rossi S lab, Britten R lab or earlier Fike J lab and Robin M lab support this. Next, 5 fractions of 2 Gy will be more protective than a single dose of 10 Gy and neurocognitive decrements will require at least 5-6 months to occur if they ever occur. In Figure 1, the decrement reported is marginal, the number of animals included (4 to 5 at most?) The number of animals is not specified) is too low to draw any significant conclusions. In addition to the timing issue, the strategy described for NOR analysis shows methodological issues with the habituation period being too short and exploration level being very low.

eLife assessment

This paper presents a valuable pipeline based on state-of-the-art analytical software that was used to study genetic pleiotropy between neuropsychiatric disorders. The presented evidence supporting the claims is solid, although the paper is lacking an appropriate comparison to previously published methods as well as a more detailed exploration of some of the findings. The created pipeline is made publicly available and can thus be used by researchers from diverse fields to study different combinations of diseases and traits.

Reviewer #1 (Public Review):

The authors investigate pleiotropy in the genetic loci previously associated to a range of neuropsychiatric disorders: Alzheimer's disease, amyotrophic lateral sclerosis (ALS), frontotemporal dementia, Parkinson's disease, and schizophrenia. The local statistical fine-mapping and variant colocalisation approaches they use have the potential to uncover not only shared loci but also shared causal variants between these disorders. There is existing literature describing the pleiotropy between ALS and these other disorders but here the authors apply state of the art, local genetic correlation approaches to further refine any relationships.

Complex disease and GWAS is not my area of expertise but the authors managed to present their methods and results in a clear, easy to follow manner. Their results statistically support several correlations between the disorders and, for ALS and AD, a shared variant in the vicinity of the lead SNP from the original ALS GWAS. Such findings could have important implications for our understanding of the mechanisms of such disorders and eventually the possibility of managing and treating them.

The authors have built a useful pipeline that plugs together all the gold-standard, existing software to perform this analysis and made it openly available which is commendable. However, there is little discussion of what software is available to perform global and local correlation analysis and, if there are multiple tools available, why they consider the ones they selected to be the gold-standard.

There is some mention of previous findings of genetic pleiotropy between ALS and these other disorders in the introduction, and discussion of their improved ALS-AD evidence relative to previous work. However, detailed comparisons of their other correlations to what was described before for the same pairs of disorders (if any) is missing. Adding this would strengthen the impact of this paper.

Finally, being new to this approach I found the abstract a little confusing. Initially, the shared causal variant between ALS and AD is mentioned but immediately in the following sentence they describe how their study "suggested that disease- implicated variants in these loci often differ between traits". After reading the whole paper I understood that the ALS-AD shared variant was the exception but it may be best to restructure this part of the abstract. Additionally, in the abstract the authors state that different variants "suggests the role of distinct mechanisms across diseases despite shared loci". Is it not possible that different variants in the same regulatory region or protein-coding parts of a gene could be having the same effect and mechanism? Or does the methodology to establish that different variants are involved automatically mean that the variants are too distant for this to be possible?

Reviewer #2 (Public Review):

Summary:

Spargo and colleagues present an analysis of the shared genetic architectures of Schizoprehnia and several late-onset neurological disorders. In contrast to many polygenic traits for which global genetic correlation estimates are substantial, global genetic correlation estimates for neurological conditions are relatively small, likely for several reasons. One is that assortative mating, which will spuriously inflate genetic correlation estimates, is likely to be less salient for late-onset conditions. Another, which the authors explore in the current manuscript, is that some loci affecting two or more conditions (i.e., pleiotropic loci) may have effects in opposite directions, or shared loci are sparse, such that the global genetic correlation signal washes out.

The authors apply a local genetic correlation approach that assesses the presence and direction of pleiotropy in much smaller spatial windows across the genome. Then, within regions evidencing local genetic correlations for a given trait pair, they apply fine-mapping and colocalization methods to attempt to differentiate between two scenarios: that the two traits share the same causal variant in the region or that distinct loci within the region influence the traits. Interestingly, the authors only discover one instance of the former: an SNP in the HLA region appearing to confer risk for both AD and ALS. This is in contrast to six regions with distinct causal loci, and twenty regions with no clear shared loci.

Finally, the authors have published their analysis pipeline such that other researchers might easily apply the same techniques to other collections of traits.

Strengths:<br /> - All such analysis pipelines involve many decision points where there is often no clear correct option. Nonetheless, the authors clearly present their reasoning behind each such decision.<br /> - The authors have published their analytic pipeline such that future researchers might easily replicate and extend their findings.

Weaknesses:<br /> - The majority of regions display no clear candidate causal variants for the traits, whether shared or distinct. Further, despite the potential of local genetic correlation analysis to identify regions with effects in opposing directions, all of the regions for causal variants were identified for both traits evidenced positive correlations. The reasons for this aren't clear and the authors would do well to explore this in greater detail.<br /> - The authors very briefly discuss how their findings differ from previous analyses because of their strict inclusion for "high-quality" variants. This might be the case, but the authors do not attempt to demonstrate this via simulation or otherwise, making it difficult to evaluate their explanation.

eLife assessment

Chang et al. provide glutamate co-expression profiles in the central noradrenergic system and test the requirement of Vglut2-based glutamatergic release in respiratory and metabolic activity under physiologically relevant gas challenges. Their experiments provide compelling evidence that conditional deletion of Vglut2 in noradrenergic neurons does not impact steady-state breathing or metabolic activity in room air, hypercapnia, or hypoxia. This study provides an important contribution to our understanding of how noradrenergic neurons regulate respiratory homeostasis in conscious adult mice.

Reviewer #1 (Public Review):

Summary: Chang et al. provide glutamate co-expression profiles in the central noradrenergic system and test the requirement of Vglut2-based glutamatergic release in respiratory and metabolic activity under physiologically relevant gas challenges. Their experiments show that conditional deletion of Vglut2 in NA neurons does not impact steady-state breathing or metabolic activity in room air, hypercapnia, or hypoxia. Their observations challenge the importance of glutamatergic signaling from Vglut2 expressing NA neurons in normal respiratory homeostasis in conscious adult mice.

Strengths: The comprehensive Vglut1, Vglut2, and Vglut3 co-expression profiles in the central noradrenergic system and the combined measurements of breathing and oxygen consumption are two major strengths of this study. Observations from these experiments provide previously undescribed insights into (1) expression patterns for subtypes of the vesicular glutamate transporter protein in the noradrenergic system and (2) the dispensable nature of Vglut2-dependent glutamate signaling from noradrenergic neurons to breathing responses to physiologically relevant gas challenges in adult conscious mice.

Weaknesses: Although the cellular expression profiles for the vesicular glutamate transporters are provided, the study fails to document that glutamatergic-based signaling originating from noradrenergic neurons is evident at the cellular level under normal, hypoxic, and/or hypercapnic conditions. This limits the reader's understanding of why conditional Vglut2 knockdown is dispensable for breathing under the conditions tested.

Reviewer #2 (Public Review):

The authors characterized the recombinase-based cumulative fate maps for vesicular glutamate transporters (Vglut1, Vglut2 and Vglut3) expression and compared those maps to their real-time expression profiles in central NA neurons by RNA in situ hybridization in adult mice. Authors have revealed a new and intriguing expression pattern for Vglut2, along with an entirely uncharted co-expression domain for Vglut3 within central noradrenergic neurons. Interestingly, and in contrast to previous studies, the authors demonstrated that glutamatergic signaling in central noradrenergic neurons does not exert any influence on breathing and metabolic control either under normoxic/normocapnic conditions or after chemoreflex stimulation. Also, they showed for the first-time the Vglut3-expressing NA population in C2/A2 nuclei. In addition, they were also able to demonstrate Vglut2 expression in anterior NA populations, such as LC neurons, by using more refined techniques, unlike previous studies.

A major strength of the study is the use of a set of techniques to investigate the participation of NA-based glutamatergic signaling in breathing and metabolic control. The authors provided a full characterization of the recombinase-based cumulative fate maps for Vglut transporters. They performed real-time mRNA expression of Vglut transporters in central NA neurons of adult mice. Further, they evaluated the effect of knocking down Vglut2 expression in NA neurons using a DBH-Cre; Vglut2cKO mice on breathing and control in unanesthetized mice. Finally, they injected the AAV virus containing Cre-dependent Td tomato into LC of v-Glut2 Cre mice to verify the VGlut2 expression in LC-NA neurons. A very positive aspect of the article is that the authors combined ventilation with metabolic measurements. This integration holds particular significance, especially when delving into the exploration of respiratory chemosensitivity. Furthermore, the sample size of the experiments is excellent.

Despite the clear strengths of the paper, some weaknesses exist. It is not clear in the manuscript if the experiments were performed in males and females and if the data were combined. I believe that the study would have benefited from a more comprehensive analysis exploring the sex specific differences. The reason I think this is particularly relevant is the developmental disorders mentioned by the authors, such as SIDS and Rett syndrome, which could potentially arise from disruptions in central noradrenergic (NA) function, exhibit varying degrees of sex predominance. Moreover, some of the noradrenergic cell groups are sexually dimorphic. For instance, female Wistar rats exhibit a larger LC size and more LC-NA neurons than male subjects (Pinos et al., 2001; Garcia-Falgueras et al., 2005). More recently, a detailed transcriptional profiling investigation has unveiled the identities of over 3,000 genes in the LC. This revelation has highlighted significant sexual dimorphisms, with more than 100 genes exhibiting differential expression within LC-NA neurons at the transcript level. Furthermore, this investigation has convincingly showcased that these distinct gene expression patterns have the capacity to elicit disparate behavioral responses between sexes (Mulvey et al., 2018). Therefore, the authors should compare the fate maps, Vglut transporters in males and females, at least considering LC-NA neurons. Even in the absence of identified sex differences, this information retains significant importance.

An important point well raised by the authors is that although suggestive, these experiments do not definitively rule out that NA-Vglut2 based glutamatergic signaling has a role in breathing control. Subsequent experiments will be necessary to validate this hypothesis.

An improvement could be made in terms of measuring body temperature. Opting for implanted sensors over rectal probes would circumvent the need to open the chamber, thereby preventing alterations in gas composition during respiratory measurements. Further, what happens to body temperature phenotype in these animals under different gas exposures? These data should be included in the Tables.

Is it plausible that another neurotransmitter within NA neurons might be released in higher amounts in DBH-Cre; Vglut2 cKO mice to compensate for the deficiency in glutamate and prevent changes in ventilation?

Continuing along the same line of inquiry is there a possibility that Vglut2 cKO from NA neurons not only eliminates glutamate release but also reduces NA release? A similar mechanism was previously found in VGLUT2 cKO from DA neurons in previous studies (Alsio et al., 2011; Fortin et al., 2012; Hnasko et al., 2010). Additionally, does glutamate play a role in the vesicular loading of NA? Therefore, could the lack of effect on breathing be explained by the lack of noradrenaline and not glutamate?

Reviewer #1 (Public Review):

Qin et al., demonstrate, convincingly, that plasticity of ocular dominance of binocular neurons in the visual thalamus persists in adulthood. The adult plasticity is similar to that described in critical period juveniles in that it is absent in transgenic mice with the deletion of the GABA a1 receptor in thalamus, which also blocks ocular dominance plasticity in primary visual cortex. However, the adult plasticity is not dependent on feedback from primary visual cortex, an important difference from juveniles. These findings are an important contribution to a growing body of work identifying plasticity in the adult visual system, and identifies the visual thalamus as a potential target for therapies to reverse adult amblyopia.

Reviewer #2 (Public Review):

In this work, the authors found in the mouse line of GABA a1 subunit KO in thalamic neurons, which was previously reported lacking ocular dominance (OD) plasticity in juvenile V1 and dLGN (Sommeijer et al., 2017), the adult V1 and dLGN OD plasticity was also missing. Through muscimol inhibiting the V1 feedback, thalamic OD plasticity was unaffected in both WT and KO adult mice. However, during the critical period, the thalamic OD plasticity was dependent on V1 feedback in WT mice.

Strengths:

1. The experiments were well designed. The authors used both MD and No MD controls with both WT and KO mice. The authors used in vivo SU recording, which is broadly accepted as the major method for evaluating OD plasticity.

2. The data analysis was solid. The authors used proper statistical tests for non-parametric data set.

Weaknesses:

1. In my previous review I pointed out that an alternative interpretation of the results is that the lack of OD plasticity in adult V1 and dLGN was caused by an early blockade of the development of the inhibitory circuit in dLGN, which causes life-long deficits in the functional connection of dLGN. The best way to rule out this possibility is by using conditional KO mice that dLGN synaptic inhibition was only interfered in adulthood. In response to my concern, the authors replied with a long text of reasoning why the current results are solid enough and the proposed experiment was unnecessary. I agree with most of the explanation that the current conclusion is solid, but I still think that the cKO experiment will be a good supplement to the current study, and if we do see a similar result in the cKO mice, the conclusion that the adult perturbation of thalamic inhibitory circuit interfere with the OD plasticity will be more convincing. However, I do understand that repeating the experiments again in another mouse line will be difficult and time-consuming, so the authors could choose if they want to perform the experiment or not.

2. Now the discussion part is very long and complex. Rearranging the discussion with sub-sections will make it easy to read.

eLife assessment

Multimodal experiences that for example contain both visual and tactile components are encoded as associative memories. This manuscript is a valuable contribution supporting structural and functional brain plasticity following associative training protocols that pair together different types of sensory stimuli. The results provide solid support for this plasticity being a basis for cross-modal associative memories.

Reviewer #1 (Public Review):

This manuscript by Xu and colleagues addresses the important question of how multi-modal associations are encoded in the rodent brain. They use behavioral protocols to link stimuli to whisker movement and discover that the barrel cortex can be a hub for associations. Based on anatomical correlations, they suggest that structural plasticity between different areas can be linked to training. Moreover, they provide electrophysiological correlates that link to behavior and structure. Knock-down of nlg3 abolishes plasticity and learning.

This study provides an important contribution as to how multi-modal associations can be formed across cortical regions.

Reviewer #2 (Public Review):

This manuscript by Xu et al. explores the potential joint storage/retrieval of associated signals in learning/memory and how that is encoded by some associative memory neurons using a mouse model. The authors examined mouse associative learning by pairing multimodal mouse learning including olfactory, tactile, gustatory, and pain/tail heating signals. The key finding is that after associative learning, barrel neurons respond to other multi-model stimulations. They found these barrel cortical neurons interconnect with other structures including piriform cortex, S1-Tr and gustatory cortical neurons. Further studies showed that Neuroligin 3 mediated the recruitment of associative memory neurons during paired stimulation group. The authors found that knockdown Neuroligin 3 in the barrel cortex suppressed the associative memory cell recruitment in the paired stimulation learning. Overall, while the findings of this study are interesting, the concept of associative learning involving multiple functionally connective cortical regions is not that novel. While some data presented are convincing, the other seems to lack rigor. In addition, more details and clarification of the experimental methods are needed.

eLife assessment

This fundamental study provides a unique tool for assessing the range of phosphorylation in insulin reactions due to genetic variation and dietary influence through the utilization of genetically distinct mouse strains. The discoveries of this study hold substantial importance, as they shed light on the interplay between genetic attributes and environmental conditions in shaping the insulin-signaling network within skeletal muscle, a crucial regulator of metabolism. The supporting evidence presented is compelling, and the work is anticipated to captivate a wide audience within the metabolism discipline due to its extensive appeal and by providing inspiration for further hypothesis-driven research.

Reviewer #1 (Public Review):

The authors focused on genetic variability in relation to insulin resistance. They used genetically different lines of mice and exposed them to the same diet. They found that genetic predisposition impacts the overall outcome of metabolic disturbances. This work provides a fundamental novel view on the role of genetics and insulin resistance.

Reviewer #2 (Public Review):

Summary:<br /> In the present study, van Gerwen et al. perform deep phosphoproteomics on muscle from saline or insulin-injected mice from 5 distinct strains fed a chow or HF/HS diet. The authors follow these data by defining a variety of intriguing genetic, dietary, or gene-by-diet phosphor-sites that respond to insulin accomplished through the application of correlation analyses, linear mixed models, and a module-based approach (WGCNA). These findings are supported by validation experiments by intersecting results with a previous profile of insulin-responsive sites (Humphrey et al, 2013) and importantly, mechanistic validation of Pfkfb3 where overexpression in L6 myotubes was sufficient to alter fatty acid-induced impairments in insulin-stimulated glucose uptake. To my knowledge, this resource provides the most comprehensive quantification of muscle phospho-proteins which occur as a result of diet in strains of mice where genetic and dietary effects can be quantifiably attributed in an accurate manner. Utilization of this resource is strongly supported by the analyses provided highlighting the complexity of insulin signaling in muscle, exemplified by contrasts to the "classically-used" C57BL6/J strain. As it stands, I view this exceptional resource as comprehensive with compelling strength of evidence behind the mechanism explored. Therefore, most of my comments stem from curiosity about pathways within this resource, many of which are likely well beyond the scope of incorporation in the current manuscript. These include the integration of previous studies investigating these strains for changes in transcriptional or proteomic profiles and intersections with available human phospho-protein data, many of which have been generated by this group.

Strengths:<br /> Generation of a novel resource to explore genetic and dietary interactions influencing the phospho-proteome in muscle. This is accompanied by the elegant application of in silico tools to highlight the utility.

Weaknesses:<br /> Some specific aspects of integration with other data among the same fixed strains could be strengthened and/or discussed.

Reviewer #3 (Public Review):

Summary:<br /> The authors aimed to investigate how genetic and environmental factors influence the muscle insulin signaling network and its impact on metabolism. They utilized mass spectrometry-based phosphoproteomics to quantify phosphosites in the skeletal muscle of genetically distinct mouse strains in different dietary environments, with and without insulin stimulation. The results showed that genetic background and diet both affected insulin signaling, with almost half of the insulin-regulated phosphoproteome being modified by genetic background on an ordinary diet, and high-fat high-sugar feeding affecting insulin signaling in a strain-dependent manner.

Strengths:<br /> The study uses state-of-the-art phosphoproteomics workflow allowing quantification of a large number of phosphosites in skeletal muscle, providing a comprehensive view of the muscle insulin signaling network. The study examined five genetically distinct mouse strains in two dietary environments, allowing for the investigation of the impact of genetic and environmental factors on insulin signaling. The identification of coregulated subnetworks within the insulin signaling pathway expanded our understanding of its organization and provided insights into potential regulatory mechanisms. The study associated diverse signaling responses with insulin-stimulated glucose uptake, uncovering regulators of muscle insulin responsiveness.

Weaknesses:<br /> Different mouse strains have huge differences in body weight on normal and high-fat high-sugar diets, which makes comparison between the models challenging. The proteome of muscle across different strains is bound to be different but the changes in protein abundance on phosphosite changes were not assessed. Authors do get around this by calculating 'insulin response' because short insulin treatment should not affect protein abundance. The limitations acknowledged by the authors, such as the need for larger cohorts and the inclusion of female mice, suggest that further research is needed to validate and expand upon the findings.

eLife assessment

This important study presents convincing evidence for the utility of orangutan teeth as terrestrial proxies to reconstruct rainfall regimes, while exploring the potentially conflicting impact of breastfeeding signals. The findings have ramifications for the methods and tools used by the field in the reconstruction of environmental conditions in the historical and archeological past.

Reviewer #1 (Public Review):

Summary:<br /> The authors measured the oxygen stable isotope ratios in six orangutan teeth using a state-of-the-art micro-sampling technique (SHRIMP SI) to gather substantial multi-year isotopic data for six modern and five fossil orangutan individuals from Borneo and Sumatra. This fine-scale sampling technique allowed them to address the fundamental question of whether breastfeeding affects the oxygen isotope ratios in teeth forming in the first one to two years of life, during which orangutans are assumed to largely depend on breastmilk. The authors provide compelling evidence that the consumption of milk does not appear to affect the overall isotopic profile in early-forming teeth. They conclude that this allows us to use these teeth as terrestrial/arboreal isotopic proxies in paleoenvironmental research, which would provide an invaluable addition to otherwise largely marine climate records in these regions.

Strengths:<br /> The overall large sample size of orangutan dental isotope records as well as the rigorous dating of the fossil specimens provide a strong dataset for addressing the outlined questions. The direct comparison of modern and fossil orangutan specimens provides a valuable evaluation of the use of these modern and past environmental proxies, with some discussion of the implications for the environmental conditions during the expansion of early modern humans into this region of the world.

Weakness:<br /> Although the overall conclusions of this paper are well supported and discussed, one important aspect could have more detailed consideration: the ecology and behavior of orangutans. As one example, orangutans are almost exclusively (~96%) arboreal creatures foraging for plant foods in the forest canopy, and as such they mostly meet their water requirements from the plants they eat, only very rarely drinking surface water (Ashbury et al. 2015). As a result, all orangutan water and foods are strongly affected by the so-called canopy effect, which could have found stronger consideration in this study. The canopy effect in primate plant foods has been demonstrated to easily exceed 5‰ within the same forest canopy and even within the same tree, mainly depending on stratigraphy/height (Lowry et al. 2021). This variation may explain the noise in the isotopic data within a given orangutan tooth, which lies well within this 5% range, and could easily obscure any possible breastfeeding effect in dental isotope ratios. If the canopy effect may indeed introduce so much noise in the oxygen isotope data, this should be also considered in the use of the data as a climate proxy. The question arises if a terrestrial long-lived mammal species may be a more suitable proxy than an arboreal one.

Reviewer #2 (Public Review):

Summary:<br /> This manuscript provides microprobe serial oxygen isotope data from thin-sectioned modern and fossil orangutan teeth in an effort to reconstruct the seasonality of rainfall in Borneo and Sumatra. The authors also explore the hypothesis that nursing could affect early tooth (first molar) isotope values. They find that all molars yield similar oxygen isotope values and therefore conclude that future research need not exclude the use of first molars. With regard to seasonality, the modern orangutans yield similar results from both islands. The authors suggest differences between modern and fossil orangutan teeth, but the comparisons could be more fully explored.

Strengths:<br /> The study employs a sampling method that captures serial isotope values within thin sections of teeth using a microprobe that provides a much higher resolution than traditional hand-held drilling.

Weaknesses:<br /> The study only examines six modern and six fossil orangutan individuals. Of those, only four modern individuals were samples across multiple molars. The comparisons between modern and fossil teeth are difficult to follow, making unclear the conclusion that climate has changed.

Author Response

We appreciate the opportunity to publish our research in eLife. Both reviewers highlight our state-of-the-art oxygen isotope sampling approach, which has allowed us to establish that early-formed primate enamel does not show a large or consistent isotopic offset due to intensive nursing. This means we can be more confident in employing early-forming teeth to probe environmental conditions—an issue that has handicapped past paleoenvironmental studies—documenting seasonal rainfall variation in the tropics at an extremely fine-scale.

Reviewer 1 requests that we elaborate on the ecology and behavior of orangutans, particularly in reference to the issue of isotopic enrichment within forest canopies—a topic we devote a paragraph to in the discussion. We appreciate the opportunity to add additional context during revision, noting here that our previous comparisons of terrestrial baboons and semi-terrestrial tantalus monkeys in the Bushenyi District (Uganda) do show modest isotopic differences between species, consistent with a canopy effect (Green et al. 2022). However, this is less of an issue for comparisons of Sumatran and Bornean orangutans given their ecological and behavioral similarities. We agree that variation in the canopy heights/positions of orangutan food sources may contribute to enamel oxygen isotope variation, in addition to the seasonal rainfall trends we observe in our datasets. Importantly, our published and on-going work on western chimpanzees has revealed strong annual oxygen isotope trends concordant with local rainfall patterns. The consistency and amplitude of seasonal oxygen isotope oscillations in such datasets suggest that arboreal primates are not less useful than terrestrial primates for reconstruction of rainfall seasonality.

We clarify that while Reviewer 1 states that we measured 6 teeth, Tables 1 and 2 and the first sentence of the results make it clear that we measured 18 teeth in this study.

Reviewer 2 asks for further detail about comparisons between modern and fossil orangutan teeth that support inferences of climate variation, which we will endeavour to add in the revised manuscript.

eLife assessment

This study presents valuable new insights from the protist Tetrahymena regarding radial spokes, conserved protein complexes that are important for cilia motility. The work employs interdisciplinary approaches to provide convincing support for radial spoke composition with some experiments, but there are weaker areas with partially incomplete support, such as relying on knockouts alone rather than including localization studies of tagged proteins.

Reviewer #1 (Public Review):

Summary:<br /> Radial spokes (RS) are made of >20 proteins and are believed to be a transducer to coordinate axonemal dyneins to enable the beating motion of motile cilia. While the atomic structure of RS from green algae Chlamydomonas and H. Sapience has been solved by single particle cryo-EM recently, this work by Bicka et al. provided the atomic structure of RS from ciliate Tetrahymena. They identified component proteins of Tetrahymena RS, which correspond to those in the atomic structure of Chlamydomonas and human RS. These proteins were likely already guessed as RS components, based on sequence similarity, but in this work experimentally identified for the first time. Furthermore, they discovered novel isoforms of RS proteins and characterized them structurally and functionally. RSP3 has three isoforms (A, B, and C). They are distributed specifically in the three radial spokes within the repeating unit as proved by mutant analysis, cryo-EM, and proteomics. By high-speed video microscopy, they proved the essential roles of RSP3B for ciliary beating. These isoforms have never been reported in past works and this demonstrates the novelty of this work.

Strength:<br /> Their discovery of RSP3 isoforms is unexpected and, although it is still not clear why Tetrahymena needs to have these isoforms, will evoke future research. The authors characterized the multi-facet aspects of these proteins precisely, structurally by cryo-EM, functionally by waveform and velocity analysis, and in terms of protein networking by co-IP and bioID studies.

Weakness:<br /> While the first half of this manuscript about RSP3 isoforms is very well organized and described (this reviewer still has some advice to make this story convincing and attractive), the later part has room for improvement. Some results are presented in the current manuscript without mentioning figures or tables, for example in "250: The components of the Tetrahymena radial spoke stalks" no figure/table is cited. There is also inconsistency - in 327 RSP9 is mentioned as a MORN protein, but in Fig.6 Sup.3 Table.1, it is mentioned as "unknown".

Reviewer #2 (Public Review):

Summary:<br /> Radial spokes are evolutionarily conserved protein complexes that are important for cilia motility. So far, the composition of certain radial spokes was investigated in the algae Chlamydomonas, mice, and humans. This work by Bicka et al. investigated the composition of radial spokes in the ciliate Tetrahymena by analyzing knockouts and strains that express tagged radial spoke proteins, using mass spectrometry and cryo-electron tomography. While three specific types of radial spokes have been reported thus far, this study suggests that in Tetrahymena, there is another layer to the variability in radial spokes. Additionally, many proteins with predicted enzymatic folds have now been assigned to radial spokes. The comparison of ciliary complexes between species is important to define the basic principles that govern cilia motility, as well as to reveal the differences that enable cilia of various organisms to beat in diverse environments.

Strengths:<br /> The manuscript includes a thorough bioinformatic analysis of radial spoke proteins in Tetrahymena and reveals the presence of multiple orthologs to certain algae and mammalian radial spoke proteins. The mass spectrometry analysis and cryo-electron tomography experiments are solid and informative. This work provides a lot of important data and thus, opens the door to resolve the exact composition and structures of radial spokes in Tetrahymena and perhaps other species.

Weaknesses:<br /> The assignment of the three RSP3 orthologs to RS1, RS2, and RS3 is based only on missing structures in the knockouts. Although this method is informative, it is not sufficient to draw conclusions regarding the positions of the missing proteins. There are numerous examples where a structure was missing, but the absent protein was localized elsewhere (i.e., absence of central pair protrusions in patients with mutations in radial spoke proteins). To directly demonstrate the position of an RSP3 ortholog in a certain radial spoke, the protein can be labeled with a tag that is visualized in subtomogram averages (as was done in Oda et al., 2014 and other studies). Relying on the data from knockouts alone, the model for radial spoke composition in Tetrahymena (Fig. 6) may be incomplete.

The control for the bio-ID experiment was WT cells. Since there are many hits in the experiment, a better control would have been a strain with free BirA, or BirA fused to a protein that is distant from the radial spokes, such as one of the outer-dynein arm proteins, or a ciliary membrane protein.

Reviewer #3 (Public Review):

Summary:<br /> The authors aim to study the role of axoneme radial spoke proteins in forming the three radial spokes that connect the central pair microtubules with the doublet microtubules of the ciliary axoneme. They combined existing and novel mutants to first study ciliary dynamics, followed by cryoET structure and proteomics to identify known and new radial spoke protein components, and assign those with radial spoke(s) to which they belong.

Strengths: / Weaknesses:<br /> The strengths of this study are in the genetic mutants combined with the cryoET to study the unique structural impacts of each mutant on the three radial spokes. The proteomics to study protein loss and interactions also enabled a comprehensive comparison of proteins at the radial spoke under normal and mutant conditions. This allowed the authors to predict that there are several classes of each type of radial spoke. While there are some limitations with overlapping phenotypes between the mutants, this tactic allows the authors to predict known and new proteins that are predicted to localize to each of the three radial spokes. However, in some places, the conclusions are overstated and the list of molecules without functional insight simply identifies new components that will need to be the target of future studies. Two examples of this are that the authors claim to have "solved the composition of individual radial spokes" and that "adenylate kinases [that] dock to specific RSs". Neither of these statements should be made based on the results in this manuscript. Moreover, the authors state that Rsp3Bp does not change in rsp3C knockouts and conclude that Rsp3B from the A-C heterodimer is still attached to the axoneme without maintaining the RS2 structure. To me, this makes a series of strongly stated conclusions without the results to justify the statement. The authors also report on unique features of ciliary dynamics resulting from the loss of each of the three Tetrahymena RSP3 genes. This showed a strong phenotype for rsp3b knockout. However, a quantitative measure of ciliary dynamics to understand how much the presented data represent the ciliary dynamics was not clear. Furthermore, the authors argue that metachrony or coordination between cilia was affected but the presented data are not interpretable or quantified. Furthermore, the authors state that all three Rsp3 paralogs localize along the entire length of the cilium. However, Rsp3A and B do not localize to ciliary tips, while Rsp3C does. This may inform the differences found in the ciliary waveform for rsp3C mutants compared to rsp3A and rsp3B. The authors state that they have defined a "large part of the protein composition of individual RSs...". It is not clear to me that they know how much of the total RS proteome they have identified.

This manuscript identifies new candidate proteins that may function with radial spokes, future work will be required to 1) confirm their localization to the radial spoke and 2) to study their function within radial spokes.

Author Response

We thank Editors and Reviewers for their positive evaluation of our work and appreciation of new findings and applied interdisciplinary approaches. We also thank for pointing out manuscript weaknesses as well as for all suggestions and advices that can strengthen this manuscript. We apologise for mistakes, overstatements or discrepancies in citing figures as well as omitted references.

The first part of the manuscript focuses on the Tetrahymena RSP3 genes mutants.  Tetrahymena genome encodes three RSP3 paralogs that are the components of different radial spokes and likely form homo- and heterodimers. Thus, the proteomic analyses of Tetrahymena radial spokes are more complicated compared to the similar analyses in organisms having a single RSP3 protein.

Next, we attempted to identify proteins specific for each RS type. Conducting this research, we took advantage of six different radial spoke knockout mutants (RSP3A-KO, RSP3B-KO, RSP3C-KO, CFAP206-KO, CFAP61-KO, and CFAP91-KO) and compared wild-type and mutants’ ciliomes using two methods, LFQ and TMT (for each mutant the experiment was repeated three times). Comparative analyses of the wild-type and mutants ciliomes allowed us to identify Tetrahymena radial spoke proteins, in the case of RS1 (WT versus RSP3A-KO), RS2 (WT versus RSP3B-KO, RSP3C-KO, and CFAP206-KO), and RS3 (wild-type versus  CFAP61-KO and CFAP91-KO). The extensive proteomic analyses were combined with detailed bioinformatics studies and co-immunoprecipitation and BioID assays to verify the presence of identified proteins in RS complexes. 

Importantly, in the case of RS1 and RS2 spokes, our findings are in agreement with data obtained for Chlamydomonas and mammalian radial spokes. Thus, it is very likely, that newly discovered RS1 and RS2 proteins as well as identified Tetrahymena RS3 proteins are also true RS subunits.

As an outcome of this part, we propose a model of the RS protein composition in a ciliate Tetrahymena. We agree that this model requires further experimental verification (for example by pull-down experiments).  However, considering the number of identified proteins, this is a considerable amount of additional work that we would like to publish as separate papers. We would like to add that our current analyses of additional RS3 mutant (that will be published separately) support findings regarding RS3 proteomic composition.

Reviewer 2:

The control for the bio-ID experiment was WT cells. Since there are many hits in the experiment, a better control would have been a strain with free BirA, or BirA fused to a protein that is distant from the radial spokes, such as one of the outer-dynein arm proteins, or a ciliary membrane protein.

The BirA* tag is approximately 30 kDa protein and thus it can be transported to cilia by diffusion. BirA* ligase present throughout the cilia could randomly biotinylate proteins including radial spoke proteins. Thus, expression of the BirA* alone is not the best control. We have performed numerous BioID experiments in which BirA* tag was fused with T/TH subunits (CFAP43, CFAP44, Urbanska et al., 2018), subunits of the small complex positioned parallel to N-DRC (CCDC113, CCDC96, Bazan et al., 2021), CFAP69, SPEF2A (C1b central apparatus complex, Joachimiak et al., 2021), N-DRC proteins (Ghanaeian et al., Biorxiv, 2023) and subunits of other ciliary complexes (our unpublished data). The comparison of the earlier obtained BioID data with RSP BioID data, prove that identified proteins are specifically associated with radial spokes. Therefore, in our model, wild-type cells are a good control for BioID experiments.

Author Response

Reviewer 1 Public Review

The authors aim to theoretically explain the wide range of time scales observed in cortical circuits in the brain – a fundamental problem in theoretical neuroscience. They propose that the variety of time scales arises in recurrent neural networks with heterogeneous units that represent neuronal assemblies of different sizes that transition through sequences of high- and low-activity metastable states. When transitions are driven by intrinsically generated noise, the heterogeneity leads to a wide range of escape times (and hence time scales) across units. As a mathematically tractable model, they consider a recurrent network of heterogeneous bistable rate units in the chaotic regime. The model is an extension of the previous model by Stern et al (Phys. Rev. E, 2014) to the case of heterogeneous self-coupling parameters. Biologically, this heterogeneous parameter is interpreted as different assembly sizes. The chaoticity acts as intrinsically generated noise-driving transitions between bistable states with escape times that are indeed widely distributed because of the heterogeneity. The distribution is successfully fitted to experimental data. Using previous dynamic mean-field theory, the self-consistent auto-correlation function of the driving noise in the mean-field model is computed (I guess numerically). This leaves the theoretical problem of calculating escape times in the presence of colored noise, which is solved using the unified colored-noise approximation (UCNA). They find that the log of the correlation time of a given unit increases quadratically with the self-coupling strength of that unit, which nicely explains the distribution of time scales over several orders of magnitude. As a biologically plausible implementationof the theory, they consider a spiking neural network with clustered connectivity and heterogeneous cluster sizes (extension of the previous model by Mazzucato et al. J Neurosci 2015). Simulations of this model also exhibit a quadratic increase in the log dwell time with cluster size. Finally, the authors demonstrate that heterogeneous assemblies might be useful to differentially transmit different frequency components of a broadband stimulus through different assemblies because the assembly size modulates the gain.

I found the paper conceptually interesting and original, especially the analytical part on estimating the mean escape times in the rate network using the idea of probe units and the UCNA. It is a nice demonstration of how chaotic activity serves as noise-driving metastable activity. Calculating the typical time scales of such metastable activity is a hard theoretical problem, for which the authors made considerable advancement. The conclusions of this paper are mostly well supportedby simulations and mathematical analysis, but some aspects need to be clarified and extended, especially concerning the biological plausibility of the rate network model and its relation to the spiking neural network model as well as the analytical calculation of the mean dwell time.

Question 1a. The theory is based on a somewhat unbiological network of bistable rate units. It seems to only loosely apply to the implementation with a spiking neural network with clustered architecture, which is used as a biological justification of the rate model. In the spiking model, a wide distribution of time scales also emerges as a consequence of noise-induced escapes in combination with heterogeneity. Apart from this analogy, however, the mechanisms for metastability seem to be quite different: firstly, the functional units in the spiking neural network are presumably not bistable themselves but multistability only emerges as a network effect, i.e. from the interaction with other assemblies and inhibitory neurons. (This difference yields anti-correlations between assemblies in the spiking model, in marked contrast to the independence of bistable rate units (if N is large).) Secondly, transitions between metastable states are presumably not driven by chaotic dynamics but by finite-size fluctuations (e.g. Litwin-Kumar and Doiron 2012). The latter is also strongly dependent on assembly size. More precisely, the mechanism of how assembly size shapes escape times T seems to be different: in the rate model the self-coupling ("assembly size") predominantly affects the effective potential, whereas in the spiking network, the assembly size predominantly affects the noise. Therefore, the correspondence between the rate model and the spiking model should probably be regarded in a looser sense than presented in the paper.

Answer 1a. We thank the Reviewer for suggesting to clarify the relationship between the rate and spiking model. In this answer, we first show that the dynamicalmodes in the spiking network are E/I cluster pairs, then we show that assemblies are bistable due to the large self-couplings, and third we discuss whether transitions between high and low activity states are driven by chaos or finite size effects, including correlations between assemblies.

We first elucidated the dynamical modes in the spiking network and how those can be related to the rate network. Using an approach from (1, 2), we considered the mean-field theory for the spiking network, reducing the degrees of freedom from N neurons to 2p+2 E/I assemblies (plus E/I background populations), then we identified the approximate dynamical modes as E/I cluster pairs emerging as the Schur eigenvectors of the mean field-reduced coupling matrix. Comparing the eigenvalue distribution of the full vs. the mean field-reduced coupling matrix, we found that the slow timescales capturing the assemblies metastable dynamics correspond to the p−1 large positive eigenvalues corresponding to the Schur modes. The heterogeneity in timescales of the spiking model arises from the heterogeneous distribution of these gapped eigenvalues, reflecting the hierarchy in assembly sizes and assembly self-couplings in the mean field approach. We then analyzed the eigenvalues in the rate network with a lognormal self-coupling distribution and found a similar picture, where the slow units are related to the large eigenvalues in the coupling matrix (Appendix 2). We also note that in the rate network, there is no gap in the eigenvaluedistributionas there are many units with small values of the self-couplings. On the other hand in the spiking network the large eigenvalues are p − 1, where p is the number of assemblies, and they are gapped. These new analyses clarify the correspondence between rate network units and spiking network E/I cluster pairs, arising from the Schur picture.

We now discuss previous studies to examine whether bistability in the spiking network arises from assembly self-couplings or from other effects. Previous mean-field analyses of spiking networks with clustered connectivity showed that the bistability of assembly dynamics is due to the presence of a large self-coupling, rather than from the interactions with other assemblies. We briefly summarize the published evidence for this. The seminal work of (3) showed that in a network with assemblies, a bifurcation in network dynamics emerges when the assembly self-coupling JEE+ > Jc exceeds a critical value Jc; beyond this value, a low and a high activity stable state coexist. Although in this network these two states are stable, more recent work from (4, 5) showed that finite size effects (small assembly size) can destabilize the states, leading to the metastable regime. When the inhibitory population is homogeneous, as in these last two articles, metastability arises from finite size effects and it is sensitive to network parameters (5) and (6). Specifically, when one scales both the network size and the E assembly size, metastability disappears (5). Moreover, when the I population is homogeneous, then E clusters are anti-correlated, as correctly suggested by the Reviewer. However, our model differs from the ones just discussed in that the inhibitory population is also arranged in assemblies, which are reciprocally paired with E assemblies. In this class of E/I clustered models, metastability is robust to changes in network parameters (see (6)). More specifically, in our revised version, we show that metastable dynamics persists when scaling up the network size to N = 10,000 neurons (and scaling up network size with N). A crucial difference between the model with homogeneous I population vs the model with I assemblies (i.e., our model), is that in the former the assemblies are anti-correlated, while in the latter case the assemblies are uncorrelated (see Fig. 1), the same as in the rate network. These results suggest that transitions between metastable states in the spiking network may be driven by a coexistence of two effects: on the one hand, finite size effects due to the small assembly size, and on the other hand, by the heterogeneity in the inter-assembly couplings. Although the former effect is absent in the rate network, the latter is the driver of the chaotic activity observed in the rate network. Thus it is plausible that rate-based chaotic dynamics might also contribute to the metastable activity in the spiking network, although more targeted work should be performed to answer this question. In the revised version of the manuscript, we overhauled the subsection ’A reservoir of timescales in E-I spiking networks’, Fig. 5, and Appendix 2, by adding an extensive explanation of the emergence of slow timescales from the large eigenvalues in the Schur basis, and its comparison between spiking and rate network. In particular, we highlighted the differences between rate and spiking networks and the fact that finite size effects might be at play in the latter case.

Furthermore, the prediction of the rate model is a quadratic increase of log(T), however, the data shown in Fig.5b do not seem to strongly support this prediction. More details and evidence that the data "was best fit with a quadratic polynomial" would be necessary to test the theoretical prediction.

We increased the clarity and strengthened the support for the data in Fig 5b as "best fit with a quadratic polynomial" by addinga plot, inset in Fig 5b, alongsidea detailed explanation of the fitting procedure in Methods section (e). Figure 5b inset displays a cross-validatedmodel selection’s training and test errors for polynomial fit. The test error shows a minimal error at a polynomial degree 2, supporting the claim that the best fit was achieved with a quadratic polynomial. In Methods section (e), under "Model selection for timescale fit," we added a detailed description of the cross-validation procedure by which the fit was obtained. A quote from that section in the revised manuscript can also be found in this document under answer 11.

Question 2. The time scale of a bistable probe unit driven by network-generated "noise" is taken to be the mean dwell time T (mean escape time) in a metastable state. It seems that the expressions Eq.4 and Eq.21 for this time are incorrect. The mean dwell time is given by the mean first-passage time (MFPT) from one potential minimum to the opposite one includingthe full passage across the barrier. At least, the final point for the MFPT should be significantly beyond the barrier to complete the escape. However, the authors only compute the MFPT to a location −xc slightly before the barrier is reached, at which point the probe unit has not managed to escape yet (e.g. it could go back to −x2 after reaching −xc instead of further going to +x2). It is not clear whether the UCNA can be applied to such escape problems because it is valid only in regions, where the potential is convex, and thus the UCNA may break down near the potential barrier. Indeed, the effective potential is not defined near the barrier (see forbidden zone in Fig.4b), and hence it is not clear how to calculate the mean escape time. Nonetheless, the incomplete MFPT computedby the authors seems to qualitatively predict the dependence on the self-coupling parameter s, at least in the example of Fig.4c. However, if the incomplete MFPT is taken as a basis, then the incomplete MFPT should also be used for the white-noise case for a fair comparison. It seems that the corresponding white-noise case is given by Eq.4 with τ1 = 0, which still has the same dependence on the self-coupling s2, contrary to what is claimed in the paper (it is unclear how the curve for the white-noise case in Fig.4 was obtained). Note that the UCNA has been designed such that it is valid for both small and large τ1 (thus, it is also unclear why the assumption of large τ1 is needed).

Answer 2. We are deeply grateful to the Reviewer for this critical evaluation of our UCNA calculation of the escape times. We will first clarify our rationale and then discuss comparison with the white noise case. The idea behind our calculation is indeed that when starting from the left minimum −x2, the probe effectively escapes to +x2 before reaching the limit of the UCNA support region at −xc. First, our simulations show (Fig 4b light blue colored area) that the probe almost exclusively visits the valid areas |x| > xc: our new analysis shows that the fraction of activity spent in the forbidden region is (1.8+/ −0.4)×10−3 (mean±SD over 10 probe units run with parameters as in Fig. 4a-b), confirming the fact that the histogram of x values from simulations has almost null support in the forbidden region |x| < xc. This is also supported by the representative simulation time course in Fig. 4a which exhibits abrupt jumps between the two bistable states. We then estimated the ’escape point’ from simulations as follows: for a transition from the x = −s2 well towards the x = +x2 well, the escape point is defined as the point where x on the side of the source well, i.e. x < 0, but the trajectory starts accelerating towards the target well (positive second derivative). We found that the distribution of escape points was predominantly in the allowed region (93.8%). This analysis supports our method to calculate the MFPT and confirms that our calculation is performed in the valid UCNA region. In the revised version of the manuscript, we added a clarification of this point with text and a new supplementary figure in Fig. 4 Suppl. 1. Regarding the comparison with white noise, we compared white-noise-driven probe dynamics with a probe driven by a network (effectively represented by the colored noise). To adequately make this comparison, we replaced the input coming from the network into the probe unit (Eq 1. rhs last term) with white noise. The rest of the terms in this equation were left untouched to maintain the exact probe’s self-response properties. This procedure aims to understand the unique contribution of the colored noise generated by the network to each unit dynamics by removing its "colored" correlated input contribution but otherwise leaving all dynamical properties the same. For clarity of the manuscript on this subject, we added a paragraph about it under "A comparison with white noise" in Methods section (d).

We can estimate the mean first passage time (MFPT) of a probe unit driven by white noise with Eq. 4. The procedure described above for switching the network drive with white noise also dictates the parameter values to use in Eq. 4 for the case of white noise. First, with no correlation in white noise τ1 = 0. Second, D, the magnitude of the drive inherits its value from the network (see also Eq. 22) as the strength of the white noise (its integral around zero as a δ function). The results are presented in Fig 4. To strengthen the results and improve the clarity of the text, we expanded the content of Fig 4c. The plot now includes both the results of simulations (Fig. 4c green line) and estimation by mean first passage time (Fig. 4c green dashed line) for white noise, as explained above. We note that the potential in the white noise case (Fig. 4b green dashed line) does include a concave part. Indeed,the agreementbetweenthe distributionretrieved from simulations (Fig. 4b light green area) and its locations’ visit probability approximated by theory (Eq. 19 with τ1 = 0, Fig. 4b green line) are not in full agreement. However, this probability is still a good approximation. As a result, the mean first passage time (Eq. 4, Fig. 4c green dashed line) is a good approximation. The great advantage of having Eq. 4 as an approximation for the mean first passage time is that it clearly explainsthe contributionof each part of the dynamical equation (Eq. 1) towards achieving long timescales. Mainly, since log<T> depends on τ1 linearly, its exponent, the mean first passage time depends on tau1 exponentially. Hence the importance of the color in the input and the vast differences between the network drive and the white noise.

Question 3. The given argument that the time-scale separation arises as network effect is not very clear. Apart from the issue of a fair comparison of colored and white noise raised in point 1 above, an external colored noise with matched statistics that drives a single bistable unit would yield the same MFPT and thus would be an alternative explanation independent of the network dynamics.

Answer 3. The goal of our investigation was to uncover a neural mechanism that induces heterogeneous timescales in a self-consistent way. The idea of self-consistencyis the central tenet of our approach, namely, that a timescale distribution must arise due to the internal dynamics of a recurrent circuit without the need to invoke an external auxiliary force driving it. If we had an external colored noise with matched statistics driving the probe unit, then we would still have to explain which mechanism would give rise to that particular statistics of the colored noise - with the most natural explanation being a recurrent network with time-varying activity.

The second ingredient in our argument demonstrating that it is a network effect is the following. If the time-scale separation was not a network effect, but rather a property of a single probe unit, then it would persist regardless of the specific features of the noise driving the unit. To test this hypothesis, we compared the scenarios of the same probe unit driven by the self-consistent noise generated by the rest of the network, as opposed to white noise, and found that the time-scale separation is not present in the second case. Thus, the time-scale separation is not an intrinsic property of the probe unit, but, rather, it relies on the unit being part of a recurrent network generating a specific kind of noise. This argument is explained in the last paragraph of the section ’Separation of timescales in the bistable chaotic regime’.

Question 4. The UCNA has assumptions and regimes of validity that are not stated in the paper. In particular, it assumes an Ornstein-Uhlenbeck noise, which has an exponential auto-correlation function, and local stability (region where potential is convex). Because the self-consistent auto-correlation function is generally not exponential and the probe unit also visits regions where the potential is concave, the validity of the UCNA is not clear. On the other hand, the assumption of large correlation time might be dropped as the UCNA’s main feature is that it works for both large and small correlation times.

Answer 4. We thanks the Reviewer again for this critical evaluation of our assumptions, however, we believe that our approach is justified because of the following two arguments. First, although the UCNA was derived in case of an OU process, it has since then been successfully applied to different classes of noise, including multiplicative noise, harmonic noise, and others (see e.g. (7–9). To the best of our knowledge, the UCNA has never been applied before to noise whose autocorrelation arises from chaotic dynamics, whose hallmark is a vanishing slope at zero lag, markedly different from the OU process. To address the concern about concavity, we performed the additional analyses discussed in our answer to Question 2, showing that the probe unit never visits regions where the potential is concave, which would lie outside of the support of the potential. Because of these two considerations, we believe that the UCNA is valid in our scenario, as suggested by the good agreement between theory and simulation at large values of the self-couplingsin Fig. 4c. Finally, we thank the Reviewer for bringing up the fact that UCNA works for both large and small correlation times, we fixed that in the revised manuscript.

eLife assessment

This fundamental work uses computational network models to suggest a possible origin of the wide range of time scales observed in cortical activity. This claim is supported by convincing evidence based on comparisons between mathematical theory, simulations of spiking network models, and analysis of recordings from the orbitofrontal cortex. This manuscript will be of interest to the broad community of systems and computational neuroscience.

Reviewer #1 (Public Review):

The authors aim to theoretically explain the wide range of time scales observed in cortical circuits in the brain -- a fundamental problem in theoretical neuroscience. They propose that the variety of time scales arises in recurrent neural networks with heterogeneous units that represent neuronal assemblies of different sizes that transition through sequences of high- and low-activity metastable states. When transitions are driven by intrinsically generated noise, the heterogeneity leads to a wide range of escape times (and hence time scales) across units. As a mathematically tractable model, they consider a recurrent network of heterogeneous bistable rate units in the chaotic regime. The model is an extension of the previous model by Stern et al (Phys. Rev. E, 2014) to the case of heterogeneous self-coupling parameters. Biologically, this heterogeneous parameter is interpreted as different assembly sizes. The chaoticity acts as intrinsically generated noise-driving transitions between bistable states with escape times that are indeed widely distributed because of the heterogeneity. The distribution is successfully fitted to experimental data. Using previous dynamic mean-field theory, the self-consistent auto-correlation function of the driving noise in the mean-field model is computed (I guess numerically). This leaves the theoretical problem of calculating escape times in the presence of colored noise, which is solved using the unified colored-noise approximation (UCNA). They find that the log of the correlation time of a given unit increases quadratically with the self-coupling strength of that unit, which nicely explains the distribution of time scales over several orders of magnitude. As a biologically plausible implementation of the theory, they consider a spiking neural network with clustered connectivity and heterogeneous cluster sizes (extension of the previous model by Mazzucato et al. J Neurosci 2015). Simulations of this model also exhibit a quadratic increase in the log dwell time with cluster size. Finally, the authors demonstrate that heterogeneous assemblies might be useful to differentially transmit different frequency components of a broadband stimulus through different assemblies because the assembly size modulates the gain.

I found the paper conceptually interesting and original, especially the analytical part on estimating the mean escape times in the rate network using the idea of probe units and the UCNA. It is a nice demonstration of how chaotic activity serves as noise-driving metastable activity. Calculating the typical time scales of such metastable activity is a hard theoretical problem, for which the authors made considerable advancement. The conclusions of this paper are mostly well supported by simulations and mathematical analysis, but some aspects need to be clarified and extended, especially concerning the biological plausibility of the rate network model and its relation to the spiking neural network model as well as the analytical calculation of the mean dwell time.

1) The theory is based on a somewhat unbiological network of bistable rate units. It seems to only loosely apply to the implementation with a spiking neural network with clustered architecture, which is used as a biological justification of the rate model. In the spiking model, a wide distribution of time scales also emerges as a consequence of noise-induced escapes in combination with heterogeneity. Apart from this analogy, however, the mechanisms for metastability seem to be quite different: firstly, the functional units in the spiking neural network are presumably not bistable themselves but multistability only emerges as a network effect, i.e. from the interaction with other assemblies and inhibitory neurons. (This difference yields anti-correlations between assemblies in the spiking model, in marked contrast to the independence of bistable rate units (if N is large).) Secondly, transitions between metastable states are presumably not driven by chaotic dynamics but by finite-size fluctuations (e.g. Litwin-Kumar & Doiron 2012). The latter is also strongly dependent on assembly size. More precisely, the mechanism of how assembly size shapes escape times T seems to be different: in the rate model the self-coupling ("assembly size") predominantly affects the effective potential, whereas in the spiking network, the assembly size predominantly affects the noise.

Furthermore, the prediction of the rate model is a quadratic increase of log(T), however, the data shown in Fig.5b do not seem to strongly support this prediction. More details and evidence that the data "was best fit with a quadratic polynomial" would be necessary to test the theoretical prediction. Therefore, the correspondence between the rate model and the spiking model should probably be regarded in a looser sense than presented in the paper.

2) The time scale of a bistable probe unit driven by network-generated "noise" is taken to be the mean dwell time T (mean escape time) in a metastable state. It seems that the expressions Eq.4 and Eq.21 for this time are incorrect. The mean dwell time is given by the mean first-passage time (MFPT) from one potential minumum to the opposite one including the full passage across the barrier. At least, the final point for the MFPT should be significantly beyond the barrier to complete the escape. However, the authors only compute the MFPT to a location -x_c slightly before the barrier is reached, at which point the probe unit has not managed to escape yet (e.g. it could go back to -x_2 after reaching -x_c instead of further going to +x_2). It is not clear whether the UCNA can be applied to such escape problems because it is valid only in regions, where the potential is convex, and thus the UCNA may break down near the potential barrier. Indeed, the effective potential is not defined near the barrier (see forbidden zone in Fig.4b), and hence it is not clear how to calculate the mean escape time. Nonetheless, the incomplete MFPT computed by the authors seems to qualitatively predict the dependence on the self-coupling parameter s, at least in the example of Fig.4c. However, if the incomplete MFPT is taken as a basis, then the incomplete MFPT should also be used for the white-noise case for a fair comparison. It seems that the corresponding white-noise case is given by Eq.4 with tau_1=0, which still has the same dependence on the self-coupling s_2, contrary to what is claimed in the paper (it is unclear how the curve for the white-noise case in Fig.4 was obtained). Note that the UCNA has been designed such that it is valid for both small and large tau_1 (thus, it is also unclear why the assumption of large tau_1 is needed).

3) The given argument that the time-scale separation arises as network effect is not very clear. Apart from the issue of a fair comparison of colored and white noise raised in point 1 above, an external colored noise with matched statistics that drives a single bistable unit would yield the same MFPT and thus would be an alternative explanation independent of the network dynamics.

4) The UCNA has assumptions and regimes of validity that are not stated in the paper. In particular, it assumes an Ornstein-Uhlenbeck noise, which has an exponential auto-correlation function, and local stability (region where potential is convex). Because the self-consistent auto-correlation function is generally not exponential and the probe unit also visits regions where the potential is concave, the validity of the UCNA is not clear. On the other hand, the assumption of large correlation time might be dropped as the UCNA's main feature is that it works for both large and small correlation times.

Reviewer #2 (Public Review):

It is well known that introducing clusters in balanced random networks leads to metastable dynamics that potentially span long time scales. The authors build on their previous work (Stern et al. 2014) and here show that the lifetime of metastable states depends on the size of the individual activated clusters. Showing qualitative similarities between clustered spiking networks and networks of bistable rate units, the authors further derive dynamic mean-field predictions for the separation of time scales of the dynamics in relation to differences in the strength of self-couplings in rate networks. Further, they confirm these results in simulations of spiking networks and compare them to time scales observed in the orbitofrontal cortex. Finally, the authors show that assemblies of a particular size (and thus time scale) get entrained by specific external input frequencies, allowing the network to demix temporal signals in a spatial manner.

The manuscript is in general well written and addresses a timely and important topic in neuroscience. However, there are concerns related to the discussion of alternative mechanisms for a large repertoire of time scales as well as the relation between the spiking and rate network model.

Author Response

Reviewer #2 (Public Review):

The work is very clearly designed, executed, and written. The transcription output data is rigorous and well quantified, and the fit of the TF binding model clearly shows agreement with experiments in the case of cooperativity, but not in its absence, making a strong case for the authors' conclusion.

How the Hidden Markov Model fit results (promoter kon and koff values) lead to the observed effects on transcription output is less clear. For instance, Dl1 deletion results in a small increase in kon and a moderate increase in koff, which seems at odds with the other variants. Yet all variants exhibit similar transcription output profiles. One other intriguing observation is that the promoter states in Fig. 4C&D do not look dramatically different in their kinetics, yet the input transcription traces exhibit a 3-fold amplitude difference. Maybe the authors can clarify these apparent discrepancies.

We thank the reviewer for insightful comments. The reduction in transcription output is mainly due to the decrease in transcription amplitude. We have done further analysis to demonstrate that the loading rate of Pol II, correlated to the initial slope of transcription, is significantly reduced in the mutants. We measured the initiation rate by calculating the slope of the MS2 traces and correlated it to the Pol II loading rate. As expected, the initiation rate in wildtype is higher than in mutant embryos. This additional analysis suggests that the drastic reduction in transcriptional amplitude is due to the reduced Pol II loading rate, not kon, and corroborates the previously shown results and conclusions (Bothma et al., PNAS 2014, PMID: 24994903; Garcia et al., Curr. Biol. 2013, PMID: 24139738). We have added this plot in Figure 4H in the revised manuscript, which shows the initiation rates of the wildtype and mutant embryos, and revised the manuscript as follows.

We have added this in the Introduction (Page 4):

We find that mutating a single TF (Dl or Twi) binding site in the enhancer significantly reduces mRNA production of the target gene, mainly through lowering transcriptional amplitude by reducing RNA polymerase (Pol) II loading rate, without significantly delaying the timing of initiation or affecting the probability of activation.

We have added this in the Results (Page 15):

Previously, we demonstrated that the mutations affect mRNA production through transcriptional amplitude (Figure 2E). This could be because either the mutations hinder the Pol II loading rate or reduce the time the promoter is in the ON state….

In addition, we find that the Pol II loading rate is significantly reduced in the mutant embryos compared to the wildtype (Figure 4H). This confirms that the lower transcriptional amplitude mainly results from the promoter’s inability to effectively load Pol II, along with an additional contribution from the reduced time the promoter spends in the ON state.

We have added this in the Discussion (Page 16):

This reduction is mainly due to the decreased transcriptional amplitude, driven by a lower rate of Pol II loading… and, Since the amount of time the promoter spends in the ON state is not affected by the mutations, the lower transcriptional amplitude can be mainly attributed to the promoter’s inability to effectively load Pol II (Figure 2E, Figure 4D-F).

The HMM is utilized to tease apart the changes in transcriptional kinetics. Our analysis revealed that the HMM provides some explanation for the reduction in transcriptional output in TF binding site mutants. For this reason, we must examine the results in a broader context. As pointed out, Dl1 site deletion has a slightly different effect on kon and koff. However, its transcription output is similar to the other mutants (Figure 4D and E). This is due to the fact that the changes in kon and koff are significantly less drastic than the changes in the transcription amplitude and Pol II loading rates, contributing less to the mRNA production. In our analysis, the amplitude is a separate parameter than the kon and koff rates, which are calculated from the HMM.

We have added the following in the Discussion to address this concern (Page 17):

However, we note that the HMM only provides some explanation for the reduction in transcriptional activity since the changes in kon and koff are less drastic than the changes in transcriptional output. Since the amount of time the promoter spends in the ON state is not affected by the mutations, the lower transcriptional amplitude can be mainly attributed to the promoter’s inability to effectively load Pol II (Figure 2E, Figure 4D, H).

The authors observe cooperativity between TF binding sites and transcription output, which their model suggests is driven by TF binding cooperativity ("We propose that the cooperativity allows TF binding sites with moderate or weak affinities to recruit more TFs to the enhancer"). This is plausible and likely, but not rigorously demonstrated; another possibility could be cooperativity at the step of transcription activation. One could verify that the binding step is the cooperative one via ChIP-qPCR in the different variants, but given the cautious wording of the paper, this is not absolutely necessary.

We thank the reviewer for suggesting this experiment. Unfortunately, due to the experimental design, performing ChIP-qPCR was not feasible. There are two copies of snaSEmin enhancer region, one within the endogenous genome and the one within the transgene. For this reason, proper amplification in qPCR was challenging as the primers would recognize two distinct portions of the genome. We designed primers such that the forward primer would recognize both the endogenous and transgene enhancer region (inevitable) and the reverse primer would recognize only the transgene. Yet, we did not observe the expected fold change in amplification as the concentration of DNA was modulated. Hence, we did not proceed to perform ChIPqPCR.

eLife assessment

This valuable work explores how transcription factors regulate transcription through cooperative binding to enhancers. Through experiments and modeling, the authors show convincingly that the cooperativity of transcription factor binding regulates transcriptional bursting and the extent of the amount of time that the target promoter remains in an active state.

Reviewer #1 (Public Review):

In this work, the authors were aiming to probe why enhancers tend to have multiple binding sites for the same transcription factor (TF). As a test bed, they use the snail distal enhancer, which drives a band of expression in the early Drosophila embryo and is composed of multiple, generally weak binding sites for several activating TFs. Using the MS2-MCP reporter system, the authors characterize the live mRNA dynamics driven by the wild-type and mutant enhancers, in which individual or pairs of binding sites have been deleted. They complement these experimental measurements with two computational models - a simple thermodynamic model to explore the cooperativity of TF binding to enhancers and a Hidden Markov Model to analyze the kinetic parameters of their dynamic measurements. The key finding from the experiments is that ablating any of several TF binding sites individually or in pairs dramatically reduces the expression levels, though not the spatial extent, of the snail distal enhancer. This effect holds true in a ~600 bp minimal enhancer and a ~1800 bp extended enhancer. The bulk of this effect is due to a marked decrease in transcriptional amplitude. A simple thermodynamic model confirms the intuition that synergy between the TF binding sites can explain the experimental results and further analysis shows that the modest decline in transcriptional burst duration in mutant enhancers is likely due to more frequent dissociation of the enhancer-promoter complex.

The paper's strengths include the use of well-established measurement and analysis techniques to uncover the surprisingly dramatic effect of single TF binding site mutations, even in the extended enhancer which contains ~20 TF binding sites. This work starts to chip away at the question of why multiple TF binding sites are so frequently observed in enhancers and complement studies of other similar enhancers. It is likely to be of interest to the enhancer biology community. It also sets the stage to explore whether this observation will generalize to other enhancers with different properties, e.g. those with stronger TF binding sites or whose activity is more strongly shaped by repressive TFs.

Reviewer #2 (Public Review):

The work is very clearly designed, executed, and written. The transcription output data is rigorous and well quantified, and the fit of the TF binding model clearly shows agreement with experiments in the case of cooperativity, but not in its absence, making a strong case for the authors' conclusion.

How the Hidden Markov Model fit results (promoter kon and koff values) lead to the observed effects on transcription output is less clear. For instance, Dl1 deletion results in a small increase in kon and a moderate increase in koff, which seems at odds with the other variants. Yet all variants exhibit similar transcription output profiles. One other intriguing observation is that the promoter states in Fig. 4C&D do not look dramatically different in their kinetics, yet the input transcription traces exhibit a 3-fold amplitude difference. Maybe the authors can clarify these apparent discrepancies.

The authors observe cooperativity between TF binding sites and transcription output, which their model suggests is driven by TF binding cooperativity ("We propose that the cooperativity allows TF binding sites with moderate or weak affinities to recruit more TFs to the enhancer"). This is plausible and likely, but not rigorously demonstrated; another possibility could be cooperativity at the step of transcription activation. One could verify that the binding step is the cooperative one via ChIP-qPCR in the different variants, but given the cautious wording of the paper, this is not absolutely necessary.

Author Response

Reviewer #1 (Public Review):

In this manuscript, the authors investigated plausible circuit mechanisms for their recently reported effect of NMDAR antagonists on the synchrony of prefrontal neurons in a cognitive task. On the basis of previously proposed computational network models of spiking excitatory and inhibitory neurons and their mean-field and linear stability analysis descriptions, they show that a specific network configuration set close to the onset of instability of the asynchronous state can replicate qualitatively key empirical observations. For such a network, a small increase in external drive causes a large increase in neuronal synchrony, and this is not happening if NMDAR-dependent transmission is reduced. This shows parallelism with the empirical data thus representing its first neural network explanation.

The paper provides valuable insights into possible mechanisms related to cortical dysfunction under NMDAR hypofunction, a topic of importance for several neuropsychiatric disorders. However, the fact that the manuscript remains at a rather abstract level and does not attempt a closer match to the experimental data is a limitation of the study.

1) The manuscript is strongly based on state diagrams and parametric descriptions of neural dynamics in a computational model that has been extensively studied before (Brunel, Wang 2003). Many of the parametric dependencies of this model shown here were already reported before, although not specifically altering concurrently external inputs and NMDAR-dependent transmission as done now. The main novelty of the study is the application of this framework to a specific empirical dataset of great scientific relevance. However, the manuscript emphasizes the model exploration in relation to a limited set of effects in the data (changes in synchrony immediately before motor response) and not so much the comparison to the neural recordings more generally (for instance, firing rates, other time periods in the task, etc.)

We are grateful to the Reviewer for thoroughly reviewing the manuscript and the constructive critique. Our work is built on the computational framework that has been developed earlier in several seminal computational and theoretical studies, including Compte et al. (2000) and Brunel and Wang (2003), that we acknowledge throughout our paper. However, we would like to emphasize, without diminishing the importance of these earlier studies, that our work provides new theoretical and computational insights on the impact of NMDAR synaptic transmission modulation on spiking dynamics by further developing the theoretical framework of Brunel and Wang (2003). For example, in Brunel and Wang (2003) it is stated that “NMDA conductances could be removed from all simulations without affecting any of the results” (p. 416). In fact, equations provided in Brunel and Wang (2003) are only for the special case of the oscillatory instability growth rate λ=0 and they do not include the NMDAR synaptic conductances. Thus, the consideration presented in Brunel and Wang (2003) cannot explain the NMDAR-dependent modulation of synchrony effect observed in Zick et al. (2018). In our study, we extended the theoretical framework of Brunel and Wang (2003) and provided equations that explicitly include both λ and NMDAR conductance. It is this extension of the framework that allowed us to provide an NMDAR dependent mechanism to explain the Zick et al. (2018) effect.

In the revised manuscript, by suggestion of Reviewer 2, we have further extended theoretical consideration and obtained an analytic approximation in closed form for the oscillatory instability growth rate λ describing the dependence on the AMAPR, NMDAR, GABAR synaptic conductances and external rate. We believe that this is the first paper in which such approximation for the instability growth rate λ accounting for the effects of more realistic synaptic currents is obtained. Based on this consideration, we have now provided in a new Results section “Dependence of oscillatory instability growth rate on synaptic parameters” a substantially more detailed theoretical account of the precise mechanism implemented in our model for the transition between the steady and oscillatory states and the lack thereof when the NMDAR conductance is blocked.

We agree with the reviewer that it would be beneficial for the paper to extend the model exploration in relation to other measurable variables provided by neural data such us firing rates. At the reviewers’ suggestions we have now carried out new series of simulations with transient external inputs and compared the simulation results with the dynamics of both synchrony and firing rates that were estimated from neural data. We address these questions in more detail in the corresponding points in the Recommendations for the authors section below.

2) As discussed in the introduction, empirical data available suggests that 0-lag synchrony in prefrontal networks is affected by manipulations that reduce NMDAR function (Zick et al. 2018) and by manipulations that enhance NMDAR function (Zick et al. 2021). The computational model presented in this manuscript does not show this U-shaped behavior and the discussion does not mention this. It should be discussed whether the model can accommodate this or not.

This is a very good point which we now explicitly address in a new section in the revised Discussion (‘Potential U-shaped relation between NMDAR function and spike synchrony’, see new text in blue starting at line 953). The reviewer provides an excellent insight by noting that that our prior neural recording data (specifically convergent reduction in 0-lag synchrony in monkey drug and mouse genetic models) could be explained by an inverted U-shaped relationship between NMDAR function and 0-lag synchrony. In the new section we also note the precedent for such a relationship by drawing a parallel to the work of Vijayraghavan, Arnsten and colleagues (2007) showing an inverted U-shaped relationship between D1R synaptic actions and the strength of persistent activity in monkey prefrontal neurons during working memory tasks.

However, in the new section we note also that we cannot yet conclude that the relationship between 0-lag spike synchrony and NMDAR activation is indeed an inverted U-shaped function based on our neural data. Reaching this conclusion would require completing a dose-response function between the concentration of NMDAR agonist (or antagonist) administered and the strength of 0-lag synchrony (which we have not done). In addition, we note in the new section that we can’t conclude the reduction of 0-lag synchrony in mouse prefrontal cortex is indeed due to increased expression of NMDAR, since deletion of Dgcr8, given its role in miRNA synthesis, would be expected to upregulate the expression of many different mRNA corresponding to many different genes. However, the possibility of a U-shaped relation is an important and interesting one, which we now fully discuss.

Reviewer #2 (Public Review):

In this paper, the authors carry out neural circuit modeling to theoretically elucidate the mechanism underlying the empirically observed (in a previous study by some of the current authors) reduction in neural synchrony in the monkey prefrontal cortex (PFC), as a result of NMDAR blockade. Empirically it was previously found that in monkeys performing a cognitive control task, PFC neurons exhibit precisely timed synchronous firing, especially in the short period before the monkey's response, leading to "0-lag" (zero in the 1-2 millisecond timescale) spiking correlations. This signature of synchrony was then found to be extinguished or diminished with the systemic administration of an NMDAR antagonist.

In the current study, the authors simulate and analyze a network of excitatory and inhibitory spiking neurons as a model of a local PFC circuit, to elucidate the mechanism underlying this effect. The model network is composed of leaky integrate-and-fire neurons with conductance-based synaptic inputs and is sparsely and randomly connected as in the classic studies of balanced networks in which neurons fire irregularly as observed in the cortex. Using mean-field theory, the authors start by mapping out the phase boundary between the asynchronous irregular and synchronous irregular states in the network as a function of network parameters controlling synaptic connectivity and external background inputs (which they parametrize as ratios of recurrent or external currents mediated by AMPAR, NMDAR or GABAA). The transition between the two phases corresponds to a Hopf-like bifurcation above which synchronous oscillations with frequency in the gamma-band (or above) emerge. It is found that with an increase in external inputs, a network in the asynchronous state (but close to criticality) can switch to the synchronous state. Based on this, the authors hypothesize that an increase in the external drive is the mechanism underlying the empirically observed increase in synchrony before the behavioral response. It is then shown that a reduction in NMDAR conductance (keeping AMPAR or GABAR conductances fixed) has the opposite effect, and pushes the network towards the asynchronous state, and can counteract or weaken the effect of increased external input. In both cases increase or decrease in synchrony is quantified by an increase or decrease in 0-lag pairwise correlations; transition to synchrony is shown to also lead to the development of nonzero-lag peaks in the average spiking correlation reflecting gamma-band oscillations. The authors then show that (with the appropriate choice of primary network parameters) their proposed mechanisms for the (natural) increase in synchrony via an increase in external inputs and the weakening of this effect with the weakening of NMDA conductances do semi-quantitatively match the observed changes in 0-lag synchrony and nonzero lag peaks in spiking correlations. Finally, they discuss the effect of the balance between average NMDA and GABA currents in the primary (baseline) network on the above effects.

Strengths:

• The modeling and analysis are solid and overall this work succeeds in providing a convincing mechanistic explanation for the specific empirically observed effects in monkey PFC: the natural task-dependent modulation of 0-lag synchrony and its extinction with NMDA blockage.

• The manuscript is very readable and the figures and plots are clearly described.

• The mathematical mean-field analysis in the Methods section is also sound and well written and does/can (see below) provide a sufficient mathematical explanation of the simulation results.

We appreciate the positive comments.

Weaknesses:

1) I found the intuitive explanation of the effects of external input or NMDAR conductance on synchrony incomplete. While simulations and mean-field analysis both predict this effect, the mean-field theory and the linearization analysis and stability analysis can be used to further shed light on the precise mechanism by which external input and NMDAR conductance promote synchrony (or destabilization of the asynchronous state).

2) An important natural question (which is relevant to the connection with schizophrenia) is what are the distinct roles of AMPAR-based and NMDAR-based excitation on the transition to synchrony, and this is not addressed in this study. It would be important to clarify what is special/distinct about NMDAR in the current findings.

3) In the Introduction and Discussion, the authors speculate on the possible connection between their empirical and theoretical findings (on the effect of NMDAR hypofunction on synchronous spiking) and the pathogenesis of schizophrenia. While this is not central to the findings of the paper, because it is relevant to the broader significance and impact of this work I will note the following. Their proposed specific link to pathogenesis is as follows: the reduction in precisely timed synchrony resulting from NMDAR hypofunction can disrupt spike-timing dependent plasticity (STDP) and lead to "disconnection" of cortical circuits as observed in schizophrenia. Letting aside the fact that observations in schizophrenia relate to functional connectivity and not synaptic connectivity, previous theoretical studies of STDP in spiking networks do not support the claim that lack of synchronous activity would lead to disconnection of the circuit.

Thank you for the thorough review and critique, bringing up these important issues. We address them in detail in the corresponding points in the Recommendations for the authors section below.

Reviewer #3 (Public Review):

The starting point of the paper is the observation by the group of Matthew Chafee that zero-lag correlations in pairs of prefrontal cortex neurons transiently increase close to the motor response in a dot-pattern expectancy task', and that this increase in synchrony is abolished by NMDA blockers. The goal of this paper is to understand the mechanisms of this NMDA-dependent increase in synchrony using computational modeling. They simulate and analyze a network of sparsely connected spiking neurons in which synaptic interactions are mediated by AMPA, NMDA, and GABA conductances with realistic time constants. In this network, it had been shown previously that when parameters are such that the network is close to a bifurcation separating asynchronous from synchronous oscillatory states, an increase in external inputs can push the network towards synchrony. They show that when the NMDA component of synaptic inputs is removed, the network moves away from the bifurcation, and thus the same increase in external inputs no longer leads to a significant increase in synchronization.

Thus, this study provides a potential explanation for the NMDA-dependent increase of synchrony observed in their data. The authors further argue that this effect might be responsible for symptoms observed in schizophrenia, through spike-timing-dependent mechanisms. Overall, this is an interesting study, but there are several weaknesses that dampened my initial enthusiasm: In particular, the model predicts a tight link between synchrony and mean firing rate that should hold during the whole task, not only at the time of the motor response but this is not explored by the authors.

Thank you for critically reviewing the manuscript and valuable comments. We address them in the corresponding points in the Recommendations for the authors section below.

Also, the relationship between changes in synchrony due to NMDAR dysfunction and schizophrenia is not very convincing. Many forms of synaptic plasticity, including STDP are dependent on NMDA receptors, and thus synaptic plasticity in schizophrenic patients is likely to be impacted independently of any synchrony. Thus, the link between the results of this paper and schizophrenia seems tenuous.

These are good points. To address them we have limited the link between the current study and schizophrenia in the Introduction to the motivation for the original neurophysiological experiments (as this link dictated the pharmacological and genetic manipulations we employed in the animal models). We have also added a new section to the Discussion with the heading ‘Spike timing disruptions and rewiring of prefrontal local circuits via STDP’ where we discuss the complexity of the interaction between STDP, synchrony, and connectivity in prior modeling studies. Namely, it is difficult to predict whether loss of synchronous spiking would cause disconnection via STDP without additional data. We acknowledge this constraint on our original hypothesis that asynchrony would cause disconnection considering these prior theoretical studies in this new section. In this section, we also note that altered NMDAR function that has been implicated in schizophrenia could impact STDP directly independently of any change in spike synchrony (see new blue text, starting at line 950) as suggested.

eLife assessment

This manuscript reports important new results, but it provides incomplete support for its claims. Recent data has shown that schizophrenia-related synaptic alterations induce changes in neural network synchrony, and this manuscript provides the first theoretical understanding of the underlying network mechanisms. Proper support for this result, however, requires a tighter link between the computational model and the experimental data and a more in-depth understanding of the model mechanisms.

Reviewer #1 (Public Review):

In this manuscript, the authors investigated plausible circuit mechanisms for their recently reported effect of NMDAR antagonists on the synchrony of prefrontal neurons in a cognitive task. On the basis of previously proposed computational network models of spiking excitatory and inhibitory neurons and their mean-field and linear stability analysis descriptions, they show that a specific network configuration set close to the onset of instability of the asynchronous state can replicate qualitatively key empirical observations. For such a network, a small increase in external drive causes a large increase in neuronal synchrony, and this is not happening if NMDAR-dependent transmission is reduced. This shows parallelism with the empirical data thus representing its first neural network explanation.

The paper provides valuable insights into possible mechanisms related to cortical dysfunction under NMDAR hypofunction, a topic of importance for several neuropsychiatric disorders. However, the fact that the manuscript remains at a rather abstract level and does not attempt a closer match to the experimental data is a limitation of the study.

1) The manuscript is strongly based on state diagrams and parametric descriptions of neural dynamics in a computational model that has been extensively studied before (Brunel, Wang 2003). Many of the parametric dependencies of this model shown here were already reported before, although not specifically altering concurrently external inputs and NMDAR-dependent transmission as done now. The main novelty of the study is the application of this framework to a specific empirical dataset of great scientific relevance. However, the manuscript emphasizes the model exploration in relation to a limited set of effects in the data (changes in synchrony immediately before motor response) and not so much the comparison to the neural recordings more generally (for instance, firing rates, other time periods in the task, etc.)

2) As discussed in the introduction, empirical data available suggests that 0-lag synchrony in prefrontal networks is affected by manipulations that reduce NMDAR function (Zick et al. 2018) and by manipulations that enhance NMDAR function (Zick et al. 2021). The computational model presented in this manuscript does not show this U-shaped behavior and the discussion does not mention this. It should be discussed whether the model can accommodate this or not.

Reviewer #2 (Public Review):

In this paper, the authors carry out neural circuit modeling to theoretically elucidate the mechanism underlying the empirically observed (in a previous study by some of the current authors) reduction in neural synchrony in the monkey prefrontal cortex (PFC), as a result of NMDAR blockade. Empirically it was previously found that in monkeys performing a cognitive control task, PFC neurons exhibit precisely timed synchronous firing, especially in the short period before the monkey's response, leading to "0-lag" (zero in the 1-2 millisecond timescale) spiking correlations. This signature of synchrony was then found to be extinguished or diminished with the systemic administration of an NMDAR antagonist.

In the current study, the authors simulate and analyze a network of excitatory and inhibitory spiking neurons as a model of a local PFC circuit, to elucidate the mechanism underlying this effect. The model network is composed of leaky integrate-and-fire neurons with conductance-based synaptic inputs and is sparsely and randomly connected as in the classic studies of balanced networks in which neurons fire irregularly as observed in the cortex. Using mean-field theory, the authors start by mapping out the phase boundary between the asynchronous irregular and synchronous irregular states in the network as a function of network parameters controlling synaptic connectivity and external background inputs (which they parametrize as ratios of recurrent or external currents mediated by AMPAR, NMDAR or GABAA). The transition between the two phases corresponds to a Hopf-like bifurcation above which synchronous oscillations with frequency in the gamma-band (or above) emerge. It is found that with an increase in external inputs, a network in the asynchronous state (but close to criticality) can switch to the synchronous state. Based on this, the authors hypothesize that an increase in the external drive is the mechanism underlying the empirically observed increase in synchrony before the behavioral response. It is then shown that a reduction in NMDAR conductance (keeping AMPAR or GABAR conductances fixed) has the opposite effect, and pushes the network towards the asynchronous state, and can counteract or weaken the effect of increased external input. In both cases increase or decrease in synchrony is quantified by an increase or decrease in 0-lag pairwise correlations; transition to synchrony is shown to also lead to the development of nonzero-lag peaks in the average spiking correlation reflecting gamma-band oscillations. The authors then show that (with the appropriate choice of primary network parameters) their proposed mechanisms for the (natural) increase in synchrony via an increase in external inputs and the weakening of this effect with the weakening of NMDA conductances do semi-quantitatively match the observed changes in 0-lag synchrony and nonzero lag peaks in spiking correlations. Finally, they discuss the effect of the balance between average NMDA and GABA currents in the primary (baseline) network on the above effects.

Strengths:<br /> - The modeling and analysis are solid and overall this work succeeds in providing a convincing mechanistic explanation for the specific empirically observed effects in monkey PFC: the natural task-dependent modulation of 0-lag synchrony and its extinction with NMDA blockage.

- The manuscript is very readable and the figures and plots are clearly described.

- The mathematical mean-field analysis in the Methods section is also sound and well written and does/can (see below) provide a sufficient mathematical explanation of the simulation results.

Weaknesses:<br /> 1) I found the intuitive explanation of the effects of external input or NMDAR conductance on synchrony incomplete. While simulations and mean-field analysis both predict this effect, the mean-field theory and the linearization analysis and stability analysis can be used to further shed light on the precise mechanism by which external input and NMDAR conductance promote synchrony (or destabilization of the asynchronous state).

2) An important natural question (which is relevant to the connection with schizophrenia) is what are the distinct roles of AMPAR-based and NMDAR-based excitation on the transition to synchrony, and this is not addressed in this study. It would be important to clarify what is special/distinct about NMDAR in the current findings.

3) In the Introduction and Discussion, the authors speculate on the possible connection between their empirical and theoretical findings (on the effect of NMDAR hypofunction on synchronous spiking) and the pathogenesis of schizophrenia. While this is not central to the findings of the paper, because it is relevant to the broader significance and impact of this work I will note the following. Their proposed specific link to pathogenesis is as follows: the reduction in precisely timed synchrony resulting from NMDAR hypofunction can disrupt spike-timing dependent plasticity (STDP) and lead to "disconnection" of cortical circuits as observed in schizophrenia. Letting aside the fact that observations in schizophrenia relate to functional connectivity and not synaptic connectivity, previous theoretical studies of STDP in spiking networks do not support the claim that lack of synchronous activity would lead to disconnection of the circuit.

Reviewer #3 (Public Review):

The starting point of the paper is the observation by the group of Matthew Chafee that zero-lag correlations in pairs of prefrontal cortex neurons transiently increase close to the motor response in a dot-pattern expectancy task', and that this increase in synchrony is abolished by NMDA blockers. The goal of this paper is to understand the mechanisms of this NMDA-dependent increase in synchrony using computational modeling. They simulate and analyze a network of sparsely connected spiking neurons in which synaptic interactions are mediated by AMPA, NMDA, and GABA conductances with realistic time constants. In this network, it had been shown previously that when parameters are such that the network is close to a bifurcation separating asynchronous from synchronous oscillatory states, an<br /> increase in external inputs can push the network towards synchrony. They show that when the NMDA component of synaptic inputs is removed, the network moves away from the bifurcation, and thus the same increase in external inputs no longer leads to a significant increase in synchronization.

Thus, this study provides a potential explanation for the NMDA-dependent increase of synchrony observed in their data. The authors further argue that this effect might be responsible for symptoms observed in schizophrenia, through spike-timing-dependent mechanisms. Overall, this is an interesting study, but there are<br /> several weaknesses that dampened my initial enthusiasm: In particular, the model predicts a tight link between synchrony and mean firing rate that should hold during the whole task, not only at the time of the motor response but this is not explored by the authors.

Also, the relationship between changes in synchrony due to NMDAR dysfunction and schizophrenia is not very convincing. Many forms of synaptic plasticity, including STDP are dependent on NMDA receptors, and thus synaptic plasticity in schizophrenic patients is likely to be impacted independently of any synchrony. Thus, the link between the results of this paper and schizophrenia seems tenuous.

Author Response

Reviewer #1 (Public Review):

Esmaily and colleagues report two experimental studies in which participants make simple perceptual decisions, either in isolation or in the context of a joint decision-making procedure. In this "social" condition, participants are paired with a partner (in fact, a computer), they learn the decision and confidence of the partner after making their own decision, and the joint decision is made on the basis of the most confident decision between the participant and the partner. The authors found that participants' confidence, response times, pupil dilation, and CPP (i.e. the increase of centro-parietal EEG over time during the decision process) are all affected by the overall confidence of the partner, which was manipulated across blocks in the experiments. They describe a computational model in which decisions result from a competition between two accumulators, and in which the confidence of the partner would be an input to the activity of both accumulators. This model qualitatively produced the variation in confidence and RTs across blocks.

The major strength of this work is that it puts together many ingredients (behavioral data, pupil and EEG signals, computational analysis) to build a picture of how the confidence of a partner, in the context of joint decision-making, would influence our own decision process and confidence evaluations. Many of these effects are well described already in the literature, but putting them all together remains a challenge.

We are grateful for this positive assessment.

However, the construction is fragile in many places: the causal links between the different variables are not firmly established, and it is not clear how pupil and EEG signals mediate the effect of the partner's confidence on the participant's behavior.

We have modified the language of the manuscript to avoid the implication of a causal link.

Finally, one limitation of this setting is that the situation being studied is very specific, with a joint decision that is not the result of an agreement between partners, but the automatic selection of the most confident decisions. Thus, whether the phenomena of confidence matching also occurs outside of this very specific setting is unclear.

We have now acknowledged this caveat in the discussion in line 485 to 504. The final paragraph of the discussion now reads as follows:

“Finally, one limitation of our experimental setup is that the situation being studied is confined to the design choices made by the experimenters. These choices were made in order to operationalize the problem of social interaction within the psychophysics laboratory. For example, the joint decisions were not made through verbal agreement (Bahrami et al., 2010, 2012). Instead, following a number of previous works (Bang et al., 2017, 2020) joint decisions were automatically assigned to the most confident choice. In addition, the partner’s confidence and choice were random variables drawn from a distribution prespecified by the experimenter and therefore, by design, unresponsive to the participant’s behaviour. In this sense, one may argue that the interaction partner’s behaviour was not “natural” since they did not react to the participant's confidence communications (note however that the partner’s confidence and accuracy were not entirely random but matched carefully to the participant’s behavior prerecorded in the individual session). How much of the findings are specific to these experimental setting and whether the behavior observed here would transfer to real-life settings is an open question. For example, it is plausible that participants may show some behavioral reaction to a human partner’s response time variations since there is some evidence indicating that for binary choices such as those studied here, response times also systematically communicate uncertainty to others (Patel et al., 2012). Future studies could examine the degree to which the results might be paradigm-specific.”

Reviewer #2 (Public Review):

This study is impressive in several ways and will be of interest to behavioral and brain scientists working on diverse topics.

First, from a theoretical point of view, it very convincingly integrates several lines of research (confidence, interpersonal alignment, psychophysical, and neural evidence accumulation) into a mechanistic computational framework that explains the existing data and makes novel predictions that can inspire further research. It is impressive to read that the corresponding model can account for rather non-intuitive findings, such as that information about high confidence by your collaborators means people are faster but not more accurate in their judgements.

Second, from a methodical point of view, it combines several sophisticated approaches (psychophysical measurements, psychophysical and neural modelling, electrophysiological and pupil measurements) in a manner that draws on their complementary strengths and that is most compelling (but see further below for some open questions). The appeal of the study in that respect is that it combines these methods in creative ways that allow it to answer its specific questions in a much more convincing manner than if it had used just either of these approaches alone.

Third, from a computational point of view, it proposes several interesting ways by which biologically realistic models of perceptual decision-making can incorporate socially communicated information about other's confidence, to explain and predict the effects of such interpersonal alignment on behavior, confidence, and neural measurements of the processes related to both. It is nice to see that explicit model comparison favor one of these ways (top-down driving inputs to the competing accumulators) over others that may a priori have seemed more plausible but mechanistically less interesting and impactful (e.g., effects on response boundaries, no-decision times, or evidence accumulation).

Fourth, the manuscript is very well written and provides just the right amount of theoretical introduction and balanced discussion for the reader to understand the approach, the conclusions, and the strengths and limitations.

Finally, the manuscript takes open science practices seriously and employed preregistration, a replication sample, and data sharing in line with good scientific practice.

We are grateful to the reviewer for their positive assessment of our work.

Having said all these positive things, there are some points where the manuscript is unclear or leaves some open questions. While the conclusions of the manuscript are not overstated, there are unclarities in the conceptual interpretation, the descriptions of the methods, some procedures of the methods themselves, and the interpretation of the results that make the reader wonder just how reliable and trustworthy some of the many findings are that together provide this integrated perspective.

We hope that our modifications and revisions in response to the criticisms listed below will be satisfactory. To avoid redundancies, we have combined each numbered comment with the corresponding recommendation for the Authors.

First, the study employs rather small sample sizes of N=12 and N=15 and some of the effects are rather weak (e.g., the non-significant CPP effects in study 1). This is somewhat ameliorated by the fact that a replication sample was used, but the robustness of the findings and their replicability in larger samples can be questioned.

Our study brings together questions from two distinct fields of neuroscience: perceptual decision making and social neuroscience. Each of these two fields have their own traditions and practical common sense. Typically, studies in perceptual decision making employ a small number of extensively trained participants (approximately 6 to 10 individuals). Social neuroscience studies, on the other hand, recruit larger samples (often more than 20 participants) without extensive training protocols. We therefore needed to strike a balance in this trade-off between number of participants and number of data points (e.g. trials) obtained from each participant. Note, for example, that each of our participants underwent around 4000 training trials. Strikingly, our initial study (N=12) yielded robust results that showed the hypothesized effects nearly completely, supporting the adequacy of our power estimate. However, we decided to replicate the findings because, like the reviewer, we believe in the importance of adequate sampling. We increased our sample size to N=15 participants to enhance the reliability of our findings. However, we acknowledge the limitation of generalizing to larger samples, which we have now discussed in our revised manuscript and included a cautionary note regarding further generalizations.

To complement our results and add a measure of their reliability, here we provide the results of a power analysis that we applied on the data from study 1 (i.e. the discovery phase). These results demonstrate that the sample size of study 2 (i.e. replication) was adequate when conditioned on the results from study 1 (see table and graph pasted below). The results showed that N=13 would be an adequate sample size for 80% power for behavoural and eye-tracking measurements. Power analysis for the EEG measurements indicated that we needed N=17. Combining these power analyses. Our sample size of N=15 for Study 2 was therefore reasonably justified.

We have now added a section to the discussion (Lines 790-805) that communicates these issues as follows:

“Our study brings together questions from two distinct fields of neuroscience: perceptual decision making and social neuroscience. Each of these two fields have their own traditions and practical common sense. Typically, studies in perceptual decision making employ a small number of extensively trained participants (approximately 6 to 10 individuals). Social neuroscience studies, on the other hand, recruit larger samples (often more than 20 participants) without extensive training protocols. We therefore needed to strike a balance in this trade-off between number of participants and number of data points (e.g. trials) obtained from each participant. Note, for example, that each of our participants underwent around 4000 training trials. Importantly, our initial study (N=12) yielded robust results that showed the hypothesized effects nearly completely, supporting the adequacy of our power estimate. However, we decided to replicate the findings in a new sample with N=15 participants to enhance the reliability of our findings and examine our hypothesis in a stringent discovery-replication design. In Figure 4-figure supplement 5, we provide the results of a power analysis that we applied on the data from study 1 (i.e. the discovery phase). These results demonstrate that the sample size of study 2 (i.e. replication) was adequate when conditioned on the results from study 1.”

We conducted Monte Carlo simulations to determine the sample size required to achieve sufficient statistical power (80%) (Szucs & Ioannidis, 2017). In these simulations, we utilized the data from study 1. Within each sample size (N, x-axis), we randomly selected N participants from our 12 partpincats in study 1. We employed the with-replacement sampling method. Subsequently, we applied the same GLMM model used in the main text to assess the dependency of EEG signal slopes on social conditions (HCA vs LCA). To obtain an accurate estimate, we repeated the random sampling process 1000 times for each given sample size (N). Consequently, for a given sample size, we performed 1000 statistical tests using these randomly generated datasets. The proportion of statistically significant tests among these 1000 tests represents the statistical power (y-axis). We gradually increased the sample size until achieving an 80% power threshold, as illustrated in the figure.The the number indicated by the red circle on the x axis of this graph represents the designated sample size.

Second, the manuscript interprets the effects of low-confidence partners as an impact of the partner's communicated "beliefs about uncertainty". However, it appears that the experimental setup also leads to greater outcome uncertainty (because the trial outcome is determined by the joint performance of both partners, which is normally reduced for low-confidence partners) and response uncertainty (because subjects need to consider not only their own confidence but also how that will impact on the low-confidence partner). While none of these other possible effects is conceptually unrelated to communicated confidence and the basic conclusions of the manuscript are therefore valid, the reader would like to understand to what degree the reported effects relate to slightly different types of uncertainty that can be elicited by communicated low confidence in this setup.

We appreciate the reviewer’s advice to remain cautious about the possible sources of uncertainty in our experiment. In the Discussion (lines 790-801) we have now added the following paragraph.

“We have interpreted our findings to indicate that social information, i.e. partner’s confidence, impacts the participants beliefs about uncertainty. It is important to underscore here that, similar to real life, there are other sources of uncertainty in our experimental setup that could affect the participants' belief. For example, under joint conditions, the group choice is determined through the comparison of the choices and confidences of the partners. As a result, the participant has a more complex task of matching their response not only with their perceptual experience but also coordinating it with the partner to achieve the best possible outcome. For the same reason, there is greater outcome uncertainty under joint vs individual conditions. Of course, these other sources of uncertainty are conceptually related to communicated confidence but our experimental design aimed to remove them, as much as possible, by comparing the impact of social information under high vs low confidence of the partner.”

In addition to the above, we would like to clarify one point here with specific respect to the comment. Note that the computer-generated partner’s accuracy was identical under high and low confidence. In addition, our behavioral findings did not show any difference in accuracy under HCA and LCA conditions. As a consequence, the argument that “the trial outcome is determined by the joint performance of both partners, which is normally reduced for low-confidence partners)” is not valid because the low-confidence partner’s performance is identical to that of the high-confidence partner. It is possible, of course, that we have misunderstood the reviewer’s point here and we would be happy to discuss this further if necessary.

Third, the methods used for measurement, signal processing, and statistical inference in the pupil analysis are questionable. For a start, the methods do not give enough details as to how the stimuli were calibrated in terms of luminance etc so that the pupil signals are interpretable.

Here we provide in Author response image 1 the calibration plot for our eye tracking setup, describing the relationship between pupil size and display luminance. Luminance of the random dot motion stimuli (ie white dots on black background) was Cd/m2 and, importantly, identical across the two critical social conditions. We hope that this additional detail satisfies the reviewer’s concern. For the purpose of brevity, we have decided against adding this part to the manuscript and supplementary material.

Author response image 1.

Calibration plot for the experimental setup. Average pupil size (arbitrary units from eyelink device) is plotted against display luminance. The plot is obtained by presenting the participant with uniform full screen displays with 10 different luminance levels covering the entire range of the monitor RGB values (0 to 255) whose luminance was separately measured with a photometer. Each display lasted 10 seconds. Error bars are standard deviation between sessions.

Moreover, while the authors state that the traces were normalized to a value of 0 at the start of the ITI period, the data displayed in Figure 2 do not show this normalization but different non-zero values. Are these data not normalized, or was a different procedure used? Finally, the authors analyze the pupil signal averaged across a wide temporal ITI interval that may contain stimulus-locked responses (there is not enough information in the manuscript to clearly determine which temporal interval was chosen and averaged across, and how it was made sure that this signal was not contaminated by stimulus effects).

We have now added the following details to the Methods section in line 1106-1135.

“In both studies, the Eye movements were recorded by an EyeLink 1000 (SR- Research) device with a sampling rate of 1000Hz which was controlled by a dedicated host PC. The device was set in a desktop and pupil-corneal reflection mode while data from the left eye was recorded. At the beginning of each block, the system was recalibrated and then validated by 9-point schema presented on the screen. For one subject was, a 3-point schema was used due to repetitive calibration difficulty. Having reached a detection error of less than 0.5°, the participants proceeded to the main task. Acquired eye data for pupil size were used for further analysis. Data of one subject in the first study was removed from further analysis due to storage failure.

Pupil data were divided into separate epochs and data from Inter-Trials Interval (ITI) were selected for analysis. ITI interval was defined as the time between offset of trial (t) feedback screen and stimulus presentation of trial (t+1). Then, blinks and jitters were detected and removed using linear interpolation. Values of pupil size before and after the blink were used for this interpolation. Data was also mid-pass filtered using a Butterworth filter (second order,[0.01, 6] Hz)[50]. The pupil data was z-scored and then was baseline corrected by removing the average of signal in the period of [-1000 0] ms interval (before ITI onset). For the statistical analysis (GLMM) in Figure 2, we used the average of the pupil signal in the ITI period. Therefore, no pupil value is contaminated by the upcoming stimuli. Importantly, trials with ITI>3s were excluded from analysis (365 out of 8800 for study 1 and 128 out 6000 for study 2. Also see table S7 and Selection criteria for data analysis in Supplementary Materials)”

Fourth, while the EEG analysis in general provides interesting data, the link to the well-established CPP signal is not entirely convincing. CPP signals are usually identified and analyzed in a response-locked fashion, to distinguish them from other types of stimulus-locked potentials. One crucial feature here is that the CPPs in the different conditions reach a similar level just prior to the response. This is either not the case here, or the data are not shown in a format that allows the reader to identify these crucial features of the CPP. It is therefore questionable whether the reported signals indeed fully correspond to this decision-linked signal.

Fifth, the authors present some effective connectivity analysis to identify the neural mechanisms underlying the possible top-down drive due to communicated confidence. It is completely unclear how they select the "prefrontal cortex" signals here that are used for the transfer entropy estimations, and it is in fact even unclear whether the signals they employ originate in this brain structure. In the absence of clear methodical details about how these signals were identified and why the authors think they originate in the prefrontal cortex, these conclusions cannot be maintained based on the data that are presented.

Sixth, the description of the model fitting procedures and the parameter settings are missing, leaving it unclear for the reader how the models were "calibrated" to the data. Moreover, for many parameters of the biophysical model, the authors seem to employ fixed parameter values that may have been picked based on any criteria. This leaves the impression that the authors may even have manually changed parameter values until they found a set of values that produced the desired effects. The model would be even more convincing if the authors could for every parameter give the procedures that were used for fitting it to the data, or the exact criteria that were used to fix the parameter to a specific value.

Seventh, on a related note, the reader wonders about some of the decisions the authors took in the specification of their model. For example, why was it assumed that the parameters of interest in the three competing models could only be modulated by the partner's confidence in a linear fashion? A non-linear modulation appears highly plausible, so extreme values of confidence may have much more pronounced effects. Moreover, why were the confidence computations assumed to be finished at the end of the stimulus presentation, given that for trials with RTs longer than the stimulus presentation, the sensory information almost certainly reverberated in the brain network and continued to be accumulated (in line with the known timing lags in cortical areas relative to objective stimulus onset)? It would help if these model specification choices were better justified and possibly even backed up with robustness checks.

Eight, the fake interaction partners showed several properties that were highly unnatural (they did not react to the participant's confidence communications, and their response times were random and thus unrelated to confidence and accuracy). This questions how much the findings from this specific experimental setting would transfer to other real-life settings, and whether participants showed any behavioral reactions to the random response time variations as well (since several studies have shown that for binary choices like here, response times also systematically communicate uncertainty to others). Moreover, it is also unclear how the confidence convergence simulated in Figure 3d can conceptually apply to the data, given that the fake subjects did not react to the subject's communicated confidence as in the simulation.

eLife assessment

In this important study, Esmaily and colleagues investigate the "confidence matching" between two agents and present a useful exploration of its computational and physiological correlates. Further analyses would be helpful to provide a tighter link between fluctuations of confidence, pupil size, EEG response, and computational variables, to delineate the causal relations between these quantities, which are nevertheless incompletely documented at present.

Reviewer #1 (Public Review):

Esmaily and colleagues report two experimental studies in which participants make simple perceptual decisions, either in isolation or in the context of a joint decision-making procedure. In this "social" condition, participants are paired with a partner (in fact, a computer), they learn the decision and confidence of the partner after making their own decision, and the joint decision is made on the basis of the most confident decision between the participant and the partner. The authors found that participants' confidence, response times, pupil dilation, and CPP (i.e. the increase of centro-parietal EEG over time during the decision process) are all affected by the overall confidence of the partner, which was manipulated across blocks in the experiments. They describe a computational model in which decisions result from a competition between two accumulators, and in which the confidence of the partner would be an input to the activity of both accumulators. This model qualitatively produced the variation in confidence and RTs across blocks.

The major strength of this work is that it puts together many ingredients (behavioral data, pupil and EEG signals, computational analysis) to build a picture of how the confidence of a partner, in the context of joint decision-making, would influence our own decision process and confidence evaluations. Many of these effects are well described already in the literature, but putting them all together remains a challenge. However, the construction is fragile in many places: the causal links between the different variables are not firmly established, and it is not clear how pupil and EEG signals mediate the effect of the partner's confidence on the participant's behavior.

Finally, one limitation of this setting is that the situation being studied is very specific, with a joint decision that is not the result of an agreement between partners, but the automatic selection of the most confident decisions. Thus, whether the phenomena of confidence matching also occurs outside of this very specific setting is unclear.

Reviewer #2 (Public Review):

This study is impressive in several ways and will be of interest to behavioral and brain scientists working on diverse topics.

First, from a theoretical point of view, it very convincingly integrates several lines of research (confidence, interpersonal alignment, psychophysical, and neural evidence accumulation) into a mechanistic computational framework that explains the existing data and makes novel predictions that can inspire further research. It is impressive to read that the corresponding model can account for rather non-intuitive findings, such as that information about high confidence by your collaborators means people are faster but not more accurate in their judgements.

Second, from a methodical point of view, it combines several sophisticated approaches (psychophysical measurements, psychophysical and neural modelling, electrophysiological and pupil measurements) in a manner that draws on their complementary strengths and that is most compelling (but see further below for some open questions). The appeal of the study in that respect is that it combines these methods in creative ways that allow it to answer its specific questions in a much more convincing manner than if it had used just either of these approaches alone.

Third, from a computational point of view, it proposes several interesting ways by which biologically realistic models of perceptual decision-making can incorporate socially communicated information about other's confidence, to explain and predict the effects of such interpersonal alignment on behavior, confidence, and neural measurements of the processes related to both. It is nice to see that explicit model comparison favor one of these ways (top-down driving inputs to the competing accumulators) over others that may a priori have seemed more plausible but mechanistically less interesting and impactful (e.g., effects on response boundaries, no-decision times, or evidence accumulation).

Fourth, the manuscript is very well written and provides just the right amount of theoretical introduction and balanced discussion for the reader to understand the approach, the conclusions, and the strengths and limitations.

Finally, the manuscript takes open science practices seriously and employed preregistration, a replication sample, and data sharing in line with good scientific practice.

Having said all these positive things, there are some points where the manuscript is unclear or leaves some open questions. While the conclusions of the manuscript are not overstated, there are unclarities in the conceptual interpretation, the descriptions of the methods, some procedures of the methods themselves, and the interpretation of the results that make the reader wonder just how reliable and trustworthy some of the many findings are that together provide this integrated perspective.

First, the study employs rather small sample sizes of N=12 and N=15 and some of the effects are rather weak (e.g., the non-significant CPP effects in study 1). This is somewhat ameliorated by the fact that a replication sample was used, but the robustness of the findings and their replicability in larger samples can be questioned.

Second, the manuscript interprets the effects of low-confidence partners as an impact of the partner's communicated "beliefs about uncertainty". However, it appears that the experimental setup also leads to greater outcome uncertainty (because the trial outcome is determined by the joint performance of both partners, which is normally reduced for low-confidence partners) and response uncertainty (because subjects need to consider not only their own confidence but also how that will impact on the low-confidence partner). While none of these other possible effects is conceptually unrelated to communicated confidence and the basic conclusions of the manuscript are therefore valid, the reader would like to understand to what degree the reported effects relate to slightly different types of uncertainty that can be elicited by communicated low confidence in this setup.

Third, the methods used for measurement, signal processing, and statistical inference in the pupil analysis are questionable. For a start, the methods do not give enough details as to how the stimuli were calibrated in terms of luminance etc so that the pupil signals are interpretable. Moreover, while the authors state that the traces were normalized to a value of 0 at the start of the ITI period, the data displayed in Figure 2 do not show this normalization but different non-zero values. Are these data not normalized, or was a different procedure used? Finally, the authors analyze the pupil signal averaged across a wide temporal ITI interval that may contain stimulus-locked responses (there is not enough information in the manuscript to clearly determine which temporal interval was chosen and averaged across, and how it was made sure that this signal was not contaminated by stimulus effects).

Fourth, while the EEG analysis in general provides interesting data, the link to the well-established CPP signal is not entirely convincing. CPP signals are usually identified and analyzed in a response-locked fashion, to distinguish them from other types of stimulus-locked potentials. One crucial feature here is that the CPPs in the different conditions reach a similar level just prior to the response. This is either not the case here, or the data are not shown in a format that allows the reader to identify these crucial features of the CPP. It is therefore questionable whether the reported signals indeed fully correspond to this decision-linked signal.

Fifth, the authors present some effective connectivity analysis to identify the neural mechanisms underlying the possible top-down drive due to communicated confidence. It is completely unclear how they select the "prefrontal cortex" signals here that are used for the transfer entropy estimations, and it is in fact even unclear whether the signals they employ originate in this brain structure. In the absence of clear methodical details about how these signals were identified and why the authors think they originate in the prefrontal cortex, these conclusions cannot be maintained based on the data that are presented.

Sixth, the description of the model fitting procedures and the parameter settings are missing, leaving it unclear for the reader how the models were "calibrated" to the data. Moreover, for many parameters of the biophysical model, the authors seem to employ fixed parameter values that may have been picked based on any criteria. This leaves the impression that the authors may even have manually changed parameter values until they found a set of values that produced the desired effects. The model would be even more convincing if the authors could for every parameter give the procedures that were used for fitting it to the data, or the exact criteria that were used to fix the parameter to a specific value.

Seventh, on a related note, the reader wonders about some of the decisions the authors took in the specification of their model. For example, why was it assumed that the parameters of interest in the three competing models could only be modulated by the partner's confidence in a linear fashion? A non-linear modulation appears highly plausible, so extreme values of confidence may have much more pronounced effects. Moreover, why were the confidence computations assumed to be finished at the end of the stimulus presentation, given that for trials with RTs longer than the stimulus presentation, the sensory information almost certainly reverberated in the brain network and continued to be accumulated (in line with the known timing lags in cortical areas relative to objective stimulus onset)? It would help if these model specification choices were better justified and possibly even backed up with robustness checks.

Eight, the fake interaction partners showed several properties that were highly unnatural (they did not react to the participant's confidence communications, and their response times were random and thus unrelated to confidence and accuracy). This questions how much the findings from this specific experimental setting would transfer to other real-life settings, and whether participants showed any behavioral reactions to the random response time variations as well (since several studies have shown that for binary choices like here, response times also systematically communicate uncertainty to others). Moreover, it is also unclear how the confidence convergence simulated in Figure 3d can conceptually apply to the data, given that the fake subjects did not react to the subject's communicated confidence as in the simulation.

Author Response

Joint Public Review

This manuscript utilizes Drosophila melanogaster as a model system to functionally characterize the role of genes previously associated with obstructive pulmonary disease (COPD) in epithelial barrier function. Using genetic and imaging approaches, the authors characterised a previously unrecognised role of intestinal Acetylcholine receptor (AchR) signalling, in the regulation of epithelial barrier function. The working model proposes that Acetylcholine (Ach) produced by enteroendocrine cells (EEs) and enteric neurons signals to AchR in enterocytes (ECs). This signalling activates the secretion of the Peritrophic membrane (PM) through the regulation of the exocytic protein Syt4. In this way, Ach/AchR signalling works to protect epithelial barrier function and organismal tolerance to ingested damaging agents, such as those causing oxidative stress.

Overall, the data presented support the main model of the paper: EC AchR activation is necessary to maintain epithelial barrier function. The evidence, however, on the mechanisms downstream of AchR, namely, the involvement of this signalling pathway in the regulation of Syt4 is weak.

The work in this manuscript represents an important proof of concept for the use of the Drosophila midgut as a model to functionally interrogate genes from human genetic association studies in pathologies affecting epithelial homeostasis.

We would like to thank the reviewers for their positive assessment of the significance of the study. The reviewers point out that the reported data support the conclusions of the manuscript and request additional studies to elucidate the downstream mechanism in more detail. We have now edited our manuscript according to the specific requests, including additional data and further clarifications of our model. We believe these new data and edits significantly improve the manuscript and hope that it is now acceptable for publication in eLife

eLife assessment

This study reveals a novel mechanism of Acetylcholine- Acetylylcholine receptor signaling in regulating gut barrier function in Drosophila, which provides important implications on the pathway played in human diseases, such as Chronic Obstructive Pulmonary DiseaseCOPD. The evidence supporting the claims of the authors is solid.

Joint Public Review

This manuscript utilizes Drosophila melanogaster as a model system to functionally characterize the role of genes previously associated with obstructive pulmonary disease (COPD) in epithelial barrier function. Using genetic and imaging approaches, the authors characterised a previously unrecognised role of intestinal Acetylcholine receptor (AchR) signalling, in the regulation of epithelial barrier function. The working model proposes that Acetylcholine (Ach) produced by enteroendocrine cells (EEs) and enteric neurons signals to AchR in enterocytes (ECs). This signalling activates the secretion of the Peritrophic membrane (PM) through the regulation of the exocytic protein Syt4. In this way, Ach/AchR signalling works to protect epithelial barrier function and organismal tolerance to ingested damaging agents, such as those causing oxidative stress.

Overall, the data presented support the main model of the paper: EC AchR activation is necessary to maintain epithelial barrier function. The evidence, however, on the mechanisms downstream of AchR, namely, the involvement of this signalling pathway in the regulation of Syt4 is weak.

The work in this manuscript represents an important proof of concept for the use of the Drosophila midgut as a model to functionally interrogate genes from human genetic association studies in pathologies affecting epithelial homeostasis.

Author Response

Reviewer #1 (Public Review):

Mano et. al. use a combination of behavioral, genetic silencing, and functional imaging experiments to explore the temporal properties of the optomotor response in Drosophila. They find a previously unreported inversion of the behavior under high contrast and luminance conditions and identify potential pathways mediating the effect.

Strengths:

Quantifications of optomotor behavior have been performed for many decades. Despite a large number of previous studies, the authors still find something fundamentally novel: under high contrast conditions and extended stimulation periods, the behavior becomes dynamic over time. The turning response shows an initial transient positive following response. The amplitude of the behavior then decreases and even inverts such that animals show an anti-directional rotation response. The authors systematically explore the stimulation feature space, including large ranges of spatial and temporal frequencies and conditions with high and low contrast. They also test two wild-type fly species and even compare experiments across two different labs and setups. From these data, it seems clear that the behavior is robust and largely depends on the brightness of the stimulation, rearing conditions, and genetic background. The authors discuss that these effects have not clearly been reported elsewhere beforehand, and convincingly argue why this may be the case.

In general, the presented behavioral quantifications illustrate the importance of further experimental studies of the temporal dynamics of behavior in response to dynamically varying stimulus features, across different stimulus types, genetic backgrounds, and model animal systems. It also illustrates the importance of relating the conditions that animals experience in the laboratory to the ones they would experience in the wild. As the authors mention, the brightness during a sunny day can reach values as high as 4000 cd/m2, while experimental stimulation in the lab has so far often been orders of magnitude below that.

The study then systematically explores potential neural elements involved in the behavior. Through a set of silencing experiments, they find that T4 and T5 neurons, as expected, are required for motion behaviors. On the other hand, silencing HS cells largely abolishes the 'classical' syn-directional response but leaves anti-directional turning intact. On the other hand, silencing CH cells abolishes the anti-directional response but leaves the syn-directional behavior intact. Through functional imaging in T4, T5, HS, and CH neurons, the authors could show that none of these neurons shows a response inversion depending on contrast level. Together, these experiments nicely illustrate that the dynamics do not seem to be computed within the early parts of visual processing, but they must happen on the level of the lobula plate or further downstream.

Weaknesses:

While the authors have already explored various parameters of the experiment, it would have been nice to see additional experiments regarding the initial adaptation phase. The experiments in Figure 2e, where the authors show front-to-back or back-to-front gratings before the rotation phase, are a good start. What would the behavioral dynamics look like if they had exposed animals to long periods of static high or low contrast gratings, whole field brightness, or darkness? Such experiments would surely help to better understand the stimulus features on which the adaptation elements operate. It would be interesting to explore to what degree such static stimuli impact the subsequent behavioral dynamics.

To address this question, we have added a new adaption condition, in which a high contrast, stationary sinusoidal grating is presented for 5 seconds before the high contrast rotational stimulus is presented (new Figure 2 – Supp. Fig. 1). We find that the turning looks identical to the case of a gray adapter. These results drive home the point that the direction of motion of the adapter is what matters most.

Given the dynamics of the behavior, it would probably also be worth looking at the turning dynamics after the stimulus has stopped. If direction-selective adaptation mechanisms are regulating the turning response, one may find long-lasting biases even in the absence of stimulation. If the authors have more data after the stimulus end, it would be good to further expand the time range by a few seconds to show if this is the case or not (for example, in Figure 1b).

We now show these dynamics in Figure 1. See Essential Revision #1.

Another important experiment could be to initially perform experiments in a closed-loop configuration, and then quickly switch to open-loop. The closed-loop configuration should allow the motion computing circuitry to adapt to the chosen environmental conditions. Explorations of the changes in turning response dynamics after such treatments should then enable further dissections of the mechanisms of adaptation. Closed-loop experiments under different contrast conditions have already been performed (for example, Leonhardt et al. 2016), which also showed complex response dynamics after stimulus on- and offset. It would be great to discuss the current open-loop experiments, and maybe some new closed-loop results, in relation to the previous work.

We have performed these suggested experiments; please see Essential Revision #2.

The authors mention the different rearing conditions, and there is one experiment in Figure S2 which mentions running experiments at 25 deg C. But it is not clear from the Methods at which temperature all other experiments have been performed. It is also not clear at which temperature the shibire block experiments were performed. As such experiments require elevated temperatures, I assume that all behavioral experiments have been performed at such levels? How high were those?

Our apologies for leaving out this important information. In DAC’s lab, behavioral experiments are run at 34-36ºC in a room maintaining ~50% relative humidity (this yields ~25% RH in the box with the experiment, as we now note in the methods). These conditions yield high quality, reproducible behavior, especially since this temperature elicits strong walking behavior. In TRC’s lab, behavioral experiments are similarly run at 34ºC in a room maintaining ~50% relative humidity (similarly with ~25% RH in the experimental box), for similar reasons. We have now added these details to the methods sections for each lab’s behavioral experiments.

What does the fly see before and after the stimulus (i.e. the gray boxes in all figures)? Are these periods of homogenous gray levels or are these non-moving gratings with the luminance and contrast of the subsequent stimulus? It would be important to add this information to the methods and to the figure illustrations or legends.

In the figures, gray is a uniform luminance screen that appears before and after the stimuli, with luminance matched to the mean stimulus luminance. We have now included this in the methods section where we describe how stimuli were generated in each lab.

It would be nice to discuss the potential location where the motion adaptation may be implemented in the brain. A small model scheme as an additional figure could further help to discuss how such computations may be mechanistically implemented, helping readers to think about future experimental dissections of the behavior.

Following this suggestion, we have created a diagram that shows a potential mechanistic implementation of the behavior observed, and summarizes our results (new Figure 6 – Supp. Fig. 2). There are many other possible alternatives that we do not show, including exactly how an opposing signal could ramp up under the conditions of these experiments. In the figure caption, we remind readers what locations have been excluded for this sort of computation. We reference this diagram where we discuss subtraction in the Discussion.

For setting up similar experiments in other labs, the authors need to better describe how they measured the luminance of the arena. Do they simply report the brightness delivered by the Lightcrafter system, or did they measure this with a lux-meter? If so, at which distance was the measurement performed and with which device? Given that the behavior is sensitive to the specific properties of the stimulus, it will be important to report these numbers carefully to enable other groups to reproduce effects.

In brief, since these are rear projection screens, we can easily measure light intensity by placing a power meter in front of the screen. This gives us the photon flux in watts, which can be converted to lumens by a standard conversion and then into candelas by making the approximation that our screen scatters into 2π steradians. Dividing by the sensor area gives us our desired candelas per square-meter. We have now added this methodology to the methods section.

eLife assessment

The present study provides a valuable new perspective on the optomotor response based on an inversion of the behavior under specific (non-natural) conditions that may help elucidate the principles of this specific behavior. The evidence provided is convincing.

Reviewer #1 (Public Review):

Mano et. al. use a combination of behavioral, genetic silencing, and functional imaging experiments to explore the temporal properties of the optomotor response in Drosophila. They find a previously unreported inversion of the behavior under high contrast and luminance conditions and identify potential pathways mediating the effect.

Strengths:<br /> Quantifications of optomotor behavior have been performed for many decades. Despite a large number of previous studies, the authors still find something fundamentally novel: under high contrast conditions and extended stimulation periods, the behavior becomes dynamic over time. The turning response shows an initial transient positive following response. The amplitude of the behavior then decreases and even inverts such that animals show an anti-directional rotation response. The authors systematically explore the stimulation feature space, including large ranges of spatial and temporal frequencies and conditions with high and low contrast. They also test two wild-type fly species and even compare experiments across two different labs and setups. From these data, it seems clear that the behavior is robust and largely depends on the brightness of the stimulation, rearing conditions, and genetic background. The authors discuss that these effects have not clearly been reported elsewhere beforehand, and convincingly argue why this may be the case.

In general, the presented behavioral quantifications illustrate the importance of further experimental studies of the temporal dynamics of behavior in response to dynamically varying stimulus features, across different stimulus types, genetic backgrounds, and model animal systems. It also illustrates the importance of relating the conditions that animals experience in the laboratory to the ones they would experience in the wild. As the authors mention, the brightness during a sunny day can reach values as high as 4000 cd/m2, while experimental stimulation in the lab has so far often been orders of magnitude below that.

The study then systematically explores potential neural elements involved in the behavior. Through a set of silencing experiments, they find that T4 and T5 neurons, as expected, are required for motion behaviors. On the other hand, silencing HS cells largely abolishes the 'classical' syn-directional response but leaves anti-directional turning intact. On the other hand, silencing CH cells abolishes the anti-directional response but leaves the syn-directional behavior intact. Through functional imaging in T4, T5, HS, and CH neurons, the authors could show that none of these neurons shows a response inversion depending on contrast level. Together, these experiments nicely illustrate that the dynamics do not seem to be computed within the early parts of visual processing, but they must happen on the level of the lobula plate or further downstream.

Weaknesses:<br /> While the authors have already explored various parameters of the experiment, it would have been nice to see additional experiments regarding the initial adaptation phase. The experiments in Figure 2e, where the authors show front-to-back or back-to-front gratings before the rotation phase, are a good start. What would the behavioral dynamics look like if they had exposed animals to long periods of static high or low contrast gratings, whole field brightness, or darkness? Such experiments would surely help to better understand the stimulus features on which the adaptation elements operate. It would be interesting to explore to what degree such static stimuli impact the subsequent behavioral dynamics.

Given the dynamics of the behavior, it would probably also be worth looking at the turning dynamics after the stimulus has stopped. If direction-selective adaptation mechanisms are regulating the turning response, one may find long-lasting biases even in the absence of stimulation. If the authors have more data after the stimulus end, it would be good to further expand the time range by a few seconds to show if this is the case or not (for example, in Figure 1b).

Another important experiment could be to initially perform experiments in a closed-loop configuration, and then quickly switch to open-loop. The closed-loop configuration should allow the motion computing circuitry to adapt to the chosen environmental conditions. Explorations of the changes in turning response dynamics after such treatments should then enable further dissections of the mechanisms of adaptation. Closed-loop experiments under different contrast conditions have already been performed (for example, Leonhardt et al. 2016), which also showed complex response dynamics after stimulus on- and offset. It would be great to discuss the current open-loop experiments, and maybe some new closed-loop results, in relation to the previous work.

The authors mention the different rearing conditions, and there is one experiment in Figure S2 which mentions running experiments at 25 deg C. But it is not clear from the Methods at which temperature all other experiments have been performed. It is also not clear at which temperature the shibire block experiments were performed. As such experiments require elevated temperatures, I assume that all behavioral experiments have been performed at such levels? How high were those?

What does the fly see before and after the stimulus (i.e. the gray boxes in all figures)? Are these periods of homogenous gray levels or are these non-moving gratings with the luminance and contrast of the subsequent stimulus? It would be important to add this information to the methods and to the figure illustrations or legends.

It would be nice to discuss the potential location where the motion adaptation may be implemented in the brain. A small model scheme as an additional figure could further help to discuss how such computations may be mechanistically implemented, helping readers to think about future experimental dissections of the behavior.

For setting up similar experiments in other labs, the authors need to better describe how they measured the luminance of the arena. Do they simply report the brightness delivered by the Lightcrafter system, or did they measure this with a lux-meter? If so, at which distance was the measurement performed and with which device? Given that the behavior is sensitive to the specific properties of the stimulus, it will be important to report these numbers carefully to enable other groups to reproduce effects.

Reviewer #2 (Public Review):

This study looks at how optomotor turning in fruit flies varies with stimulus conditions. Although the response has usually been observed in the same direction of rotation as the stimulus, they find that in many situations the flies turn strongly in the opposite direction to the stimulus. This 'anti-directional' turning increases with stimulus brightness, contrast, and duration of the stimulus, and also varies with many factors such as rearing temperature, lab, strain, and developmental stage. They show that the anti-directional response depends on neurons in the visual system that are also important for the more standard response, but they don't find clear changes in the activity of these neurons that could explain the directional switch. The main conclusion is that supposedly simple behaviors may be more complicated than they first appear, and careful consideration needs to be given to the precise stimulus conditions and the response dynamics when measuring such behaviors, and especially when comparing data across labs.

eLife assessment

Using a novel micropipette-based, minimally invasive approach in combination with theoretical and computational analysis, this important work probes tissue mechanics in the Drosophila embryo. The authors provide compelling evidence for the applicability of their method, which reveals important differences between the mechanical properties on the apical and basal tissue sides. This work should be of broad interest to scientists studying tissue mechanics, membranes, and developmental processes.

Reviewer #1 (Public Review):

In this work, Cheikh et al. develop a novel method to probe tissue mechanics in vivo, with particular application to the early Drosophila embryo. The method is based on filling a pulled micropipette with a mixture of fluorescent dye and PDMS, which is cured and allowed to harden. Etching away the tip of the glass micropipette leaves exposed the PDMS core, which, like the bristles held in a brush handle, is easily deformed. Calibration of the stiffness of the PDMS tip allows for direct measurement of forces through the tip displacement. Apart from the particular application here, this method should prove to be widely useful in biological physics.

The authors then inserted this force probe into Drosophila embryos at the stage when cellularization has occurred, and demonstrate the ability to deform the tissue (visualized by fluorescently labelled cell walls). Crucially, the time course of the deformation can be controlled by the rate at which the pipette is translated, allowing for the study of potential viscous or viscoelastic effects.

The authors find from their experiments and extensive computational analysis of mechanical models of the embryo that there must be a significant difference between the mechanical properties of the apical and basal sides of the tissue.

This is a very well executed paper that provides compelling evidence for the utility of the experimental method and the particular issues in Drosophila mechanics. A strength of the paper is the clear and simple focus on a particular deformation and its experimental and theoretical analysis. The computational section is a bit less clearly connected to the observations, in the sense that some kind of very simplified model incorporating the apicobasal differences is lacking.

Reviewer #2 (Public Review):

This is a very interesting study with a potential impact on understanding the 3D mechanics of cells in epithelia. The assay that the authors developed is novel and quite useful for future studies. However, I was hoping to see more experimental results in the manuscript. For example, there is a zoo of mutants that the community speculates about possible mechanical changes in cells. I was hoping to see if the authors can settle some of these arguments by using their novel technique and analysis.

eLife assessment

This elegantly performed and rigorous study generates new and conceptually important insights into the interaction between an essential malaria parasite invasion ligand (and vaccine candidate) called PfRH5, and its erythrocyte surface integral membrane receptor basigin. The authors provide compelling evidence based on rigorous biochemical assays that erythrocyte basigin is predominantly expressed in a complex with one of two distinct erythrocyte membrane proteins called PMCA and MCT1 and that PfRH5 binds to these complexes better than to isolated basigin. Certain invasion-inhibitory antibodies, that do not prevent binding of PfRH5 to isolated basigin, do in contrast prevent binding to the basigin complexes, explaining the mode of action of these previously enigmatic antibodies and providing valuable data towards the improved design of vaccines based on PfRH5.

Reviewer #1 (Public Review):

In this study, the authors investigate the interactions between Plasmodium falciparum RH5, an essential ligand mediating erythrocyte invasion by the malaria parasite, and its cognate receptor basigin. Based on published observations that basigin forms complexes with the plasma membrane Ca2+-ATPase PMCA1/4 or monocarboxylate transporter MCT1, the authors asked whether RH5 can interact with basigin complexed with PMCA or MCT1, whether this modulates the function of PMCA and whether these interactions may explain the mechanism of action of neutralising antibodies targeting RH5. The objectives and rationale of the study are very clear.

Using size exclusion chromatography, 2D blue native PAGE, antibody shift, and depletion assays, the authors demonstrate that native basigin in human erythrocytes is essentially found in heteromeric complexes with either PMCA4 or MCT1. They measured the binding of PfRH5 to purified basigin-PMCA and basigin-MCT1 complexes by surface plasmon resonance and found that RH5 interacts with complexed basigin with higher affinity than with isolated basigin. RH5 did not alter the ATPase activity of PMCA, either in purified PMCA-basigin complexes or in CHO cells expressing human basigin and PMCA4, leading the authors to rule out RH5-mediated alteration of PMCA-mediated calcium export as a mechanism underlying the changes in calcium flux at the interface between the erythrocyte and the invading parasite. Finally, the authors used structural modelling to show that growth-inhibitory antibodies sterically block the binding of RH5 to basigin-PMCA and basigin-MCT1 complexes, providing a molecular explanation for why most potent anti-RH5 neutralising antibodies do not prevent RH5 binding to isolated basigin.

The paper is well-written and the claims are well-supported by the data. The study provides new insight into an essential interaction during blood-stage malaria and reveals the mode of action of growth-inhibitory antibodies, with potential implications for the design of RH5-based malaria vaccines. The study does not address whether PMCA and MCT1 are required during erythrocyte invasion by P. falciparum merozoites, and does not provide direct evidence to completely rule out a role of RH5-PMCA interaction in calcium flux modulation in the context of erythrocyte invasion by the parasite.

Reviewer #2 (Public Review):

Plasmodium falciparum RH5 (PfRH5) is an integral membrane protein of P. falciparum merozoites that acts as an essential ligand involved in host erythrocyte invasion, functioning by binding to the erythrocyte surface protein basigin. Previous work by the authors of this study and other groups has demonstrated that antibodies to PfRH5 can block invasion and can be protective in in vivo challenge studies, so PfRH5 is a promising malaria vaccine candidate. This study by Jamwal et al addresses the paradoxical observation, made in earlier work by these authors, that certain antibodies to PfRH5 efficiently inhibit parasite invasion of erythrocytes yet does not block the binding of PfRH5 to recombinant basigin ectodomain. The authors first demonstrate through a range of approaches that most native erythrocyte basigin is expressed in the form of detergent-stable complexes with one of two distinct erythrocyte membrane proteins, plasma membrane calcium ATPase (PMCA) or monocarboxylate transporter (MCT). Using in vitro biophysical techniques, they then show that recombinant PfRH5 binds more tightly (and with slower off-rates) to the native basigin-PMCA or basigin-MCT1 complexes than to the isolated recombinant basigin ectodomain. Finally and crucially, the authors then show that 2 of these known invasion-inhibitory anti-PfRH5 antibodies (called R5.016 and 9AD4) that do not block the interaction between recombinant basigin and PfRH5 do in contrast block the interaction between PfRH5 and basigin-PMCA and basigin-MCT1 complexes. By docking known atomic structures of the R5.016 and 9AD4 Fab-basigin structures onto the known or modelled basigin complex structures, the authors present a convincing argument that the invasion-inhibitory antibodies function through steric hindrance, preventing PfRH5 binding to the basigin-PMCA or basigin-MCT1 complexes. The work provides a rational explanation for the invasion-inhibitory activity of this class of PfRH5-specific antibodies and demonstrates the potential complexity underlying the mode of action of invasion-inhibitory anti-malarial antibodies.

Reviewer #3 (Public Review):

Higgins et al. examine the interaction between erythrocyte basigin and malaria parasite RH5. They use sophisticated biochemical and biophysical studies to establish that basigin on erythrocyte membranes exists primarily in association with either MCT1 or PMCA4b, that these complexes facilitate tighter binding of RH5 to basigin, and that RH5-basigin interaction does not appear to change the activity of the PMCA4b Ca++ pump. They determine that some antibodies that interfere with RH5-basigin interaction to interfere with the pathogen's entry into erythrocytes are effective only when tested in the presence of MCT1 or PMCA4b association. The studies are rigorously performed and have the potential to guide the development of better vaccines that block this invasion process.

Author Response

Reviewer #1 (Public Review):

Summary:

The study assesses the impact of testing contacts of cases in school classes when identified, rather than at the end of quarantine, on various outcomes such as secondary infections, tracing delay, and identification of the possible source of infection. The authors find that the intervention likely reduced tracing delay and increased the number of possible infection sources. However, due to unmeasured confounding, it remains unclear if secondary transmission actually decreased. The analysis requires clarification and further explanation in parts.

Major strengths and weaknesses:

The study benefits from the assessment of various outcomes in contact tracing in addition to changes in transmission, such as tracing delay, and the identification of putative infectors; however the assumption that other cases found in households are infectors of the index case rather than putative infectees, may introduce significant bias, but this is not mentioned in the Discussion despite being significant. It is difficult to understand the intervention in Figure 1 due to unclear labelling and incomplete descriptions in the caption. The authors mention that the same school class could be included multiple times for multiple outbreaks - was there a time cutoff for inclusion? I had a lot of trouble interpreting or reproducing the values given in Table 1. Firstly, the methods used to produce the RRs given are not described in the methods section of the paper. What are the outcomes - "classes" and "indexes" are poroly defined. Is this output from univariate or multivariate regression model, and what is the link function? I was also unable to reproduce the RRs listed in the table despite attempting several methods. The closest numbers I achieved were by crudely dividing the risks (e.g. for the RR for known infection source I took the ratio of indexes for which a school contact was suspected pre and post-intervention (644/1175)/(146/429) = 1.61), but if this is the case then the unknown class is by definition not the reference category. This is the same for the other RRs stated in the table. The methods used should be clarified and results updated if erroneous. The mediation analysis components and their relevance to the study could be better explained in the methods and results.

Achievement of aims and support for conclusions:

The authors partially achieved their aims by demonstrating a likely decrease in tracing delay and an increase in possible infection sources. However, the study's inability to determine if secondary transmission decreased due to unmeasured confounding limits the conclusiveness of the findings. The authors should reiterate the main numerical results in the first few paragraphs of the discussion.

Impact on the field and utility of methods and data:

This study has the potential to impact the field by highlighting the benefits of testing contacts earlier in school classes. The findings on reduced tracing delay and increased identification of infection sources can inform future strategies and interventions. However, clarity on the analysis methods, as well as the results, are necessary to ensure the utility and reliability of the findings.

We thank the reviewer for his encouraging comments, we completely agree with the interpretation of our findings. Nevertheless, the intervention under evaluation is not exactly as descried by the reviewer. In fact, the change of contact tracing targeted mostly the tracing in household cases. Investigation in schools used the immediate testing of all contacts already before the intervention, even if after the intervention the timeliness increased. It was in the household where we had a clear change with immediate testing of all asymptomatic family contacts.

The assumption of direction of infection: We understand the reviewer’s point and we agree that such an assumption would introduce an important bias. Nevertheless, we do not assume any direction of the infection. We only report the conclusions of the field investigation conducted during the school outbreak about a known source of infection for the index case.

On the contrary, in our conceptual framework, we make the hypothesis that introducing backward contact tracing for all cases in the community (mostly household infections) asymptomatic cases in school age were more promptly identified and this improved the surveillance of school outbreaks and possibly reduced transmission in school outbreaks. This increase in timeliness could occur whatever the direction of infection within the household was, i.e. from the symptomatic adult to the asymptomatic child or the other way round.

Figure 1: we completely changed figure 1 according to reviewer’s suggestions.

Table 1: it has been split in two tables, the first describe the characteristics of the classes and index cases and the outcomes of the outbreaks, and the second is a table showing the association between possible confounders and the main outcome. We are sorry; trying to make the paper shorter, we made the table very unclear.

Repeated outbreaks in the same class: we thank the reviewer for this point. We did not define a time limit to distinguish two episodes. The outbreaks were defined by the field investigations. If the class was involved in two investigations, public health operators firstly tried to assess if there was a direct link between the two. Actually, it was impossible that two outbreaks were considered independent if there was less than 21 days between the two index cases notifications. We added a sentence in the methods.

Mediation analysis rationale: we added a DAG to explain the mediation analysis, we also changed the results reporting following step by step the preliminary results to introduce the mediation analysis to justify the selection of the mediators and the confounders.

Discussion: we added the main findings in a quantitative way at the beginning of the discussion.

Reviewer #2 (Public Review):

This is a review of "Effect of an enhanced public health contact tracing intervention on the secondary transmission of SARS-CoV-2 in educational settings: the four-way decomposition analysis", by Djuric et al.

In late 2020, a province in northern Italy implemented a new testing regimen for all contacts of people known to have COVID-19, offering them SARS-CoV-2 testing immediately after the detection of the index case instead of at the end of a quarantine period. The authors of this study investigated whether this policy change reduced secondary transmission of SARS-CoV-2 in schools. In addition to studying this primary outcome, they examined two "process" outcomes; whether this policy of testing earlier enabled public health officials to more successfully identify the source of infection of the index case, and if the time interval from detection of the index case to testing of contacts in the educational setting reduced.

They concluded that the time between detection of the index case and testing of contacts did reduce before and after the policy change. Similarly, the proportion of cases for which the source of infection was identified also increased after the policy change. Both of these "process" indicators correlated with reduced secondary transmission, though only identifying the source of infection was associated with a statistically significant (at the 5% level) reduction in secondary transmission.

Strengths of this paper

Educational settings experienced significant disruption during the COVID-19 pandemic, and efforts to better understand the spread of SARS-CoV-2 in schools - and how to mitigate this spread - are of significant public health importance. This paper, therefore, addresses an important topic.

Additionally, the authors describe a detailed dataset comprising case and contact tracing data from over 1,600 index cases with in-school contacts. The richness of the data described in Table 1 provides a good opportunity to conduct a natural experiment on the potential impact of testing contacts immediately after exposure on secondary transmission. The authors also appropriately acknowledge that this interrupted time series study would be insufficient to provide causal information, given the potential for confounders.

Finally, the primary statistical method (a four-way decomposition analysis) was new to me, but - from the references cited - seems appropriate. Given the relative novelty of this method, more space could be dedicated to explaining it in the methods.

Weakness of this paper

Although the paper tackles an important topic with an appropriate dataset, the analyses feel insufficient to fully support the authors' conclusions.

First and most critically, it is difficult to understand exactly what the primary outcome of the study is. Both the median number of secondary cases per class and the proportion of classes that experienced any secondary transmission are presented in Table 1, but - at least in the unadjusted analyses - point in different directions regarding the impact of the effect of the intervention (albeit neither strongly). For example, before the policy change, the median number of secondary cases per index case is 2, while after the policy change, it has reduced to 1. In contrast, before the policy change 37% of classes experienced any secondary transmission, but after the policy change, this had increased to 39% of classes. In some of the adjusted analyses, "number of secondary cases" is stated as the outcome variable, but that is not fully defined. The "attack rate", which is well defined in the methods, could be one option for use as a consistent primary outcome, however, it is only provided for the total study population and the attack rates pre- or post-policy change are not presented or compared.

Additionally, although using a "process measure" as a secondary outcome could be valuable - especially in a natural experiment like this, where identifying a causal relationship with a complex outcome like secondary transmission will be difficult - it was somewhat unclear how the process measures described in this study were measured, or their validity. For example, the reduced time between detection of the index case and testing of contacts seems unsurprising, since the intervention itself is to test contacts immediately after the index case is identified. Additionally, the results describe reductions in median testing delay and median tracing delay, but only testing delay is defined in the methods.

Finally, there is existing published literature that provides additional context on the impact of testing on secondary transmission within schools that arguably provides a higher level of evidence than the current study, but is not cited by the authors. A key limitation of this study - which the authors acknowledge - is the interrupted time series nature of their study, which is open to confounding by other important factors that happened at the same time, including but not limited to: changes in overall incidence of COVID-19; viral evolution (e.g. the emergence of the Alpha variant (B.1.1.7) which occurred during this study and which significantly altered the risk of secondary transmission); the efficiency of the contact tracing system (including skill and size of the contact tracing workforce); and the availability of non-molecular diagnostic tests (e.g. lateral flow devices) that might allow individuals to change their behaviors even without enrolling in this study. Examples of alternative studies which might reduce some of this potential confounding include around 400 schools in Los Angeles County, California, USA, that implemented "test to stay" in 2021 and were compared to 1,600 schools that did not implement "test to stay" [https://www.cdc.gov/mmwr/volumes/70/wr/mm705152e1.htm] and a cluster-randomized trial of daily testing of exposed contacts to study in-school transmission in England, UK, also in 2021 [https://www.sciencedirect.com/science/article/pii/S0140673621019085]. Although these examples describe slightly different interventions involving enhanced testing of exposed contacts, they both compared educational settings with and without the intervention across the same time periods; and the UK study in particular has methodological advantages over this current paper, including randomization. While the findings in the current paper did not contradict these earlier, stronger papers, the example from this province should be placed in context with the totality of evidence around testing in schools.

We thank the reviewer for his encouraging and useful comments.

We have completely reframed Table 1 and split it in two separate tables. We have added suggested references.

According to the reviewer’s suggestions, we tried to better describe the main outcome and to justify our choice. We also added a definition of testing delay that was missing. We added a box explaining in plain language all the outputs of the mediation analysis. We improved reporting of the descriptive data in table 1, including attack rate.

Furthermore, we better explained the choice of process outcomes and how they were related to the main outcome a priori and what changes were expected under the hypothesis that the intervention worked correctly. In particular, we agree that a reduction in the time to testing was unsurprising, in fact, this was just to check that the intervention was actually and correctly implemented; increasing the proportion of index cases with a known source of infection (and the proportion of asymptomatic index cases, that was not identified in the initial protocol but we identified later as an important process indicator) is a process indicator suggesting that more index cases have been identified as a consequence of a household investigation, i.e. the change in tracing helped in early detection of school exposure.

Regarding the proportion of classes with secondary transmission, we added a sentence in the discussion explaining why we did not expect that this would change after the intervention. In fact, as described in the new figure 1, household contacts were immediately quarantined before as well as after the intervention, what changed is that they are timely identified as contacts and therefore school contacts are identified and isolated. Only if a secondary transmission in the class already occurred we could reduce transmission in the class, i.e. we are preventing tertiary cases not secondary. Nevertheless, the number of classes investigated is also expected to change, so it was difficult to predict if the proportion of investigated classes with transmission should increase or decrease.

In the discussion, we reported examples of studies that applied an experimental or semi-experimental design and thus overcame the main limits of our observational study. Nevertheless, we also highlighted that the intervention we are evaluating in this study was particularly difficult to be conducted in a trial or a semi-experimental setting, in fact, we are trying to evaluate a change in the contact tracing in the community that occurred during the peak of the second wave.

eLife assessment

This study provides a potentially useful assessment of the effect of testing contacts of cases in school classes when identified, rather than at the end of quarantine, on both the number of secondary infections and other outcomes including tracing delay and identification of the possible source of infection. The authors find that the intervention likely led to a decrease in tracing delay and an increase in the number of possible sources of infection, though were unable to determine whether secondary transmission decreased, due in part to unmeasured confounding. While the surveillance system described provides a solid dataset appropriate for this analysis, the description of methods, study outcomes, and consideration of potential confounding factors is incomplete.

Reviewer #1 (Public Review):

Summary:<br /> The study assesses the impact of testing contacts of cases in school classes when identified, rather than at the end of quarantine, on various outcomes such as secondary infections, tracing delay, and identification of the possible source of infection. The authors find that the intervention likely reduced tracing delay and increased the number of possible infection sources. However, due to unmeasured confounding, it remains unclear if secondary transmission actually decreased. The analysis requires clarification and further explanation in parts.

Major strengths and weaknesses:<br /> The study benefits from the assessment of various outcomes in contact tracing in addition to changes in transmission, such as tracing delay, and the identification of putative infectors; however the assumption that other cases found in households are infectors of the index case rather than putative infectees, may introduce significant bias, but this is not mentioned in the Discussion despite being significant. It is difficult to understand the intervention in Figure 1 due to unclear labelling and incomplete descriptions in the caption. The authors mention that the same school class could be included multiple times for multiple outbreaks - was there a time cutoff for inclusion? I had a lot of trouble interpreting or reproducing the values given in Table 1. Firstly, the methods used to produce the RRs given are not described in the methods section of the paper. What are the outcomes - "classes" and "indexes" are poroly defined. Is this output from univariate or multivariate regression model, and what is the link function? I was also unable to reproduce the RRs listed in the table despite attempting several methods. The closest numbers I achieved were by crudely dividing the risks (e.g. for the RR for known infection source I took the ratio of indexes for which a school contact was suspected pre and post-intervention (644/1175)/(146/429) = 1.61), but if this is the case then the unknown class is by definition not the reference category. This is the same for the other RRs stated in the table. The methods used should be clarified and results updated if erroneous. The mediation analysis components and their relevance to the study could be better explained in the methods and results.

Achievement of aims and support for conclusions:<br /> The authors partially achieved their aims by demonstrating a likely decrease in tracing delay and an increase in possible infection sources. However, the study's inability to determine if secondary transmission decreased due to unmeasured confounding limits the conclusiveness of the findings. The authors should reiterate the main numerical results in the first few paragraphs of the discussion.

Impact on the field and utility of methods and data:<br /> This study has the potential to impact the field by highlighting the benefits of testing contacts earlier in school classes. The findings on reduced tracing delay and increased identification of infection sources can inform future strategies and interventions. However, clarity on the analysis methods, as well as the results, are necessary to ensure the utility and reliability of the findings.

Reviewer #2 (Public Review):

This is a review of "Effect of an enhanced public health contact tracing intervention on the secondary transmission of SARS-CoV-2 in educational settings: the four-way decomposition analysis", by Djuric et al.

In late 2020, a province in northern Italy implemented a new testing regimen for all contacts of people known to have COVID-19, offering them SARS-CoV-2 testing immediately after the detection of the index case instead of at the end of a quarantine period. The authors of this study investigated whether this policy change reduced secondary transmission of SARS-CoV-2 in schools. In addition to studying this primary outcome, they examined two "process" outcomes; whether this policy of testing earlier enabled public health officials to more successfully identify the source of infection of the index case, and if the time interval from detection of the index case to testing of contacts in the educational setting reduced.

They concluded that the time between detection of the index case and testing of contacts did reduce before and after the policy change. Similarly, the proportion of cases for which the source of infection was identified also increased after the policy change. Both of these "process" indicators correlated with reduced secondary transmission, though only identifying the source of infection was associated with a statistically significant (at the 5% level) reduction in secondary transmission.

Strengths of this paper

Educational settings experienced significant disruption during the COVID-19 pandemic, and efforts to better understand the spread of SARS-CoV-2 in schools - and how to mitigate this spread - are of significant public health importance. This paper, therefore, addresses an important topic.

Additionally, the authors describe a detailed dataset comprising case and contact tracing data from over 1,600 index cases with in-school contacts. The richness of the data described in Table 1 provides a good opportunity to conduct a natural experiment on the potential impact of testing contacts immediately after exposure on secondary transmission. The authors also appropriately acknowledge that this interrupted time series study would be insufficient to provide causal information, given the potential for confounders.

Finally, the primary statistical method (a four-way decomposition analysis) was new to me, but - from the references cited - seems appropriate. Given the relative novelty of this method, more space could be dedicated to explaining it in the methods.

Weakness of this paper

Although the paper tackles an important topic with an appropriate dataset, the analyses feel insufficient to fully support the authors' conclusions.

First and most critically, it is difficult to understand exactly what the primary outcome of the study is. Both the median number of secondary cases per class and the proportion of classes that experienced any secondary transmission are presented in Table 1, but - at least in the unadjusted analyses - point in different directions regarding the impact of the effect of the intervention (albeit neither strongly). For example, before the policy change, the median number of secondary cases per index case is 2, while after the policy change, it has reduced to 1. In contrast, before the policy change 37% of classes experienced any secondary transmission, but after the policy change, this had increased to 39% of classes. In some of the adjusted analyses, "number of secondary cases" is stated as the outcome variable, but that is not fully defined. The "attack rate", which is well defined in the methods, could be one option for use as a consistent primary outcome, however, it is only provided for the total study population and the attack rates pre- or post-policy change are not presented or compared.

Additionally, although using a "process measure" as a secondary outcome could be valuable - especially in a natural experiment like this, where identifying a causal relationship with a complex outcome like secondary transmission will be difficult - it was somewhat unclear how the process measures described in this study were measured, or their validity. For example, the reduced time between detection of the index case and testing of contacts seems unsurprising, since the intervention itself is to test contacts immediately after the index case is identified. Additionally, the results describe reductions in median testing delay and median tracing delay, but only testing delay is defined in the methods.

Finally, there is existing published literature that provides additional context on the impact of testing on secondary transmission within schools that arguably provides a higher level of evidence than the current study, but is not cited by the authors. A key limitation of this study - which the authors acknowledge - is the interrupted time series nature of their study, which is open to confounding by other important factors that happened at the same time, including but not limited to: changes in overall incidence of COVID-19; viral evolution (e.g. the emergence of the Alpha variant (B.1.1.7) which occurred during this study and which significantly altered the risk of secondary transmission); the efficiency of the contact tracing system (including skill and size of the contact tracing workforce); and the availability of non-molecular diagnostic tests (e.g. lateral flow devices) that might allow individuals to change their behaviors even without enrolling in this study. Examples of alternative studies which might reduce some of this potential confounding include around 400 schools in Los Angeles County, California, USA, that implemented "test to stay" in 2021 and were compared to 1,600 schools that did not implement "test to stay" [https://www.cdc.gov/mmwr/volumes/70/wr/mm705152e1.htm] and a cluster-randomized trial of daily testing of exposed contacts to study in-school transmission in England, UK, also in 2021 [https://www.sciencedirect.com/science/article/pii/S0140673621019085]. Although these examples describe slightly different interventions involving enhanced testing of exposed contacts, they both compared educational settings with and without the intervention across the same time periods; and the UK study in particular has methodological advantages over this current paper, including randomization. While the findings in the current paper did not contradict these earlier, stronger papers, the example from this province should be placed in context with the totality of evidence around testing in schools.

Author Response

Reviewer #1 (Public Review):

Briggs et al use a combination of mathematical modelling and experimental validation to tease apart the contributions of metabolic and electronic coupling to the pancreatic beta cell functional network. A number of recent studies have shown the existence of functional beta cell subpopulations, some of which are difficult to fully reconcile with established electrophysiological theory. More generally, the contribution of beta cell heterogeneity (metabolism, differentiation, proliferation, activity) to islet function cannot be explained by existing combined metabolic/electrical oscillator models. The present studies are thus timely in modelling the islet electrical (structural) and functional networks. Importantly, the authors show that metabolic coupling primarily drives the islet functional network, giving rise to beta cell subpopulations. The studies, however, do not diminish the critical role of electrical coupling in dictating glucose responsiveness, network extent as well as longer-range synchronization. As such, the studies show that islet structural and functional networks both act to drive islet activity, and that conclusions on the islet structural network should not be made using measures of the functional network (and vice versa).

Strengths:

• State-of-the-art multi-parameter modelling encompassing electrical and metabolic components.

• Experimental validation using advanced FRAP imaging techniques, as well as Ca2+ data from relevant gap junction KO animals.

• Well-balanced arguments that frame metabolic and electrical coupling as essential contributors to islet function.

• Likely to change how the field models functional connectivity and beta cell heterogeneity.

Weaknesses:

• Limitations of FRAP and electrophysiological gap junction measures not considered.

• Limitations of Cx36 (gap junction) KO animals not considered.

• Accuracy of citations should be improved in a few cases.

We thank reviewer 1 for their positive comments, including the many strengths in the approaches, arguments and impact. We do note the weaknesses raised by the reviewer and have addressed them following the comments below.

We would like to also note that when we refer to metabolic activity driving the functional network, we are not referring to metabolic coupling between beta cells. Rather we mean that two cells that show either high levels of metabolic activity (glycolytic flux) or that show similar levels metabolic activity will show increased synchronization and thus a functional network edge as compares to cells with elevated gap junction conductance. Increased metabolic activity would likely generate increased depolarizing currents that will provide an increased coupling current to drive synchronization; whereas similar metabolic activity would mean a given coupling current could more readily drive synchronized activity. We have substantially rewritten the manuscript to clarify this point.

Reviewer #2 (Public Review):

In their present work, Briggs et al. combine biophysical simulations and experimental recordings of beta cell activity with analyses of functional network parameters to determine the role played by gap-junctional coupling, metabolism, and KATP conductance in defining the functional roles that the cells play in the functional networks, assess the structure-function relationship, and to resolve an important current open question in the field on the role of so-called hub cells in islets of Langerhans.

Combining differential equation-based simulations on 1000 coupled cells with demanding calcium, NAPDH, and FRAP imaging, as well as with advanced network analyses, and then comparing the network metrics with simulated and experimentally determined properties is an achievement in its own right and a major methodological strength. The findings have the potential to help resolve the issue of the importance of hub cells in beta cell networks, and the methodological pipeline and data may prove invaluable for other researchers in the community.

However, methodologically functional networks may be based on different types of calcium oscillations present in beta cells, i.e., fast oscillations produced by bursts of electrical activity, slow oscillations produced by metabolic/glycolytic oscillations, or a mixture of both. At present, the authors base the network analyses on fast oscillations only in the case of simulated traces and on a mixture of fast and slow oscillations in the case of experimental traces. Since different networks may depend on the studied beta cell properties to a different extent (e.g., fast oscillation-based networks may, more importantly, depend on electrical properties and slow oscillationbased networks may more strongly depend on metabolic properties), it is important that in drawing the conclusions the authors separately address the influence of a cell's electrical and metabolic properties on its functional role in the network based on fast oscillations, slow oscillations, or a mixture of both.

We thank reviewer 2 for their positive comments, including addressing the importance of this study as it pertains to islet biology and acknowledging methodological complexities of this study. We also thank the reviewer for their careful reading and providing useful comments. We have integrated each comment into the manuscript. Most importantly, we have now extended our analysis to both fast and slow oscillations by incorporating an additional mathematical model of coupled slow oscillations and performing additional experimental analysis of fast, slow, and mixed oscillations.

Reviewer #3 (Public Review):

Over the past decade, novel approaches to understanding beta cell connectivity and how that contributes to the overall function of the pancreatic islet have emerged. The application of network theory to beta cell connectivity has been an extremely useful tool to understand functional hierarchies amongst beta cells within an islet. This helps to provide functional relevance to observations from structural and gene expression data that beta cells are not all identical.

There are a number of "controversies" in this field that have arisen from the mathematical and subsequent experimental identification of beta "hub" cells. These are small populations of beta cells that are very highly connected to other beta cells, as assessed by applying correlation statistics to individual beta cell calcium traces across the islet.

In this paper Briggs et al set out to answer the following areas of debate:

They use computational datasets, based on established models of beta cells acting in concert (electrically coupled) within an islet-like structure, to show that it is similarities in metabolic parameters rather than "structural" connections (ie proximity which subserves gap junction coupling) that drives functional network behaviour. Whilst the computational models are quite relevant, the fact that the parameters (eg connectivity coefficients) are quite different to what is measured experimentally, confirm the limitations of this model. Therefore it was important for the authors to back up this finding by performing both calcium and metabolic imaging of islet beta cells. These experimental data are reported to confirm that metabolic coupling was more strongly related to functional connectivity than gap junction coupling. However, a limitation here is that the metabolic imaging data confirmed a strong link between disconnected beta cells and low metabolic coupling but did not robustly show the opposite. Similarly, I was not convinced that the FRAP studies, which indirectly measured GJ ("structural") connections were powered well enough to be related to measures of beta cell connectivity.

The group goes on to provide further analytical and experimental data with a model of increasing loss of GJ connectivity (by calcium imaging islets from WT, heterozygous (50% GJ loss), and homozygous (100% loss). Given the former conclusion that it was metabolic not GJ connectivity that drives small world network behaviour, it was surprising to see such a great effect on the loss of hubs in the homs. That said, the analytical approaches in this model did help the authors confirm that the loss of gap junctions does not alter the preferential existence of beta cell connectivity and confirms the important contribution of metabolic "coupling". One perhaps can therefore conclude that there are two types of network behaviour in an islet (maybe more) and the field should move towards an understanding of overlapping network communities as has been done in brain networks.

Overall this is an extremely well-written paper which was a pleasure to read. This group has neatly and expertly provided both computational and experimental data to support the notion that it is metabolic but not "structural" ie GJ coupling that drives our observations of hubs and functional connectivity. However, there is still much work to do to understand whether this metabolic coupling is just a random epiphenomenon or somehow fated, the extent to which other elements of "structural" coupling - ie the presence of other endocrine cell types, the spatial distribution of paracrine hormone receptors, blood vessels and nerve terminals are also important.

We thank reviewer 3 for their positive comments, including the methodology, writing style, and the importance of this paper to the broader islet community. We thank the reviewer for their very in-depth and helpful comments. We have addressed each comment below and made significant changes to the manuscript according. We conducted more FRAP experiments and separated results into slow, fast, and mixed oscillations. We included analysis of an additional computational model that simulates slow calcium oscillations. Additionally, we substantially rewrote the paper to clarify that we are not referring to metabolic coupling and speak on the broader implications of network theory and our findings.

Reviewer #4 (Public Review):

This manuscript describes a complex, highly ambitious set of modeling and experimental studies that appear designed to compare the structural and functional properties of beta cell subpopulations within the islet network in terms of their influence on network synchronization. The authors conclude that the most functionally coupled cell subpopulations in the islet network are not those that are most structurally coupled via gap junctions but those that are most metabolically active.

Strengths of the paper include (1) its use of an interdisciplinary collection of methods including computer simulations, FRAP to monitor functional coupling by gap junctions, the monitoring of Ca2+ oscillations in single beta cells embedded in the network, and the use of sophisticated approaches from probability theory. Most of these methods have been used and validated previously. Unfortunately, however, it was not clear what the underlying premise of the paper actually is, despite many stated intentions, nor what about it is new compared to previous studies, an additional weakness.

Although the authors state that they are trying to answer 3 critical questions, it was not clear how important these questions are in terms of significance for the field. For example, they state that a major controversy in the field is whether network structure or network function mediates functional synchronization of beta cells within the islet. However, this question is not much debated. As an example, while it is known that there can be long-range functional coupling in islets, no workers in the field believe there is a physical structure within islets that mediates this, unlike the case for CNS neurons that are known to have long projections onto other neurons. Beta cells within the islets are locally coupled via gap junctions, as stated repeatedly by the authors but these mediate short-range coupling. Thus, there are clearly functional correlations over long ranges but no structures, only correlated activity. This weakness raises questions about the overall significance of the work, especially as it seems to reiterate ideas presented previously.

We thank reviewer 4 for their positive comments, including our multidisciplinary use of mathematical models and experimental imaging techniques. We have now included an additional model of slow oscillations (the Integrated Oscillator Model) to improve our conclusions. We also thank reviewer 4 for the insightful comments. We have carefully reviewed each comment and made significant changes to the manuscript accordingly. In particular, we have significantly rewritten the introduction and discussion attempting to clarify what is new in our manuscript and what is previously shown. Additionally, we agree with the reviewers’ sentiment that there is little debate over whether, for example, there are physical structures within the islet that mediate long-range functional connections. However, there is current debate over whether functional beta-cell subpopulations can dictate islet dynamics (see [11]–[13]). This debate can be framed by observing whether these functional subpopulations emerge from the islet due to physical connections (structural network) or something more nuisance (such as intrinsic dynamics). We have reframed the introduction and discussion to clarify this debate as well as more clearly state the premise of the paper.

Specific Comments

1). The authors state it is well accepted that the disruption of gap junctional coupling is a pathophysiological characteristic of diabetes, but this is not an opinion widely accepted by the field, although it has been proposed. The authors should scale back on such generalizations, or provide more compelling evidence to support such a claim.

Thank you for pointing this out, we have provided more specific citations and changes the wording from “well accepted” to “has been documented”. See Discussion page 13 lines 415-416.

2) The paper relies heavily on simulations performed using a version of the model of Cha et al (2011). While this is a reasonable model of fast bursting (e.g. oscillations having periods <1 min.), the Ca2+ oscillations that were recorded by the authors and shown in Fig. 2b of the manuscript are slow oscillations with periods of 5 min and not <1 min, which is a weakness of the model in the current context. Furthermore, the model outputs that are shown lack the well-known characteristics seen in real islets, such as fast-spiking occurring on prolonged plateaus, again as can be seen by comparing the simulated oscillations shown in Fig. 1d with those in Fig. 2b. It is recommended that the simulations be repeated using a more appropriate model of slow oscillations or at least using the model of Cha et al but employed to simulate in slower bursting.

The reviewer raises an important point and caveat associated with our simulated model and experimental data. This point was also made by other reviewers, and a similar response to this comment can be found elsewhere in response to reviewer 2 point 6. To address this comment, we have performed several additional experiments and analyses:

1) We collected additional Ca2+ (to identify the functional network and hubs) and FRAP data (to assess gap junction permeability) in islets which show either pure slow, pure fast, or mixed oscillations. We generated networks based on each time scale to compare with FRAP gap junction permeability data. We found that the conclusions of our first draft to be consistent across all oscillation types. There was no relationship between gap junction conductance, as approximated using FRAP, and normalized degree for slow (Figure 3j), fast (Figure 3 Supp 1d,e), or mixed (Figure 3 Supp 1g,h) oscillations. We also include discussion of these conclusions - See Results page 7 lines 184-186 and lines 188-191, Discussion page 12 lines 357-360.

2) We also performed additional simulations with a coupled ‘Integrated Oscillator Model’ which shows slow oscillations because of metabolic oscillations (Figure 2). We compared connectivity with gap junction coupling and underlying cell parameters. In this case, there is an association between functional and structural networks, with highly-connected hub cells showing higher gap junction conductance (Figure 2f) but also low KATP channel conductance (gKATP) (Figure 2e). However, there are some caveats to these findings – given the nature of the IOM model, we were limited to simulating smaller islets (260 cells) and less heterogeneity in the calcium traces was observed. Additional analysis suggests the greater association between functional and structural networks in this model was a result of the smaller islets, and the association was also dependent on threshold (unlike in the Cha-Noma fast oscillator model) robust. These limitations and results are discussed further (Discussion page 11 lines 344-354).

Additionally, in the IOM, the underlying cell dynamics of highly-connected hub cells are differentiated by KATP channel conductance (gKATP), which is different than in the fast oscillator model (differentiated by metabolism, kglyc). However this difference between models can be linked to differences in the way duty cycle is influenced by gKATP and kglyc (Figure 1h, Figure 2g). In each model there was a similar association between duty cycle and highly-connected hub cells. We also discuss these findings (Discussion page 11 lines 334-343).

Overall these results and discussion with respect to the coupled IOM oscillator model can be found in Figure 2, Results page 6 lines 128-156 and Discussion page 11 lines 332-354.

3) Much of the data analyzed whether obtained via simulation or through experiment seems to produce very small differences in the actual numbers obtained, as can be seen in the bar graphs shown in Figs. 1e,g for example (obtained from simulations), or Fig. 2j (obtained from experimental measurements). The authors should comment as to why such small differences are often seen as a result of their analyses throughout the manuscript and why also in many cases the observed variance is high. Related to the data shown, very few dots are shown in Figs. 1eg or Fig 4e and 4h even though these points were derived from simulations where 100s of runs could be carried out and many more points obtained for plotting. These are weaknesses unless specific and convincing explanations are provided.

We thank the reviewer for these comments, which are similar to those of reviewer 2 (point 4) and reviewer 3 (point 6). Indeed there is some variability between cells in both simulations and experiments related to the metabolic activity in hubs and non-hubs. The variability points to potentially other factors being involved in determining hubs beyond simply kglyc, including a minor role for gap junction coupling structural network and potentially cell position and other intrinsic factors. We now discuss this point – see Discussion page 12 lines 364-266.

The differences between hubs and nonhubs appear small because the value of kglyc is very small. For figure 1e, the average kglyc for nonhubs was 1.26x10-4 s-1 (which is the average of the distribution because most cells are non hubs) while the average kglyc for hubs was 1.4x10-4 s-1 which is about half of a standard deviation higher. The paired t-test controls for the small value of average kglyc.

For simulation data each of the 5 dots corresponds to a simulated islet averaged over 1000 cells (or 260 cells for coupled IOM). The computational resources are high to generate such data so it is not feasible to conduct 100s of runs. Again, we note the comparisons between hubs and non-hubs are paired, and we find statistically significant differences for kglyc in figure 1 using only 5 paired data points. That we find these differences indicates the substantial difference between hubs and non-hubs. This is further supported all effect sizes being much greater than 0.8 for all significantly different findings (Cha Noma - kglyc: 2.85, gcoup: 0.82) (IOM: gKATP: 1.27, gcoup: 2.94) – We have included these effect sizes in the captions see Figure 1 and 2 captions (pages 34, 36)

To consider all of the available data rather than the average across an entire islet, we created a kernel density estimate the kglyc for hubs and nonhubs created by concatenating every single cell in each of the five islets. A kstest results in a highly significant difference (P<0.0001) between these two distributions.

Author response image 1.

4) The data shown in Fig. 4i,j are intended to compare long-range synchronization at different distances along a string of coupled cells but the difference between the synchronized and unsynchronized cells for gcoup and Kglyc was subtle, very much so.

Thank you for pointing out these subtle differences. The y-axis scale for i and j is broad to allow us to represent all distances on a single plot. After correction for multiple comparison, the differences were still statistically significant. As the reviewer mentioned in point 3, each plot contains only five data points, each of which represent the average of a single simulated islet, therefore we are not concerned about statistical significance coming from too large of a sample size. We also checked the differences between synchronized and nonsynchronized cell pairs in figure 4 panels e and h (now figure 5 e, h). These are the same data as i and j but normalized such that all of the distances could be averaged together. We again found statistical significance between synchronized and non-synchronized cell pairs. As can be seen in Author response image 2 the difference between synchronized and non-synchronized cell pairs is greater than the variability between simulated islets. Thus, in this case the variability is not substantial.

Author response image 2.

5) The data shown in Fig. 5 for Cx36 knockout islets are used to assess the influence of gap junctional coupling, which is reasonable, but it would be reassuring to know that loss of this gene has no effects on the expression of other genes in the beta cell, especially genes involved with glucose metabolism.

This is an important point. Previous studies have assessed that no significant change in NAD(P)H is observed in Cx36 deficient islets – see Benninger et al J.Physiol 2011 [14]. Islet architecture is also retained. Further the insulin secretory response of dissociated Cx36 knockout beta cells is the same as that of dissociated wildtype beta cells, further indicating no significant defect in the intrinsic ability of the beta cell to release insulin – see Benninger et al J.Physiol 2011 [14]. We now Mention these findings in the discussion. See Discussion page 14 lines 459-464.

6) In many places throughout the paper, it is difficult to ascertain whether what is being shown is new vs. what has been shown previously in other studies. The paper would thus benefit strongly from added text highlighting the novelty here and not just restating what is known, for instance, that islets can exhibit small-world network properties. This detracts from the strengths of the paper and further makes it difficult to wade through. Even the finding here that metabolic characteristics of the beta cells can infer profound and influential functional coupling is not new, as the authors proposed as much many years ago. Again, this makes it difficult to distill what is new compared to what is mainly just being confirmed here, albeit using different methods.

Thank you for the suggestion, we have made significant modifications throughout the Introduction, Discussion and Results to be clearer about what is known from previous work and what is newly found in this manuscript.

Reviewer #5 (Public Review):

The authors use state-of-the-art computation, experiment, and current network analysis to try and disaggregate the impact of cellular metabolism driving cellular excitability and structural electrical connections through gap junctions on islet synchronization. They perform interesting simulations with a sophisticated mathematical model and compare them with closely associated experiments. This close association is impressive and is an excellent example of using mathematics to inform experiments and experimental results. The current conclusions, however, appear beyond the results presented. The use of functional connectivity is based on correlated calcium traces but is largely without an understood biophysical mechanism. This work aims to clarify such a mechanism between metabolism and structural connection and comes out on the side of metabolism driving the functional connectivity, but both are required and more nuanced conclusions should be drawn.

We thank reviewer 5 for their positive comments, including our multifaceted experimental and computational techniques. We also found the reviewers careful reading and thoughtful comments to be very helpful and we have worked to integrate each comment into our manuscript. It is evident from the reviewer comments that we did not clearly explain what was meant by our conclusions concerning the functional network reflecting metabolism rather than gap junctions. We have conducted significant rewriting to show that we are not concluding that communication (metabolic or electric) occurs due to conduits other than gap junctions. Rather, our data suggest that the functional network (which reflects calcium synchronization) reflects intrinsic dynamics of the cells, which include metabolic rates, more than individual gap junction connections.

References referred to in this response to reviewers document:

[1] A. Stožer et al., “Functional connectivity in islets of Langerhans from mouse pancreas tissue slices,” PLoS Comput Biol, vol. 9, no. 2, p. e1002923, 2013.

[2] N. L. Farnsworth, A. Hemmati, M. Pozzoli, and R. K. Benninger, “Fluorescence recovery after photobleaching reveals regulation and distribution of connexin36 gap junction coupling within mouse islets of Langerhans,” The Journal of physiology, vol. 592, no. 20, pp. 4431–4446, 2014.

[3] C.-L. Lei, J. A. Kellard, M. Hara, J. D. Johnson, B. Rodriguez, and L. J. Briant, “Beta-cell hubs maintain Ca2+ oscillations in human and mouse islet simulations,” Islets, vol. 10, no. 4, pp. 151–167, 2018.

[4] N. R. Johnston et al., “Beta cell hubs dictate pancreatic islet responses to glucose,” Cell metabolism, vol. 24, no. 3, pp. 389–401, 2016.

[5] V. Kravets et al., “Functional architecture of pancreatic islets identifies a population of first responder cells that drive the first-phase calcium response,” PLoS Biology, vol. 20, no. 9, p. e3001761, 2022.

[6] H. Ren et al., “Pancreatic α and β cells are globally phase-locked,” Nature Communications, vol. 13, no. 1, p. 3721, 2022.

[7] A. Stožer et al., “From Isles of Königsberg to Islets of Langerhans: Examining the function of the endocrine pancreas through network science,” Frontiers in Endocrinology, vol. 13, p. 922640, 2022.

[8] J. Zmazek et al., “Assessing different temporal scales of calcium dynamics in networks of beta cell populations,” Frontiers in physiology, vol. 12, p. 337, 2021.

[9] M. E. Corezola do Amaral et al., “Caloric restriction recovers impaired β-cell-β-cell gap junction coupling, calcium oscillation coordination, and insulin secretion in prediabetic mice,” American Journal of Physiology-Endocrinology and Metabolism, vol. 319, no. 4, pp. E709–E720, 2020.

[10] J. M. Dwulet, J. K. Briggs, and R. K. P. Benninger, “Small subpopulations of beta-cells do not drive islet oscillatory [Ca2+] dynamics via gap junction communication,” PLOS Computational Biology, vol. 17, no. 5, p. e1008948, May 2021, doi: 10.1371/journal.pcbi.1008948.

[11] B. E. Peercy and A. S. Sherman, “Do oscillations in pancreatic islets require pacemaker cells?,” Journal of Biosciences, vol. 47, no. 1, pp. 1–11, 2022.

[12] G. A. Rutter, N. Ninov, V. Salem, and D. J. Hodson, “Comment on Satin et al.‘Take me to your leader’: an electrophysiological appraisal of the role of hub cells in pancreatic islets. Diabetes 2020; 69: 830–836,” Diabetes, vol. 69, no. 9, pp. e10–e11, 2020.

[13] L. S. Satin and P. Rorsman, “Response to comment on satin et al.‘Take me to your leader’: An electrophysiological appraisal of the role of hub cells in pancreatic islets. Diabetes 2020; 69: 830–836,” Diabetes, vol. 69, no. 9, pp. e12–e13, 2020.

[14] R. K. Benninger, W. S. Head, M. Zhang, L. S. Satin, and D. W. Piston, “Gap junctions and other mechanisms of cell–cell communication regulate basal insulin secretion in the pancreatic islet,” The Journal of physiology, vol. 589, no. 22, pp. 5453–5466, 2011.

[15] R. Fried, Erectile dysfunction as a cardiovascular impairment. Academic Press, 2014. [16] T. Pipatpolkai, S. Usher, P. J. Stansfeld, and F. M. Ashcroft, “New insights into KATP channel gene mutations and neonatal diabetes mellitus,” Nature Reviews Endocrinology, vol. 16, no. 7, pp. 378–393, 2020.

[17] A. M. Notary, M. J. Westacott, T. H. Hraha, M. Pozzoli, and R. K. P. Benninger, “Decreases in Gap Junction Coupling Recovers Ca2+ and Insulin Secretion in Neonatal Diabetes Mellitus, Dependent on Beta Cell Heterogeneity and Noise,” PLOS Computational Biology, vol. 12, no. 9, p. e1005116, Sep. 2016, doi: 10.1371/journal.pcbi.1005116.

[18] J. V. Rocheleau, G. M. Walker, W. S. Head, O. P. McGuinness, and D. W. Piston, “Microfluidic glucose stimulation reveals limited coordination of intracellular Ca2+ activity oscillations in pancreatic islets,” Pro ceedings of the National Academy of Sciences, vol. 101, no. 35, pp. 12899–12903, 2004. [19] R. K. Benninger, M. Zhang, W. S. Head, L. S. Satin, and D. W. Piston, “Gap junction coupling and calcium waves in the pancreatic islet,” Biophysical journal, vol. 95, no. 11, pp. 5048–5061, 2008.

eLife assessment

The manuscript describes a set of detailed modeling and experimental studies to disentangle the respective roles of gap junctional electrical vs. metabolic coupling mechanisms in the synchronization of islet activity. This is of interest due to the importance of islet synchronization and generally islet network properties in the regulation of insulin secretion from the pancreas. The significance of the findings was judged to be mostly important and the strength of evidence provided was judged to be mostly solid overall.

Reviewer #1 (Public Review):

Briggs et al use a combination of mathematical modelling and experimental validation to tease apart the contributions of metabolic and electronic coupling to the pancreatic beta cell functional network. A number of recent studies have shown the existence of functional beta cell subpopulations, some of which are difficult to fully reconcile with established electrophysiological theory. More generally, the contribution of beta cell heterogeneity (metabolism, differentiation, proliferation, activity) to islet function cannot be explained by existing combined metabolic/electrical oscillator models. The present studies are thus timely in modelling the islet electrical (structural) and functional networks. Importantly, the authors show that metabolic coupling primarily drives the islet functional network, giving rise to beta cell subpopulations. The studies, however, do not diminish the critical role of electrical coupling in dictating glucose responsiveness, network extent as well as longer-range synchronization. As such, the studies show that islet structural and functional networks both act to drive islet activity, and that conclusions on the islet structural network should not be made using measures of the functional network (and vice versa).

Strengths:

- State-of-the-art multi-parameter modelling encompassing electrical and metabolic components.

- Experimental validation using advanced FRAP imaging techniques, as well as Ca2+ data from relevant gap junction KO animals.

- Well-balanced arguments that frame metabolic and electrical coupling as essential contributors to islet function.

- Likely to change how the field models functional connectivity and beta cell heterogeneity.

Weaknesses:

- Limitations of FRAP and electrophysiological gap junction measures not considered.

- Limitations of Cx36 (gap junction) KO animals not considered.

- Accuracy of citations should be improved in a few cases.

Reviewer #2 (Public Review):

In their present work, Briggs et al. combine biophysical simulations and experimental recordings of beta cell activity with analyses of functional network parameters to determine the role played by gap-junctional coupling, metabolism, and KATP conductance in defining the functional roles that the cells play in the functional networks, assess the structure-function relationship, and to resolve an important current open question in the field on the role of so-called hub cells in islets of Langerhans.

Combining differential equation-based simulations on 1000 coupled cells with demanding calcium, NAPDH, and FRAP imaging, as well as with advanced network analyses, and then comparing the network metrics with simulated and experimentally determined properties is an achievement in its own right and a major methodological strength. The findings have the potential to help resolve the issue of the importance of hub cells in beta cell networks, and the methodological pipeline and data may prove invaluable for other researchers in the community.<br /> However, methodologically functional networks may be based on different types of calcium oscillations present in beta cells, i.e., fast oscillations produced by bursts of electrical activity, slow oscillations produced by metabolic/glycolytic oscillations, or a mixture of both. At present, the authors base the network analyses on fast oscillations only in the case of simulated traces and on a mixture of fast and slow oscillations in the case of experimental traces. Since different networks may depend on the studied beta cell properties to a different extent (e.g., fast oscillation-based networks may, more importantly, depend on electrical properties and slow oscillation-based networks may more strongly depend on metabolic properties), it is important that in drawing the conclusions the authors separately address the influence of a cell's electrical and metabolic properties on its functional role in the network based on fast oscillations, slow oscillations, or a mixture of both.

Reviewer #3 (Public Review):

Over the past decade, novel approaches to understanding beta cell connectivity and how that contributes to the overall function of the pancreatic islet have emerged. The application of network theory to beta cell connectivity has been an extremely useful tool to understand functional hierarchies amongst beta cells within an islet. This helps to provide functional relevance to observations from structural and gene expression data that beta cells are not all identical.

There are a number of "controversies" in this field that have arisen from the mathematical and subsequent experimental identification of beta "hub" cells. These are small populations of beta cells that are very highly connected to other beta cells, as assessed by applying correlation statistics to individual beta cell calcium traces across the islet.

In this paper Briggs et al set out to answer the following areas of debate:<br /> 1. They use computational datasets, based on established models of beta cells acting in concert (electrically coupled) within an islet-like structure, to show that it is similarities in metabolic parameters rather than "structural" connections (ie proximity which subserves gap junction coupling) that drives functional network behaviour. Whilst the computational models are quite relevant, the fact that the parameters (eg connectivity coefficients) are quite different to what is measured experimentally, confirm the limitations of this model. Therefore it was important for the authors to back up this finding by performing both calcium and metabolic imaging of islet beta cells. These experimental data are reported to confirm that metabolic coupling was more strongly related to functional connectivity than gap junction coupling. However, a limitation here is that the metabolic imaging data confirmed a strong link between disconnected beta cells and low metabolic coupling but did not robustly show the opposite. Similarly, I was not convinced that the FRAP studies, which indirectly measured GJ ("structural") connections were powered well enough to be related to measures of beta cell connectivity.<br /> 2. The group goes on to provide further analytical and experimental data with a model of increasing loss of GJ connectivity (by calcium imaging islets from WT, heterozygous (50% GJ loss), and homozygous (100% loss). Given the former conclusion that it was metabolic not GJ connectivity that drives small world network behaviour, it was surprising to see such a great effect on the loss of hubs in the homs. That said, the analytical approaches in this model did help the authors confirm that the loss of gap junctions does not alter the preferential existence of beta cell connectivity and confirms the important contribution of metabolic "coupling". One perhaps can therefore conclude that there are two types of network behaviour in an islet (maybe more) and the field should move towards an understanding of overlapping network communities as has been done in brain networks.

Overall this is an extremely well-written paper which was a pleasure to read. This group has neatly and expertly provided both computational and experimental data to support the notion that it is metabolic but not "structural" ie GJ coupling that drives our observations of hubs and functional connectivity. However, there is still much work to do to understand whether this metabolic coupling is just a random epiphenomenon or somehow fated, the extent to which other elements of "structural" coupling - ie the presence of other endocrine cell types, the spatial distribution of paracrine hormone receptors, blood vessels and nerve terminals are also important.

Reviewer #4 (Public Review):

This manuscript describes a complex, highly ambitious set of modeling and experimental studies that appear designed to compare the structural and functional properties of beta cell subpopulations within the islet network in terms of their influence on network synchronization. The authors conclude that the most functionally coupled cell subpopulations in the islet network are not those that are most structurally coupled via gap junctions but those that are most metabolically active.

Strengths of the paper include (1) its use of an interdisciplinary collection of methods including computer simulations, FRAP to monitor functional coupling by gap junctions, the monitoring of Ca2+ oscillations in single beta cells embedded in the network, and the use of sophisticated approaches from probability theory. Most of these methods have been used and validated previously. Unfortunately, however, it was not clear what the underlying premise of the paper actually is, despite many stated intentions, nor what about it is new compared to previous studies, an additional weakness.

Although the authors state that they are trying to answer 3 critical questions, it was not clear how important these questions are in terms of significance for the field. For example, they state that a major controversy in the field is whether network structure or network function mediates functional synchronization of beta cells within the islet. However, this question is not much debated. As an example, while it is known that there can be long-range functional coupling in islets, no workers in the field believe there is a physical structure within islets that mediates this, unlike the case for CNS neurons that are known to have long projections onto other neurons. Beta cells within the islets are locally coupled via gap junctions, as stated repeatedly by the authors but these mediate short-range coupling. Thus, there are clearly functional correlations over long ranges but no structures, only correlated activity. This weakness raises questions about the overall significance of the work, especially as it seems to reiterate ideas presented previously.

Specific Comments

1. The authors state it is well accepted that the disruption of gap junctional coupling is a pathophysiological characteristic of diabetes, but this is not an opinion widely accepted by the field, although it has been proposed. The authors should scale back on such generalizations, or provide more compelling evidence to support such a claim.<br /> 2. The paper relies heavily on simulations performed using a version of the model of Cha et al (2011). While this is a reasonable model of fast bursting (e.g. oscillations having periods <1 min.), the Ca2+ oscillations that were recorded by the authors and shown in Fig. 2b of the manuscript are slow oscillations with periods of 5 min and not <1 min, which is a weakness of the model in the current context. Furthermore, the model outputs that are shown lack the well-known characteristics seen in real islets, such as fast-spiking occurring on prolonged plateaus, again as can be seen by comparing the simulated oscillations shown in Fig. 1d with those in Fig. 2b. It is recommended that the simulations be repeated using a more appropriate model of slow oscillations or at least using the model of Cha et al but employed to simulate in slower bursting.<br /> 3. Much of the data analyzed whether obtained via simulation or through experiment seems to produce very small differences in the actual numbers obtained, as can be seen in the bar graphs shown in Figs. 1e,g for example (obtained from simulations), or Fig. 2j (obtained from experimental measurements). The authors should comment as to why such small differences are often seen as a result of their analyses throughout the manuscript and why also in many cases the observed variance is high. Related to the data shown, very few dots are shown in Figs. 1e-g or Fig 4e and 4h even though these points were derived from simulations where 100s of runs could be carried out and many more points obtained for plotting. These are weaknesses unless specific and convincing explanations are provided.<br /> 4. The data shown in Fig. 4i,j are intended to compare long-range synchronization at different distances along a string of coupled cells but the difference between the synchronized and unsynchronized cells for gcoup and gKglyc was subtle, very much so.<br /> 5. The data shown in Fig. 5 for Cx36 knockout islets are used to assess the influence of gap junctional coupling, which is reasonable, but it would be reassuring to know that loss of this gene has no effects on the expression of other genes in the beta cell, especially genes involved with glucose metabolism.<br /> 6. In many places throughout the paper, it is difficult to ascertain whether what is being shown is new vs. what has been shown previously in other studies. The paper would thus benefit strongly from added text highlighting the novelty here and not just restating what is known, for instance, that islets can exhibit small-world network properties. This detracts from the strengths of the paper and further makes it difficult to wade through. Even the finding here that metabolic characteristics of the beta cells can infer profound and influential functional coupling is not new, as the authors proposed as much many years ago. Again, this makes it difficult to distill what is new compared to what is mainly just being confirmed here, albeit using different methods.

Reviewer #5 (Public Review):

The authors use state-of-the-art computation, experiment, and current network analysis to try and disaggregate the impact of cellular metabolism driving cellular excitability and structural electrical connections through gap junctions on islet synchronization. They perform interesting simulations with a sophisticated mathematical model and compare them with closely associated experiments. This close association is impressive and is an excellent example of using mathematics to inform experiments and experimental results. The current conclusions, however, appear beyond the results presented. The use of functional connectivity is based on correlated calcium traces but is largely without an understood biophysical mechanism. This work aims to clarify such a mechanism between metabolism and structural connection and comes out on the side of metabolism driving the functional connectivity, but both are required and more nuanced conclusions should be drawn.

eLife assessment

In this study, Sparta et al., generated and employed a battery of fluorescent reporters that allowed them to perform time-resolved monitoring of mechanistic target of rapamycin complex 1 (mTORC1) responses to stimuli including glucose, amino acids, and insulin at the single cell resolution. The results of this elegant approach support a model of graded mTORC1 activation in response to the aforementioned stimuli when applied individually or in combination. This model is consistent with continuous adjustment of mTORC1 signaling to changes in cellular environment and opposed to the "on/off" model of mTORC1 function. Considering the pivotal role of mTORC1 in integrating signals such as nutrients, hormones, growth factors, oxygen, and energy status with a plethora of outputs that affect cell fate and organismal physiology, it was thought that this study will be of interests across a variety of biomedical disciplines. Overall, the elegance and robustness of the approach was highly appreciated, though the paper would be strengthened by addressing some technical issues and concerns regarding the positioning of the proposed model of mTORC1 regulation in the field.

Reviewer #1 (Public Review):

Notwithstanding that the molecular underpinnings of the mechanistic target of rapamycin complex 1 (mTORC1) signaling are relatively well understood, quantitative data pertinent to mTORC1-dependent integration of a variety of stimuli is lacking. To address this question, Sparta et al., developed a series of fluorescent reporters that in combination with live cell microscopy allowed them to determine responses of mTORC1 to several stimuli including glucose, amino acids, and insulin at the single cell resolution. Considering the central role of mTORC1 in homeostasis and its dysregulation across a variety of pathological states, it was thought that this study should be of broad interest to a wide spectrum of biomedical disciplines ranging from biochemistry, molecular and cellular biology to neurobiology and cancer research.

Strengths: This study employs powerful approach based on use of live cell imaging of multiple fluorescent reports that are indicative of alterations in mTORC1 activity. In contrast to traditional approaches based on querying phosphorylation status of mTORC1 substrates by Western blotting this approach allows time-resolved measurement of mTORC1 activity at the single cell resolution. Using this approach, the authors provide solid evidence to corroborate a model of graded activation of mTORC1 by amino acids, insulin, and combination thereof.

Weaknesses: The major weaknesses were thought to be related to the interpretation of the current model of mTORC1 regulation as AND gate and reliance on a single cell line. Some minor technical issues were also observed pertinent to the lack of controls demonstrating the effectiveness of manipulations of nutrients and/or insulin as well as the effects of such manipulation on the expression of reporters used to monitor mTORC1 activity.

Reviewer #2 (Public Review):

Using fluorescent-TFEB fusion proteins and mutants thereof for live-cell imaging single cells, the authors investigated how mTORC1 responds to amino acids and growth factors. First, they demonstrated that the stably expressed fusion protein behaves as endogenous TFEB with regards to mTORC1 activation. Next, using the phosphodeficient TFEB mutant, they showed that GSK3 phosphorylation amplifies the C/N ratio, supporting the role of GSK3 and mTORC1 in co-regulating TFEB. When amino acids or insulin were added to starved cells, they found a graded response depending on amounts of AA or insulin, respectively, thus suggesting an incremental response. When multiple inputs were assessed, they found that TFEB C/N ratio also increased in increments when nutrients were added first followed by insulin. But when insulin was added first before nutrients, a minimal response occurred although this could be subsequently increased upon addition of the nutrients. Lastly, by tracking down TFEB C/N in response to different amounts of nutrients over longer periods (12 hr), they observed that a new steady state is achieved, indicating adaptation of mTORC1 activity and that this correlates with signal inputs from Akt and AMPK. Based on these findings, the authors conclude that the mTORC1-TFEB signaling continuously adjust to nutrient availability rather than just behave in "AND" gate logic fashion.

Overall, the results are robust and supportive of their conclusion. The use of fluorescent fusion proteins/mutants is nicely done. The authors have created useful tools to further analyze mTOR signaling at the single-cell level. However, the findings that mTORC1 signaling behaves like a rheostat is not really new and rather more confirmatory of previous studies. The current studies further support this model with their use of TFEB as mTORC1 target in single cells.

Reviewer #3 (Public Review):

This is an interesting manuscript from Sparta and colleagues that investigates dynamics of MTOR and TFEB signalling. The main strength is that the study is based on a systems biology approach using live cell imaging of a range of MTOR downstream readouts, capturing data on a single-cell level with capabilities to multiplex tracking over time. To monitor downstream signalling, the authors primarily rely on measuring nuclear translocation of a fluorescent reporter of TFEB, truncated to remove C-terminal DNA-binding domain and the AKT phosphorylation site. The authors further show that a TFEB reporter with 3x S>A mutations at 3 GSK3beta phosphorylation sites (134, 138 and 142) was dramatically less sensitive to stimulation by amino acids, or by insulin. The authors use these single cell tools to determine whether MTOR-TFEB signalling better fits a gated / digital pattern of response vs a gradual/ analogue mode. Data based on concentration-dependent titrations provide further support of the ability of MTOR-TFEB to respond to amino acid or insulin stimulations with gradual/incremental sensitivity. To understand how MTOR, AMPK and AKT pathways respond and integrate to multiple signals, the authors were also able to use single cell imaging approaches, comparing: TFEB, AMPK-FRET, and FOXO reporters. As follows, the authors were able to track downstream signalling following various patterns of sequential stimulation by glucose, amino acids and insulin. This work is thus able to provide further insight and illustrate how single cells within a population function during nutrient sensing signalling. The results highlight the power of single cell multi-channel imaging to interrogate signalling in real time.

eLife assessment

This important study describes a high performance computational approach to interrogate how microscopic epistasis and clonal interference affect evolutionary dynamics in a spin glass model of microbial evolution. The study offers several insights that can aid in our understanding of the forces that operate in adaptive evolution. The evidence provided is compelling, with its rigorous use of models and analytical descriptions of how these forces manifest in evolution.

Reviewer #1 (Public Review):

This paper presents extensive numerical simulations using a model that incorporates up to second-order epistasis to study the joint effects of microscopic epistasis and clonal interference on the evolutionary dynamics of a microbial population. Previous works that explicitly modeled microscopic epistasis typically assumed strong selection & weak mutation (SSWM), a condition that is generally not met in real-life evolutionary processes. Alternatively, another class of models coarse-grained the effects of microscopic epistasis into a generic distribution of fitness effects. The framework introduced in this paper represents an important advance with respect to these previous approaches, allowing for the explicit modeling of microscopic epistasis in non-SSWM scenarios. The modeling framework presented promises to be a valuable tool to study microbial evolution in silico.

Reviewer #2 (Public Review):

This paper presents an extensive numerical study of microbial evolution using a model of fitness inspired by spin glass physics. It places special emphasis on elucidating the combined effects of microscopic epistasis, which dictates how the fitness effect of a mutation depends on the genetic background on which it occurs, and clonal interference, which describes the proliferation of and competition between multiple strains. Both microscopic epistasis and clonal interference have been observed in microbial evolution experiments, and are chief contributors to the complexity of evolutionary dynamics. Correlations between random mutations and nonlinearities associated with interactions between sub-populations consisting of competing strains make it extremely challenging to make quantitative theoretical predictions for evolutionary dynamics and associated observables such as the mean fitness. While the body of theoretical and computational research on modeling evolutionary dynamics is extensive, most theoretical efforts rely on making simplifications such as the strong selection weak mutation (SSWM) limit, which neglects clonal interference, or assumptions about the distribution of fitness effects that are not experimentally verifiable.

The authors have addressed this challenge by running a numerical microbial evolution experiment over realistic population sizes (~ 100 million cells) and timescales (~ 10,000 generations) using a spin glass model of fitness that considers pairwise interactions between mutations on distinct genetic loci. By independently tuning mutation rate as well as the strength of epistasis, the authors have shown that epistasis generically slows down the growth of fitness trajectories regardless of the amount of clonal interference. On the other hand, in the absence of epistasis, clonal interference speeds up the growth of fitness trajectories, but leaves the growth unchanged in the presence of epistasis. The authors quantitatively characterize these observations using asymptotic power law fits to the mean fitness trajectories. Further, the authors employ more simplified macroscopic models that are informed by their empirical findings, to reveal the mechanistic origins of the epistasis mediated slowing down of fitness growth. Specifically, they show that epistasis leads to a broadening of the distribution of fitness increments, leading to the fixation of a large number of mutations that confer small benefits. Effectively, this leads to an increase in the number of fixed mutations required to climb the fitness peak. This increased number of required beneficial mutations together with the decreasing availability of beneficial mutations at high fitness lead to the slowdown of fitness growth. The authors' data analysis is quite solid and their conclusions are well supported by quantitative macroscopic models. The paper also includes an interesting analysis of dynamical correlations between mutations, using tools developed in the spin glass literature.

One of the highlights of this paper is the author's astute choice of model, which strikes an impressive balance between complexity, flexibility, and numerical accessibility. In particular, the authors were able to achieve results over realistic population sizes and timescales largely because of the amenability of the model to the implementation of an efficient simulation algorithm. At the same time, the strength of epistasis and clonal interference can be tuned in a facile manner, enabling the authors to map out a phase diagram spanning these two axes. One could argue that the numerical scheme employed here would only work for a specific class of models, and is therefore not generalizable to all models of evolutionary dynamics. While this is likely true, the model is capable of recapitulating several complex aspects of microbial evolution, and is therefore not unduly restrictive.

Spin glass physics has already provided significant insights into a wide range of topics in the life sciences including protein folding, neuroscience, ecology and evolution. The present work carries this approach forward, with immediate implications for microbial evolution, and potential implications in related areas of research such as microbial ecology. In addition to the theoretical value of spin glass physics, the high performance algorithm developed in this work lays the foundation for formulating data driven approaches aimed at understanding evolutionary dynamics. In the future, there is considerable scope for utilizing data generated by such models to train machine learning algorithms for quantifying parameters associated with epistasis, clonal interference, and the distribution of fitness effects in laboratory experiments.

Author Response

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

Reviewer #1 (Public Review):

This article is interested in how butterfly, or more precisely, butterfly wing scale precursor cells, each make precisely patterned ultrastructures made of chitin.

To do this, the authors sought to use the butterfly Parides eurimedes, a papilionid swallowtail, that carries interesting, unusual structures made of 1) vertical ridges, that lack a typical layered stacking arrangement; and 2) deep honeycomb-like pores. These two features make the organism chosen a good point of comparison with previous studies, including classic papers that relied on electronic microscopy (SEM/TEM), and more recent confocal microscopy studies.

The article shows good microscopy data, including detailed, dense developmental series of staining in the Parides eurimedes model. The mix of cell membrane staining, chitin precursor, and F-actin staining is well utilized and appropriately documented with the help of 3D-SIM, a microscopy technique considered to provide super-resolution (here needed to visualize sub-cellular processes).

The key message from this article is that F-actin filaments are later repurposed, in papilionid butterflies, to finish the patterning of the inter-ridge space, elaborating new structures (this was not observed so far in other studies and organisms). The model proposed in Figure 6 summarized these findings well, with F-actin reshaping it itself into a tulip that likely pulls down a chitin disk to form honeycombs. These interpretations of the microscopy data are interesting and novel.

There are two other points of interest, that deserve future investigation:

1) The authors performed immunolocalizations of Arp2 and pharmacological inhibitions of Arp2/3, and found some possible effect on honeycomb lattice development. The inter-ridge region of the butterfly Papilio polytes, which lacks these structures, did not seem to be affected by drug treatments. Effects where time- dependent, which makes sense. These data provide circumstantial evidence that Arp2/3 is involved in the late role of F-actin formation or re-organisation.

2) The authors perform a comparative study in additional papilionids (Fig. 6 in particular). I find these data to be quite limited without a dense sampling, but they are nonetheless interesting and support a second-phase role of F-actin re- organisation.

The article is dense, well produced and succinctly written. I believe this is an interesting and insightful study on a complex process of cell biology, that inspires us to look at basic phenomena in a broader set of organisms.

We thank the reviewer for the positive appraisal.

Reviewer #2 (Public Review):

The manuscript by Seah and Saranathan investigates the cell-based growth mechanism of so called honeycomb-structures in the upper lamina of papilionid wing scales by investigating a number of different species. The authors chose Parides eurimedes as a focus species with the developmental pathway of five other papilionid as a comparative backup. Through state-of-the-art microscopy images of different developmental steps, the author find that the intricate f-actin filaments reorganise, support cuticular discs that template the air holes that form the honeycomb lattice. The manuscript is well written and easy to follow, yet based on a somewhat limited sample size for their focus species, limiting attempts to suppress expression and alter structure shape.

The fact that the authors find a novel reorganisation mechanism is exciting and warrants further research, e.g. into the formation of other microscale features or smaller scale structures (e.g. the mentioned gyroid networks).

We thank the reviewer for the positive appraisal.

The authors place their results in the discussion in the light of current literature (although the references could be expanded further to include the breadth of the field). However, the mechanistic explanation completely ignores the mechanical properties of the membranes as an origin of some of the observed phenomena (see McDougal's work for example) and places the occurence of some features into Turing patterns and Ostwald ripening, which I find somewhat unlikely and I suggest that the authors discover this aspects further in the discussion.

We thank the reviewer for these suggestions. We have added more references from the current literature to more accurately reflecting the breadth of the field. McDougal et al. 2021. discuss the nature of biomechanical forces (differential growth and buckling) on the membrane and deposited cuticle shaping the formation of longitudinal ridges. However, here it is the invagination of the plasma membrane bearing the deposited cuticle that is our main concern. Nevertheless, we agree future studies should indeed consider the mechanical properties of the membranes, in addition, to explain some of the observed features. We have clarified this in our discussion.

I have little concerns regarding the experimental approach beyond the somewhat limited sample size. One thing the authors should more clearly mention are the pupation periods for all investigated species as only the periods for two species are named.

Yes, unfortunately, we were only able to obtain pupae with pupation dates for two species. We have clarified this point in the methods.

Reviewer #1 (Recommendations For The Authors):

Suggestion for improvement.

I recommend adopting a magenta/green (or orange/azure) color scheme to make the figures accessible to most color vision types. This does not require re-doing the figure and could be processed on the rendered JPG/TIF figures with the following procedure :

1) open the rendered figures in Photoshop in RGB mode

2) go to Channel Mixer

3) Select Output Channel : Blue

4) set Blue 100%-->0% and Red 0-->100%

This will change Red to Magenta without affecting luminosity.

Similar solutions should be available in other software including GIMP.

Of note this is a late fix and ideally, color encoding could be done upstream in the microscopy file extraction software (e.g. Fiji), but I do not think this heavier solution is needed here.

We thank the reviewer for this suggestion. In order to be more inclusive, we have redone the figures and videos in a yellow+magenta color scheme.

Reviewer #2 (Recommendations For The Authors):

References: Some literature is missing that could be considered by the authors e.g.

https://doi.org/10.1098/rstb.2020.0505 https://doi.org/10.1101/2023.06.01.542791

https://doi.org/10.1098/rsfs.2011.0082 https://doi.org/10.1557/mrs.2019.21

https://iopscience.iop.org/article/10.1088/2040- 8986/aaff39/meta https://doi.org/10.1364/OE.20.008877

We have added more references as suggested.

Placing the captions next to the figures, particularly in the SI will help accessibility.

We agree. We believe this would be done during article production.

113: chiefly?

We have replaced ‘chiefly’ with ‘focusing mainly on’.

160: how do you know the scales are more scletorized already? Just because it's later in development?

Yes, that is what we are alluding to here. We have made edits to clarify this sentence.

186: Specify sample size.

We have specified the sample size ‘(N = 15)’ here.

309: Multilayered cover scales would be more accurate.

Thanks for the suggestion. We have changed ‘structurally-colored cover scales’ to ‘multilayered cover scales’ as suggested.

Please check the literature list again for accurate references.

Thanks for the suggestion. We have gone through the references and fixed any missing information.

eLife assessment

This important study reports how swallowtail butterflies pattern structures composed of chitin at the nanometer scale to produce structural colors. The work uses state-of-the-art microscopy techniques to convincingly show that F-actin is utilized in these butterflies in a novel way to produce structure, paving the way for further studies on growth regulation leading to precise ultrastructures and structural colors.

Reviewer #1 (Public Review):

This article is interested in how butterfly, or more precisely, butterfly wing scale precursor cells, each make precisely patterned ultrastructures made of chitin.

To do this, the authors sought to use the butterfly Parides eurimedes, a papilionid swallowtail, that carries interesting, unusual structures made of 1) vertical ridges, that lack a typical layered stacking arrangement; and 2) deep honeycomb-like pores (rather than. These two features make the organism chosen a good point of comparison with previous studies, including classic papers that relied on electronic microscopy (SEM/TEM), and more recent confocal microscopy studies.

The article shows good microscopy data, including detailed, dense developmental series of staining in the Parides eurimedes model. The mix of cell membrane staining, chitin precursor, and F-actin staining is well utilized and appropriately documented with the held of 3D-SIM, a microscopy technique considered to provide super-resolution (here needed to visualize sub-cellular processes).

The key message from this article is that F-actin filaments are later repurposed, in papilionid butterflies, to finish the patterning of the inter-ridge space, elaborating new structures (this was not observed so far in other studies and organisms). The model proposed in Figure 6 summarized these findings well, with F-actin reshaping itself into a tulip that likely pulls down a chitin disk to form honeycombs. These interpretations of the microscopy data are interesting and novel.

There are two other points of interest, that deserve future investigation:

1) The authors performed immunolocalizations of Arp2 and pharmacological inhibitions of Arp2/3, and found some possible effect on honeycomb lattice development. The inter-ridge region of the butterfly Papilio polytes, which lacks these structures, did not seem to be affected by drug treatments. Effects were time-dependent, which makes sense. These data provide circumstantial evidence that Arp2/3 is involved in the late role of F-actin formation or re-organisation.

2) The authors perform a comparative study in additional papilionids (Fig. 6 in particular). I find these data to be quite limited without a dense sampling, but they are nonetheless interesting and support a second-phase role of F-actin re-organisation.

The article is dense, well produced and succinctly written. I believe this is an interesting and insightful study on a complex process of cell biology, that inspires us to look at basic phenomena in a broader set of organisms.

Reviewer #2 (Public Review):

The manuscript by Seah and Saranathan investigates the cell-based growth mechanism of so called honeycomb-structures in the upper lamina of papilionid wing scales by investigating a number of different species. The authors chose Parides eurimedes as a focus species with the developmental pathway of five other papilionid as a comparative backup. Through state-of-the-art microscopy images of different developmental steps, the authors find that the intricate f-actin filaments reorganise, support cuticular discs that template the air holes that form the honeycomb lattice.

The revised manuscript is well written and easy to follow, yet based on a somewhat limited sample size for their focus species, limiting attempts to suppress expression and alter structure shape. I have no further comments.

Author Response

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

eLife assessment

This important manuscript reveals signatures of co-evolution of two nucleosome remodeling factors, Lsh/HELLS and CDCA7, which are involved in the regulation of eukaryotic DNA methylation. The results suggest that the roles for the two factors in DNA methylation maintenance pathways can be traced back to the last eukaryotic common ancestor and that the CDC7A-HELLS-DNMT axis shaped the evolutionary retention of DNA methylation in eukaryotes. The evolutionary analyses are solid, although more refined phylogenetic approaches could have strengthened some of the claims. Overall, this study should be useful for researchers studying DNA methylation pathways in different organisms, and it should be of general interest to colleagues in the fields of evolutionary biology, chromatin biology and genome biology.

We sincerely appreciate constructive comments and suggestions by the reviewers and a fair and accurate summary by the monitoring editor. Below we made point-by-point responses to reviewers’ comments.

Reviewer #1 (Public Review):

Overall, I find the work performed by the authors very interesting. However, the authors have not always included literature that seems relevant to their study. For instance, I do not understand why two papers Dunican et al 2013 and Dunican et al 2015, which provide important insight into Lsh/HELLS function in mouse, frog and fish were not cited. It is also important that the authors are specific about what is known and in particular about what is not known about CDCA7 function in DNA methylation regulation. Unless I am mistaken, there is currently only one study (Velasco et al 2018) investigating the effect of CDCA7 disruption on DNA methylation levels (in ICF3 patient lymphoblastoid cell lines) on a genome-wide scale (Illumina 450K arrays). Unoki et al 2019 report that CDCA7 and HELLS gene knockout in human HEK293T cells moderately and extremely reduces DNA methylation levels at pericentromeric satellite-2 and centromeric alpha-satellite repeats, respectively. No other loci were investigated, and it is therefore not known whether a CDCA7-associated maintenance methylation phenotype extends beyond (peri)centromeric satellites. Thijssen et al performed siRNA- mediated knockdown experiments in mouse embryonic fibroblasts (differentiated cells) and showed that lower levels of Zbtb24, Cdca7 and Hells protein correlate with reduced minor satellite repeat methylation, thereby implicating these factors in mouse minor satellite repeat DNA methylation maintenance. Furthermore, studies that demonstrate a HELLS-CDCA7 interaction are currently limited to Xenopus egg extract (Jenness et al 2018) and the human HEK293 cell line (Unoki et al 2019). Whether such an interaction exists in any other organism and is of relevance to DNA methylation mechanisms remains to be determined. Therefore, in my opinion, the conclusion that "Our co- evolution analysis suggests that DNA methylation-related functionalities of CDCA7 and HELLS are inherited from LECA" should be softened, as the evidence for this scenario is not very compelling and seems premature in the absence of molecular data from more species.

We appreciate this reviewer’s thorough reading of our manuscript.

Regarding the citation issues, we will cite Dunican 2013 and Dunican 2015. In addition, we went through the manuscript to update the citations.

As pointed out by the reviewer, the role of CDCA7 in genome DNA methylation was extensively studied in Velasco et al 2018. The result, together with Thijssen et al (2015), and Unoki et al. (2018), supports the idea that ZBTB24, CDCA7 and HELLS act within the same pathway to promote DNA methylation, the pattern of which is overlapping but distinct from DNMT3B-mediated methylation. This observation suggests that a ZBTB24- CDCA7-HELLS mechanism for DNA methylation may involve an alternative DNMT. Interestingly, our analysis of the gene presence-absence pattern revealed that the presence of CDCA7 coincides with DNMT1 more than DNMT3 genes. Indeed, while CDCA7 is lost from diverse branches of eukaryote species, genomes encoding CDCA7 always encode HELLS, and almost always encode DNMT1. Based on this observation, we speculate the role of CDCA7 is tightly linked to HELLS and DNA methylation throughout evolution.

As pointed out by Reviewer 1, the link between CDCA7, HELLS and DNA methylation has not been determined experimentally across these species. However, based on our previously published and unpublished data, we are confident about the functional interaction between CDCA7 and HELLS in Xenopus laevis and Homo sapiens.

Furthermore, the importance of HELLS homologs in DNA methylation has been extensively studied in human, mice and plants. We hope our current study will motivate the field to experimentally test the evolutionary conservation of HELLS-CDCA7 interaction, as well as their importance in DNA methylation, in other species.

The authors used BLAST searches to characterize the evolutionary conservation of CDCA7 family proteins in vertebrates. From Figure 2A, it seems that they identify a LEDGF binding motif in CDCA7/JPO1. Is this correct and if yes, could you please elaborate and show this result? This is interesting and important to clarify because previous literature (Tesina et al 2015) reports a LEDGF binding motif only in CDCA7L/JPO2.

We searched for a LEDGF binding motif ({E/D}-X-E-X-F-X-G-F, also known as IBM described in Tesina et al 2015) in vertebrate CDCA7 proteins, and reported their positions in Figure 2A. Examples of identified LEDGF-binding motifs are now presented in Fig. 2C.

To provide evidence for a potential evolutionary co-selection of CDCA7, HELLS and the DNA methyltransferases (DNMTs) the authors performed CoPAP analysis. Throughout the manuscript, it is unclear to me what the authors mean when referring to "DNMT3". In the Material and Methods section, the authors mention that human DNMT3A was used in BLAST searches to identify proteins with DNA methyltransferase domains. Does this mean that "DNMT3" should be DNMT3A? And if yes, should "DNMT3" be corrected to "DNMT3A"? Is there a reason that "DNMT3A" was chosen for the BLAST searches?

As described in the Methods section, both Human DNMT1 and DNMT3A were used to initially identify any proteins containing a domain homologous to the DNA methyltransferase catalytic domain. Within Metazoa, if their orthologs exist, the top hit from BLAST search using human DNMT1 and DNMT3A show E-value 0.0, and thus their orthology is robust. This is even true for DNMT1 and DNMT3 homologs in the sponge Amphimedon queenslandica, which is one of the earliest-branching metazoan species. For other DNMTs, such as DNMT2, DNMT4, DNMT5, DNMT6, we conducted separate BLAST searches using those proteins as baits as described in Methods. The methyltransferase domain was then isolated using the NCBI conserved domains search. The selected DNMT domain sequences were aligned with CLUSTALW to generate a phylogenetic tree to further classify DNMTs. In response to reviewer #2’s comments, we also generated another multi-sequence alignment of DNMTs using MUSCLE v5 and conducted maximum-likelihood-based phylogenetic tree assembly using IQ-TREE (new Fig. S6). The overall topology of these trees is consistent except for orphan DNMTs. It has been suggested that vertebrate DNMT3A and DNMT3B are derived from duplication of a DNMT3 gene of chordates ancestor (e.g., Liu et al 2020, PMID 31969623). As such many invertebrates encode only one DNMT3. As previously shown (Yaari et al., 2019, PMID 30962443), plants have two distinct DNMT3-like protein family, the ‘true DNMT3’ and DRM, the plant specific de novo DNMT that is often considered to be a DNMT3 homolog (see Reviewer 2’s comment). Our phylogenetic analysis successfully deviated the clade of DNMT3 and DRM from the rest of DNMTs (Figure S6). Yaari et al noted that PpDNMT3a and PpDNMT3b, the two DNMT3 orthologs encoded by the basal plant Physcomitrella patens, are not orthologs of mammalian DNMT3A and DNMT3B, respectively. Therefore, to minimize such nomenclature confusions, any DNMTs that belong to either the DNMT3 or DRM clades indicated in Figure S6 are collectively referred to as ‘DNMT3’ throughout the paper (see Figure S2 for overview).

CoPAP analysis revealed that CDCA7 and HELLS are dynamically lost in the Hymenoptera clade and either co-occurs with DNMT3 or DNMT1/UHRF1 loss, which seems important. Unfortunately, the authors do not provide sufficient information in their figures or supplementary data about what is already known regarding DNA methylation levels in the different Hymenoptera species to further consider a potential impact of this observation. What is "the DNA methylation status" of all these organisms? This information cannot be easily retrieved from Table S2. A clearer presentation of what is actually known already would improve this paragraph.

As the DNA methylation status of the species in the Hymenoptera clade has not been comprehensively tested, we initially did not include this information to Figure 7. However, during the course of the revision, we realized that Bewick et al.2017 (PMID 28025279) reported that DNA methylation is absent from the braconid wasp Aphidius ervi. We originally conducted synteny analysis on Aphidius gifuensis, which has a chromosome-level genome assembly with annotated proteins available in NCBI, whereas annotated proteins for Aphidius ervi protein are not available in NCBI. By conducting tBLASTn search against the Aphidius ervi genome, we now found that the presence/absence pattern of CDCA7, HELLS, DNMT1, DNMT3 and UHRF1 in Aphidius ervi is identical to that of Aphidius gifuensis, with a caveat that genome assembly of Aphidius ervi is at scaffold-level. In other words, DNA methylation, DNMT1 and CDCA7 are absent in Aphidius ervi, where 5mC is undetectable. Additionally, we also realized that the DNA methylation status reported for some species in Bewick et al. 2017 was inferred from the CpG frequency instead of the direct experimental detection of methylated cytosines. Therefore, we have amended Table S3 to indicate the presence of 5mC only for those species where this was experimentally tested. As such, we now consider the DNA methylation status of Fopius arisanus, which lacks DNMT1 and CDCA7, to be unknown.

Altogether, among the 17 Hymenoptera species that we analyzed (listed in the amended Table S3), the 8 species that have detectable DNA methylation all encode CDCA7, whereas the 2 species that do not have detectable DNA methylation lack CDCA7. We will note this finding in the revised text, and include the known 5mC status in the new Figure 7.

Furthermore, A. thaliana DDM1, and mouse and human Lsh/Hells are known to preferably promote DNA methylation at satellite repeats, transposable elements and repetitive regions of the genome. On the other hand, DNA methylation in insects and other invertebrates occurs in genic rather than intergenic regions and transposable elements (e.g. Bewick et al 2017; Werren JH PlosGenetics 2013). It would be helpful to elaborate on these differences.

We were aware of this interesting point, which was discussed in the third paragraph of the Discussion. To better illustrate this point, we now expanded the Discussion (page 14) to speculate about the role of DNA methylation in insects, where emerging evidence indicates the importance of DNMT1 in meiosis. It should be noted that, in the Arabidopsis ddm1 mutant, reduction of CG methylation of gene bodies is common (50% of all methylated euchromatic genes) (Zemach et al, 2013). In addition, hypomethylation is not limited to satellite repeats and transposable elements in ICF patients defective in HELLS or CDCA7 (Velasco et al., 2018).

Reviewer #2 (Public Review):

In this manuscript, Funabiki and colleagues investigated the co-evolution of DNA methylation and nucleosome remolding in eukaryotes. This study is motivated by several observations: (1) despite being ancestrally derived, many eukaryotes lost DNA methylation and/or DNA methyltransferases; (2) over many genomic loci, the establishment and maintenance of DNA methylation relies on a conserved nucleosome remodeling complex composed of CDCA7 and HELLS; (3) it remains unknown if/how this functional link influenced the evolution of DNA methylation. The authors hypothesize that if CDCA7-HELLS function was required for DNA methylation in the last eukaryote common ancestor, this should be accompanied by signatures of co-evolution during eukaryote radiation.

To test this hypothesis, they first set out to investigate the presence/absence of putative functional orthologs of CDCA7, HELLS and DNMTs across major eukaryotic clades. They succeed in identifying homologs of these genes in all clades spanning 180 species. To annotate putative functional orthologs, they use similarity over key functional domains and residues such as ICF related mutations for CDCA7 and SNF2 domains for HELLS. Using established eukaryote phylogenies, the authors conclude that the CDCA7-HELLS-DNMT axis arose in the last common ancestor to all eukaryotes. Importantly, they found recurrent loss events of CDCA7-HELLS-DNMT in at least 40 eukaryotic species, most of them lacking DNA methylation.

Having identified these factors, they successfully identify signatures of co-evolution between DNMTs, CDCA7 and HELLS using CoPAP analysis - a probabilistic model inferring the likelihood of interactions between genes given a set of presence/absence patterns. As a control, such interactions are not detected with other remodelers or chromatin modifying pathways also found across eukaryotes. Expanding on this analysis, the authors found that CDCA7 was more likely to be lost in species without DNA methylation.

In conclusion, the authors suggest that the CDCA7-HELLS-DNMT axis is ancestral in eukaryotes and raise the hypothesis that CDCA7 becomes quickly dispensable upon the loss of DNA methylation and/or that CDCA7 might be the first step toward the switch from DNA methylation-based genome regulation to other modes.

The data and analyses reported are significant and solid. However, using more refined phylogenetic approaches could have strengthened the orthologous relationships presented. Overall, this work is a conceptual advance in our understanding of the evolutionary coupling between nucleosome remolding and DNA methylation. It also provides a useful resource to study the early origins of DNA methylation related molecular process. Finally, it brings forward the interesting hypothesis that since eukaryotes are faced with the challenge of performing DNA methylation in the context of nucleosome packed DNA, loosing factors such as CDCA7-HELLS likely led to recurrent innovations in chromatin-based genome regulation.

Strengths:

• The hypothesis linking nucleosome remodeling and the evolution of DNA methylation.

• Deep mapping of DNA methylation related process in eukaryotes.

• Identification and evolutionary trajectories of novel homologs/orthologs of CDCA7.

• Identification of CDCA7-HELLS-DNMT co-evolution across eukaryotes.

Weaknesses:

• Orthology assignment based on protein similarity.

• No statistical support for the topologies of gene/proteins trees (figure S1, S3, S4, S6) which could have strengthened the hypothesis of shared ancestry.

We appreciate the reviewers’ accurate summary, nicely emphasizing the importance of the our study. We agree that better phylogenetic analysis for orthology assignment will strengthen our conclusion. Having anticipated this weakness, however, we specifically conducted a CoPAP analysis exclusively for Ecdysozoa specieswhich supported our major conclusion, as orthology assignment is straightforward in these species. For example, if we conduct BLAST search against the clonal raider ant Oocerea biroi protein dataset using human HELLS as a query, top 1 hit is a protein sequence annotated as one of three isoforms of ‘lymphoid-specific helicase” (i.e., HELLS), with E value 0.0. Similarly, the top BLAST hit from the Oocerea biroi dataset using human DNMT1 as a query also returns with isoforms of DNMT1 with E value 0.0. As such, there are little disputes in orthology assignment in Ecdysozoa. Outside of Chordata, classification of DNMTs, particularly in Excavata and SAR, require more extensive identification in these supergroups. Our current orthology assignment for the major targets in this study (HELLS, DNMT1, DNMT3, DNMT5) is largely consistent with published results (Ponger et al., 2005 PMID 15689527; Huff et al, 2014 PMID 24630728; Yaari et al., 2019 PMID 30962443; Bewick et al., 2019 PMID 30778188). However, while we are preparing this response and re-crosschecking our assignments with these references, we realized that we had erroneously missed DNMT5 orthologs in Leucosporidium creatinivorum, Postia placenta, Armillaria gallica and Saitoella complicata, and a DNMT6 ortholog in Fragilariopsis cylindrus. We also recognized that DNMT4 orthologs were identified in Fragilariopsis cylindrus and Thalassiosira pseudonana in Huff et al 2014 (PMID 24630728), but in our phylogenetic analysis, these proteins form a distinct clade between DNMT1/Dim-2 and DNMT4 (original Figure S6), although the confidence level of this classification by Huff et al was not strong. To resolve this potential confusion in DNMT annotations, we generated new multiple sequence alignments with MUSCLE v5 and IQ-TREE 2 (maximum likelihood-based method, coupled with selection of optimal substitution model and bootstrapping). The tree topology was not significantly altered between the two methods, except for the unambiguous location of orphan DNMTs and DNMT4-related proteins. To avoid unnecessary confusion in the DNMT annotations, we decided to present MUSCLE-IQ- TREE for the DNMT phylogenetic tree and classification (new Fig. S6). The raw results of IQ-TREE analysis for CDCA7/zf-4CXXC_R1, HELLS SNF2 domain, and DNMTs are included as Dataset S1-S3. We then conducted CoPAP analysis using the corrected classification. As it is not clear a priori if fungal specific CDCA7-like proteins (now referred to as CDCA7F with class II zf-4CXXC_R1) should be considered CDCA7 orthologs, we conducted CoPAP against two lists; the first list includes CDCA7F in the CDCA7 group, whereas the second list includes a separate category of class II zn-4CXXC_R1, which includes CDCA7F. Both results show slightly different topology in the coevolutionary linkages but support our major conclusion that CDCA7 coevolved with DNMT1-UHRF1 and HELLS. These new CoPAP results are shown in Fig. S7.

Reviewer #1 (Recommendations For The Authors):

Summary

Last sentence: "...a unique specialized role of CDCA7 in HELLS-dependent DNA methylation maintenance...". What do the authors mean?

Our analysis strongly indicates that CDCA7 is dispensable in systems lacking HELLS and DNMT (particularly DNMT1). In other words, species preserve CDCA7 only if it has both HELLS and DNMT1 (or in some cases DNMT5). The importance of HELLS homologs in DNA methylation has been extensively studied in human, mouse and plants. However, in these studies, substantial DNA methylation remains despite the defective HELLS/DDM1 (especially in euchromatic regions). Additionally, there are species (e.g., Bombyx mori) that have DNMT1 and detectable DNA methylation but lacks HELLS and CDCA7. These observations suggest that the role of CDCA7 must be unique and specialized in a way that it is strongly coupled to HELLS-dependent DNA methylation (but not HELLS-independent DNA methylation), and that this function of CDCA7 seems to be inherited from the last eukaryotic common ancestor.

Introduction

• page 3: "DNMTs are largely subdivided into maintenance and de novo DNMTs" - Which species are the authors referring to?

As described in the cited reference (Lyko 2018), maintenance DNA methylation and de novo DNA methylation are well accepted functional classification of DNA methylation. It is also currently accepted that distinct DNMTs execute maintenance DNA methylation or de novo DNA methylation, although crosstalk between these processes has been reported. Therefore, we stated, “DNMTs are largely subdivided into maintenance DNMTs and de novo DNMTs”, and this subdivision is species independent.

• page 3" "Maintenance DNMTs recognize hemimethylated CpGs. " - Can the authors please define the species and/or literature they are referring to? This seems important to clarify. For instance, mammalian DNMT1 requires a co-factor, UHRF1, which recognizes hemimethylated DNA and H3K9me3 (Bostick et al 2007).

We meant to describe, “Maintenance DNMTs directly or indirectly recognize hemimethylated CpGs…”. The specific requirement of UHRF1 for DNMT1-mediated maintenance DNA methylation is explained in the subsequent sentence “In animals…”. In the case of Cryptococcus neoformans, DNMT5 recognizes hemimethylated DNA independently of UHRF1 in vitro to execute maintenance methylation.

• page 3: The authors may want to mention that A. thaliana also has a de novo DNA methyltransferase, DRM2, a homolog of the mammalian DNMT3 methyltransferases. This seems important, since they show in Figure 1 that a de novo methyltransferase is found in A. thaliana. Also, later in their manuscript they mention plant de novo DNA methylation.

Thanks for pointing this out. As shown in Figure 5, we classified plant DRMs as DNMT3-like proteins, but we now note this in the Introduction.

• page 3: Sentence starting "In about 50% of ICF patients,..." - Why is DNMT3B referred to as "de novo", is it not a de novo DNA methyltransferase?

You are correct. Quotation marks are now removed to avoid unnecessary confusion.

• page 4: Sentence starting "Indeed, the importance of HELLS/CDCA7 in DNA methylation maintenance...", - Which references (Han et al., 2020; Ming et al., 2021; Unoki, 2021; Unoki et al., 2020) provide experimental evidence for a role of CDCA7 in DNA methylation maintenance by DNMT1?

Thanks for pointing out the typo. “/CDCA7” is now removed.

• page 5: Sentence starting "Indeed, it has been shown that DNMT3A..." - Should DNMTB be DNMT3B?

Yes. This is now corrected.

Results

• Page 5: Sentence starting "However, we identified a protein..." - No A. thaliana reference?

We added Zemach et al 2010, and Chan et al 2005.

• Figure 2B: "ICF4 mutations" should this be "ICF3 mutations"?

• Figure 3: "ICF4 mutations" should this be "ICF3 mutations"?

• Figure 4: "ICF4 mutations" should this be "ICF3 mutations"?

• Figure S1: Orange colored "CDC7L (fish), CDC7e, CDC7, CDC7L" is there an "A" missing?

• Figure S5: "ICF4 mutations" should this be "ICF3 mutations"?

These typos are now corrected. Thank you.

• Figure S7: What is "CDCA7(II)" referring to, "zf-4CXXC_R1 class II (plants)"?

The original CDCA7 (II) included proteins with class II zf-4CXXC_R1, which are found in plants, fungi, Acanthamoeba castellanii and Amphimedon. Among those species, the prototypical CDCA7 orthologs are absent only in fungi. It has been a priori unclear if fungal proteins with class II zf-4CXXC_R1 (now we term CDCA7F) should be included in CDCA7 for CoPAP analysis. Although we originally included CDCA7F in CDCA7, we now show the results of two analyses. In the first one (Fig. S7A) CDCA7F was included in CDCA7, whereas in in the second one (Fig. S7B) CDCA7F was included in the separate category of class II zf-4CXXC_R1. Topologies of two results are slightly different, but they both show coevolutionary linkage between the CDCA7 and DNMT1- UHRF1 cluster.

• Figure 4 and 5: In the case of preliminary genome assemblies what is the difference between empty squares with dotted lines and filled squares without dotted lines?

As it is difficult to be certain of a gene’s absence (did the species lose the gene or is it simply not annotated due to incomplete genome coverage?), we illustrated the absence of a gene in preliminary genome assemblies with an empty square with dotted outline. Since the presence of a gene is evident regardless of the level of genome assembly, the presence of a gene is represented with filled squares with solid lines, even for preliminary genome assemblies.

• Figure 1: Why was Mus musculus - one of the main model organisms used for many DNA methylation studies not included? Also what are empty and filled squares?

Filled and empty squares indicate the presence and absence of the indicated genes, respectively. Clarifying statement is now added in the figure legends. Mus musculus is now included in the figure.

• Figure S2: Adding the existence of DNA methylation and DNMT3 in the bottom right part of the figure (overall no of species) would make this panel more informative

We included this overview to summarize the co-retention of CDCA7, HELLS and maintenance DNMTs across the analyzed species. We decided not to include DNA methylation, since DNA methylation status is known for only a fraction of the listed species. Inclusion of DNMT3 will introduce too many possible gene presence-absence combinations to convey a clear message. However, we now mention in the revised text (page 11, second paragraph) that unlike the prevalent co-retention of DNMT1 in species with CDCA7, we identified several species that possess CDCA7, HELLS and DNMT1 but lack DNMT3. These examples include insects such as the bed bug Cimex lectularius and the red paper wasp Polistes canadensis.

• Page 6: Sentence starting "This leucine zipper sequence is highly conserved..." - Figure/Reference missing?

The sequence alignment of the leucine zipper is now shown in Fig. 2C.

• page 6: Sentence starting "In contrast to zf-4CXXC_R1 motif-containing proteins..." - The authors may want to mention the role of the CXXC zf domain in KDM2A/B, DNMT1, MLL1/2 and TET1/3 and what the CDCA7 CXXC zf domain is/could be required for.

The notion that zf-CXXC binds to nonmethylated CpG is now included. Due to the substantial difference between zf-CXXC and zf-4CXXC_R1, we hesitated to relate the function of zf-4CXXC_R1 with zf-CXXC, but we now discuss a potential role of zf- 4CXXC_R1 in sensing DNA methylation status in Discussion (Page 13).

• page 7: Sentence starting "Second, the fifth cysteine is replaced..."- Zoopagomycota" - Figure 4A does not have this labeling, one has to deduce this from Figure 4B.

We fixed this by including the list of Zoopagomycota species in the main text.

• page 7: Sentence containing "Neurospora crassa DMM-1 does not directly regulate DNA methylation or demethylation but rather..." - How does the information about DMM- 1 relate to what is shown in Figure 4B, to CDCA7, HELLS and DNMTs? Please clarify.

Both Neurospora DMM-1 and Arabidopsis IBM1 contain the JmjC domain and are implicated in an indirect control mechanism of DNA methylation. Since it has never been pointed out that they have a divergent zf-4CXXC_R1 domain, which clearly shares the origin with CDCA7 proteins, we thought that this is important to note. We realized that we did not clearly mark Neurospora XP-956257 as DMM-1 in Fig. 4B. This is now fixed.

• Heading "Systematic identification of CDCA7, HELLS and DNMT homologs in eukaryotes". When mentioning CDCA7 the authors may want to decide on the use of one consistent definition of "prototypical (Class I) CDCA7-like proteins (i.e. CDCA7 orthologs)" "Class I CDCA7 proteins". Constantly changing the way how they refer to these proteins is very confusing.

We now make it clear that we call proteins with class I zf-CXXC_R1 motif CDCA7 orthologs. We also define class II zf-4CXXC_R1 (as those with a substitution at ICF- associated glycine residue). Since no clear CDCA7 orthologs can be found in fungi, we now call fungi proteins with class II zf-4CXXC_R1 “CDCA7F”, implying its ambiguous orthology assignment.

Under this heading there is also no mention of DNMTs. Instead, the authors introduce DNMTs under the heading "Classification of DNMTs in eukaryotes" - Please clarify.

This is now corrected.

• page 9: Sentence containing "... presence of DNMT1, UHRF1 and CDCA7 outside of Viridiplantae and Opisthokonta is rare". What does "rare" mean? How is UHRF1 relevant here?

Among the 32 species outside of Viridiplantae and Opisthokonta, only the Acanthamoeba castellanii genome encodes clear orthologs of DNMT1, UHRF1 and CDCA7. Although it is often difficult to deduce if the selected panel of species is a reasonable representation, we think that it is not unreasonable to state that Acanthamoeba is a rare case to encode this set of proteins outside of Viridiplantae and Opisthokonta. We include UHRF1 since it is a well-established activator of DNMT1, and indeed our CoPAP analysis showed a tight coevolution of UHRF1 with DNMT1. Outside of Viridiplantae and Opisthokonta, only Acanthamoeba castellanii and Naegleria gruberi encode UHRF1. Interestingly, these two species also encode CDCA7 and HELLS.

Having said that, we rephrased this sentence, which reads; “Species that encode a set of DNMT1, UHRF1, CDCA7 and HELLS are particularly enriched in Viridiplantae and Metazoa.”

• page 11: Sentence containing "..., that the function of CDCA7-like proteins is strongly linked to HELLS and DNMT1,..." What do the authors mean with "the function of CDCA7-like proteins"? And what happened to DNMT3?

Our observation that almost all species that contain CDCA7 (including fungal CDCA7F) also have DNMT1 and HELLS, despite the frequent loss of these genes in species that do not contain CDCA7, indicates “that the function of CDCA7-like proteins is strongly linked to HELLS and DNMT1”. We found only 2 species that possesses CDCA7 (class I or class II) but not DNMT1 among the panel of 180 species. These 2 exceptional species, Naegleria gruberi and Taphrina deformans, do encode UHRF1-like proteins and a DNMT (an orphan DNMT in N. gruberi and DNMT4 in T. deformans). In contrast, we found 26 species that possess CDCA7 (or CDCA7F) but not DNMT3 (Table S1), so the linkage between CDCA7 and DNMT3 is weaker.

• page 11: Sentence containing "..., CDCA7 is lost from this gene cluster in parasitoid wasps, including Ichneumonoidea wasps and chalcid wasps". This sentence is confusing because already in an earlier paragraph the authors say that "Microplitis demolitor lost CDCA7" and in the following sentence they say "...among Ichneumonoidea wasps, CDCA7 appears to be lost in the Braconidae clade, ...". It would greatly help this reader if the authors could streamline these sentences and also decide on whether CDCA7 is lost in M. demolitor or CDCA7 appears to be lost in M.demolitor.

The confusion was in part due to the difficulty in differentiating between the true loss of a gene versus its apparent absence in a species due to an incomplete genome assembly, including for of M. demolitor. To verify that the loss of CDCA7 was not due to gaps in the genome assembly, we executed the synteny analysis. However, we edited this section to improve the readability (Page 12-13).

What could be the role for HELLS/CDCA7 in insect DNA methylation? In several cases, the authors analyses reveal co-evolutionary links between DNMT3 (DNMT3A?) and CDCA7/HELLS. I do not understand why this finding is not really discussed by the authors. Instead there is a strong focus on replication-uncoupled DNA methylation maintenance. Could the authors elaborate why?

The role of DNA methylation in insects is largely unclear, so discussion must be highly speculative. A recent finding in the clonal raider ant, showing that DNMT1 is not essential for development but is critical for oogenesis, pointed toward a possible more universal role of DNA methylation in meiosis. Stimulated from a finding in Neurospora, where DNA methylation is required for homolog pairing during meiosis, we discuss a speculative model that DNA methylation status acts as a hallmark to distinguish between healthy/young DNA and old/mutated (or competitive/pathogenic) DNA at homolog pairing during meiosis (page 14).

Regarding the cases where CDCA7 and DNMT3 are co-lost, we had discussed about this phenomenon at the last section of Result, stating, “This co-loss of CDCA7 and DNA methylation (together with either DNMT1-UHRF1or DNMT3) in braconid wasps suggests that evolutionary preservation of CDCA7 is more sensitive to DNA methylation status per se than to the presence or absence of a particular DNMT subtype.” Please note that we found several lineages that lacks CDCA7 but has DNMT1 (and DNMT3), whereas almost all species that has CDCA7 also has DNMT1 (but not necessarily DNMT3). Supported with our CoPAP analyses, our results indicate the tight functional link between CDCA7 and DNMT1, but it does not necessarily mean that CDCA7 does not play any role related to DNMT3 or de novo methylation. Clarification of this point and our speculation of how CDCA7 loss is linked to reduced requirement of DNA methylation are discussed in page 13 and 14 with additional texts.

Discussion

• page 12: Where is the data supporting. "... the red flour beetle Tribolium castaneum possesses DNMT1 and HELLS, but lost DNMT3 and CDCA7"?

Figure 5, Figure S2 and Table S1. This is now noted in the text.

• page 14: Based on which parts of their analyses or evidence from the literature can the authors speculate that "...the evolutionary arrival of HELLS-CDCA7 in eukaryotes might have been required to transmit the original immunity-related role of DNA methylation from prokaryotes to nucleosome-containing (eukaryotic) genomes"? Please clarify.

This is inferred from the well-known role of DNA methylation in bacteria for defending against phage viruses. However, it was not correct to state that such a function was inherited from prokaryotes. It should be stated that it was inherited from the last universal common ancestor (LUCA). We also admit that it is not clear if such an immunity-related role was inherited from LUCA, or if it emerged through convergent evolution. Therefore, we amended this description to emphasize our hypothesis that the advent of CDCA7 was “a key step to transmit the DNA methylation system from the LUCA to the eukaryotic ancestor with nucleosome-containing genomes”.

Supplementary Figures/Tables

• page 26: Table S2 and Table S3, it seems that these tables show data that supports what is shown in Figure 7 and not Figure 5.

You are correct. Thank you for pointing out the typos.

Has the methylation status been assessed in C. glomerata, C. typhae, Chelonus insularis, Diachasma alloeum or Aphidius gifuensis? Please clarify in Table S2.

Not to our knowledge. However, as we realized that absence of DNA methylation in Aphidius ervi was previously reported (Bewick et al 2017), we now included this data together with presence/absence analysis of DNMT1, UHRF1, DNMT3, CDCA7 and HELLS. Known presence/absence of DNA methylation is now shown in Fig.7.

Reviewer #2 (Recommendations For The Authors):

Recommendation to strengthen the paper:

1) Phylogenetics:

• Test and report the appropriateness of the substitution model used in protein alignments/trees.

• Use Maximum likelihood methods and/or MCM Bayesian inference to build and report trees with well supported topologies. This is required to properly assign orthology (shared ancestry). This will avoid false interpretation due to technical limitation of similarity-based phylogenies (without statistical support). Figure S1, S3, S4 and S6.

To address these points, we made new multisequence alignments using MUSCLE v6 and generated phylogenetic trees using the maximum likelihood-based IQ-TREE 2, where multiple models were screened. A consensus tree was generated after 1000 bootstrap replicates from the best alignment and model. The topology and assignment of these new trees were largely consistent with the original trees, except for some corrections in DNMT assignment as discussed below.

1. We realized that we erroneously missed DNMT5 orthologs of Leucosporidium creatinivorum, Postia placenta, Armillaria gallica and Saitoella complicata., and DNMT6 orthologs from Fragilariopsis cylindrus reported in Huff et al 2014 (PMID 24630728). They are now included in the new list and CoPAP analysis.

2. DNMT4 orthologs were identified in Fragilariopsis cylindrus and Thalassiosira pseudonana by Huff et al 2014 (PMID 24630728), but in our original phylogenetic analysis, these proteins form a distinct clade between DNMT1/Dim-2 and DNMT4. The new tree and classification are more consistent with Huff et al, so we present the new tree in Fig. S6 and conducted the classification based on this tree.

Beside Fig. S6, we decided to maintain original Fig. S1, S3 and S4 (with a few adjustments) for better visibility, but we included the results of IQ-TREE analysis as Dataset S1-S3.

The CoPAP analysis based on the revised assignment slightly changed the topology of coevolutionary linkages. In addition, we obtained a slightly different result depending on whether fungal specific CDCA7 with class II zn-4CXXC_R1 (now referred to as CDCA7F) is included as a CDCA7 ortholog or not. Despite this difference, we reproducibly observed the coevolutionary linkage between CDCA7 and DNMT1- UHRF1.

• Be more careful with wording: RBH is not sufficient to call gene/proteins orthologs (e.g. Page 8). The above mentioned method will help you support this claim (+ synteny when you can).

We were aware of this issue. This is why we conducted phylogenetic tree building based on sequence alignment of full-length HELLS (Fig. S3) and SNF2 domain only (Fig. S4), as explained in the text. We found that the RBH criterion is robust in Metazoa; orthologs are easily recognizable with very low E-value (0.0) and extensive homology over the full length of the protein, while synteny is not practical to employ in the diverse set of species.

• Also, use "co-retention" or "co-evolution" but not "co-selection" when describing CoPAP results - as CoPAP does not test for signature of natural selection.

This is a good point and is now corrected.

• The statistics (p-val...) underlying the CoPAP analyses should be explained.

The explanation is now added in Methods section.

“A method to calculate p-value for CoPAP was described previously (Cohen et al., 2012, PMID 22962457). Briefly, for each pair of tested genes, Pearson's correlation coefficient was computed. Parametric bootstrapping was used to compute a p-value by comparing it with a simulated correlation coefficient calculated based on a null distribution of independently evolving pairs with a comparable exchangeability (a value reporting the likelihood of gene gain and loss events across the tree).”

2) Figure S2 and S3 could be improved for readability

After consideration of this criticism, we decided to keep their original formats for following reasons.

Figure S2. The purpose of this list is to better visualize the comprehensive list shown in Table S2. A consolidated list is already shown in Figure 5. An alternative choice is to make a diagram where individual species names are unreadable. This kind of presentation is seen in many published papers, but we found that they are not helpful to check the details. As this is a supplementary figure, we prefer to show the detailed data that can be visible without a specialized software.

Figure S3. This figure is included to show which SNF2 family proteins are more likely to be misassigned as HELLS/DDM1 orthologs. We believe that the figure serves this purpose.

3) What is the meaning of the coloring patterns of ICF residues in znf?

ICF residues are highlighted as light blue in the schematics to indicate its conservation. In the alignment, the coloring reflects the level of conservation within the shown set of proteins, and the choice of coloring was set by Jalview.

4) To improve clarity: the introduction could be more focused on evolutionary considerations and functional link between CDCA7-HELLS and DNMTs.

We revised the first paragraph of the introduction to illustrate this point.

5) Could indicate the CDC7A loss / DNA methylation hypothesis in the abstract.

We now included this hypothesis in the Abstract.

eLife assessment

This important manuscript reveals signatures of co-evolution of two nucleosome remodeling factors, Lsh/HELLS and CDCA7, which are involved in the regulation of eukaryotic DNA methylation. The results suggest that the roles for the two factors in DNA methylation maintenance pathways can be traced back to the last eukaryotic common ancestor and that the CDC7A-HELLS-DNMT axis shaped the evolutionary retention of DNA methylation in eukaryotes. The solid evolutionary analyses form an interesting basis for experimental follow-up studies. The work should be of interest to colleagues in the fields of evolutionary biology, chromatin biology and genome biology.

Reviewer #1 (Public Review):

Funabiki et al, performed a co-evolutionary analysis of Lsh/HELLS and CDCA7, two factors with links to DNA methylation pathways in mammals, amphibia and fish. The authors suggest that conserved roles for the two factors in DNA methylation maintenance pathways can be traced back to the last eukaryotic common ancestor. Overall, the findings are important and the results could be useful for researchers studying DNA methylation pathways in many different organisms.

Comments on current version:

In the revised version of this manuscript the authors addressed all previously raised issues. I would like to thank them for that. The data is now clearly presented and interpreted and more experimental detail has been added. Thus, the manuscript is much improved and provides an interesting basis for experimental follow-up and further functional investigations.

Reviewer #2 (Public Review):

In this manuscript, Funabiki and colleagues investigated the co-evolution of DNA methylation and nucleosome remolding in eukaryotes. This study is motivated by several observations: (1) despite being ancestrally derived, many eukaryotes lost DNA methylation and/or DNA methyltransferases; (2) over many genomic loci, the establishment and maintenance of DNA methylation relies on a conserved nucleosome remodeling complex composed of CDCA7 and HELLS; (3) it remains unknown if/how this functional link influenced the evolution of DNA methylation. The authors hypothesize that if CDCA7-HELLS function was required for DNA methylation in the last eukaryote common ancestor, this should be accompanied by signatures of co-evolution during eukaryote radiation.

To test this hypothesis, they first set out to investigate the presence/absence of putative functional orthologs of CDCA7, HELLS and DNMTs across major eukaryotic clades. They succeed in identifying homologs of these genes in all clades spanning 180 species. To annotate putative functional orthologs, they use similarity over key functional domains and residues - such as ICF related mutations for CDCA7 and SNF2 domains for HELLS - as well as maximum likelihood phylogenetic analyses. Using established eukaryote phylogenies, the authors conclude that the CDCA7-HELLS-DNMT axis arose in the last common ancestor to all eukaryotes. Importantly, they found recurrent loss events of CDCA7-HELLS-DNMT in at least 40 eukaryotic species, most of them lacking DNA methylation.

Having identified these factors, they successfully identify signatures of co-evolution between DNMTs, CDCA7 and HELLS using CoPAP analysis - a probabilistic model inferring the likelihood of interactions between genes given a set of presence/absence patterns. As a control, such interactions are not detected with other remodelers or chromatin modifying pathways also found across eukaryotes. Expanding on this analysis, the authors found that CDCA7 was more likely to be lost in species without DNA methylation.

In conclusion, the authors suggest that the CDCA7-HELLS-DNMT axis is ancestral in eukaryotes and raise the hypothesis that CDCA7 becomes quickly dispensable upon the loss of DNA methylation and/or that CDCA7 might be the first step toward the switch from DNA methylation-based genome regulation to other modes.

The data and analyses reported are significant and solid. Overall, this work is a conceptual advance in our understanding of the evolutionary coupling between nucleosome remolding and DNA methylation. It also provides a useful resource to study the early origins of DNA methylation related molecular process. Finally, it brings forward the interesting hypothesis that since eukaryotes are faced with the challenge of performing DNA methylation in the context of nucleosome packed DNA, loosing factors such as CDCA7-HELLS likely led to recurrent innovations in chromatin-based genome regulation.

Strengths:<br /> - The hypothesis linking nucleosome remodeling and the evolution of DNA methylation.<br /> - Deep mapping of DNA methylation related process in eukaryotes.<br /> - Identification and evolutionary trajectories of novel homologs/orthologs of CDCA7.<br /> - Identification of CDCA7-HELLS-DNMT co-evolution across eukaryotes.

eLife assessment

Large scale cell movements occur during gastrulation in vertebrate embryos but their role in this major morphogenetic transition in formation of the body plan is poorly understood. Using the chick embryo model system, this study makes important advances using elegant methods to show that extension of the primitive streak during gastrulation, occurring through cell proliferation, polarisation and intercalation, and large-scale polonaise cell movements, can be uncoupled. Although the driving mechanism and precise role of these movements remains a mystery, the study provides convincing evidence for the uncoupling through independent approaches, the most creative of which are the effects shown after induction of a supernumerary primitive streak.

Reviewer #1 (Public Review):

In chicken embryos, the counter-rotating migration of epiblast cells on both sides of the forming primitive streak (PS), a process referred to as polonaise movements, has attracted longstanding interest as a paradigm of morphogenetic cell movements. However, the association between these cell movements and PS development is still controversial. This study investigated PS development and polonaise movements separately at their initial stage, showing that both could be uncoupled (at least at the initial phase), being activated via Vg1 signaling.

Strengths of this study

Polonaise movements, i.e., the circular cell migration of epiblast cells on both sides of the forming PS in avian embryos, have been the subject of research through live imaging and promoted the development of new tools to analyze quantitatively such movements. However, conclusions from previous studies remain controversial, at least partly due to the nature of perturbations to PS development and polonaise movements.

This study performed the challenging technique of electroporation to successfully mark and manipulate Wnt/PCP pathways in unincubated chicken embryo cells at the initiation phase of these two processes. In addition, the authors separately altered PS development and polonaise movements: PS development was perturbed by inhibiting either the Wnt/PCP pathway or DNA synthesis using aphidicolin, while polonaise movements were modified by the development of a second PS after engrafting Vg1-expressing COS cells located at the opposite end of the blastoderm. The study concluded that Vg1 elicits both PS development and polonaise movements, which occur in a parallel and are not inter-dependent.

To support these conclusions, particle image velocimetry (PIV) of cell trajectories captured by live imaging was performed. These tools delineated visually appealing cell movements and gave rise to vorticity profiles, adding more value to this study.

Weaknesses of this study

Engrafted Vg1-expressing COS cells located at the anterior end of the blastoderm elicited both the development of a second PS and marked bilateral polonaise movements while perturbing these movements along the original PS. How do polonaise movements along the second PS dominate over those along the normal PS? The authors suggested a model in which Vg1 acts in a graded or dose-dependent manner since engrafted COS cells over-expressed Vg1. This model can be tested by reducing the mass of engrafted COS cells. Although the authors propose performing this analysis in further investigations, it would be preferable to incorporate into this study for better consistency.

The authors claim that chicken embryo development is representative of "amniotes," but it does not hold for all groups. Avian and mammal species are exceptional among amniotes in the sense they develop a PS (e.g., Coolen et al. 2008). Moreover, in certain mammalian embryos like mouse embryos, cells laterally to the PS do not move much (Williams et al. 2012). The authors should avoid the generalization that chicken embryos unequivocally represent amniotes as opposed to the observed in non-amniote embryos. The observations in chicken embryos as they stand are significant enough.

References:<br /> Coolen M, et al. (2008). Molecular characterization of the gastrula in the turtle Emys orbicularis: an evolutionary perspective on gastrulation. PLoS One. 3(7):e2676. doi: 10.1371/journal.pone.0002676

Williams M, et al. (2012). Mouse primitive streak forms in situ by initiation of epithelial to mesenchymal transition without migration of a cell population. Dev Dyn. 241(2):270-283. doi: 10.1002/dvdy.23711

Reviewer #2 (Public Review):

Summary:<br /> The authors are interested in large-scale cell flow during gastrulation and in particular in the polonaise movement. This movement corresponds to a bilateral vortex-like counter-rotating cell flow and transport the mesendodermal cells allowing ingression of cells through the primitive streak and ultimately the formation of the mesoderm and endoderm. The authors specifically wanted to investigate the coupling of the polonaise movement and primitive streak to understand whether the polonaise movement is a consequence of the formation of the primitive streak or the other way around. They propose a model where the primitive streak elongation is not required for the cell flow but rather for its maintenance and that robust cell flow is not required for primitive streak extension.

Strengths:<br /> Overall, the manuscript is well written with clear experimental designs. The authors have used live imaging and cell flow analysis in different conditions, where either the formation of the primitive streak or the cell flow was perturbed.<br /> Their live imaging and PIV-based analyses convincingly support their conclusions that primitive streak deformation or mitotic arrest do not impact the initiation of the polonaise movement but rather the location or maintenance of these rotations. They additionally showed that disruption of the polonaise movement in the authentic primitive streak by elegant addition of an ectopic primitive streak does not impact the original primitive streak elongation.

Weaknesses:<br /> - When using the delta-DEP-GFP construct, the authors showed that they can manipulate the shape of the primitive streak without affecting the identity and number of primitive streak cells. It is not clear however how this can affect the shape, volume or adhesion of the cells. Some mechanistic insights would strengthen the paper.<br /> - Overall, frequencies of observation are missing for a better view of the phenomenon. For example, do Vg1/Cos cells always disrupt the flow at the authentic primitive streak? Can replicate vector fields be integrated to reflect quantification?<br /> - Since myosin cables have been shown to be instrumental for the polonaise movement, it would be interesting to better investigate how the manipulations by the delta-DEP-GFP construct, or Vg1/Cos affect the myosin cables (as shown in preliminary form for the aphidicolin-treated embryos).

Author Response:

Thank you very much for selecting our paper for peer review and for the thorough evaluation of our manuscript. We appreciate your assessment and the reviewers’ comments that value our work and identify important points that will enable us to improve the paper. We are now working on key experiments to further test the hypothesis that ROCK is essential for the formation, growth, and morphology of the sea urchin larval skeleton. We will address the reviewers’ comments in detail in the revised version of the paper that we will submit after completing the experiments, but for now, there are two points we would like to clarify.

We thank the first reviewer for the appreciation of this paper and of our previous work where we studied calcium vesicle dynamics in whole embryos (Winter et al, Plos Com Biol 2021). In Winter et al 2021, we found that the skeleton (spicules) doesn’t grow when the embryos are immobilized in either control or treated embryos. As a consequence, we cannot determine the role of ROCK in vesicle trafficking and exocytosis based on experiments conducted in whole embryos. We are developing an alternative assay for vesicle tracking using cell cultures, but that is beyond the scope of this current work.

As for the second reviewer’s criticism of the usage of Y-27632 to block ROCK activity: The ROCK inhibitor concentrations we used (30-80µM) are similar the those commonly used in mammalian systems and in Drosophila to block ROCK activity, for example: (Becker et al., 2022; Canellas-Socias et al., 2022; Fischer et al., 2009; Kagawa et al., 2022; Segal et al., 2018; Su et al., 2022). The manufactory datasheet indicates that: “Y-27632 dihydrochloride is a selective ROCK inhibitor (Ki values are 0.14-0.22, 0.3, 25, 26 and > 250 μM for ROCK1 (p160 ROCK), ROCK2, PKA, PKC and MLCK respectively)”. That is, the affinities of Y-27632 for ROCK kinases are at least 100 times higher than those for PKC, PKA, and MLCK. Furthermore, these Ki values are based on biochemistry assays where the activity of the inhibitor is tested in-vitro with the purified protein. Therefore, these concentrations are not relevant to cell or embryo cultures where the inhibitor has to penetrate the cells and affect ROCK activity in-vivo. Y-27632 activity was studied both in-vitro and in-vivo in Narumiya, Ishizaki and Ufhata, Methods in Enzymology 2000 (Narumiya et al., 2000). This paper reports similar concentrations to the ones indicated in the manufactory data sheet for the in-vitro experiments, but shows that 10µM concentration or higher are effective in cell cultures. As stated above, we will add additional experimental verifications to the revised version, but even at this stage, the concentrations we used and the agreement between our pharmacological and genetic perturbations suggests that the affected protein is indeed ROCK.

We share the reviewers and editors wish to identify the molecular targets of ROCK and the specific cellular processes that ROCK is involved in, and we are actively working on achieving this goal. However, we believe that this paper is an important step towards illuminating the cellular components that participate in biomineral construction and the feedback between the cellular machinery and gene expression.

Best,

Smadar, in the name of all co-authors.

References:

• Becker, K.N., Pettee, K.M., Sugrue, A., Reinard, K.A., Schroeder, J.L., Eisenmann, K.M., 2022. The Cytoskeleton Effectors Rho-Kinase (ROCK) and Mammalian Diaphanous-Related (mDia) Formin Have Dynamic Roles in Tumor Microtube Formation in Invasive Glioblastoma Cells. Cells 11.
• Canellas-Socias, A., Cortina, C., Hernando-Momblona, X., Palomo-Ponce, S., Mulholland, E.J., Turon, G., Mateo, L., Conti, S., Roman, O., Sevillano, M., Slebe, F., Stork, D., Caballe-Mestres, A., Berenguer-Llergo, A., Alvarez-Varela, A., Fenderico, N., Novellasdemunt, L., Jimenez-Gracia, L., Sipka, T., Bardia, L., Lorden, P., Colombelli, J., Heyn, H., Trepat, X., Tejpar, S., Sancho, E., Tauriello, D.V.F., Leedham, S., Attolini, C.S., Batlle, E., 2022. Metastatic recurrence in colorectal cancer arises from residual EMP1(+) cells. Nature 611, 603-613.
• Fischer, R.S., Gardel, M., Ma, X., Adelstein, R.S., Waterman, C.M., 2009. Local cortical tension by myosin II guides 3D endothelial cell branching. Curr Biol 19, 260-265.
• Kagawa, H., Javali, A., Khoei, H.H., Sommer, T.M., Sestini, G., Novatchkova, M., Scholte Op Reimer, Y., Castel, G., Bruneau, A., Maenhoudt, N., Lammers, J., Loubersac, S., Freour, T., Vankelecom, H., David, L., Rivron, N., 2022. Human blastoids model blastocyst development and implantation. Nature 601, 600-605.
• Narumiya, S., Ishizaki, T., Uehata, M., 2000. Use and properties of ROCK-specific inhibitor Y-27632. Methods Enzymol 325, 273-284.
• Segal, D., Zaritsky, A., Schejter, E.D., Shilo, B.Z., 2018. Feedback inhibition of actin on Rho mediates content release from large secretory vesicles. J Cell Biol 217, 1815-1826.
• Su, Y., Huang, H., Luo, T., Zheng, Y., Fan, J., Ren, H., Tang, M., Niu, Z., Wang, C., Wang, Y., Zhang, Z., Liang, J., Ruan, B., Gao, L., Chen, Z., Melino, G., Wang, X., Sun, Q., 2022. Cell-in-cell structure mediates in-cell killing suppressed by CD44. Cell Discov 8, 35.

Reviewer #2 (Public Review):

This manuscript reports on the role of Rho-associated coiled-coil kinase (ROCK) in biomineralization of sea urchin larval skeletons. A number of experiments examine the initiation, growth, and patterning of the skeleton in an effort to determine if, and how, ROCK participates in skeletal formation. The authors conclude that ROCK controls the formation, growth, and morphology (patterning) of the skeleton based on a number of inhibition studies. The main target of the experiments is the actomyosin cytoskeleton which has been the focus of many ROCK studies in vertebrates. Based on similar experimental outcomes when comparing the results here with published data from vertebrates, they suggest that ROCK and the actomyosin network operate in a similar way in biomineralization despite independent evolutionary origins of the sea urchin larval skeletons and the skeletons of vertebrates.

My concerns are the interpretation of the experiments. The main overriding concern is a possible over-interpretation of the role of ROCK. In the literature that ROCK participates in many biological processes with a major contribution to the actin cytoskeleton. And when a function is attributed to ROCK, it is usually based on the determination of a protein that is phosphorylated by this kinase. Here that is not the case. The observation here is in most cases stunted growth of the spicule skeleton and some mis-patterning occurs or there is an absence of skeleton if the inhibitor is added prior to initiation of skeletal growth. They state in the abstract that ROCK impairs the organization of F-actin around the spicules. The evidence for that as a direct role is absent. They use morpholino data and ROCK inhibitor data to draw their conclusion. My main concern is the concentration of the inhibitor used since at the high concentrations used, the inhibitor chosen is known to inhibit other kinases as well as ROCK (PKA and PKC). They indicate that this inhibition is specifically in the skeletogenic cells based on the isolation of skeletogenic cells in culture and spicule production either under control or ROCK inhibition and they observe the same - stunting and branching or absence of skeletons if treated before skeletogenesis commences. Again, however, the high concentrations are known to inhibit the other kinases. They use blebbistatin and latrunculin and show that these known inhibitors of actin cytoskeleton lead to abnormal spiculogenesis, This coincidence is suggestive but is not proof that it is ROCK acts on the actomyosin cytoskeleton given the specificity concerns.

Reviewer #1 (Public Review):

Using a pharmacological and knock-down approach, the authors could demonstrate that ROCK activity is required for the normal development of the larval skeleton. The presence of ROCK in the pluteus stage depends on the activity of VEGF that is responsible for the formation of the tubular syncytial sheath of the calcifying primary mesenchyme cells in which the skeleton forms. The importance of ROCK in skeleton formation was confirmed in cell culture experiments, demonstrating that ROCK inhibition leads to decreased elongation and abnormal branching of spicules. µCT analyses underline this finding demonstrating that the inhibition of ROCK mainly affects the elongation of spicules while growth in girth is little affected. F-actin labeling experiments could demonstrate that ROCK inhibition interferes with the organization of the actomyosin network in the early phase of skeleton formation, while f-actin organization in the tips of the elongating spicule is unaffected by the pharmacological inhibition of ROCK. Finally, ROCK inhibition strongly affects the expression of major regulatory and calcification-related genes in the calcifying cells. Based on these findings the authors propose a model for the regulatory interaction between the skeletogenic GRN, ROCK, and the f-actin system relevant for skeletogenesis.

I reviewed this paper previously for submission to another Journal. I emphasize again, that this is an interesting and important work that aims to uncover the interaction between the Rho-associated Kinase, ROCK, the actomyosin network, and its relevance for the formation of the calcitic skeleton of the sea urchin larva. I carefully went through the revised manuscript. In their new version, the authors rearranged the figures to provide a more direct comparison between the in vivo and cell culture experiments which mitigates the criticism of collateral effects by the inhibitors on the whole organism. The authors also performed an additional experiment localizing the F-Actin signal in spicules of PMC cell cultures under ROCK inhibition. This experiment strengthens the concept that ROCK activity is important for tip dominance rather than CaCO3 deposition rates. The results section was substantially reorganized and only very minor changes were made to the introduction and discussion.

I think that this work has great potential to provide seminal insights into an understudied aspect of the biomineralization process - the role and regulation of the cytoskeleton in calcifying cells. As I mentioned in my previous review there are some gaps in this work that need to be answered to provide a conclusive dataset on the role of ROCK and the actomyosin system in the mineralization process. The manuscript in its current form provides evidence for the interaction of ROCK with the actomyosin system in the sea urchin larva and that perturbation of this system affects skeletogenesis. However, it is missing an explanation regarding the mechanism by which ROCK affects skeleton formation. No difference in f-actin localization was found at the spicule tips in control and ROCK-inhibited larvae. A slight hint was found in the difference in vesicle size and f-actin organization within calcifying cells, but it remains unresolved if ROCK activity impacts the trafficking of calcification vesicles. The authors provide an interesting discussion on the involvement of f-actin and ROCK on vesicular trafficking, and exocytosis based on existing knowledge from animal and plant models. But for the sea urchin larva, this important link between ROCK, f-actin, and the biomineralization process remains unanswered. In their previous work by Winter et al. 2021, the authors demonstrated excellent technologies to monitor vesicular dynamics in the calcifying cells. This tool would be ideal to investigate the role of ROCK and the actomyosin network on the trafficking dynamics of Ca2+-rich vesicles. These experiments (among others suggested in the following review) may help to uncover the critical mechanism to resolve the missing gap in this manuscript.

Major comments<br /> One MASO led to reduced skeleton formation while the other one additionally induced ectopic branching. How was the optimum concentration for the MASOs determined? Did the authors perform a dose-response curve? What is the reason for this difference? Which of the two MASOs can be validated by reduced ROCK protein abundance? Since the ROCK antibody works, I would like to see a control experiment on Rock protein abundance in control and ROCK MO injected larvae which is the gold-standard for validating the knock-down.

L212 "Together, these measurements show that ROCK is not required for the uptake of calcium into cells." But what about trafficking and exocytosis? As mentioned earlier, I think this is a really important point that needs to be confirmed to understand the function of ROCK in controlling calcification. In their previous study (reference 45) the authors demonstrated that they have superior techniques in measuring vesicle dynamics in vivo. Here an acute treatment with the ROCK inhibitor would be sufficient to test if calcein-positive vesicle motion, including the observed reduction in velocity close to the tissue skeleton interface, is affected by the inhibitor.

Is there a colocalization of ROCK and f-actin in the tips of the spicules? This would support the mechano-sensing-hypothesis by ROCK.

L 283. "F-actin is enriched at the tips of the spicules independently of ROCK activity" The results of this paragraph clearly demonstrate that ROCK inhibition has no effect on the localization of f-actin at the tips of the growing spicules. In addition, the new cell culture experiments underline this observation. Still, the central question that remains is, what is the interaction between ROCK, f-actin, and the mineralization process, that leads to the observed deformations? What does the f-actin signal look like in a branched phenotype or in larvae that failed to develop a skeleton (inhibition from Y20)?

Immunohistochemical analyses on f-actin localization and abundance should be additionally performed with ROCK knock-down phenotypes to confirm the pharmacological inhibition.

L 365 "...supporting its role in mineral deposition..." "...Overall, our studies indicate that ROCK activity....is essential for the formation of the spicule cavity......which could be essential for mineral deposition..." I think the authors need to do a better job in clearly separating between the potential processes impacted by ROCK perturbation. Is it stabilization and mechano-sensing in the spicule tip or the intracellular trafficking and deposition of the ACC? If the dataset does not allow for a definite conclusion, I suggest clearly separating the different possibilities combined with thorough discussion-based findings from other mineralizing systems where the interaction between ROCK and F-actin has been described.

eLife assessment

This valuable study uses phylogenetic linear mixed models in a Bayesian framework to explore the relationships between taste qualities and the therapeutic use of botanicals from the ancient Graeco-Roman vademecum. The evidence supporting the authors' conclusions is, however, incomplete, and the paper would benefit from a more exhaustive methodological description. The work is nevertheless of broad relevance to ethnobotanists, pharmacologists, and scientists working on drug discovery, particularly those interested in natural products.

Reviewer #1 (Public Review):

Summary:<br /> The authors explored correlations between taste features of botanical drugs used in ancient times and therapeutic uses, finding some potentially interesting associations between intensity and complexity of flavors and therapeutic potential, plus some more specific associations described in the discussion sections. I believe the results could be of potential benefit to the drug discovery community, especially for those scientists working in the field of natural products.

Strengths:<br /> Owing to its eclectic and somehow heterodox nature, I believe the article might be of interest to a general audience. In fact, I have enjoyed reading it and my curiosity was raised by the extensive discussion.

The idea of revisiting a classical vademecum with new scientific perspectives is quite stimulating.

The authors have undertaken a significant amount of work, collecting 700 botanical drugs and exploring their taste and association with known uses via eleven trained panelists.

Weaknesses:<br /> I have some methodological concerns. Was subjective bias within the panel of participants explored or minimized in any manner? Were the panelists exposed to the drugs blindly and on several occasions to assess the robustness of their perceptions? Judging from the total number of taste assessments recorded and from Supplementary Material, it seems that not every panelist tasted every drug. Why? It may be a good idea to explore the similarity in the assessments of the same botanical drug by different volunteers. If a given descriptor was reported by a single volunteer, was it used anyway for the statistical analysis or filtered out?

The idea of "versatility" is repeatedly used in the manuscript, but the authors do not clearly define what they call "versatile".

The introduction should be expanded. There are plenty of studies and articles out there exploring the evolution of bitter taste receptors, and associating it with a hypothetical evolutionary advantage since bitter plants are more likely to be poisonous. Since plant secondary metabolites are one of the most important sources of therapeutic drugs and one of their main functions is to protect plants from environmental dangers (e.g., animals), this evolutionary interplay should be at least briefly discussed in the introductory section. Since the authors visit some classical authors, Parecelsus' famous quote "All things are poison and nothing is without poison. Solely the dose determines that a thing is not a poison" may be relevant here. Also note that some authors have explored the relationship between taste receptors and pharmacological targets (e.g., Bioorg Med Chem Lett. 2012 Jun 15;22(12):4072-4).

Reviewer #2 (Public Review):

Summary:<br /> This is an unusual, but interesting approach to link the "taste" of plants and plant extracts to their therapeutic use in ancient Graeco-Roman culture. The authors used a panel of 11 trained tasters to test ~700 different medicinal plants and describe them in terms of 22 "taste" descriptors. They correlated these descriptors with the plant's medical use as reported in the De Materia Medica (DMM 1st Century, CE). Correcting for some of the plants' evolutionary phylogenetic relationships, the authors found that taste descriptors along with intensity measures were correlated with the "versatility" and/or specific therapeutic use of the medicine. For example, simple but intense tastes were correlated with the versatility of a medicine. Specific intense tastes were linked to versatility while others were not; intense bitter, starchy, musky, sweet, cooling, and soapy were associated with versatility, but sour and woody were negatively associated. Also, some specific tastes could be associated with specific uses - both positive and negative associations. Some of these findings make sense immediately, but others are somewhat surprising, and the authors propose some links between taste and medicinal use (both historical and modern use) in the discussion. The authors state that this study allows for a re-evaluation of pre-scientific knowledge, pointing toward a central role of taste in medicine.

Strengths:<br /> The real strength of this study is the novelty of this approach - using modern-day tasters to evaluate ancient medicinal plants to understand the potential relationships between taste and therapeutic use, lending some support to the idea that the "taste" of a medicine is linked to its effectiveness as a treatment.

Weaknesses:<br /> While I find this study very interesting and potentially insightful into the development and classification of certain botanical drugs for specific medicinal use, I would encourage the authors to revise the manuscript and the accompanying figures significantly to improve the reader's understanding of the methods, analyses, and findings. A more thorough discussion of the limitations of this particular study and this general type of approach would also be very important to include.

The metric of versatility seems somewhat arbitrary. It is not well explained why versatility is important and/or its relationship with taste complexity or intensity. Similarly, the rationale for examining the relationships between individual therapeutic uses and taste intensity/complexity is not well explained, and given that a similar high intensity/low complexity relationship is common for most of the therapeutic uses, it restates the same concepts that were covered by the initial versatility comparison. There are multiple issues with the figures - the use of icons is in many cases counterproductive and other representations are not clear or cause confusion (especially Figure 3). The phylogenetic information about the botanicals is missing. Also missing is any reference/discussion about how that analysis was able to disambiguate the confounding effects of shared uses and tastes of drugs from closely related species.

eLife assessment

This important study uncovers a surprising link between two self-cleaving RNAs that belong to the same structural family. The evidence supporting the main conclusions is convincing and based on extensive biochemical and bioinformatic analysis. This research will be of broad interest to RNA molecular biologists and biochemists.

Reviewer #1 (Public Review):

Summary:<br /> The overall analysis and discovery of the common motif are important and exciting. Very few human/primate ribozymes have been published and this manuscript presents a relatively detailed analysis of two of them. The minimized domains appear to be some of the smallest known self-cleaving ribozymes.

Strengths:<br /> The manuscript is rooted in deep mutational analysis of the OR4K15 and LINE1 and subsequently in modeling of a huge active site based on the closely-related core of the TS ribozyme. The experiments support the HTS findings and provide convincing evidence that the ribozymes are structurally related to the core of the TS ribozyme, which has not been found in primates prior to this work.

Weaknesses:<br /> 1. Given that these two ribozymes have not been described outside of a single figure in a Science Supplement, it is important to show their locations in the human genome, present their sequence and structure conservation among various species, particularly primates, and test and discuss the activity of variants found in non-human organisms. Furthermore, OR4K15 exists in three copies on three separate chromosomes in the human genome, with slight variations in the ribozyme sequence. All three of these variants should be tested experimentally and their activity should be presented. A similar analysis should be presented for the naturally-occurring variants of the LINE1 ribozyme. These data are a rich source for comparison with the deep mutagenesis presented here. Inserting a figure (1) that would show the genomic locations, directions, and conservation of these ribozymes and discussing them in light of this new presentation would greatly improve the manuscript. As for the biological roles of known self-cleaving ribozymes in humans, there is a bioRxiv manuscript on the role of the CPEB3 ribozyme in mammalian memory formation (doi.org/10.1101/2023.06.07.543953), and an analysis of the CPEB3 functional conservation throughout mammals (Bendixsen et al. MBE 2021). Furthermore, the authors missed two papers that presented the discovery of human hammerhead ribozymes that reside in introns (by de la PeÃ{plus minus}a and Breaker), which should also be cited. On the other hand, the Clec ribozyme was only found in rodents and not primates and is thus not a human ribozyme and should be noted as such.

2. The authors present the story as a discovery of a new RNA catalytic motif . This is unfounded. As the authors point out, the catalytic domain is very similar to the Twister Sister (or "TS") ribozyme. In fact, there is no appreciable difference between these and TS ribozymes, except for the missing peripheral domains. For example, the env33 sequence in the Weinberg et al. 2015 NCB paper shows the same sequences in the catalytic core as the LINE1 ribozyme, making the LINE1 ribozyme a TS-like ribozyme in every way, except for the missing peripheral domains. Thus these are not new ribozymes and should not have a new name. A more appropriate name should be TS-like or TS-min ribozymes. Renaming the ribozymes to lanterns is misleading.

3. In light of 2) the story should be refocused on the fact the authors discovered that the OR4K15 and LINE1 are both TS-like ribozymes. That is very exciting and is the real contribution of this work to the field.

4. Given the slow self-scission of the OR4K15 and LINE1 ribozymes, the discussion of the minimal domains should be focused on the role of peripheral domains in full-length TS ribozymes. Peripheral domains have been shown to greatly speed up hammerhead, HDV, and hairpin ribozymes. This is an opportunity to show that the TS ribozymes can do the same and the authors should discuss the contribution of peripheral domains to the ribozyme structure and activity. There is extensive literature on the contribution of a tertiary contact on the speed of self-scission in hammerhead ribozymes, in hairpin ribozyme it's centered on the 4-way junction vs 2-way junction structure, and in HDVs the contribution is through the stability of the J1/2 region, where the stability of the peripheral domain can be directly translated to the catalytic enhancement of the ribozymes.

5. The argument that these are the smallest self-cleaving ribozymes is debatable. LÃ1/4nse et al (NAR 2017) found some very small hammerhead ribozymes that are smaller than those presented here, but the authors suggest only working as dimers. The human ribozymes described here should be analyzed for dimerization as well (e.g., by native gel analysis) particularly because the authors suggest that there are no peripheral domains that stabilize the fold. Furthermore, Riccitelli et al. (Biochemistry) minimized the HDV-like ribozymes and found some in metagenomic sequences that are about the same size as the ones presented here. Both of these papers should be cited and discussed.

6. The authors present homology modeling of the OR4K15 and LINE1 ribozymes based on the crystal structures of the TS ribozymes. This is another point that supports the fact that these are not new ribozyme motifs. Furthermore, the homology model should be carefully discussed as a model and not a structure. In many places in the text and the supplement, the models are presented as real structures. The wording should be changed to carefully state that these are models based on sequence similarity to TS ribozymes. Fig 3 would benefit from showing the corresponding structures of the TS ribozymes.

Reviewer #2 (Public Review):

Summary:<br /> This manuscript applies a mutational scanning analysis to identify the secondary structure of two previously suggested self-cleaving ribozyme candidates in the human genome. Through this analysis, minimal structured and conserved regions with imminent importance for the ribozyme's activity are suggested and further biochemical evidence for cleavage activity are presented. Additionally, the study reveals a close resemblance of these human ribozyme candidates to the known self-cleaving ribozyme class of twister sister RNAs. Despite the high conservation of the catalytic core between these RNAs, it is suggested that the human ribozyme examples constitute a new ribozyme class. Evidence for this however is not conclusive.

Strengths:<br /> The deep mutational scanning performed in this study allowed the elucidation of important regions within the proposed LINE-1 and OR4K15 ribozyme sequences. Part of the ribozyme sequences could be assigned a secondary structure supported by covariation and highly conserved nucleotides were uncovered. This enabled the identification of LINE-1 and OR4K15 core regions that are in essence identical to previously described twister sister self-cleaving RNAs.

Weaknesses:<br /> I am skeptical of the claim that the described catalytic RNAs are indeed a new ribozyme class. The studied LINE-1 and OR4K15 ribozymes share striking features with the known twister sister ribozyme class (e.g. Figure 3A) and where there are differences they could be explained by having tested only a partial sequence of the full RNA motif. It appears plausible, that not the entire "functional region" was captured and experimentally assessed by the authors.

They identify three twister sister ribozymes by pattern-based similarity searches using RNA-Bob. Also comparing the consensus sequence of the relevant region in twister sister and the two ribozymes in this paper underlines the striking similarity between these RNAs. Given that the authors only assessed partial sequences of LINE-1 and OR4K15, I find it highly plausible that further accessory sequences have been missed that would clearly reveal that "lantern ribozymes" actually belong to the twister sister ribozyme class. This is also the reason I do not find the modeled structural data and biochemical data results convincing, as the differences observed could always be due to some accessory sequences and parts of the ribozyme structure that are missing.

Highly conserved nucleotides in the catalytic core, the need for direct contacts to divalent metal ions for catalysis, the preference of Mn2+ oder Mg2+ for cleavage, the plateau in observed rate constants at ~100mM Mg2+, are all characteristics that are identical between the proposed lantern ribozymes and the known twister sister class.

The difference in cleavage speed between twister sister (~5 min-1) and proposed lantern ribozymes could be due to experimental set-up (true single-turnover kinetics?) or could be explained by testing LINE-1 or OR4K15 ribozymes without needed accessory sequences. In the case of the minimal hammerhead ribozyme, it has been previously observed that missing important tertiary contacts can lead to drastically reduced cleavage speeds.

eLife assessment

This valuable study presents a solid transcriptomic analysis of enterochromaffin cells, but there are weaknesses in the functional and physiological data describing the role of enterochromaffin cell mechanosensory receptors (Piezo2 channels) in regulating colonic motility.

Reviewer #1 (Public Review):

The authors have performed extensive work generating reporter mice and performing single-cell analysis combined with in situ hybridization to arrive at 14 clusters of enterochromaffin (EC) cells. Then, they focus on Piezo channel expression in distal EC cells and find that these channels might play a role in regulating colonic motility. Overall, this is an informative study that comprehensively classifies EC cells in different regions of the small and large intestine. From a functional point of view, however, the authors seem to ignore the fact that the expression of Piezo-2-IRES-Cre is broad, which would raise concerns regarding their physiological conclusions.

The authors may wish to consider the following specific points:

It is surprising that the number of ileal EC cells is less than that of the distal colon, and it would be interesting to know whether the authors can comment about ileal EC cells. It is unclear why ileal ECs were not included in the study, even though they are mentioned in the diagram (Fig. 2c).

Based on their analysis, there are 10 EC cell clusters in SI while there are only 4 clusters in the colon. The authors should comment on whether this is reflective of lesser diversity among colonic ECs or due to the smaller number of colonic ECs collected.

The authors previously described that distal colonic EC cells exhibit various morphologies (Kuramoto et al., 2021). Do Ascl1(+) EC cells particularly co-localize with EC cells with long basal processes? Also, to validate the RNA seq data, the authors might show co-localization between Piezo2/Ascl1/Tph1 in distal EC cells. It would be interesting to see whether Ascl1-CreER (which is available in Jax) specifically labels distal colonic EC cells as this could provide a good genetic tool to specifically manipulate distal colonic EC cells.

The authors used Piezo2-IRES-Cre mice, whose expression is rather broad. They might examine the distribution of Chrm3-mCitrine in the intestine (IF/IHC would be straightforward). And if the expression is in other cell types (which is most likely the case), they should justify that the observed phenotype derives from Piezo2-expressing EC cells. Alternatively, they could use Piezo2-Cre;ePetFlp (or Vil-Flp);Chrm3 to specifically express DREADD receptors in distal colonic EC cells. Also, what does 5HT release look like in jejunal EC cells in Piezo-CHRM3 mice?

For the same reasons as above, DTR experiments may also be non-specific. For example, based on the IF staining (Fig. 6b,d), there seems to be a loss of Tph1+ cells in the proximal colon of Piezo2-DTR mice, so the effects of the Piezo2-DTR likely extend beyond the distal colon.

It is unclear why the localized loss of Piezo2 in Piezo2-DTR mice alters small intestinal transit (Fig. 6g,h). The authors should discuss the functional differences observed between Piezo2-DTR (intraluminal app) and Vil1-Piezo2 KO mice i.e., small intestinal transit, 5HT release, etc. Are these differences due to the residual Piezo2 expression in Piezo2 KO mice? In this context, the authors may want to discuss their findings in the context of recent papers, such as those from the Patapoutian and Ginty groups.

Reviewer #2 (Public Review):

Summary:<br /> The authors investigated the expression profile of enterochromaffin (EC) cells after creating a new tryptophan hydroxylase 1 (Tph1) GFP-reporter mouse using scRNAseq and confirmative RNAscope analysis. They distinguish 14 clusters of Tph1+ cells found along the gut axis. The manuscript focuses on two of these, (i) a multihormonal cell type shown to express markers of pathogen/toxin and nutrient detection in the proximal small intestine, and (ii) on a EC-cluster in the distal colon, which expresses Piezo2, rendering these cells mechanosensitive. In- and ex- vivo data explore the role of the mechanosensitive EC population for intestinal/colonic transit, using chemogenetic activation, diptheria-toxin receptor dependent cell ablation and conditional gut epithelial specific Piezo2 knock-out. Whilst some of these data are confirmative of previous reports - Piezo2 has been implicated in mechanosensitive serotonin release previously, as referred to by the authors - the data are solid and emphasize the importance of mechanosensitive serotonin release for colonic propulsion. The transcriptomic data will guide future research.

Strengths:<br /> The transcriptomic data, whilst confirmative, is more granular than previous data sets. Employing new tools to establish a role of mechanosensitive EC cells for colonic and thus total intestinal transit.

Weaknesses:<br /> 1) The proposed villus/crypt distribution of the 14 cell types is not verified adequately. The RNAscope and immunohistochemistry samples presented do not allow assessment of whether this interpretation is correct - spatial transcriptomics, now approaching single-cell resolution, would be likely to help verify this claim.

2) The physiological function and/or functionality of most of the transcriptomically enriched gene products has not been assessed. Whilst a role for Piezo2 expressing cells for colonic transit is convincingly demonstrated, the nature of the mechanical stimulus or the stimulus-secretion coupling downstream of Piezo2 activation is not clear.

eLife assessment

This important study reconstructs the evolutionary history of Heliconius butterflies, a well-established model system for understanding speciation in the presence of gene flow between species. Using a convincing statistical phylogenetic approach that relies on the multispecies coalescent, the authors reconstruct the evolution of the lineage, including the timing of speciation events and the history of gene flow. The new phylogeny will be of interest to all researchers working on Heliconius butterflies, and the phylogenetic approach to investigators aiming to understand the history of lineages that have experienced extensive gene flow.

Reviewer #1 (Public Review):

Summary:<br /> This study aims to further resolve the history of speciation and introgression in Heliconius butterflies. The authors break the data into various partitions and test evolutionary hypotheses using the Bayesian software BPP, which is based on the multispecies coalescent model with introgression. By synthesizing these various analyses, the study pieces together an updated history of Heliconius, including a multitude of introgression events and the sharing of chromosomal inversions.

Strengths:<br /> Full-likelihood methods for estimating introgression can be very computationally expensive, making them challenging to apply to datasets containing many species. This study provides a great example of how to apply these approaches by breaking the data down into a series of smaller inference problems and then piecing the results together. On the empirical side, it further resolves the history of a genus with a famously complex history of speciation and introgression, continuing its role as a great model system for studying the evolutionary consequences of introgression. This is highlighted by a nice Discussion section on the implications of the paper's findings for the evolution of pollen feeding.

Weaknesses:<br /> The analyses in this study make use of a single method, BPP. The analyses are quite thorough so this is okay in my view from a methodological standpoint, but given this singularity, more attention should be paid to the weaknesses of this particular approach. Additionally, little attention is paid to comparable methods such as PhyloNet and their strengths and weaknesses in the Introduction or Discussion. BPP reduces computational burden by fixing certain aspects of the parameter space, such as the species tree topology or set of proposed introgression events. While this approach is statistically powerful, it requires users to make informed choices about which models to test, and these choices can have downstream consequences for subsequent analyses. It also might not be as applicable to systems outside of Heliconius where less previous information is available about the history of speciation and introgression. In general, it is likely that most modelling decisions made in the study are justified, but more attention should be paid to how these decisions are made and what the consequences of them could be, including alternative models.

• Co-estimating histories of speciation and introgression remains computationally challenging. To circumvent this in the study, the authors first estimate the history of speciation assuming no gene flow in BPP. While this approach should be robust to incomplete lineage sorting and gene tree estimation, it is still vulnerable to gene flow. This could result in a circular problem where gene flow causes the wrong species tree to be estimated, causing the true species tree to be estimated as a gene flow event. This is a flaw that this approach shares with summary-statistic approaches like the D-statistic, which also require an a-priori species tree. Enrichment of particular topologies on the Z chromosome helps resolve the true history in this particular case, but not all datasets will have sex chromosomes or chromosome-level assemblies to test against.

• The a-priori specification of network models necessarily means that potentially better-fitting models to the data don't get explored. Models containing introgression events are proposed here based on parsimony to explain patterns in gene tree frequencies. This is a reasonable and common assumption, but parsimony is not always the best explanation for a dataset, as we often see with phylogenetic inference. In general, there are no rigorous approaches to estimating the best-fitting number of introgression events in a dataset. Likewise, the study estimates both pulse and continuous introgression models for certain partitions, though there is no rigorous way to assess which of these describes the data better.

• Some aspects of the analyses involving inversions warrant additional consideration. Fewer loci were able to be identified in inverted regions, and such regions also often have reduced rates of recombination. I wonder if this might make inferences of the history of inverted regions vulnerable to the effects of incomplete lineage sorting, even when fitting the MSC model, due to a small # of truly genealogically independent loci. Additionally, there are several models where introgression events are proposed to explain the loss of segregating inversions in certain species. It is not clear why these scenarios should be proposed over those in which the inversion is lost simply due to drift or selection.

Reviewer #2 (Public Review):

Thawornwattana et al. reconstruct a species tree of the genus Heliconius using the full-likelihood multispecies coalescent, an exciting approach for genera with a history of extensive gene flow and introgression. With this, they obtain a species tree with H. aoede as the earliest diverging lineage, in sync with ecological and morphological characters. They also add resolution to the species relationships of the melpomene-silvaniform clade and quantify introgression events. Finally, they trace the origins of an inversion on chromosome 15 that exists as a polymorphism in H. numata, but is fixed in other species. Overall, obtaining better species tree resolutions and estimates of gene flow in groups with extensive histories of hybridization and introgression is an exciting avenue. Being able to control for ILS and get estimates between sister species are excellent perks. One overall quibble is that the paper seems to be best suited to a Heliconius audience, where past trees are easily recalled, or members of the different clades are well known.

Overall, applying approaches such as these to gain greater insight into species relationships with extensive gene flow could be of interest to many researchers. However, the conclusions could be strengthened with a bit more clarity on a few points.

1) The biggest point of concern was the choice of species to use for each analysis. In particular the omission of H. ismenius in the resolution of the BNM clade species tree. The analysis of the chromosome 15 inversion seems to rely on the knowledge that H. ismenius is sister to H. numata, so without that demonstrated in the BNM section the resulting conclusions of the origin of that inversion are less interruptible.

2) An argument they make in support of the branching scenario where H. aoede is the earliest diverging branch is based on which chromosomes support that scenario and the key observation that less introgression is detected in regions of low recombination. Yet, they go no further to understand the relationship between recombination rate and species trees produced.

3) How the loci were defined could use more clarity. From the methods, it seems like each loci could vary quite a bit in total bp length and number of informative sites. Understanding the data processing would make this paper a better resource for others looking to apply similar approaches.

Reviewer #3 (Public Review):

The authors use a full-likelihood multispecies coalescent (MSC) approach to identify major introgression events throughout the radiation of Heliconius butterflies, thereby improving estimates of the phylogeny. First, the authors conclude that H. aoede is the likely outgroup relative to other Heliconius species; miocene introgression into the ancestor of H. aoede makes it appear to branch later. Topologies at most loci were not concordant with this scenario, though 'aoede-early' topologies were enriched in regions of the genome where interspecific introgression is expected to be reduced: the Z chromosome and larger autosomes. The revised phylogeny is interesting because it would mean that no extant Heliconius species has reverted to a non-pollen-feeding ancestral state. Second, the authors focus on a particularly challenging clade in which ancient and ongoing gene flow is extensive, concluding that silvaniform species are not monophyletic. Building on these results, a third set of analyses investigates the origin of the P1 inversion, which harbours multiple wing patterning loci, and which is maintained as a balanced polymorphism in H. numata. The authors present data supporting a new scenario in which P1 arises in H. numata or its ancestor and is introduced to the ancestor of H. pardilinus and H. elevatus - introgression in the opposite direction to what has previously been proposed using a smaller set of taxa and different methods.

The analyses were extensive and methodologically sound. Care was taken to control for potential sources of error arising from incorrect genotype calls and the choice of a reference genome. The argument for H. aoede as the earliest-diverging Heliconius lineage was compelling, and analyses of the melpomene-silvaniform clade were thorough.

The discussion is quite short in its current form. In my view, this is a missed opportunity to summarise the level of support and biological significance of key results. This applies to the revised Melpomene-silvaniform phylogeny and, in particular, the proposed H. numata origin of P1. It would be useful to have a brief overview of the relationships that remain unclear, and which data (if any) might improve estimates.

It was good to see the authors reflect on the utility of full-likelihood approaches more generally, though the discussion of their feasibility and superiority was at times somewhat overstated and reductive. Alternative MSC-based methods that use gene tree frequencies or coalescence times can be used to infer the direction and extent of introgression with accuracy that is satisfactory for a wide variety of research questions. In practice, a combination of both approaches has often been successful. Although full-likelihood approaches can certainly provide richer information if specific parameter estimates are of interest, they quickly become intractable in large species complexes where there is extensive gene flow or significant shifts in population size. In such cases, there may be hundreds of potentially important parameters to estimate, and alternate introgression scenarios may be impossible to disentangle. This is particularly challenging in systems, unlike Heliconius where there is little a priori knowledge of reproductive isolation, genome evolution, and the unique life history traits of each species. It would be useful for the authors to expand on their discussion of strategies that can simplify inference problems in such systems, acknowledging the difficulties therein.

eLife assessment

This important study reports a new mutant mouse with compromised function of one of the BRCT repeats of TOPBP1, a DNA damage response protein. Mutant mice are viable but males are sterile, owing to the lack of spermatocytes beyond the late pachynema stage of the first meiotic prophase. Using immunofluorescence, phospho-proteomics and single-cell sequencing of cells in the testis, the authors provide solid evidence that the animals are defective in the maintenance (but not the initiation) of sex chromosome inactivation in pachynema. The report is of interest to researchers in the fields of meiosis as well as gene silencing/sex chromosome inactivation.

Reviewer #1 (Public Review):

Summary:

This is a very well written and performed study describing a TOPBP1 separation of function mutation, resulting in defective MSCI maintenance but normal sex body formation. The phenotype differs from that of a previous TOPBP1 null allele, in which both MSCI and sex body formation were defective. Additional defects in CHK phosphorylation and SETX localization are also described.

Strengths:

The study is very rigorous, with a remarkably large number of MSCI marks assayed, phosphoproteomics (leading to the interesting SETX discovery) and 10X RNAseq, allowing the MSCI phenotype to be further deconvolved. The approaches in most cases are robust.

Weaknesses:

There aren't many; please find list below:

1. The authors are committed to the idea that maintenance of MSCI is the major defect here. However, based on the data, an alternative would be that some cells achieve sex body formation and MSCI normally, while others do not. It would only take a small percentage of cells exhibiting MSCI failure to kill all the cells in the same germinal epithelium, so this could still explain the complete pachytene block. This isn't a major point...this phenotype is clearly different to the TOPBP1 KO, but a broader discussion of possibilities in the discussion would help. I raise this in the context of both the cytology and 10X analysis:

a) The assessment that sex body formation is normal is based on cytology in Supp 8 and 9, but a more rigorous approach would be to assess condensation of the XY pair in stage-matched spread cells (maybe they have that data already) by measuring distances between the X and Y centromere, or looking at stage IV of the seminiferous cycle, where all cells should have oval sex bodies but sex body mutants have persistent elongated XY pairs (see work of Namekawa and Turner). The authors do actually mention that gH2AX spreading is defective in many cells....and if this is true, condensation to form a sex body would almost certainly not have taken place in those cells.

b) Regarding the 10X data, the finding that expression of some XY genes is elevated and others are not is also consistent with a "partial" phenotype (some cells have normal XY bodies and MSCI, others fail in both). In Fig 6E, X expression looks to be elevated in B5 vs wt at all stages...if this were a maintenance issue, shouldn't it be equal to that in wt and then elevate later?

2. How is the quantitation showing impaired localization of select markers (e.g. SETX) normalized? How do we know that the antibody staining simply didn't work as well on the mutant slides?

3. Is testis TOPBP1 protein expression reduced in the B5 mutant?

4. 10X analysis: how were the genes on the y-axis in Supp 24 arranged? Is this by location on the X chromosome?

5. The final analyses in Fig 7: X-genes are subdivided based on their behavior (up, down, unchanged). What isn't clear to me is whether the authors have considered the fact that there are global changes in gene expression during meiosis (very low in lep , zyg and early pach, then ramps up hugely from mid pach). In other words, is this normalized to autosomal gene expression?

6. Again regarding the 10X analysis, my prediction would be that not ALL X and Y gene would increase in pach if MSCI were ablated...we should remember that XY genes have been subject to MSCI for some 160 million years of evolution, and this will mean that many enhancers that originally drove their expression prior to the evolution of MSCI will now be lost. This has been our experience: many XY genes aren't elevated at pach even in mutants in which MSCI is totally defective. I'd urge the authors to consider this possibility when they use XY gene expression patterns to diagnose the severity or timing of the MSCI phenotype. This could be a discussion point.

Reviewer #2 (Public Review):

Summary:<br /> This paper described the role of BRCT repeat 5 in TOPBP1, a DNA damage response protein, in the maintenance of meiotic sex chromosome inactivation (MSCI). By analyzing a Topbp1 mutant mouse with amino acid substitutions in BRCT repeat 5, the authors found reduced phosphorylation of a DNA/RNA helicase, Sentaxin, and decreased localization of the protein to the X-Y sex body in pachynema. Moreover, the authors also found decreased repression of several genes on the sex chromosomes in the male mice.

Strengths:<br /> The works including phospho-proteomics and single-cell RNA sequencing with lots of data have been done with great care and most of the results are convincing.

Weaknesses:<br /> One concern is that, although the Topbp1 mutant spermatocytes show very severe defects after the stage of late pachynema, the defect in the gene silencing in the sex body is relatively weak. It is a bit difficult to explain how such a weak misregulation of the gene silencing in mice causes the complete loss of cells in the late stage of spermatogenesis.

Reviewer #3 (Public Review):

The work presented by Ascencao and coworkers aims to deepen into the process of sex chromosome inactivation during meiosis (MSCI) as a critical factor in the regulation of meiosis progression in male mammals. For this purpose, they have generated a transgenic mouse model in which a specific domain of TOPBP1 protein has been mutated, hampering the binding of a number of protein partners and interfering with the regulatory cascade initiated by ATR. Through the use of immunolocalization of an impressive number of markers of MSCI, phosphoproteomics and single cell RNA sequencing (scRNAseq), the authors are able to show that despite a proper morphological formation of the sex body and the incorporation of most canonical MSCI makers, sex chromosome-liked genes are reactivated at some point during pachytene and this triggers meiosis progression breakdown, likely due to a defective phosphorylation of the helicase SETX.

The manuscript presents a clear advance in the understanding of MSCI and meiosis progression with two main strengths. First, the generation of a mouse model with a very uncommon phenotype. Second, the use of a vast methodological approach. The results are well presented and illustrated. Nevertheless, the discussion could be still a bit tuned by the inclusion of some ideas, and perhaps speculations, that have not been considered.

eLife assessment

The manuscript by Kahraman et al. describes the use of a fluorescent dye for purifying and analyzing human islet alpha cells. The study provides solid evidence that the alpha cells can be purified using this method and the cells remained viable and functional after culturing for several days. The significance of the study is access to a new tool that will be useful for islet biologists and researchers studying diabetes mechanisms.

Reviewer #1 (Public Review):

The study by Kahraman et al describes the application of a reaction-based probe "diacetylated Zinpyr1" (DA-ZP1) that was developed for the enrichment of human islet beta cells (Lee et al. 2020 to purify human cadaveric alpha cells. The probe binds zinc with high enough affinity to allow the authors to separate beta cells from alpha cells based on the fluorescence intensity; beta cells had high intensity and alpha cells had medium intensity. FACs sorting of cells with intermediate fluorescent intensity were enriched for glucagon expression indicating they were alpha cells. They went on to reaggregate the purified alpha cells into pseudo-islets to test for viability, proliferation, ability to secrete glucagon and transcriptome analysis. These studies demonstrated that the pseudo-alpha cell islets were able to be maintained in culture for up to 10 days without losing their function and with only minor changes in gene expression.

The strengths of the manuscript include:<br /> 1. The description and characterization of a novel tool with which to purify human islet alpha cells<br /> 2. The ability to use the same DA-ZP1 probe to purify both human alpha and beta cells<br /> 3. The functional analysis to show that purified alpha cells retain their identity and maintain function even after in vitro culturing.<br /> 4. Providing a comparison of the transcriptome between whole islets, unsorted islets and sorted alpha cell pseudo-islets. The data is strengthened by the use of four donor islets and several timepoints for the transcriptomic analysis.<br /> 5. The quality of the data and data presentation

Weaknesses include:<br /> 1. Lack of a comparison with other published methods to purify human alpha cells<br /> 2. Unbiased transcriptome analysis of the sorted "high" vs. "medium" fluorescent populations to assess the degree of cross contamination between the 2 populations<br /> 3. Use of only one donor islet for functional analyses

Overall, this study represents a solid characterization of a new tool for purifying cadaveric human alpha cells that will be useful to researchers in the islet biology and diabetes fields.

Reviewer #2 (Public Review):

In the manuscript by Kahraman et al. the authors tested a recently developed Zn2+ indicator fluorogenic sensor as a tool to sort and purify human alpha cells from cadaveric donor islets, for downstream transcriptional and functional analysis. They demonstrate that their previously published sensor DA-ZP1, which was used to sort adult human islet beta cells in their previous work (Lee et al. 2020) they have now adapted for sorting alpha cells based on the 'intermediate' fluorescence intensity of these cells during staining. FACS purification of DA-ZP1-intermediate cells reveals they are strongly enriched for GCG+ cells (alpha cells). The sorted alpha cells can be reaggregated into alpha-pseudoislets for further studies. They carry out a variety of assays to characterize the viability, proliferation, apoptosis, glucagon secretion and transcriptomic changes in their sort purified alpha cells as compared with unsorted islet cells and intact islets. They conclude that sorting alpha cells with DA-ZP1 staining does not alter their function or transcriptome and allows stable maintenance of alpha-pseudoislets in culture for up to 10 days with no deleterious effects.

Strengths:<br /> 1. The study is a nice resource for the field, particularly with the ongoing interest in studying alpha cell biology and function relevant to health and diabetes. The probe that they have previously published can now be used to simultaneously sort alpha and beta cells, which would be a great approach for the field. The results are generally supportive of the conclusions.

2. The study used several human cadaveric donor islet preparations (four in total) representing different ancestries, limiting bias and inter-donor variation. A variety of cellular/molecular assays are employed to provide detailed phenotypic information.

3. The transcriptomic profiling are very strong and provide solid evidence that the reaggregated alpha-pseudoislets are not dedifferentiating or losing function during prolonged (10 day) culture times.

4. Visual presentation is clear and easy to follow for non-specialists.

Weaknesses:

1. The authors are presenting a previously developed probe/tool and also mention that other probes have been developed that can perform a very similar function, so the overall novelty is limited. They did not provide experimental evidence of how their probe is comparable or superior to other probes (e.g. ZIGIR, Newport Green).

2. The authors performed glucagon secretion assays to monitor the function of the sort purified and reaggregated alpha-pseudoislets, but this was only done on 1 of the 4 human islet donors, limiting the generalizability of the conclusions. Also very few experiments were performed to examine alpha cell function in the sort purified cells.

Reviewer #3 (Public Review):

This study presents a new method to highly purify live human pancreatic α cells using the zinc-based reaction probe DA-ZP1. After demonstrating this probe is capable of separating β and α cells from other islet and non-islet cells based on florescence intensity, the authors employ a variety of experimental approaches to demonstrate that these isolated α cells are functional and capable of maintaining their viability and identity in culture over time. The authors also investigate the impact of islet dissociation and cell reaggregation on the islet cell transcriptome, where they primarily identified downregulation of pathways associated with extracellular matrix organization, cell surface interactions, and focal adhesion. Overall, this study adds an additional tool to isolate human α cells to the islet biology community, which may aid in further understanding of human α cell biology under both normal and diabetic conditions. However, some caveats of this study include:

1) While the authors claim that this method improves human α cell yield over antibody-based approaches, they provide no direct comparison between the two methods.<br /> 2) The strength of studies determining cell fraction purity and α cell characteristics (function, viability, proliferation, and apoptosis rates) would be strengthened by performing these studies across multiple donors rather than multiple replicates from the same donor.<br /> 3) Given the heterogeneous nature of the human islet, the use of bulk RNA-sequencing makes the interpretation of genes obtained via the comparison of α-pseudoislets and unsorted pseudoislets difficult. Some cell-specific signals will be missed or masked by differences in cell mixture between groups. It is unclear whether these expression changes are due to α-intrinsic changes or simply the loss of other cell types.<br /> 4) Supplementary files concerning bulk sequencing data is not transparent, with only the direction of the gene expression noted.

Author Response

Reviewer #1 (Public Review):

In this genetic and imaging based analysis of stem-cell maintenance and organ initiation, two phases important for continued production of shoot organs in plants, the authors tested whether SHR and targets/partners (SCR, SCL23, JKD) provide the circuitry to maintain stem cell pool and contribute to the production of lateral organs. Finding that these factors are indeed expressed in and required for SAM activities, and furthermore, behaviors of SHR and SCR in the root are recapitulated in the meristem, including mobility of SHR (here to epidermis from internal layers), activation of SCR by SHR, and "trapping" of SHR movement by complexing with SCR. Strengths include high quality imaging of reporters and FRET-FLIM measurement to assess in vivo complex formation. The analysis is then extended to link SHR and SCR to shoot-specific factors and auxin, again by testing expression, genetic dependencies and physical interaction. This is repeated for a number of factors and individually, each is well done experiment. Conclusions about causal relationships are somewhat overstated (for example, the idea that SHR-SCR act through CYCD6 to alter cell division is based on expression patterns, not a functional analysis of cycd6).

We concluded that SHR and cofactors drive cell proliferation through CYCD6;1, substantiated by the significant reduction in pCYCD6;1-GFP expression within the lateral organ primordia of the shr-2 mutant. This decrease in expression corresponds with the reduction in the number of cell layers within the L3 of the lateral organ primordia in shr-2 mutants, compared to wild-type. To further support this conclusion, we have added new data by analyzing the meristem of the cycd6;1 mutant. Our findings reveal a small, but significant reduction in both meristem size and the number of cell layers in the L3, relative to the wild type, as depicted in Fig4-FigSuppl2I-N. Collectively, these findings underscore our assertion that the SHR regulatory network plays a role in activating CYCD6;1 expression, thereby promoting cell division within the lateral organ primordia.

In general, there are many high-quality studies included in this paper, and the presentation of imaging data (both the images themselves and quantification of data) is excellent. There is also a lot of data, and while each section was presented in a logical way, connections between sections, and the overarching developmental questions were sparse. Because the authors found that many of the relationships defined in the root were recapitulated in the shoot, the present organization leaves one with somewhat of a sense that little new was learned, and yet, the shoot meristem IS different and there are shoot specific inputs into the core regulatory factors. Rewriting to highlight the different activities (and thus expectation about regulation) could make the finding of the same network more interesting and creating a summary figure that highlights the input of shoot specific signals would bring the unique analysis to the forefront.

We greatly appreciate your positive feedback on the imaging data presentation and the quality of the included studies! We tried to address your and the other reviewer´s comments and strengthened the connections between the different sections of the manuscripts. We made substantial revisions to the organization and presentation of the paper. Our focus has been on highlighting the distinct activities and regulatory aspects of the SHR network within the shoot meristem, underscoring the novel insights gained from this analysis. We also created a summary figure that features the input of shoot-specific signals, thereby emphasizing the unique analysis conducted. These changes have allowed us to better convey the significance of our findings and showcase the novel aspects of shoot meristem regulation. We believe these revisions align more closely with the paper's objectives and will make the study's contributions more engaging and apparent.

Reviewer #2 (Public Review):

This study contains a huge amount of data and the images are of high quality. However, the conclusions are not really well supported. The authors may have reached too far from their results. The roles of SHR, SCR and SCL23 in the shoot apex are not really clarified. The manuscript by Bahafid et al., reports a study of the functions of SHORTROOT (SHR), a well-established root development regulator in the shoot apical meristem (SAM) development with focus on lateral organ initiation. A large amount of data is included in this paper. This study highly depends on imaging, and the images are in general of very good quality. The authors show reciprocal interactions between SHR and SCR with auxin/MP. There are also a large amount of genetic interactions among several genes, including WUS and CLV3. Although the study provides a vast amount of data, the conclusions are not so well supported. There seem to be many interactions, at the protein level, and at the transcriptional regulation level, but the conclusion is nevertheless ambiguous.

We have refined our manuscript.

Author Response

Evaluation Summary:

The manuscript shows that retinal ganglion cell light responses in awake mice differ substantially from those under two forms for anesthesia and previously attained ex vivo recordings. This difference is central to our understanding of how ganglion cell responses relate to behavior. There are a few technical issues and issues about how the work is presented that could be strengthened.

We thank the reviewers for their constructive comments. We have addressed all the issues, and added substantially more data and analysis results in the revised manuscript, further supporting our findings that awake responses are larger, faster, and more linearly decodable in the mouse retina than those responses under anesthesia or ex vivo.

Reviewer #1 (Public Review):

This paper compares output signals from the mouse retina in three conditions: awake mice, anaesthetized mice, and isolated retinas. The paper reports substantial differences, particularly between awake and either of the other conditions. Retinal signaling has been well studied using ex vivo preparations, with an assumption that the findings from those studies can be carried over to how the retina operates in vivo. The results from this paper at a minimum indicate a need to be cautious about that assumption. There are several technical issues that need testing or further explanation, and several issues about the presentation that could be clarified.

Spike sorting

The paper does not describe any control analyses that test for contamination in spike sorting. These are needed to evaluate the work.

We have reported the details of our spike sorting procedure in the revised manuscript (Data Analysis section in Methods and Figure 1). In short, single-units were identified by clustering in principal component space, followed by manual inspection of spike waveform (triphasic as expected from axonal signals; e.g., revised Figure 1F-H; Barry, 2015) as well as auto- and cross-correlograms (minimal inter-spike interval above 1 ms for a refractory period; e.g., revised Figure 1I-K). A small fraction of visually responsive cells (20/282, awake; 21/325, isoflurane; 1/103, FMM) had a small fraction of interspike intervals below 2 ms; but, whether or not including them in the analysis did not affect our main conclusions.

Light levels

The paper argues that differences in light level cannot account for the results. According to the methods, light levels were about two-fold higher at the retina in array recordings as compared to the front of the eye for in vivo recordings. The main text indicates that they differ less, it's not clear why the numbers in the main text and methods are different. Aside from this issue, this comparison does not consider the loss of light between the front of the eye and the retina. It is crucial that the paper provide a more detailed description of light levels. This should include converting those light levels to units that include the spectral output of the light source used (e.g. to isomerizations per rod or cone per second).

The maximum light intensity of our in vivo setup was 31.3 mW/m2 (with 15.9 mW for UV LED and 15.4 mW/m2 for blue LED). Following the suggestion by the reviewer, we calculated the photon flux on the mouse retina in vivo by taking into account the loss of light by the eye optics. In short, assuming 50% and 68% transmittance at 365 nm and 454 nm, respectively (Jacobs & Williams 2007), the pupil size of 1 mm and the retinal diameter of 4 mm with the stimulus covering 73° in azimuth and 44° in elevation, we obtained the photon flux on the mouse retina in vivo as 3.81×103 and 6.64×103 photons/s/μm2 for UV and blue light, respectively. Assuming a total photon collecting area of 0.2 μm² for cones and 0.5 μm² for rods (Nikonov et al. 2006), and a relative sensitivity of rods, S- and M-cones to be [UV, Blue]=[25, 60], [90, 0], [25, 60]%, respectively (Jacobs & Williams 2007), we then estimated the photoisomerization (R) rate as: 2.5×103 R/rod/s, 0.7×103 R/S-cone/s, and 1.0×103 R/M-cone/s.

In contrast, the maximum light intensity of the in vitro set up was 36 mW/m2 as reported in Vlasiuk and Asari (2021). The photon flux on the isolated retina was then estimated to be around 9×104 photons/s/μm2 (under the assumption that the white light from a CRT monitor is centered around 500 nm). Assuming the sensitivity of rods, S- and M-cones to be 40, 2 and 40%, respectively, we then obtained 4×104 R/rod/s, 2×103 R/S-cone/s, and 4×104 R*/Scone/s.

Thus, the light intensity level was about ten times larger for the in vitro recordings than for the in vivo recordings. The amount of light reaching the retina in the awake condition should also be somewhat smaller than that under anesthesia due to pupillary reflexes. Past studies suggest that the darker the stimulus is, the slower the kinetics is and the smaller the response is for RGCs in an isolated retina (Wang et al 2011). Thus, the light intensity difference cannot simply account for the higher firing and faster kinetics in the awake condition than ex vivo or in the anesthestized condition.

We have revised the manuscript accordingly.

Comparison with other work

The authors accurately point out that there is not much prior work on retinal outputs in awake animals. The paper, however, minimally describes the work that does exist. The Hong et al. (2018) paper, in particular, should be discussed. There are several differences between the results of that paper and the present paper. These include the fraction of recorded cells that are DS cells, and the maintained firing rates (though this does not appear to be studied systematically in Hong et al.).

In the discussion section of the revised manuscript, we clarified connections to the existing studies on the retinal activity in vivo. To our knowledge, none of the past studies provided descriptive statistics on the awake RGC response properties (Hong et al., 2018; Schroeder et al., 2020; Sibille et al., 2022). Nevertheless, consistent with our study, we can see high baseline activity in the reported examples from C57BL6 mice (Figure 3C, Schroeder et al. 2020; Figure S7h, Sibille et al. 2022).

Hong et al (2018), in contrast, reported somewhat different as pointed out by the reviewer. Firstly, they found a relatively low baseline activity in RGCs of albino CD1 mice. We think that this is likely due to general impairments of the vision/retina associated with albinism. While equipped with normal electroretinogram signals, CD1 mice showed no optomotor response and a reduced number of rods (Abdeljalil et al 2005; Brown et al 2007). This suggests a certain level of retinal dysfunction in these mice. Secondly, Hong et al (2018) reported a higher fraction of direction-selective RGCs in their recordings (>50% at a DS index threshold of 0.3). This is even higher than one would expect from anatomical and physiological studies ex vivo on BL6 mice (about a third; Sanes and Masland, 2015; Baden et al., 2016; Jouty et al 2013). Besides the effect of albinism, we think that this overrepresentation of DS cells in Hong et al (2018) arose as a consequence of the low baseline activity. As discussed above, the higher the baseline activity, the lower the DS/OS index by definition (Eq.(3) in Methods). Indeed we found much more cells with high DS/OS index values in our anesthetized data than in awake ones (42-54% vs 17% at an index value threshold of 0.15; Figure 2), even though these recordings were done in the same experimental set up.

A related issue is that there are a few comparisons of ex vivo RGC responses with behavioral sensitivity. Smeds et al. (2019) is one example. More generally, the long-standing observation that dark-adapted sensitivity approaches limits set by Poisson fluctuations in photon absorption, and that prior RGC measurements are consistent with this result, is hard to explain if the RGCs are firing at high spontaneous rates under these conditions. RGC responses will certainly change with light level, but this merits discussion in the paper.

As the reviewer pointed out, the retina may employ different coding principles under different light levels. In a scotopic condition, ex vivo studies reported a high tonic firing rate for OFF RGC types (~50 Hz, OFF sustained alpha cells in mice; Smeds et al 2019; ~20 Hz, OFF parasol cells in primates; Ala-Laurila and Rieke, 2014), while a low tonic firing for ON cell types (<1Hz for both ON sustained alpha in mice and ON parasol in primates). These ON cells were shown to be responsible for light detection by firing in the silent background, hence compatible with the sparse feature detection strategy. In contrast, our recordings were done in a high mesopic / low photopic range where both rods and cones are supposedly active. Unlike the scotopic condition with rod vision, we then found high firing in awake recordings in general, indicating that no visual feature can be readily detectable as brief firing events in the silent background. To explore the implications of such firing patterns on visual coding, we took a modelling approach in the revised manuscript. We found that a latency-based temporal code was not preferable in the awake condition (Figure 7); and that a linear decoder worked significantly better with the population responses in the awake condition to capture the presented random fluctuation of the light intensity (Figure 8). While we have not tested any behavioural relevance in our study besides correlation to locomotion/pupil size, it is then possible that the retina may work in different modes under different light intensity regimes (Tikidji-Hamburyan et al 2015).

We clarified these points in the revised discussion section.

Sampling bias

The paper argues that sampling bias is not likely to contribute substantially to the results because of the wide variety of cell types recorded (line 431). This does not seem like a particularly strong argument, especially given the large degree of overlap in the distributions of most quantities across preparations. The argument about many cell types could be made more strongly if the distributions were completely separated, but that is not the case.

We cannot deny the presence of a sampling bias in our datasets, and as the reviewer pointed out, we made comparisons only at a population level, but not at the level of individual cells or cell-types. However, the anesthetized and awake recordings were done with the same recording setup and techniques, and thus subject to the same sampling bias. Hence, the difference in the RGC response properties between these conditions cannot be explained by the sampling bias per se.

Sensitivity

The firing rates in response to 10% contrast sinusoids are quite low, as are the maximal firing rates for high contrast sinusoids. Relatedly, the modulation produced by the noise stimuli, particularly for the array recordings, is weak. This raises concerns about the health of some of the preparations.

To our knowledge, in vivo contrast responses reported here were comparable to ex vivo data in previous reports (mouse, Jouty et al 2018, Pearson and Kerschensteiner 2015; rat, Jensen 2017, 2019). Likewise, the static nonlinearity and its upper bound for ex vivo responses were comparable between this study and previous reports (Santina et al. 2013; Kerschensteiner et al 2008; Cantrell et al 2010; Trapani et al 2022).

We also examined batch effects in the response to the noise stimuli. We found certain variabilities across preparations in each recording condition, but not to the extent to discard any particular data as an obvious outlier (Figure 6 – figure supplement 1). While it is difficult to tell the health status of preparations retrospectively, we thus believe that the effects were negligible.

Efficient coding

Sparse firing is not a universal property of retinal ganglion cell responses. Primate midget RGCs, for example, have pretty high maintained firing rates as shown in many past studies. Mouse RGCs have also been reported to operate in a mode similar to the high firing rate On cells reported here (Ke et al. 2014). A more balanced discussion of this past work is needed.

As the reviewer pointed out, some retinal ganglion cells show high firing under certain conditions. In a scotopic condition, for example, OFF cells have high firing rates, while ON cells fire virtually nothing unless a light stimulus is presented (Ke et al 2014; Smeds et al 2019). At the behavoural level, a single-photon detection above chance level nevertheless relies on the information from the ON but not the OFF pathway (Smeds et al 2019). Thus, the sparse coding framework still works as a valid strategy here, if not universal.

This is, however, very different from what we report here. In a high-mesopic/low-photopic light level, we found a general increase of firing across all cell categories in the awake condition, compared to the anesthetized or ex vivo recordings (Figures 3 and 6). While this lowers information transfer rate (bits/spike; Figure 7), we found that the awake responses were more linearly decodable than the responses in the other conditions (Figure 8). We also ran a simulation and showed that a latency-based temporal code is not preferable for the awake responses (Figure 7 – figure supplement 1). These results suggest that the retina in awake condition is in favor of a rate code, though we have not tested all light levels or any behavioural relevance here.

We clarified these points in the revised manuscript.

Role of eye movements

Could eye movements be at least partially responsible for the differences in response properties? Specifically, small fixational eye movements might produce a constantly varying input that could modulate firing.

As described above (Essential Review item #2), eye movements were rarely observed during the head-fixed awake recordings. Eliminating those events from the analysis did not change our overall conclusions, and thus their contributions should be minimal in this study. It should also be noted that we mainly used full-field stimulation, and thus microsaccades should not substantially affect the amount of light impinging on the retina. We clarified these points in the revised manuscript.

Reviewer #2 (Public Review):

The technical achievements presented in the manuscript represent a tour de force, as optical tract recordings in awake mice have only rarely been done before. The substantial number of neurons recorded in both awake and anaesthetized conditions form a precious and worldwide unique dataset. However, since the recordings represent a non-standard approach, it would be, in my view, highly beneficial to show more details about the success of the method. How did the authors post-hoc identify electrode contacts located in the optical tract, how did the spike waveforms look like, what were the metrics of spike sorting quality, etc.

We added more details about our recording and analysis methods in the revised manuscript. Below are answers to the reviewer’s specific questions:

• The probe was coated with a fluorescent dye (DiI stain) and its location was verified histologically after the recordings (Figure 1E).

• Spike waveforms typically had a triphasic shape (e.g., Figure 1F-H) as expected from axonal signals (Barry, 2015).

• Single-units were identified by clustering in principal component space, followed by manual inspection of spike shape as well as auto- and cross-correlograms. Most units had a minimum interspike interval above 2 ms (93%, awake; 94%, isoflurane; 99%, FMM); and no units had the interspike intervals below 1 ms for a refractory period (e.g., Figure 1I-K), except for 1 (out of 103) for FMM-anesthetized recordings.

We then selected visually responsive cells (SNR>0.15; see Eq.(1) in Methods) for the analyses.

The authors go a long way in characterising the functional response properties of the recorded neurons and relating them to previous ex-vivo recordings. Based on the responses they find, the authors claim that they identified "... a new response type [which] likely emerged due to high baseline firing in awake mice". Regarding this claim, how do the authors rule out that it corresponds to any of the previously described cell types? For instance, the very sharp transient or brief modulations by the contrast part of the stimulus might have been missed in previous classifications based on calcium responses (e.g. Baden et al. 2016), where a number of cell types seem to respond equally strong to grey and white and have an elevated response throughout the sinusoidal modulation of contrast. I acknowledge that the authors touch upon the possibility that the newly described OFFsuppressive ON cells correspond to a known cell type in the discussion, but I would recommend changing the phrasing of the results to avoid potential misunderstandings.

We agreed with the reviewer and revised the manuscript accordingly. Here we have two possibilities. Firstly, as the reviewer pointed out, this kind of response dynamics could be overlooked previously because of a difference in the recording modality (Ca imaging; Baden et al 2016) or clustering methods (Jouty et al 2019). Secondly, these cells may belong to one of the cell-types described in the past ex vivo studies, but exhibited distinct response dynamics in vivo as an emerging property of the awake condition. This is an interesting topic to pursue in future studies.

The manuscript makes the interesting suggestion that "the retinal output characteristics [...] observed in vivo, [...] provide a completely different view on the retinal code". Given that this conclusion would change the way we should think about and do retinal neuroscience, in my view, the authors should take a few more steps to quantitatively demonstrate the implications of their findings on retinal coding, e.g. how much lower is the information transmitted per spike, how much does a temporal code based on spike timing suffer with the latencies observed in vivo. If the authors could quantify through computational modelling approaches the consequences of the observed differences, they might also be able to revise their title / main message, i.e. that "Awake responses SUGGEST inefficient dense coding in the mouse retina".

To explore functional implications of our findings, we performed three more analyses as suggested by the reviewer. Specifically,

1) We showed that the information transmitted per spike was significantly lower in awake condition, while the total information rate was comparable (Figure 7).

2) We tested the performance of a linear decoder applied on the firing rate in response to full-field noise, and showed that it worked significantly better for the awake population responses (Figure 8).

3) We simulated RGC responses to a full-field contrast change at different intensities in different conditions, and showed that a latency coding did not work well with awake responses, compared to ex vivo or anesthetized responses (Figure 7 – figure supplement 1).

These results strengthened our conclusion that awake response dynamics were different from anesthetized or ex vivo responses, all arguing against the sparse efficient coding principles at least at a light level we examined. We nevertheless kept the title as is because we have not explored the retinal coding properties per se. Our main claim stays on the visual response characteristics of retinal outputs in awake mice.

Reviewer #3 (Public Review):

The manuscript by Boissonnet, Tripodi, and Asari compares retinal ganglion cell (RGC) light responses in awake mice (recorded in the optic nerve) with those under two forms for anaesthesia and previously attained ex vivo recordings. This is a well motivated study looking at a question that is really critical to the field.

The presentation is generally clear and compelling. My suggestions are relatively minor and aimed at improving an already very strong article.

1) More cells in the awake condition would help strenghten the conclusions. Only 51 cells are reported, and mouse RGCs comprise more than 40 different types. The authors are well aware of the possible confound of sampling bias, and the best way to mitigate this issue in this experimental paradigm is simply to record more cells. The anesthsia conditions each have about 100 cells, which is better.

We made substantially more recordings in the awake condition, reaching 282 cells (in 15 animals) in total in the revised manuscript. This does not yet allow for a full cell-type classification as in the past ex vivo studies. Nevertheless, we did our best to broadly classify visual responses, and showed that the overall conclusions remained the same: awake RGCs had higher baseline firing and faster response kinetics in general. For details, see above our response to the Essential Revision item #1.

2) It took me longer than it should have (had to look up the previous paper cited) to figure out that the ex vivo comparison data were recorded at 37{degree sign}C. This is an important detail since most ex vivo recordings are at 32{degree sign}C. The authors should make this clear in the text and perhaps say something in the Discussion about comparisons to the larger body of literature of ex vivo studies at 32{degree sign}.

We are aware that most ex vivo studies on the retina were performed at 32 °C, which is lower than physiological body temperature (37 °C). However, the temperature of the ocular surface is around 37 °C (Vogel et al 2016), suggesting that the retina should operate at 37 °C in vivo. This is why we decided to perform ex vivo experiments at 37 °C in our previous study (Vlasiuk and Asari, 2021), allowing us to make a fair comparison between the ex vivo and in vivo recordings.

We clarified the point in the revised manuscript.

3) Direction and orientation selectivity should be separated in Fig. 2 and not combined into the confusing term "motion sensitive." Motion sensitivity has another meaning in the literature for RGCs that respond preferentially to moving over static stimuli without direction or orientation preference (Kuo et al., 2016; Manookin et al., 2018)

We agree with the reviewer. In the revised manuscript, we separated the direction and orientation selective cells (Figure 2), and avoided the term “motion sensitive.”

4) While I am certainly sympathetic to the argument that the RGC spike code is "inefficient" in the sense that it does not conform to efficient coding theory (ETC), I think it's oversimplified to claim that the present data is a key argument against ETC. Plenty of ex vivo data has already shown ETC to be incomplete at best, and misguided at worst, since it includes the implicit assumption that image reconstruction is the retina's objective function (or even that the experimenter has any idea what that objective function is). For example, OFF sustained alpha (OFF delta in guinea pig) RGCs are not quite sparse feature detectors even ex vivo, and they seem to be optimized to transmit contrast with high SNR (Homann and Freed, 2017). In general, the enormous coverage factor of the RGC population seems to make ETC untenable to begin with, as discussed in (Schwartz, 2021) and elsewhere. I realize that there are still people attached to simplistic forms of ETC as a key principle of retinal computatiion, so I am not asking for the authors to completely remove this angle. Rather, a more nuanced treatment of the issue both in the introduction and the discussion is warranted.

We totally agree that we are not the first to argue against the efficient coding principles in the retina (Schwartz, 2021). The main argument in this study is that certain aspects of the RGC activity are distinct in an awake condition, such as the baseline firing and response kinetics, and thus we cannot simply translate our knowledge obtained from ex vivo studies into awake animals. To explore the implications on retinal computations, we showed in the revised manuscript that 1) awake responses have a comparable total information transfer rate (in bits per second; Figure 7A) but are less efficient (i.e., lower bits per spikes; Figure 7B); 2) awake responses are not in favor of a latency-based temporal code (Figure 7 – figure supplement 1); and 3) a linear decoder worked significantly better with awake responses (Figure 8), even though an image reconstruction is not necessarily the objective function of the retina. These results point out a need to rethink about retinal function in vivo, including the efficient coding theory.

We thank the reviewer for the suggestion, and revised the manuscript accordingly.

References

Homann, J., and Freed, M.A. (2017). A mammalian retinal ganglion cell implements a neuronal computation that maximizes the SNR of its postsynaptic currents. Journal of Neuroscience 37, 1468-1478.

Kuo, S.P., Schwartz, G.W., and Rieke, F. (2016). Nonlinear Spatiotemporal Integration by Electrical and Chemical Synapses in the Retina. Neuron 90, 320-332.

Manookin, M.B., Patterson, S.S., and Linehan, C.M. (2018). Neural Mechanisms Mediating Motion Sensitivity in Parasol Ganglion Cells of the Primate Retina. Neuron 97, 13271340.e4. Schwartz, G.W. (2021). Retinal Computation (Academic Press).

Author Response

Reviewer #1 Public Review:

In this manuscript, Berne et al apply state-of-the-art methodology for quantifying animal behavior to identify distinct behavioral components associated with the repeated application of mechanical stimuli. A central strength of this manuscript is the development of a sophisticated system for precisely applying mechanical stimuli and measuring behavior. This is a significant advance over commonly used approaches and has the potential to broadly impact the field. I have some concerns about the methods used to define discrete behaviors and the interpretations drawn from them (see point 2), the opposing phenotypes of memory mutants, and the circuit modeling. However, the overall results provide strong evidence that a small set of behaviors reflect the intensity of response to stimuli, and these combine to reflect an overall complex behavioral response to mechanical stimuli. Overall the manuscript is well written, and clearly communicates results. The level of analysis has the potential to broadly impact many fields examining innate and learned responses to sensory stimuli.

1) A central strength of this manuscript is the resolution of behavioral analysis. Implicit in this is the potential to use a wealth of genetic analysis and sophisticated genetic tools to dissect the neural basis of these behaviors. These implications would be clearer if the introduction provided more description of this literature.

This is certainly true, where the findings from behavior experiments should lead to interesting investigations at the neural circuit level. This is especially true for Drosophila, which has a wealth of genetic tools readily available. We have added a new paragraph at the end of the Introduction section to discuss this, and provide citations to a number of commonly used tools that could be used to identify and characterize the circuit side of mechano-sensation and adaptation in flies.

2) It is unclear how the 4 discrete behaviors were decided upon, and whether there are rarer behaviors, or subcategories within them (for example, sideways crawl).

We do list a number of behaviors in the third paragraph of the Introduction, and describe some of these in more detail in the next paragraph, but agree that a clearer justification needs to be given for focusing on the four specific behaviors in the paper. The answer is that these are the only behaviors that larvae perform given the constraints we place on their movement (hard, flat agar gel), and because we avoid overly strong stimuli that would cause more drastic pain responses. This is now noted directly near the end of the 5th paragraph of the Introduction.

3) From figure 1A it looks like the mechanical transducer remains in the center independently of where the larvae is. Could it be possible that subtle differences in mechanical force are detected across the arena and this impacts the response? Does the degree of turning matter?

While the first paragraph of the Results section notes we use a “customized platform,” and the details and purpose of this are listed later in the second paragraph of Materials and Methods, I think it is warranted to include more details up front, as many readers will likely have the same question. We now clearly state what is customized about the platform and that its purpose is to achieve a spatially uniform vibration stimulus, and point the reader to Materials and Methods for further details.

4) I am not clear about the application of statistics. For example, 2D states that as a general trend, increasing vibration also increases reversals. I can see this, clearly but is there reason not to run statistics on these data?

We agree, it is not sufficient to simply state there is a general trend, when statistics can be readily applied (especially to binary/fractional data like this!). We have performed statistical comparison tests for reverse crawling response probabilities in the data in Figure 2C, which shows fractional behavior usage for a wide range of vibration frequency and acceleration. We show the statistics in two ways. (1) Adjacent graphs are connected with bridging lines that are black (p>0.05) or yellow (p<0.05) (Fisher’s exact test for both), which shows the onset of significant reverse crawling behavior when looking along gamma or f axes. (2) Each of the 29 graphs was tested against the baseline (zero vibration) reverse crawl fraction, and red dots indicate significant reverse crawl use. The graphs and captions for Figure 2C have been updated accordingly.

We also did more serious statistics with the data in Figure 5 (habituation model compared to data) and Figure 7 (simple circuit model compared to data), and those are described below with their associated comments.

5) The importance of vibration behavior in research is discussed but the ecological relevance of these behaviors is not described.

A very good idea for setting the context better. We have added a new paragraph to the Introduction with 56 references for readers interested in learning more about this side of things. Vibration response is important in real larvae in nature too, it helps them communicate and avoid predators.

6) The results of habituation times in mutants are not clear to me. One might predict dnc and rut would have the same phenotype but they have opposing phenotypes with rut being a super-habituate.

The dnc and rut mutants both desensitize faster than the CS control larvae (comparing the traces in Fig6A to the gray wild type version), which would agree with this prediction, but the details are still finer details to sort out. For example that rut is faster than dnc, or that rut is faster at both desensitizing and re-sensitizing than wild type, but dnc is slow to re-sensitize. This would be interesting to piece together, but for now the mutant results highlight the importance of extracting the finer details (and multiple time constants) involved in vibration response, and explaining why the mutants (or other future strains tested) have the specific values is a bit beyond the scope of this paper.

We have noted the comparisons with dnc and rut more directly in the text now, accompanying the descriptions of Fig. 6A and 6B in the Results section.

7) I appreciate the application of circuit modeling, but it would seem that this would be strengthened by including what is already known about the biological circuit.

We were not very clear about describing the purpose of the circuit model – we did not intend the circuit components of the model to directly match the actual neural circuit elements. It is primarily a visualization tool for what appears to be happening based on the empirical results (although the math behind the circuit might suggest some possible real mechanisms, noted in Discussion). In earlier drafts the visualization tool was a water bucket pouring into a second bucket with a hole in the bottom, with water volume analogous to habituation (the math was identical to the capacitor circuit). We have added a sentence at the beginning of the circuit model section to clarify its purpose better.

That said, we agree it is important to discuss the context of the real neural circuit. This was in the Introduction already, but not emphasized or introduced very well. This section now has its own paragraph, which we have expanded and added additional references (paragraph starting with “Some aspects of the neural circuitry…”).

We have also substantially edited the Results section about the circuit model in response to other comments below, and it should be more focused and clearer now.

Reviewer #2 (Public Review):

Berne et al. establish the responses of Drosophila larvae to mechanical vibrations as a novel paradigm to study habituation. The authors first comprehensively quantify the different types of locomotor responses to vibrations and find that larvae respond to faster and stronger vibrations with more avoidance-type behaviors, like pauses, turns, and reversals. The authors then combine genetic and computational methods to characterize the strong de-sensitization of avoidance responses to vibrations. De-sensitization of reversals follows a simple, exponential decay with a single time constant. By contrast, re-sensitization dynamics are more complex and strongly accelerate after repeated exposure to a vibration stimulus. The authors then test mutants for genes involved in learning and memory (rut, dnc, cam) and find altered desensitization and re-sensitization dynamics, suggesting that these genes mediate this behavior. Finally, a simple and intuitive electrical circuit model is used to explain these complex dynamics results. Overall, the results are interesting and they successfully combine behavioral characterization, genetic manipulations, and computational modeling to explain the behavior.

The analyses are all sound and support most of the conclusions but additional control experiments and analyses are required.

1) To convincingly show that the computational models capture the key aspects of the behavior and therefore provide insight into the underlying phenomenon, model predictions and behavioral data need to be compared systematically and quantitatively. This is not sufficiently done for the electrical circuit model, and the analyses shown in Fig. 7C need to be extended. The model should be fitted to the data and the match between model and data should be A) quantified using a suitable measure of goodness-of-fit and B) illustrated by overlaying behavioral data and model predictions.

We agree, and thank the referee for pointing this out. The circuit model was intended as primarily a visualization tool, but it was not fair of us to say that it correctly predicts anything real without being more precise and quantitative, including using significance metrics. We also feel that Fig. 7C was not a very compelling demonstration and not very interesting. We have replaced 7C with a new panel that shows empirical reverse crawl probability overlayed with the circuit model’s prediction of reverse crawl behavior (where FREV ~ exp(-Q2). The peak values match very closely, although the overall shape does not, due to the simplicity of the model. This is discussed fully in the Results text and in a redone Fig. 7 caption.

Moreover, the contribution of individual circuit elements should be quantified, for instance by removing key elements from the model like the second capacitor. If a good quantitative fit is for some reason hard to obtain, then more effort should be spent to demonstrate a good qualitative agreement between model and data.

We have shown what we think is the bare minimum circuit model that can include the accumulation and decay of a substance (the charge Q2 standing in for “habituation”). We could have built a more complicated circuit and essentially forced it to have the same time constants as we extracted from data, but felt that would lose sight of its appeal as a visualization tool and qualitative idea. We could not remove C2, for example, because the “output” of the circuit model itself is the charge on that capacitor.

In response to further comments below we have overhauled and simplified the section about the circuit model, and hope this also helps alleviate any concerns.

The same goes for the phenomenological model in Fig. 5. Predictions of model variants with a constant re-sensitization time constant and a time constant that changes with pulse number should be shown and their fit to the data should be quantified.

Absolutely. We have added two other versions of the model to Fig. 5E (one with only desensitization and the other that doesn’t have the time constant changing with pulse number) and performed significance tests on the peak values for each pulse response. The model with all three aspects of habituation performs the best. Fig. 5E has been made larger to better see the traces, we have added visual cues and a legend for the significance tests, and the caption has been expanded accordingly.

2) The Markov model in Fig. 3 is used to state that habituation is a one-way process from reversals to other behaviors, with only rare transitions back to reversals. However, the low transition rates to reversals (Fig. 3) seem at odds with the fast re-sensitization after repeated stimulation (Fig. 5). This should be explained and both results should be linked.

This is a really good observation, and fortunately does have an explanation. The assigned behaviors in Fig. 3 are what we observe during the first 3 seconds after vibration onset. Habituation sets in as the stimulus stays on, then re-sensitization (even if not complete) occurs while the stimulus is off. Then when the stimulus turns on again, we assign the next behavior. An individual with a strong (reversal) response will most often (85% of the time) reverse again the next time the stimulus turns on. We would not classify that as a transition back to reversal, but as a repeat of the reversal behavior following de-sensitization and resensitization. For the 15% of individuals that did not reverse the second time, they will only very rarely (< 2%) reverse the third time. The re-sensitization process in fact explains why strong response behaviors so often repeat for the next vibration pulse response.

We have expanded a paragraph in the Results section to add text similar to what we have written here to clear up this point. It’s the last paragraph in the “Re-sensitization rates increase…” subsection.

3) Based on altered de-sensitization and re-sensitization dynamics in mutants, the authors claim that three different genes - rut, dnc, cam - are involved in the molecular pathway that mediates habituation of larval locomotor responses to vibrations. This is interesting and deserves further study. However, it is unclear whether the observed effects are specific to the genes that were altered or whether the effects stem from differences in the genetic background across the mutants. This could be resolved in two ways: Ideally, with rescue experiments; if this is not feasible, then data from different wild-type strains could be used to show that the de-sensitization and re-sensitization dynamics are similar across wild types and somewhat robust to genetic background.

Additional control data with other wild type strains was not doable due to personnel issues noted in our resubmission letter, and also time constraints (for example, each trace like the one in Fig. 5A requires 1000 animals to construct – we suspect that the required number of larva-hours to determine habituation parameters is a large part of why other researchers have not observed these habituation characteristics in larvae before). We do acknowledge this limitation directly in the manuscript now, and highlight why it would be important for further experiments like these to be carried out in the future. A new paragraph in the “Conclusions” subsection of Discussion discusses this. We now state directly that the mutant results are there to highlight the importance of characterizing multiple time constants and other dependencies when determining anything about habituation. The fact that habituation parameters are not the same as this particular CS wild type is suggestive, but given the lack of additional controls it would not be fair to make specific statements about any of the mutants at this stage.

Author Response

Reviewer #1 (Public Review):

1) Comment: To determine the effect of diseased monocytes on retinal health, light-injured mouse retinas were injected with monocytes isolated from AMD patients (Figure 1 - figure supplement 1). This resulted in a reduction in photoreceptor number and ERG b-wave amplitude. However, the light-injured control eye was injected with PBS only, so no cells were present. The reasoning for using this control was not provided. The appropriate injection control would include monocytes isolated from non-AMD patients. This control should be performed side-by-side with cells from AMD patients.

We thank the reviewer for this important comment. The purpose of the current study was to identify the macrophage subtype that may be associated with cell death in aAMD. We have previously reported that macrophages from AMD patient demonstrate a different phenotype compared with healthy patient in the rodent model for laser induced CNV (Hagbi-Levi S et al, 2016). Per the reviewer comment, we have performed additional experiments to assess the effect of monocytes from healthy controls in the photic retinal injury model. Results showed that monocytes from AMD and healthy patients exert different impact on the retina in this rodent model for aAMD. Interestingly, we found that monocytes from healthy patients were more neurotoxic to photoreceptors compared with monocytes from AMD patients. These results are included in the revised ms. as Figure 1- figure supplement 1H. A possible explanation for these findings is discussed in lines 179-190 of the revised manuscript. This finding reinforces the idea that the use of monocytes from AMD patients in the experiments is required to obtain a comprehensive understanding of their involvement in the progression of the disease.

2) Comment: The authors hypothesize, from the experiments presented in Figure 1 - figure supplement 1, that the injected monocytes generated macrophages in the retina, which were responsible for the observed neurotoxicity (Lines 143-145). However, no direct evidence was presented. This idea should be tested in vivo. This could be done by injecting tracer-labeled human AMD-derived monocytes into light-injured mouse retinas. If the authors' hypothesis is true, collected retinas should contain tracer-labeled cells that express macrophage markers. Tracer-labeled M2a macrophage cells should be present since subsequent experiments identify this subclass as being associated with retinal cell death.

Thank you for this important comment. To address the reviewers comment, retinal section from mice exposed to photic-retinal injury and injected with Dio-tracer labelled monocytes were stained with two M2a macrophages markers, CD206 (mannose receptor) and VEGF (Kadomoto, S et al, 2022; Jayasingam SD et al, 2019). Interestingly, we found co-localization of Dio-tracer staining (representing the injected human macrophages) with CD206 and VEGF markers in monocytes localized in different retinal layers, but not in monocytes remaining in the vitreous cavity. These data indicate that M2a markers are expressed during the polarization of monocytes into M2a phenotype which is maintained only upon entry into the retina tissue. These results were included in Figure 1- figure supplement 1K-S and discussed in the revised manuscript in lines 179-182.

3) Comment: Photoreceptor number and b-wave amplitudes were measured in light-injured retinas injected with one of four macrophage cell types generated from human AMD-derived monocytes. The authors conclude that only injection of M2a cells reduced photoreceptor number and b-wave amplitudes (Figure 1C, E). This may be true, but it is difficult for the reader to make a conclusion (especially in Fig. 1E) due to the large error bars and five different traces overlapping each other. To make these results easier to interpret, graph control cells with only one experimental sample (cell type) at a time.

Thank you for this comment. Per the reviewer comment, the graphs were modified in the revised ms. (Figure 1, panel H-K).

4) Comment: Most injected macrophages were located in the vitreous. In the case of M2a cells, the authors note that "several of the cells migrated across the retinal layers reaching the subretinal space" (Lines 167,168). One possible explanation for why M0, M1, and M2c macrophages did not induce retinal degeneration is that they did not migrate to the subretinal space and around the optic nerve head. Supplementary figures should be added to demonstrate that this is not the case.

Thank you for this comment. To address the reviewer comment we compared the migration patterns of the different macrophage phenotypes following intravitreal injection in mice exposed to photic-injury. Our results indicated that M0, M1 and M2c macrophages, similarly to M2a macrophages, migrated to the subretinal space and around the optic nerve. Thus, the neurotoxic effect of M2a is not explained by their capacity to infiltrate the retinal tissues. These results was included in Figure 1- figure supplement 2 E-H of the revised manuscript. These results are supported by our ex-vivo experiments, showing that co-culture of M2a macrophages with a retinal explants was associated with increased photoreceptor cells death compared to M1 macrophages. The results are presented and discussed in the revised manuscript in lines 200-203.

5) Comment: Figure 1 - figure supplement 2: Panel A, B cells were stained with CD206 to demonstrate the presence of M2a macrophages (panel B). The authors conclude that panel A contains M1 and panel B contains M2a cells. The lack of CD206 expression illustrates that panel A cells are not M2a macrophages but do not demonstrate they are M1 macrophages. A control using an M1 cell marker is necessary to show that panel A cells are M1 and M1 cells are not detected in M2a cultures.

Thank you for this comment. We have validated the phenotype of each macrophages subtype by qPCR (Figure 1 panel A). To further address the reviewer comment, we have performed additional immunocytochemistry for M1 macrophages using anti-CD80 antibody which is utilized as M1 macrophages marker (Bertani FR et al.2017). Results of the staining confirmed the identity of the M1 macrophages. These new results were included in Figure 1- figure supplement 2A, and are discussed in lines 168-170.

6) Comment: Ex vivo, apoptotic photoreceptor and RPE cells are observed when cultured with M2a macrophages (Figure 2). Do injected M2a cells also induce apoptosis of RPE cells in vivo? This is important to establish that retinal explants are a good model for in vivo experiments.

Thank you for this comment. To address the reviewer comment, we assessed RPE apoptosis (using TUNEL, Caspase 3 staining and RPE65 marker) after M2A cells delivery, in the in-vivo photic injury model. We could not detect apoptotic signal in the RPE layers 7 days after photic injury and therefore could not evaluate the effect of M2a macrophages on the RPE cells in-vivo (see Author response image 1). One possible explanation is that RPE cells that have undergone apoptosis are rapidly removed from the damaged tissue and are no longer detectable unlike photoreceptors. Furthermore, a study that investigated the impact of bright light on RPE cells in-vivo, showed that although RPE cells undergone structural and chemical modifications after photic-injury, TUNEL signal was not detected because RPE cell die by necrosis mechanism and not apoptosis (Jaadane I et al, 2017). Other studies validated that blue light induces RPE necrosis (Song W et al, 2022; Mohamed A et al, 2022). Taken together, it seems that ex-vivo retinal explant and in-vivo photic injury both simulate the mechanism of retinal cell death. However, the use of ex-vivo model allows for establishing the direct impact of M2a macrophages on retina in non-inflammatory context.

Author responnse image 1.

7) Comment: Reactive oxygen species (ROS) production was measured to determine if M2a cell-mediated neurotoxicity was due to oxidative stress. It is concluded that a ROS increase is partly responsible (Line 218). The data do not support this conclusion. ROS was detected in cultured M2a macrophages. More importantly, however, there was no increase in oxidative damage in vivo. The in vivo and cell culture results contradict each other so no conclusion can be made. The lack of in vivo confirmation weakens the argument that ROS drives M2a neurotoxicity. Text suggesting a role for ROS in neurotoxicity should be appropriately edited (Lines including 218, 244, 401,406,481).

Thank you for this comment. The manuscript was revised according to the reviewer suggestion (Lines 250-256).

8) Comment: The authors ask if the photoreceptor cell death is cytokine-mediated. Multiple cytokines were enriched in M2a-conditioned media. Of particular interest were CCR1 ligands MPIF1 and MCP4. The implication is that these two ligands mediate the M2a macrophages to photoreceptor cell death through CCR1. However, there is no attempt to show that either MPIF1 or MCP4 are present in vivo, or are sufficient to induce the retinal response observed. This could be demonstrated by injection of MPIF1 or MCP4. Evidence that either ligand phenocopies M2a macrophage injection would be direct evidence that CCR1 ligands activate the retinal response. Furthermore, co-injection with BX174 should block the effect of these ligands if they work through CCR1.

Thank you for this comment. The identification of CCR1 ligands expression from M2a polarized macrophages directed our decision to study CCR1 in the context of atrophic AMD. We do not claim that these specific CCR1 ligands are sufficient to activate CCR1 and exert retinal injury. The mechanism is likely more complex. Yet, to address the reviewer comment, we have performed the experiments suggested by the reviewer. Mice were exposed to photic injury and immediately injected in one eye with MPIF1, MCP-4, or a combination of both and in second eye with PBS as vehicle. Intravitreal cytokines delivery was repeated two days later (following the half-life time of these cytokines) and ERG were recorded two days after the last injection. Injection of cytokines at a concentration of 300 ng per eye did not exacerbated photoreceptor death. Then, the same experiment was repeated with two higher concentrations of cytokine, 1.2 ug/eye and 2 ug/eye, but no changes are observed between the cytokines treated-eyes and the vehicle treated-eyes. Based on previous studies reporting the physiological concentration of different cytokines in eyes of un/healthy individuals and on experiments in which different cytokines are injected in rodent eye (Estevao C et al, 2021. Zeng Y et al, 2019; Roybal CN et al, 2018; Mugisho OO et al, 2018), the cytokine concentrations used in our experiment are in the range in which effect on the retina is expected.

It is likely that a synergistic effect of M2a-secreted proteins in a particular microenvironment is necessary to increase the level of retinal damage (Bartee E et al, 2013). It is also likely that in the photic retinal injury model there is upregulation of cytokines that may mask additional delivery of exogenous cytokines. Comprehensive understanding of the complex interactions of these cytokines during retinal degeneration is beyond the scope of the current manuscript which is not focus on identifying ligand-induced CCR1 activation and its consequences. Additionally, we suggest that due to cytokine redundancy (Nicola NA; 1994), demonstrating that MPIF-4 or MCP-3 can increase photoreceptor death is not required for proving CCR1 receptor involvement.

eLife assessment

This study presents valuable findings that could be utilized for the identification of women at risk for preeclampsia prior to the onset of the disease. The novel aspect of this study lies in the utilization of exosomes with two different sizes. However, the data is incomplete: the patient population has not been well-defined, and the study only measured the proteins at a single time point.

Reviewer #1 (Public Review):

The authors primary objective in this study was to identify differences between patients with preeclampsia and normal patients with respect to the placental syncytiotrophoblast extracellular vesicle proteome.

One of the strengths of this study is that it is one of only a few studies that investigated the difference in proteome between patients with preeclampsia and those with normal pregnancies using placental extracellular vesicles obtained by an ex-vivo dual lobe placenta perfusion technique.

The main weaknesses of this study are:

1. The small sample size in that there were only 12 cases.<br /> 2. The study patients and control population of normal pregnancies were not matched for gestational age at delivery.

The authors were able to achieve their study aims and the results support their conclusions.

These findings could be used in future studies of the disease mechanisms and as biomarkers for prediction of preeclampsia. As such, they may be very useful for the identification of women at risk for preeclampsia well before the onset of disease.

Reviewer #2 (Public Review):

Summary:

Preeclampsia is a disorder of pregnancy that affects 4-5% of pregnancies worldwide. Identifying this condition early is clinically relevant as it will help clinicians to make management decisions to prevent adverse outcomes. The placenta holds a key to many pregnancy-related pathologies including preeclampsia and studies have shown many differences in the placenta of women with preeclampsia as compared to controls. However as the placenta cannot be collected directly during pregnancy, the exosomes secreted by it are considered a good alternative to tissue biopsy. In this study, the authors have compared the proteins in different sizes of exosomes from the placenta of women with and without preeclampsia. The idea is to eventually use these as biomarkers for early detection of preeclampsia.

Strengths:

The novelty factor of this study is the use of two different-sized exosomes which has not been achieved earlier.

Weaknesses:

There is already enough information about the differences in exosome contents from the placentas of women with and without preeclampsia. There are some issues with the methods which may influence the outcomes of the data.

The patient population described in the methods section is of HELLP syndrome while the title and the manuscript describe preeclampsia. While it is an important life-threatening condition to address, it is extremely rare and needs careful assessment by clinicians in terms of patient characteristics and outcomes measured.

The study measured the proteins at only a single time point after the disease has already occurred. However, the placenta is an ever-changing tissue throughout pregnancy and different proteins can come up at different times in pregnancy. Thus serial measurements are necessary and a single time point measurement like that done here does little value addition. Unfortunately, this site has not validated the identified biomarkers in plasma or circulating placental exosomes from women with and without preeclampsia. Thus the validity of these findings in real-life situations can not be judged.

eLife assessment

This important study describes how the composition and stabilization of nanodomains in the plasma membrane are an integral part of plant defence against viruses, with a focus on the calcium-dependent kinase CPK3 and its apparent interaction with a plasma-membrane nano domain scaffold protein from the remorin family. While the evidence for a specific role of CPK3 in limiting viral spread is convincing, the claims regarding the CPK3-remorin interaction would be strengthened by additional experimental support. The work, which will be of interest to plant cell biologists and plant virology, opens new avenues for understanding the role of plasma membrane nanodomains in limiting viral spread.

Reviewer #1 (Public Review):

Summary:

How plants perceive their environment and signal during growth and development is of fundamental importance for plant biology. Over the last few decades, nano domain organisation of proteins localised within the plasma-membrane has emerged as a way of organising proteins involved in signal pathways. Here, the authors addressed how a non-surface localised signal (viral infection) was resisted by PM localised signalling proteins and the effect of nano domain organisation during this process. This is valuable work as it describes how an intracellular process affects signalling at the PM where most previous work has focused on the other way round, PM signalling effecting downstream responses in the plant. They identify CPK3 as a specific calcium dependent protein kinase which is important for inhibiting viral spread. The authors then go on to show that CPK3 diffusion in the membrane is reduced after viral infection and study the interaction between CPK3 and the remorins, which are a group of scaffold proteins important in nano domain organisation. The authors conclude that there is an interdependence between CPK3 and remorins to control their dynamics during viral infection in plants.

Strengths:

The dissection of which CPK was involved in the viral propagation was masterful and very conclusive. Identifying CPK3 through knockout time course monitoring of viral movement was very convincing. The inclusion of overexpression, constitutively active and point mutation non functioning lines further added to that.

Weaknesses:

My main concerns with the work are twofold.<br /> 1) Firstly, the imaging described and shown is not sufficient to support the claims made. The PM localisation and its non-PM localised form look similar and with no PM stain or marker construct used to support this. The sptPALM data conclusions are nice and fit the narrative. However, no raw data or movie is shown, only representative tracks. Therefore the data quality cannot be verified and in addition, the reporting of number of single particle events visualised per experiment is absent, only number of cells imaged is reported. Therefore it is impossible for the reader to appreciate the number of single molecule behaviours obtained and hence the quality of the data.

2) Secondly, remorins are involved in a lot of nano domain controlled processes at the PM. The authors have not conclusively demonstrated that during viral infection the remorin effects seen are solely due to its interaction with CPK3. The sptPALM imaging of REM1.2 in a cpk3 knockout line goes part way to solve this but more evidence would strengthen it in my opinion. How do we not know that during viral infection the entire PM protein dynamics and organisation are altered? Or that CPK3 and REM are at very distant ends of a signalling cascade. Negative control experiments are required here utilising other PM localised proteins which have no role during viral infection. In addition, if the interaction is specific, the transiently expressed CPK3-CA construct (shown to from nano domains) should be expressed with REM1.2-mEOS to show the alterations in single particle behaviour occur due to specific activations of CPK3 and REM1.2 in the absence of PIAMV viral infection and it is not an artefact of whole PM changes in dynamics during viral infection.

In addition, displaying more information throughout the manuscript (such as raw particle tracking movies and numbers of tracks measured) on the already generated data would strengthen the manuscript further.

Overall, I think this work has the potential to be a very strong manuscript but additional reporting of methods and data are required and additional lines of evidence supporting interaction claims would significantly strengthen the work and make it exceptional.

Reviewer #2 (Public Review):

Summary:

The paper provides evidence that CPK3 plays a role in plant virus infection, and reports that viral infection is accompanied by changes in the dynamics of CPK3 and REM1.2, the phosphorylation substrate of CPK3, in the plasma membrane. In addition, the dynamics of the two proteins in the PM are shown to be interdependent.

Strengths:

The paper contains novel, important information.

Weaknesses:

The interpretation of some experimental data is not justified, and the proposed model is not fully based on the available data.

Reviewer #3 (Public Review):

Summary:

This study examined the role that the activation and plasma membrane localisation of a calcium dependent protein kinase (CPK3) plays in plant defence against viruses.

The authors clearly demonstrate that the ability to hamper the cell-to-cell spread of the virus P1AMV is not common to other CPKs which have roles in defence against different types of pathogens, but appears to be specific to CPK3 in Arabidopsis. Further they show that lateral diffusion of CPK3 in the plasma membrane is reduced upon P1AMV infection, with CPK3 likely present in nano-domains. This stabilisation however, depends on one of its phosphorylation substrates a Remorin scaffold protein REM1-2. However, when REM1-2 lateral diffusion was tracked, it showed an increase in movement in response to P1AMV infection. These contrary responses to P1AMV infection were further demonstrated to be interdependent, which led the authors to propose a model in which activated CPK3 is stabilised in nano-domains in part by its interaction with REM1.2, which it binds and phosphorylates, allowing REM1-2 to diffuse more dynamically within the membrane.

The likely impact of this work is that it will lead to closer examination of the formation of nano-domains in the plasma membrane and dissection of their role in immunity to viruses, as well as further investigation into the specific mechanisms by which CPK3 and REM1-2 inhibit the cell-to-cell spread of viruses.

Strengths:

The paper provided compelling evidence about the roles of CPK3 and REM1-2 through a combination of logical reverse genetics experiments and advanced microscopy techniques, particularly in single particle tracking.

Weaknesses:

There is a lack of evidence for the downstream pathways, specifically whether the role that CPK3 has in cytoskeletal organisation may play a role in the plant's defence against viral propagation. Also, there is limited discussion about the localisation of the nano-domains and whether there is any overlap with plasmodesmata, which as plant viruses utilise PD to move from cell to cell seems an obvious avenue to investigate.

Reviewer #2 (Public Review):

Summary: Shotgun data have been analysed to obtain fungal and bacterial organisms' abundance. Through their metabolic functions and through co-occurrence networks, a functional relationship between the two types of organisms can be inferred. By means of metabolomics, function-related metabolites are studied in order to deepen the fungus-bacteria synergy.

Strengths:<br /> Data obtained from bacteria correlate with data from other authors.<br /> The study of metabolic "interactions" between fungi and bacteria is quite new.<br /> The inclusion of metabolomics data to support the results is a great contribution.

Weaknesses: Methodological descriptions are minimal.

Some example:<br /> *The CON group (line 147) has not been defined. I supposed it is the control group.<br /> * There are no statistics related to shotgun sequencing. How many reads have been sequenced? How many have been removed from the host? How many are left to study bacteria and fungi? Are these reads proportional among the 48 samples? If not, what method has been used to normalise the data?<br /> * ggClusterNet has numerous algorithms to better display the modules of the microbiome network. Which one has been used?

eLife assessment

This study has uncovered some interesting findings about the fungal composition and its interaction with bacteria in Caesarean section scar diverticulum (CSD). While the study's findings are valuable and with translation possibilities, the strength of the conclusions obtained is incomplete due to the small sample size and methodological issues indicated by the reviewers such as the lack of controls and the location of samples analyzed.

Reviewer #1 (Public Review):

Summary:<br /> Chen et al. describe the bacterial and fungal composition of cervical samples from women with/without Cesarean-section scar diverticulum (CSD) using whole metagenomic sequencing. Also, they report the metabolomic profile associated with CSD and built correlation networks at the taxonomical and taxonomic-metabolic levels to establish potential bacteria-fungi interactions. These interactions could be used, long-term, as therapeutic options to treat or prevent CSD.

Strengths:<br /> - The authors have used advanced techniques in shotgun sequencing which is a powerful tool able to characterize the microbiome at the species (or lower) level and metabolomics.<br /> - These are novel results showing the interaction of bacteria and fungi and present a wider view of the role of the microbiome in female infertility.

Weaknesses:<br /> - This is a pilot study with only 24 cases and 24 controls. Because the human microbiota entails individual variability, this work should be confirmed with a higher sample size to achieve enough statistical power.<br /> - The authors do not report here the use of blank controls. The use of this type of control is important to "subtract" the potential background from plasticware, buffer or reagents from the real signal. Lack of controls may lead to microbiome artefacts in the results. This can be seen in the results presented where the authors report some bacterial contaminants (Agrobacterium tumefaciensis, Aequorivita lutea, Chitinophagaceae, Marinobacter vinifirmus, etc) as part of the most common bacteria found in cervical samples.<br /> - Samples used for this study were collected from the cervix. Why not collect samples from the uterine cavity and isthmocele fluid (for cases)? In their previous paper using samples from the same research protocol ((IRB no. 2019ZSLYEC-005S) they used endometrial tissue from the patients, so access to the uterine cavity was guaranteed.<br /> - Through the use of shotgun genomics, results from all the genomes of the organisms present in the sample are obtained. However, the authors have only used the metagenomic data to infer the taxonomical annotation of fungi and bacteria.

Reviewer #3 (Public Review):

In the present study, Chen et al. revealed the fungal composition and explored its interaction with bacteria in Caesarean section scar diverticulum (CSD) patients. Performing metagenomic and mass spectrometry analysis, they found specific fungi could alter bacterial abundance through regulating the production of several metabolites such as Goyaglycoside A and Janthitrem E, which results in disruption of bacterial composition stability. Their study drew a conclusion that abnormal fungal composition and activity are essential drivers for bacterial dysbiosis in CSD patients. However, the results are not substantial enough and there are many format errors throughout the manuscript. In addition, I have some concerns or suggestions that may help to improve this work.

Major<br /> 1. Smoke or drink conditions, as well as diseases like hypertension and diabetes are important factors that could influence the metabolism of the host, thus the authors should add them in the exclusion criteria in the Methods.<br /> 2. The sample size of this study is not large enough to draw a convincing conclusion.

Reviewer #3 (Public Review):

The major strength of this manuscript is the "anvi-estimate-metabolism' tool, which is already accessible online, extensively documented, and potentially broadly useful to microbial ecologists. However, the context for this tool and its validation is lacking in the current version of the manuscript. It is unclear whether similar tools exist; if so, it would help to benchmark this new tool against prior methods. Simulated datasets could be used to validate the approach and test its robustness to different levels of bacterial richness, genome sizes, and annotation level.

The concept of metabolic independence was intriguing, although it also raises some concerns about the overinterpretation of metagenomic data. As mentioned by the authors, IBD is associated with taxonomic shifts that could confound the copy number estimates that are the primary focus of this analysis. It is unclear if the current results can be explained by IBD-associated shifts in taxonomic composition and/or average genome size. The level of prior knowledge varies a lot between taxa; especially for the IBD-associated gamma-Proteobacteria. It can be difficult to distinguish genes for biosynthesis and catabolism just from the KEGG module names and the new normalization tool proposed herein markedly affects the results relative to more traditional analyses. As such, it seems safer to view the current analysis as hypothesis-generating, requiring additional data to assess the degree to which metabolic dependencies are linked to IBD.

eLife assessment

This study describes an important bioinformatics tool for normalizing gene copy number from metagenomic assemblies. The tool is used in a meta-analysis of data from inflammatory bowel disease (IBD) patients and healthy controls. While some of the evidence for the power of the method is compelling, other evidence seems incomplete. The inclusion of additional computational and/or experimental validation would markedly strengthen the study. This paper will likely be of broad interest to researchers studying the role of complex microbial communities in host health and disease.

Reviewer #1 (Public Review):

In this work, Veseli et al. present a computational framework to infer the functional diversity of microbiomes in relation to microbial diversity directly from metagenomic data. The framework reconstructs metabolic modules from metagenomes and calculates the per-population copy number of each module, resulting in the proportion of microbes in the sample carrying certain genes. They applied this framework to a dataset of gut microbiomes from 109 inflammatory bowel disease (IBD) patients, 78 patients with other gastrointestinal conditions, and 229 healthy controls. They found that the microbiomes of IBD patients were enriched in a high fraction of metabolic pathways, including biosynthesis pathways such as those for amino acids, vitamins, nucleotides, and lipids. Hence, they had higher metabolic independence compared with healthy controls. To an extent, the authors also found a pathway enrichment suggesting higher metabolic independence in patients with gastrointestinal conditions other than IBD indicating this could be a signal for a general loss in host health. Finally, a machine learning classifier using high metabolic independence in microbiomes could predict IBD with good accuracy. Overall, this is an interesting and well-written article and presents a novel workflow that enables a comprehensive characterization of microbiome cohorts.

Reviewer #2 (Public Review):

This study builds upon the team's recent discovery that antibiotic treatment and other disturbances favour the persistence of bacteria with genomes that encode complete modules for the synthesis of essential metabolites (Watson et al. 2023). Veseli and collaborators now provide an in-depth analysis of metabolic pathway completeness within microbiomes, finding strong evidence for an enrichment of bacteria with high metabolic independence in the microbiomes associated with IBD and other gastrointestinal disorders. Importantly, this study provides new open-source software to facilitate the reconstruction of metabolic pathways, estimate their completeness and normalize their results according to species diversity. Finally, this study also shows that the metabolic independence of microbial communities can be used as a marker of dysbiosis. The function-based health index proposed here is more robust to individuals' lifestyles and geographic origin than previously proposed methods based on bacterial taxonomy.

The implications of this study have the potential to spur a paradigm shift in the field. It shows that certain bacterial taxa that have been consistently associated with disease might not be harmful to their host as previously thought. These bacteria seem to be the only species that are able to survive in a stressed gut environment. They might even be important to rebuild a healthy microbiome (although the authors are careful not to make this speculation).

This paper provides an in-depth discussion of the results, and limitations are clearly addressed throughout the manuscript. Some of the potential limitations relate to the use of large publicly available datasets, where sample processing and the definition of healthy status varies between studies. The authors have recognised these issues and their results were robust to analyses performed on a per-cohort basis. These potential limitations, therefore, are unlikely to have affected the conclusions of this study.

Overall, this manuscript is a magnificent contribution to the field, likely to inspire many other studies to come.

Author Response

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

Reviewer #1 (Public Review):

Summary of the major findings -

1) The authors used saturation mutagenesis and directed evolution to mutate the highly conserved fusion loop (98 DRGWGNGCGLFGK 110) of the Envelope (E) glycoprotein of Dengue virus (DENV). They created 2 libraries with parallel mutations at amino acids 101, 103, 105-107, and 101-105 respectively. The in vitro transcribed RNA from the two plasmid libraries was electroporated separately into Vero and C6/36 cells and passaged thrice in each of these cells. They successfully recovered a variant N103S/G106L from Library 1 in C6/36 cells, which represented 95% of the sequence population and contained another mutation in E outside the fusion loop (T171A). Library 2 was unsuccessful in either cell type.

2) The fusion loop mutant virus called D2-FL (N103S/G106L) was created through reverse genetics. Another variant called D2-FLM was also created, which in addition to the fusion loop mutations, also contains a previously published, evolved, and optimized prM-furin cleavage sequence that results in a mature version of the virus (with lower prM content). Both D2-FL and D2-FLM viruses grew comparably to wild type virus in mosquito (C6/36) cells but their infectious titers were 2-2.5 log lower than wild type virus when grown in mammalian (Vero) cells. These viruses were not compromised in thermostability, and the mechanism for attenuation in Vero cells remains unknown.

4) Next, the authors probed the neutralization of these viruses using a panel of monoclonal antibodies (mAbs) against fusion loop and domain I, II and III of E protein, and against prM protein. As intended, neutralization by fusion loop mAbs was reduced or impaired for both D2-FL and D2-FLM, compared to wild type DENV2. D2-FLM virus was equivalent to wild type with respect to neutralization by domain I, II, and III antibodies tested (except domain II-C10 mAb) suggesting an intact global antigenic landscape of the mutant virion. As expected, D2-FLM was also resistant to neutralization by prM mAbs (D2-FL was not tested in this batch of experiments).

5) Finally, the authors evaluated neutralization in the context of polyclonal serum from convalescent humans (n=6) and experimentally infected non-human primates (n=9) at different time points (27 total samples). Homotypic sera (DENV2) neutralized D2-FL, D2-FLM, and wild type DENV similarly, suggesting that the contribution of fusion loop and prM epitopes is insignificant in a serotype-specific neutralization response. However, heterotypic sera (DENV4) neutralized D2-FL and D2-FLM less potently than wild type DENV2, especially at later time points, demonstrating the contribution of fusion loop- and prM-specific antibodies to heterotypic neutralization.

Impact of the study-

1) The engineered D2-FL and D2-FLM viruses are valuable reagents to probe antibodies targeting the fusion loop and prM in the overall polyclonal response to DENV.

2) Though more work is needed, these viruses can facilitate the design of a new generation of DENV vaccine that does not elicit fusion loop- and prM-specific antibodies, which are often poorly neutralizing and lead to antibody-dependent enhancement effect (ADE).

3) This work can be extended to other members of the flavivirus family.

4) A broader impact of their work is a reminder that conserved amino acids may not always be critical for function and therefore should not be immediately dismissed in substitution/mutagenesis/protein design efforts.

Evaluating this study in the context of prior literature -

The authors write "Although the extreme conservation and critical role in entry have led to it being traditionally considered impossible to change the fusion loop, we successfully tested the hypothesis that massively parallel directed evolution could produce viable DENV fusion-loop mutants that were still capable of fusion and entry, while altering the antigenic footprint."

".....Previously, a single study on WNV successfully generated a viable virus with a single mutation at the fusion loop, although it severely attenuated neurovirulence. Otherwise, it has not been generated in DENV or other mosquito-borne flaviviruses"

The above claims are a bit overstated. In the context of other flaviviruses:

• A previous study applied a similar saturation mutagenesis approach to the full length E protein of Zika virus and found that while the conserved fusion loop was mutationally constrained, some mutations, including at amino acid residue 106 were tolerated (PMID 31511387).

• The Japanese encephalitis virus (JEV) SA14-14-2 live vaccine strain contains a L107F mutation in the fusion loop (in addition to other changes elsewhere in the genome) relative to the parental JEV SA14 strain (PMID: 25855730).

• For tickborne encephalitis virus (TBEV-DENV4 chimera), H104G/L107F double mutant has been described (PMID: 8331735)

There have also been previous examples of functionally tolerated mutations within the DENV fusion loop:

• Goncalvez et al., isolated an escape variant of DENV 2 using chimpanzee Fab 1A5, with a mutation in the fusion loop G106V (PMID: 15542644). G106 is also mutated in D2-FL clone (N103S/G106L) described in the current study.

• In the context of single-round infectious DENV, mutation at site 102 within the fusion loop has been shown to retain infectivity (PMID 31820734).

We thank the reviewer for these comments. We have adjusted the text above to better reflect and credit the prior literature. Text is modified as follows in the discussion session.

“Previous reported mutations in the fusion loop are mainly derived from experimental evolution using FL-Ab to select for escape mutant or by deep mutational scanning (DMS) of the Env protein for Ab epitope mapping. Mutations in the FL epitope were observed in a DENV2-NGC-V2 (G106V)39, attenuated JEV vaccine strain SA14-14-2 (L107F)40, attenuated WNV-NY99 (L107F)41. While most of the mutations, including the double mutations reported here lead to attenuation of the virus. A recent DMS study showed that Zika-G106A has no observable impact on viral fitness42. Interestingly, we also recovered a mutation G106L, suggesting position 106 and 107 might be the most tolerable position for mutation in mosquito borne flavivirus FL. On the other hand, tick borne flavivirus as well as vector only flavivirus show a more diverse FL composition. The inflexibility of mosquito borne flavivirus might be due to the evolution constraint of the virus to switch between mosquito and vertebrate hosts.”

Appraisal of the results -

The data largely support the conclusions, but some improvements and extensions can benefit the work.

1) Line 92-93: "This major variant comprised ~95% of the population, while the next most populous variant comprised only 0.25% (Figure 1C)".

What is the sequence of the next most abundant variant?

The sequence of the next most abundant variant has been added to the text.

2) Lines 94-95: "Residues W101, C105, and L107 were preserved in our final sequence, supporting the structural importance of these residues." L107F is viable in other flaviviruses.

We acknowledge that the L107F mutation has been described in other flaviviruses, including the tick-borne flaviviruses DTV and POWV. This mutation in JEV is associated with viral attenuation. This sentence is referring to the fact that, in our libraries, we did not recover variants with mutations at these positions, in contrast to D2-FL with variants at N103 and G106, indicating less mutational tolerance. However, we want to re-direct the focus of this manuscript to engineer a viable DENV that is antigenically different in the FL epitope, but not which residue is more tolerance for mutation.

3) Figure 2c: The FLM sample in the western blot shows hardly any E protein, making E/prM quantitation unreliable.

The samples used in Figure 2C derive from the growth curve endpoint (Figure 2A), in which there is a 1-log difference in viral titer between D2 and D2-FLM. Equivalent volumes of viral supernatant were loaded in the gel, explaining the reduced intensity of the E band in D2-FLM. The higher exposure on the right shows the E band more clearly for D2-FLM. The Western blot assay comparing prM/E ratio as a measure of maturation state was described and validated in our previous study (Tse et al. 2022, mbio). The methods and figure legend have been updated to include greater detail. The polyclonal E antibody was specifically chosen for this study as our previously used monoclonal antibody targeted the fusion loop. The polyclonal antibody was raised against a fragment of E (AA 1-495) and should have minimal effect by the fusion loop mutations.

4) Lines 149 -151: "Importantly, D2-FL and D2-FLM were resistant to antibodies targeting the fusion loop. While neutralization by 1M7 is reduced by ~2-logs, no neutralization was observed for 1N5, 1L6, and 4G2 for either variant (Figure 3 A)".

a) Partial neutralization was observed for 1N5, for D2-FL.

The text has been updated to more accurately describe the 1N5 neutralization data.

b) Do these mAbs cover the full spectrum of fusion loop antibodies identified thus far in the field?

We did not test every known fusion loop antibody that has been described, instead focusing on 1M7, 1N5, 1L6, and 4G2, which were previously described by Smith et al and Crill et al. We also modified the text in discussion to reflect the possibility of other FL-Ab that are not affected by out mutations.

“We have tested a panel of FL-Ab; however, we cannot exclude the possibility that other FL-Abs may not be affected by N103S and G106L. However, we have shown that saturation mutagenesis could generate mutants with multiple amino acid changes, and we are currently using D2-FLM as backbone to iteratively evolve additional mutations in FL to further deviate the FL antigenic epitope.”

c) Are the epitopes known for these mAbs? It would be useful to discuss how the epitope of 1M7 differs from the other mAbs? What are the critical residues?

Critical residues for these antibodies have been described. They are as follows: 1M7: W101R, W101C, G111R; 1N5: W101R, L107P, L107R, G111R; 1L6: G100A, W101A, F108A; 4G2: G104H, G106Q, L107K. The critical residues for 1M7 are slightly different than the others, perhaps explaining the residual binding to D2-FL. Note that the critical residue identified previously for 1M7 and 1N5 do not overlap with D2-FLM mutations, suggesting the FL mutations has extending effect on the antigenic FL epitope.

d) Maybe the D2-FL mutant can be further evolved with selection pressure with fusion loop mAbs 1M7 +/-1N5 and/or other fusion loop mAbs.

We agree that it may be possible to further evolve D2-FL using antibody selection, although we have not yet performed these experiments, we are currently performing iterative saturation mutagenesis and directed evolution to further evolve away from the natural FL.

5) It would have been useful to include D2-M for comparison (with evolved furin cleavage sequence but no fusion loop mutations).

Neutralization data for some of the mAbs against D2-M can be found in our previous study (Tse et al. 2022 mBio), in which no difference in neutralization was observed compared to DV2 wildtype. Given the limited resources of the anti-DENV NHP and human serum, we did not add D2-M for comparison. Although some insight can be deduced from the D2-FL vs D2-FLM comparison, we agree future studies that are designed to delineate CR-Ab population between prM, FL and other CR-epitopes should include D2-M for comparison.

6) Data for polyclonal serum can be better discussed. Table 1 is not discussed much in the text. For the R1160-90dpi-DENV4 sample, D2-FL and D2-FLM are neutralized better than wild type DENV2? The authors' interpretation in lines 181-182 is inconsistent with the data presented in Figure 3C, which suggests that over time, there is INCREASED (not waning) dependence on FL- and prM-specific antibodies for heterotypic neutralization.

We remade Table 1 to show dilution factors instead of dilution factor-1 of FRNT50.

In general, our human convalescent sera from heterotypic infection (DENV1, 3 and 4) showed none to low neutralization against our DENV2. FRNT50s were between 1: 40 – 1:200. Given the weak potency of the antiserum, it is difficult to compare the FRNT50s between DV2-WT and D2-FLM.

Similarly, in a different NHP cohort (2nd NHP cohort shown in Table 1), only one DENV4 infected NHP (R1160) showed a low heterotypic titer against DENV2. The detectable FRNT50s were between 1: 50 – 1:90. The value was extrapolated based on a single data point (1:40) which has above 50% neutralization. Given the Hill slope of all the neutralization curves were below 0.5, the FRNT50 values is should not be

In conclusion, we do not think serum from Table 1 is potent enough to shows difference between the viruses. The intension to show the negative data in Table 1 is to highlight the difference in serum heterogeneity in DENV infected patients and experimental infected NHPs.

As the reviewer pointed out, the dependence of FL-Ab in later time points increased (the difference between DV2 and D2-FL at 20dpi vs 60dpi vs 90dpi), suggesting non-FL CR-Ab is waning but not prM- and FL-Abs. We rewrote the sentence as follow:

“These data suggest that after a single infection, many of the CR Ab responses target prM and the FL and the reliance on these Abs for heterotypic neutralization increase overtime (Figure 3C).”

Suggestions for further experiments-

1) It would be interesting to see the phenotype of single mutants N103S and G106L, relative to double mutant N103S/G106L (D2-FL).

2) The fusion capability of these viruses can be gauged using liposome fusion assay under different pH conditions and different lipids.

3) Correlative antibody binding vs neutralization data would be useful.

We thank the reviewer for the suggestions; we agree these would be of interest and, indeed, these studies are currently underway. In regard to single mutants, these were present in the initial plasmid library but did not enrich after viral production and passage. Two possible explanations can be drawn, 1) The stochastic of directed evolution prevents a single mutant with similar fitness to enriched. 2) The two mutations are compensatory to each other to make a functional mutant. The 2nd hypothesis highlights the difference between saturation mutagenesis (this study) and DMS (in previous studies).

Fusion capability is indeed very interesting, however, the mechanistic difference or not between wildtype FL and the mutated FL in supporting fusion is not the focus of this study. Instead, we are currently working on adapting the D2-FLM in mammalian cells. If successful, the difference in fusion mechanism between the Vero adapted and D2-FLM in different lipid, insect vs mammalian would be of interest.

We are currently developing whole virus ELISA; we avoid using rE monomer for the study as it might neglect the conformation Ab.

Reviewer #2 (Public Review):

Antibody-dependent enhancement (ADE) of Dengue is largely driven by cross-reactive antibodies that target the DENV fusion loop or pre-membrane protein. Screening polyclonal sera for antibodies that bind to these cross-reactive epitopes could increase the successful implementation of a safe DENV vaccine that does not lead to ADE. However, there are few reliable tools to rapidly assess the polyclonal sera for epitope targets and ADE potential. Here the authors develop a live viral tool to rapidly screen polyclonal sera for binding to fusion loop and pre-membrane epitopes. The authors performed a deep mutational scan for viable viruses with mutations in the fusion loop (FL). The authors identified two mutations functionally tolerable in insect C6/36 cells, but lead to defective replication in mammalian Vero cells. These mutant viruses, D2-FL and D2-FLM, were tested for epitope presentation with a panel of monoclonal antibodies and polyclonal sera. The D2-FL and D2-FLM viruses were not neutralized by FL-specific monoclonal antibodies demonstrating that the FL epitope has been ablated. However, neutralization data with polyclonal sera is contradictory to the claim that cross-reactive antibody responses targeting the pre-membrane and the FL epitopes wane over time.

Overall, the central conclusion that the engineered viruses can predict epitopes targeted by antibodies is supported by the data and the D2-FL and D2-FLM viruses represent a valuable tool to the DENV research community.

Reviewer #1 (Recommendations For The Authors):

1) Line 51-52: "Currently, there is a single approved DENV vaccine, Dengvaxia." Line 56-57: "Other DENV vaccines have been tested or are currently undergoing clinical trial, but thus far none have been approved for use."

It should be specified for the global audience that this applies to the United States. Takeda's DENV vaccine, QDENGA is approved in Indonesia, European Union, and Brazil.

The text has been modified to include this information.

2) Line 62-63: - "The core fusion loop-motif DRGWGNGCGLFGK is highly conserved..." Lines 78-80: - We generated two different saturation mutagenesis libraries, each with 5 randomized amino acids: DRGXGXGXXXFGK (Library 1) and 79 DRGXXXXXGLFGK (Library 2).

It may be useful for the readers if the amino acid numbers are stated. The core fusion loop motif DRGWGNGCGLFGK (Eaa98-110) is highly conserved. We generated two different saturation mutagenesis libraries, each with 5 randomized amino acids: DRGXGXGXXXFGK (Library 1; Xaa 101,103, 105-7) and DRGXXXXXGLFGK (Library 2; Xaa 101-105).

This information has been added to the text.

3) Line 91-92: "Bulk Sanger sequencing revealed an additional Env-91 T171A mutation outside of the fusion-loop region."

It looks like the mutation T171A is in domain I of the E protein and does not seem to interface with the fusion loop. Is that why it wasn't pursued further?

The E171A mutation was included in the infectious clone for D2-FL and D2-FLM. The text has been modified to clarify this inclusion.

4) Lines 82-85: "Saturation mutagenesis plasmid libraries were used to produce viral libraries in either C6/36 (Aedes albopictus mosquito) or Vero 81 (African green monkey) cells and passaged three times in their respective cell types."

a) What was the size of the libraries? How does one make sure that the experimental library actually has all the amino acid combinations that were intended?

Each library has 5 randomized amino acids, so there are 205 = 3.2 million combinations. In these experiments, sequencing of the plasmid libraries revealed about 2 million unique amino acid sequences, or approximately 62.5% library coverage. The actual plasmid diversity is expected to be higher than 2 million as our deep sequencing has limited coverage.

b) The wild type sequence was excluded from the libraries, correct?

The wild-type sequence was not specifically excluded from the libraries, as there is no easy method to do so. Wild-type sequence was detected in the plasmid libraries but was not selected in the C6/36 library. However, in the Vero library, we recovered WT virus.

5) Table 1: - Please include in the table description, what the colors indicate.

We remade Table 1 to show dilution factors instead of dilution factor-1 of FRNT50 and removed the unnecessary color code. We also added all relevant information in the table legend.

6) Lines 246-248: "Previously, a single study on WNV successfully generated a viable virus with a single mutation at the fusion loop, although it severely attenuated neurovirulence."

It may be worthwhile to mention the WNV mutation (L107F) as some readers may be curious about where this mutation is relative to the ones described in this study.

This information has been added to the text. We also included the previously described FL mutations in flaviviruses in the text.

Reviewer #2 (Recommendations For The Authors):

Major Critique:

• There is a disconnect between Fig 2A and 2C. FL and FLM viruses have much lower levels of prM-E expression in the viral supernatants based on the western blot in 2C. Why isn't E being detected in the Western? Is the particle-to-pfu ratio skewed in the mutant viruses? Is it possible that the polyclonal is targeting the cross-reactive prM and FL epitopes, and if so would using a monoclonal antibody targeting a known DIII-epitope (2D22) yield a different western result? Also, the legend and methods for Fig 2C are not clear. What is actually being tested in the Western blot? Were equivalent volumes of the different viral preps used?

The samples used in Figure 2C derive from the growth curve endpoint (Figure 2A), in which there is a 1-log difference in viral titer between D2 and D2-FLM. Equivalent volumes of viral supernatant were loaded in the gel, explaining the reduced intensity of the E band in D2-FLM. The higher exposure on the right shows the E band more clearly for D2-FLM. The Western blot assay comparing prM/E ratio as a measure of maturation state was described and validated in our previous study (Tse et al. 2022, mBio) and the methods have been updated to include greater detail. The polyclonal E antibody was specifically chosen for this study as our previously used monoclonal antibody targeted the fusion loop. The polyclonal antibody was raised against a fragment of E (AA 1-495) and should not be affected by the fusion loop mutations. 2D22 is a conformational antibody and does not work in western blot.

• Table 1: The data within Table 1 is ignored in the text, and some of this data contradicts the central conclusions of the manuscript.

o A.) Some of the convalescent data contradicts the hypothesis. DS0275 had an equivalent neut between DV2 and D2-FLM, DS1660, and R1160 (90) had better neut against the D2-FLM than DV2. Discussion of these samples is warranted.

o C.) The description in the legend does not adequately describe the table. What do the colors represent? What are the numerical values being displayed? What is in parentheses, (I assume the challenge strain)? The limit of detection is reported as 1:40; 0.25. 1:40 is 0.025 which matches most of the data? There is inadequate description of these experiments in the materials and methods.

We remade Table 1 to show dilution factors instead of dilution factor-1 of FRNT50 and removed the unnecessary color code. We also added discussion for Table 1 and clarify the difference between the three cohorts of serum in the text with the corresponding references.

In general, our human convalescent sera from heterotypic infection (DENV1, 3 and 4) showed none to low neutralization against our DENV2. FRNT50s were between 1: 40 – 1:200. Given the weak potency of the antiserum, it is difficult to compare the FRNT50s between DV2-WT and D2-FLM.

Similarly, in a different NHP cohort (2nd NHP cohort shown in Table 1), only one DENV4 infected NHP (R1160) showed a low heterotypic titer against DENV2. The detectable FRNT50s were between 1: 50 – 1:90. The value was extrapolated based on a single data point (1:40) which was above 50% neutralization. Given the Hill slope of all the neutralization curves were below 0.5, the FRNT50 values are not reliable.

In conclusion, we do not think sera from Table 1 is potent enough to show difference between the viruses. The intension to show the negative data in Table 1 is to highlight the difference in serum heterogeneity in DENV infected patients and experimental infected NHPs.

Minor critique:

Figure 1C: Legend is not clear for this panel. What is on the x-axis of the bubble plots? Are these mutations across the entire viral genome or is this just the prM-E sequence?

The X-axis is a scatter of all of the sequences contained in the library, similar to graphs used for plotting CRISPR screen results. These represent individual sequences from the saturation mutagenesis libraries in the fusion loop of E as described in Figure 1B.

The wording in Lines 92-94 is not clear. It looks like the T171A mutation was present in 95% of the sequences (Line 92). Yet this sequence was not incorporated into the variant virus. What is the rationale for omitting this mutation in downstream variant virus generation?

The 95% in Line 92 refers to the variant containing N103S/G106L mutations as seen in Figure 1C. The high-throughput sequencing approach did not include residue 171, so the presence of the T171A mutation in combination with fusion loop mutations cannot be determined. However, the E171A mutation was included in the infectious clone for D2-FL and D2-FLM. The text has been modified to clarify this inclusion.

The authors discuss the potential of the D2-FL or D2-FLM virus as a potential vaccine platform in the abstract, introduction, and conclusion. This is a good idea, but the authors provide no evidence of feasibility in this manuscript.

The ultimate goal to engineer a viable DENV with distinct FL antigenic epitope is for it use as live attenuated vaccine. As this is the rationale for the study, we introduce the concept throughout the manuscript. The current study demonstrated the possibility to mutate a novel fusion loop motif in DENV and provided evidence to show the favorable antigenic properties of D2-FLM. We agree with the reviewer that definitive work in animal to show vaccine efficacy need to be done and are currently undergoing. To avoid misleading our audience, we tone down the emphasis of vaccine use in the text.

Line 150-153: Figure 3A demonstrates that the FL-specific antibodies broadly do not neutralize the mutant viruses. However, the conclusions are overstated in the text. 1N5 neutralizes the D2-FL variant.

The text has been updated to more accurately describe the 1N5 neutralization data.

Lines 175-182: The authors make a lot of assumptions about the target of the polyclonal target without any evidence.

These lines reference studies that showed greater enhancement by antibodies targeting the fusion loop and prM as compared to other cross-reacting antibodies. The assumption that both our manuscript and others have drawn was that Abs that are cross-reactive and weakly neutralizing are more prone for ADE. As discussed, other groups have attempted to mutate the FL from recombinant E protein to achieve similar goal to remove the fusion loop epitope to reduce ADE. We have re-written the sentence in the followings:

“As FL and prM targeting Abs are the major species demonstrated to cause ADE in vitro, we and others hypothesized these Abs are responsible for ADE-driven negative outcomes after primary infection and vaccination,10–12,32 we propose that genetic ablation of the FL and prM epitopes in vaccine strains will minimize the production of these subclasses of Abs responsible for undesirable vaccine responses. Indeed, covalently locked E-dimers and E-dimers with FL mutations have been engineered as potential subunit vaccines that reduce the availability of the FL, thereby reducing the production of FL Abs.33–36”

Reviewer #1 (Public Review):

Summary of the major findings -

1. The authors used saturation mutagenesis and directed evolution to mutate the highly conserved fusion loop (98 DRGWGNGCGLFGK 110) of the Envelope (E) glycoprotein of Dengue virus (DENV). They created 2 libraries with parallel mutations at amino acids 101, 103, 105-107, and 101-105 respectively. The in vitro transcribed RNA from the two plasmid libraries was electroporated separately into Vero and C6/36 cells and passaged thrice in each of these cells. They successfully recovered a variant N103S/G106L from Library 1 in C6/36 cells, which represented 95% of the sequence population and contained another mutation in E outside the fusion loop (T171A). Library 2 was unsuccessful in either cell type.

2. The fusion loop mutant virus called D2-FL (N103S/G106L) was created through reverse genetics. Another variant called D2-FLM was also created, which in addition to the fusion loop mutations, also contains a previously published, evolved, and optimized prM-furin cleavage sequence that results in a mature version of the virus (with lower prM content). Both D2-FL and D2-FLM viruses grew comparably to wild type virus in mosquito (C6/36) cells but their infectious titers were 2-2.5 log lower than wildtype virus when grown in mammalian (Vero) cells. These viruses were not compromised in thermostability, and the mechanism for attenuation in Vero cells remains unknown.

4. Next, the authors probed the neutralization of these viruses using a panel of monoclonal antibodies (mAbs) against fusion loop and domain I, II and III of E protein, and against prM protein. As intended, neutralization by fusion loop mAbs was reduced or impaired for both D2-FL and D2-FLM, compared to wild type DENV2. D2-FLM virus was equivalent to wild type with respect to neutralization by domain I, II, and III antibodies tested (except domain II-C10 mAb) suggesting an intact global antigenic landscape of the mutant virion. As expected, D2-FLM was also resistant to neutralization by prM mAbs (D2-FL was not tested in this batch of experiments).

5. Finally, the authors evaluated neutralization in the context of polyclonal serum from convalescent humans (n=6) and experimentally infected non-human primates (n=9) at different time points (27 total samples). Homotypic sera (DENV2) neutralized D2-FL, D2-FLM, and wild type DENV similarly, suggesting that the contribution of fusion loop and prM epitopes is insignificant in a serotype-specific neutralization response. However, heterotypic sera (DENV4) neutralized D2-FL and D2-FLM less potently than wild type DENV2, especially at later time points, demonstrating the contribution of fusion loop- and prM-specific antibodies to heterotypic neutralization.

Impact of the study-

1. The engineered D2-FL and D2-FLM viruses are valuable reagents to probe antibodies targeting the fusion loop and prM in the overall polyclonal response to DENV.

2. Though more work is needed, these viruses can facilitate the design of a new generation of DENV vaccine that does not elicit fusion loop- and prM-specific antibodies, which are often poorly neutralizing and lead to antibody-dependent enhancement effect (ADE).

3. This work can be extended to other members of the flavivirus family.

4. A broader impact of their work is a reminder that conserved amino acids may not always be critical for function and therefore should not be immediately dismissed in substitution/mutagenesis/protein design efforts.

Appraisal of the results -

The data largely support the conclusions, but some improvements and extensions can benefit the work.

1. In Figure 3A, the authors concluded that the engineered dengue virus fusion loop mutant viruses are insensitive to monoclonal antibodies (mAbs) targeting the fusion loop. However, the reduction in neutralization sensitivity varied depending on the mAb tested. The contribution of the optimized prM cleavage site (D2-FLM) to sensitivity to fusion loop mAbs also varied.

a) Are the epitopes known for these mAbs? It would be useful to discuss how the epitope of 1M7 differs from the other mAbs. What are the critical residues?<br /> d) Maybe the D2-FL mutant can be further evolved with selection pressure with fusion loop mAbs 1M7 +/-1N5 and/or other fusion loop mAbs.

2. It would have been useful to include D2-M for comparison (with evolved furin cleavage sequence but no fusion loop mutations).

3. Data for polyclonal serum can be better discussed. Table 1 is not discussed much in the text.

Suggestions for further experiments-

1. It would be interesting to see the phenotype of single mutants N103S and G106L, relative to double mutant N103S/G106L (D2-FL).<br /> 2. The fusion capability of these viruses can be gauged using liposome fusion assay under different pH conditions and different lipids.<br /> 3. Correlative antibody binding vs neutralization data would be useful.

eLife assessment

This valuable study describes engineered dengue virus variants that can be used to dissect epitope specificities in polyclonal sera, and to design candidate vaccine antigens that dampen antibody responses against undesirable epitopes. While the major claims are supported by solid evidence, experiments to distinguish the impact on antibody binding from neutralizing activities would have strengthened the study. This work will be of interest to virologists and structural biologists working on antibody responses to flaviviruses.

Reviewer #2 (Public Review):

Antibody-dependent enhancement (ADE) of Dengue is largely driven by cross-reactive antibodies that target the DENV fusion loop or pre-membrane protein. Screening polyclonal sera for antibodies that bind to these cross-reactive epitopes could increase the successful implementation of a safe DENV vaccine that does not lead to ADE. However, there are few reliable tools to rapidly assess the polyclonal sera for epitope targets and ADE potential. Here the authors develop a live viral tool to rapidly screen polyclonal sera for binding to fusion loop and pre-membrane epitopes. The authors performed a deep mutational scan for viable viruses with mutations in the fusion loop (FL). The authors identified two mutations functionally tolerable in insect C6/36 cells, but lead to defective replication in mammalian Vero cells. These mutant viruses, D2-FL and D2-FLM, were tested for epitope presentation with a panel of monoclonal antibodies and polyclonal sera. The D2-FL and D2-FLM viruses were not neutralized by FL-specific monoclonal antibodies demonstrating that the FL epitope has been ablated.

Overall the central conclusion that the engineered viruses can predict epitopes targeted by antibodies is supported by the data and the D2-FL and D2-FLM viruses represent a valuable tool to the DENV research community.

Author Response

We thank all three reviewers for their detailed reviews, and generally agree with their feedback. To accompany the reviewed preprint of this manuscript, we wished to respond to comments from the reviewers so that they (and the public) will know what we are planning to incorporate in the revised manuscript we are currently preparing. If there are any comments on our plans in the meantime, please let us know.

• Reviewer 1, on concerns regarding identification of ontogenetic stage and comparison of taxa from different ontogenetic stages: It is fair to say that enantiornithine ontogeny is still poorly understood, though we believe all current evidence points to each specimen used in this study to being adequately mature for comparison to the extant birds used in the study. Stages of skeletal fusion are the standard method of assessing enantiornithine ontogeny (Hu and O'Connor 2017), and our comparison of histological work (Atterholt, Poust et al. 2021) to skeletal stages in Table S4 suggests a transition from juvenile to subadult in stage 0 or 1 and from subadult to adult within stage 3. Thus, the specimens we quantitatively examine in this study, all at stages 2 or 3 (Figure S10), are advanced subadults or adults. It is well-known that many living animals considered “adults” would be considered subadults or even juveniles to a palaeontologist (Hone, Farke et al. 2016). So, even if some individuals in this study are not fully skeletally mature, they should have obtained the morphology which they would possess for most of their lives and thus the morphology which undergoes selective pressure. We will add this context to the “Bohaiornithid Ontogeny” section and thank the reviewer for seeking more detail for this point.

• Reviewer 2, on need of a context figure: We have an artistic life reconstruction of a bohaiornithid in preparation, and can include that in the revised manuscript as a figure.

• Reviewer 2, on raptor claw categories: We explain these categories in-depth in a previous work (Miller, Pittman et al. 2023). However, we will now add a short summary of that explanation to this work so that this manuscript will become self-contained in this regard. In short, the “large raptor” category includes extant birds with records of regularly taking prey which cannot be encircled with the pes, while birds in the “small raptor” have no such records. As Reviewer 2 points out this does often follow phylogenetic lines, but not always. E.g. most owls specialise in taking small prey, but the great horned owl Bubo virginianus regularly takes mammals and birds larger than its pes (Artuso, Houston et al. 2020); and conversely we can only find reports of the common black hawk Buteogallus anthracinus taking prey samll enough for the pes to encircle (Schnell 2020) despite other accipiters frequently taking large prey. In both cases these taxa plot in PCA nearer to other large or small raptors (respectively) than to their phylogenetic relatives.

• Reviewer 3, on teeth vs beaks: We are not aware of any foods which are exclusive to toothed or beaked animals. There are some aspects of extant bird biology that may affect the way a certain diet may need to be adapted to which we do comment on, e.g. discussion of alternatives to the crop and ventriculus for processing plant matter in the Bohaiornithid Ecology and Evolution section. For functional studies, e.g. FEA, we have included the rhamphotheca in toothless models which serves the same role as teeth, to be a feeding surface. It should not matter, in theory, if the feeding surface is hard or soft as mechanical failure occurs in high stress/strain states regardless of the medium. If having teeth necessarily increases or decreses overall stress/strain relative to a beak (and from our work this does not appear to be the case), this would in turn necessarily limit dietary options. So, all models in our work should be directly comparable.

As an additional note on this topic, we address tooth shape in bohaiornithids at the end of the Bohaiornithid Ecology and Evolution section. We specifically note that their tooth shape is likley controlled by phylogeny in the current version, though we will add a note in the upcoming version that the morphospace of bohaiorntihid teeth overlaps that of many other clades with purportedly diverse diets, which is consistent with a hypothesis of diverse diets within the clade.

• Reviewer 3, on cranial kinesis: Our FE models should be unaffected by cranial kinesis, as these are two-dimensional and model the akinetic lower jaw only. Some mediolateral kinesis may be relevant in the mandible in the form of “wishboning” in different taxa, but its prevalence in extant birds is currently unknown. The preservation of enantiornithines (two-dimensionally and typically in lateral view) limits the ability to capture any mediolateral function regardless.

Our models of mechanical advantage do not account for any cranial kinesis. This is a necessary simplifcation. The nature of cranial kinesis in extant birds, and the role that it plays in feeding, is poorly understood. Cranial kinesis will increase gape, but we don’t yet know how/if it affects jaw closing force and speed (moreover, given the variation in quadrate and hinge morphology present in extant birds, this is also something that is likely to be highly diverse). We have therefore modelled the extant birds’ jaw closing systems as having one, akinetic out lever (the jaw joint to the bite point), to match the situation in our fossil taxa. This is a common simplification that has been used previously with success (Corbin, Lowenberger et al. 2015, Olsen 2017). However, we acknowledge that this simplification may introduce some error. Unfortunately, until the mechanics of cranial kinesis – and the variation in the anatomy and performance of kinetic structures in extant birds – are better understood, we cannot determine exactly what that error looks like. We therefore have greater confidence in the inter-species comparability this conservative, akinetic approach (in other words, we may not be making assumptions that are 100% accurate, but we are at least making the same assumption across all taxa, so it should be comparable in its error). We will add a section in the Mechanical Advantage and Functional Indices discussion calling for further research into the mechanics of cranial kinesis so future mechanical advantage work in birds can take this matter into account.

• Reviewer 3, on skull reconstruction: This issue is partly addressed in the Bohaiornithid Skull Reconstruction section, though we agree that adding more mentions of it in the MA and FEA Discussion sections and the Bohaiornithid Ecology and Evolution sections will benefit the manuscript. Most notably Shenqiornis and Sulcavis have similar ecological interpretations, but much of the Shenqiornis skull reconstruction uses Sulcavis bones. Longusunguis is the only other taxon which takes more than two bones from a different taxon, and in this case all but the quadrate are not used in any quanitative measurements. We have ensured that the skull reconstructions presented in Figure 2 show what portions of the skull come from what specimen so that as new material is discovered and phylogenetic relationships are updated it will be clear to future readers which parts of reconstructions will need to be updated.

• Reviewer 3, on data availability: All data including FEA models and raw measurement data are included in the same repository as the scripts, which we will make clear in the manuscript. Good catch on the data link being dead, we will publish it now.

As a final note, it was brought to our attention by another colleague that the original manuscript’s ancestral state reconstrction lacked an outgroup. An updated reconstruction using Sapeornis as an outgroup will be included in the revised manuscript. The addition of the outgroup does not change any conclusions of the manuscript.

We once again thank our reviewers for their valuable feedback and will submit a revised version of this manuscript for publication shortly. Please let us know if you have any additional comments after reading our response that we can take onboard in our revision.

References

Artuso, C., C. S. Houston, D. G. Smith and C. Rohner (2020). Great Horned Owl (Bubo virginianus), version 1.0. Birds of the World. A. F. Poole. Ithaca, NY, USA, Cornell Lab of Ornithology.

Atterholt, J., A. W. Poust, G. M. Erickson and J. K. O'Connor (2021). "Intraskeletal osteohistovariability reveals complex growth strategies in a Late Cretaceous enantiornithine." Frontiers in Earth Science 9: 640220.

Corbin, C. E., L. K. Lowenberger and B. L. Gray (2015). "Linkage and trade‐off in trophic morphology and behavioural performance of birds." Functional ecology 29(6): 808-815.

Hone, D. W. E., A. A. Farke and M. J. Wedel (2016). "Ontogeny and the fossil record: what, if anything, is an adult dinosaur?" Biology letters 12(2): 20150947.

Hu, H. and J. K. O'Connor (2017). "First species of Enantiornithes from Sihedang elucidates skeletal development in Early Cretaceous enantiornithines." Journal of Systematic Palaeontology 15(11): 909-926.

Miller, C. V., M. Pittman, X. Wang, X. Zheng and J. A. Bright (2023). "Quantitative investigation of Mesozoic toothed birds (Pengornithidae) diet reveals earliest evidence of macrocarnivory in birds." iScience 26(3): 106211.

Olsen, A. M. (2017). "Feeding ecology is the primary driver of beak shape diversification in waterfowl." Functional Ecology 31(10): 1985-1995.

Schnell, J. H. (2020). Common Black Hawk (Buteogallus anthracinus), version 1.0. Birds of the World. A. F. Poole and F. B. Gill. Ithaca, NY, USA, Cornell Lab of Ornithology.

eLife assessment

This important study explores numerous lines of evidence for the surprisingly diverse diets of a group of toothed birds that lived over 100 million years ago. The large amount of data the authors collected forms a solid dataset for their statistical analyses. The methods are, to various extents, extensible to other limbed vertebrates. The conclusions in the article itself will be of interest to anyone who studies ecological evolution in birds or dinosaurs more generally, as well as to anyone who studies the impact of the mass extinction event 66 million years ago on ecology and evolution.

Reviewer #1 (Public Review):

Understanding the ecology including the dietary ecology of enantiornithines is challenging by all means. This work explores the possible trophic diversity of the "opposite-bird" enantiornithines by referring to the body mass, jaw mechanical advantage, finite element analysis of the jaw bones, and morphometrics of the claws and skull of both fossil and extant avian species. By incorporation of the dietary information of longipterygids and pengornithinds, the authors predicted a wide variety of foods for enantiornithine ancestors. This indicates the evolutionary successes of enantiornitine during Cretaceous is very likely to have been driven by the wide range of recipes. I believe this work represented the most comprehensive analysis of enantiornithines' diet and trophic diversity by far and the first systematic dietary analysis of bohaiornithids, though the analysis themselves are largely based on the indirect evidence including jaw bone morphologies and claw and skull morphometrics. Anyway, I believe the authors did most the paleontologists could do, and I do not know whether the conclusions could be further supported by incorporating some geochemical data, as most of the specimens the authors analyzed were recovered from a small geographic area. The results also indicate that the developmental trajectories of enantiornithines, at least for jaw bones, might also have been diverse to some extent in response to the diverse ecological niches they adapted. My only concern regarding the analysis is to what extent the conclusions are convincing by comparing specimens representing various ontogenetic stages.

Reviewer #2 (Public Review):

Miller et al. take a variety of measurements and analytical techniques to assess the ecology of various species of the enantiornithine clade Bohaiornithidae. From this they suggest that the ancestral enantiornithine was a generalist and that the descendant clades occupied a breadth of niches similar to that of the radiation of derived birds after the K-Pg extinction.

I am not a statistician so I found much of the paper to be outside my ability to review. I also am not an expert on enantiornithines or cranial morphology of birds, so these areas I also am not the best reviewer.

However, I have published on bird foot functional morphology, notably that of birds of prey. This area thus is where I concentrated my efforts in the review.

Overall, I find the idea that enantiornithines had occupied a similar niche breadth to post-K-Pg derived birds to be a curious, thought provoking proposal. On methodology, I have a few questions about bird feet comparisons. Whether my comments require minor or major edits is not really possible to say since I am not commenting on e.g. the skull-based analyses.

STRENGTHS<br /> The paper uses a multi-proxy approach to assess ecological categories. This is broader than in previous works and is to be commended. I am not well placed to comment on the specifics of the statistical methods however.

LANGUAGE<br /> The manuscript is very well written. I don't recall seeing many or possibly any grammatical issues. That's rare these days and I commend the authors on checking their manuscript and making it readable. This said, I found the extensive use of acronyms and abbreviations to be difficult to follow. This is not much of a criticism but in a general-readership journal, perhaps not having everything abbreviated might be preferential.

The manuscript uses phrases like "superficially resembles" and "is similar to" a lot. I'm trying not to be picky, but very often these phrasings don't say how the features are similar (or not). Is it the curvature etc? Could these be expanded upon a bit more in the text please? It isn't very easy to assess similarity r dissimilarity without some point of reference.

FIGURES<br /> The figures are generally very good, and the captions are generously descriptive. However, all figures are graphs, tables, etc. It would be nice, somewhere, to have an image or group of images showing us what a bohaiornithine is.. especially since this is a general-readership journal. I wasn't aware of the details of enantiornithine clades before reading this manuscript, and I suspect other readers would be in the same place. Can we get some images of fossils, a skeletal diagram, or something?

RAPTOR CLAWS<br /> This is my main criticism.

The foot morphometrics suggest that there is a morphological difference between claws of raptors that feed on large prey, and those of raptors that feed on small prey. I am curious what these morphological differences are.

In our paper(s) (Fowler et al., 2009; 2011), we looked at the feet (especially the claws) of various birds of prey, and studied foot functional morphology compared with prey choice, capture and immobilization strategy. We devised a behavioural categorization that separated the behavior (mainly in subduing the prey) between "small" and "large" prey, that being whether they can be fully contained within the foot of the raptor. Most if not all raptors take small prey, and these are typically killed using constriction. Some raptors have specialized in small prey/constriction (e.g. most owls). Some raptors might also take large prey, but since (by definition) large prey cannot be fully contained within the foot then the prey item cannot be constricted and a different immobilization (kill) mechanism must be employed (which differs among clades).

We never made a morphological distinction between small and large prey specialists largely because all raptors take small prey. I am thus interested in what taxa are designated small vs large prey specialists in this study. Perhaps these authors have found characters that distinguish primarily small-prey-specialist raptors, but I do not know what they are and maybe this should be included in the text somewhere.

Owls are mainly small prey specialists. Compared with other raptors, they have a unusual foot that has (I am generalising here) short non-ungual phalanges contrasting with long ungual phalanges which are relatively low curvature. We (Fowler et al 2009) suggest that this gives owls a more tightly closable foot (short non-ungual phalanges), but maintains reach of each toe (long claw). This could be seen as indicative of small -prey specialization, but again, other raptor clades take small prey without this very specialized foot. If the "small prey specialist" category here is really just owls then it might be slightly misleading.

This is my main criticism. I would at least like some explanation of what is in this category.

Otherwise I must leave assessment of cranial functional morphology, and general statistical analysis to other reviewers.

IMPACT<br /> As I have already stated, the idea that Enantiornithines occupied a similar breadth of niches to post K-Pg birds is thought provoking, moreso than upon initial reading. The authors note that this raises questions about the adaptations or survivorship of derived birds, and this is what I find most intriguing, and is what I think will appeal to most readers.

Reviewer #3 (Public Review):

Summary:<br /> The authors use several quantitative approaches to characterize the feeding ecologies of bohaiornithid enantiornithines, including allometric data, mechanical advantage and finite element analyses of the jaw, and morphometric analyses of the claws. The authors combine their results with data for other enantiornithines collected from the literature to shed new insight on the ecological evolution of Enantiornithes as a clade.

The approaches used by the authors are generally appropriate for the questions being asked, their comparisons are thorough, and the interpretations are generally reasonable. However, there are a number of major flaws to the comparisons used that should at least be addressed by the authors, if not overcome by modifying the methodology. Smaller concerns/comments are provided in "Recommendations for the authors."

My first major concern is about how the presence of teeth might influence both qualitative and quantitative comparisons to extant birds. The authors should discuss how the presence of teeth might facilitate or prevent feeding strategies that might be inconsistent with (for example) patterns reconstructed using finite element analysis for a comparative sample of toothless birds.

Next, the authors should discuss the potential impact that cranial kinesis might have on the functionality of the jaws - especially with regards to the mitigation of stresses experienced by the skull. Do the quantitative approaches used here to characterize the mechanics of the jaws account for kinesis in extant birds? If so, how? If not, how do the authors' account for that mechanical difference in their interpretations?

My next concern regards potential biases introduced by the approach taken to reconstruct the bohaiornithid skulls sampled here. Using elements from closely related taxa to fill out an incomplete skull during reconstruction is reasonable, but it may influence the results of subsequent shape comparisons - especially when the "donor" skull is compared to the recipient. The authors should explain how they accounted for this possibility in their methods or their interpretations.

Next, it is unclear how or where much of the data used or generated by this study are made available. I appreciate that the authors thoroughly cite the literature from which some data (e.g., extant FEA data), but all data used should be provided in the supplement. Likewise for the FEA models generated for the newly sampled taxa. The authors indicate that some R scripts are available online (Lines 787-788), but that link is currently non-functional, so I could not verify what was made available. And unless I missed it, the authors don't indicate that other data (e.g., FEA models) are also available there. Any data used in, or generated by, this paper should be made available online - including FEA models, tree files and analysis output files.

Also pertaining to the methods, in some places, the methods the authors used to analyze their data were not specified. For example, the authors mention that "all analyses of the [MA/FEA] data were performed in R" and "scripts [are] available" online (Lines 786-787), but the authors don't specify what those analyses actually are - unless that was specified elsewhere and I missed it? I know very little about FEA or MA analyses, so maybe these approaches are well understood in those circles, but I am unable to assess the approaches here without downloading and digging into the scripts.

A broader recommendation here: in several places, I found this paper difficult to follow. That's partly understandable, the authors are discussing and comparing trends across a wide variety of data types and analyses - which is certainly both challenging and commendable. But that variety of analyses has resulted in a staggering variety of acronyms that I found nearly impossible to keep track of. Minimally, I recommend that the authors redefine the most important acronyms at the start of each major subheading.

Related to that last point, in the discussion, I often found myself missing the forest for the trees, so to speak - the authors paid much attention to interpreting the results of each analytical approach for each taxon (which I appreciate), but I found it difficult to keep track of the take-home message the authors were trying to convey. I would recommend a reorganization of the discussion that follows a backbone based on the authors' key messages - for example, a section on species-level interpretations (maybe with sub-headings for each approach discussed), followed by larger-picture discussions of Bohaiornithidae and Enantiornithes more generally. The authors included a section at the end of their discussion that already provides that larger picture for Enantiornithes, but the section on "Bohaiornithid Ecology and Evolution" includes a lot of species-level comparison that I think would be better suited for species-focused sub-sections, and I think the paper would be better served by reserving this section for a bohaiornithid-level survey.

eLife assessment

This study has uncovered some important initial findings about cellular responses to aneuploidy through analysis of gene expression in a set of donated human embryos. While the study's findings are in general solid, some experiments lack statistical power due to small sample sizes. The authors should try to get much more insight with their data highlighting the novel findings.

Reviewer #1 (Public Review):

This study investigated an important question in human reproduction: why most fully aneuploid embryos is incompatible with normal fetal development. Specifically, the authors investigated the cellular responses to aneuploidy through analysis of gene expression in a set of donated human blastocysts. The samples included uniform aneuploid embryos of meiotic origin and mosaic aneuploid embryos from the SAC inhibitor reversine treatment. The authors relied mainly on low-input RNA sequencing and immunofluorescence staining. Pathway analysis with RNA-seq data of trophectoderm cells suggested activation of p53 and possibly apoptosis, and this cellular signature appeared to be stronger in TE cells with a higher degree of aneuploidy. Immunostaining also found some evidence of apoptosis, increased expression of HSP70 and autophagy in some aneuploid cells. With combinational OCT4 and GATA4 as lineage markers, it appeared that aneuploidy could alter the second lineage segregation and primitive endoderm formation in particular.

Although this study is largely descriptive, it generated valuable RNA-seq data from a set of aneuploid TE cells with known karyotypes. Immunostaining results in general were consistent with findings in mouse embryos and human gastruloids.

While there is a scarcity of human embryo materials for research, the lack of single cell level data limits further extension of the presented data on the consequences of mosaic embryos. A major concern is that the gene list used for pathway analysis is not FDR controlled. It is also unclear how the many plots generated with the "supervised approach" were actually performed. The authors also appear to have ignored the possibility that high-dosage group could have a higher mitotic defects. Assuming a fully aneuploid embryo, why do only some cells display p53 and autophagy marker? The conclusion about proteotoxic stress was largely based on staining of HSP70. It appears from Figure 3 d,h that the same cells exhibited increased HSP70 and CASP8 staining. Since HSP70 is known to have anti-apoptotic effect, could the increased expression of Hsp70 be an anti-apoptotic response?

Reviewer #2 (Public Review):

A high fraction of cells in early embryos carry aneuploid karyotypes, yet even chromosomally mosaic human blastocysts can implant and lead to healthy newborns with diploid karyotypes. Previous studies in other models have shown that genotoxic and proteotoxic stresses arising from aneuploidy lead to the activation of the p53 pathway and autophagy, which helps eliminate cells with aberrant karyotypes. These observations have been here evaluated and confirmed in human blastocysts. The study also demonstrates that the second lineage and formation of primitive endoderm are particularly impaired by aneuploidy.

This is a timely and potentially important study. Aneuploidy is common in early embryos and has a negative impact on their development, but the reasons behind this are poorly understood. Furthermore, how mosaic aneuploid embryos with a fraction of euploidy greater than 50 % can undergo healthy development remains a mystery. Most of our current information comes from studies on murine embryos, making a substantial study on human embryos of great importance. However, there are only very few new findings or insights provided by this study. Some of the previous findings were reproduced, but it is difficult to say whether this is a real finding, or whether it is a consequence of a low sample number. The authors could get much more insight with their data.

Author Response

Reviewer #1 (Public Review):

This paper addresses the question of Prdm9-dependent hotspots and Prdm9 alleles evolution. Two properties underlie this question: the erosion of hotspots by biased gene conversion and the high mutation rate of the Prdm9 zinc finger domain. Here the authors include an additional recently observed property of Prdm9: its role in DSB repair, by enhancing DSB repair efficiency when binding on both homologs (symmetric sites). The status of symmetric binding depends on Prdm9 level and affinity, possibly other factors. The authors present a model for simulating Prdm9 and hotspots co-evolution based on several assumptions (Number of DSB independent of Prdm9, two types of hotspots, strong or weak; hotspots compete; at least one symmetric DSB is required on the smallest autosome). Although the in vivo context is obviously more complex, these assumptions are reasonable (except for the number of Prdm9 bound sites) as they qualitatively recapitulate or get close to what is known about the requirement for fertility. The model leads to several important conclusions and predictions that Prdm9 limits the number of sites used since such conditions are predicted to allow for a weaker contribution of asymmetric sites.

The presentation of the model is clear, but the results are difficult to follow and require many readings to follow the text and the associated figures.

We edited the results section to make the progression of the argument clearer (as detailed below).

A few specific points also require clarification:

Competition: It seems that in the context defined Prdm9 is limiting (since most Prdm9 can be bound to all weak sites); in addition, it is not clear how the competition for DSB activity between Prdm9 sites is taken into account.

We now clarify throughout the text that we have assumed conditions under which PRDM9 is limiting (as detailed below). We state in the Model that we assume “all PRDM9 bound sites are equally likely to experience a DSB”.

The number of Prdm9-bound sites in vivo is not known, thus several values must be tested.

We have run additional simulations (when considering strong and weak hotspots, k_1=5 or 50, and when considering large and small population sizes, N= 10^3 or 10^6), using P_T = 500, 1000 and 2500. The results of these simulations are included and discussed in Appendix 4.

It would be interesting to discuss the model prediction in the context of several observations published on hybrids with variable Prdm9 gene dosage.

We now include a section in the Discussion, entitled “PRDM9-mediated hybrid sterility”, which discusses the reported gene dosage effects in mice.

Reviewer #2 (Public Review):

In mammalian genomes (with some exceptions), the location of recombination hotspots is driven by the PRDM9 zinc-finger protein that recognizes some specific DNA motifs and recruits the machinery inducing double-strand breaks (DSBs) initiating recombination. As DSBs are repaired with the homologous chromosome, "hot motifs" can be rapidly eroded through gene conversion occurring during the repair. This led to the "hotspot paradox" question and to the development of red queen models of hotspot evolution where the lack of enough DSB motifs can select for new PRDM9 alleles recognizing new sets of motifs, which in turn are eroded. However, this model fails to explain some observations, in particular, that the number of DSB seems not limited by PRDM9 sites. Recent findings also showed that PRDM9 played a central role in the symmetrical binding of homologous chromosomes.

In this study, the author incorporated this new finding (and more realistic assumptions compared to previous models) in a model of hotspot evolution. Their main result is that it affects the evolution dynamics and in particular the causes of selection on new PRDM9 alleles. Instead of selection pressure to increase the number of DSB targets, they showed that selection likely occurred instead to limit the number of hotspots to the hottest and symmetrical ones. These results are important as they changed our view and understanding of the evolution of mammalian hotspots and should have general implications for the study of recombination. The article focuses on complex mechanisms and can appear rather specific and technical. However, it nicely exemplifies the importance of taking molecular mechanisms into account to model genome evolution.

Overall, the model is sound with no apparent flaw and should be an important contribution to the field. The model is rather complex but the authors focused on a few key parameters while fixing others based on empirical knowledge. This allows for highlighting the novelty of the results without being lost within too many scenarios and hypotheses. However, two main issues should be addressed but they mostly concern the way the model and the results are presented and do not. First, partly due to the complexity of the mechanisms, the core of the manuscript is rather difficult to follow and would deserve a more careful and explicit presentation to guide the reader, as detailed below. Second, the implications of the model and the practical and testable predictions it makes could be developed more, in particular, to compare with previous models. The main comments are listed below.

1) The introduction reads very well and clearly explains complex mechanisms. It is a bit long and could be reduced a bit.

Following this suggestion, we have reduced the length of the Introduction.

2) It is quite helpful to analyze the model step by step. However, the objective of each step is not clearly explained, and it is left to the reader to understand where the authors want to go. At first read, it is not clear whether the authors present an analysis of the model or simulation results and why they do that. So, the results part deserves rewriting and re-organization to guide the reader.

• In the two first parts (Fitness with one heat and two heats) it should be stated more explicitly that it corresponds to an analysis of the fitness landscapes generated by the molecular mechanisms than results on the evolutionary dynamics

• The part "Dynamics of the two-heat model" corresponds to simulations and it is only at this point that mutation on PRDM9 is introduced.

• In the present form, the presentation of the results describes many mechanisms (which is fine). However, as the model is complex, stressing the main conclusion for each part could be useful as then making a clear link between the different steps of the reasoning.

We have rewritten the results sections to include more signposting and to make clearer the intentions behind each step taken.

3) The choice of key parameters is well justified with a detailed review of the literature and it is well justified to fix most of them to focus on the key unknown (or not well-known) ones. However, in a few cases, additional simulations or at least better justification would be welcome, in particular on the mutation dynamics of PRDM9.

Thank you for your suggestion. We have now added an additional appendix (Appendix 5), which investigates the dynamics of our model when newly arising PRDM9 alleles are initiated with hotspot numbers set near values that would be reasonable for perfect matches to motifs with 10 or 11 non-degenerate bases. We show that this sometimes affects the dynamics (compared to the case in the main text), but when it does, the differences can be readily understood using the same kind of reasoning developed in the main text.

4) The model clearly gives new insights into the evolution of recombination hotspots and appears better to explain some results. However, it is not clear what are the predictions of the model that could be properly tested with data, in particular against previous models. Some predictions are proposed but remain mainly qualitative. For example, can one quantify that this model predicts a skewer distribution of hotspots compared to previous red-queen models? How good is the model at predicting the number of PRDM9 alleles in human and mouse for example? Only the diversity at PRDM9 is given, it may be interesting to also give the number of alleles to compare to observations. The discussion on this remains a bit vague. Finally, are there additional predictions of the model that could be used to test it?

In previous Red Queen models, the specific distribution of heats was not important: fitness was determined by the sum of the heats of all available binding sites. Accordingly, these models do not predict a specific distribution, only that PRDM9 alleles that bind more overall would be favored. Our model thus provides the first theoretical framework under which there is an explicit benefit to localizing PRDM9 to smaller numbers of loci, a premise consistent with the use of hotspots, i.e., the use of only a small proportion of the genome for recombination.

We chose the two-heat model as a reasonable first approximation to the true distribution. If we were to consider a more realistic binding distribution (or similarly, if we relaxed our assumption about most PRDM9 molecules being bound), the quantitative conclusions would likely be affected. Accordingly, while our simplified model provides robust insights into the dynamics of PRDM9 evolution, quantities such as the predicted levels of diversity in our model may be off and cannot be readily compared to what is observed in human and mice populations. We now better clarify the scope of our results and what may be done to extend it, in the Discussion.

5) The Penrose stair metaphor is appealing but it seems to be dependent on the definition of hotspot, so not to represent a real biological process. Related to metaphors, it is also not very clear whether the authors suggest abandoning the red-queen metaphor for the benefit of the Penrose stair one. Actually, we can still consider that it is a red-queen dynamics but with a different underlying driver.

We have expanded our discussion of the difference between these two analogies in the discussion section “Does the decay of hotspots by GC lead to more or fewer hotspots?” to clarify that the Penrose stairs model is a specific kind of Red Queen model. However, precisely because a hotspot has a somewhat arbitrary definition, we can imagine her running in either direction–towards fewer or more hotspots– depending on our perspective on the Penrose stairs.