Author Response

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

We would like to thank the reviewers for their strong interest in our studies and their excellent suggestions for improvement.

Reviewer #1:

Weaknesses:

Comment 1. The authors identified NPR-15 and ASJ neurons that are involved in both molecular and behavioral responses to pathogen attack. This finding, by itself, is significant. However, how the NPR-15/ASJ circuit regulates the interplay between the two defense strategies was not explored. Therefore, emphasizing the interplay in the title and the abstract is misleading.

Response to comment 1. We have removed the word “interplay.”

Comment 2. Although the discovery of a single GPCR regulating both immunity and avoidance behavior is significant and novel, NPR-15 is not the first GPCR identified with these functions. Previously, the same lab reported that the GPCR OCTR-1 also regulates immunity and avoidance behavior through ASH and ASI neurons respectively (PMID: 29117551). This point was not mentioned in the current manuscript.

Response to Comment 2. We’d like to clarify that it remains unclear whether OCTR-1 itself controls both immunity and behavior (PMID: 29117551). The reference study showed that OCTR-1-expressing neurons ASH and ASI control immunity and behavior, respectively. We modified the manuscript to make this point clearer: “While OCTR-1-expressing neurons ASI play a role in avoidance (34), the specific role of OCTR-1 in ASH and ASI neurons remains unclear. “

Comment 3. The authors discovered that NPR-15 regulates avoidance behavior via the TRPM gene, GON-2. Only two factors (GON-2 and GTL-2) were examined in this study, and GON-2 happens to function through the intestine.

Response to comment 3. We studied GON-2 and GTL-2 because a recent screen of intestinal TRPM genes showed that they are the only two involved in the control of pathogen avoidance. We modified the manuscript to make this rationale clearer: “Because transient receptor potential melastatin (TRPM) ion channels, GON-2 and GTL-2, are required for pathogen avoidance (32), we studied whether they may be part of the NPR-15 pathway that controls pathogen avoidance”

Comment 3b. It is possible that NPR-15 may broadly regulate multiple effectors in multiple tissues. Confining the regulation to the amphid sensory neuron-intestinal axis, as stated in the title and elsewhere in the manuscript, is not accurate.

Response to comment 3b. We agree that NPR-15 may broadly regulate multiple effectors in different tissues. Indeed, we have shown that the transcriptional activity of ELT-2, HLH-30, DAF-16, and PMK-1 is higher in npr-15 than in WT animals. We found that expression of NPR-15 only in ASJ cells rescues both the survival and behavioral phenotypes of npr-15 animals (Figs. 4F and 5C).

Comment 4. The C. elegans nervous system is simple, and hermaphrodites only have 302 neurons. Individual neurons possessing multiple regulatory functions is expected. Whether this is conserved in mammals and other vertebrates is unknown, because in higher animals, neurons and neuronal circuits could be more specialized.

Response to Comment 4. We agreed. We have removed the statements discussing conservation in that manner.

Comment 5. A key question, that is, why would NPR-15 suppress immunity (which is bad for defense) but enhance avoidance behavior (which is good for defense), is not addressed or explained. This could be due to temporal regulation, for example, upon pathogen exposure, NPR-15 could regulate behavior to avoid the pathogen, but after infection, NPR-15 could suppress excessive immune responses or quench the responses for the resolution of infection.

Response to comment 5. We found that NPR-15 controls the expression of immune genes in the absence of an infection. Without further experiments, we think it would be too speculative to discuss the possibility of a temporal regulation. However, we modified the manuscript to address the control of both molecular and behavioral immunity by NPR-15. The revised discussion reads: “Our findings shed light on the role of NPR-15 in the control of the immune response. NPR-15 seems to suppress specific immune genes while activating pathogen avoidance behavior to minimize potential tissue damage and the metabolic energy cost associated with activating the molecular immune response against pathogen infections. Overall, the control of immune activation is essential for maintaining homeostasis and preventing excessive tissue damage caused by an overly aggressive and energy-costly response against pathogens (60-63).”

Comment 6. Discussion appears timid in scope and contains some repetitive statements. Point 5 can be addressed in the Discussion.

Response to comment 6. We have removed repetitive concepts and modified the discussion as mentioned in the response to point 5.

Comment 7. Overall, the authors presented an impactful study that identified specific molecules and neuronal cells that regulate both molecular and behavioral immune responses to pathogen attack. Most conclusions are supported by solid evidence. However, some statements are overreaching, for example, regulation of the interplay between molecular and behavioral immune responses was emphasized but not explored. Nonetheless, this study reported a significant and novel discovery and has laid a foundation for investigating such an interplay in the future.

Response to comment 7: We removed the statements that may have appeared to be overreaching and addressed the weakness raised by the reviewer. The revised discussion reads “Our findings shed light on the role of NPR-15 in the control of the immune response. NPR-15 seems to suppress specific immune genes while activating pathogen avoidance behavior to minimize potential tissue damage and the metabolic energy cost associated with activating the molecular immune response against pathogen infections. Overall, the control of immune activation is essential for maintaining homeostasis and preventing excessive tissue damage caused by an overly aggressive and energy-costly response against pathogens (60-63).”

Recommendations for the authors:

Recommendations 1. The title, abstract and some statements in the main text need to be re-written to reflect the fact that regulation of the interplay between molecular and behavioral immune responses was not explored in this study.

Response to recommendations 1. We modified the title and abstract accordingly.

Recommendations 2. It should be mentioned in the manuscript that OCTR-1 is the first GPCR that was identified to regulate both immunity and avoidance behavior.

Response to recommendation 2. We addressed this issue as discussed in the response to comment 2.

Recommendations 3. Repetitive statements should be removed from Discussion.

Response to recommendations 3. The statements were removed.

Recommendations 4. It is surprising to see that pmk-1 RNAi did not affect the survival of npr-15(tm12539) animals against S. aureus because PMK-1 has a general role in defense against S. aureus infection.

Response to recommendations 4. We agree. However, the RNAi studies were validated using mutants (Fig. S3B).

Recommendations 4b. Also, the rationale for using skn-1 RNAi as a control was not given. These need to be explained adequately in the manuscript.

Response to recommendations 4b. There’s no need to include skn-1 RNAi and we removed the data.

Recommendations 5. The conclusion that the lack of avoidance behavior by NPR-15 loss-of-function is independent of immunity and neuropeptide genes was drawn entirely based on experiments with RNAi of individual genes. Functional redundancy among genes could render RNAi of individual genes ineffective, thus masking the dependence of avoidance behavior on these genes. More experiments are needed to support this conclusion, or the wording of the conclusion need to be changed.

Response to recommendations 5. We modified the conclusion to address this issue: “Given the possibility of functional redundancy among these genes, we cannot rule out the possibility that different combinations may play a role in controlling avoidance behavior.”

Recommendations 6. What is representation factor in Fig. 2B and 2C?

Response to recommendations 5. Figure 2B shows significantly enriched terms with a Q value < 0.1, sorted by P values. Figure 2 C shows the representation factor that is calculated using a tool, http://nemates.org/MA/progs/overlap_stats.html. The calculation is based on the number of genes in set 1, the number of genes in set 2, and the Overlap between set 1 and set 2, as well as the number of genes in the genome.

We corrected the Figure legends and included the corresponding information in Material and Methods.

Recommendations 7. The legend of Fig. 6 was wrong and should be changed to 'GPCR/NPR-15 suppressed immune response and enhanced avoidance behavior via sensory neurons'.

Response to recommendations 7. Thank you for pointing this out. We changed the legend.

Reviewer #2:

Comments 1. There is some variance in lawn occupancy of wt strains between the different trials in WT animals (e.g. in Fig. 1: 25 for wt vs 60% for npr mutant; S1c 5% for wt and 60% for npr mutant).

Response to comment 1. We appreciate the observation. We did notice some variation in both the WT and npr-15(tm12539) animals during our study. Notably, the variation appeared to be more in the WT compared to the npr-15(tm12539) animals. However, it's important to note that these variations did not significantly affect the outcome of our findings. We calculated the means, standard deviation, and standard error across different experimental trials that are presented in the manuscript (Table S2) (new Table). It's worth noting that these variations did not significantly impact the observed differences in lawn occupancy between the wild-type (WT) and npr-15 mutant strains.

We addressed this issue in the revised manuscript: “Interestingly, we noticed that the variation in lawn occupancy is greater in WT than in npr-15(tm12539) animals across experiments (Table S2), which suggests that the strong lack of avoidance of npr-15(tm12539) somehow counteracts the experimental variation”

Comment 2. Does this reflect rates of migration or re-occupancy in WT?

Response to comment 2. We did not observe any re-occupancy in either the WT or npr-15 animals at 24-hour time points (which we mostly use in this study) or beyond. To address the comment, we performed a new experiment and found that the re-occupancy of npr-15 mutants is comparable to that of WT animals at 4 hours post-exposure (Figure S1B).

Comment 3. Does pathogen avoidance persist and/or the rate of avoidance differ in npr mutant worms?

Response to comment 3. As illustrated in new Figure S1B, the avoidance behavior in response to pathogens remained consistent even when we extended our observations up to 48 hours (Figure S1B).

Comment 4. if animals were exposed then re-exposed, could the authors to determine whether a learned avoidance was similarly affected by this mutation by assessing rate changes?

Response to comment 4. We conducted the proposed experiment and observed that the WT animals learned to avoid the pathogen but not npr-15(tm12539) mutants (Figure S1C). The revised manuscript reads: “We also found that npr-15(tm12539) exhibited reduced learned avoidance compared to WT animals (Figure S1C).”

Comment 5: Is there any difference in gene expression of animals that have migrated off the lawn to those remaining on the lawn (e.g. in partial lawn experiments?).

Response to comment 5. This is an interesting question that has not been addressed in the field yet. While we think the study is exciting, we believe that it is outside the scope of our work. All the gene expression studies performed here are in non-avoiding conditions.

Comment 6. No concerns but the P values in the legends are a pain to read. Why not put them in figures as in above figures.

Response to comment 6. We included the P values as suggested.

Recommendations for the authors:

Recommendation 1. Fig. 1/S1. Comments: There is some variance in lawn occupancy of wt strains between the different trials in WT animals (e.g. in Fig. 1: 25 for wt vs 60% for npr mutant; S1c 5% for wt and 60% for npr mutant).

Response to recommendation 1. We addressed this issue as discussed in the response to comment 1.

Recommendation 2. Fig. 1/S1. Comments. Does this reflect rates of migration or re-occupancy in WT?

Response to recommendation 2. We have responded to this issue in comment 2.

Recommendations 3. Fig. 1/S1. Comments. Does pathogen avoidance persist and/or the rate of avoidance differ in npr mutant worms.

Response to recommendation 3. We have responded to this issue in comment 3.

Recommendation 4. Fig. 1/S1. Comments B. and if animals were exposed then re- exposed, could the authors to determine whether a learned avoidance was similarly affected by this mutation by assessing rate changes?

Response to recommendation 4: We have responded to this issue in comment 4 above.

Recommendation 5. Fig. 2/S2. Comment: Is there any difference in gene expression of animals that have migrated off the lawn to those remaining on the lawn (e.g. in partial lawn expts?).

Response to recommendation 5. We have responded to this issue in comment 5 above.

Recommendation 6. Fig. 3/S3. Comment. No concerns but the P values in the legends are a pain to read. Why not put them in figures as in above figures.

Response to recommendation 6. We included the P values.

Recommendation 7. Fig. 5. Comments: The authors suggest that the ASJ/NPR15 effect to limit avoidance acts via inhibition of GON-2 in the intestine. The observation that GON-2 inhibition effects on pathogen avoidance occur independently of neurons could suggest that it is a redundant way of accomplishing the same thing, which then makes one wonder if or what the connection is exists between the neuron and the gut. The effect of ASJ via NPR on pathogen avoidance is not neuropeptide dependent, which they show. So how the neuronal-gut communication works. Specific Transmitters... perhaps.

Response to Recommendation 7 Fig. 5. Thanks for this observation. To address the recommendation, we modified the discussion: “Our research additionally indicates that the regulation of NPR-15-mediated avoidance is not influenced by intestinal immune and neuropeptide genes. Given the potential for functional redundancy and our focus on genes upregulated in the absence of NPR-15, we cannot entirely rule out the possibility that unexamined immune effectors or neuropeptides, not transcriptionally controlled by NPR-15, might be involved. Different intestinal signals may also participate in the NPR-15 pathway that controls pathogen avoidance.”

Recommendation 8. Comment. Since ASJ neurons control entry into dauer, perhaps isn't surprising that DAF-16 showed up as an NPR-15. induced factor (and dauer worms are resistant to a lot of stressors); that said dauer hormones might be involved as well. Is there any evidence that DAF-16 down-regulates GON-2 expression (see Murphy, Kenyon et al. 2005), and along these lines would GON-2 RNAi work in a DAF-16 mutant? I think addressing these issues are the subject of future studies.

Response to recommendation 8. We checked the data in the study by Murphy, Kenyon et al., and found that the gon-2 gene was not downregulated.

Recommendation 9. Minor: Regarding the description to Fig. 5. "Consistently with our previous findings, we found that only " The adverb form of consistent should not be used here.

Response to recommendation 9. Thank you for pointing this out. The description of Figure 5 was corrected.

Author Response

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

Reviewer #1:

A weakness of the paper is that the power of the model is illustrated for only one specific set of parameters, added in a stepwise manner and the comparison to one specific empirical TGM, assumed to be prototypical; And that this comparison remains descriptive. (That is could a different selection of parameters lead to similar results and is there TGM data which matches these settings less well.)

The fact that the comparisons in the paper are descriptive is a central point of criticism from both reviewers. As mentioned in my preliminary response, I intentionally did not optimise the model to a specific TGM or show an explicit metric of fitness. As I now explicitly mention in the new experimental section of the paper:

“The previous analyses were descriptive in the sense that they did not quantify how much the generated TGMs resembled a specific empirical TGM. This was deliberate, because empirical TGMs vary across subjects and experiments, and I aimed at characterising them as generally as possible by looking at some characteristic features in broad terms. For example, while TGMs typically have a strong diagonal and horizontal/vertical bars of high accuracy, questions such as when these effects emerge and for how long are highly dependent on the experimental paradigm. For the same reason, I did not optimise the model hyperparameters, limiting myself to observing the behaviour of the model across some characteristic configurations”

And, in the Discussion:

“The demonstrations here are not meant to be tailored to a specific data set, and are, for the most part, intentionally qualitative. TGMs do vary across experiments and subjects; and the hyperparameters of the model can be explicitly optimised to specific scientific questions, data sets, and even individuals. In order to explore the space of configurations effectively, an automatic optimisation of the hyperparameter space using, for instance, Bayesian optimisation (Lorenz, et al., 2017) could be advantageous. This may lead to the identification of very specific (spatial, spectral and temporal) features in the data that may be neurobiologically interpreted.”

Nonetheless, it is possible to fit the model to a specific TGMs by using a explicit metric of fitness. For illustration, this is what I did in the new experimental section Fitting and empirical TGM, where I used correlation with an empirical TGM to optimise two temporal parameters: the rise slope and the fall slope. As can be seen in the Figure 8, the correlation with the empirical TGM was as high as 0.7, even though I did not fit the other parameters of the model. As mentioned in the paragraph above, more sophisticated techniques such as Bayesian optimisation might be necessary for a more exhaustive exploration, but this would be beyond the scope of the current paper.

I would also like to point out that fitting the parameters in a step-wise manner was a necessity for interpretation. I suggest to think of the way we use F-tests in regression analyses as a comparison: if we want to know how important a feature is, we compare the model with and without this feature and see how much we loss.

It further remained unclear to me, which implications may be drawn from the generative model, following from the capacities to mimic this specific TGM (i) for more complex cases, such as the comparison between experimental conditions, and (ii) about the complex nature of neural processes involved.

Following on the previous points, the object of this paper (besides presenting the model and the associated toolbox) was not to mimic a specific TGM, but to characterise the main features that we generally see across studies in the field. To clarify this, I have added Figure 2 (previously a Supplemental Information figure), and added the following to the Results section:

“Figure 2 shows a TGM for an example subject, where some archetypal characteristics are highlighted. In the experiments below, specifically, I focus on the strong narrow diagonal at the beginning of the trial, the broadening of accuracy later in the trial, and the vertical/horizontal bars of higher-than-chance accuracy. Importantly, this specific example in Figure 2 is only meant as a reference, and therefore I did not optimise the model hyperparameters to this TGM (except in the last subsection), or showed any quantitative metric of similarity.”

I mention the possibility of using the model to explore more complex cases in the Introduction, although doing so here would be out of scope:

“Other experimental paradigms, including motor tasks and decision making, can be investigated with genephys”

Towards this end, I would appreciate (i) a more profound explanation of the conclusions that can be drawn from this specific showcase, including potential limitations, as well as wider considerations of how scientists may empower the generative model to (ii) understand their experimental data better and (iii) which added value the model may have in understanding the nature of underlying brain mechanism (rather than a mere technical characterization of sensor data).

To better illustrate how to use genephys to explore a specific data set, I have added a section (Fitting an empirical TGM) where I show how to fit specific hyperparameters to an empirical TGM in a simple manner.

In the Introduction, I briefly mentioned:

“This (not exhaustive) list of effects was considered given previous literature (Shah, et al., 2004; Mazaheri & Jensen, 2006; Makeig, et al., 2002; Vidaurre, et al., 2021), and each effect may be underpinned by distinct neural mechanisms. For example, it is not completely clear the extent to which stimulus processing is sustained by oscillations, and disentangling these effects can help resolving this question”

In the Discussion, I have further commented:

“Genephys has different available types of effect, including phase resets, additive damped oscillations, amplitude modulations, and non-oscillatory responses. All of these elements, which may relate to distinct neurobiological mechanisms, are configurable and can be combined to generate a plethora of TGMs that, in turn, can be contrasted to specific empirical TGMs. This way, we can gain insight on what mechanisms might be at play in a given task.

The demonstrations here are not meant to be tailored to a specific data set, and are, for the most part, intentionally qualitative. TGMs do vary across experiments and subjects; and the hyperparameters of the model can be explicitly optimised to specific scientific questions, data sets, and even individuals. In order to explore the space of configurations effectively, an automatic optimisation of the hyperparameter space using, for instance, Bayesian optimisation (Lorenz, et al., 2017) could be advantageous. This may lead to the identification of very specific (spatial, spectral and temporal) features in the data that may be neurobiologically interpreted. “

On p. 15 "Having a diversity of frequencies but not of latencies produces another regular pattern consisting of alternating, parallel bands of higher/lower than baseline accuracy. This, shown in the bottom left panel, is not what we see in real data either. Having a diversity of latencies but not of frequencies gets us closer to a realistic pattern, as we see in the top right panel." The terms frequency and latency seem to be confused.

The Reviewer is right. I have corrected this now. Thank you.

Reviewer #2:

The results of comparisons between simulations and real data are not always clear for an inexperienced reader. For example, the comparisons are qualitative rather than quantitative, making it hard to draw firm conclusions. Relatedly, it is unclear whether the chosen parameterizations are the only/best ones to generate the observed patterns or whether others are possible. In the case of the latter, it is unclear what we can actually conclude about underlying signal generators. It would have been different if the model was directly fitted to empirical data, maybe of different cognitive conditions. Finally, the neurobiological interpretation of different signal properties is not discussed. Therefore, taken together, in its currently presented form, it is unclear how this method could be used exactly to further our understanding of the brain.

This critique coincides with that of Reviewer 1. In the current version, I made more clear the fact that I am not fitting a specific empirical TGM and why, and that, instead, I am referring to general features that appear broadly throughout the literature. See more detailed changes below.

Regarding whether the chosen parameterizations are the only/best ones to generate the observed patterns, the Discussion reflects this limitation:

“Also importantly, I have shown that standard decoding analysis can differentiate between these explanations only to some extent. For example, the effects induced by phase-resetting and the use of additive oscillatory components are not enormously different in terms of the resulting TGMs. In future work, alternatives to standard decoding analysis and TGMs might be used to disentangle these sources of variation (Vidaurre, et al., 2019). ”

And

“Importantly, the list of effects that I have explored here is not exhaustive …”

Of course, since the list of signal features I have explored is not exhaustive, it cannot be claimed without a doubt that these features are the ones generating the properties we observe in real TGMs. The model, however, is a step forward in that direction, as it provides us with a tool to at least rule out some causes.

Firstly, it was not entirely clear to me from the introduction what gap exactly the model is supposed to fill: is it about variance in neural responses in general, about which signal properties are responsible for decoding, or about capturing stability of signals? It seems like it does all of these, but this needs to be made clearer in the introduction. It would be helpful to emphasize exactly what insights the model can provide that are unable to be obtained with the current methods.

I have now made this explicit in in the Introduction, as suggested:

“To gain insight into what aspects of the signal underpin decoding accuracy, and therefore the most stable aspects of stimulus processing, I introduce a generative model”

To help illustrating what insights the model can provide, I have added the following sentence as an example:

“For example, it is not completely clear the extent to which stimulus processing is sustained by oscillations, and disentangling these effects can help resolving this question.”

Furthermore, I was unclear on why these specific properties were chosen (lines 71 to 78). Is there evidence from neuroscience to suggest that these signal properties are especially important for neural processing? Or, if the logic has more to do with signal processing, why are these specific properties the most important to include?

To clarify this the text now reads:

“In the model, when a channel responds, it can do it in different ways: (i) by phase-resetting the ongoing oscillation to a given target phase and then entraining to a given frequency, (ii) by an additive oscillatory response independent of the ongoing oscillation, (iii) by modulating the amplitude of the stimulus-relevant oscillations, or (iv) by an additive non-oscillatory (slower) response. This (not exhaustive) list of effects was considered given previous literature (Shah, et al., 2004; Mazaheri & Jensen, 2006; Makeig, et al., 2002; Vidaurre, et al., 2021), and each effect may be underpinned by distinct neural mechanisms”

The general narrative and focus of the paper could also be improved. It might help to start off with an outline of what the goal is at the start of the paper and then explicitly discuss how each of the steps works toward that goal. For example, I got the idea that the goal was to capture specific properties of an empirical TGM. If this was the case, the empirical TGM could be placed in the main body of the text as a reference picture for all simulated TGMs. For each simulation step, it could be emphasized more clearly exactly which features of the TGM is captured and what that means for interpreting these features in real data.

Thank you. To clarify the purpose of the paper better, I have brought Figure 2 to the front (before a Supplementary Figure), and in the first part of Results I have now added:

“Figure 2 shows a TGM for an example subject, where some archetypal characteristics are highlighted. In the experiments below, specifically, I focus on the strong narrow diagonal at the beginning of the trial, the broadening of accuracy later in the trial, and the vertical/horizontal bars of higher-than-chance accuracy. Importantly, this specific example in Figure 2 is only meant as a reference, and therefore I did not optimise the model hyperparameters to this TGM (except in the last subsection), or showed any quantitative metric of similarity. ”

I have enunciated the goals more clearly in the Introduction:

“To gain insight into what aspects of the signal underpin decoding accuracy, and therefore the most stable aspects of stimulus processing, …”

Relatedly, it would be good to connect the various signal properties to possible neurobiological mechanisms. I appreciate that the author tries to remain neutral on this in the introduction, but I think it would greatly increase the implications of the analysis if it is made clearer how it could eventually help us understand neural processes.

The Reviewer is right in pointing out that I preferred to remain neutral on this. While I have still kept that tone of neutrality throughout the paper, I have now included the following sentence as an example of a neurobiological question that could be investigated with the model:

“For example, it is not completely clear the extent to which stimulus processing is sustained by oscillations, and disentangling these effects can help resolving this question.”

And, more generally,

“Genephys has different available types of effect, including phase resets, additive damped oscillations, amplitude modulations, and non-oscillatory responses. All of these elements, which may relate to distinct neurobiological mechanisms, are configurable and can be combined to generate a plethora of TGMs that, in turn, can be contrasted to specific empirical TGMs. This way, we can gain insight on what mechanisms might be at play in a given task. ”

Line 57: this sentence is very long, making it hard to follow, could you break up into smaller parts?

Thank you. The sentence is fragmented now.

Please replace angular frequencies with frequencies in Hertz for clarity.

Here I have preferred to stick to angular frequencies because it is more general than if I talk about Hertz, because that would entail having a specific sampling frequency. I think doing so would create confusion precisely of the sorts that I am trying to clarify in this revision: that is, that these results are not specific of one TGM but reflect general features that we see broadly in the literature.

There are quite some types throughout the paper, please recheck

Thank you. I have revised and have made my best to clear them out.

Author Response

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

Reviewer #1:

A weakness of the paper is that the power of the model is illustrated for only one specific set of parameters, added in a stepwise manner and the comparison to one specific empirical TGM, assumed to be prototypical; And that this comparison remains descriptive. (That is could a different selection of parameters lead to similar results and is there TGM data which matches these settings less well.)

The fact that the comparisons in the paper are descriptive is a central point of criticism from both reviewers. As mentioned in my preliminary response, I intentionally did not optimise the model to a specific TGM or show an explicit metric of fitness. As I now explicitly mention in the new experimental section of the paper:

“The previous analyses were descriptive in the sense that they did not quantify how much the generated TGMs resembled a specific empirical TGM. This was deliberate, because empirical TGMs vary across subjects and experiments, and I aimed at characterising them as generally as possible by looking at some characteristic features in broad terms. For example, while TGMs typically have a strong diagonal and horizontal/vertical bars of high accuracy, questions such as when these effects emerge and for how long are highly dependent on the experimental paradigm. For the same reason, I did not optimise the model hyperparameters, limiting myself to observing the behaviour of the model across some characteristic configurations”

And, in the Discussion:

“The demonstrations here are not meant to be tailored to a specific data set, and are, for the most part, intentionally qualitative. TGMs do vary across experiments and subjects; and the hyperparameters of the model can be explicitly optimised to specific scientific questions, data sets, and even individuals. In order to explore the space of configurations effectively, an automatic optimisation of the hyperparameter space using, for instance, Bayesian optimisation (Lorenz, et al., 2017) could be advantageous. This may lead to the identification of very specific (spatial, spectral and temporal) features in the data that may be neurobiologically interpreted.”

Nonetheless, it is possible to fit the model to a specific TGMs by using a explicit metric of fitness. For illustration, this is what I did in the new experimental section Fitting and empirical TGM, where I used correlation with an empirical TGM to optimise two temporal parameters: the rise slope and the fall slope. As can be seen in the Figure 8, the correlation with the empirical TGM was as high as 0.7, even though I did not fit the other parameters of the model. As mentioned in the paragraph above, more sophisticated techniques such as Bayesian optimisation might be necessary for a more exhaustive exploration, but this would be beyond the scope of the current paper.

I would also like to point out that fitting the parameters in a step-wise manner was a necessity for interpretation. I suggest to think of the way we use F-tests in regression analyses as a comparison: if we want to know how important a feature is, we compare the model with and without this feature and see how much we loss.

It further remained unclear to me, which implications may be drawn from the generative model, following from the capacities to mimic this specific TGM (i) for more complex cases, such as the comparison between experimental conditions, and (ii) about the complex nature of neural processes involved.

Following on the previous points, the object of this paper (besides presenting the model and the associated toolbox) was not to mimic a specific TGM, but to characterise the main features that we generally see across studies in the field. To clarify this, I have added Figure 2 (previously a Supplemental Information figure), and added the following to the Results section:

“Figure 2 shows a TGM for an example subject, where some archetypal characteristics are highlighted. In the experiments below, specifically, I focus on the strong narrow diagonal at the beginning of the trial, the broadening of accuracy later in the trial, and the vertical/horizontal bars of higher-than-chance accuracy. Importantly, this specific example in Figure 2 is only meant as a reference, and therefore I did not optimise the model hyperparameters to this TGM (except in the last subsection), or showed any quantitative metric of similarity.”

I mention the possibility of using the model to explore more complex cases in the Introduction, although doing so here would be out of scope:

“Other experimental paradigms, including motor tasks and decision making, can be investigated with genephys”

Towards this end, I would appreciate (i) a more profound explanation of the conclusions that can be drawn from this specific showcase, including potential limitations, as well as wider considerations of how scientists may empower the generative model to (ii) understand their experimental data better and (iii) which added value the model may have in understanding the nature of underlying brain mechanism (rather than a mere technical characterization of sensor data).

To better illustrate how to use genephys to explore a specific data set, I have added a section (Fitting an empirical TGM) where I show how to fit specific hyperparameters to an empirical TGM in a simple manner.

In the Introduction, I briefly mentioned:

“This (not exhaustive) list of effects was considered given previous literature (Shah, et al., 2004; Mazaheri & Jensen, 2006; Makeig, et al., 2002; Vidaurre, et al., 2021), and each effect may be underpinned by distinct neural mechanisms. For example, it is not completely clear the extent to which stimulus processing is sustained by oscillations, and disentangling these effects can help resolving this question”

In the Discussion, I have further commented:

“Genephys has different available types of effect, including phase resets, additive damped oscillations, amplitude modulations, and non-oscillatory responses. All of these elements, which may relate to distinct neurobiological mechanisms, are configurable and can be combined to generate a plethora of TGMs that, in turn, can be contrasted to specific empirical TGMs. This way, we can gain insight on what mechanisms might be at play in a given task.

The demonstrations here are not meant to be tailored to a specific data set, and are, for the most part, intentionally qualitative. TGMs do vary across experiments and subjects; and the hyperparameters of the model can be explicitly optimised to specific scientific questions, data sets, and even individuals. In order to explore the space of configurations effectively, an automatic optimisation of the hyperparameter space using, for instance, Bayesian optimisation (Lorenz, et al., 2017) could be advantageous. This may lead to the identification of very specific (spatial, spectral and temporal) features in the data that may be neurobiologically interpreted. “

On p. 15 "Having a diversity of frequencies but not of latencies produces another regular pattern consisting of alternating, parallel bands of higher/lower than baseline accuracy. This, shown in the bottom left panel, is not what we see in real data either. Having a diversity of latencies but not of frequencies gets us closer to a realistic pattern, as we see in the top right panel." The terms frequency and latency seem to be confused.

The Reviewer is right. I have corrected this now. Thank you.

Reviewer #2:

The results of comparisons between simulations and real data are not always clear for an inexperienced reader. For example, the comparisons are qualitative rather than quantitative, making it hard to draw firm conclusions. Relatedly, it is unclear whether the chosen parameterizations are the only/best ones to generate the observed patterns or whether others are possible. In the case of the latter, it is unclear what we can actually conclude about underlying signal generators. It would have been different if the model was directly fitted to empirical data, maybe of different cognitive conditions. Finally, the neurobiological interpretation of different signal properties is not discussed. Therefore, taken together, in its currently presented form, it is unclear how this method could be used exactly to further our understanding of the brain.

This critique coincides with that of Reviewer 1. In the current version, I made more clear the fact that I am not fitting a specific empirical TGM and why, and that, instead, I am referring to general features that appear broadly throughout the literature. See more detailed changes below.

Regarding whether the chosen parameterizations are the only/best ones to generate the observed patterns, the Discussion reflects this limitation:

“Also importantly, I have shown that standard decoding analysis can differentiate between these explanations only to some extent. For example, the effects induced by phase-resetting and the use of additive oscillatory components are not enormously different in terms of the resulting TGMs. In future work, alternatives to standard decoding analysis and TGMs might be used to disentangle these sources of variation (Vidaurre, et al., 2019). ”

And

“Importantly, the list of effects that I have explored here is not exhaustive …”

Of course, since the list of signal features I have explored is not exhaustive, it cannot be claimed without a doubt that these features are the ones generating the properties we observe in real TGMs. The model, however, is a step forward in that direction, as it provides us with a tool to at least rule out some causes.

Firstly, it was not entirely clear to me from the introduction what gap exactly the model is supposed to fill: is it about variance in neural responses in general, about which signal properties are responsible for decoding, or about capturing stability of signals? It seems like it does all of these, but this needs to be made clearer in the introduction. It would be helpful to emphasize exactly what insights the model can provide that are unable to be obtained with the current methods.

I have now made this explicit in in the Introduction, as suggested:

“To gain insight into what aspects of the signal underpin decoding accuracy, and therefore the most stable aspects of stimulus processing, I introduce a generative model”

To help illustrating what insights the model can provide, I have added the following sentence as an example:

“For example, it is not completely clear the extent to which stimulus processing is sustained by oscillations, and disentangling these effects can help resolving this question.”

Furthermore, I was unclear on why these specific properties were chosen (lines 71 to 78). Is there evidence from neuroscience to suggest that these signal properties are especially important for neural processing? Or, if the logic has more to do with signal processing, why are these specific properties the most important to include?

To clarify this the text now reads:

“In the model, when a channel responds, it can do it in different ways: (i) by phase-resetting the ongoing oscillation to a given target phase and then entraining to a given frequency, (ii) by an additive oscillatory response independent of the ongoing oscillation, (iii) by modulating the amplitude of the stimulus-relevant oscillations, or (iv) by an additive non-oscillatory (slower) response. This (not exhaustive) list of effects was considered given previous literature (Shah, et al., 2004; Mazaheri & Jensen, 2006; Makeig, et al., 2002; Vidaurre, et al., 2021), and each effect may be underpinned by distinct neural mechanisms”

The general narrative and focus of the paper could also be improved. It might help to start off with an outline of what the goal is at the start of the paper and then explicitly discuss how each of the steps works toward that goal. For example, I got the idea that the goal was to capture specific properties of an empirical TGM. If this was the case, the empirical TGM could be placed in the main body of the text as a reference picture for all simulated TGMs. For each simulation step, it could be emphasized more clearly exactly which features of the TGM is captured and what that means for interpreting these features in real data.

Thank you. To clarify the purpose of the paper better, I have brought Figure 2 to the front (before a Supplementary Figure), and in the first part of Results I have now added:

“Figure 2 shows a TGM for an example subject, where some archetypal characteristics are highlighted. In the experiments below, specifically, I focus on the strong narrow diagonal at the beginning of the trial, the broadening of accuracy later in the trial, and the vertical/horizontal bars of higher-than-chance accuracy. Importantly, this specific example in Figure 2 is only meant as a reference, and therefore I did not optimise the model hyperparameters to this TGM (except in the last subsection), or showed any quantitative metric of similarity. ”

I have enunciated the goals more clearly in the Introduction:

“To gain insight into what aspects of the signal underpin decoding accuracy, and therefore the most stable aspects of stimulus processing, …”

Relatedly, it would be good to connect the various signal properties to possible neurobiological mechanisms. I appreciate that the author tries to remain neutral on this in the introduction, but I think it would greatly increase the implications of the analysis if it is made clearer how it could eventually help us understand neural processes.

The Reviewer is right in pointing out that I preferred to remain neutral on this. While I have still kept that tone of neutrality throughout the paper, I have now included the following sentence as an example of a neurobiological question that could be investigated with the model:

“For example, it is not completely clear the extent to which stimulus processing is sustained by oscillations, and disentangling these effects can help resolving this question.”

And, more generally,

“Genephys has different available types of effect, including phase resets, additive damped oscillations, amplitude modulations, and non-oscillatory responses. All of these elements, which may relate to distinct neurobiological mechanisms, are configurable and can be combined to generate a plethora of TGMs that, in turn, can be contrasted to specific empirical TGMs. This way, we can gain insight on what mechanisms might be at play in a given task. ”

Line 57: this sentence is very long, making it hard to follow, could you break up into smaller parts?

Thank you. The sentence is fragmented now.

Please replace angular frequencies with frequencies in Hertz for clarity.

Here I have preferred to stick to angular frequencies because it is more general than if I talk about Hertz, because that would entail having a specific sampling frequency. I think doing so would create confusion precisely of the sorts that I am trying to clarify in this revision: that is, that these results are not specific of one TGM but reflect general features that we see broadly in the literature.

There are quite some types throughout the paper, please recheck

Thank you. I have revised and have made my best to clear them out.

Author Response

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

We are grateful for the comments and suggestions from the reviewers and have followed the recommendation in producing our revised manuscript. We have modified the text and performed additional statistical analysis as detailed below, which we believe has improved the overall manuscript.

Reviewer #1 (Public Review):

Establishing direct links between the neuronal connectivity information of connectomics datasets with circuit physiology and behavior and exciting current research area in neurobiology. Until recently, studies of aggression in Drosophila had been conducted largely in males, and many of the neurons involved in this behavior are male-specific clusters. Since the currently available fly brain connectomes come from female brains, their applicability for the study of the circuitry underlying aggressive behavior is very limited.

The authors have previously used the Janelia hemibrain connectome paired with behavior analysis to show that activating either the aIPg or pC1d cell types can induce short-term aggression in females, while activation of other PC1 clusters (a-c and e) does not. Here they expand on those findings, showing that optogenetic stimulation of aIPg neurons was sufficient to promote an aggressive internal state lasting at least 10 minutes following a 30-second activation. In addition, the authors show that while stimulation of PC1d alone is not sufficient to induce this persistent aggressive state, simultaneous activation of PC1d + PC1e is, suggesting a synergistic effect. Connectomics analysis performed in the authors' previous study had shown that PC1d and aIPg are interconnected. However, silencing pC1d neuronal activity did not reduce aIPg-evoked persistent aggression, indicating that the aggressive state did not depend on pC1d-aIPg recurrent connectivity.

The conclusions are well supported by the data, and the results presented in this manuscript represent an important contribution to our understanding of the neuronal circuitry underlying female aggression.

Reviewer #1 (Recommendations For The Authors):

1. Previously, the authors have shown that the activation of PC1e alone does not induce female aggression. In this study, they investigate the role of aIPg, PC1d, or PC1d+e on aggression persistence, but they do not explore the effect of activation of PC1e alone. It is possible that PC1e activation may not produce an immediate short-term effect but could lead to a gradual increase in aggression over time, potentially explaining at least in part the observed effect upon PC1d+e activation. Incorporating an examination of the long-term impact of PC1e activation on aggression could provide valuable information.

We did perform mixed pair experiments with the pC1e-SS1 line from the Schretter et al. (2020) paper and did not find any significant changes in aggression over time in this setup as well. We have now added a reference to these experiments in the revised submission in lines 135 to 136.

1. Some important controls are missing: flies with the genetic combinations employed in the activation experiments shown in Figure 2 but in the absence of activation and under the exact same conditions and for a similar observation period.

For Figure 2, we used an empty split-Gal4 driver as a genetic control for our activation paradigms. As these flies contain the same number of copies of mini-white while not labeling the targeted cell types, we believe that they provide an appropriate control for these experiments. The control information is specified in all figure legends as well.

1. The quantification shown in Fig 3- Supplementary Figure 1 shows no effect during stimulation (13 s + 15s), but based on the plots of Figure 3, there may be an effect of silencing PC1d on aIPg-induced aggression during the initial 13 second period. Those two time periods (13 s vs 15 s) could be quantified separately to determine if this is the case.

We examined the two stimulation periods separately and did not find any significant differences in either period (13s period, p = 0.2978; 15s period, p = 0.6650). We have now added this into the figure legend for Figure 3 and Figure 3 supplement 1.

1. Expression of Kir2.1 in pC1d neurons while aIPg neurons were activated did not suppress aggression after aIPg stimulation, suggesting that connections from pC1d neurons are not necessary for the persistent aggressive state promoted by aIPg. Since previously the authors have shown that TNT-mediated inhibition of aIPg reduces aggression, the reciprocal experiment would be informative: determining if stimulation of PC1d+e no longer produces persistent aggression when aIPg neurons are silenced.

In this manuscript, we were primarily testing if the connections from aIPg to pC1d were necessary for the persistent aggressive state induced by aIPg activation. Therefore, we believe the suggested experiment is beyond the scope of the current manuscript.

1. How many times was each experiment repeated? This is important information and should be in the methods section for each type of experiment or in each figure legend.

We have now added this information in the appropriate figure legends.

1. Determining the effect on persistent aggression of silencing sNPF (for example via RNAi or Crispr-Cas9 mediated mutagenesis) in aIPG neurons would be an important addition to the manuscript. If peptidergic signaling is underlying the persistence phenotype of aIPg neurons, that would explain why the recurrent connectivity found between those cells and the PC1 cluster does not play a role.

We agree with the reviewer that this would be a logical next step in extending this work.

Reviewer #2 (Public Review):

The mechanisms that mediate female aggression remain poorly understood. Chiu, Schretter, and colleagues, employed circuit dissection techniques to tease apart the specific roles of particular doublesex and fruitless expressing neurons in the fly Drosophila in generating a persistent aggressive state. They find that activating the fruitless positive alPg neurons, generated an aggressive state that persisted for >10min after the stimulation ended. Similarly, activating the doublesex positive pC1de neurons also generated a persistent state. Activating pC1d or pC1e individually did not induce a persistent state. Interestingly, while neural activation of alPGs and pC1d+e neurons induced persistent behavioural states it did not induce persistent activity in the neurons being activated.

The conclusions of this paper are well supported by the data, there were only a few points where clarification might help:

1. Figure 3 is a little confusing. This is a circuit behavioural epistasis experiment where the authors activate alPg with CsChrimson while inhibiting pC1d with Kir2.1. In Fig. 2 flies were separated for 10 min following stimulation which allowed for identification of a persistent state. However, in Fig 3 it appears as if flies were allowed to freely interact during and immediately post-stimulation. It is unclear why flies were not separated as in Fig. 2, which makes it difficult to compare the two results. Some discussion of this point would help. Also, from the rasters it appears as if inhibition of pC1d reduced aggression induced by alPg during the stimulation period. Is this true?

We thank the reviewer for pointing out the need for clarification and we have modified the legend in Figure 3 to address the points raised. The flies were allowed to freely interact during the experiments shown in Figure 3 and we have added this information to the figure legend. To obtain a high level of aggressive behavior that would make it easier to observe a suppression of aggression, the epistasis experiments were performed with freely moving same-genotype pairs. The level of aggression triggered by the generation 1 LexA line labeling aIPg was lower than that observed when using with the aIPg-SS GAL4 line. The experiment was performed as in Schretter et al. (2020) where we found that aIPg activation induced persistent fighting in same genotype pairs. We have added a brief explanation in lines 152 to 155.

Inhibition of pC1d does not significantly reduce the overall aggression induced by aIPg stimulation in the 13s + 15s period. We also examined the differences within the two stimulation periods and did not find any significant differences (13s period, p = 0.2978; 15s period, p = 0.6650). We have now added this information to the figure legends for Figure 3 and Figure 3 supplement 1.

1. pC1e neurons also have recurrent connectivity with alPg neurons. It might help to also discuss the potential role of this arm of the microcircuit.

We thank the review for this suggestion. The number of synapses that aIPg sends back to pC1e is a very low proportion of its total output (0.177%). However, based on the experiments that we have performed, we cannot rule out that this microcircuit might contribute to maintaining persistence. We have added this point into the discussion in lines 210 to 211.

Reviewer #2 (Recommendations For The Authors):

1. Line 129-130: A citation for group-housed flies showing lower aggression would be helpful.

We have now added in the reference to Chiu et al. (2021), as they showed this effect for females, in line 130.

1. Figure 2 - figure supplement 1: In the legend, change "when pC1d neurons were stimulation" to "when pC1d neurons were stimulated".

We thank the reviewer for finding this error and have now corrected this.

Reviewer #3 (Public Review):

Two studies published in 2020 independently identified the alPg, pC1d, and pC1e neurons to be involved in initiating and maintaining a state of aggression in female Drosophila. Both studies combined behavioural analyses, optogenitic manipulation of neurons, and connectomics. One of these studies proposed that the extensive interconnections seen between the alPg and pC1d+e neurons might represent a recurrent motif known to support persistent behvioural states in other systems. In this manuscript, the authors test this idea and report that their data do not support it. Specifically, they report that alPg or pC1d+e (but not pC1d alone) can initiate a persistent state of aggression. But they find that the persistent aggressive state is maintained even when the pC1d neurons are inactivated. Finally, they show that neither of these neurons themselves sustains neuronal activity upon stimulation, nor do either of them induce a persistent activity in the other. Together, their data suggest that the recurrent connection between alPg and pC1d is not what supports the persistent state. The data underlying these claims are convincing. A possibility to explore before ruling out recurrent motifs (at this circuit level) in maintaining aggression is that the connections between alPg and pC1e can compensate for the loss of pC1e. Overall, the study is important and will be of interest to those who study the circuit basis of persistent behavioural states, but also to neuroscientists in general.

Reviewer #3 (Recommendations For The Authors):

I enjoyed reading this manuscript for its clarity in writing and data presentation.

I would like the authors to comment on the possibility that pC1e can compensate for the loss of pC1d. It is possible that if they silence both pC1d+e in the context of alPg activation, the persistent aggression is lost?

We agree with the reviewer that this is an intriguing hypothesis. In order to examine if pC1e does compensate for pC1d, we would need to also activate pC1e while inhibiting pC1d. However, such an experiment is not currently possible as we do not have a LexA line that specifically labels either pC1d or pC1e alone.

For the pC1d+e silencing experiments, we were primarily testing to see if the most prominent recurrent connection, which is between pC1d and aIPg, was responsible for the behavioral persistence. We agree with the reviewer that this would be a logical follow up experiment to be performed in the future.

Have the authors looked for activity in the pC1e neuron upon simulation of alPg? (Deutsch et al 2020 observed many regions in the brain that maintained sustained activity upon pC1d+e stimulation.)

We have not examined this activity. We agree that this would be a good follow up experiment; however, we believe it is beyond the scope of the current work.

Would the more appropriate experiment in Figure 4c be the co-stimulation of pC1d+e while imaging from alPg?

For these experiments, we were testing to see if the most prominent recurrent connection, which is between pC1d and aIPg, was responsible for the behavioral persistence. We agree with the reviewer that this would be a good follow up experiment

Author Response

Reviewer #1 (Public Review):

Summary:

This study uses whole genome sequencing to characterise the population structure and genetic diversity of a collection of 58 isolates of E. coli associated with neonatal meningitis (NMEC) from seven countries, including 52 isolates that the authors sequenced themselves and a further 6 publicly available genome sequences. Additionally, the study used sequencing to investigate three case studies of apparent relapse. The data show that in all three cases, the relapse was caused by the same NMEC strain as the initial infection. In two cases they also found evidence for gut persistence of the NMEC strain, which may act as a reservoir for persistence and reinfection in neonates. This finding is of clinical importance as it suggests that decolonisation of the gut could be helpful in preventing relapse of meningitis in NMEC patients.

Strengths:

The study presents complete genome sequences for n=18 diverse isolates, which will serve as useful references for future studies of NMEC. The genomic analyses are high quality, the population genomic analyses are comprehensive and the case study investigations are convincing.

We agree

Weaknesses:

The NMEC collection described in the study includes isolates from just seven countries. The majority (n=51/58, 88%) are from high-income countries in Europe, Australia, or North America; the rest are from Cambodia (n=7, 12%). Therefore it is not clear how well the results reflect the global diversity of NMEC, nor the populations of NMEC affecting the most populous regions.

The virulence factors section highlights several potentially interesting genes that are present at apparently high frequency in the NMEC genomes; however, without knowing their frequency in the broader E. coli population it is hard to know the significance of this.

We acknowledged the limitations of our NMEC collection in the Discussion. We agree the prevalence of virulence factors in our collection is interesting. The limited size of our collection prevented further evaluation of the prevalence of these virulence factors in a broader E. coli population.

Reviewer #2 (Public Review):

Summary:

In this work, the authors present a robust genomic dataset profiling 58 isolates of neonatal meningitis-causing E. coli (NMEC), the largest such cohort to be profiled to date. The authors provide genomic information on virulence and antibiotic resistance genomic markers, as well as serotype and capsule information. They go on to probe three cases in which infants presented with recurrent febrile infection and meningitis and provide evidence indicating that the original isolate is likely causing the second infection and that an asymptomatic reservoir exists in the gut. Accompanying these results, the authors demonstrate that gut dysbiosis coincides with the meningitis.

Strengths:

The genomics work is meticulously done, utilizing long-read sequencing.

The cohort of isolates is the largest to be sampled to date.

The findings are significant, illuminating the presence of a gut reservoir in infants with repeating infection.

We agree

Weaknesses:

Although the cohort of isolates is large, there is no global representation, entirely omitting Africa and the Americas. This is acknowledged by the group in the discussion, however, it would make the study much more compelling if there was global representation.

We agree. In the Discussion we state this is likely a reflection of the difficulty in acquiring isolates causing neonatal meningitis, in particular from countries with limited microbiology and pathology resources.

Reviewer #3 (Public Review):

Summary:

In this manuscript, Schembri et al performed a molecular analysis by WGS of 52 E. coli strains identified as "causing neonatal meningitis" from several countries and isolated from 1974 to 2020. Sequence types, virulence genes content as well as antibiotic-resistant genes are depicted. In the second part, they also described three cases of relapse and analysed their respective strains as well as the microbiome of three neonates during their relapse. For one patient the same E. coli strain was found in blood and stool (this patient had no meningitis). For two patients microbiome analysis revealed a severe dysbiosis.

Major comments:

Although the authors announce in their title that they study E. coli that cause neonatal meningitis and in methods stipulate that they had a collection of 52 NMEC, we found in Supplementary Table 1, 29 strains (therefore most of the strains) isolated from blood and not CSF. This is a major limitation since only strains isolated from CSF can be designated with certainty as NMEC even if a pleiocytose is observed in the CSF. A very troubling data is the description of patient two with a relapse infection. As stated in the text line 225, CSF microscopy was normal and culture was negative for this patient! Therefore it is clear that patient without meningitis has been included in this study.

We have reviewed the clinical data for our 52 NMEC isolates, noting that for some of the older Finish isolates we relied on previous publications. This data is shown in Table S1. To address the Reviewer’s comment, we have added the following text to the methods section (new text underlined).

‘The collection comprised 42 isolates from confirmed meningitis cases (29 cultured from CSF and 13 cultured from blood) and 10 isolates from clinically diagnosed meningitis cases (all cultured from blood).’

Patient 2 was initially diagnosed with meningitis based on a positive blood culture in the presence of CSF pleocytosis (>300 WBCs, >95% polymorphs). We understand there may be some confusion with reference to a relapsed infection, which we now more accurately describe as recrudescent invasive infection in the revised manuscript.

Another major limitation (not stated in the discussion) is the absence of clinical information on neonates especially the weeks of gestation. It is well known that the risk of infection is dramatically increased in preterm neonates due to their immature immunity. Therefore E. coli causing infection in preterm neonates are not comparable to those causing infection in term neonates notably in their virulence gene content. Indeed, it is mentioned that at least eight strains did not possess a capsule, we can speculate that neonates were preterm, but this information is lacking. The ages of neonates are also lacking. The possible source of infection is not mentioned, notably urinary tract infection. This may have also an impact on the content of VF.

We agree. In the Discussion we now note the following (new text underlined):

‘… we did not have clinical data on the weeks of gestation for all patients, and thus could not compare virulence factors from NMEC isolated from preterm versus term infants.’

Submission to Medrxiv, a requirement for review of our manuscript at eLife, necessitated the removal of some patient identifying information, including precise age and detailed medical history.

Sequence analysis reveals the predominance of ST95 and ST1193 in this collection. The high incidence of ST95 is not surprising and well previously described, therefore, the concluding sentence line 132 indicating that ST95 E. coli should exhibit specific virulence features associated with their capacity to cause NM does not add anything. On the contrary, the high incidence of ST1193 is of interest and should have been discussed more in detail. Which specific virulence factors do they harbor? Any hypothesis explaining their emergence in neonates?

We compared the virulence factors of ST95 and ST1193 and summarized this information in Figure 4. We also discussed how the K1 polysialic acid capsule in ST95 and ST1193 could contribute to the emergence of these STs in NM. Specifically, we stated the following: ‘We speculate this is due to the prevailing K1 polysialic acid capsule serotype found in ST95 and the newly emerged ST1193 clone [22, 37] in combination with other virulence factors [15, 28, 29] (Figure 4) and the immature immune system of preterm infants.’

In the paragraph depicted the VF it is only stated that ST95 contained significantly more VF than the ST1193 strains. And so what? By the way "significantly" is not documented: n=?, p=?

We compared the prevalence of known virulence factors between ST95 and ST1193, and showed that ST95 strains in our collection contained significantly more virulence factors than the ST1193 strains. The P-value and the statistical test used were included in Supplementary Figure 3. To address the reviewers concern, we have now also added this to the main manuscript text as follows (new text underlined):

‘Direct comparison of virulence factors between ST95 and ST1193, the two most dominant NMEC STs, revealed that the ST95 isolates (n = 20) contained significantly more virulence factors than the ST1193 isolates (n=9), p-value < 0.001, Mann-Whitney two-tailed unpaired test (Supplementary Table 1, Supplementary Figure 3).’

The complete sequence of 18 strains is not clear. Results of Supplementary Table 2 are presented in the text and are not discussed.

NMEC isolates that were completely sequenced in this study are indicated in bold and marked with an asterisk in Figure 1. This information is indicated in the figure legend and was provided in the original submission. All information regarding genomic island composition and location, virulence genes and plasmid and prophage diversity is included in Supplementary Table 2. This information is highly descriptive and thus we elected not to include it as text in the main manuscript.

46 years is a very long time for such a small number of strains, making it difficult to put forward epidemiological or evolutionary theories. In the analysis of antibiotic resistance, there are no ESBLs. However, Ding's article (reference 34) and other authors showed that ESBLs are emerging in E. coli neonatal infection. These strains are a major threat that should be studied, unfortunately, the authors haven't had the opportunity to characterize such strains in their manuscript.

We agree 46 years is a long time-span. The study by Ding et al examined 56 isolates comprised of 25 different STs isolated in China from 2009-2015, with ST1193 (n=12) and ST95 (n=10) the most common. Our study examined 58 isolates comprised of 22 different STs isolated in seven different geographic regions from 1974-2020, with ST1193 (n=9) and ST95 (n=20) the most common. Thus, despite differences in the geographic regions from which isolates in the two studies were sourced, there are similarities in the most common STs identified. The fact that we observed less antibiotic resistance, including a lack of ESBL genes, in ST1193 is likely due to the different regions from which the isolates were sourced. We acknowledged and discussed the potential of ST1193 harbouring multidrug resistance including ESBLs in our manuscript as follows:

‘Concerningly, the ST1193 strains examined here carry genes encoding several aminoglycoside-modifying enzymes, generating a resistance profile that may lead to the clinical failure of empiric regimens such as ampicillin and gentamicin, a therapeutic combination used in many settings to treat NM and early-onset sepsis [35, 36]. This, in combination with reports of co-resistance to third-generation cephalosporins for some ST1193 strains [22, 34], would limit the choice of antibiotic treatment.’

Second part of the manuscript:

The three patients who relapsed had a late neonatal infection (> 3 days) with respective ages of 6 days, 7 weeks, and 3 weeks. We do not know whether they are former preterm newborns (no term specified) or whether they have received antibiotics in the meantime.

As noted above, patient ages were not disclosed to comply with submission to Medrxiv, a requirement for review of our manuscript at eLife.

Patient 1: Although this patient had a pleiocytose in CSF, the culture was negative which is surprising and no explanation is provided. Therefore, the diagnosis of meningitis is not certain. Pleiocytose without meningitis has been previously described in neonates with severe sepsis. Line 215: no immunological abnormalities were identified (no details are given).

We respectfully disagree with the reviewer. The diagnosis of meningitis is made unequivocally by the presence of a clearly abnormal CSF microscopy (2430 WBCs) and an invasive E. coli from blood culture. This does not seem controversial to the authors. We had believed it unnecessary to include this corroborative evidence, but have added the following to support our assertion:

‘The child was diagnosed with meningitis based on a cerebrospinal fluid (CSF) pleocytosis (>2000 white blood cells; WBCs, low glucose, elevated protein), positive CSF E. coli PCR and a positive blood culture for E. coli (MS21522).’

On the contrary, the authors are surprised by the statement that CSF pleocytosis occurs in neonatal sepsis ‘without meningitis’ and do not know of any definitions of neonatal meningitis that are not tied to the presence of a CSF pleocytosis. Furthermore, the later isolation of E. coli from the CSF during the relapsed infection re-enforces the initial diagnosis.

Patient 2: This patient had a recurrence of bacteremia without meningitis (line 225: CSF microscopy was normal and culture negative!). This case should be deleted.

In a similar vein to the previous comment, we respectfully assert that this patient has clear evidence of meningitis (330 WBCs in the CSF, taken 24h after initiation of antibiotic treatment). In this case, molecular testing was not performed as, under the principle of diagnostic stewardship, it was not considered necessary by the clinical microbiologists and treating clinicians following the culture of E. coli in the bloodstream. We agree that this is not a case of recurrent meningitis, but our intention was to highlight the recrudescence of an invasive infection (urinary sepsis requiring admission to hospital and intravenous antibiotics) which we hypothesise has arisen from the intestinal reservoir. We did not state that all patients suffered from relapsed meningitis.

Despite this, to address this reviewers concern, we have changed all reference to ‘relapsed infection’ to now read ‘recrudescent invasive infection’ in the revised manuscript.

Patient 3: This patient had two relapses which is exceptional and may suggest the existence of a congenital malformation or a neurological complication such as abscess or empyema therefore, "imaging studies" should be detailed.

This patient underwent extensive imaging investigation to rule out a hidden source. This included repeated MRI imaging of head and spine, CT imaging of head and chest, USS imaging of abdomen and pelvis and nuclear medicine imaging to detect a subtle meningeal defect and CSF leak. All tests were normal, and no abscess or empyema found.

We have modified the text to include this information:

Text in original submission: ‘Imaging studies and immunological work-up were normal.’

New text in revised manuscript (underlined): ‘Extensive imaging studies including repeated MRI imaging of the head and spine, CT imaging of the head and chest, ultrasound imaging of abdomen and pelvis, and nuclear medicine imaging did not show a congenital malformation or abscess. Immunological work-up did not show a known primary immunodeficiency. At two years of age, speech delay is reported but no other developmental abnormality.’

The authors suggest a link between intestinal dysbiosis and relapse in three patients. However, the fecal microbiomes of patients without relapse were not analysed, so no comparison is possible. Moreover, dysbiosis after several weeks of antibiotic treatment in a patient hospitalized for a long time is not unexpected. Therefore, it's impossible to make any assumption or draw any conclusion. This part of the manuscript is purely descriptive. Finally, the authors should be more prudent when they state in line 289 "we also provide direct evidence to implicate the gut as a reservoir [...] antibiotic treatment". Indeed the gut colonization of the mothers with the same strain may be also a reservoir (as stated in the discussion line 336). Finally, the authors do not discuss the potential role of ceftriaxone vs cefotaxime in the dysbiosis observed. Ceftriaxone may have a major impact on the microbiota due to its digestive elimination.

We addressed the limitations of our study in the Discussion, including that we did not have access to urine or stool samples from the mother of the infants that suffered recrudescence, and thus cannot rule out mother-to-child transmission as a mechanism of reinfection. We have now added that we did not have clinical data on the weeks of gestation for all patients, and thus could not compare virulence factors from NMEC isolated from preterm versus term infants. The limitations of our study are summarised as follows in the Discussion (new text underlined):

‘This study had several limitations. First, our NMEC strain collection was restricted to seven geographic regions, a reflection of the difficulty in acquiring strains causing this disease. Second, we did not have access to a complete set of stool samples spanning pre- and post-treatment in the patients that suffered NM and recrudescent invasive infection. This impacted our capacity to monitor E. coli persistence and evaluate the effect of antibiotic treatment on changes in the microbiome over time. Third, we did not have access to urine or stool samples from the mother of the infants that suffered recrudescence, and thus cannot rule out mother-to-child transmission as a mechanism of reinfection. Finally, we did not have clinical data on the weeks of gestation for all patients, and thus could not compare virulence factors from NMEC isolated from preterm versus term infants.’

Author Response

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

Shibl et al., studied the possible role of dicarboxylate metabolite azelaic acid (Aze) in modulating the response of different bacteria, it was used as a carbon source by Phycobacter and possibly toxic for Alteromonas. The experiments were well conducted using transcriptomics, transcriptional factor coexpression networks, uptake experiments, and chemical methods to unravel the uptake, catabolism, and toxicity of Aze on these two bacteria. They identified a putative Aze TRAP transporter in bacteria and showed that Aze is assimilated through fatty acid degradation in Phycobacter. Meanwhile, in Alteromonas it is suggested that Aze inhibits the ribosome and/or protein synthesis, and that efflux pumps shuttles Aze outside the cytoplasm. Further on, they demonstrate that seawater amended with Aze selects for microbes that can catabolize Aze.

Major strengths:

The manuscript is well written and very clear. Through the combination of gene expression, transcriptional factor co-expression networks, uptake experiments, and chemical methods Shibl et al., showed that Aze has a different response in two bacteria.

Major weakness:

There is no confirmation of the Aze TRAP transporters through mutagenesis.

Impact on the field:

Metabolites exert a significant influence on microbial communities in the ocean, playing a crucial role in their composition, dynamics, and biogeochemical cycles. This research highlights the intriguing capacity of a single metabolite to induce contrasting responses in distinct bacterial species, underscoring its role in shaping microbial interactions and ecosystem functions.

We thank the reviewer for their comments on the paper and we appreciate their suggestion to confirm the activity of Aze TRAP transporters through mutagenesis. We agree that this would be a valuable addition to the study, and we mention in the text that “Despite numerous attempts, our efforts to knock-out azeTSL in Phycobacter failed.”

The success rate of mutagenesis experiments is often low and time-consuming. There are a few reasons why our knock-out experiments with Phycobacter have not been successful. Despite using several modified protocols for electroporation, no Phycobacter colonies grew on the antibiotic plate. We then tried the homologous recombination approach for conjugation but were not successful in selecting for Phycobacter cells, even when grown in high salinity conditions that favor Phycobacter and disfavor the carrier, E. coli . While we would love to include a mutagen to confirm the function of this cluster, the task seems to be unattainable at the moment .

Reviewer #2 (Public Review):

This study explores the breadth of effects of one important metabolite, azelaic acid, on marine microbes, and reveals in-depth its pathway of uptake and catabolism in one model bacterial strain. This compound is known to be widely produced by phytoplankton and plants, and to have complex effects on associated microbiomes.

This work uses transcriptomics to assay the response of two strains that show contrasting responses to the metabolite: one catabolizes the compound and assimilates the carbon, while the other shows growth inhibition and stress response. A highly induced TRAP transporter, adjacent to a previously identified regulator, is inferred to be the specific uptake system for azelaic acid. However the transport function was not directly tested via genetic or biochemical methods. Nevertheless, this is a significant finding that will be useful for exploring the distribution of azelaic acid uptake capability across metagenomes and other bacteria.

The authors use pulse-chase style metabolomics experiments to beautifully demonstrate the fate of azelaic acid through catabolic pathways. They also measure an assimilation rate per cell, though it remains unclear how this measured rate relates to natural systems. The metabolomics approach is an elegant way to show carbon flux through cells, and could serve as a model for future studies.

The study seeks to extend the results from two model strains to complex communities, using seawater mesocosm experiments and soil/Arabidopsis experiments. The seawater experiments show a community shift in mesocosms with added azelaic acid. However, the mechanisms for the shift were not determined; further work is necessary to demonstrate which community members are directly assimilating the compound vs. benefitting indirectly or experiencing inhibition. In my opinion the soil and Arabidopsis experiments are quite preliminary. I appreciate the authors' desire to broaden the scope beyond marine systems, but I believe any conclusions regarding different modes of action in aquatic vs terrestrial microbial communities are speculative at this stage.

This work is a nice illustration of how we can begin to tease apart the effects of chemical currencies on marine ecosystems. A key strength of this work is the combination of transcriptomics and metabolomics methods, along with assaying the impacts of the metabolite on both model strains of bacteria and whole communities. Given the sheer number of compounds that probably play critical roles in community interactions, a key challenge for the field will be navigating the tradeoffs between breadth and depth in future studies of metabolite impacts. This study offers a good compromise and will be a useful model for future studies.

We thank the reviewer for their thoughtful comments on the manuscript. We appreciate their feedback on the breadth of effects of Aze on marine microbes, and their insights into the strengths and limitations of our study.

We agree that the specific mechanisms underlying community-level shifts in seawater mesocosm experiments with added Aze are not yet fully understood and we believe such work is beyond the scope of this paper and warrants an in-depth study of its own. This can perhaps be conducted at a larger scale by using a combination of meta-omics and targeted enrichment to identify the community members directly assimilating Aze, as well as those that are benefitting indirectly or experiencing inhibition.

We also agree that the soil and Arabidopsis experiments are exploratory. However, we believe that these experiments are a valuable first step in highlighting the potential for Aze to have different modes of action in aquatic versus terrestrial microbial communities. Our interest in contrasting bacterial molecular responses in terrestrial plant rhizospheres and marine algal phycospheres stems from the fact that both environments share similar molecules and related bacteria, yet exhibit significantly different evolutionary histories and fluid dynamic profiles (Seymour et al 2017, Nature Microbiol ). Although more is known about Aze in Arabidopsis than phytoplankton, there are still gaps in this knowledge. For example, recent work has shown that Aze and derivatives can be secreted into soil (Korenblum et al 2020, PNAS ), but whether Aze directly influences microbial communities in soil as we have shown in seawater has not been explored. Thus, we feel our preliminary experiments in soil are important to provide such a distinction with seawater. Additional studies in these systems to further investigate the importance of Aze, which were beyond the scope of this current work, would be quite beneficial.

Reviewer #1 (Recommendations For The Authors):

General comments:

A complete supplemental file of differentially expressed genes should be provided in the supplemental. Please add tables with the entire DESeq output for Aze additions in the genomes of Phycobacter (0.5 and 8 h) and Alteromonas (0.5 h). While it makes sense to focus the paper on Aze related genes, the full dataset should be made available in a more curated form than just the raw reads in the SRA.

We thank the reviewer for this suggestion. We have included three more sheets in Supplementary Table 1 file where readers can find the entire DESeq outputs of Phycobacter (0.5 and 8 h) and Alteromonas (0.5 h) experiments.

Specific comments:

• L82 indicates the TRAP transporter for Aze. Looking at the table for gene expression of Phycobacter there are 26 significantly enriched transport genes at 0.5 h other than the putative Aze TRAP transporter. Even though the TRAP transporter is likely transporting Aze, it would be good to let the readers know that other transporters showed transcript enrichment.

Thank you for this helpful comment. We modified the sentence accordingly to read as follows: “Among 26 enriched transporter genes in our dataset, a C 4 - dicarboxylate tripartite ATP-independent periplasmic (TRAP) transporter substrate-binding protein (INS80_RS11065) was the most and the third most upregulated gene in Phycobacter grown on Aze at 0.5 and 8 hours, respectively.”

• Figure 1: There are many genes enriched from -1 to 1. Is there a cut off, p-val (can you add it to the caption)? It would be good to have a dashed line or something that indicates the -1 and 1 log2 fold change in the figure.

We thank the reviewer for this suggestion. We added the following sentence to the legend of Fig. 1: “Genes were considered DE with a p -adjusted value of < 0.05 and a log2 fold-change of ≥ ±0.50.”

• Supplementary tables: Add a title on all the supplementary tables. It's hard to tell what each one of the tables means without looking at the text and content of each tables.

A short descriptive title is now added to all supplementary tables.

• Not sure if it matters, though Table S1 was not available in the attached files, though it is in the complete pdf.

Table S1 is now in the attached files and the DESeq output has been added to it as suggested in the general comment above.

Reviewer #2 (Recommendations For The Authors):

Here I offer some more specific suggestions and comments on the methods and presentation.

I recommend being careful throughout with the language regarding conclusions. For instance, the study does not directly demonstrate the activity of the TRAP transporter (as mentioned above), and does not directly demonstrate that the bacteria that increase in abundance in the mesocosm experiments are actually assimilating azelaic acid.

We thank the reviewer for this comment. We agree that further studies are required to get definitive answers regarding the direct activity of the transporter genes and direct assimilation of Aze by bacteria in the mesocosm. These complex experiments would require establishing a reproducible workflow for knocking out genes and further isotope labeling experiments to track Aze assimilation in a natural setting. To that end, we were keen on using language throughout the manuscript indicating that transporter activity is putative. We went through the manuscript again to make sure it was clear that the transporter activity is putative at this time and is not confirmed. For the mesocosms, we cannot rule out that the changes in community structure is not due to other factors besides Aze. We have added this sentence in the discussion of the mesocosm experiments to indicate that the observed changes in microbial community cannot be directly attributed to Aze activity and may be a byproduct of other mechanisms.

Additionally, I find the soil and plant experiments to be very preliminary, and would personally recommend removing them from the manuscript. This is of course the authors' choice, but I find they detract from an otherwise more solid story. I wonder whether 16 hours was sufficient to see community changes and whether adding azelaic acid directly into the plant is necessary or relevant. The study does not measure any plant immune responses so I caution against drawing conclusions about the mechanism. It seems the connection to plant immunity was already shown in the literature, in which case I'm not sure whether these experiments presented here really add anything new to the paper.

We thank the reviewer for these comments. Our 16-hour sampling time point (similar to the seawater experiment) represents an overnight incubation period that should allow sufficient change in the natural microbial composition yet avoids the long-term succession of microbes with high metabolic capacities that may outcompete the rest of the community at long incubation periods. Deciding on this length of incubation was also informed by the uptake rate of Aze and its influence on either bacteria assimilating it as a carbon source or being inhibited by it.

Since no significant changes were observed in the soil, it was necessary to test the hypothesis that the plant host might be indirectly influencing the rhizosphere microbial communities by infiltrating A. thaliana leaves with Aze. As the reviewer mentions, the association between Aze and plant immunity was previously shown; however, the overall influence on the microbial community has not been fully explored yet. The soil and plant experiments were meant to serve an exploratory purpose and we find them necessary to keep in the manuscript as a first step in comparing the mode of action of Aze within marine and terrestrial ecosystems. They are by no means the answer to what role Aze plays in soil systems, but rather they are the starting point. We hope that our results encourage some readers to investigate similar common metabolites to further elucidate the molecular underpinnings of microbial modulation in both environments.

Regarding the transcriptomics data, I am not clear on why the "expression ratio" -- i.e. the fraction of pathway genes that were differentially abundant -- was used. I would not expect all transcripts in a pathway to behave the same way in response to a perturbation, due to variation in half-life/stability, post-transcriptional and post-translational regulation, etc. I recommend removing the expression ratio (right panel) from Figure 1. The left panel shows the data more clearly and more directly.

We thank the reviewer for their insight and we agree that not all transcripts in a pathway behave the same way. However, we find the expression ratio panel visually informative to highlight the importance of a pathway in response to Aze, taking into consideration the total number of key genes involved in a pathway. For example, despite the larger number of DE genes associated with the Amino Acid Metabolism & Degradation pathway compared to the Fatty Acid Degradation pathway, the expression ratio for the former in each transcriptome is lower than its Fatty Acid Degradation counterpart, indicating that the response of key fatty acid degradation genes to Aze is more pronounced. We have qualified the reasons for including expression ratios in Figure 1 legend.

Overall I enjoyed reading the manuscript and applaud the authors on a nice contribution to this important field.

Author Response

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

Public Reviews:

Reviewer #1 (Public Review):

Peng et al develop a computational method to predict/rank transcription factors (TFs) according to their likelihood of being pioneer transcription factors--factors that are capable of binding nucleosomes--using ChIP-seq for 225 human transcription factors, MNase-seq and DNase-seq data from five cell lines. The authors developed relatively straightforward, easy to interpret computational methods that leverage the potential for MNase-seq to enable relatively precise identification of the nucleosome dyad. Using an established smoothing approach and local peak identification methods to estimate positions together with identification of ChIP-seq peaks and motifs within those peaks which they referred to as "ChIP-seq motifs", they were able to quantify "motif profiles" and their density in nucleosome regions (NRs) and nucleosome depleted regions (NDRs) relative to their estimated nucleosome dyad positions. Using these profiles, they arrived at an odd-ratio based motif enrichment score along with a Fisher's exact test to assess the odds and significance that a given transcription factor's ChIP-seq motifs are enriched in NRs compared to NDRs, hence, its potential to be a pioneer transcription factor. They showed that known pioneer transcription factors had among the highest enrichment scores, and they could identify a number of relatively novel pioneer TFs with high enrichment scores and relatively high expression in their corresponding cell line. They used multiple validation approaches including (1) calculating the ROC-AUC and Matthews correlation coefficient (MCC) and generating ROC and precision-recall curves associated with their enrichment score based on 32 known pioneer TFs among their 225 TFs which they used as positives and the remaining TFs (among the 225) as negatives; (2) use of the literature to note that known pioneer TFs that acted as key regulators of embryonic stem cell differentiation had a highest enrichment scores; (3) comparison of their enrichment scores to three classes of TFs defined by protein microarray and electromobility shift assays (1. strong binder to free and nucleosomal DNA, 2. weak binder to free and nucleosomal DNA, 3. strong binding to free but not nucleosomal DNA); and (4) correlation between their calculated TF motif nucleosome end/dyad binding ratio and relevant data from an NCAP-SELEX experiment. They also characterize the spatial distribution of TF motif binding relative to the dyad by (1) correlating TF motif density and nucleosome occupancy and (2) clustering TF motif binding profiles relative to their distance from the dyad and identifying 6 clusters.

The strengths of this paper are the use of MNase-seq data to define relatively precise dyad positions and ChIP-seq data together with motif analysis to arrive at relatively accurate TF binding profiles relative to dyad positions in NRs as well as in NDRs. This allowed them to use a relatively simple odds ratio based enrichment score which performs well in identifying known pioneer TFs. Moreover, their validation approaches either produced highly significant or reasonable, trending results.

The weaknesses of the paper are relatively minor, and the authors do a good job describing the limitations of the data and approach.

Reviewer #2 (Public Review):

In this study, the authors utilize a compendium of public genomic data to identify transcription factors (TF) that can identify their DNA binding motifs in the presence of nuclosome-wrapped chromatin and convert the chromatin to open chromatin. This class of TFs are termed Pioneer TFs (PTFs). A major strength of the study is the concept, whose premise is that motifs bound by PTFs (assessed by ChIP-seq for the respective TFs) should be present in both "closed" nucleosome wrapped DNA regions (measured by MNase-seq) as well as open regions (measured by DNAseI-seq) because the PTFs are able to open the chromatin. Use of multiple ENCODE cell lines, including the H1 stem cell line, enabled the authors to assess if binding at motifs changes from closed to open. Typical, non-PTF TFs are expected to only bind motifs in open chromatin regions (measured by DNaseI-seq) and not in regions closed in any cell type. This study contributes to the field a validation of PTFs that are already known to have pioneering activity and presents an interesting approach to quantify PTF activity.

For this reviewer, there were a few notable limitations. One was the uncertainty regarding whether expression of the respective TFs across cell types was taken into account. This would help inform if a TF would be able to open chromatin. Another limitation was the cell types used. While understandable that these cell types were used, because of their deep epigenetic phenotyping and public availability, they are mostly transformed and do not bear close similarity to lineages in a healthy organism. Next, the methods used to identify PTFs were not made available in an easy-to-use tool for other researchers who may seek to identify PTFs in their cell type(s) of interest. Lastly, some terms used were not define explicitly (e.g., meaning of dyads) and the language in the manuscript was often difficult to follow and contained improper English grammar.

Reviewer #3 (Public Review):

Peng et al. designed a computational framework for identifying pioneer factors using epigenomic data from five cell types. The identification of pioneer factors is important for our understanding of the epigenetic and transcriptional regulation of cells. A computational approach toward this goal can significantly reduce the burden of labor-intensive experimental validation.

The authors have addressed my previous comments.

The main issue identified in this re-review is based on the authors' additional experiments to investigate the reproducibility of the pioneer factors identified in the previously analysis that anchored on H1 ESCs.

The additional analysis that uses the other four cell types (HepG2, HeLa-S3, MCF-7, and K562) as anchors reveals the low reproducibility/concordance and high dependence on the selection of anchor cell type in the computational framework. In particular, now several stem cell related TFs (e.g. ESRRB, POU5F1) are ranked markedly higher when H1 ESC is not used as the anchor cell type as shown in Supplementary Figure 5.

Of note, the authors have now removed the shape labels that denote Yamanaka factors in Figure 2c (revised manuscript) that was presented in the main Figure 2a in the initial submission. The NFYs and ESRRB labels in Supplementary 4a are also removed and the boxplot comparing NFYs and ESRRB with other TF are also removed in this figure. Removing these results effectively hides the issues of the computational framework we identified in this revision. Please justify why this was done.

In summary, these new results reveal significant limitations of the proposed computational framework for identifying pioneer factors. The current identifications appear to be highly dependent on the choice of cell types.

Response: We thank all reviewers for their thoughtful and constructive comments and suggestions, which helped us to strengthen our paper. Following the suggestions, we have further addressed the reviewer’s comments and the detailed responses are itemized below.

Reviewer #1 (Recommendations For The Authors):

The following few minor mistakes/discrepancies/omissions should be addressed:

1. In Figure 3, the Nucleosome Occupancy curves and legend are orange and the Binding Motif Profiles are blue; however, the y-axis label for Nucleosome occupancy profile is blue, and the y-axis label of Binding motif profile is orange. The colors seem to be switched, or I'm missing something.

Response: We thank the reviewer for pointing it out. We have changed the colors to make it consistent.

1. The text at the bottom of p. 11 of the main manuscript describing Supplementary Fig. 5 states: "If we repeat our anaysis by redefining differentially open regions as those closed in differentiated cell lines and open in H1 embryonic cell line, then ESSRB and Yamanaka pioneer transcription factor POU5F1 (OCT4) showed significantly higher enrichment scores (Supplementary Figure 5)." However, Supplementary Fig. 5 legend states: "Enrichment analysis of different TFs using the differentially open from one cell line (shown in the title) and conserved open regions from other four cell lines.". These two descriptions of the differential chromatin criteria used in the analysis don't appear to match. The description in the text is the one that makes much more sense to me. The legend should be written a little more clearly and reflect the statement in the main text. One can see from the cut and paste the "analysis" is also misspelled.

Response: We have rewritten the legend of Supplementary Figure 5 to make it clear and consistent. The misspelling has also been corrected.

1. It might be helpful to add that a random classifier would yield a constant precision recall (PR) curve (as a function of Recall) with the Precision = P/(P+N) or the fraction of positives for all plotted PR curves which in the case of Fig. 2a is 32/225 = 0.142, for example.

Response: We thank the reviewer for the suggestions. We have added the fraction of positives for Figure 2.

1. On p. 17 line 513, the authors refer to "Supplementary 7, 9 and 13". I'm assuming it's "Supplementary Tables 7, 9 and 13".

Response: It has been corrected.

1. On p. 18 line 539, "essays" should be "assays".

Response: It has been corrected.

Reviewer #2 (Recommendations For The Authors):

We are satisfied with the revisions in this version of the manuscript.

Reviewer #3 (Public Review):

The main issue identified in this re-review is based on the authors' additional experiments to investigate the reproducibility of the pioneer factors identified in the previously analysis that anchored on H1 ESCs.

The additional analysis that uses the other four cell types (HepG2, HeLa-S3, MCF-7, and K562) as anchors reveals the low reproducibility/concordance and high dependence on the selection of anchor cell type in the computational framework. In particular, now several stem cell related TFs (e.g. ESRRB, POU5F1) are ranked markedly higher when H1 ESC is not used as the anchor cell type as shown in Supplementary Figure 5.

Of note, the authors have now removed the shape labels that denote Yamanaka factors in Figure 2c (revised manuscript) that was presented in the main Figure 2a in the initial submission. The NFYs and ESRRB labels in Supplementary 4a are also removed and the boxplot comparing NFYs and ESRRB with other TF are also removed in this figure. Removing these results effectively hides the issues of the computational framework we identified in this revision. Please justify why this was done.

In summary, these new results reveal significant limitations of the proposed computational framework for identifying pioneer factors. The current identifications appear to be highly dependent on the choice of cell types.

Response: We would like to clarify that our enrichment score used for TF classification, defined by Equation 3, is expected to be cell-type specific. The value of the enrichment score is modulated by a number of factors beyond the property of a TF to act as a PTF, such as the abundance of a given TF in a given cell line, cell type-specific nucleosome binding maps and interactions with other TFs. Thus, it is expected that the enrichment scores calculated for the same TF in different cell lines should be quantitatively different. Following the initial suggestion of Reviewer 3, we have diversified our analysis by using different cell lines as anchors. This analysis showed that most PTFs that we identified could be confirmed based on different cell lines, when comparing the relative enrichment scores within each cell line. On the other hand, it is not expected that the values of enrichment scores of a given TF should be similar across different cell lines.

Regarding a specific comment about ESRRB and POU5F1, these TFs are known pioneer factors with roles in reprogramming of somatic cells into induced pluripotent stem cells and suppressing cell differentiation. They have the ability to open closed chromatin regions in the differentiated cell lines. Therefore, if we redefine the differentially open regions as those closed in differentiated cell lines and open in H1 embryonic cell line, these pioneer factors are expected to have high enrichment scores. Indeed, our new results validated the roles of these PTFs in cell reprogramming. As mentioned above, their enrichment scores in different cell lines are not expected to be the same.

We also would like to clarify that no results were removed during the update of the figures, and all modifications of the manuscript following the suggestions of the reviewers were only made to improve the figures and make them clearer and the message more straightforward.

Author Response

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

We would like to thank the Editors and Reviewers for their additional comments and constructive feedback on our manuscript. We have made minor adjustments to the figures and texts based on their suggestions, including improved images in Figure 1 and correction of figure labels.

Reviewer #1 (Public Review):

In their previous paper (Lari et al, 2019; Azra Lari Arvind Arul Nambi Rajan Rima Sandhu Taylor Reiter Rachel Montpetit Barry P Young Chris JR Loewen Ben Montpetit (2019) A nuclear role for the DEAD-box protein Dbp5 in tRNA export eLife 8:e48410.) as well as in the current manuscript the authors states that Dbp5 is involved in the export of tRNA that is independent of and parallel to Los1. They state that Dbp5 binds to the tRNA independent of known tRNA export proteins. The obtained conclusion is both intriguing and innovative, since it suggests that there are other variables, beyond the ones previously identified as tRNA factors, that might interact with Dbp5 to facilitate the export process. In order to find out additional factors aiding this process the authors may employ total RNA-associated protein purification (TRAPP) experiments ( Shchepachevto et al., 2019; Shchepachev V, Bresson S, Spanos C, Petfalski E, Fischer L, Rappsilber J, Tollervey D. Defining the RNA interactome by total RNA-associated protein purification. Mol Syst Biol. 2019 Apr 8;15(4):e8689. doi: 10.15252/msb.20188689. PMID: 30962360; PMCID: PMC6452921) to identify extra factors involved in conjunction with Dbp5. The process elucidates hitherto uninvestigated tRNA export components that function in conjunction with Dbp5.

Author Response: We greatly appreciate this suggestion and agree with the reviewer that identification of the composition of the export competent Dbp5 containing tRNA complex is a critical next step for understanding the mechanism of Dbp5 mediated tRNA export, which will form the foundation of a future investigation in the laboratory and warrants its own study.

Reviewer #1 (Public Review):

Various reports suggest that eukaryotic translation elongation factor 1 eEF1A is involved tRNA export Bohnsack et al., 2002 (Bohnsack MT, Regener K, Schwappach B, Saffrich R, Paraskeva E, Hartmann E, Görlich D. Exp5 exports eEF1A via tRNA from nuclei and synergizes with other transport pathways to confine translation to the cytoplasm. EMBO J. 2002 Nov 15;21(22):620515. doi: 10.1093/emboj/cdf613. PMID: 12426392; PMCID: PMC137205), Grosshans etal., 2002; Grosshans H, Hurt E, Simos G. An aminoacylation-dependent nuclear tRNA export pathway in yeast. Genes Dev. 2000 Apr 1;14(7):830-40. PMID: 10766739; PMCID: PMC316491). The presence of mutations in eEF1A has been seen to hinder the nuclear export process of all transfer RNAs (tRNAs). eEF1A has been shown to interact with Los1 aiding in tRNA export. The authors can also explore the crosstalk between Dbp5 and eEF1A in this study. Additionally, suppressor screening analysis in dbp5R423A , los1∆dbp5R423A los1∆msn∆dbp5R423A could shed more light on this.

Author Response: Thank you for this suggestion and raising an important possible role for Dbp5 in eEF1A mediated tRNA export. Based on more recent investigation of eEF1A function in tRNA export (PMID: 25838545), it is likely that eEF1A functions in re-export of charged tRNAs specifically (likely in conjunction with Msn5). The current manuscript has largely focused on the role of Dbp5 in pre-tRNA export, but a more careful mechanistic characterization of Dbp5 and re-export will be conducted in follow-up studies given the physical interaction between Dbp5 and spliced tRNAs we previously reported. Similarly, suppressor screens with the Dbp5 and los1Δmsn5Δ mutants will likely be a useful tool in identifying additional tRNA export factors and we thank the reviewer for this suggestion.

Reviewer #1 (Public Review):

The addition of Gle1 is potentially novel but it's unclear why the authors didn't address the potential involvement of IP6.

Author Response: The text has been revised to highlight the importance of InsP6 in Gle1 mediated activation of Dbp5. This includes referencing InsP6 throughout the manuscript during discussions of Gle1 activation of Dbp5 and lines 401-404 discussing the potential role for the small molecule in regulating mRNA and tRNA export in different cellular contexts (e.g., stress and disease).

Author Response

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

Public Reviews:

Reviewer #1 (Public Review):

Weaknesses:

Reviewer comment: Here, the activity of SWIFT molecules was assessed in single cell types with or without BKlotho expression. Ultimately, the ability of the SWIFT molecules to activate Wnt signaling in a cell type-specific manner should be tested in the context of many different cellular identities that express BKlotho to different extents. It would be good to demonstrate that Wnt activation by SWIFT correlates with BKlotho expression level in multiple cell types - such data would strengthen the claim of cell-type specificity.

Response: We agree with the reviewer’s comment, it would be interesting to correlate the signaling level to the expression levels of βKlotho. The tools to carry out such an experiment are not currently available, as this would require a culture system that allows efficient growth of different cell types, and the reagents to detect both the receptor protein levels of βKlotho (as well as FZD/LRP) and signaling levels. We did perform an additional experiment to further support this targeting approach using a 2-layered (transwell) cell culture system. In this culture system, one cell type is put into the top well and the other cell type is put into the bottom well. Molecules to be tested were added to the media which is shared and freely diffuse across the two cell types. In this 2-layer cell system, the results again demonstrate the ability of the SWIFT molecules to specifically induce signaling only in βKlotho expressing hepatoma Huh7 cells and not in non-targeting HEK293 cells. This new data is included as Fig. 3H in the revised manuscript.

Reviewer comment: The study does not address whether the targeted cells express FGFR1c/2c/3c and whether the FGF21 full-length moiety or the 39F7 IgG moiety of SWIFT molecules could unintentionally activate FGF signaling in these cells.

Response: We agree with the reviewer’s comment. The receptor βKlotho and its binders (FGF21 and 39F7) were used to test the BRAID/SWIFT concept, the effects on FGF signaling were not the focus of the current study. This comment has now been added to the revised manuscript in the discussion. Inclusion of αGFP controls in the study also suggests the observed reporter activity in the targeted scenario is unlikely caused indirectly by any unexpected FGF signaling.

Reviewer #2 (Public Review):

Weaknesses:

Reviewer comment: The study shows the SWIFT approach works in vitro using cell lines, primary human hepatocytes, and human intestinal organoids, but it lacks an in vivo animal model or clinical validation. The applicability of this approach to therapy is still unknown.

Response: The βKlotho binder, 39F7, is specific to the human receptor and does not cross react with mouse. Unfortunately, we are not able to test these SWIFTs in a mouse model.

Reviewer comment: The success of SWIFT depends on the presence and expression of the bridging receptor (βKlotho) on target cells. The approach may fail if the target receptor is not expressed or available.

Response: We agree with the reviewer, the SWIFT molecules should not induce signaling on cells where bridging receptor is not expressed, therefore, achieving target cell specificity. As pointed out by the reviewer, finding the right bridging receptor on the target cell is critical.

Reviewer #1 (Recommendations For The Authors):

Reviewer comment 1: One way to further validate the specificity of SWIFT molecules would be to apply them to a mix of different cell types and quantify BKlotho level and Wnt reporter activity at the single cell level, potentially through imaging, FACS, or transcriptomics.

Response: We agree with the reviewer’s comment, it would be interesting to correlate the signaling level to the expression levels of βKlotho. The tools to carry out such an experiment are not currently available, as this would require a culture system that allows efficient growth of different cell types, and the reagents to detect both the receptor protein levels of βKlotho (as well as FZD/LRP) and signaling levels. We did perform an additional experiment to further support this targeting approach using a 2-layered (transwell) cell culture system. In this culture system, one cell type is put into the top well and the other cell type is put into the bottom well. Molecules to be tested were added to the media which is shared and freely diffuse across the two cell types. In this 2-layer cell system, the results again demonstrate the ability of the SWIFT molecules to specifically induce signaling only in βKlotho expressing hepatoma Huh7 cells and not in non-targeting HEK293 cells. This new data is included as Fig. 3H in the revised manuscript.

Reviewer comment 2: The experiments presented demonstrate activation of one signaling pathway in cells specifically expressing a target receptor rather than demonstrating "the feasibility of combining different signaling pathways" as stated in the abstract.

Response: We thank the reviewer for pointing this out and have adjusted the sentence accordingly.

Reviewer comment 3: What are the biological consequences of activating Wnt signaling in cells expressing BKlotho and why is that of interest? Could these biological outcomes be used as an additional, perhaps more consequential, readout for SWIFT activity?

Response: βKlotho is expressed on several different cell types that include hepatocytes, WAT, BAT, and certain regions in CNS. Our studies here focused on the WNT signaling pathway, and βKlotho/FGF21/39F7 receptor ligand system was used to illustrate the BRAID/SWIFT cell targeting concept. Whether these molecules may additional modulate endocrine FGF signaling and metabolic homeostasis, and whether there is any interaction between βKlotho and Wnt signaling pathways could be the subject of future studies. This is now added to the revised manuscript.

Reviewer comment 4: The manuscript would benefit from a careful review to improve wording and address grammatical errors.

Response: We thank the reviewer for this suggestion, and we have now had another round of language editing by a professional service.

Reviewer #2 (Recommendations For The Authors):

Reviewer comment 1. The expression of KLB in Fig 3G and 4B seems way too low and may not represent the amount on the cell surface. Did the authors validate the expression on the cell surface?

Response: In both figures we have displayed the expression level normalized to housekeeping gene ACTB. Housekeeping genes such as ACTB can be among the most abundant transcripts in a cell. The observation that KLB mRNA detection is below ACTB mRNA levels is expected and we would argue not too low. The average real-time PCR cycle threshold (Ct) for KLB in Huh7 and primary hepatocytes was 18 and 24 respectively. To avoid any confusion, we have now displayed the expression data normalized to HEK293 and intestinal organoids as a fold difference in a new Figure 3G and 4B.

Comment 2. Fig 3G needs statistical significance.

Response: We thank the reviewer for highlighting this, we have now included the statistical analysis in an updated Figure 3G.

Author Response

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

Public Reviews:

Reviewer #1 (Public Review):

In the manuscript there is not much comparison between the crystal and cryoEM structures provided, and on inspection they appear to be very similar. The crystal structures also reveal parts of the CC domains in Las1, which is not present in the cryoEM structures. It is interesting the CC domains in Sc and Cj are quite different as illustrated in Figure 4B. They also seem to be somewhat disconnected from the rest of the complex (more so for Cj), even though that's not apparent in Figures 2-4. Despite this, it would be very useful to show the cryoEM densities when describing the catalytic site and C-terminal domain interactions, for example, as this can be very useful to increase confidence in the model and proposed mechanisms.

We thank the reviewer for this suggestion. We have added a figure (Figure 5- Figure supplement 3) to show cryo-EM and crystal densities of key amino acids, when describing the catalytic site and C-terminal domain interactions. In analyzing the interaction between Las1 and Grc3, we have also provided additional comparisons of the crystal structure and the cryo-EM structure (Figure 5, Figure 5-figure supplement 1, 2 and 3, Figure 6, Figure 6-figure supplement 1).

The description of the complex as a butterfly is engaging, and from a certain angle it can be made to look as such; this was also described previously in (Pillon et al., 2019, NSMB) for the same complex from a different organism (Ct). However, it is a bit misleading, because the complex is actually C2 symmetric. Under this symmetry, the 'body' would consist of two 'heads' one pointing up, one down facing towards the back, and one wing would have its back toward the viewer, the other the front. The structures presented here in Sc and Cj seem quite similar to the previous structure of the same complex in Ct, though the latter was only solved with cryoEM, and was also lacking the structure of the CC domain in Las1.

We thank the reviewer for pointing out this issue. We have re-wrote these sentences and changed the butterfly description of Las1-Grc3 complex in the revised manuscript.

For the model suggested in Figure 8, perhaps in the 'weak activity' state, the LCT in Las1 could still be connected to Grc3, via the LCT, rather than disconnected as shown. This could facilitate faster assembly of the 'high activity' state. The complex is described as 'compact and stable', but from the structure and this image, it appears more dynamic, which would serve its purpose and the illustrated model better. The two copies of HEPN appear to have more connective area, meaning they are indeed more likely to remain assembled in the 'weak activity' state. On the other hand, HEPN in one protein appears to have less binding surface with PNK in Grc3, and even less so with the CTD (both PNK and CTD being from the other associated protein), meaning these bindings could release easily to form the 'weak activity' state.

There is also the potential to speculate that the GCT is bound to HEPN near the catalytic area in the 'weak activity' state. The reduced activity when the GCT residues are replaced by Alanine could then be explained by the complex not being able to assemble as quickly upon binding of the substrate, as it could if the GCT remained bound, rather than by a conformational change that it induces upon binding. The conformational change is also likely to be influenced by the combined binding of PNK and CTD in the assembled state, which also contact HEPN, rather than by GCT alone.

We thank the reviewer for this suggestion. We have revised our model in the new Figure 8 of our revised manuscript. We apologize for the un-clarity description of the 'weak activity' state in our model. In fact, we believe that Las1 is in a "weakly activity" state before binding to Grc3 and is in a "highly activity" state when it forms a complex with Grc3. We strongly agree that the Las1-Grc3 complex is more dynamic than compact and stable, so it is easy to change its active state. We have changed our description and revised our model in the revised manuscript.

When comparing the structure of the HEPN domain in the lone Las1 protein to the structure of Las1-HEPN in the Las1-Grc3 complex, it is mentioned that 'large conformational changes are observed'. These could be described a bit better. The conformational change is ~3-4Å C-alpha RMSD across all ~150 residues in the domain (~90 residues forming a stable core that only changes by ~1Å). There is also a shift in the associated HEPN domain in Las1B domain compared to the bound HEPN in the Las1-Grc3 complex, as shown in Figure 7D: ~1Å shift and ~12degrees rotation. This does point to the conformation of HEPN changing upon complex formation, as does the relative positions of the HEPN domains in Las1A and Las1B. The conformational change and relative shift could indeed by key for the catalysis of the substrate as mentioned.

We thank the reviewer for this great suggestion. We have replaced the sentence describing the conformational changes in our revised manuscript.

Overall, the structures presented should be very useful in further study of this system, even though the exact dynamics and how the substrate is bound are aspects that are perhaps not fully clear yet. The addition of the structures of the CC domain in two different organisms and the Las1 HEPN domain (not in complex with Grc3) as new structural information should allow for increasing our understanding of the overall complex and its mechanism.

We thank this reviewer for these encouraging comments, which helped us with greatly improving our manuscript.

Reviewer #2 (Public Review):

In this manuscript, Chen et al. determined the structural basis for pre-RNA processing by Las1-Grc3 endoribonuclease and polynucleotide kinase complexes from S. cerevisiae (Sc) and C. jadinii (Cj). Using a robust set of biochemical assays, the authors identify that the sc- and CjLas1-Grc3 complexes can cleave the ITS2 sequence in two specific locations, including a novel C2' location. The authors then determined X-ray crystallography and cryo-EM structures of the ScLas1-Grc3 and CjLas1-Grc3 complexes, providing structural insight that is complimentary to previously reported Las1-Grc3 structures from C. thermophilum (Pillon et al., 2019, NSMB). The authors further explore the importance of multiple Las1 and Grc3 domains and interaction interfaces for RNA binding, RNA cleavage activity, and Las1-Grc3 complex formation. Finally, evidence is presented that suggests Las1 undergoes a conformational change upon Grc3 binding that stabilizes the Las1 HEPN active site, providing a possible rationale for the stimulation of Las1 cleavage by Grc3.

Several of the conclusions in this manuscript are supported by the data provided, particularly the identification and validation of the second cleavage site in the ITS2. However, several aspects of the structural analysis and complimentary biochemical assays would need to be addressed to fully support the conclusions drawn by the authors.

We thank the reviewer for the positive comments.

• There is a lack of clarity regarding the number of replicates performed for the biochemical experiments throughout the manuscript. This information is critical for establishing the rigor of these biochemical experiments.

We apologize for not providing the detailed information on the number of replicates of biochemical experiments. All the biochemical experiments were repeated three times. We have provided this information in the figure legends.

• The authors conclude that Rat1-Rai1 can degrade the phosphorylated P1 and P2 products of ITS2 (lines 160-162, Figure 1H). However, the data in Fig. 1H shows complete degradation of 5'Phos-P2 and 5'Phos-P4 of ITS2, while the P1 and 5'Phos-P3 fragments remain in-tact. Additional clarification for this discrepancy should be provided.

We thank the reviewer for pointing out this issue. “phosphorylated P1 and P2 products” should be “phosphorylated P2 and P4 products”. We have corrected this clerical error. In addition, we have also provided an explanation for why phosphorylated P3 product show only partial degradation. We suspect that P3 product may be too short to completely degrade.

• The authors determined X-ray crystal structures of the ScLas1-Grc3 (PDB:7Y18) and CjLas1-Grc3 (PDB:7Y17) complexes, which represents the bulk of the manuscript. However, there are major concerns with the structural models for ScLas1-Grc3 (PDB:7Y18) and CjLas1-Grc3 (PDB:7Y17). These structures have extremely high clashscores (>100) as well as a significant number of RSRZ outliers, sidechain rotamer outliers, bond angle outliers, and bond length outliers. Moreover, both structures have extensive regions that have been modeled without corresponding electron density, and other regions where the model clearly does not fit the experimental density. These concerns make it difficult to determine whether the structural data fully support several of the conclusions in the manuscript. A more careful and thorough reevaluation of the models is important for providing confidence in these structural conclusions.

We thank the reviewer for pointing out this issue. We have used the cryo-EM datasets to further validate our conclusions of the manuscript. We analyzed the active site of Las1-Grc3 complex and the interactions between Las1 and Grc3 using the cyro-EM structures and presented new figures (Figure 5- Figure supplement 1, Figure 5- Figure supplement 2, Figure 5- Figure supplement 3, Figure 6- Figure supplement 1) in our revised manuscript. Both the refinement and validation statistical parameters of the cryo-EM datasets are within a reasonable range (Table 2), which will provide confidence for our structure conclusions. The X-ray crystal structures of ScLas1-Grc3 (PDB:7Y18) and CjLas1-Grc3 (PDB:7Y17) complexes has high calshscores and many outliers, which is mainly due to the great flexibility of Las1-Grc3 complex, especially the CC domain of Las1. We have improved our crystal structure models with better refinement and validation of statistical parameters. The clashscores of ScLas1-Grc3 complex and CjLas1-Grc3 complex are 25 and 45, respectively. There are no rotamer outliers and C-beta outliers to report for both ScLas1-Grc3 complex and CjLas1-Grc3 complex.

• The presentation of the cryo-EM datasets is underdeveloped in the results section drawing and the contribution of these structures towards supporting the main conclusions of the manuscript are unclear. An in-depth comparison of the structures generated from X-ray crystallography and cryo-EM would have greatly strengthened the structural conclusions made for the ScLas1-Grc3 and CjLas1-Grc3 complexes.

We thank the reviewer for this suggestion. We have performed structural comparisons between X-ray crystal structure and cyro-EM structure in analyzing the active site of Las1-Grc3 complex and the interactions between Las1 and Grc3 (Figure 5- Figure supplement 1, Figure 5- Figure supplement 2, Figure 6- Figure supplement 1). We have also added a figure (Figure 5- Figure supplement 3) to show cryo-EM and crystal densities of the Las1 active site as well as the key amino acids for Las1 and Grc3 interactions. These comparisons and densities have greatly strengthened our structural conclusions.

• The authors conclude that truncation of the CC-domain contributes to Las1 IRS2 binding and cleavage (lines 220-222, Fig. 4C). However, these assays show that internal deletion of the CC-domain alone has minimal effect on cleavage (Fig 4C, sample 3). The loss in ITS2 cleavage activity is only seen when truncating the LCT and LCT+CC-domain (Fig 4C, sample 2 and 4, respectively). Consistently, the authors later show that Las1 is unable to interact with Grc3 when the LCT domain is deleted (Fig. 6 and Fig. 6-figure supplement 2). These data indicate the LCT plays a critical role in Las1-Grc3 complex formation and subsequent Las1 cleavage activity. However, it is unclear how this data supports the stated conclusion that the CC-domain is important for LasI cleavage.

Our EMSA data shows that the CC domain contributes to the binding of ITS2 RNA (Figure 4D), suggesting that the CC domain may play a role of ITS2 RNA stabilization in the Las1 cutting reaction. The in vitro RNA cleavage assays (Figure 4C) indicate that the LCT is important for Las1 cleavage because it plays a critical role in the formation of the Las1-Grc3 complex. Compared with LCT, the CC domain, although not particularly important for Las1 cutting ITS2, still has some influence (Fig 4C, sample 1 and 3, sample 2 and 4,). Therefore, we conclude that the CC domain may mainly play a role in the stabilization of ITS2 RNA, thereby enhancing ITS2 RNA cleavage.

• The authors conclude that the HEPN domains undergo a conformational change upon Grc3 binding, which is important for stabilization of the Las1 active site and Grc3-mediated activation of Las1. This conclusion is based on structural comparison of the HEPN domains from the CjLas1-Grc3 complex (PDB:7Y17) and the structure of the isolated HEPN domain dimer (PDB:7Y16). However, it is also possible that the conformational changes observed in the HEPN domain are due to truncation of the Las1 CC and CGT domains. A rationale for excluding this possibility would have strengthened this section of the manuscript.

We thank the reviewer for pointing out this issue. We agree that the complete Las1 structure information is helpful in illuminating the conformational activation of the Las1 by Grc3. We screened about 1200 crystallization conditions with full-length Las1 proteins, but ultimately did not obtain any crystals, probably due to flexibility. The CC domain exhibits a certain degree of flexibility, which has not been observed in the structure obtained from electron microscopy. The LCT is involved in binding to the CTD domain of Grc3. The coordination of the active center of HEPN domains by LCT and CC domains is unlikely due to the limited nuclease activity observed in full-length Las1. The conformational changes of the active center are essential for HEPN nuclease activation. Our structure shows that the GCTs of Grc3 interact with the active residues of Las1 HEPN domains, which probably induce conformational changes in the active center of the HEPN domain to activate Las1. Of course, we cannot exclude the possibility that truncation of the Las1 CC and LCT domains will result in little conformational change in the HEPN domains. We have explained this possibility in our revised manuscript.

Reviewer #1 (Recommendations For The Authors):

1) It would be very useful to show the cryoEM densities when describing the catalytic site and C-terminal domain interactions.

The new Figure 5-figure supplement 2 have showed the Cyro-EM densities of the catalytic site of ScLas1 and the C-terminal domain of ScGrc3.

2) "ScLas1 cleaves the 33-nt ITS2 at C2 site to theoretically generate a 10-nt 5′-terminal product and a 23-nt 3′-terminal product (Figure 1A). Our merger data shows that the final 5′-terminal and 3′-terminal product bands are at nearly the same horizontal position on the gel (Figure 1B), indicating that they are similar in size." These two sentences seem to contradict, i.e. 10-nt and 23-nt are similar in size even though they are different lengths?

We apologize for the contradiction in these two sentences mentioned above. We have re-wrote these two sentences in the revised manuscript.

3) We observed four cleavage bands of approximately 23-nt (P2), 14-nt (P3), 10-nt (P1), and 9-nt (P4) in length (Figure 1C). "

Figure 1C. The bands show 23 nt, 22 nt, 21nt, 14 nt, 13nt, and 11nt, so this text does not seem to describe the figure.

We have re-wrote this sentence in the revised manuscript.

4) "We obtained similar cleavage results with a longer 81-nt ITS2 RNA substrate 6 (Figure 1D, E). " Figure 1D,E. The lengths in Figure 1E do not correspond to all bands in Figure 1E, e.g. the 13 nt band, though the others do, e.g. 14 nt, 30nt, 37nt, etc.

In order to better evaluate the size of the cut product, we used an RNA marker as a comparison. The RNA marker will have more bands than the cleavage products. To further confirm the cleavage site of C2′, we also mapped the cleavage sites of the 81-nt ITS2 using reverse transcription coupling sequencing methods (Figure 1F).

5) In Figure 3, domains are colored different but it's hard to know which are different proteins.

We have added a diagram in Figure 3 to show the Las1-Grc3 complex structure, and it is now clear how Las1 and Grc3 are assembled into a tetramer.

6) Line 267. "we screened a lot of crystallization conditions with full-length Las1 proteins" How many? Rough numbers ok, but 'a lot' is not very informative

We have provided the approximate numbers of crystallization conditions in our revised manuscript.

Reviewer #2 (Recommendations For The Authors):

1) The authors missed an excellent opportunity to compare and contrast the ScLas1-Grc3 and CjLas1-Grc3 complex structures presented here with that of the previously determined CtLas1-Grc3 structure (Pillon et al., 2019, NSMB). For example, His130 in the ScLas1-Grc3 complex active site adopts a similar conformation to His142 in the TcLas1-Grc3 complex active site (Pillon et al., 2019, NSMB). Interestingly, the analogous His134 active site residue in the CjLas1-Grc3 adopts an alternative (maybe inactive) conformation. This observation could provide a structural rationale for the activation of scLas1 and TcLas1 by Grc3, while also providing a rationale for the fairly weak activation of CjGrc3 by CjGrc3.

We thank the reviewer for this suggestion. We have performed structural comparisons between ScLas1-Grc3, CjLas1-Grc3 and CtLas1-Grc3 complexes, especially the Las1 nuclease active center. We added two figures (Figure7-figure supplement 3A and 3B) in the revised manuscript to contrast and highlight the conformational differences of active amino acids in active centers between ScLas1-Grc3, CtLas1-Grc3 and CjLas1-Grc3. These structural comparisons provide stronger evidence that further reinforces the conclusions of our manuscript.

2) Can the authors speculate as to whether the structural data can provide any insight into how the Las1-Grc3 may cleave both C2 and C2' positions in the ITS2 RNA? This commentary would further strengthen the discussion section of the manuscript.

We thank the reviewer for this suggestion. We have provided a speculation in the discussion section of the revised manuscript.

We think that the structural data may provide some insight into how Las1-Grc3 complex cleaves ITS2 RNA at both C2 and C2' positions. The Las1-Grc3 tetramer complex has one nuclease active center and two kinase active centers. The nuclease active center consists of two Las1 molecules in a symmetric manner, while the kinase active center consists of only one Grc3 molecule. The ITS2 RNA is predicted to form a stem-loop structure. The symmetrical nuclease active center recognizes the stem region of ITS2 RNA and makes it easy to perform C2 and C2' cleavages on both sides of the stem. C2 and C2' cleavage products are further phosphorylated by two Grc3 kinase active centers, respectively.

3) The method used for the plasmid generation, expression, and purification of the Las1 truncations and the Las1 and Grc3 point mutants should be provided in the methods section.

The method used for the plasmid generation, expression, and purification of the Las1 truncations and the Las1 and Grc3 point mutants have be provided in the methods section.

4) The exact amino acid cutoffs for the truncated forms of Las1 used for the biochemical assays in Fig. 4 should be provided.

We have provided the exact amino acid cutoffs for the truncated forms of Las1 in the figure legend of Figure 4C.

5) The models associated with the cryo-EM datasets should be deposited in the PDB.

The models associated with the Cryo-EM datasets have be deposited in the PDB with the following accession codes: 8J5Y (ScLas1-Grc3 complex), and 8J60 (CjLas1-Grc3 complex).

6) Lines 232-234: Arg129 should be changed to His134.

We have corrected it.

7) Figure 5B: the bottom half of the HEPN active site has been labeled incorrectly. The labels should be Arg129, His130, and His134 (from left to right).

We have corrected it.

8) Line 252: "multitudinous" should be changed to "multiple."

We have corrected it.

Author Response

We are grateful to the reviewers for their thorough and thoughtful critiques, including their agreement on the significant value of this dataset. We intend to respond to their comments in full with a revision in the near future. However, we would like to make an initial comment at this stage. A key concern raised by the reviewers was that the analyses described do not adequately support the claim that "movie-watching data can identify retinotopic regions" (quoted from R2, similar sentiment expressed by R1). To be clear, we agree with this assessment. Our primary aim was not to identify visual areas with movie-watching data. Rather, our focus was on how movies can reveal fine-grained organization in infant visual cortex, which would support their potential utility for understanding the development of dynamic visual processing. To demonstrate this potential, we tested and found that maps of visual activity generated from movies are significantly similar to those generated by a retinotopy task. Nevertheless, we did not intend to argue that movie-based maps are sufficiently accurate to replace task-based retinotopic maps when defining visual areas, nor did we test this possibility. We accept that this point was unclear in the original manuscript and will make edits to avoid this miscommunication. We also plan to incorporate the reviewers’ many other helpful recommendations, including addressing concerns about the clarity of the presentation and double dipping, as well as adding several new analyses we hope will provide greater confidence in the findings and interpretation.

Author Response

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

We would like the reviewers for their positive and useful comments. Below please find our answers to the issues raised.

Reviewer #1 (Public Review):

Overall, the experiments are well-designed and the results of the study are exciting. We have one major concern, as well as a few minor comments that are detailed in the following.

Major:

1) The authors suggest that "Visuomotor experience induces functional and structural plasticity of chandelier cells". One puzzling thing here, however, is that mice constantly experience visuomotor coupling throughout life which is not different from experience in the virtual tunnel. Why do the authors think that the coupled experience in the VR induces stronger experience-dependent changes than the coupled experience in the home cage? Could this be a time-dependent effect (e.g. arousal levels could systematically decrease with the number of head-fixed VR sessions)? The control experiment here would be to have a group of mice that experience similar visual flow without coupling between movement and visual flow feedback.

Either change would be experience-dependent of course, but having the "visuomotor experience dependent" in the title might be a bit strong given the lack of control for that. We would suggest changing the pitch of the manuscript to one of the conclusions the authors can make cleanly (e.g. Figure 4).

Although the plasticity is induced by the visuomotor experience in the tunnel, we agree that we do not know what aspect of the repeated exposure to the virtual tunnel caused the plasticity. We cannot rule out that it was the exposure to the visual stimuli alone that caused it. Therefore, we rephrased sentences that suggested that it was the coupling between visual stimuli and motor behavior that was responsible for the plasticity. We also changed the title to “Experience Shapes Chandelier Cell Function and Structure in the Visual Cortex”.

We do believe that training the mice in the virtual tunnel does significantly increase experience with coupled visuomotor activity, though. In their home cage, mice are mostly active in the dark and there is litle space to run.

Minor:

2). "ChCs shape the communication hierarchy of cortical networks providing visual and contextual information." We are not sure what this means.

We thank the reviewer for helping to raise clarity and we rephrased this sentence to: “…ChCs may establish a hierarchical relationship among cortical networks.”

3) "respond to locomotion and visuomotor mismatch, indicating arousal-related activity" This is not clear. We think we understand what the authors mean but would suggest rephrasing.

Agreed. We rephrased this sentence to: "...respond to events that are known to increase arousal levels, such as locomotion and visuomotor mismatch.”

4) 'based on morphological properties revealed that 87% (287/329) of labeled neurons were ChCs" Please specify the morphological properties used for the classification somewhere in the methods.

We added that the neurons were positioned at the border of L1 and L2 and had a dendrite reaching into layer 1.

5) We may have missed this - in the patch clamp experiment (Fig.1 H-K), please add information about how many mice/slices these experiments were performed in.

We have added the information to the legend of Fig. 1.

6) "These findings suggest that the rabies-labeled L1-4 neurons providing monosynaptic input to ChCs are predominantly inhibitory neurons". We are not sure this conclusion is warranted given the sparse set of neurons labelled and the low number of cells recorded in the paired patch experiment. We would suggest properly testing (e.g. stain for GABA on the rabies data) or rephrasing.

We weakened the statement to: “These findings suggest that the rabies-labeled L1-4 neurons providing monosynaptic input to ChCs may include many inhibitory neurons.”

7) Figure 2E. A direct comparison of dF/F across different cell types can be subject to a problematic interpretation. The transfer function from spikes to calcium can be different from cell type to cell type. Additionally, the two cell populations have been marked with different constructs (despite the fact that it's the same GECI) further reducing the reliability of dF/F comparisons. We would recommend using a different representation here that does not rely on a direct comparison of dF/F responses (e.g. like the "response strength" used in Figure 3B). Assuming calcium dynamics are different in ChCs and PyCs - this similarity in calcium response is likely a coincidence.

We have removed the quantification in this figure.

8) If ChCs are more strongly driven by locomotion and arousal, then it's a bit counterintuitive that at the beginning of the visual corridor when locomotion speed consistently increases, the activity of ChCs consistently decreases. This does not appear to be driven by suppression by visual stimuli as it is present also in the first and last 20cm of the tunnel where there are no visual stimuli. How do the authors explain this?

We do believe that this is suppression driven by visual stimuli. Although on average the strongest visual suppression happens between 20-80 cm, neurons that have their receptive fields toward the center of the visual field will already respond to the stimuli before the mouse reaches the 20 cm location of the tunnel. In addition, although the visual stimuli are the strongest sensory inputs, the background of the visual part of the tunnel has a black and white noise patern, which might already mildly suppress ChC activity. Both arguments are supported by the observation that the visual PyCs (V-PyCs, blue line) in Fig. 4D are already activated at the beginning of the tunnel and that the activity of V-PyCs matches well with the suppression of ChC activity.

9) The authors mention that "ChC responses underwent sensory-evoked plasticity during the repeated visual exposure, even though the visual stimuli were different from those encountered during training in the virtual tunnel". How would this work? And would this mean all visual responses are reduced? What is special about the visual experience in the virtual tunnel? It does not inherently differ from visual experience in the home cage, given that the test stimuli (full field gratings) are different from both.

As mentioned in our answer to point 1, the exposure to visual stimuli is strongly increased since, firstly, they are presented during the dark phase when the mice are most active and, secondly, they do not get these types of visual inputs in their home cage.

10) Just as a point to consider for future experiments: For the open-loop control experiments, the visual flow is constant (20cm/s) - ideally, this would be a replay of the running speed the mouse previously generated to match statistics.

We agree with this point and will implement replay of earlier sessions in future experiments.

11) We would recommend specifying the parameters used for neuropil correction in the methods section.

This is described on page 24, under “preprocessing”. We also refer to the analysis package (Spectral Segmentation - SpecSeg) in which the neuropil correction as used by us here is explained in more detail.

12) If we understand correctly, the F0 used for the dF/F calculation is different from that used for division. Why is this?

We apologize for this mistake, which was based on an older version of the software. We have now corrected this in the revised manuscript.

13) Authors compare neuronal responses using "baseline-corrected average". Please specify the parameters of the baseline correction (i.e. what is used as baseline here).

In the revised version we have now beter explained this in the methods, page 24, under “Passive Sessions”.

Reviewer #2 (Public Review):

Summary:

Seignete et al. investigated the potential roles of axo-axonic (chandelier) cells (ChCs) in a sensory system, namely visual processing. As introduced by the authors, the axo-axonic cell type has remained (and still is) somehow mysterious in its function. Seignete and colleagues leveraged the development of a transgenic mouse line selective for ChC, and applied a very wide range of techniques: transsynaptic rabies tracing, optogenetic input activation, in vitro electrophysiology, 2-photon recording in vivo, behavior and chemogenetic manipulations, to precisely determine the contribution of ChCs to the primary visual cortex network.

The main findings are 1) the identification of synaptic inputs to ChC, with a majority of local, deep layer principal neurons (PN), 2) the demonstration that ChC is strongly and synchronously activated by visual stimuli with low specificity in naive animals, 3) the recruitment of ChC by arousal/visuomotor mismatch, 4) the induction of functional and structural plasticity at the ChC-PN module, and, 5) the weak disinhibition of PNs induced by ChCs silencing. All these findings are strongly supported by experimental data and thoroughly compared to available evidence.

Strengths:

This article reports an impressive range of very demanding experiments, which were well executed and analyzed, and are presented in a very clear and balanced manner. Moreover, the manuscript is well- writen throughout, making it appealing to future readers. It has also been a pleasure to review this article.

In sum, this is an impressive study and an excellent manuscript, that presents no major flaws.

Notably, this study is one of the first studies to report on the activities and potential roles of axo-axonic cells in an active, integrated brain process, beyond locomotion as reported and published in V1. This type of research was much awaited in the fields of interneuron and vision research.

Weaknesses:

There are no fundamental weaknesses; the later mainly concern the presentation of the main results. The main weakness may be that the different sections appear somehow disconnected conceptually.

Additionally, some parts deserve a more in-depth clarification/simplification of concepts and analytic methods for scientists outside the subfield of V1 research. Indeed, this paper will be of key interest to researchers of various backgrounds.

Reviewer #3 (Public Review):

Summary:

The authors set out to characterize the anatomical connectivity profile and the functional responses of chandelier cells (ChCs) in the mouse primary visual cortex. Using retrograde rabies tracing, optogenetics, and in vitro electrophysiology, they found that the primary source of input to ChCs are local layer 5 pyramidal cells, as well as long-range thalamic and cortical connections. ChCs provided input to local layer 2/3 pyramidal neurons, but did not receive reciprocal connections.

With two-photon calcium imaging recordings during passive viewing of drifting gratings, the authors showed that ChCs exhibit weakly selective visual responses, high correlations within their own population, and strong responses during periods of arousal (assessed by locomotion and pupil size). These results were replicated and extended in experiments with natural images and prediction of receptive field structure using a convolutional neural network.

Furthermore, the authors employed a learned visuomotor task in a virtual corridor to show that ChCs exhibit strong responses to mismatches between visual flow and locomotion, locomotion-related activation (similar to what was shown above), and visually-evoked suppression. They also showed the existence of two clusters of pyramidal neurons with functionally different responses - a cluster with "classically visual" responses and a cluster with locomotion- and mismatch-driven responses (the later more correlated with ChCs). Comparing naive and trained mice, the authors found that visual responses of ChCs are suppressed following task learning, accompanied by a shortening of the axon initial segment (AIS) of pyramidal cells and an increase in the proportion of AIS contacted by ChCs. However, additional controls would be required to identify which component(s) of the experimental paradigm led to the functional and anatomical changes observed.

Finally, using a chemogenetic inactivation of ChCs, the authors propose weak connectivity to pyramidal cells (due to small effects in pyramidal cell activity). However, these results are not unequivocally supported, as the baseline activity of ChCs before inactivation is considerably lower, suggesting a potentially confounding homeostatic plasticity mechanism might already be operating.

Strengths:

The authors bring a comprehensive, state-of-the-art methodology to bear, including rabies tracing, in vivo two-photon calcium imaging, in vitro electrophysiology, optogenetics and chemogenetics, and deep neural networks. Their analyses and statistical tests are sound and for the most part, support their claims. Their results are in line with previous findings and extend them to the primary visual cortex.

Weaknesses:

• Some of the results (e.g. arousal-related responses) are not entirely surprising given that similar results exist in other cortical areas.

We agree that previous studies have shown arousal-related responses of ChC cells and our study confirms those findings. However, this is not the main message of the article and we present many findings that are novel.

• Control analyses regarding locomotion paterns before and atier learning the task (Figure 5), and additional control experiments to identify whether functional and anatomical changes following task learning were due to learning, repeated visual exposure, exposure to reward, or visuomotor experience would strengthen the claims made.

In figure 5 we excluded running trials, so locomotion paterns are unlikely to play a major role. We agree that testing what are the factors that contribute to the observed plasticity are important to investigate in future experiments.

• The strength of the results of the chemogenetics experiment is impacted by the lower baseline activity of ChCs that express the KORD receptor. At present, it is not possible to exclude the presence of homeostatic plasticity in the network before the inactivation takes place.

Although we do not know why there is a difference in the baseline df/f (e.g. expression levels), we consider it unlikely that expression of the KORD receptor itself without exposure to the ligand causes reduction of ChC activity. Moreover, we are not sure how homeostatic plasticity in the network would occur selectively in KORD-expressing ChCs. Finally, we do not find evidence for a relationship between lower ChC calcium signals and the effects of ChC silencing on PyC activity. We performed an additional analysis in which we correlated baseline ChC activity (before salvinorin B injection) with the effect of ChC silencing on PyC activity (post – pre) across mice, and found that this correlation was not significant (R = 0.41, p = 0.18).

Reviewer #1 (Recommendations For The Authors):

In the spirit of openness of the scientific discussion, all our feedback and recommendations to the authors are included in the public reviews.

Reviewer #2 (Recommendations For The Authors):

Most of my comments and suggestions concern the presentation of the data, to (hopefully) help and convey as clearly as possible the messages of this important article.

Main

The main weakness of the paper may be that the different sections appear somehow disconnected conceptually. This is particularly true for:

-structural plasticity: how can we link this finding with the rest of the study? Are there ways to correlate this finding with physiological recordings in individual animals, or to directly test whether particular functional types of PNs (visual, non-visual) undergo plasticity at their AIS?

This is a very interesting question that may be addressed in future experiments.

-the indirect finding suggesting that ChC weakly inhibits PNs using chemogenetic silencing of PNs. Do chemogenetic manipulations of ChCs affect PN responses in visual paradigm and/or modify the induction of structural plasticity at the ChC-AIS connection?

This is also a very interesting question for future work.

Additionally, some parts would deserve a more in-depth clarification/simplification of concepts and analytic methods (OSI, DSI, MEI...) for scientists outside the subfield of V1 research. Indeed, this paper will be of key interest to researchers of various backgrounds.

In the revised manuscript we briefly explain what an MEI is when first introduced, and introduce the abbreviations OSI and DSI at the correct location. We believe orientation and direction selectivity are well-known concepts for the audience reading this article.

Minor

These are discussed by order of appearance in the text.

Abstract

The alternative interpretation of error/mismatch negativity to explain ChC activation deserves to appear in the abstract. Arousal consistency in prediction should be in the introduction. "In mice running in a virtual tunnel, ChCs respond strongly to locomotion and halting visual flow, suggesting arousal-related activity."

This comment holds for the end of the introduction and the beginning of the discussion, as well.

"These findings suggest that ChCs provide an arousal-related signal to layer 2/3 pyramidal cells that may modulate their activity". This statement appears to be in contradiction with the weak effect mentioned just before. This comment holds for the end of the introduction.

The full sentence was: “These findings suggest that ChCs provide an arousal-related signal to layer 2/3 pyramidal cells that may modulate their activity and/or gate plasticity of L2/3 PyCs in V1.” Our results show that activity of layer 2/3 pyramidal cells is modulated (albeit weakly) and it is well possible that ChCs regulate plasticity at the AIS. Therefore, we do not believe that this statement contradicts the weak direct effect of ChCs on layer 2/3 pyramidal cell activity. Therefore , we think that this statement does not contradict the weak direct effect of ChCs on layer 2/3 pyramidal cell activity.

We changed the last sentence of the introduction to “Our findings suggest that ChCs predominantly respond to arousal related to locomotion or unexpected events/stimuli, and act to weakly modulate activity and/or gate plasticity of L2/3 PyCs in V1.”

Introduction First paragraph

Coming from a field outside of vision research, it is not obvious to me what has been learned from interneuron classes in the past. An example would be welcome in the introduction.

The literature on the role of different interneuron types in visual processing and plasticity is too large to pick one or two examples. For the sake of conciseness, we have therefore provided some important references and reviews for the interested readers (references 1 to 10).

Interneuron "subtypes" seem to refer to main classes (e.g. PV+): please rephrase accordingly (ChC being a type and PV+ ChC a subtype).

We changed interneuron “subtypes” to “types” and left L2/3 pyramidal cell “subtypes” unchanged.

Second paragraph

Beyond the reversal potential of GABA-ARs at the axon initial segment, GABA may inhibit action potential generation in various conditions (Lipkin et al. 2023, DOI: 10.1523/JNEUROSCI.0605-23.2023 : should be cited).

We added this citation.

Fourth paragraph

"ChCs alter the number of synapses at the AIS based on the activity of their postsynaptic targets": the concept of alteration is too vague to let the reader grasp the concept: could the authors rephrase?

We have rephrased the sentence to:

“…ChCs increase the number of synapses at the AIS if their postsynaptic targets are chemogenetically activated…”

Results 1) ChCs receive input from long-range sources and L5 PyCs in V1 It is not clear how morphological identification of ChC was performed. Did dendrites and/or axons of starter cells occasionally overlap as can be expected, complicating the cell-by-cell morphological classification?

"Most labeled neurons were located on the border between L1 and L2/3 and displayed typical ChC morphology": maybe clarify that this concerns neurons expressing eYFP-TVA?

We assessed the location (at the border of L1 and L2) and spatial distribution of the labeled cells and whether they had a dendrite extending upwards towards into L1. We have now indicated this in the results section and clarified that these neurons express eYFP-TVA.

-Likewise the following would benefit from clarification " This is further supported by the distributed localization of the labeled neurons": it would also help here to remind the reader of the labelling (presumably retrogradely-labeled mCherrry+ neurons).

We have now clarified in the text that these are mCherry+ neurons labeled by the rabies virus

2) Chandelier cells are modulated by arousal and show high correlations

-The authors indicate that the results "(suggest) that ChCs distribute a synchronized signal during high arousal." : it would be stronger to defend this claim by showing a higher ChC-ChC correlation during "arousal" vs. baseline (i.e. analyze high arousal epochs outside of movement). It may be difficult to perform this analysis due to low fluorescence changes outside running episodes, but this should be discussed accordingly. In this respect, the title of the section is more in line with the data presented.

We believe our statement is correct. The activity of ChCs is highly synchronized and their firing rates increase during arousal. We do not state that synchronization increases with arousal.

-A brief explanation of DSI and OSI meaning would be nice for the audience that will definitely extend beyond vision research given the importance of this study.

See above

3) ChCs are weakly selective to visual information

-I may very well miss the point, but the equivalence in response strength among cell classes (Fig3B) seems inconsistent with the wider distribution of high response strength in ChCs (Fig3C). Perhaps a graphical representation taking into account the distribution of single data points in Fig3B would help resolve this discrepancy.

This is because in panel C the response strengths are normalized. We now also state this in the legend to avoid confusion.

-"clearly oriented edge-like paterns with sharp ON and OFF regions": it would help if a representative example was highlighted in Figure 3F.

The majority of L2/3 pyramidal MEIs presented in this panel show this patern.

-It is interesting and surprising that properties of ChCs appear more distinct from those of L5 PNs than from those of L2-3 PNs (Fig 3G-J), given the fact that V1 ChCs were found by the authors to derive their inputs from V1 L5 PNs (please see comments of the discussion for this specific point).

How ChCs respond based on L5 input depends strongly on how the connections between L5 and ChCs are organized. Similarity between responses of L5 and ChC neurons is not required.

4) Locomotion and visuomotor mismatch drive chandelier cell activity in a virtual tunnel This is the least convincing part in terms of presentation.

-It is unclear where/when visuomotor mismatch has been induced in the tunnel: please clarify in the text and in Fig 4B.

We realized that the title of the paragraphs was indeed confusing. In fig. 4A-D and the first paragraph about the virtual tunnel, we do not discuss the visuomotor mismatch. This comes later, when we describe the results in Fig. 4E. The titles have been changed.

-No result on visuomotor mismatch is reported in the text of this section, while this is presented in the subsequent section: this needs to be corrected (merge this section with the next?).

We agree, apologies for the confusion. See above.

-It would be interesting to further analyze responses to CS and US. Regarding the US: is water rewarding in non-water-restricted mice? This should be mentioned.

We realized that we did not mention that the mice were water restricted during behavioral training and during the imaging sessions when mice performed the virtual tunnel task. We have now added this to the methods section. Sorry for the omission.

-Along this line: was water sometimes omited? This would provide a complementary way to test the prediction error theory for ChC activation with an alternative modality.

We never omited the water reward. It would be interesting to test this in a future experiment.

5) ChCs have similar response properties as non-visual PyCs

• It would help to explicitly mention that in Ai65 mice, only Cre and Flp+ cells express tdTomato (here Vipr2 and PV+).

We added the following sentence: “In these mice, tdTomato was only expressed in cells expressing both Vipr2 and PV.”

6) Visuomotor experience in the virtual tunnel induces plasticity of ChC-AIS connectivity

• In relation to the previous section, Jung et al. (doi.org/10.1038/s41593-023-01380-x) recently reported that motor learning reduced ChC-ChC synchrony in M2. Did the author observe a similar change in ChC- ChC synchrony with visual experience/habituation to the task? If available, these data should be reported to help build a clearer picture of ChC functions in the neocortex.

We tested this and also found reduced correlations between ChCs in trained mice vs naïve mice. We added this as text on p14 in the results section.

• The low number of ChC boutons' appositions per AIS may be misleading: "While the average number of ChC boutons per AIS remained constant (~2-3 ChC boutons/AIS)"). It would be helpful to make it clear that these are "virally" labelled boutons, as opposed to absolute numbers, if compared with the detailed quantification of Schneider-Mizell et al, 2021 (7.4 boutons per AIS in average; doi: 10.7554/eLife.73783.).

We added "virally labeled"

• It may be difficult to clearly isolate boutons in light microscopic images of ChC boutons. could the authors comment on this and explain how they solved this issue (in the methods section for instance)?

We elaborated on our definition of a bouton under confocal microscopy conditions. We also added that the analysis was performed under blinded conditions for the experimenter (i.e. the experimenter did not know whether the images came from trained or untrained mice).

• Is there any suggestion for heterogeneity/selectivity for a subset of PNs (the distribution does not seem to show this, though)? It would be interesting to discuss this and try to link this finding to the rest of the study a bit more directly. Future work could also investigate if genetically defined PN types undergo different pre-synaptic plasticity at their AISs (e.g. work cited by the authors by O'Toole et al, 2023 doi: 10.1016/j.neuron.2023.08.015 -this reference can be updated as well, since the work has been published in the meantime).

In our data, we did not find evidence for heterogeneity or selectivity of targeting, also not in the physiology using KORD (see below). We do agree that it is an interesting question and deserves atention in future experiments. We also updated the reference.

7) ChCs weakly inhibit PyC activity independent of locomotion speed

The authors state that "recent work in adult mice has reported hyperpolarizing and shunting effects in prelimbic cortex, S1 and hippocampus (18, 26, 27)": however, to my knowledge studies presented in refs 26 & 27 found reduced activity/firing of PNs upon optogenetic activation of ChCs in vivo, but did not perform intracellular recordings to assess GABA-A reversal potential at the AIS. I would like to kindly ask the authors to correct this sentence.

If the polarity of responses is discussed, they may rather refer to the corresponding literature including Rinetti Vargas et al (doi: 10.1016/j.celrep.2017.06.030), Lipkin et al (doi: 10.1523/JNEUROSCI.0605- 23.2023), and Khirug et al (doi: 10.1523/JNEUROSCI.0908-08.2008.).

We added the reference to Lipkin et al and changed the sentence so that it matches the references..

• In an atempt to link findings from several parts of the article, did the authors investigate whether chemogenetic effects were different in visual vs non-visual PNs? As ChCs are functionally related to visual PNs, one might indeed speculate that these cells are synaptically connected.

We did not find evidence for selectivity in the chemogenetic effect. We compared the chemogenetic effect to locomotion modulation (see text accompanying Fig 7.) – based on our observation that non- visual PyCs were more strongly modulated by locomotion (see Fig. 4) – but did not find any significant correlation.

• " We first looked at the average activity of neurons in both essions.": sessions

Thank you for noticing. We corrected this.

Discussion

Summary of findings

-It would be worthwhile to include in the summary the finding of mismatch-related activity, that appears to explain more convincingly ChC activation than arousal per se (with the data available).

We updated the summary of the discussion accordingly.

-Moreover, the last part of the article (weak inhibition of PNs by ChCs), despite being very important, is not mentioned.

We now mention this in the summary of the discussion (“Finally, ChCs only weakly inhibit PyCs.”)

Discussion of findings

-" Optogenetic activation of cortical feedback": it is not clear what the authors mean by cortical feedback. As RS was retrogradely labeled, this region may rather provide feedforward inhibition to V1 via ChCs.

Retrosplenial cortex is a higher order cortical area and only provides feedback to V1.

-"This means that each ChC receives input from many L5 PyCs, which could explain the low selectivity of ChC responses we observed to natural images compared to those of L2/3 and L5 PyCs". : perhaps state explicitly that the convergence of many PN inputs each carrying different RF/visual properties "averages out" in ChC (as you do a few lines below for MEI).

At this point, we do not know how the connections from L5 to ChCs are organized. Whether this converge results in “average out” is therefore not so certain. We have made an atempt to clarify the situation. (“This convergence of L5 PyC inputs, if not strongly organized, could explain the low selectivity of ChC responses we observed to natural images compared to those of L2/3 and L5 PyCs.”)

-"However, we did not identify neuromodulatory inputs to ChCs in our rabies tracing experiment. Possibly, these inputs act predominantly through extrasynaptic receptors and were therefore not labeled by the transsynaptic rabies approach.": here, the authors should cite the work by Lu et al (doi: 10.1038/nn.4624) which found basal forebrain (diagonal band of Broca) cholinergic inputs to ChC of the PFC in the Nkx2.1CreER mouse model. Moreover, the authors should discuss potential technical differences (?) responsible for this discrepancy. Beyond the extrasynaptic release of neuromodulators, rabies strains may display different tropism profiles for neuron classes.

We have now added a sentence discussing this and added the reference in the revised manuscript.

-The section dedicated to prediction error is particularly interesting and relevant. In my opinion, this interpretation should be further emphasized in the abstract and summary of findings paragraph in the discussion (as already indicated).

Yes, we agree and have added some emphasis.

-" These findings are thus in contrast with the general notion that ChCs exert powerful control over PyC output (28, 78), but consistent with computational simulations predicting a relatively small inhibitory effect of GABAergic innervation of the AIS, possibly involving shunting inhibition (79, 80)." These findings are also consistent with results from PFC and dCA1 studies showing, with electrophysiological recordings combined with optogenetic stimulation of ChCs, that a small proportion of putative PNs was inhibited upon ChC stimulation (doi: 10.1038/nn.4624 doi: 10.1016/j.neuron.2021.09.033).

Perhaps the effect of ChCs is limited in all these experiments by a suboptimal efficiency of ChC targeting. Moreover, inhibition might be restricted to a subset of PNs carrying a specific function. This could be discussed.

We added an explanation for the weak effects of silencing to the discussion and stated that our results are in line with findings in PFC and CA1. (“One explanation for the weak effects we observed is the high variability in the number of GABAergic boutons that PyCs receive at their AISs. Possibly, only a smaller fraction of PyCs with high numbers of AIS synapses are inhibited when ChCs are active. Indeed, we find that only a small fraction of PyCs increased their activity upon chemogenetic silencing of ChCs, in line with findings by others showing that manipulating ChC activity in vivo has relatively weak effects on small populations of PyCs (27, 28).”)

Although we cannot rule out that ChC targeting is suboptimal in our and other experiments, the expression of the KORD receptor as visualized by mCyRFP1 fluorescence appeared very strong. In addition, the common notion in the ChC field is that ChCs exert powerful control over PyC firing. Even suboptimal labeling should in that case show clear inhibitory effects. Similar experiments with PV+ interneurons would show very convincing inhibition, even if labeling is suboptimal. To keep the discussion concise, we prefer to leave this particular point out.

-" ChC activation could prevent homeostatic AIS shortening of L2/3 PyCs if their activity occurs during behaviorally relevant, arousal inducing events": this postulate seems to be very interesting but is not very clear and lacks some mechanistic speculation.

We considered elaborating more on this hypothesis. However – given that it is merely a speculation at this point – we do not wish to lengthen the discussion further on this point.

• A reference to previous studies demonstrating high levels of synchronous ChC activities is missing: the authors may cite Dudok et al., Schneider-Mizell et al., and Jung et al. (and discuss a change in synchrony with learning or habituation in the case of this study; see above).

We have now also referred to these papers in the context of high correlations between ChCs.

Methods

Beyond references to reagents (eg antibodies, viruses), lot numbers should be provided whenever this is possible. Indeed, there might be strong lot-to-lot variations in specificity and efficiency.

Reviewer #3 (Recommendations For The Authors):

Major:

• (Figure 5) Control analysis missing. Mice before and after training in VR will almost definitely exhibit different running paterns when viewing driftng gratings. Since ChCs are strongly modulated by locomotion, assess whether results depend on changes in running.

Although we did not compare locomotion paterns before and after training, we removed all trials in which the mice were running (see methods). Therefore, we can exclude that these results are caused by changes in running behavior.

• (Figure 5 & 6) What would happen with simple passive visual experience, not in a visuomotor task? What if there was no reward? What if there was an open-loop experiment with random reward? To which specific aspect of the experiment are the results atributable?

These are indeed very interesting questions that may be tested in future experiments.

(Figure 7 B, H) The pre-injection ChC activity in the KORD group is less than 50% of that in control mice! Discuss the effect of such a shift in baseline. Plasticity of PyCs even before ChC inactivation?

See answer to the above question in the public section of reviewer 3.

• (Figure 3 H) Contrast tuning results, as far as I understand, come only from the CNN. However, if I understood correctly, during the passive viewing of gratings there were already different contrasts. Why not show contrast tuning there? Do the results disagree?

We did indeed show stimuli at different contrasts during the passive viewing of gratings. Although the results from those recordings were not optimal for defining contrast sensitivity, they also showed that ChC responses were less modulated by contrast than PyCs.

Minor: - (Figure 3) Explain the potential impact of different indicators 8m vs 6f due to different baselines and dynamics.

We believe there is no impact of different indicators, because for the CNN analyses we estimated spikes using CASCADE. This toolbox is specifically designed to generalize across different calcium indicators. Although GCaMP8m was not included in their training set, the wide variety of indicators used provides a solid basis for generalizable spike estimation. Importantly, comparisons between L2/3 PyCs and ChCs also would not be affected by this concern.

• (Figure 4) NV-PyCs. Would you call all of these mismatch-responsive neurons? Discuss the difference in the percentage of neurons (more than 50% of total PyCs here, compared to significantly less - up to 40% in previous studies, as far as I'm aware)

Not all NV-PyCs appeared to be mismatch-responsive neurons.

• (Figure 6 D) No error bars?

This is a representation of the fraction of all contacted AISs, which has no error bars indeed.

• (Figure 6 E-F and H-I) These pairs of panels contain essentially the same information. The first panel of each pair seems redundant.

We prefer to keep both plots in place, as in this case the skewness of the histogram can be helpful, which is less clear in the boxplot (which in itself displays the quantiles beter).

• The equation for direction tuning still has ang_ori, instead of ang_dir which I'm assuming should be there.

Thank you for noticing, we corrected it.

• The response for drifting gratings is calculated from a different interval (0.2-1.2s) compared to natural images (0-0.5s). Why?

Because we used spike probability in the case of the natural images to shorten the signal, and the visual stimuli were presented for 0.5 s (instead of 1 s as with the gratings).

Very minor:

• It would be helpful for equations to have numbers.

Done

• Sparsity equation. Beter to have it as a general equation, with N instead of 40. Then below it can be explained that N is the number of images = 40.

Done

• "The similarity of these MEIs with those we found for ChCs is in line with the idea that ChCs are driven by input from a large number of L5 PyCs (but do not exclude alternative explanations)." - in parenthesis it should be does not exclude.

Corrected.

• "In contrast, the response strength of PyCs was only mildly and non-significantly reduced after training"

• statistically non-significant..

Corrected.

"We first looked at the average activity of neurons in both essions." - sessions

Corrected.

• (Figure 7 C) Explain what points and error bars represent

Done.

Author Response

Reviewer #2 (Public Review):

The study from Gumaste et al investigates whether mice can use changes of intermittency, a temporal odor feature, to locate an odor source. First, the study tries to demonstrate that mice can discriminate between low and high intermittency and that their performance is not affected by the odor used or the frequency of odor whiffs. Then, they show that there is a correlation between glomerular responses (OSNs and mitral cells) and intermittency. Finally, they conclude that sniffing frequency impacts the behavioral discrimination of intermittency as well as its neural representation. Overall, the authors seek to demonstrate that intermittency is an odor-plume property that can inform olfactory navigation.

The paper explored an interesting question, the use of intermittency of an odor plume as a behavioral cue, which is a new and intriguing hypothesis. However, it falls short in demonstrating that the animal is actually sensitive to intermittency but not other flow parameters, and is missing some important details.

Major concerns

1) One of the cornerstones of this paper consists in showing that mice are behaviorally able to distinguish among different intermittency values (high or low), across a variety of different stimuli and without confounds such as the number of whiffs or concentration. However, I could not find in the paper a convincing explanation of how these confounds were tested. It is clear that the authors repeat their measurements in different conditions (low or high concentration, and different whiff numbers) but it is not specified how: do the authors mix all stimuli in the same session, and so the animals simply generalize across all the stimuli and only consider intermittency for the behavioral choices? Or do authors repeat different sessions for different parameters? For example: do they perform two separate sessions with low concentration and high concentration? If this last one is the case, I would argue that this is not enough proof that animals generalize across concentrations, as the animals might simply use concentration as a cue and change the decision criteria at each session. Please clarify.

We appreciate the reviewer pointing out our oversight in including this information in the manuscript. Trials of the two gain values (which modulate the maximum concentration) are presented interleaved within a session. These trials are solely separated for post-session analysis to test the effect of gain on animal performance. To make this point clearer we have included the following text on line 952 of the manuscript:

“Additionally, trials of a gain of 0.5 and a gain of 1 are interwoven randomly during the session with each unique stimulus being presented at both a gain of 0.5 and 0.1. Thus, after the initial engagement trials, animals are presented with a total of 28 trials at a gain of 0.5 and 28 trials at a gain of 0.1.”

Additionally, to address one of the reviewer’s overarching points, that the manuscript “falls short in demonstrating that the animal is actually sensitive to intermittency but not other flow parameters,” we would like to highlight that through our olfactometer design (described in the Olfactometer Design subsection of the Methods section and illustrated in Figure 1C) the flow rate is held constant throughout the experiment. To further ensure that the animal is not using flowrate or other experimental conditions to perform the task, we tested all animals on a “no odor” condition in which the vial of odor is replaced with a vial of mineral oil. In this condition, their hit rate significantly lowered, as shown in Figure 2C and described in Lines 240- 245:

“Animals’ hit rate also significantly decreased when tested on the Go/No-Go task with the odor vial replaced with mineral oil (n=12 mice, two-sample t-test Naturalistic: odor hit rate = 0.87 ±0.01, no odor hit rate= 0.23 ±0.05, p<0.0001; two-sample t-test Binary Naturalistic: odor hit rate= 0.89±0.01, no odor hit rate= 0.18±0.07, p<0.0001; two-sample t-test Synthetic: odor hit rate= 0.86±0.007, no odor hit rate= 0.23±0.07, p<0.0001), confirming that mice are using odor to perform the task.”

2) It looks to me that the measure of intermittency strongly depends on the set. What is the logic of setting a specific threshold? Do the results hold when this threshold changes within a reasonable range? The same questions (maybe even more important) go for the measure of glomerular intermittence. Unfortunately, a sensitivity analysis for both measures is missing, which makes it hard to interpret the results.

We assume the reviewer suggests that we could have tested discrimination at various Intermittency thresholds. This is indeed wat we did, though not by varying the threshold parametrically (due to abovementioned time constraints), but rather qualitatively/categorically. We tested our mice on 3 stimulus "types" (Figure 1F): actual continuous plume concentration traces (naturalistic), thresholded traces (binarized by threshold 0.1) and square wave (odor agnostic periodic binary). Further, each was tested at 2 gain levels. Figure 2B demonstrates mice discriminate similarly across these 3 widely differing stimuli, while traces were spanning most of the range of possible intermittencies. Reducing the threshold by 1 or 2 orders would skew the range of trials toward many more CS+ trials. We hence conclude that the mice are robustly discriminating and that the paradigm chosen and its associated constraints provide a reasonable test of "intermittency space".

We agree nonetheless that future work should address your suggestion directly by implementing an alternate paradigm. For example, in such a paradigm, mice may be trained to discriminate high vs low intermittencies at varying absolute levels (e.g. 1 vs 0.9 and 0.1 vs 0), etc., however that was well outside the scope of what we aimed to test.

See Figure 1- Supplement 1A. We varied the threshold half a log unit around the 0.1 threshold used in the neuro-behavioral research. As expected, the higher the odor threshold, the more left-shifted the curve. You can see that the monotonic relationship is qualitatively the same across thresholds.

3) The logic of choosing the decision boundary for the discrimination task is not clear: low intermittency is considered to be below 0.15 and high intermittency is considered to be between 0.2 and 0.8. Do these values correspond to natural intermittency distribution? How were these values chosen?

Intermittency drops as function of distance from the source (downwind). It also has a close to normal (with kurtosis) distribution across wind, peaking at the center (see e.g. Crimaldi 2002, Connor 2018). So, animals may encounter any and all intermittencies (0-1). Given our Go/No-Go paradigm we had to set a CS-/CS+ boundary. Typically, to generate an adequate psychometric curve using this paradign, either the CS- or CS+ stimuli need to represent a wide range of values of which the animals are required to compare against a narrow range (or single value). Again, bounded by effective behavioral paradigm design, the number of CS+ and CS- trials need to be even in order to appropriately motivate animals to engage in the task. Thus, considering the entire range of intermittency values animals can encounter while navigating through a plume in conjunction with effective behavioral design, we arrived at our chosen values for low and high intermittency.

As you can see in Figure 1- Supplement 1A (and also reviewer #1, comment 2), I=0.15 is roughly at the knee where the monotonic decrease begins to asymptote. This is roughly true for all 3 concentration thresholds. Consequently, I=0.2-0.8 effectively samples the region where intermittency clearly relates to distance to the source, which is where we hypothesize animals.

4) Only 2 odors were used in the whole study and some results were in disagreement between the two odors. By looking at only two odors it is very difficult to make a general conclusion about intermittency encoding in the OB.

We agree 2 odors are limited, but we were constrained in terms of number of tests that we could run on our cohort of animals. Nonetheless intermittency of both odors is clearly discriminable. As explained to comment 3 by Reviewer 1:

“We indeed considered several odorants and associated properties. Given time constrains we were limited to 2 stimuli of which we had to vary many parameters (type, I, gain, sniffing) in assessing both discrimination and neural processing.”

“Additionally, these two odorants recruit glomeruli in different regions of the dorsal olfactory bulb, have different functional groups and elicit different spatiotemporal response properties in the olfactory bulb (Figure 6- figure supplement 1A, stated on line 507). Both odorants are fruit-associated odors with neutral preference indices (Saraiva et al., 2016, Fletcher, 2012). Thus, while we do not explore a panel of odorants, we do explore the generalizability of intermittency processing with two distinct odorants.”

We decided to test 2 monomolecular odorants (2-heptanone and methyl valerate) as these have been widely used in rodent olfactory bulb imaging, providing distinct and clear glomerular response patterns. They are both fruity smelling odors, implying a relationship to edible food (at least, for humans). Methyl valerate is a methyl ester of pentanoic acid with a fruity (apple) smell and 2-Heptanone is a ketone with a fruity (green banana) smell.

5) Assuming that all the above issues are resolved, one can conclude that intermittency can be perceived by an animal. The study puts a strong accent on the fact that this feature could be used for navigation. I understand that it is extremely hard to demonstrate that this feature is actually used for navigation, however, the analysis of relevance of this measure is missing. Even if it is used in navigation, most probably this would be in combination with other features, thus its relative importance needs to be discussed, or even better, established.

We fully appreciate the reviewers reasoning. Our approach indeed intended to establish a conditio sine qua non: if mice could not discriminate these stimuli they would likely not be able to use intermittency in general for navigation (at least for the odorants tested, for the intermittency ranges tested). We show however that they can, and hence they could use it. To demonstrate their use of intermittency alone or combined with other modalities or properties is well beyond the scope of this manuscript and we agree is a very interesting endeavor.

We discussed other temporal properties on line 58-71 and 657-664 and other general properties on lines 46-56. The relative roles were briefly addressed on lines 664-676 and we hesitate to speculate beyond this.

Author Response

Reviewer #1 (Public Review):

With MERGEseq, the authors sought to develop a scalable and accessible method for getting both projectome and transcriptome information at the single-cell level from multiple projection targets within a single animal. MERGEseq uses a retro rAAV2 to deliver a 15-nucleotide barcode driven by a CAG promoter with co-expression of eGFP to enrich barcoded cells using FACS. Injection of this rAAV2 in distinct regions (with each injection region distinguished by a unique barcode that is specific to the virus used) allows retrograde trafficking and expression of the barcodes in cells that project to the injected region. In this manuscript, rAAVs harboring 5 unique barcodes were stereotactically delivered to 5 targets of the mouse: dorsomedial striatum (DMS), mediodorsal thalamic nucleus (MD), basal amygdala (BLA), lateral hypothalamus (LH), and agranular insular cortex (AI). After a 6-week period to allow for viral transduction and expression, the ventromedial prefrontal cortex (vmPFC) was harvested for scRNAseq. vmPFC scRNAseq data were validated against previously published PFC datasets, demonstrating that MERGEseq does not disrupt transcript expression and identifies the same principal cell types as annotated in previous studies. Importantly, MERGEseq enabled the identification of cell types in the vmPFC that project to distinct areas, with separation occurring largely based on cell type and cortical layer. The application of stringent criteria for barcode index determination is rigorous and improves confidence that barcoded cells are correctly identified. The observation that all barcoded cells were excitatory is consistent with prior work, although it is not clear if viral tropism contributes to this in some way. In a parallel experiment, FAC-sorted cells (vmPFC cells expressing EGFP) were isolated as a comparison. Notably, EGFP+ cells were exclusively excitatory neurons, consistent with literature showing PFC projection neurons are excitatory. Next, barcode analysis was combined with transcriptional identification of neuronal subtypes to define general projection patterns and single-cell projection patterns, which were validated by the DMS and MD in situ using retrograde tracing in combination with RNA FISH. MERGEseq data were also used to identify transcriptional differences between neurons with dedicated and bifurcated projections. DMS+LH and DMS+MD projecting neurons had distinct transcriptional profiles, unlike cells with other targets. RNA FISH for marker gene Pou3f and retrograde tracing from DMS+LH projecting cells demonstrate enrichment of this gene in this projection population. Finally, machine-learning was used to predict projection targets based on transcriptional profiles. In this dataset, 50 highly variable genes (HVGs) were optimal for predicting projection patterns, though this might vary in different circuits. Overall, the results of this manuscript are well presented and include rigorous validation for select vmPFC targets with in situ techniques. The application of unique barcodes for retro-AAV delivery is an accessible tool that other labs can implement to study other brain circuits.

Ultimately, MERGEseq is a subtle conceptual advancement over VECTORseq (retro-AAV delivered transgenes rather than barcodes, in combination with scRNAseq) that offers higher confidence in the described projectome diversity in comparison. The use of a retrograde AAV inherently limits the number of projection areas that can be assessed, a weakness compared to anterograde approaches such as MAPseq/BARseq. However, BARseq demands more time and resources; further, the use of the highly toxic Sindbis virus limits the application of this technique. This manuscript builds upon previous work by utilizing machine learning to predict projection targets. BARseq2 could be used to rigorously validate predicted projectomes and gain single-cell information regarding target neurons. Overall, MERGEseq is an accessible technique that can be used across many animal models and serve as an important starting point to define circuits at the single-cell level.

We thank reviewer for the comprehensive review. We are grateful for reviewer’s recognition of the conceptual advancement of MERGE-seq and the rigorous criteria we applied for projection barcode determination. We have revised the Introduction to highlight advancements in our method. We also discussed the balance of transcriptomic comprehensiveness against spatial resolution in the revised Discussion. Reviewer’s comments have been invaluable in enhancing the clarity and depth of our manuscript.

Reviewer #2 (Public Review):

Investigating the relationship between transcriptomic profiles, their axonal projection and collateralization patterns will help define neuronal cell types in the mammalian central nervous system. The study by Xu et al. combined multiple retrograde viruses with barcodes and single-cell RNA-sequencing (MERGE-seq) to determine the projection and collateralization patterns of transcriptomically defined ventral medial prefrontal cortex (vmPFC) projection neurons. They found a complex relationship: the same transcriptomically defined cell types project to multiple target regions, and the same target region receives input from multiple transcriptomic types of vmPFC neurons. Further, collateralization patterns of vmPFC to the five target regions they investigated are highly non-random.

While many of the biological conclusions are not surprising given recent studies on the collateralization patterns of vmPFC neurons using single neuron tracing and other methods that integrate transcriptomics and projections, MERGE-seq provides validation, at the single cell level, collateralization patterns of individual vmPFC neurons, and thus offer new and valuable information over what has been published. The method can also be used to study collateralization patterns of other neuron types.

Some of the conclusions the authors draw depend on the efficiency of retrograde labeling, which was not determined. Without quantitative information on retrograde labeling efficiency, and unless such efficiency is close to 100%, these conclusions are likely misleading.

We thank reviewer for recognizing the contributions of our MERGE-seq technique in advancing the understanding of projection patterns of vmPFC neurons. We concur that while our conclusions align with previous findings, our single-cell level analysis provides additional depth to the existing knowledge of the field. We acknowledge the challenge to quantify retrograde labeling efficiency to draw quantitive conclusions based on our findings. Alternatively, we have used fMOST-based single-neuron tracing data and analysis to validate our projection patterns and ensure the robustness of our conclusions in the revised manuscript. We also more explicitly clarified the limitations of the quantitive conclusion drawn from MERGE-seq in the revised Discussion. The insights of reviewer are greatly appreciated and will inform the improvement of our research methodology.

Reviewer #3 (Public Review):

This manuscript describes a multiplexed approach for the identification of transcriptional features of neurons projecting to specific target areas at the single-cell level. This approach, called MERGE-seq, begins with multiplexed retrograde tracing by injecting distinctly barcoded rAAV-retro viruses into different target areas. The transcriptomes and barcoding of neurons in the source area are then characterized by single-cell RNA sequencing (scRNAseq) on the 10xGenomics platform. The projection targets of barcoded neurons in the source area can be inferred by matching the detected barcodes to the barcode sequences to of rAAV-retro viruses injected into the target areas.

The authors validated their approach by injecting five rAAV-retro GFP viruses, each encoding a different barcode, into five known targets of the ventromedial prefrontal cortex (vmPFC). The transcriptomes and barcoding of vmPFC neurons were then analyzed by scRNA-seq with or without enrichment of retrogradely labeled neurons based on GFP fluorescence. The authors confirmed the previously described heterogeneity of vmPFC neurons. In addition, they showed that most transcriptionally defined cell types project to multiple targets and that the five targets received projections from multiple transcriptomic types. The authors further characterized the transcriptomic features of barcoded vmPFC neurons with different projection patterns and defined Pou3f1 as a marker gene of neurons extending collateral branches to the dorsomedial striatum and lateral hypothalamus.

Overall, the results of the manuscript are convincing: the transcriptomic vmPFC cell types defined by scRNAseq in this study appear to correlate well with previous studies, the bifurcated projection patterns inferred by barcoding are validated using dual-color retro-AAV tracing, and marker genes for projection-specific cell subclasses are validated in retrogradely labeled vmPFC using RNA FISH for marker detection.

The concept of combining retrograde tracing and scRNAseq is not new. Previous studies have applied recombinase-expressing viruses capable of retrograde labeling, such as CAV, rabies virus, and AAV2-Retro, to retrogradely label and induce the expression of fluorescence markers in projection neurons, therefore facilitating enrichment and analysis of neurons projecting to a specific target. Multiplexed analysis can be achieved with the combination of different reporter viruses or viruses expressing different recombinases and appropriate reporter mouse lines. The advantages of MERGE-seq include that no transgenic lines are required and that it could be applied at even higher levels of multiplexity.

We thank reviewer for the insightful review of our manuscript and the recognition of the advantages of MERGE-seq. We appreciate reviewer acknowledged the robust validation of the method through dual-color retro-AAV tracing and RNA FISH, and the confirmation of previous findings on vmPFC neuronal heterogeneity and collateral projection patterns. We provided additional joint analysis with fMOST-based single-neuron projectome data (Gao et al., 2022, Nature Neuroscience) to further validate the projection patterns (>= 3 targets) that cannot be easily validated with dual-color retro-AAV tracing.

However, previously existing datasets that have already profiled this region with scRNAseq have not been utilized to their full extent. Therefore, for the proper context with prior literature, bioinformatic integration of these scRNAseq and prior scRNAseq data is needed.

Moreover, robust detection of barcodes in neurons labeled by barcoded AAV-retro viruses remains a challenge. The authors should clearly discuss the difficulties with barcode detection in this approach, as well as discuss potential solutions, which are important for others interested in its approach.

While this study is limited to the five known targets of vmPFC, the results suggest that MERGE-seq is a valuable tool that could be used in the future to characterize projection targets and transcriptomes of neurons in a multiplexed manner. As MERGE-seq uses AAVs to deliver barcodes, this method has the potential for application in model organisms for which transgenic lines are not available. Further improvements in experimental design and data analysis should be considered when applying MERGE-seq to poorly characterized source areas or with increased multiplexity of target areas.

In summary, this is a valuable approach, but the authors should clearly provide the context for their study within the existing literature, transparently discuss the limitations of MERGE-seq, as well as suggest improvements for the future.

We appreciate your positive assessment of MERGE-seq as a valuable approach with future potential. As recommended, we have performed integration analysis with existing vmPFC scRNA-seq studies, including Bhattacherjee et al., 2019, Lui et al., 2021, Yao at al., 2021, and specifically recently published MERFISH data of PFC (Bhattacherjee et al., 2023).

In the revised Discussion, we have transparently addressed the current limitations of MERGE-seq, including imperfect retrograde labeling efficiency, variable barcode recovery rates and cell loss during dissociation. We also addressed the challenges in detecting and recovering projection barcodes and suggested potential solutions such as using FAC-sorted EGFP-negative cells for control and applying single-molecule FISH techniques. We sincerely appreciate reviewer’s rigorous and insightful feedback, which has substantially strengthened our manuscript.

Author Response

Reviewer #1 (Public Review):

In this paper, the authors develop new models of sequential effects in a simple Bernoulli learning task. In particular, the authors show evidence for both a "precision-cost" model (precise posteriors are costly) and an "unpredictabilitycost" model (expectations of unpredictable outcomes are costly). Detailed analyses of experimental data partially support the model predictions.

Strengths:

• Well-written and clear.

• Addresses a long-standing empirical puzzle.

• Rigorous modeling.

Weaknesses:

• No model adequately explains all of the data.

• New empirical dataset is somewhat incremental.

• Aspects of the modeling appear weakly motivated (particularly the unpredictability model).

• Missing discussion of some relevant literature.

We thank Reviewer #1 for her/his positive comments on our work and her/his comments and suggestions.

Reviewer #2 (Public Review):

This paper argues for an explanation of sequential effects in prediction based on the computational cost of representing probability distributions. This argument is made by contrasting two cost-based models with several other models in accounting for first- and second-order dependencies in people's choices. The empirical and modeling work is well done, and the results are compelling.

We thank Reviewer #2 for her/his positive comments on our work.

The main weaknesses of the paper are as follows:

1) The main argument is against accounts of dependency based on sensitivity to statistics (ie. modeling the timeseries as having dependencies it doesn't have). However, such models are not included in the model comparison, which makes it difficult to compare these hypotheses.

Many models in the sequential-effects literature (Refs. [7-12] in the manuscript) are ‘leaky-integration’ models that interpret sequential effects as resulting from an attempt to learn the statistics of a sequence of stimuli, through exponentiallydecaying counts of the simple patterns in the sequence (e.g., single stimuli, repetitions, and alternations). In some studies, the ‘forgetting’ of remote observations that results from the exponential decay is justified by the fact that people live in environments that are usually changing: it is thus natural that they should expect that the statistics underlying the task’s stimuli undergo changes (although in most experiments, they do not), and if they expect changes, then they should discard old observations that are not anymore relevant. This theoretical justification raises the question as to why subjects do not seem to learn that the generative parameters in these tasks are in fact not changing — all the more as other studies suggest that subjects are able to learn the statistics of changes (and consistently they are able to adapt their inference) when the environment does undergo changes (Refs. [42,57]).

Our models are derived from a different approach: we derive behavior from the resolution of a problem of constrained optimization of the inference process. It is not a phenomenological model. When the constraint that weighs on the inference process is a cost on the precision of the posterior, as measured by its entropy, we find that the resulting posterior is one in which remote observations are ‘forgotten’, through an exponentially discount, i.e., we recover the predictions of the leaky-integration models, which past studies have empirically found to be reasonably good accounts of sequential effects. (Thus these models are already in our model comparison.) In our framework, the sequential effects do not stem from the subjects’ irrevocable belief that the statistics of the stimuli change from time to time, but rather from the difficulty that they have in representing precise belief; a rather different theoretical justification.

Furthermore, we show that a large fraction of subjects are not best-fitted by precision-cost models (i.e., they are not best-fitted by leaky integration), but instead they are best fitted by unpredictability-cost models. These models suggest a different explanation of sequential effects: that they result from the subjects favoring predictable environments, in their inference. In the revised version of the manuscript, we have made clearer that the derivation of the optimal posterior under a precision cost results in the exponential forgetting of remote observations, as in the leaky-integration models. We mention it in the abstract, in the Introduction (l. 76-78), in the Results when presenting the precision-cost models (l. 264-278), and in the Discussion (l.706-716).

2) The task is not incentivized in any way. Since incentives are known to affect probability-matching behaviors, this seems important. In particular, we might expect incentives would trade off against computational costs - people should increase the precision of their representations if it generates more reward.

We thank Reviewer #2 for her/his attention to our paper and for her/his comments. As for the point on the models, see answer above (point 1).

As for the point on incentivization: we agree that it would be very interesting to measure whether and to which extent the performance of subjects increases with the level of incentivization. Here, however, we wanted, first, to establish that subjects’ behavior could be understood as resulting from inference under a cost, and second, to examine the sensitivity of their predictions to the underlying generative probability — rather than to manipulating a tradeoff involving this cost (e.g. with financial reward). We note that we do find that subjects are sensitive to the generative probability, which implies that they exhibit some degree of motivation to put some effort in the task (which is the goal of incentivization), in spite of the lack of economic incentives. But it would indeed be interesting to know how the potential sensitivity to reward interacts with the sensitivity to the generative probability. Furthermore, as Reviewer #2 mentions, some studies show that incentives affect probability-matching behavior: it is then unclear whether the introduction of incentives in our task would change the inference of subjects (through a modification of the optimal trade-off that we model); or whether it would change their probability-matching behavior, as modeled by our generalized probability-matching response-selection strategy; or both. Note that we disentangled both aspects in our modeling and that our conclusions are about the inference, not the response-selection strategy. We deem the incentivization effects very much worth investigating; but they fall outside of the scope of our paper.

We now mention this point in the Discussion of the revised manuscript (l. 828-840).

3) The sample size is relatively small (20 participants). Even though a relatively large amount of data is collected from each participant, this does make it more difficult to evaluate the second-order dependencies in particular (Figure 6), where there are large error bars and the current analysis uses a threshold of p < .05 across a large number of tests hence creating a high false-discovery risk.

Indeed we agree with Reviewer #2 that as the number of tests increases, so does the probability that at least one null hypothesis is rejected at a given level, even if the null hypothesis is correct. But in the panels a, b and c of Figure 6, about half of the tests are rejected, which is very unlikely under the null hypothesis that there is no effect of the stimulus history on the prediction, all the more as the signs of the non-significant results are in most cases consistent with the direction of the significant results. (In panel e, which reports a finer analysis in which the number of subjects is essentially divided by 2, about a fourth of the tests are rejected, and here also the non-significant results are almost all in the same direction as the significant ones.)

However, we agree that there remains a risk of false discovery, thus we applied a Bonferroni-Holm-Šidák correction to the p-values in order to mitigate this risk. With these more conservative p-values, a lower number of tests are rejected, but in most cases in Fig. 6abc the effects remain significant. In particular, we are confident that there is a repulsive effect of the third-to-last stimulus in the case of Fig. 6c, while there is an attractive effect in the other cases.

In the revised manuscript, Figure 6 now reports whether the tests are rejected when the p-values are corrected with the Bonferroni-Holm-Šidák correction.

(We also applied this correction to the p-values of the tests in Fig. 2, which has more data: the corrected p-values are all below 1e-13, which we now indicate in the caption of this figure.)

4) In the key analyses in Figure 4, we see model predictions averaged across participants. This can be misleading, as the average of many models can produce behavior outside the class of functions the models themselves can generate. It would be helpful to see the distribution of raw model predictions (ideally compared against individual data from humans). Minimally, showing predictions from representative models in each class would provide insight into where specific models are getting things right and wrong, which is not apparent from the model comparison.

In the main text of the original manuscript, we showed the behavior of the pooled responses of the best-fitting models, and we agree with Reviewer #2 that it did not make clear to the reader that the apparent ability of the models to reproduce the subjects’ behavioral patterns was not a misleading byproduct of the averaging of different models. In the original version of the manuscript, we had put a figure showing the behavior of each individual model (each cost type with each Markov order) in the Methods section of the paper; but this could easily be overlooked, and indeed it would be beneficial for the reader to be shown the typical behaviors of the models, in the main text. We have reorganized the presentation of the models’ behaviors: the first panels in Fig. 4 (in the main text) are now dedicated to showing the individual sequential effects of the precision-cost and of the unpredictabilitycost models with Markov order 0 and 1. The Figure 4 is reproduced in the response to Reviewer #1, above, along with comments on the sequential effects produced by these models (and also on the impact of the generalized probability-matching response-selection strategy, in comparison with the traditional probability matching). We believe that this figure makes clearer how the individual models are able to reproduce the patterns in subjects’ predictions — in particular it shows that this ability of the models is not just an artifact of the averaging of many models, as was the legitimate concern of Reviewer #2. We have left the illustration of the firstorder sequential effects of the other models (with Markov order 2 and 3) in the Methods section (Fig. 7), so as not to overload Fig. 4, and because they do not bring new critical conceptual points.

As for the higher-order sequential effects, the updated Figure 5, also reproduced above in the responses to Reviewer #1, now includes the sequential effects obtained with the precision-cost model of a Bernoulli observer (m=0), in addition to the precision-cost model of a Markov observer (m=1) and to the unpredictabilitycost model of a Markov observer (m=3), in order to better illustrate the behaviors of the different models. The higher-order sequential effects of the other models can be found in Fig. 8 in Methods.

Reviewer #3 (Public Review):

This manuscript offers a novel account of history biases in perceptual decisions in terms of bounded rationality, more specifically in terms of finite resources strategy. Bridging two works of literature on the suboptimalities of human decision-making (cognitive biases and bounded rationality) is very valuable per se; the theoretical framework is well derived, building upon the authors' previous work; and the choice of experiment and analysis to test their hypothesis is adequate. However, I do have important concerns regarding the work that do not enable me to fully grasp the impact of the work. Most importantly, I am not sure whether the hypothesis whereby inference is biased towards avoiding high precision posterior is equivalent or not to the standard hypothesis that inference "leaks" across time due to the belief that the environment is not stationary. This and other important issues are detailed below. I also think that the clarity and architecture of the manuscript could be greatly improved.

We thank Reviewer #3 for her/his positive comments on our work and her/his comments and suggestions.

1) At this point it remains unclear what is the relationship between the finite resources hypothesis (the only bounded rationality hypothesis supported by the data) and more standard accounts of historical effects in terms of adaptation to a (believed to be) changing environment. The Discussion suggests that the two approaches are similar (if not identical) at the algorithmic level: in one case, the posterior belief is stretched (compared to the Bayesian observer for stationary environments) due to precision cost, in other because of possible changes in the environment. Are the two formalisms equivalent? Or could the two accounts provide dissociable predictions for a different task? In other words, if the finite resources hypothesis is not meant to be taken as brain circuits explicitly minimizing the cost (as stated by the authors), and if it produces the same type of behavior as more classical accounts: is the hypothesis testable experimentally?

We agree with Reviewer #3 that the relation between our approach and other approaches in the literature should be made clearer to the reader.

Since the 1990s, in the psychology and neuroscience literature, many models of perception and decision-making have featured an exponential decay of past observations, resulting in an emphasis, in decisions, of the more recent evidence (‘leaky integration’, Refs. [7-12, 76-86]). In the context of sequential effects, this mechanism has found a theoretical justification in the idea that people believe that statistics typically change, and thus that remote observations should indeed be discarded [8,12]. In inference tasks with binary signals, in which the optimal Bayesian posterior is in many cases a Beta distribution whose two parameters are the counts of the two signals, one way to conveniently incorporate a forgetting mechanism is to replace these counts with exponentially-filtered counts, in which more recent observations have more weight (e.g., Ref. [12]).

Our approach to sequential effects is not grounded in the history of leakyintegration models: we assume, first, that subjects attempt at learning the statistics of the signals presented to them (this is also the assumption in many studies [712]), and second, that their inference is subject to a cost, which prevents them from reaching the optimal, Bayesian posterior; but under the constraint of this cost, they choose the optimal posterior. We formalize this as a problem of constrained optimization.

The two formalisms are thus not equivalent. Beyond the fact that we clearly state the problem which we assume the brain is solving, we do not propose that the origin of sequential effects resides in an adaptation to putatively changing environments: instead, we assume that they originate in a cognitive cost internal to the decision-maker. If this cost is proportional to the entropy of the posterior, as in our precision cost, then the optimal approximate posterior is one in which remote observations are ‘forgotten’ through an exponential filter, as in the leakyintegration models. In other words, in the context of this task and with this kind of cost, the models are, as Reviewer #3 writes, identical at the algorithmic level. As for the unpredictability cost, it does not result in a solution that resembles leaky integration; about half the subjects, however, are best fitted by unpredictabilitycost models. We thus provide a different rationale for sequential effects — that the brain favors predictive environment, in its inference — and this alternative account is successful in capturing the behavior of a large fraction of the subjects.

In the revised manuscript, we now clarify that the precision cost results in leaky integration, in the abstract, in the Introduction (l. 76-78), in our presentation of the precision-cost models (Results section, l. 264-275), and in the Discussion (l. 706716). (We also refer Reviewer #3 to our response to the first comment of Reviewer #2, above.)

Finally, Reviewer #3 asks the interesting question as to whether the “two accounts provide dissociable predictions for a different task”. Given that the leakyintegration approach is justified by an adaptation to potential changes, and our approach relies on the hypothesis that precision in beliefs is costly, one way to disentangle the two would be to eliminate the sequential nature of the task and presenting instead observations simultaneously. This would eliminate the mere notion of change across time. In this case, the leaky account would predict that subjects’ inference becomes optimal (because the leak should disappear in the absence of change), while in the second approach the precision cost would still weigh on the inference, and result in approximate posteriors that are “wider” (less precise) than the optimal one. The resulting divergence in the predictions of these models is very interesting, but out of the scope of this study on sequential effects.

2) The current analysis of history effects may be confounded by effects of the motor responses (independently from the correct response), e.g. a tendency to repeat motor responses instead of (or on top of) tracking the distribution of stimuli.

We thank Reviewer #3 for pointing out the possibility that subjects may have a tendency to repeat motor responses that is not related to their inference.

We note that in Urai et al., 2017, as in many other sensory 2AFC tasks, successive trials are independent: the stimulus at a given trial is a random event independent of the stimulus at the preceding trial; the response at a given trial should in principle be independent of the stimulus at the preceding trial; and the response at the preceding trial conveys no information about the response that should be given at the current trial (although subjects might exhibit a serial dependency in their responses). By contrast, in our task an event is more likely than not to be followed by the same event (because observing this event suggests that its probability is greater than .5); and a prediction at a given trial should be correlated with the stimuli at the preceding trials, and with the predictions at the preceding trials. In a logit model (or any other GLM), this would mean that the predictors exhibit multicollinearity, i.e., they are strongly correlated. Multicollinearity does not reduce the predictive power of a model, but it makes the identification of parameters extremely unreliable: in other words, we wouldn’t be able to confidently attribute to each predictor (e.g., the past observations and the past responses) a reliable weight in the subjects’ decisions. Furthermore, our study shows that past stimuli can yield both attractive and repulsive effects, depending on the exact sequence of past observations. To capture this in a (generalized) linear model, we would have to introduce interaction terms for each possible past sequence, resulting in a very high number of parameters to be identified.

However, this does not preclude the possibility that subjects may have a motor propensity to repeat responses. In order to take this hypothesis into account, we examined the behavior and the ability to capture subjects’ data of models in which the response-selection strategy allows for the possibility of repeating, or alternating, the preceding response. Specifically, we consider models that are identical to those in our study, except for the response-selection strategy, which is an extension of the generalized probability-matching strategy, in which a parameter eta, greater than -1 and lower than 1, determines the probability that the model subject repeats its preceding response, or conversely alternates and chooses the other response. With probability 1-|η|, the model subject follows the generalized probability-matching response-selection strategy (parameterized by κ). With probability |η|, the model subject repeats the preceding response, if η > 0, or chooses the other response, if η < 0. We included the possibility of an alternation bias (negative η), but we find that no subject is best-fitted by a negative η, thus we focus on the repetition bias (positive η). We fit the models by maximizing their likelihoods, and we compared, using the Bayesian Information Criterion (BIC), the quality of their fit to that of the original models that do not include a repetition propensity.

Taking into account the repetition bias of subjects leaves the assignment of subjects into two families of inference cost mostly unchanged. We find that for 26% of subjects the introduction of the repetition propensity does not improve the fit (as measured by the BIC) and can therefore be discarded. For 47% of subjects, the fit is better with the repetition propensity (lower BIC), and the best-fitting inference model (i.e., the type of cost, precision or unpredictability, and the Markov order) is the same with or without repetition propensity. Thus for 73% (=26+47) of subjects, allowing for a repetition propensity does not change the inference model. We also find that the best-fitting parameters λ and κ, for these subjects, are very stable, when allowing or not for the repetition propensity. For 11% of subjects, the fit is better with the repetition propensity, and the cost type of the inference model is the same (as without the repetition propensity), but the Markov order changes. For the remaining 16%, both the cost type and the Markov order change.

Thus for a majority of subjects, the BIC is improved when a repetition propensity is included, suggesting that there is indeed a tendency to repeat responses, independent of the subjects’ inference process and generative stimulus probability. In Figure 7, in Methods, we show the behavior of the models without repetition propensity, and with repetition propensity, with a parameter η = 0.2 close to the average best-fitting value of eta across subjects. We show, in Methods, that (i) the unconditional probability of a prediction A, p(A), is the same with and without repetition propensity, and that (ii) the conditional probabilities p(A|A) and p(A|B) when η≠0 are weighted means of the unconditional probability p(A) and of the conditional probabilities when eta=0 (see p. 47-49 of the revised manuscript).

In summary, our results suggest that a majority of subjects do exhibit a propensity to repeat their responses. Most subjects, however, are best-fitted by the same inference model, with or without repetition propensity, and the parameters λ and κ are stable, across these two cases; this speaks to the robustness of our model fitting. We conclude that the models of inference under a cost capture essential aspects of the behavioral data, which does not exclude, and is not confounded by, the existence of a tendency, in subjects, to repeat motor responses.

In the revised manuscript, we present this analysis in Methods (p.47-49), and we refer to it in the main text (l. 353-356 and 400-406).

3) The authors assume that subjects should reach their asymptotic behavior after passively viewing the first 200 trials but this should be assessed in the data rather than hypothesized. Especially since the subjects are passively looking during the first part of the block, they may well pay very little attention to the statistics.

The assumptions that subjects reach their asymptotic behavior after being presented with 200 observations in the passive trials should indeed be tested. To that end, we compared the behavior of the subjects in the first 100 active trials with their behavior in the remaining 100 active trials. The results of this analysis are shown in Figure 9.

For most values of the stimulus generative probability, the unconditional proportions of predictions A, in the first and the second half (panel a, solid and dashed gray lines), are not significantly different (panel a, white dots), except for two values (p-value < 0.05; panel a, filled dots). Although in most cases the difference between the two is not significant, in the second half the proportions of prediction A seem slightly closer to the extremes (0 and 1), i.e., closer to the optimal proportions. As for the sequential effects, they appear very similar in the two halves of trials. We conclude that for the purpose of our analysis we can reasonably consider that the behavior of the subjects is stationary throughout the task.

4) The experiment methods are described quite poorly: when is the feedback provided? What is the horizontal bar at the bottom of the display? What happens in the analysis with timeout trials and what percentage of trials do they represent? Most importantly, what were the subjects told about the structure of the task? Are they told that probabilities change over blocks but are maintained constant within each block?

We thank Reviewer #3 for her/his close attention to the details of our experiment. Here are the answers to the reviewer’s questions:

• The feedback (i.e., a lightning strike on the left or the right rod, with the rod and the battery turning yellow if the strike is on the side predicted by the subject,) is immediate, i.e., it is provided right after the subject makes a prediction, with no delay. We now indicate this in the caption of Figure 1.

• The task is presented to the subjects as a game in which predicting the correct location of the lightning strike results in electric power being collected in the battery. The horizontal bar at the bottom of the display is a gauge that indicates the amount of power collected in the current block of trials. It has no operational value in the task. We now mention it in the Methods section (l. 872-874).

• The timeout trials were not included in the analysis. The timeout trials represented 1.27% of the trials, on average (across subjects); and for 95% of the subjects the timeout trials represented less than 2.5% of the trials. This information was added in Methods (l. 887-889).

• Each new block of trials was presented to the subject as the lightning strikes occurring in a different town. The 200 passive trials at the beginning of each block, in which subjects were asked to observe a sequence of 200 strikes, were presented as the ‘track record’ for that town, and the instructions indicated that it was ‘useful’ to know this track record. No information was given on the mechanism governing the locations of the strikes. In the main text of the revised manuscript, we now include these details when describing the task (p. 6).

Author Response

Reviewer #1 (Public Review):

Sun et al. investigated the circuit mechanism of a novel type of synaptic plasticity in the projection from the visual cortex to the auditory cortex (VC-AC), which is thought to play an important role in visuo-auditory associative learning. The key question behind this paper is what is the role of CCK positive projection from the entorhinal cortex in the plasticity of VC-AC projections? They discover that the strength of VC-AC projections does not change when pairing the stimulation of this pathway with the acoustic stimulation of the auditory cortex (AC) unless CCK is applied to the AC or CCK positive projection from the entorhinal cortex to auditory cortex (EC-AC) is optogenetically stimulated. In contrast, optogenetically stimulating VC-AC projections, which express a lower level of CCK than the EC-AC projection, do not induce such synaptic plasticity. Interestingly, the data also indicates that even if the EC-AC pathway is stimulated 500ms ahead of the pairing of stimulating VC-AC pathway and the AC, the VC-AC synaptic strength can still be potentiated, consistent with the long-lasting nature of CCK as a neuropeptide. By performing a fear conditioning assay, the authors demonstrate that the CCK signaling is indeed required for the association of visual and auditory cues.

The proposed mechanism is interesting because it not only helps explain the heterosynaptic plasticity of the visual-auditory projection but also will provide insight into how the entorhinal cortex as an association area contributes to the association of visual and auditory cues. Nevertheless, this study suffers from the lack of a few key experiments, which prevents drawing a conclusion on the contribution of CCK release from the EC-AC projection to the plasticity of the VC→AC projection.

We are grateful for the constructive comments provided by the reviewers and appreciate the significant effort they have dedicated to reviewing our manuscript. To enhance our study and strengthen our conclusions, we have made the following revisions in response to their feedback.

1) One main conclusion from figures 1-3 is that CCK released from the EC-AC projection is required for the plasticity of VC-AC projection in addition to pairing VALS with noise/electrical stimulation. But the data in those figures cannot exclude alternative explanations that CCK alone or the pairing CCK with either VALS or noise are sufficient to make the VC-AC synaptic connection more potent. It concerns the mechanism underlying the effect of CCK: CCK may function simply as a neuromodulator to regulate the excitatory synaptic transmission, but not to promote long term synaptic plasticity.

Thanks for the valuable comment and pointing out the weakness. In response to the comment, we have conducted additional control experiments to reinforce our conclusions. These include: For Figure 1G, we introduced three control groups: CCK alone (Figure1-figure supplement 1F-G), CCK + presynaptic activation of VC-to-AC inputs (Figure 1-figure supplement 1H-I), and CCK + postsynaptic firing induced by noise (Figure 1-figure supplement 1J-K). Our findings from these control experiments indicate that in all three scenarios, there was no potentiation of the VC-to-AC inputs. Further details can be found in Figure 1-figure supplement 1F-K.

For Figure 2E, we introduced three control groups: HFS laser EC-to-AC alone (Figure 2-figure supplement 1H-I), HFS laser EC-to-AC + presynaptic activation of VC-to-AC inputs (Figure 2-figure supplement 1L-M), and HFS laser + postsynaptic firing induced by noise (Figure 2-figure supplement 1P-Q). And we found that in all three scenarios, the VC-to-AC inputs were not significantly potentiated. Please see details in Figure 2-figure supplement 1.

Given that our in vivo results already demonstrated that neither HFS laser EC-to-AC alone, nor its combination with presynaptic or postsynaptic activation, potentiated the VC-to-AC inputs, we did not replicate these control groups in our ex vivo setup. These additional experiments enhance the robustness of our findings and address the initial concerns raised.

2) Similar issue exists in Fig. 2H and 3J. Without proper controls, it is impossible to tell whether all three conditions (HFLSEA, VALA, noise/electrical stimulation) are necessary for potentiated AC responses to acoustic/electrical stimulation.

Same as above, we have conducted additional control experiments to reinforce our conclusions. These include:

For Figure 2H, we also tested the noise response in the above three control groups: HFS laser EC to AC alone (Figure 2-figure supplement 1J-K), HFS laser EC-to-AC + presynaptic activation of VC-to-AC inputs (Figure 2-figure supplement 1N-O), and HFS laser + postsynaptic firing induced by noise (Figure 2-figure supplement 1R-S). And we found that fEPSPs evoked by noise stimuli were significantly potentiated after HFS laser EC-to-AC + Post (Figure 2-figure supplement 1R-S). However, there was no potentiation observed following HFS laser EC-to-AC alone (Figure 2-figure supplement 1J-K) and HFS laser EC-to-AC + Pre (Figure 2-figure supplement 1N-O).

These results suggest that both HFS laser targeting the EC-to-AC projection and noise-induced AC firing are required to potentiate the AC's response to acoustic stimuli. In contrast, activation of the VC-to-AC projection is not necessary. This finding aligns with our previous research (Li et al., 2014).

Given the similarity in experimental design, we opted not to replicate these specific control groups in our ex vivo setup.

These additional control experiments have been crucial in reinforcing the conclusions of our study.

3) Fig. 2E and 3G show that the stimulation of CCK-positive EC-AC projection is required for the plasticity of VC-AC projection. Considering most EC-AC projection neurons co-release glutamate and CCK, however, we cannot tell if CCK or glutamate or both matter to this type of plasticity. Even though the long delay in Fig 5B is consistent with the neuropeptide nature of CCK, direct experimental evidence is needed, since it is where the novelty of the paper is.

Thank you for your constructive feedback. In response to the suggestions, for Figure 2E, we have incorporated two additional experiments: one with a CCKB receptor (CCKBR) antagonist and another with ACSF infused into the AC prior to HFS laser EC-to-AC + Pre/Post Pairing (Figures 2N-P). Our findings demonstrate that the CCKBR antagonist effectively inhibited the potentiation of the VC-to-AC inputs following the HFS laser EC-to-AC + Pre/Post Pairing. Conversely, ACSF did not exhibit this inhibitory effect. For further information, please refer to Figures 2N-P. Given the similarity in experimental design, we opted not to replicate these groups in our ex vivo setup.

4) In Fig. 6, the authors examined the necessity of CCK for the generation of the visuo-auditory association. The experimental approach of injection CCK receptor blocker or CCK-4 is not specific to the EC-AC pathway. There is neither a link between VC-AC plasticity nor this behavioral result. Thus, the explanatory power of this experiment is limited in the context set up by the first 5 figures.

Thank you for highlighting this area for improvement. To enhance the explanatory power of our behavioral experiments, we conducted the following additional studies:

1) Assessing the Necessity of CCK+ EC-to-AC Projection in Establishing Visuo-Auditory Association:

We bilaterally injected AAV9-syn-DIO-hM4Di-eYFP or AAV9-syn-DIO-eYFP into the EC and implanted cannulae in the AC of Cck Ires-Cre mice. During the encoding phase, we inactivated the CCK+ EC-to-AC pathway via CNO infusion into the AC. Our results show that this inactivation prevents the behavioral establishment of an association between the visual stimulus (VS) and auditory stimulus (AS), without affecting the fear conditioning memory to the AS (Figure 6B, beige).

2) Determining the Role of VC-to-AC Projection in Establishing Visuo-Auditory Association: We bilaterally injected AAV9-syn-hM4Di-eYFP or AAV9-syn-eYFP into the visual cortex (VC) and also implanted cannulae in the AC of Cck Ires-Cre mice. Inactivating the VC-to-AC pathway during the encoding phase with CNO infusion in the AC, we observed that this inactivation hinders the establishment of a behavioral association between VS and AS, but does not interfere with the fear conditioning memory to the AS (Figure 6B, red).

3) Investigating the Importance of CCK+ EC-to-AC Projection in Recalling Recent Visuo-Auditory Association:

Again, AAV9-syn-DIO-hM4Di-eYFP or AAV9-syn-DIO-eYFP was injected bilaterally into the EC, and cannulae were implanted in the AC of Cck Ires-Cre mice. By inactivating the CCK+ EC-AC pathway during the retrieval phase with CNO infusion into the AC, we found that such inactivation disrupted the recall of the recent association between VS and AS behaviorally, yet did not affect the fear conditioning memory to the AS (Figure 6D, beige).

4) Assessing the Necessity of VC-to-AC Projection in Recalling Recent Association Memory: For this experiment, AAV9-syn-hM4Di-eYFP or AAV9-syn-DIO-eYFP was injected bilaterally into the VC, and cannulae were placed in the AC of Cck Ires-Cre mice. Inactivating the VC-AC pathway during the retrieval phase with CNO infusion in the AC led to the discovery that this inactivation disrupted the behavioral recall of the recent association between VS and AS but did not disrupt the fear conditioning memory to the AS (Figure 6D, red).

These additional experiments significantly contribute to our understanding of the roles played by the CCK+ EC-AC and VC-AC projections in both the establishment and recall of visuo-auditory associative memories.

5) In page 16, line 322-326, the authors concluded that to induce the plasticity of VC→AC projection, Delay 1 should be longer than 10 ms and Delay 2 should be longer than 0 ms. This conclusion was not fully supported by the data from Figure 5B-D, because there is no data point between -65 ms and 10 ms for Delay 1 (for example 0 ms), and no negative values for Delay 2.

We rewrote this paragraph and hope it is more accurate now.

“Taken together, our study indicates that significant potentiation of the VC-to-AC inputs can be observed (Figure 5D, black cube) across five pairing trials with a 10-second inter-trial interval, under certain tested conditions: (i) the frequency of repetitive laser stimulation of the CCK+ entorhinal cortex (EC) to AC projection was maintained at 10 Hz or higher (as we did not test frequencies between 1 to 10 Hz), (ii) Delay 1 was set within the tested range of 10 to 535 ms (noting the absence of data between -65 to 10 ms), and (iii) Delay 2 was within the range of 0 to 200 ms (acknowledging that negative values for Delay 2 were not explored).”

Reviewer #2 (Public Review):

The manuscript by Sun et al., investigates the synaptic plasticity underlying visuo-auditory association. Through a series of in vivo and ex vivo electrophysiology recordings, the authors show that high-frequency stimulation (HFLS) of the cholecystokinin (CCK) positive neurons in the entorhino-auditory projection paired with an auditory stimulus can evoke long-term potentiation (LTP) of the visuo-auditory projection. However, LTP of the visuo-auditory projection could not be elicited by HFLS of the visuo-auditory projection itself or by an unpaired stimulus. They further demonstrate that auditory stimulus pairing with CCK is required to elicit LTP of the visuo-auditory projection as well as visuo-auditory association in a fear conditioning behavioral experiment. As they found elevated expression of CCK in entorhinal neurons which project to the auditory cortex, they conclude that HFLS of the entorhino-auditory projection causes CCK release.

Strengths:

The authors use an elegant approach with Chrimson and Chronos to stimulate different auditory inputs in the same mouse in vivo and also in slice and demonstrate that potentiation of the visuo-auditory projection is dependent on HFLS of the entorhino-auditory projection paired with auditory stimulus. Furthermore, they test several parameters in a systematic fashion, generating a comprehensive analysis of the plasticity changes that regulate visuo-auditory association.

Weaknesses:

In their previous publications (Chen et al., 2019; Li et al., 2014; Zhang et al., 2020), it has been established that HFLS of the entorhino-auditory projection and CKK release are important for visuo-auditory association via electrophysiology and behavioral experiments. The Chrimson and Chronos approach was applied by Zhang et al., 2020, where they already found that the visuo-auditory projection was potentiated through HFLS of entorhino-neocortical fibers. This manuscript extends those findings by testing different parameters of pairing, which may not represent a major conceptual advance. Unlike the electrophysiological recordings, drug infusion is used in behavioral manipulations to show that HFLS of the entorhino-auditory projection is important for visuo-auditory association. While the use of drugs to inhibit CKK receptors is important, it does not directly demonstrate that CCK release from the entorhino-auditory is necessary.

We deeply appreciate the reviewer's constructive and insightful feedback. Building on our previous work (Zhang et al., 2020), which highlighted the potentiation of the VC-to-AC projection through high-frequency laser stimulation (HFS laser) of entorhino-neocortical fibers, our current study probes further into the intricacies of this process. We have thoroughly explored the specific conditions necessary for the potentiation of the VC-to-AC projection, assessing a wide range of parameters.

A significant advancement in our current research is the elucidation of why HFS of the VC-to-AC pathway alone fails to induce potentiation, whereas HFS of the EC-to-AC pathway, coupled with Pre/Post Pairing, is effective. This critical distinction is linked to the heightened expression of CCK in EC neurons projecting to the AC, in contrast to those from the VC. In this revised version of our study, we have also demonstrated that HFS laser stimulation of the EC-to-AC CCK+ projection induces the release of endogenous CCK in the AC using a combination of a CCK sensor and fiber photometry.

Behaviorally, our revised research emphasizes the vital role of the CCK+ EC-AC projection in both establishing and retrieving visuo-auditory memories, thereby highlighting its fundamental importance in memory processing. Moreover, our study confirms that the CCK+ EC-AC projection is not only crucial for memory formation and retrieval but also indicates that the VC-to-AC projection is the anatomical basis for establishing visuo-auditory associations and serves as the principal storage site for visuo-auditory associative memory. These findings represent significant strides in our understanding of synaptic plasticity and memory mechanisms.

For the behavioral part, to build the link that HFS laser of the EC-to-AC CCK+ projection is important for visuo-auditory association in the behavioral context, we conducted the following additional behavioral studies (for details please see the response to comment 4 of reviewer 1):

1) Assessing the Necessity of CCK+ EC-to-AC Projection in Establishing Visuo-Auditory Associative memories, by inactivating the pathway with inhibitory DREADD during the encoding phase.

2) Investigating the Importance of CCK+ EC-to-AC Projection in Recalling Visuo-Auditory Association, by inactivating the pathway with inhibitory DREADD during the retrieving phase.

Author Response

Reviewer #1 (Public Review):

This paper combines an array of techniques to study the role of cholecystokinin (CCK) in motor learning. Motor learning in a pellet reaching task is shown to depend on CCK, as both global and locally targeted CCK manipulations eliminate learning. This learning deficit is linked to reduced plasticity in the motor cortex, evidenced by both slice recordings and two-photon calcium imaging. Furthermore, CCK receptor agonists are shown to rescue motor cortex plasticity and learning in knockout mice. While the behavioral results are clear, the specific effects on learning are not directly tested, nor is the specificity pathway between rhinal CCK neurons and the motor cortex. In general, the results present interesting clues about the role of CCK in motor learning, though the specificity of the claims is not fully supported.

Since all CCK manipulations were performed throughout learning, rather than after learning, it is not clear whether it is learning that is affected or if there is a more general motor deficit. Related to this point, Figure 1D appears to show a general reduction in reach distance in CCK-/- mice. A general motor deficit may be expected to produce decreased success on training day 1, which does not appear to be the case in Figure 1C and Figure 2B, but may be present to some degree in Figure 5B. Or, since the task is so difficult on day 1, a general motor deficit may not be observable. It is therefore inconclusive whether the behavioral effect is learning-specific.

Thanks for your comments and suggestions.

We have tested the basic movement ability of CCK-/- and WT mice and we found that there were no significant difference between CCK-/- and WT in terms of stride length, stride time, step cycle ratio and grasp force (Figure S1C, S1D, S1E, S1F). Besides, we also have tested the performance of mice injected with CCKBR antagonist or injected with hM4Di together with clozapine after learned the task (Figure S2D, S8D). The performance of mice before and after antagonist injection or chemogenetic manipulation were comparable. These results suggested that all the CCK manipulations did not cause general defects to the movement ability of mice.

The paper implicates motor cortex-projecting CCK neurons in the rhinal cortex as being a key component in motor learning. However, the relative importance of this pathway in motor learning is not pinned down. The necessity of CCK in the motor cortex is tested by injecting CCK receptor antagonists into the contralateral motor cortex (Figure 2), though a control brain region is not tested (e.g. the ipsilateral motor cortex), so the specificity of the motor cortex is not demonstrated.

Thanks for your comments and suggestions.

In this study, we focus on the role played by CCK from the rhinal cortex to the motor cortex, and how CCK affects motor learning. The single pellet reaching task was selected to study the role of CCK from the rhinal cortex to the motor cortex in motor skill learning and the motor cortex is considered as the main area generates motor memory when training in this task (Komiyama et al., 2010; Peters et al., 2014; Richard et al., 2019). We emphasized that the importance of the motor cortex in motor learning, not meant that other brain areas where also receive CCK-positive neural projections from the rhinal cortex, for example hippocampus (spatial memory), are not important for the performance of this task. In fact, specifically inhibiting the projection from the rhinal cortex to the contrallateral motor cortex is not enough to suppress the motor learning ability of, but inhibiting projecting in both sides (contro- and ipsi-lateral) could suppress the learning ability of mice, suggesting that the whole motor cortex is critical for motor skill learning (Figure 6, S8). In this paper, we studied the relationship between the rhinal cortex and the motor cortex and the role played by CCK in this circuit. The specificity of the motor cortex is task-dependent, not the main purpose in this study.

The learning-related source of CCK in the motor cortex is also unclear, since even though it is demonstrated that CCK neurons in the rhinal cortex project to the motor cortex in Figure 4D, Figure 4C shows that there is also a high concentration of CCK neurons locally within the motor cortex. Likewise, the importance of the projection from the rhinal cortex to the motor cortex is not specifically tested, as rhinal CCK neurons targeted for inactivation in Figure 5 include all CCK cells rather than motor cortex-projecting cells specifically.

Thanks for your comments and suggestions.

The specificity of the CCK-projection from the rhinal cortex to the motor cortex for motor skill learning was studies using chemogenetic methods in the revised version of the manuscript. We first determined that over 98% of neurons in the rhinal cortex that projected to the motor cortex are CCK positive (Figure 6A, S6A, S6B). Next, we injected the retro-Cre virus in the motor cortex and the Cre-dependent hM4Di in the rhinal cortex in C57BL/6 mice to specifically inhibit the CCK neurons from the rhinal cortex to the motor cortex. Compared to two control groups, the learning ability of the experimental group was significant suppressed, suggesting that CCK projections from the rhinal cortex to the motor cortex are critical for motor skill learning (Figure 6). Detailed description was added in the part of "Result" in the manuscript.

CCK is suggested to play a role in producing reliable activity in the motor cortex through learning through two-photon imaging experiments. This is useful in demonstrating what looks like normal motor cortex activity in the presence of CCK receptor antagonist, indicating that the manipulations in Figure 2 are not merely shutting off the motor cortex. It is also notable that, as the paper points out, the activity appears less variable in the CCK manipulations (Figure 3G). However, this could be due to CCK manipulation mice having less-variable movements throughout training. The Hausdorff distance is used for quantification against this point in Figure 1E, though the use of the single largest distance between trajectories seems unlikely to give a robust measure of trajectory similarity, which is reinforced by the CCK-/- traces looking much less variable than WT traces in Figure 1D. The activity effects may therefore be expected from a general motor deficit if that deficit prevented the mice from normal exploratory movements and restricted the movement (and activity) to a consistently unsuccessful pattern.

Thanks for your comments and suggestions.

To totally suppress CCK receptors in the motor cortex, the antagonist is unavoidable to diffuse to the adjacent brain areas as the motor cortex is not regularly circular. But the area inhibited most should be the motor cortex. We applied the chemogenetics method to further determine the specificity of the motor cortex in the motor skill learning. Specific projection from the RC to the MC was inhibited bilaterally, which suppressed the motor learning ability.

For a wild-type mouse, neurons were activated when it try to get the food pellet. Neuronal pattern corresponding to each trial will be remembered, and the patterns corresponding to successful movements will tend to be repeated. Manipulations of CCK prevented neurons from remembering the pattern they tried and repeated the pattern they tried before no matter it is successful or not. This is corresponding to the neuron-activation pattern showed in figure 3D, 3E and 3G, the population activities (neuronal activities) are comparable, while the trial-to-trial population correlation is a little bit higher for the CCK-manipulation groups on Day 1. In terms of the behavior, manipulations of CCK decreased the possibility to explore the best path to get food pellets and just repeating a reach for the food pellet like it was the first time. Besides, many tests including the movement ability of CCK-/-, performance of antagonist injection group and chemogenetics manipulation group after learning indicated that CCK-manipulation did not affect the basic movement ability.

Hausdorff distance is the greatest of all the distances from a point in one set to the closest point in the other set. It is not just the largest distance between two trajectories, but comprehensively takes all points in each trajectory into consideration. Hausdorff distance is widely used to assess the variation of two trajectories. The similarity of the shapes of trajectories is not applied for analysis because it is not very effective to assess the performance of a mouse. The fixed location of the initial site and food site makes all trajectories are single lines in the same direction, thus, the shapes of the trajectories are very similar among different trials. Two trajectories with similar shape but far from each other (big Hausdorff distance) should be treated as big variation because, in terms of the final results, they are quite different (success vs. miss). Therefore, Hausdorff distance is more reliable to be applied for assessment of the performance of mice.

Finally, slice experiments are used to demonstrate the lack of LTP in the motor cortex following CCK knockout, which is rescued by CCK receptor agonists. This is a nice experiment with a clear result, though it is unclear why there are such striking short-term depression effects from high-frequency stimulation observed in Figure 6A that are not observed in Figure 1H. Also, relating to the specificity of the proposed rhinal-motor pathway, these experiments do not demonstrate the source of CCK in the motor cortex, which may for example originate locally.

Thanks for your comments.

1. Because CCK4 is a small molecule, which degrades very fast with half-time less than 1 min in the rat serum and 13 min in the human serum, we injected the drug into the electrode recording dishes, while the ACSF was stopped flowing, leading to a relatively low oxygen condition. As it showed in Figure 6A, it cost about 15 min for the brain slices to recover. Compared with CCK4 manipulation, the depression of vehicle group is stronger, which could be due to the effects of CCK4 induced LTP after HFS compensated the depression.

2. In the motor cortex, many CCK-positive neurons are γ-aminobutyric acid-ergic (GABAergic) neurons, in which the role played by CCK is not very clear (Whissell et al., 2015). However, evidence showed that GABA may inhibit the release of CCK in the neocortex (Yaksh et al., 1987). Many glutamatergic neurons in the neocortex also express CCK (Watakabe et al., 2012). In this study, the stimulation electrode was placed on the layer 1, where receives most CCK projections from the rhinal cortex, to release CCK from the rhinal cortex, but can not rule out the possibility that some CCK may release from the local CCK neurons (Figure 4B). We focused on the importance of CCK for neural plasticity in the motor cortex, but did not aim to figure out the role played by the cortical CCK-positive neurons, including inhibitory and excitatory neurons, in neuronal plasticity and motor skill learning by this experiment.

Therefore, the specificity of the projections from the rhinal cortex to the motor cortex was further studied by chemogenetic manipulation. Inhibiting the activity of the projections suppressed the learning ability compared with two types of control manipulations, indicating the CCK projections from RC to the MC is critical for motor skill learning.

Reviewer #2 (Public Review):

This study aims to test whether and if so, how cholecystokinin (CCK) from the mice rhinal cortex influences neural activity in the motor cortex and motor learning behavior. While CCK has been previously shown to be involved in neural plasticity in other brain regions/behavioral contexts, this work is the first to demonstrate its relationship with motor cortical plasticity in the context of motor learning. The anatomical projection from the rhinal cortex to the motor cortex is also a novel and important finding and opens up new opportunities for studying the interactions between the limbic and motor systems. I think the results are convincing to support the claim that CCK and in particular CCK-expressing neurons in the rhinal cortex are critical for learning certain dexterous movements such as single pellet reaching. However, more work needs to be done, or at least the following concerns should be addressed, to support the hypothesis that it is specifically the projection from the rhinal cortex to the motor cortex that controls motor learning ability in mice.

1)Because CCK is expressed in multiple brain regions, as the authors recognized, results from the CCK knock-out mice could be due to a global loss of neural plasticity. In comparison, the antagonist experiment is in my opinion the most convincing result to support the specific effect of CCK in the motor cortex. However, it is unclear to me whether the CCK knock-out mice exhibited an impaired ability to learn in general, i.e., not confined to motor skills. For instance, it would be very valuable to show whether these mice also had severe memory deficits; this would help the field to understand different or similar behavioral effects of CCK in the case of global vs. local loss of function. If the CCK knock-out mice only exhibited motor learning deficits, that would be surprising but also very interesting given previous studies on its effect in other brain areas.

Thanks for your comments. According to the studies in our lab, we found that CCK is critical for the neural plasticity in the auditory cortex, hippocampus and the amygdala and CCK-/- mice performed much worse than wildtype mice in associative, spatial and fear memory (Li et al.,2014; Chen et al., 2019; Su et al. 2019; Feng et al. 2021).

2) Related to my last point, I believe that normal neural plasticity should be essential to motor skill learning throughout development not just during the current task. Thus, it would be important to show whether these CCK knock-out mice present any motor deficits that could have resulted from a lack of CCK-mediated neural plasticity during development. If not, the authors should explain how this normal motor learning during development is consistent with their major hypothesis in this study (e.g., is CCK not critical for motor learning during early development).

Thanks for your comments and suggestions.

Development is mainly gene-guided which prepares the physical structure for learning, while learning is dependent on the neural plasticity and a period of experience (such as motor training in this research). Besides, development is deemed as "experience-expectant", using common environmental information, while learning is "experience-dependent", sensitive to the specific individual experiences (Greenough et al., 1987; Galván, 2010). Moreover, development costs longer time to form a specific ability of a species in general. The role of CCK plays in the development is not clear. Duchemin et al. (1987) studied the CCK gene expression level in the brain of rats pre- and postnatally. They found that the CCK mRNA was detectable on embryonic day 14 (E14) and gradually increased to the maximum level on postnatal day 14 (P14), indicating that CCK might participate in the development of rats. Paolo et al. (2007) mapped the expression of CCK in the mouse brain. Plentiful CCK expression was observed at E12.5 in the thalamus and spinal cord and by E17.5 CCK expression extended to the cortex, hippocampus and hypothalamus, suggesting that CCK might also regulate the development of mice. Paolo et al. (2004) found that CCK suppressed the migration of GnRH-1 through CCK-A receptor in the brain. Besides, postnatal early learning may participate in development. CCK-B receptor antagonist administration (postnatal 6 hours) suppressed the infant sheep get motor preference, indicating that CCK might be important for the development of mother preference of sheep. However, what the role CCK played in the development of motor system is not known.

In this study, the performance of both CCK-/- and WT mice is at the same level without significant difference on Day one, in terms of the percentage of "miss", "no-grasp", "drop" and "success". Besides, the movement abilities, including stride length, stride time, step cycle ratio and grasp force, were comparable for both CCK-/- and WT mice (Figure S1C, S1D, S1E, S1F), suggesting that knockout of cck gene did not affect the basic movement ability. This could be because the development of basic movement ability is not learning-guided, but is physical structure-determined. However, all these tests were on physical level, but how CCK affected the motor system on the molecular and cellular level is not known. Therefore, we further applied CCK-BR antagonist and chemogenetic method to study the role of CCK in the motor learning.

3)Lines 198-200 and Fig. 2C: The authors found that the vehicle group showed significantly increased "no grasp" behavior, and reasoned that the implantation of a cannula may have caused injuries to the motor cortex. In order to support their reasoning and make the control results more convincing, I think it would be helpful to show histology from both the antagonist and control groups and demonstrate motor cortical injury in some mice of the vehicle group but not the antagonist group. Otherwise, I'm a bit concerned that the methods used here could be a significant confounding factor contributing to motor deficits.

Thanks for your comments and suggestions.

The injury of the motor cortex can not be avoided, because the cannula was inserted below the surface of the cortex (Figure S2C). The significantly increased "no-grasp" rate is because the improvement of miss rate of the Vehicle group, which turned to "no-grasp" but failed to further improve to drop or success, while for the Antagonist group, there is no significant improving from "miss" to "no-grasp", leaving no change in the "no grasp".

4) The authors showed that chemogenetic inhibition of CCK neurons in the rhinal cortex impaired motor skill learning in the pellet-reaching task. However, we know that the rhinal cortex projects to multiple brain regions besides the motor cortex (e.g., other cortical areas and the hippocampus). Thus, the conclusion/claim that the observed behavioral deficits resulted from inhibited rhinal-motor cortical projections is not strongly supported without more targeted loss-of-function or rescue experiments.

It would also be very informative to the field to compare the specific behavioral deficits, if any, of inhibiting specific downstream targets of the rhinal CCK neurons. As a concrete example, the hippocampus may be involved in learning more sophisticated motor skills (as the authors pointed out in the Discussion) besides the motor cortex. It would be a critical result if the authors could either show or exclude the possibility that the motor learning deficits observed in CCK-/- mice were at least partially due to the inhibition of hippocampal plasticity. This echoes my earlier point (point 1) that it is unclear whether the effect of lacking CCK in knock-out mice is specific in the motor cortex or engages multiple brain regions.

Lastly, because Fig. 4 only showed histology in the rhinal and motor cortices, I am not sure whether the motor cortex solely receives CCK input from the rhinal cortex. A more comprehensive viral tracing result could be important to both supporting the circuit-specificity of the observed behavior in this study and providing a clearer picture of where the motor cortex receives CCK inputs.

Thanks for your comments.

The specificity of the CCK-projection from the rhinal cortex to the motor cortex for motor skill learning was studies using chemogenetic methods in the revised version of the paper. We first determined that over 98% of neurons in the rhinal cortex that projected to the motor cortex are CCK positive (Figure 6A, S6A, S6B). Next, we injected the retro-Cre virus in the motor cortex and the Cre-dependent hM4Di in the rhinal cortex in C57BL/6 mice to specifically inhibit the CCK neurons from the rhinal cortex to the motor cortex. Compared to two control groups, the learning ability of the experimental group was significantly suppressed, suggesting that CCK projections from the rhinal cortex to the motor cortex are critical for motor skill learning (Figure 6). Detailed description was added in the part of "Result" in the manuscript.

In this study, we focus on the role played by CCK from the rhinal cortex, and how CCK affects motor learning. The single pellet reaching task was selected to study the role of CCK from the rhinal cortex in motor skill learning and the motor cortex is considered as the main area generates motor memory when training in this task (Komiyama et al., 2010; Peters et al., 2014; Richard et al., 2019). We emphasized that the importance of the contrallateral motor cortex in motor learning, not meant that other brain areas where also receive CCK-positive neural projections from the rhina cortex, for example hippocampus (spatial memory), are not important for the performance of this task. In fact, specifically inhibiting the projection from the rhinal cortex to the contrallateral motor cortex is not enough to suppress the motor learning ability, but inhibiting projecting in both sides (contro- and ipsi-lateral) could suppress the learning ability of mice, suggesting that the whole motor cortex is critical for motor skill learning (Figure 6, S8). In our lab, we found that CCK projection from the entorhinal cortex to the hippocampus is critical for spatial memory formation (Su et al., 2019). Impaired hippocampus, to some extent, affected the performance in single pellet reaching task (Shwuhuey et al., 2007). Therefore, manipulation of CCK projections from the rhinal cortex to the hippocampus may also affect the performance in the single pellet reaching task. In this paper, we aim to study the relationship between the rhinal cortex and the motor cortex and the role played by CCK in this circuit. Other brain areas involved in the single pellet reaching task are not the core concern in this study.

The motor cortex also receive CCK projections from other cortices, such as the contrallateral motor cortex, the deep layer of visual cortex and auditory cortex, and thalamus (Figure S4).

5) I am glad to see the CCK4 rescue experiment to demonstrate the sufficiency of CCK in promoting motor learning. However, the rescue experiment lacked specificity: IP injection did not allow specific "gain of function" in the motor cortex but instead, the improved learning ability in CCK knock-out mice could be a result of a global effect of CCK4 across multiple brain regions. CCK4 injection specifically targeted at the motor cortex would be necessary to support the sufficiency of CCK-regulated neuroplasticity in the motor cortex to promote motor learning.

Thanks for your comments.

First, the specificity of the circuit were studied by injecting a Cre virus in the MC and a Cre-dependent hM4Di virus in the RC. After injection with clozapine, the motor learning ability were significantly suppressed compared with the saline control and the control virus combined with clozapine.

Besides, we emphasized that the importance of the motor cortex in motor learning, not meant that other brain areas where also receive CCK-positive neuronal projections from the rhinal cortex, for example hippocampus (spatial memory), are not important for the performance of this task. Specific infusion the drug into the motor cortex is hard to rescue the motor learning ability of CCK-/- mice because the motor cortex is very large, varying from AP: -1.3 to 2.46 mm and ML: ±0.5 to ±2.75 mm and other areas receiving CCK projections from the rhinal cortex also could be important for motor learning. Actually, we tried to inject CCK into the motor cortex through a drug cannula, but the result showed that it is hard to compensate the knock out of cck gene in the whole brain, and rescue the motor learning ability (Figure S11D, S11E). Moreover, cannula implantation causes inescapable injury to the motor cortex, because the cannula must be inserted into the brain, so that the drug could be infused into the brain. This injury may affect the performance in the task, as the motor cortex is very critical for motor learning. Therefore, it is not the best method to be applied for motor skill rescuing.

Furthermore, CCK4 molecules can be transported to the whole brain by i.p. injection, as CCK4 is capable to pass through brain blood barrier, which compensates the knockout of cck gene in the whole brain, leading to the rescuing of motor learning ability. Furthermore, i.p. injection is widely accepted for drug discovery because it is very convenient, simply manipulated and does not causes any direct injury on the brain. Thus, we applied i.p. injection not only for whole brain CCK compensation, but also for the further study of the application in drug discovery.

Reviewer #3 (Public Review):

The authors elucidated the roles of cholecystokinin (CCK)-expressing excitatory neurons, which project from the rhinal cortex to the motor cortex, in motor skill learning. The authors found CCK knock-out mice exhibited learning defects in the pellet reaching task while the baseline success rate of the knock-out mice was similar to that of the wild-type mice. Application of a CCK B receptor (CCKBR) antagonist into the motor cortex lowered the success rate in the motor task. The authors found the population activity which was observed in the in vivo calcium imaging during motor learning was elevated after motor learning, but this increase disappeared in CCK knock-out mice and animals with CCKBR antagonist administration. Anterograde and retrograde viral tracing revealed that CCK-expressing excitatory neurons in the rhinal cortex projected to the motor cortex. Chemogenetic inhibition of the CCK-expressing neurons in the rhinal cortex lowered the ability for motor learning. The application of a CCKBR agonist increased the motor learning ability of CCK knock-out animals as well as long-term potentiation (LTP) observed in the slice of the motor cortex.

However, the manuscript contains several shortcomings:

First, the "Discussion" has several statements that are only supported weakly by the results, for example, ll. 429-431, ll. 432-433, and ll. 447-448. In addition, most of the sentences in this section are not divided into subsections. The paragraphs should be composed in multiple subsections with appropriate subheadings, even though the initial section summarizing the results can lack a subheading.

Thanks for your suggestions. The statements were revised and the discussion was divided into subsections.

Second, it would be important that the authors showed which area(s) of the brain is affected by the CCKBR antagonist in the experiments described in ll. 166-206 and Fig. 2. The authors injected the drug into the motor cortex, but the chemical can spread to neighboring cortical areas (e.g. somatosensory cortex) or wider brain regions. If so, the blockade of the CCKBR in the brain areas other than the motor cortex could cause the defects of the motor task learning observed in these experiments. I think it is desirable that such a possibility should be excluded. Conversely, it is possible that the antagonist had an effect on a limited subarea of the motor cortex (e.g. only the primary motor cortex (M1)). In this case, the information about the field altered by the CCKBR blocker would be useful to interpret the results of the learning defects.

Thanks for your comments and suggestions.

The drug cannula was implanted in the motor cortex (coordinates: AP, 1.4 mm, ML, -/+1.6 mm, DV, 0.25 - 0.3 mm) contralateral to the dominant hand of the mice (Figure S2C). To totally inhibit CCKBR in the motor cortex, we injected over-dosage of antagonist into the motor cortex. Thus, we cannot totally exclude the possibility that some antagonist spread to the neighboring cortices. However, the fact is that the motor cortex is very large, varying from AP: -1.3 to 2.46 mm and ML: ±0.5 to ±2.75 mm. It is not easily to spread out of the motor cortex with high concentration.

Third, the authors need to show bilateral data about their anterograde and retrograde tracking of CCK-expressing neurons in the rhinal cortex. In ll. 290-292, they described as follows: "Both anterograde and retrograde tracking results indicated that CCK-expressing neurons in the rhinal cortex projecting to the motor cortex were asymmetric, showing a preference for the ipsilateral hemisphere." However, they provided only unilateral data for the anterograde (Fig. 4B) and the retrograde (Fig. 4D) experiments.

Thanks for your comments. Both anterograde and retrograde tracking data from bilateral hemisphere were added to the supplementary file (Figure S4).

Fourth, unilateral (contralateral to the dominant forelimb) experiments are needed in the chemogenetic inhibition of the CCK neurons. In ll. 301-338 and Fig. 5, the authors inhibited the CCK -expressing neurons in both hemispheres by injecting the virus into both sides. However, the CCKBR antagonist injection into the motor cortex contralateral to the dominant forelimb caused defects in motor learning ability, as described in ll. 166-206. The authors also observed that the population neuronal activity in the motor cortex contralateral to the dominant forelimb changed in accordance with the improvement of the motor skill in ll. 208-269. Therefore, it may be the case that inhibition of CCK neurons only in the side contralateral to the dominant forelimb - not bilaterally, as the authors did - could cause the lowered ability of motor learning. Such unilateral inhibition can be carried out by unilateral injection of the virus. In relation to the point above, in the chemogenetic inhibition experiments, it would be important to show which neurons in which cortical area is inhibited. This could be done by examining the distributions of the mCherry-labeled somata in the rhinal cortex using histochemistry.

Thanks for your comments and suggestions.

The specific of the CCK-projection from the rhinal cortex to the motor cortex for motor skill learning was studied using chemogenetic methods in the revised version of the paper. We first determined that over 98% of neurons in the rhinal cortex that projected to the motor cortex are CCK positive by retrograde virus injection and immunostaining (Figure 6A, S6A, S6B). Next, we injected the retro-Cre virus in the motor cortex and the Cre-dependent hM4Di in the rhinal cortex in C57BL/6 mice to specifically inhibit the CCK neurons from the rhinal cortex to the motor cortex. Compared to two control groups, the learning ability of the experimental group was significant suppressed, suggesting that CCK projections from the rhinal cortex to the motor cortex are critical for motor skill learning (Figure 6). Furthermore, we also injected the retro-Cre virus into the single site of the motor cortex controlateral to the dominant forelimb together with Cre-dependent hM4Di virus in the rhinal cortex. The result showed that after injection of clozapine, the motor learning ability was not significantly suppressed, suggesting that the bilateral motor cortex is important for motor skill learning. This is consistent with the previous findings that the increased GluA1 expression were observed bilaterally in the motor cortex after training in the single pellet reaching task. Detailed description was added in the part of "Result" in the manuscript.

Fifth, it would be valuable to further examine differences in task performance across sessions and groups. The paragraph in ll. 138-153 needs a comparison of the "miss" rates of CCK-/- animals between Day 1 vs. Day 6 (related to ll. 429- 431). This paragraph also needs comparisons of the "no-grasp" and "drop" rates of CCK-/- animals between Day 1 vs. Day 6 (related to ll. 432- 433). The paragraph in ll. 175-190 needs comparisons of success rates between Day 1 and Day 5/6 within the antagonist group (related to ll. 447-448).

Thanks for your comments. The comparisons were made in the revised manuscript.

Author Response

Reviewer #1 (Public Review):

This thorough study expands our understanding of BMP signaling, a conserved developmental pathway, involved in processes diverse such as body patterning and neurogenesis. The authors applied multiple, state-of-art strategies to the anthozoan Nematostella vectensis in order to first identify the direct BMP signaling targets - bound by the activated pSMAD1/5 protein - and then dissect the role of a novel pSMAD1/5 gradient modulator, zwim4-6. The list of target genes features multiple developmental regulators, many of which are bilaterally expressed, and which are notably shared between Drosophila and Xenopus. The analysis identified in particular zswim4-6 a novel nuclear modulator of the BMP pathway conserved also in vertebrates. A combination of both loss-of-function (injection of antisense morpholino oligonucleotide, CRISPR/Cas9 knockout, expression of dominant negative) and gain-of-function assays, and of transcriptome sequencing identified that zwim acts as a transcriptional repression of BMP signaling. Functional manipulation of zswim5 in zebrafish shows a conserved role in modulating BMP signaling in a vertebrate.

The particular strength of the study lies in the careful and thorough analysis performed. This is solid developmental work, where one clear biological question is progressively dissected, with the most appropriate tools. The functional results are further validated by alternative approaches. Data is clearly presented and methods are detailed. I have a couple of comments.

1) I was intrigued - as the authors - by the fact that the ChiP-Seq did not identify any known BMP ligand bound by pSMAD1/5. Are these genes found in the published ChiP-Seq data of the other species used for the comparative analysis? One hypothesis could be that there is a change in the regulatory interactions and that the initial set-up of the gradient requires indeed a feedback loop, which is then turned off at later gastrula. In this case, immunoprecipitation at early gastrula, prior to the set-up of the pSMAD1/5 gradient, could reveal a different scenario. Alternately, the regulation could be indirect, for example, through RGM, an additional regulator of BMP signaling expressed on the side of lower BMP activity, which is among the targets of the ChiP-Seq. This aspect could be discussed. Additionally, even if this is perhaps outside the scope of this study, I think it would be informative to further assess the effect of ZSWIM manipulation on RGM (and vice versa).

Indeed, BMP genes are direct BMP signaling targets in Drosophila (dpp) (Deignan et al., 2016, https://doi.org/10.1371/journal.pgen.1006164) and frog (bmp2, bmp4, bmp5, bmp7) (Stevens et al., 2021, https://doi.org/10.1242/dev.145789). Of all these ligands, only the dorsally expressed Xenopus bmp2 is repressed by BMP signaling, while another dorsally expressed Xenopus BMP gene admp is not among the direct targets. All other BMP genes listed here are expressed in the pMad/pSMAD1/5/8-positive domain and are activated by BMP signaling.

In Nematostella, we do not find BMP genes among the ChIP-Seq targets, but this is not that surprising considering the dynamics of the bmp2/4, bmp5-8 and chordin expression, as well as the location of the pSMAD1/5-positive cells. In late gastrulae/early planulae, Chordin appears to be shuttling BMP2/4 and BMP5-8 away from their production source and over to the gdf5-like side of the directive axis (Genikhovich et al., 2015; Leclere and Rentsch, 2014). By 4 dpf, chordin expression stops, and BMP2/4 and BMP5-8 start to be both expressed AND signal in the mesenteries. If bmp2/4 and bmp5-8 expression were directly suppressed by pSMAD1/5 (as is the case chordin or rgm expression), this mesenterial expression would not be possible. Therefore, in our opinion, it is most likely that at late gastrula and early planula the regulation of bmp2/4 and bmp5-8 expression by BMP signaling is indirect. We do not have an explanation for why gdf5-like (another BMP gene expressed on the “high pSMAD1/5” side) is not retrieved as a direct BMP target in our ChIP data. Since we do not understand well enough how BMP gene expression is regulated, we do not discuss this at length in the manuscript.

As the Reviewer suggested, we analyzed the effect of ZSWIM4-6 KD on the expression of rgm. Expectedly, since it is expressed on the “low BMP side”, its expression was strongly expanded (Figure 6 - Figure Supplement 4)

2) I do not fully understand the rationale behind the choice of performing the comparative assays in zebrafish: as the conservation was initially identified in Xenopus, I would have expected the experiment to be performed in frog. Furthermore, reading the phylogeny (Figure 4A), it is not obvious to me why ZSWIM5 was chosen for the assay (over the other paralog ZSWIM6). Could the Authors comment on this experiment further?

The comparison was done in zebrafish because we were planning to generate zswim5 mutants, whose analysis is currently in progress. ZSWIM6 is not expressed at the developmental stages we were interested in, while ZSWIM5 was, based on available zebrafish expression data (White et al., 2017):

Reviewer #2 (Public Review):

The authors provide a nice resource of putative direct BMP target genes in Nematostella vectensis by performing ChIP-seq with an anti-pSmad1/5 antibody, while also performing bulk RNA-seq with BMP2/4 or GDF5 knockdown embryos. Genes that exhibit pSmad1/5 binding and have changes in transcription levels after BMP signaling loss were further annotated to identify those with conserved BMP response elements (BREs). Further characterization of one of the direct BMP target genes (zswim4-6) was performed by examining how expression changed following BMP receptor or ligand loss of function, as well as how loss or gain of function of zswim4-6 affected development and BMP signaling. The authors concluded that zswim4-6 modulates BMP signaling activity and likely acts as a pSMAD1/5 dependent co-repressor. However, the mechanism by which zswim4-6 affects the BMP gradient or interacts with pSMAD1/5 to repress target genes is not clear. The authors test the activity of a zswim4-6 homologue in zebrafish (zswim5) by over-expressing mRNA and find that pSMAD1/5/9 labeling is reduced and that embryos have a phenotype suggesting loss of BMP signaling, and conclude that zswim4-6 is a conserved regulator of BMP signaling. This conclusion needs further support to confirm BMP loss of function phenotypes in zswim5 over-expression embryos.

Major comments

1) The BMP direct target comparison was performed between Nematostella, Drosophila, and Xenopus, but not with existing data from zebrafish (Greenfeld 2021, Plos Biol). Given the functional analysis with zebrafish later in the paper it would be nice to see if there are conserved direct target genes in zebrafish, and in particular, is zswim5 (or other zswim genes) are direct targets. Since conservation of zswim4-6 as a direct BMP target between Nematostella and Xenopus seemed to be part of the rationale for further functional analysis, it would also be nice to know if this is a conserved target in zebrafish.

Thank you for the suggestion. In the paper by Greenfeld et al., 2021, zebrafish zswim5 was downregulated approximately 2.4x in the bmp7 mutant at 6 hpf, while zswim6 was barely expressed and not affected at this stage. We added this information to the text of the manuscript. Expression of several other zebrafish zswim genes was also affected in the bmp7 mutant, but these genes do not appear relevant for our study since their corresponding orthologs are not identified as pSMAD1/5 ChIP-Seq targets in Nematostella. Notably, zebrafish zzswim5 is not clearly differentially expressed in BMP or Chd overexpression conditions (See Supplementary file 1 in Rogers et al. 2020). Importantly, in the paper, we wanted to compare ChiP-Seq data with ChIP-Seq data, however, unfortunately, no ChIP-Seq data for pSMAD1/5/8 is currently available for zebrafish, thus precluding comparisons.

Related to this, in the discussion it is mentioned that zswim4/6 is also a direct BMP target in mouse hair follicle cells, but it wasn't obvious from looking at the supplemental data in that paper where this was drawn from.

Please see Supplementary Table 1, second Excel sheet labeled “Mx ChIP_Seq” in Genander et al., 2014, https://doi.org/10.1016/j.stem.2014.09.009. Zswim4 has a single pSMAD1 peak associated with it, Zswim6 has two.

2) The loss of zswim4-6 function via MO injection results in changes to pSmad1/5 staining, including a reduction in intensity in the endoderm and gain of intensity in the ectoderm, while over-expression results in a loss of intensity in the ectoderm and no apparent change in the endoderm. While this is interesting, it is not clear how zswim4-6 is functioning to modify BMP signaling, and how this might explain differential effects in ectoderm vs. endoderm. Is the assumption that the mechanism involves repression of chordin? And if so one could test the double knockdown of zswim4-6 and chordin and look for the rescue of pSad1/5 levels or morphological phenotype.

We do not think that the mechanism of the ZSWIM4-6 action is via repression of Chordin. As loss of chordin leads to the loss of pSMAD1/5 in Nematostella (Genikhovich et al., 2015), the proposed experiment is, unfortunately, not feasible to test this hypothesis. Currently, we see two distinct effects of the modulation of zswim4-6 expression. First, it affects the pSMAD1/5 gradient, possibly by destabilizing nuclear SMAD1/5, as has been proposed by Wang et al., 2022 for the vertebrate Zswim4. This is in line with our results shown on Fig. 6C-F’ and Fig. 6-Figure supplement 3. In our opinion, the reaction of the genes expressed on the “high BMP” side of the directive axis to the overexpression or KD of ZSWIM4-6 (Fig. 6I-K’, 6N-P’) can be explained by these changes in the pSMAD1/5 signaling intensity. Secondly, zswim4-6 appears to promote pSMAD1/5-mediated gene repression. This is in line with the reaction of the genes expressed on the “low BMP” side of the directive axis (Fig. 6G-H’, 6L-M’, Fig. 6-Figure Supplement 4). These genes are repressed by BMP signaling, but they expand their expression upon zswim4-6 KD in spite of the increased pSMAD1/5. Our ChiP experiment (Fig. 6Q) supports this view.

3) Several experiments are done to determine how zswim4-6 expression responds to the loss of function of different BMP ligands and receptors, with the conclusion being that swim4-6 is a BMP2/4 target but not a GDF5 target, with a lot of the discussion dedicated to this as well. However, the authors show a binary response to the loss of BMP2/4 function, where zswim4-6 is expressed normally until pSmad1/5 levels drop low enough, at which point expression is lost. Since the authors also show that GDF5 morphants do not have as strong a reduction in pSmad1/5 levels compared to BMP2/4 morphants, perhaps GDF5 plays a positive but redundant role in swim4-6 expression. To test this possibility the authors could inject suboptimal doses of BMP2/4 MO with GDF5 MO and look for synergy in the loss of zswim4-6 expression.

Thanks for this great suggestion! We performed this experiment (Fig. 5H’’-L) and indeed, a suboptimal dose of BMP2/4MO + GDF5lMO results in a complete radialization of the embryo and abolished zswim4–6, similar to the effect of a high dose of BMP2/4. This result suggests that rather than being a ligand-specific signaling function, GDF5-like signaling alone still provides sufficiently high pSmad1/5 levels to activate zswim4-6 expression to apparent wildtype levels, demonstrating the sensitivity of this gene to even very low amounts of BMP signaling.

4) The zswim4-6 morphant embryos show increased expression of zswim4-6 mRNA, which is said to indicate that zswim4-6 negatively regulates its own expression. However in zebrafish translation blocking MOs can sometimes stabilize target transcripts, causing an artifact that can be mistakenly assumed to be increased transcription (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7162184/). Some additional controls here would be warranted for making this conclusion.

Thanks for raising this important experimental consideration. To-date, we do not have any evidence for MO-mediated transcript stabilization in Nematostella, and we have not found such data in the literature on models other than zebrafish. mRNA stabilization by the MO also seemed unlikely because we were unable to KD zswim4-6 using several independent shRNAs - an effect we frequently observe with genes, whose activity negatively regulates their own expression. However, to test the possibility that zswim4-6MO binding stabilizes zswim4-6 mRNA, we injected mRNA containing the zswim4-6MO recognition sequence followed by the mCherry coding sequence (zswim4-6MO-mCherrry) with either zswim4-6MO or control MO. We could clearly detect mCherry fluorescence at 1 dpf if control MO was co-injected with the mRNA, but not if zswim4-6MO was coninjected with the mRNA. At 2 dpf (the stage at which we showed upregulation of zswim4-6 upon zswim4-6MO injection on Fig. 6I-I’), zswim4-6MO-mCherrry mRNA was undetectable by in situ hybridization with our standard FITC-labeled mCherry probe independent of whether zswim4-6MO-mCherrry mRNA was co-injected with the control MO or ZSWIM4-6MO, while hybridization with the FITC-labeled FoxA probe worked perfectly.

Author response image 1.

We are currently offering two alternative hypothesis for the observed increase in zswim4-6 levels in the paper rather than stating explicitly that ZSWIM4-6 negatively regulates its own expression: “The KD of zswim4-6 translation resulted in a strong upregulation of zswim4-6 transcription, especially in the ectoderm, suggesting that ZSWIM4-6 might either act as its own transcriptional repressor or that zswim4-6 transcription reacts to the increased ectodermal pSMAD1/5 (Fig. 6I-I’).” Given the sensitivity of zswim4-6 to even the weakest pSMAD1/5 signal (zswim4/6 is expressed upon GDF5-like KD, which drastically reduces pSMAD1/5 signaling intensity (see Fig. 1 and 2 in Genikhovich et al., 2015, http://doi.org/10.1016/j.celrep.2015.02.035 and Fig. 6-Figure supplement 3 of this paper), the latter option (that it reacts to the increased ectodermal pSMAD1/5) is, in our opinion, clearly the more probable one.

5) Zswim4-6 is proposed to be a co-repressor of pSmad1/5 targets based on the occupancy of zswim4-6 at the chordin BRE (which is normally repressed by BMP signaling) and lack of occupancy at the gremlin BRE (normally activated by BMP signaling). This is a promising preliminary result but is based only on the analysis of two genes. Since the authors identified BREs in other direct target genes, examining more genes would better support the model.

We suggest that ZSWIM4-6 may be a co-repressor of pSMAD1/5 targets because it is a nuclear protein (Fig. 4G), whose knockdown results in the expansion of the ectodermal expression of several genes repressed by pSMAD1/5 in spite of the expansion of pSMAD1/5 itself (Fig. 6G-H’, 6L-M’, Fig. 6-Figure Supplement 4). Our limited ChIP analysis supports this idea by showing that ZSWIM4-6 is bound to the pSMAD1/5 site of chordin (repressed by pSMAD1/5) but not on gremlin (activated by pSMAD1/5). We agree that adding the analysis of more targets in order to challenge our hypothesis would be good. However, given technical limitations (having to inject many thousands of eggs with the EF1a::ZSWIM4-6-GFP plasmid in order to get enough nuclei to extract sufficient immunoprecipitated chromatin for qPCR on 3 genes (chordin, gremlin, GAPDH) for each biological replicate, it is currently unfortunately not feasible to test more genes. It will be of great interest for follow up studies to generate a knock-in line with tagged zswim4-6 to analyze target binding on a genome-wide scale. We stress in the discussion that currently the power of our conclusion is low.

6) The rationale for further examination of zswim4-6 function in Nematostella was based in part on it being a conserved direct BMP target in Nematostella and Xenopus. The analysis of zebrafish zswim5 function however does not examine whether zswim5 is a BMP target gene (direct or indirect). BMP inhibition followed by an in situ hybridization for zswim5 would establish whether its expression is activated downstream of BMP.

In the paper by Greenfeld et al., 2021, zebrafish zswim5 was downregulated approximately 2.4x in the bmp7 mutant at 6 hpf. However, this gene was not among the 57 genes, which were considered to be direct BMP targets because their expression was affected by bmp7 mRNA injection into cycloheximide-treated bmp7 mutants (Greenfeld et al., 2021). We added this information to the text of the manuscript.

7) Although there is a reduction in pSmad1/5/9 staining in zebrafish injected with zswim5 mRNA, it is difficult to tell whether the resulting morphological phenotypes closely resemble zebrafish with BMP pathway mutations (such as bmp2b). More analysis is warranted here to determine whether stereotypical BMP loss of function phenotypes are observed, such as dorsalization of the mesoderm and loss of ventral tail fin.

We agree, and we have tuned down all zebrafish arguments. Analyses of zswim5 mutants are currently ongoing.

Author Response

Reviewer #1 (Public Review):

Strengths:

The study addresses an intriguing research question that fills a gap in existing literature, and was carefully designed and well-executed, with a series of experiments and control experiments.

We thank the reviewer for the positive statement about the conception and execution of the study as well as the potential interest to the community within a broader field.

Weaknesses:

1) My main concern is the null effect of precision estimation pattern between cued and un-cued trials. It is well established that relative to the un-cued stimuli, the cued stimuli obtain more attentional resource and this study claimed serial attentional resource allocation during parallel feature value tracking. However, all Experiments 3a-c did not find any difference in precision estimates between these two types of trials.

We would like to annotate that the terminology „cued versus uncured trials“ in the usual sense of distinguishing between stimuli being attended versus unattended is admittedly somewhat misleading in the current work. In cued and uncured trials of the present experiments 3a-c the allocation of attention is equal. The difference is that the color stream that is attended first is defined (knowable) in the cued but not in the uncued trials. In all cases subjects had to track both color streams and report any of the probed streams as accurately as possible. In other words, the overall allocation of attention in cued and uncured trials is the same. Also, the „cue“ did not provide any information regarding the following probe (no indication of likelihood for a probe in that stream as in an attention experiment). It was entirely irrelevant and was therefore expected not to alter subjects overall performance – as confirmed by the mentioned null-result. The performed test shows, that the reported bias of ~2:1 does not depend on whether in one set of the trials one stream is cued or not. The sole purpose of the “cue” was to subconsciously redirect attention briefly towards that particular stream at the start of each trial in order to ‘phase-reset’ any process, switching/oscillating feature-based resources over time. Performance imbalance across streams is hereby not altered by this phase-reset but remains constant since precision ratio is estimated across a large number of trials and durations. To clarify this issue, we rephrased relevant descriptions in the methods section.

2) Results of Exp.1 in the main text were different from those in Figure.

Thank you for spotting that error. We have corrected the figure accordingly.

3) It would be helpful to add more details for the assignation of response 1 and response 2 to target 1 and target 2, respectively, in all experiments.

For Experiment 2 and 3 only one response per trial was required by the subjects. This design was chosen to avoid potentially ambiguous response-target assignments.

However in the first experiment, as the reviewer points out, subjects gave two color estimates (one for each of the tracked color streams) within each trial. Given that we intend to split subjects’ target-response differences (precisions) into two distributions (based on the idea that each stream is being maintained by an independent attentional resource), there are two possible ways of assigning responses:

(1) We split responses into a best and worst independent of which response was given first.

(2) Alternatively, we assign target-response pairs based on the order of response. The assumption would be, that the first response would be the one with the highest confidence and would be paired with the target closest. This pairing would occur independent of the second response, which is consequently paired with the remaining target. This leaves open the possibility of the second target-response difference being better than the first one due to resource fluctuations. In general, this strategy would be less ‘rigid’ in dividing the two precision-responses into ‘good’ and ‘bad’ responses and was consequently chosen.

To avoid problems arising from the ambiguity of target-response assignments, in all following experiments (2/3), subjects were required to give one response per trial only. We will go into further detail on this issue with reviewer 3 as well, including a numerical example. The logic behind the target-response assignments in experiment 1 has been described in more detail in the methods.

Reviewer #2 (Publlic Review):

The authors asked the question about whether and how changing feature values within the same feature dimensions are tracked. Using a series of behavioral studies combined with modeling approaches, the authors report interesting results regarding a robust, uneven distribution of attentional resources between two changing feature values (in a 2:1 ratio), alternating at 1 Hz. Although the results are clear, it is important to rule out the possible biases due to computational processes. The results advanced our understanding of how parallel tracking of multiple feature values within the same dimension is achieved.

We thank the reviewer for the summary, including the potential impact on the field and we look forward to clarify methodological imprecisions.

Reviewer #3 (Public Review):

The study is interesting and the results are informative in how well people can report colors of two superimposed dot clouds. It reveals that there are trade-offs between reporting two colors. However, I have a few basic but major concerns with the present study and its conclusions about people's abilities to continuously track color values and the rate at which attention may be allocated across the two streams which I am outlining below.

We thank the reviewer for the positive description of our findings and look forward to address any remaining issues.

1) The first concern regards the task that was used to measure continuous tracking of feature values, which in my view is ambiguous in whether it truly assesses active tracking of features or rather short-term memory of the last-seen colors. Specifically, participants were viewing two colored dot clouds that then turned gray, and were asked to report each of the colors they saw using continuous report. The test usually occurred after 6-8s (in Exp. 1 &2), so while not completely predictable, participants could easily perform the task without tracking both feature streams continuously and simply perform the color report based on the very last colors they saw. In other words, it does not seem necessary to know which color belonged to which stream, or what color it was before, to perform the task successfully. Thus, it is unclear to what extent this task is actually measuring active tracking, the same way tracking of spatial locations in multiple-object tracking tasks has been studied, which is the literature that the authors are trying to draw parallels to. In multiple-object tracking tasks, targets and nontarget objects look identical and so to keep track of which of the moving objects are targets, participants need to attend to them actively and selectively. (Similarly, the original feature-tracking study by Blaser et al., at least in their main experiment, people were asked to track an object superimposed on a second object which required continuous and selective tracking of that object).

The reviewer addresses a very fundamental point regarding ‘tracking’ in general: Does tracking rely on attentional processes or mere perception.

The reviewer posits that subjects may simply ‘report based on the very last color they saw’ without the need to track both features streams continuously. Our argument supported by a broad literature on change blindness, inattentional blindness and related phenomena (c.f. Rensink, 2000) is, that one cannot consciously report a changing feature-value without continuously attending to it, in particular when it moves around randomly in feature space. The report of a feature value at a random unpredictable time t by ‘identifying it’ includes its attentive processing immediately before t. Since the time of the probing identification is random, it must continue throughout the trial. We do also rule out any strategy in which subjects only start tracking after some time (the probe appears between 6-8sec after trial onset) since such a strategy would involve processes of temporal attention as well and increase difficulty.

Lastly, the reviewer refers to Blaser et al. as an example in which attentive tracking would be required, since ‘an object [is] superimposed on a second object’. We do absolutely agree. However, the same design principle applies in the current experiment: Two objects with separate values in feature space, that continuously change, are superimposed, that is, spatially inseparable. We do believe that the continuous movement of the feature values through color space separates this work from previous feature-tracking studies like Re et al., in which the presented features remained static. The latter work gives rise to alternate explanations in terms of working memory (mentioned in the next point of the reviewer). Once feature values keep changing and are relevant, a process of updating their internal representations in order to grant access is required (i.e. attention).

2) The main claim that tracking two colors relies on a shared and strictly limited resource is primarily based on the relation between the two responses people give, such that the first response about one color tends to be higher accuracy than for the second response of the other color across participants. In my view, this is a relatively weak version of looking at trade-offs in resources, and it would have been more compelling to show such trade-offs at a single-trial level, or assess them with well-established methods that have been developed to look at attentional bottlenecks such as attention-operating characteristics that allow quantifying the cost of adding an additional task in a precise and much more direct manner.

The reviewer suggests showing trade-offs at a single trial level within subject, which is in essence what we have done in experiment 1. Testing both streams simultaneously, however, has the drawback of introducing interference effects during the report (Reporting the first stream may degrade the precision of reporting the second stream) as well as the mentioned ambiguity between targets and responses. The second and third experiment circumvent this by probing only one color stream, as to analyze the data with a minimal set of assumptions. As the dependent measure of ‘precision’ fluctuates highly across trials, we have to estimate an overall tracking resource by creating a ‘precision’ distribution across many trials.

3) Finally, the data of the last experiment is taken as evidence that feature-based selection oscillates at 1Hz between the two streams. This is based on response errors changing across time points with respect to an exogenous cue that is thought to "reset" attentional allocation to one stream. Only one of three data sets (which uses relatively sparse temporal sampling) shows a significant interaction between cue and time, and given that there was no a priori prediction of when such interaction should occur, this result begs for a replication to ensure that this is not a false positive result. Furthermore, based on the analyses done in the paper, it may very well be the case that the presumed "switching rate" is entirely non-oscillatory based on a recent very important paper by Geoffrey Brookshire (2022, Nature Human Behavior) that demonstrates that frequency analysis are not just sensitive to periodic but also aperiodic temporal structures. The paper also has a series of suggested analyses that could be used here to further test the current conclusions.

The reviewer is absolutely correct in doubting the oscillatory nature of the results in Exp3. Importantly, in our discussion we do not claim that a regular periodicity of the attentional process maintains both color streams. In contrast, we stress the point of ‘one-feature at a time’, indicating a constraint that entails alternation between two representations. We do not presume any sort of regularity of this process but, instead, consider the switching being determined by the recurrent processing of tuning towards one of the two relevant values. Our interpretation is therefore largely in line with Brookshires criticism of previous attentional oscillation studies. In fact, we entirely share the doubtful interpretation of attentional oscillations that transfer mathematical modelling onto functional processes. In our study we use the tool of Fourier transformation in a mere methodological manner, in order to quantify alternations between our color streams but not to imply an underlying oscillatory process. We cannot draw conclusions about underlying attentional oscillations especially since we quantify the alternation/switch only across one full and one half period, in exp3a and exp3b respectively.

We make the distinction between oscillations as a methodological tool and functional cognitive process more clear in the paper.

Author Response

eLife Assessment:

The fluorescently tagged SYT-1 mouse line will be useful for the field. Importantly, the authors used a comprehensive set of immunohistochemical and physiological experiments to demonstrate that the fluorescence tagging did not alter the function of SYT-1. These are important control experiments that will make the strain useful for physiological experiments in the future. However, the advance of this manuscript is less clear.

We thank the editor for raising this point. In the revised manuscript, we performed additonal experiments including testing the expression level of Syt1-TDT and testing the co-labeling of Syt1-TDT with synaptic marker in situ. We also dicussed the advantage of our model compared with the existed ones in line 285 to 300 in the section of discusion. Briefly, we conclude the advance of our models as follows: First, the Syt1-TDT could label synapse in situ, especially in glomerular layer of olfactory bulb (compared with B6SJL-Tg(Thy1-Syt1/ECFP)1Sud/J (Han et al. 2005)). Second, we provided a potential usage of our model in the study of electrophysiological recording and imaging in vivo, as the electrophyiological properties of neurons from Syt1-TDT mice are normal (not be analyzed in B6.Cg-Tg(Thy1-YFP/Syp)10Jrs/J and B6;CBA-Tg(Thy1-spH)21Vnmu/J (Umemori et al. 2004; Li et al. 2005)), which might be result from the relative low expression of Syt1-TDT compared with the native Syt1. Third, the neurons from the transgenic mice can be used in ASF screening by skiping the procedure of immunostaining. It will save the cost of time, reagents and work.

Reviewer #1 (Public Review):

In this manuscript, Zhang and colleagues created a transgenic mouse strain that expresses SYT-1-tdt in all neurons. They showed that the labelled SYT-1 colocalizes with multiple synaptic markers and label synapses in different regions. More importantly, they showed that the transgenic expression does not alter synaptic function using ephys assays. This is a straightforward paper that generated a useful reagent that will be used broadly.

We are grateful for the reviewer’s positive comments.

Reviewer #2 (Public Review):

Yang et al. produced a transgenic mouse line (Syt1-TDT) that could be used for labeling both excitatory and inhibitory synaptic sites in cultured neurons and in vivo neurons. The strength of the current study is to provide a series of thorough analyses to claim the applicability of this mouse line in the relevant neuroscience research field(s). The weakness is the potential impact/usefulness of this mouse line. To strengthen the merit of this mouse line, the authors should present evidence showing its advantage over other similar genetic approaches.

We thank the reviewer for raising this point. To strengthen the merit of this mouse line, we tested the application of Syt1-TDT in labeling synapse in situ. We found that the Syt1-TDT is highly overlapped with synapsin in the brain slice, especially in hippocampus, cerebellum and olfactory bulb, which suggest a potential usage of our model in imaging synapse in vivo. We also compared our transgenic model with the existed ones in line 285 to 300 in the section of discussion in the revised manuscript:

“Several fluorescently tagged synaptic protein transgenic mice model, such as YFP tagged synaptophysin and pHluorin tagged synaptobrevin have been developed to label synapses [49, 50]. While these models can label synapse well, it lacks the functional analysis of neurotransmitter release in the overexpressed neurons as synaptophysin and synaptobrevin were reported to play a role in regulating neurotransmitter release. Considering the overexpression of synaptobrevin or synaptophysin were reported to promote neurite elongation or enhance neurotransmitter secretion, the synaptic organization and synaptic transmission might be changed in these models. Weiping Han et al. in their previous work [47] have generated transgenic mice expressing a Syt1-ECFP fusion protein. The Syt1-ECFP mice expressed the fluorescent protein ECFP in the cortex, midbrain, and cerebellum. However, the expression pattern in their model showed some difference with ours: In the olfactory bulb, the Syt1-TDT signals were highly enriched in glomerular layer in our model, which was not observed in the previously reported Syt1-ECFP transgenic mice [47]. It suggested a potential application of our model in labeling synapse in glomerular layer of olfactory bulb compared with Syt1-ECFP transgenic mice.”

Reviewer #3 (Public Review):

Yang and colleagues provide a thorough characterization of a transgenic mouse model expressing fluorescently tagged synaptotagmin. In particular, they present key controls validating this mouse model as a tool, including co-localization of the tagged synaptotagmin with other synaptic markers as well as normalcy of synaptic transmission mediated by synaptic terminals expressing the tagged synaptotagmin. Importantly, the authors present data on the potential use of neuronal cultures obtained from these mice in synaptic co-culture assays. In these assays, synaptic cell adhesion molecules expressed on non-neuronal cell lines such as HEK-293 cells or COS cells are used to test the sufficiency of these molecules to trigger synapse assembly. This mouse model will be a useful addition to existing models expressing fluorescently-tagged synaptic vesicle proteins such as synaptophysin, synaptotagmin as well as synaptobrevin.

We are grateful for the reviewer’s positive comments.

Author Response

Reviewer #1 (Public Review):

Bakoyiannis et al. investigated the distinct contribution of ventral hippocampal outputs to the nucleus accumbens and medial prefrontal cortex on memory in mice exposed to a high-fat diet (HFD) beginning in adolescence. The authors first characterize the hippocampal to accumbens or mPFC circuits using intersectional viral approaches. They then replicate their previous finding that adolescent HFD contributes to the overactivation of the ventral hippocampus during contextual learning via quantification of c-fos+ cells. In this manuscript, the authors further explore the distinct contribution of these two outputs from the ventral hippocampus using chemogenetics to specifically inhibit one circuit or the other. Interestingly, the authors find that inhibition of either circuit returns c-fos+ cell number to control levels, but the effects on memory are dissociable. They demonstrate that inhibition of output to the NAc rescues HFD-induced deficits on object recognition, while inhibition of mPFC outputs rescues HFD-induced deficits on object location recall. The authors further confirmed that chemogenetic manipulations resulted in alterations in c-fos+ cells that were specific to CA1, and not CA3 or DG. Behaviorally, they excluded any contribution of anxiety on recall, finding no effect on the elevated plus maze.

The strengths of this manuscript include robust behavioral findings that can be attributed to specific circuits. The conclusions of this paper are largely well supported by the data, although some of the methods could provide more detail and the statistical approaches used for analysis need improvement.

We thank the Reviewer for thoroughly summarizing the main results of the study and for providing the comments that we address below.

Reliance on only one measure of anxiety to exclude this as a confound on recall performance is a weakness of the manuscript. To be more convincing that anxiety is not a confound, more than one behavioral assay should be performed.

Reviewer #2 (Public Review):

Bakoyiannis et al. aim to analyze the impact of high-fat diet (HFD) intake during the preadolescent period on memory performances by optogenetically manipulating the circuits responsible for related memory performances. In previous work, they showed the possibility to rescue object-based memory impairments in HFD-exposed animals by silencing the ventral hippocampus (vHPC). Here they investigated further the projections to the nucleus accumbens (NAc) and medial prefrontal cortex (mPFC), 2 of the main monosynaptic targets of the vHPC.

They used a precise strategy to target and manipulate only vHPC cells that project to either NAc or mPFC. They found that preadolescent HFD can induce different types of memory deficits related to different vHPC pathways. In particular, they found that silencing vHPC-NAc, but not vHPC-mPFC, pathway restored HFD-induced object recognition memory deficit. On the other side, silencing vHPC to mPFC, but not vHPC-NAc, pathway rescued HFD-induced object location memory deficits. Moreover, these pathways do not control anxiety-like behaviours since their inactivation has no effect on anxiety levels.

We thank the Reviewer for summarizing the findings of the study and for their positive comments on our manuscript.

The conclusions of the manuscript are mostly supported by the results, but there are some points and controls that need to be addressed and clarified:

• While identifying the relevance of hippocampal cells projecting to NAc and mPFC, a missing control is to verify the activity of vHPC not projecting to these 2 regions in normal conditions or when the investigated pathways are manipulated. This control is essential to refine and bring novel results related to their previous discovery that vHPC overall is involved in the process.

• A downstream effect of their optogenetic manipulation on NAc and mPFC cellular populations should be shown if they want to claim that their chemogenetic inhibition decrease the activation of the pathway and not only of vHPC projecting neurons.

New c-Fos experiments were performed. Please see our response to points 4-5-6 in the “Essential Revision” section.

Reviewer #3 (Public Review):

"Obesogenic diet induces circuit-specific memory deficits in mice" by Bakoyiannis et al., investigates the role of specific ventral hippocampal circuits (specifically to nucleus accumbens and mPFC) in high-fat diet-induced memory deficits. The authors had previously shown that increases in activity in the ventral hippocampus accompany high-fat diet-induced memory deficits, and that inhibition of activity thereby normalizes those memory deficits. In this manuscript, the authors extend these findings to specific projections, showing that they normalize different types of memories by inhibiting the two different pathways.

The strengths of the paper include the pathway-specific manipulations that reveal a difference between the two types of memory. The results are a modest step forward for the field of feeding and learning and memory and would be of interest to that subgroup of neuroscientists. However, the paper also has a number of weaknesses which I detail below.

We thank the Reviewer for summarizing the finding of our study and for the positive feedback.

1) First, the authors show an effect of cfos from both pathways in Figure 2 on object learning. However, the inactivation studies show a pathway-specific effect on object recognition and object location, with no experiments to delineate how this divergence occurs. The authors do not specify whether they compared cfos in the control group between NAc and mPFC projections (presumably they did some controls with each injection), which might reveal differences.

We have added new groups and presented/analyzed the results for each pathway (either vHPC-NAc pathway or vHPC-mPFC pathway) separately for c-Fos (new Figure 2 and Figure 2-Figure Supplement 1) or behaviours (new Figure 3 and Figure 3-Figure Supplement 1). Please see our responses to points 2, 4-5-6 and 9 in the “Essential Revision” section.

2) Related to this, it is unclear how the pathways end up diverging for memory if they do not show any differences in cfos during training. Perhaps there are pathway-specific differences in cfos following the ORM and OLM tests? It is difficult to support the claim that there are pathway differences in memory following inactivation if we do not see any pathway-specific change in activity.

We thank the Reviewer for this comment. Please see our answer to point 7 in the “Essential Revision” section above.

3) Figure 2 and Figure 3 are also hard to interpret because of the usage of a 1-way ANOVA which is not the appropriate statistical test when there are two independent variables (HFD and DREADD manipulation). Indeed, noticing the statistical test also reveals that a critical control missing: HFD -, hM4di+CNO +. It is possible that inactivation simply brings down cfos levels regardless of diet. While this might benefit memory in the case of HFD, it is critical to know whether the manipulation is specific to the overactivation caused by HFD or just provides a general decrease in activity.

Based on this comment we added new HFD-hM4di+CNO+ groups and modified statistical analyses accordingly. Indeed, inactivation of each pathway (vHPC-NAc or vHPC-mPFC) decreases c-Fos in both HFD+ and HFD- (CD+) groups (new Figure 2) whereas it has opposite effect on behaviors, improving memory performance in HFD+ groups but impairing or having no effect in HFD- (CD+) groups (new Figure 3). We have corrected this in the manuscript (please see our responses to points 2 and 9 of “Essential Revision” section).

Author Response

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

eLife assessment

This paper reports the fundamental discovery of adrenergic modulation of spontaneous firing through the inhibition of the Na+ leak channel NALCN in cartwheel cells in the dorsal cochlear nucleus. This study provides unequivocal evidence that the activation of alpha-2 adrenergic or GABA-B receptors inhibit NALCN currents to reduce neuronal excitability. The evidence supporting the conclusions is compelling, the electrophysiological data is high quality and the experimental design is rigorous.

Public Reviews:

Reviewer #1 (Public Review):

This study uses electrophysiological techniques in vitro to address the role of the Na+ leak channel NALCN in various physiological functions in cartwheel interneurons of the dorsal cochlear nucleus. Comparing wild type and glycinergic neuron-specific knockout mice for NALCN, the authors show that these channels 1) are required for spontaneous firing, 2) are modulated by noradrenaline (NA, via alpha2 receptors) and GABA (through GABAB receptors), 3) how the modulation by NA enhances IPSCs in these neurons.

This work builds on previous results from the Trussell's lab in terms of the physiology of cartwheel cells, and from other labs in terms of the role of NALCN channels, that have been characterized in more and more brain areas somewhat recently; for this reason, this study could be of interest for researchers that work in other preparations as well. The general conclusions are strongly supported by results that are clearly and elegantly presented.

I have a few comments that, in my opinion, might help clarify some aspects of the manuscript.

1. It is mentioned throughout the manuscript, including the abstract, that the results suggest a closed apposition of NALCN channels and alpha2 and GABAB receptors. From what I understand, this conclusion comes from the fact that GABAB receptors activate GIRK channels through a membrane-delimited mechanism. Is it possible that these receptors converge on other effectors, for example adenylate cyclase (see https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6374141/).

We have now tested the role of adenylyl cyclase modulation in the control of NALCN, by saturating the cells with a cAMP analogue 8-Br-cAMP and found no effect on the NA response. These data are included in the paper. While further experiments are necessary, these results argue in favor of a direct gating by G-proteins.

1. In Figure 2G, the neurons from NALCN KO mice appear to reach a significantly higher frequency than those from WT (figure 2E, 110 vs. 70 spikes/s). Was this higher frequency a feature of all experiments? The results mention a rundown of peak firing rate due to whole-cell dialysis, but, from what I understand, the control conditions should be similar for all experiments.

The peak firing rates in control solutions for WT and KO CWC are not statistically different.

1. Also in Figure 2, the firing patterns for neurons from WT and NALCN KO mice appear to be quite different, with spikes appearing to be generated during the hyperpolarization of the bursts in the second half of the current step for WT neurons but always during the depolarization in KO neurons. Was this always the case? If so, could NALCN channels be involved in this type of firing? Along these lines, it would be interesting to show an example of a firing pattern of neurons from WT mice in the presence of NA, which inhibits NALCN channels.

The specific pattern of spikes in CWC is quite variable from trial-to-trial or cell-to-cell, as it is dependent on multiple CaV and calcium dependent K channels subtypes, and is not dependent on the genotypes used here. The primary effects observed in the KO are in background firing and sensitivity to NA, both reflected alterations in rheobase. The firing pattern example requested was shown in the raster plot of fig 2B2.

1. It might be interesting to discuss how the hyperpolarization induced by the activation of GIRK channels and inhibition of NALCN channels could have different consequences due to their opposite effect on the input resistance.

We considered this as a point of discussion, but decided that making sense of it would depend on assumptions about the location of the channels (dendritic vs somatic, distance to AIS) that we do not have data for. For example, a dendritic increase in resistance through NALCN block, leading to a hyperpolarization of the soma, might have actions similar to a somatic hyperpolarizing conductance increase by GIRK, as far as the voltage at the AIS is concerned.

Reviewer #2 (Public Review):

This is a very interesting paper with several important findings related to the working mechanism of the cartwheel cells (CWC) in the dorsal cochlear nucleus (DCN). These cells generate spontaneous firing that is inhibited by the activation of α2-adrenergic receptors, which also enhances the synaptic strength in the cells, but the mechanisms underlying the spontaneous firing and the dual regulation by α2-adrenergic receptor activation have remained elusive. By recording these cells with the NALCN sodium-leak channel conditionally knocked, the authors discovered that both the spontaneous firing and the regulation by noradrenaline (NA) require NALCN. Mechanistically, the authors found that activation of the adrenergic receptor or GABAB receptor inhibits NALCN. Interestingly, these receptor activations also suppress the low [Ca2+] "activation" of NALCN currents, suggesting crosstalk between the pathways. The finding of such dominant contribution of the NALCN conductance to the regulation of firing by NA is somewhat surprising considering that NA is known to regulate K+ conductances in many other neurons.

The studies reveal the molecular mechanisms underlying well known regulations of the neuronal processes in the auditory pathway. The results will be important to the understanding of auditory information processing in particular, and, more generally, to the understanding of the regulation of inhibitory neurons and ion channels. The results are convincing and are clearly presented.

Reviewer #3 (Public Review):

The study by Ngodup and colleagues describes the contribution of sodium leak NALCN conductance on the effects of noradrenaline on cartwheel interneurons of the DCN. The manuscript is very well-written and the experiments are well-controlled. The scope of the study is of high biological relevance and recapitulates a primary finding of the Khaliq lab (Philippart et al., eLife, 2018) in ventral midbrain dopamine neurons, that Gi/o-coupled receptors inhibit NALCN current to reduce neuronal excitability. Together these studies provide unequivocable evidence for NALCN as a downstream target of these receptors. There are no major concerns. I have only minor suggestions:

Minor

1. As introduced in the introduction, NALCN is inhibited by extracellular calcium which has led to some discourse of the relevance of NALCN when recorded in 0.1 mM calcium. A strength of this study is the effect of NA on NALCN is recorded in physiological levels of calcium (1.2 mM). I suggest including the concentration of extracellular calcium in the aCSF in the Results section instead of relying on the reader to look to the Methods.

Done.

1. It would be interesting to include the basal membrane properties of the KO compared to wildtype, including membrane resistance and resting membrane potential. From the example recording in Figure 2, one might think that the KOs have lower membrane resistance, so it is interesting that the 2 mV hyperpolarization produced similar effects on rheobase. In addition, from the example in Figure 2G, it appears that NA has an effect on firing frequency with large current injection in the KO. Is this true in grouped data and if so, is there any speculation into how this occurs?

We have included in the text a comparison of the input resistance in WT and KO. These were not different. This should not be too surprising given the wide range of values between animals, and the necessity to compare populations. Measurements of resting potential are complicated by the fact that CWC are normally spontaneously active. As was discussed in the text, peak firing frequency declined with time during recording in both control and KO, necessitating normalization as shown in Fig 2E-H.

1. Please expand on the rationale for why GABAB and alpha2 must be physically close to NALCN. To my knowledge, the mechanism by which these receptors inhibit NALCN is not known. Must it be membrane-delimited?

Given the known membrane delimited modulation of GIRK by GABAB, and that alpha2 and GABAB receptors appear to share the same population of NALCN channels, and that alpha2 receptors do not appear to target GIRK channels, we felt the simplest explanation would be coupling through G-proteins, with spatial segregation of different receptor/channel pools providing the means for separating GIRK and NALCN effects. Given that the alpha2 receptor is a Gi/o GPCR, we have now included in the revision new experiments using 8-Br-cAMP, as discussed above. These showed no effect on the NA response, consistent with a direct effect membrane delimited of G-proteins. We acknowledge however that further experiments are warranted.

Reviewer #1 (Recommendations For The Authors):

1. I suggest labeling the voltage traces in Figure 2 with WT and KO for easier comprehension; in addition, I suggest adding the average data to the plots in Figure 2, as in Figure 2-supplementary Figure 1 panel F.

We have added the figure labels as requested. We chose not to add the average data as we noticed that averaging the full FI plots led to a smearing of the curves and a distortion in the apparent rheobase. Thus, we instead measured the rheobase for individual cells and report their average.

1. For readers that are not familiar with the field, more details should be given about the electrical stimulation to evoke IPSCs in cartwheel cells, and what they represent.

Done.

1. The methods should mention if and how the concentrations of divalents were adjusted in the experiments with 0.1 extracellular Ca2+

Done.

Reviewer #2 (Recommendations For The Authors):

I only have several minor comments.

1. The total lack of spontaneous firing in CWCs in the NALCN KO (Fig. 1) is interesting and provides an opportunity to probe the in vivo function of such spontaneous firing. Besides being a little smaller, do the mutant mice have any sign of abnormality in sound signal processing?

Figure 1 – Figure supplement 1 showed that there are no effects on auditory brainstem responses in the KO.

1. Figs. 3&4 (and several other figures with voltage-clamp recordings), a line indicating zero current level would be useful.

Done

1. page 7, "Outward current generated by suppression of NALCN": it might be better to state as "Outward response generated by suppression of NALCN", as the authors correctly pointed out that the NA-induced apparently outward current response is largely a result of an inhibition of NALCN-mediated inward Na+ current. One way to clarify this might be to record at the Nernst potential of K+ to isolate the contribution of Na+ currents (unclear if K+- or Cs+-based pipette was used in the experiment in Fig 3).

Text has been modified.

1. Figs. 5,6&7: do the dashed lines indicate initial current level or zero current level?

Initial current. See legends.

1. The labeling of some of the bar graphs can be made more clear. For example, in Fig. 2K, the right two columns should be labeled as WT as well. Fig. 3C & Fig. 4C, the left two columns should be labeled as WT and the right two as KO.

Added labels to Fig 2 as requested.

1. Figs. 5-7: The suppression of low extracellular [Ca2+]-induced NALCN-dependent current by NA and baclofen is very interesting. As the tonic inhibition of NALCN by extracellular Ca2+ is likely through a Ca2+-sensing GPCR (CaSR) and G-proteins (lowering [Ca2+] releases the inhibition and generates inward current) (Lu et al. 2010), the action of NA and baclofen may all converge onto the same G-protein dependent pathway of the Ca2+-sensing receptor. I'd include this in the discussion to provide a potential mechanistic explanation of the interesting observation.

This is indeed an interesting idea. We prefer not to discuss here, as 1) the source of Ca2+ sensitivity of the channel seems to be controversial (Chua et al 2020), and 2) the effect of Ca2+ reduction is enormously slower than the effect of the modulators (Fig 5-7), implying distinct mechanisms.

Reviewer #3 (Recommendations For The Authors):

Typos/general comments

1. Figure 2 would be easier to comprehend with WT and KO labels as in the other figures. Done

2. Page 11, size of the IPSCs in NA is missing the minus sign.

Corrected.

1. Is the y-axis correct on Figure 8B? This looks like it is doubling the size of the IPSC.

Thank you for catching this mistake. The formula used to calculate % change was in error. We have corrected all the data analysis in the figure, which fortunately did not change the conclusion. Regarding the axis, note that the measurement was % change, not ratio of drug vs control.

Author Response

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

We thank the reviewers and editors for their constructive comments on the manuscript. We have extensively revised the manuscript based on these concerns and comments. The followings are the specific answers.

Public Reviews:

Reviewer #1 (Public Review):

In the manuscript "Long‐read single‐cell sequencing reveals expressions of hypermutation clusters of isoforms in human liver cancer cells", S. Liu et al present a protocol combining 10x Genomics single-cell assay with Element LoopSeq synthetic long-read sequencing to study single nucleotide variants (SNVs) and gene fusions in Hepatocellular carcinoma (HCC) at single‐cell level. The authors were the first to combine LoopSeq synthetic long‐read sequencing technology and 10x Genomics barcoding for single cell sequencing. For each cell and each somatic mutation, they obtain fractions of mutated transcripts per gene and per each transcript isoform. The manuscript states that these values (as well as gene fusion information) provide better features for tumor-normal classification than gene expression levels. The authors identified many SNVs in genes of the human major histocompatibility complex (HLA) with up to 25 SNVs in the same molecule of HLA‐DQB1 transcript. The analysis shows that most mutations occur in HLA genes and suggests evolution pathways that led to these hypermutation clusters. Yet, very little is said about novel isoforms and alternative splicing in HCC cells, differences in isoform ratio between cells carrying different mutations, or diversity of alternative isoforms across cells. While the manuscript by Liu et al. presents a promising combination of technologies, it lacks significant insights, a comprehensive introduction, and has significant problems with data description and presentation.

Answer: Thanks for the precious suggestion. Our long-read single-cell sequencing has discovered an average of 442 novel isoform transcripts per benign liver cell and 450 novel isoform transcripts per HCC cell per SCANTI v1.2 analysis. These are stated in the revised manuscript. The alternative splicing was detected by differential isoform expression as demonstrated in supplemental figures 6 and 7 and supplemental tables 8-11. The examples of differences in isoform ratio between cells carrying different mutations are now shown by DOCK8 and STEAP4 (figure 5 in the revised manuscript). A new section was added in the results to discuss the mutation expression of these two genes. The diversity of isoforms of the selected genes is shown in Supplemental Figure 10.

This study showed how mutations in the same allele evolved in liver cancer. In particular, HLA hypermutations were found to develop from some specific sites of the molecules into large clusters of mutations in the same molecules. A new paragraph of introduction was added about the role of mutations in human cancer development. We also revised the figures to present the information better. All the HLA genes expressed only one known isoform, as shown in Figure 4 and Supplemental Figure 3, regardless of mutations.

Major comments:

1. The introduction section is scarce. It lacks description of important previous works focused on clustered mutations in cancers (for example, PMID35140399), on deriving the process of cancer development through somatic evolution (PMID32025013, from single cell data PMID32807900). Moreover, some key concepts e.g. mutational gene expression and mutational isoform expression are not defined. The introduction and the abstract contain slang expressions e.g. "protein mutation', a combination of terms I teach my students not to use.

Answer: We appreciate the reviewer for the idea of more solid background introduction and term definition. We added a new paragraph in the introduction section to introduce the role of mutations and hypermutations in human cancers. Some important work has been cited. We added a new section in the "Methods" to define "mutation gene expression share" and "mutation isoform expression share". "Protein mutation" has been replaced by "genetic mutation".

1. In the results section, to select the mutations of interest, the authors apply UMAP dimensionality reduction to the mutation isoforms expression and cluster samples in UMAP space, then select the mutations that are present only in one cluster, then apply UMAP to the selected mutations only and cluster the samples again. The motivation for such a procedure seems unclear, could it be replaced with a more straightforward feature selection?

Answer: Thanks for raising up this important question. The goal of the analysis is an unbiased classification of the cell populations in the samples. We found that by removal of mutated isoform expressions that were at similar levels of all cells, the UMAP clustering generated clear segregation of three population cells. When the unique mutated isoform expressions from each group were applied, it generated highly distinct 8 groups of cells, with each group having a distinct mutation isoform expression pattern. If we force known knowledge into the mix of the analysis, it may generate unwanted bias. Specifically, the first UMAP was performed in an unbiased way to cluster cells, while the second step is a supervised approach by selecting the unique mutations in each cluster to identify the classifiers. The second UMAP matches the Benign/HCC labeling well.

1. As I understand, the first "mutated isoform"-based UMAP clustering was built from expression levels of 205 "mutational isoforms". What was the purpose and outcome of the second "mutated isoform"based UMAP clustering (Figure 2E)? In the manuscript the authors just describe the clusters and do not draw any conclusions or use the results of the clustering anywhere further.

Answer: Thanks for pointing this out. Figure 2E was generated from unique mutation isoform expressions in groups A, B, and C from Figure 2D. The purpose of Figure 2E is to investigate whether these unique mutation isoforms can further classify the cell populations free of prior biological knowledge. We added a sentence in the revision to clarify the purpose of the clustering. The conclusion from this analysis, including Figure 2F and Figure 3 (which is an extension of Figure 2E), is that HLA mutation isoform expressions dominated the classifications of cell populations.

1. The authors just cluster the data three times based on expression levels of different sets of "mutational isoforms" and describe the clusters. What do we need to gather from these clustering attempts besides the set of 113 mutations used for further analysis? What was the point of the reclusterings? Did the authors observe improvement of the classification at each step?

Answer: Thanks for asking this important question. The improvement of re-clustering to classify cell populations is the obvious segregation of 8 different groups of cells without any manual classification through prior knowledge. The distances among groups were far apart in comparison to the first clustering (figure 2B). Detailed subclassifications were achieved on cell populations that otherwise could not be segregated based on the first clustering.

1. The alignment of short reads generated from hypermutated transcriptomes is non-trivial. The proposed approach could address the issue without the need for whole genome sequencing and offer insights about the cancer development through somatic evolution. Why didn't the authors use modern phylogenetic approaches in the "Evolution of mutations in HLA molecules" section or at least utilize the already performed clustering to infer cell lineages?

Answer: We appreciate for the great question. For a single molecule mutation evolution, single gene clustering may not produce a desirable and robust effect. A simple evolution snowball chart in Figure 4B may be easier to be understood.

1. I am not sure I understood the definition of "mutated gene expression levels" and "mutated isoform expression levels" in the "Mutational gene expression and fusion transcript enhanced transcriptome clustering of benign hepatocytes and HCC" section. The authors mention that gene lists included all the isoforms within the same range of standard deviation. If I understand it correctly, they are equal if there is only one expressed transcript isoform. In that case, this overlap is not surprising at all.

Answer: We thank the reviewer for the great question. The definition of mutation gene expression level, mutation isoform expression level, and fusion gene expression level are now defined in the "Methods" section. In all HLA mutation transcripts, there were multiple transcripts with or without mutations for a single dominant isoform.

1. "To investigate the roles of gene expression alterations that were not accompanied with isoform expression changes, UMAP analyses were performed based on the non‐overlapped genes." Venn diagrams (Sup Figure 8) show that there are much less "non-overlapped genes" than "genes that showed both gene and isoform level changes" for each SD threshold (for example, for SD>=0.8 59 vs 275). Could that be the reason why clustering based on the former group is worse i.e the cancer and normal cells are separated less clearly?

Answer: The number of (attributes) genes could be a contributing factor in the segregation of cell populations. However, the number of attributes is not the underlying reason for worse performance for gene only classifier because much smaller isoforms/genes (22) overlap in SD>=1 outperformed a large number of genes (59) with SD>=0.8. It suggested that 59 gene expression classifier is less efficient in segregating the cell populations. To address this concern, we took SD>=0.8 as an example for demonstration if we subsampled the 275 overlapped genes/isoforms to 59 (equal to 59 non-overlapped genes in terms of number), we can still get better separation than the 59 DEG only. We repeated this subsampling process for three times. Similar results were found. The new data were inserted into supplemental Figure 8

Reviewer #2 (Public Review):

In the present study, Liu et al present an analysis of benign and HCC liver samples which were subjected to a new technology (LOOP-Seq) and paired WES. By integrating these data, the authors find isoforms, fusions and mutations which uniquely cluster within HCC samples, such as in the HLA locus, which serve as candidate leads for further investigation. The main appeal of the study is in the potential of LOOPSeq as a method to present isoform-resolved data without actually performing long-read sequencing. While this presents an exciting new method, the current study lacks systematic comparisons with other technologies/data to test the robustness, reproducibility and utility of LOOPSeq. Further, this study could be further improved by giving more physiologic context and examples from the analyses, thus providing a new resource to the HCC community. A few suggestions based on these are below:

Answer: We appreciate the reviewer to raise up all the important questions and the great suggestions. The LOOPseq technology was compared with Oxford nanopore and PacBio long-read sequencing in our previous study. We have cited analysis in the introduction section of the paper. HLA mutation clusters in the single molecules are our finding with major physiological significance since these mutations may help liver cancer cells evade immune surveillance. We have extensively discussed the potential impact of these mutations on cancer development in the discussion. In addition, we added a new section of DOCK8 and STEAP4 mutation expressions in the results (page 11, new Figure 5) that are highly relevant to the pathogenesis of HCC.

1. A primary consideration is that this seems to be the first implementation of LOOP-Seq, where the technology, while intriguing, has not been evaluated systematically. It seems like a standard 10x workflow is performed, where exons are selectively pulled down and amplified. Subsequent ultra-deep sequencing is assumed to give isoform-resolution of the sc-seq data. To demonstrate the utility of the approach it would benefit the study to compare the isoform-resolved results with studies where long-read sequencing was actually performed (ex: https://journals.lww.com/hep/Fulltext/2019/09000/Long_Read_RNA_Sequencing_Identifies_Alternativ e.19.aspx, https://www.jhep-reports.eu/article/S2589-5559(22)00021-0/fulltext, https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1010342). Presumably, a fair amount of overlap should occur to justify the usage.

Answer: We have discussed the utility of the methodology in comparison with the previous studies by these three groups in the revision (results, page 12).

1. Related to this point, the sc-seq cell types and benign vs HCC genes should be compared with the wealth of data available for HCC sc-seq (https://www.nature.com/articles/s41467-022-322833, https://www.nature.com/articles/s41598-021-84693-w). These seem to be important to benchmark the technology in order to demonstrate that the probe-based selection and subsequent amplification does not bias cell type definition and clustering. In particular, https://www.nature.com/articles/s41586021-03974-6 seems quite relevant to compare mutational landscapes from the data.

Answer: This is a great point. The consistency probe-based analysis was demonstrated in our previous analyses and the analyses mentioned in the comments. We further discussed it in the results section of the paper (page 12).

1. From the initial UMAP clustering, it will be important to know what the identities are of the cells themselves. Presumably, there is quite a bit of immune cells and hepatocytes, but without giving identities, downstream mechanistic interpretation is difficult.

Answer: When mutation analyses were combined with cell marker analysis, i.e., immune marker positive but negative in HLA mutation, we found only one bona fide immune cell in the HCC sample. Thus, immune cells may not be significant in the current analysis.

1. In general, there are a fair amount of broad analyses, such as comparisons of hierarchical clustering of cell types, but very little physiologic interpretations of what these results mean. For example, among the cell clusters from Fig 6, knowing the pathways and cell annotations would help to contextualize these results. Without more biologically-meaningful aspects to highlight, most of the current appeal for the manuscript is dependent on the robustness of LOOP-seq and its implementation.

Answer: To address this comment, a new pathway analysis was performed on the cluster results of Figure 6. A new supplemental table was generated. The results are now discussed on page 13.

1. Many of the specific analyses are difficult and the methods are brief. Especially given that this technology is new and the dataset potentially useful, I would strongly recommend the authors set up a git repository, galaxy notebook or similar to maximize utility and reproducibility

Answer: The script file has been uploaded to GIT to facilitate the reproducibility of the analysis. We also added a new pipeline description script in the methods (pages 19-20).

1. The authors claim that clustering between benign and HCC samples was improved by including isoform & gene (Suppl fig 8). This seems like an important conclusion if true, especially to justify the use of longread implementation. Given that the combination of isoform + gene presents ~double the number of variables on which to cluster, it would be important to show that the improved separation on UMAP distance is actually due to the isoforms themselves and not just sampling more variables from either gene or isoform

Answer: The number of (attributes) genes could be a contributing factor in the segregation of cell populations. However, the number of attributes is not the underlying reason for worse performance for gene only classifier because much smaller isoforms/genes (22) overlap in SD>=1 outperformed a large number of genes (58) with SD>=0.8. It suggested that 58 gene expression classifier is less efficient in segregating the cell populations. To address this comment, we performed random subsampling to reduce the isoform/gene overlap iterates, similar results were obtained. A new supplemental figure was generated to reflect the new analyses.

1. SQANTI implementation to identify fusions relevant for the HCC/benign comparison. How do the fusions compare with those already identified for HCC? These analyses can be quite messy when performed on WES alone so it seems that having such deep RNA-seq would improve the capacity to see which fused genes are strongly expressed/suppressed. This doesn't seem as evident from current analysis. There are quite a bit of WES datasets which could be compared: https://www.nature.com/articles/ng.3252, https://www.nature.com/articles/s41467-01803276-y

Answer: Exome sequencing is not an ideal tool to identify fusion genes. Very few fusion genes have been discovered based on RNA sequencing so far. The fusion genes discovered in the study appeared mostly novel. No exome sequencing was involved in the identification of fusion genes.

1. Figure 4 is fairly unclear. The matrix graphs showing gene position mutations are tough to interpret and make out. Usually, gene track views with bars or lollipop graphs can make these results more readily interpretable. Also, how Figure 4 B infers causal directions from mutations is unclear.

Answer: We appreciate the reviewer for pointing this out. We have revised the diagram in Figure 4A to reflect the proper distance between the mutations in HLA-DQB1 NM_002123. Since these are the positions in the same alleles (protein), the gene track view or lollipop graph may not show that properly. The mutation clusters started from an isolated mutation, and mutation did not revert to wild type sequence after occurring. Based on these two principles, we showed several mutation accumulation pathways leading to hypermutation clusters.

Reviewer #3 (Public Review):

The Liu, et al. manuscript focuses on the interesting topic of evaluating in an almost genome-wide-scale, the number of transcriptional isoforms and fusion gene are present in single cells across the annotated protein coding genome. They also seek to determine the occurrences of single nucleotide variations/mutations (SNV) in the same isoform molecule emanating from the same gene expressed in normal and normal and hepatocellular carcinoma (HCC) cells. This study has been accomplished using modified LoopSeq long‐read technology (developed by several of the authors) and single cell isolation (10X) technologies. While this effort addresses a timely and important biological question, the reader encounters several issues in their report that are problematic.:

1. Much of the analysis of the evolution of mutations results and the biological effects of the fusion genes is conjecture and is not supported by empirical data. While their conclusions leave the reader with a sense that the results obtained from the LoopSeq has substantive biological implications. However, they are extended interpretations of the data. For example: The fusion protein likely functions as a decoy interference protein that negatively impacts the microtubule organization activity of EML4.(pg 9)... and other statements presented in a similar fashion.

Answer: We thank the reviewer for the helpful comment. The mutation results were experimentally validated by exome sequencing on the same samples. Furthermore, these mutations were filtered by requiring their presence in three different transcriptomes. The biological significance of these mutations is probably the subject of investigation in the next phase. Since a large number of HLA mutations did not occur overnight, the analysis of the accumulation pathways for these mutations was warranted, given the extensive evidence of such a process. The impact of mutations on HLA molecules appeared obvious and should be discussed. For ACTR2-EML4 fusion, we revised it as "The loss of microtubule binding domain may negatively impact the microtubule organization activity of EML4 domain of the fusion protein." We only discussed the obvious impact due to the loss of a large protein domain.

2, LoopSeq has the advantage of using short read sequencing analyses to characterize the exome capture results and thus benefits from low error rate compared to standard long-read sequencing techniques. However, there is no evidence obtained from standard long read sequencing that the isoforms observed with LoopSeq are obtained with parallel technologies such as long read technologies. It is not made clear how much discordance there is in comparing the LoopSeq results are with either PacBio or ONT long read technologies.

Answer: The comparative analyses among LOOPSeq, Oxford nanopore, and PacBio sequencing were performed in our previous study. We have cited the study in our introduction.

1. There is no proteome evidence (empirically derived or present in proteome databases) from the HCC and normal samples that confirms the presence or importance of the identified novel isoforms, nor is there support that indicate that changes in levels HLA genes translate to effects observed at the protein level. Since the stability and transport differences of isoforms from the same gene are often regulated at the post-transcriptional level, the biological importance of the isoform variations is unclear.

Answer: Given the transcriptome sequencing data, we can only focus on the isoform variation analysis but not directly link to the protein level variation because of the post-transcriptional level regulation. We discussed this in the revised manuscript (page 14).

4 It is unclear why certain thresholds were chosen for standard deviation (SD) <0.4 (page 5), SD >1.0 (pg 11).

Answer: The threshold is flexible and arbitrary. We showed different thresholds, and the same conclusion holds. We just choose the thresholds with better separation and a reasonable number of genes/isoforms for the downstream analysis. (Supplemental Figure 6-7 with different thresholds and supplemental tables 4-12).

1. HLA is known to accumulate considerable somatic variation. Of the many non-immunological genes determined to have multiple isoforms what are the isoform specific mutation rates in the same isoform molecule? Are the HLA genes unique in the number of mutations occurring in the same isoform?

Answer: We thank the reviewer for this important suggestion. We now show mutation expression patterns in isoforms of DOCK8 and STEAP4 in Figure 5. A new section is added to discuss the mutation expression of these two genes. As shown in supplemental figure 10, HLA-DQB1, HLA-DRB1, HLA-B, and HLA-C, have only one known isoform detected,

Editorial comments:

The present study pairs single-cell seq with LoopSeq synthetic long-read sequencing on samples of HCC and benign liver to identify mutations and fusion transcripts specific to cancer cells. The authors present a potentially important resource; however the overall support remains incomplete.

While the approach of evaluating isoform-specific changes at the cellular level to cancer seeks to address a timely and important topic, there is currently incomplete evidence in support of the major claims in the manuscript. In particular, major recommendations to provide stronger support for the combination of technologies and interpretation regarding cancer-associated genomic changes include: 1) systematic evaluation of UMAP-based clustering methods, to what subsets of data they are applied and subsequent interpretations, 2) direct comparisons of results with additional methods to quantify long-read sequencing data and those evaluating mutational consequences of HCC progression and 3) detailed expansion of the description of methods and rationale for selecting specific parameters and cell types for further analyses. Including these changes would significantly strengthen the support for utility of combining 10x single-cell with Loop-seq and provide compelling evidence for usage of this resource in dissecting HCC-associated molecular changes.

Answer: We appreciate the frank and constructive comments. The goal of UMAP is to obtain biological knowledge through unbiased data selection. Systematically, we select classifiers without any prior knowledge (blind to the samples). In our case, classifiers with high standard deviation across all the cells were chosen. We stressed this in the result section. The comparison among LOOPSeq, PacBio, and Oxford nanopore was made in our previous study. We cited that analysis in this paper. Analysis detail and pipelines were added in the revised manuscript to improve the reproducibility. The mutation expression analysis was quite clear-cut. The clustering classified the HCC and benign liver cells by itself and identified a few cancer cells in the benign liver sample. All these were accomplished without applying any knowledge.

Reviewer #1 (Recommendations For The Authors):

Overall, there are numerous problems with data presentation and insufficient description, which authors could fix.

1. Figure 4. A. It would be more clear if the figure showed the distribution of mutations in the molecule. Otherwise, it's hard to see if we see clusters of mutations or just 25 mutations spread uniformly across the transcript. B. It's unclear what the reader needs to take away from these columns of numbers.

Answer: The mutation positions are now presented as proportion to the location in a molecule. Column B is the distribution of mutation molecules from left panel in each cluster of cells (from Figure 3A) and their sample origin (HCC or benign liver). We clarify it a little more in the legend of Figure 4A.

1. As a reader, I did not understand how "mutated gene expression levels" and "mutated isoform expression levels" were calculated in terms of sequenced long reads

Answer: We defined the term and calculations in the methods section of the revised manuscript.

1. Page 6 "genes involving antigen presentation"

Answer: The full sentence of the subtitle is" Mutations of genes involving antigen presentation dominated the mutation expression landscape."

1. Page 6 "These unique mutational isoforms" - how are these isoforms unique?

Answer: We take away most of the "unique" adjectives to describe the non-redundant mutations.

1. Page 6. Unclear "All but one clusters contained cells co‐migrated with cells of their sources."

"Among 113 mutation isoforms, the major histocompatibility complex (HLA) was the most prominent with 68 iterations (60.2%) (Supplemental Table 3, Figure 3B)" There is nothing about HLA in Figure 3B.

Answer: We revised the sentence as "Cells in all but one clusters co-migrated with cells of their sources". The mutation isoform expressions were listed in supplemental Table 3. They are too small and become unreadable when put in the figure.

1. Page 10 "genes or isoforms that across all samples had with expression standard deviations less than" - probably "with" should not be there.

Answer: We correct the error and thank the reviewer for the comment.

1. Page 11 "UMAP analysis was performed using genes with standard deviations {greater than or equal to} 1.0 (182 wild‐type genes) and standard deviations >0.4 (282 mutated genes)". What do "wild-type" and "mutated" mean here?

Answer: We edited as "UMAP analysis was performed using gene expressions with standard deviations ≥ 1.0 (182 non-mutated genes) and gene mutation expression with standard deviations 0.4 (282 mutated genes)."

1. I could not find the description of Supplementary Tables.

Answer: The supplemental table legends are added in the revised manuscript.

1. In the Discussion section, the authors mention that mutations were mainly expressed in a specific isoform of a gene for a given cell. I suggest to emphasize this point in the Results section and illustrate it with a comparison of abundance of mutated and non-mutated isoforms

Answer: For HLA molecules, their expression appeared to be restricted to one known isoform, regardless of mutation status. This sentence is removed in the revision. A new section of DOCK8 and STEAP4 mutation expression is added to the result.

1. It is also mentioned that mutations may have an impact on the RNA splicing process. The authors should compare the observed isoform ratio to a prediction of the effect of variants on splicing by SpliceAI or similar tools

Answer: This sentence was removed from the discussion.

1. Figure 3c: triangles corresponding to HLA-positive cells are hard to distinguish

Answer: We provide a larger representation of the triangle and circle in figure 3c in the revision.

Reviewer #2 (Recommendations For The Authors):

Many of my comments could be addressed by spending time to provide the code/data and a walkthrough of analyses so that other users would be able to answer these questions on their own.

Answer: We have included a script section in the revision to ensure the reproducibility of the analysis. The raw data had been uploaded to GEO (see Methods).

Author Response

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

Reviewer #1:

1. The results that TF binding produces microdomains at medium and long linker DNA but not short linker is very interesting. Although the differences can be observed from the figure, it still lacks of quantitative comparison. It is not clear the exact definition of the microdomain observed from simulations and what numbers of microdomains can be identified under different conditions. A quantitative comparison of different conditions could also be provided.

We thank the reviewer for this suggestion. Our intent was to show qualitatively how TF binding locations that we design can direct fiber folding and create microdomains, which we define in the paper as high frequency contact regions in the contact maps, similar to the TADs observed in HiC maps. Together with the fiber configurations, contact maps allow us to identify formation of such microdomains, and to observe how these microdomains change depending on the conditions we build into the model, such as TF binding region or linker DNA length.

To address your point, we have added a clustering analysis of the contact matrices with nucleosome resolution and assign each contact along the genome position (nucleosome index) to a cluster. In Supporting Figure S6, we show how DBSCAN clustering provides a clustering distribution that quantitatively describes the microdomains observed in the matrices and estimates the number of microdomains. For example, in the 44 and 62 bp systems, the contacts along the genomic distance separate into 5, 2, and 1 nucleosome groups for topologies 1 to 3, and into 2 and 1 group for topology 4, respectively. In the 26 bp and Life-Like systems, where microdomains are more diffuse due to fiber rigidity or polymorphism, we see that the clustering results are not as TF-topology-dependent as in the 44 and 62 bp systems. We also decomposed the contact matrices into one dimensional plots that depict the magnitude of 𝑖, 𝑖 ± 𝑘 internucleosome interactions. We see that internucleosome patterns change with the TF binding topology, and that the 26 bp and Life-Like systems show the least changes.

1. When increasing TF concentration, from 0 to 100%, it seems that both packing ratio and sedimentation coefficients are not sensitive to the TF concentrations after 25%. Is it due to the saturation of TF binding? How many TF binding sites are considered at each concentration?

Yes, in most cases, at TF concentrations higher than 25%, the fiber compaction does not change due to saturation of TF binding. Although the TF concentrations are reached, such as 50%, 70%, or 100%, these do not influence the fiber architecture. A higher order folding and compaction cannot be reached due to excluded volume interactions that impede overlapping of beads in the model.<br /> We have clarified this in the manuscript.

As stated in the Methods section, the TF concentration refers to the number of linker DNA beads that can engage in a constraint compared to the total number of linker DNA beads. Thus, at 25% TF, 25% of linker DNA beads are engaged in TF constraints. We have added a comment on this in the Results section.

1. It is shown that the contact maps that reveal microdomains are ensemble-based maps and single trajectories do not show clear formation of microdomains. Does the formation of microdomains increase with the number of combined trajectories?

The formation of microdomains occurs in each single trajectory. However, the microdomains formed in each trajectory can be different. That is why ensemble-based maps show clearer trends of microdomains that might not be as visible in single-trajectory maps. If we increase the number of trajectories, the macrodomains will be more visible and there will be more macrodomains in the contact map, but the formation of microdomains will not increase in each single trajectory.

1. "As we see from Figure 4A, when the linker DNA is short, such as 26 and 35 bp, TF binding does not increase the packing ratio of the fiber." The results of 35bp cannot be found in Figure 4A. In addition, the color of 44 and 62 bp should be changed since they are very similar in the figure.

Thank you for catching this. The results corresponding to the 35 bp system are presented in the Supporting Figure 7. We have changed the text to read “As we see from Figure 4A and Figure S7..”.

We have changed the color of the 62 bp trace to blue in the plots of Figure 4. Consistently, we have also changed the color of the 62 bp fiber in Figure 2 and Figure 5.

1. For modelling of TF binding at increasing concentrations, it is mentioned that in these three conditions, TFs are allowed to bind to any region. Do you mean TF can also bind to nucleosomal DNA? Nucleosome structure prevents the binding of many TFs.

In our model, only linker DNA beads can engage in the constraints (bind TF).<br /> We have changed the text to read “TFs are allowed to bind to any linker DNA region”.

1. The details of the Mnase-seq dataset and how NFRs are identified should be provided, such as the coverage of the data and what read fragments are selected for NFR mapping.

MNase data in bedgraph format were downloaded from the Genome Expression Omnibus (GSM2083107) repository and loaded without further processing into the Genome Browser. NFRs were visually inspected and detected as genomic regions without peaks. As detailed in the GEO repository, the sequenced paired-end reads were mapped to the mm9 genome. Only uniquely mapped reads with no more than two mismatches were retained and reads with insert sizes less than 50 or larger than 500 bp were discarded.

We have clarified this in the manuscript.

1. The calculations of volume and area of the Eed promoter region should be further elucidated.

Thank you. We now elaborate upon these calculations. In particular, the Eed promoter region is defined between cores 123 and 129. The x,y or x,y,z coordinates of those cores are used to create the bounding area or volume by defining the shape’s vertices.

1. In Figure 3, it is not clear how different topology are identified.

In Figure 3 the topology, or TF binding regions, is the same for each of the 10 contact maps as these emerge from trajectory replicas of the same system which we named Topology 1. Different microdomains are formed in each individual trajectory as the high-frequency regions appear in different locations on each contact map. However, when these 10 maps are summed, the ensemble contact map clearly shows consensus microdomains in each region where TF binds.

Reviewer #2:

To further improve the manuscript, I have the following suggestions/comments.

1. While most of the conclusions in this paper follow from the evidence provided by the ximulations, the result in section 3.3 title "Gene locus repression is medicated by TF finding," may not follow from the results. In my opinion, repression is a more complex process, and many more factors (such as nucleosome positioning, nucleosome sliding, histone methylation, and other proteins such as PRC or HP1, etc) may be involved in repression. While compaction is often associated with repressed chromatin (heterochromatin), recent studies have shown that heterochromatin fibers are highly diverse, and compaction alone may not be the criteria for repression (eg. see Spracklin et al. Nat. Struct. Mol. Biol. 30, 38-51 (2023).). In this light, I would recommend slightly modifying the title to say, "TF binding-mediated compaction can help in gene locus repression" or something similar.

Yes! We completely agree that gene repression is a very complex phenomenon that involves many factors that we are approaching by modeling starting from the simplest strategy. Thus, we have changed the subtitle to read “TF binding-mediated compaction as possible mechanism of gene locus repression”.

1. Authors could also present the contact probability versus genomic distance. This may provide some generic features at nucleosome resolution, given the variability in linker length and LH density.

We thank the reviewer for this suggestion. We have now calculated the contact probability for the EED gene with and without TF binding (Supporting Figure 8). We see that the contact probability corresponding to short range interactions (i ± 2, 3, 4, 5, and 6) is slightly lower for the EED gene upon TF binding. However, a striking increase in the contact probability upon TF binding is seen in the genomic region between 3 and 5 kb, which corresponds to local loop interactions. Thus, TF binding slightly decreases local interactions but increases chromatin loops. Such changes are not observed for the EED system with LH density 0.8 (Supporting Figure 9), further supporting the idea that an increase in LH density hampers the effect of TF binding for the EED gene architecture. <br /> We have now added these results to the manuscript.

1. Write a short paragraph about the limitations of the model/study. For example, one of the limitations could be that, as of now, it has only the effect of a few proteins, but to predict repression, one may need to incorporate the effect of several proteins.

We agree with the reviewer that our model is a simple, first-step approach. Nonetheless, even the simplest mathematical model can be enlightening in helping dissect essential factors. Here, our model clearly shows how TF binding location modulates fiber architecture and the interplay between TF binding and other chromatin elements, like linker DNA length, LH density, and histone acetylation. We have now stated in the Discussion section that although limited due to being implicit and not considering other protein partners, our model can provide insights on the regulation of chromatin architecture by protein binding. Future modeling with explicit protein binding or combination of several proteins will further help us understand genome folding regulation.

1. The radius of gyration of 26 kb chromatin is around ~60nm in this paper. Is there any experimental measurement to compare (approximate order of magnitude)? While I do not know any measurement for Eed gene locus, I am aware of the results in the Boettiger et al. paper from Xiaowei Zhuang lab (Nature 2016). There, they find that the Rg of a 26 kb region is above 100nm. But that is for a different organism, a different set of genes. Also, see Sangram Kadam et al. Nature Communications 14 (1), 4108, 2023.

Thank you for this suggestion. To the best of our knowledge, there are no radius of gyration measurements for the EED gene. Regarding the two papers you cite, in the paper from Boettiger et al. (1) they determine by microscopy experiments that Rg ∝ 𝐿! where 𝐿 is the genomic length and 𝑐 is 0.37 ± 0.02 for active chromatin (Figure 1d of the paper). In such case, the Rg for a 26 kb region would be 43 ± 9 nm. Considering that these are Drosophila cells, our value of 62 nm is in good agreement with that estimate. Regarding the Kadam et al. paper (2), by coarse grained modeling they find an Rg of around 100 nm for different genes. Considering that the radius of gyration depends on cell type and fiber configuration (see for example (3) for the dependency of Rg on loop number and persistence length), we believe that our measurements in the same ball park as experimental results and other theoretical modeling studies are good indicators of our model’s reasonableness.

We have added this comparison to the manuscript.

1. The reason why it is useful to compare some distance measurements (physical dimension) with experiments is the following: The contact map in Hi-C only gives relative contact probabilities. It does not give absolute contact probabilities. To convert a Hi-C map into a physical distance, one requires comparison with some experimentally measured 3D distance. The radius of gyration is an ideal quantity to compare. From my experience, the contact probability is often much smaller than 1, suggesting that the chromatin is more expanded. But this could be due to the effect of many other proteins in vivo and the crowding, etc. I do not expect this work to incorporate all those effects. However, it may be useful to make a comment about it in the manuscript.

Thank you. We have added to the discussion a comment on our first-generation model of TF binding to chromatin and the neglect of many associated protein and RNA cofactors that certainly influence chromosome folding and domain formation on higher scales. Some distance measures are also added to the Results as mentioned above.

References

1. Boettiger,A.N., Bintu,B., Moffitt,J.R., Wang,S., Beliveau,B.J., Fudenberg,G., Imakaev,M., Mirny,L.A., Wu,C. and Zhuang,X. (2016) Super-resolution imaging reveals distinct chromatin folding for different epigenetic states. Nature, 529, 418–422.

2. Kadam,S., Kumari,K., Manivannan,V., Dutta,S., Mitra,M.K. and Padinhateeri,R. (2023) Predicting scale-dependent chromatin polymer properties from systematic coarsegraining. Nat. Commun., 14, 4108.

3. Wachsmuth,M., Knoch,T.A. and Rippe,K. (2016) Dynamic properties of independent chromatin domains measured by correlation spectroscopy in living cells. Epigenetics Chromatin, 9, 57.

Author Response

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

Reviewer #1 (Recommendations For The Authors):

I have only a few very minor suggestions for improvement.

• the text repeatedly uses the terms "central nervous system" and "enteric nervous system", which are not in standard use in the field. These terms are not defined until the bottom of p. 12 even though they are used earlier. It would be useful for the authors to explicitly describe their definitions of these terms earlier in the paper.

Fixed.

• the inclusion of four pre-trained models is a powerful and useful aspect of WormPsyQi. Would it be possible to develop a simple tool that, when given the user's images, could recommend which of the four models would be most appropriate?

We appreciate the reviewer for bringing this up. To address this, we have now added an additional function in the pipeline to test all pre-trained models on representative input images. Before processing an entire dataset, users can view all segmentation results for images in Fiji to assess which model performed best, judged by the user. The GUI, running guide document, and manuscript have been modified accordingly.

In addition, we would like to emphasize that the pre-trained models were developed by iterative analyses of many reporters, often with multiple rounds of parameter tuning; the results were validated post hoc to choose the optimal model for each reporter, and we have listed this information in Supplemental Table 1 to inform the choice of the pre-trained model for commonly used reporter types.

• On p. 11 (and elsewhere), the differences in the performance of WormPsyQi and human experimenters are called "statistically insignificant". This statement is not particularly informative (absence of evidence is not evidence of absence). Can the authors provide a more rigorous analysis here - or provide an estimate of the typical effect size of the machine-vs-human difference?

To address this, we have included additional analysis in Figure 2 – figure supplement 3. For two reporters - I5 GFP::CLA-1 and M4 GFP::RAB-3 - we compare WormPsyQi vs. labelers and inter-labeler puncta quantification. A high Pearson correlation coefficient (r2) reflects greater correspondence between two independent scoring methods. We chose these two test cases to demonstrate that the machine-vs-human effect size is reporter-dependent. For I5, where the CLA-1 signal is very discrete and S/N ratio is high, the discrepancy between WormPsyQi, labeler 1, and labeler 2 is minimal (r2=0.735); moreover, scoring correspondence depends on the labeler (r2=0.642 and 0.942, respectively). In other words, WormPsyQi mimics some labelers better than others, which is to be expected. For M4, where the RAB-3 signal is diffuse and synapse density is high in the ROI, the inter-labeler discrepancy is high (r2=0.083) and WormPsyQi vs labeler (1 or 2) discrepancy is slightly reduced (r2=0.322 and 0.116, respectively). The problematic regions for the M4 RAB-3 reporter are emphasized in Figure 6 - figure supplement 1A. Overall, the additional analysis suggests that the effect size is contingent on the reporter type and image quality, and importantly for scoring difficult strains WormPsyQi may average out inter-labeler scoring variability.

• p. 12: "Again, relying on alternative reporters where possible..." This is an incomplete sentence - are some words missing?

Edited.

Reviewer #2 (Recommendations For The Authors):

1. The authors effectively validated the sexually dimorphic synaptic connectivity by comparing the synapse puncta numbers of PHB>AVA, PHA>AVG, PHB>AVG, and ADL>AVA. However, these differences appear to be quite robust. It would be beneficial for the authors to test whether WormPsyQi can detect more subtle changes at the synapses, such as 10-20% changes in puncta number and fluorescence intensity.

While the dimorphic strains were used to first validate WormPsyQi based on the ground truth of very well-characterized reporters, the reviewer reasonably asks whether our pipeline can pick up on more subtle differences. To address this, we have now included an additional figure (Figure 9 – figure supplement 2), where we performed pairwise comparisons between L4 and adult timepoints for the reporter M3 GFP::RAB-3. As reflected in panels A and C, although the difference between puncta number and mean intensity between L4 and adult is marginal (22% increase in puncta number and 13% increase in mean intensity from L4 to adult), WormPsyQi can pick it up as statistically significant.

1. On page 10, the authors mentioned that "cell-specific RAB-3 reporters have a more diffuse synaptic signal compared to the punctate signal in CLA-1 reporters for the same neuron, as shown for the neuron pair ASK (Figure 4 -figure supplement 1B, C)". It is important to note that in this case, the reporter gene expressing RAB-3 is part of an extrachromosomal array, whereas the reporter gene expressing CLA-1 is integrated into the chromosome. It's possible that the observed difference in pattern may arise from variations in the transgenic strategies employed.

To emphasize the difference in puncta features inherent to the reporter type, we have now added WormPsyQi segmentation results for ASK CLA-1 extrachromosomal reporter (otEx7455) next to the ASK CLA-1 integrant (otIs789) and ASK RAB-3 reporter (otEx7231) in Figure 4 – figure supplement 1C. Importantly, otEx7455 was integrated to generate otIs789, so they belong to the same transgenic line. Literature shows that RAB-3 and CLA-1 have different localization patterns and corresponding functions at presynaptic specializations, and this is qualitatively and quantitatively shown by the significant difference in puncta area size between RAB-3 and both CLA-1 reporters, i.e., both CLA-1 reporters have smaller, discrete puncta compared to RAB-3 (Figure 4 – figure supplement 1C). Quantitatively, in the case of ASK - where the synapse density is sparse enough that even diffuse RAB-3 puncta can be segmented without confounding adjacent puncta – overall puncta number between otEx7231 and otIs789 are similar. However, RAB-3 signal is diffuse and this poses quantification problems in cases where the synapse density is higher (e.g. AIB, SAA in Figure 4 – figure supplement 1D) and WormPsyQi fails to score puncta in these reporters since the signal is not punctate. As far as integrated vs. extrachromosomal reporters go, the reviewer is right in pointing out that some differences may be stemming from reporter type as our additional analysis between otIs789 and otEx7455 indeed shows fewer puncta in the latter owing to variable expressivity.

1. The authors mentioned that having a cytoplasmic reporter in the background of the synaptic reporter enhanced performance. It would be more informative to provide comparative results with and without cytoplasmic reporters, particularly for scenarios involving dim signals or densely distributed signals.

The presence of a cytoplasmic marker is critical in two specific scenarios: 1) images where the S/N ratio is poor, and 2) when the image S/N ratio is good, but the ROI is large, which would make the image processing computationally expensive.

To demonstrate the first scenario, we have included an additional panel in Figure 4 – figure supplement 1(B) to show how WormPsyQi performs on the PHB>AVA GRASP reporter with and without the channel having cytoplasmic marker. The original image was processed as-is in the former case with both the synaptic marker in green and cytoplasmic marker in red; for comparison, only the green channel having synaptic marker was used to simulate a situation where the strain does not have a cytoplasmic marker. As shown in the figure, in the presence of background autofluorescence signal from the gut (which can be easily confounded with GRASP puncta depending on the worm’s orientation), WormPsyQi quantified GRASP puncta much more robustly with the cytoplasmic label; without the cytoplasmic marker, gut puncta are incorrectly segmented as synapses (highlighted with red arrows) while some dim synaptic puncta are not picked up (highlighted with yellow arrows).

To demonstrate the second scenario, we now highlight the case of ASK CLA-1 in Figure 2 - figure supplement 4E. Additionally, we have emphasized in the manuscript that in cases where the S/N ratio is good and the image is restricted to a small ROI, WormPsyQi will perform well even in the absence of a cytoplasmic marker. This is equally important to note as having a specific cytoplasmic marker in the background may not always be feasible and, in fact, if the cytoplasmic marker is discontinuous or dim relative to puncta signal, using a suboptimal neurite mask for synapse segmentation would result in undercounting synapses.

1. On page 12, the author stated "We also note that in several cases, GRASP quantification differed from EM scoring". However, the EM scoring is primarily based on a single sample, making it challenging to conduct a statistical analysis for the purpose of comparison.

This is correct and is indeed a limitation of EM for this type of analysis. We have now reworded this sentence (page 14) to emphasize the reviewer’s point, and it is also elaborated further in the limitations section.

1. In Figure 6F, the discrepancy between WormPsyQi and human quantification in the analysis of RAB-3 is observed. The author stated that "the RAB-3 signal was too diffuse to resolve all puncta". To better illustrate this discrepancy, it would be beneficial to include images highlighting the puncta that WormPsyQi cannot score, providing direct evidence that diffusing signals are not able to automatically detectable.

To highlight puncta that were not segmented by WormPsyQi but were successfully scored manually, we have included arrows in Figure 6. In addition, for reporter M4p::GFP::RAB-3, we have included magnified insets in Figure 6 - figure supplement 1A to highlight the region where human annotator scores more puncta than WormPsyQi owing to the high synapse density. In future implementations, additional functionality can be built for separating these merged puncta into instances based on geometrical features such as shape and intensity contour.

1. In Figure 9 S1D, the results from WormPsyQi and the manual are totally different. To address this notable discrepancy, the authors should highlight and illustrate the areas of discrepancy in the images. This visual representation can assist future users in identifying signal types that may not be well-suited for WormPsyQi analysis and inspire the development of new strategies to tackle such challenges.

This is now addressed in additional figure panels in Figure 4 – figure supplement 1B and Figure 6 - figure supplement 1A.

Reviewer #3 (Recommendations For The Authors):

I found the comparison between manual quantification and WormPsyQi-based quantification to be very informative. In my opinion, quantifying the number of puncta is not the most tedious/difficult quantification even when done manually. Would the authors be able to include manual-WormPsyQi comparison for more time-consuming and potentially more prone to human error/bias quantifications such as puncta size or distribution patterns using a few markers with some inter/intra animal variabilities?

To address this point, we have now included an additional figure supplement to Figure 2 (Figure 2 – figure supplement 4). We focused on the ASK GFP::CLA-1 reporter and had two human annotators manually label the masks of puncta for each worm by scanning Z-stacks and drawing all pixels belonging to each puncta in Fiji, which were then processed by WormPsyQi’s quantification pipeline to score puncta number, volume, and distribution. We also included a comparison of overall image processing time for each annotator and WormPsyQi. For features analyzed, the difference between WormPsyQi and human annotators for ASK CLA-1 is not statistically significant for multiple puncta features. Importantly, WormPsyQi reduces overall processing time by at least an order of magnitude, and while this is already advantageous for counting puncta, it is especially useful for other important puncta features since a) they may not be easily discernible, and b) it is extremely laborious to quantify them manually in large datasets when pixel-wise labels are required.

The authors listed minimum human errors and biases as one of the benefits of WormPsyQi. For the markers with discrepancies in quantifications between human and WormPsyQi, have the authors encountered or considered human errors/biases as potential reasons for such discrepancies?

This is the same point brought up by reviewer 1. We added Figure 2- figure supplement 3 to compare WormPsyQi to different human labelers, and show that because human labels can introduce systematic bias, WormPsyQi reduces such bias by scoring images using the same metric.

The authors noted that WormPsyQi would be useful for comparing different genotypes/environments. Some mutants have known changes in synapse patterning/number. It would be helpful if the authors could validate WormPsyQi using some of the mutants with known synapse defects. For instance, zig-10 mutant increases the cholinergic synapse density just by a bit (Cherra and Jin, Neuron 2016), and nlr-1 mutant disrupts punctated localization of UNC-9 gap junction in the nerve ring (Meng and Yan, Neuron 2020), which could only be detectable by experts' eyes. It would be interesting to see if WormPsyQi picks up such subtle phenotypes.

We agree that our pipeline would need to be tested in multiple paradigms to test its performance on detecting additional subtle phenotypes. In the context of this paper, we note that the developmental analysis of puncta in Figure 8 was performed to validate the ground truth from previous EM-based analyses (Witvliet et al., 2021), albeit the latter was limited by sample size. We extended this developmental analysis to the pharyngeal reporters, and in some cases the difference across timepoints was marginal (as emphasized by additional Figure 9 - figure supplement 2), but still detected by WormPsyQi. Lastly, our synapse localization analysis in Figure 10 assigns the probability of finding a synapse at a particular location along a neurite, which is not easily discernible by manual scoring.

One of the benefits of the automated data analysis program is to be able to notice the differences you do not expect. For example, there are situations where you feel that in certain genotypes there is something different from wild type with their synapses but you can't tell what's different from wild type. In such cases, you may not know what to quantify. I think it would be beneficial if there were more parameters to be included in the default qualifications such as puncta number/size/intensity/distributions in the pipeline, so that the users may find unexpected phenotypes from one of the default quantifications.

We apologize if this was not clearer in the manuscript where we first describe the pipeline in detail. To clarify, the output of WormPsyQi is a CSV file which includes several quantitative features, such as mean/max/min fluorescence intensity, puncta volume, and position. While most of our analyses are focused on puncta count, the user can perform downstream statistical analyses on all additional features scored to infer which features are most significantly variable across conditions. To make this clearer, we have elaborated the text when we first describe our pipeline, and along with the new Figure 2 - figure supplement 4, we hope that this point is clearer now.

In addition, most proof-of-principle analysis we performed was focused on an ROI where we expect the synapses to localize. In practice, the user can input images and perform quantification across the entire image without biasing toward an ROI (this can be done in the GUI synapse corrector window) to also evaluate synaptic changes in regions outside the usual ROI.

The authors stated that WormPsyQi could mitigate the problems stemming from scoring images with low signal-to-noise ratio or in regions with high background autofluorescence, laboriousness of scoring large datasets, and inter-dataset variability. Other than the 'laboriousness of scoring large datasets' it appeared to me that WormPsyQi does not do better than manual quantifications, especially inter-dataset variability, as the authors noted variability among the transgenes as one of the limitations of the toolkits. If two datasets are taken with completely different setups such as two independent arrays taken with two distinct confocal microscopes, would WormPsyQi make these two datasets comparable?

We have included additional figure supplements to address the reviewer’s point. A significant advantage WormPsyQi offers over manual scoring is that it provides a standardized method of quantifying synapse features. As shown in Figure 2 – figure supplement 3, human labelers can introduce systematic bias (e.g. some over count puncta, while some undercount). In addition, while puncta number may be relatively easy to quantify, especially in a high-quality dataset, more subtle puncta features such as size, intensity, and distribution are much more laborious to quantify and require a priori knowledge of signal localization (Figure 2 – figure supplement 4, Figure 10). Altogether, our pipeline facilitates multiple measurements while also enabling robust quantification in hard-to-score cases such as the example shown for PHB>AVA reporter (Figure 4 - figure supplement 1B).

Minor comments:

Limitations are not quite specific to this work but those are general limitations to the concatemeric trans genes and fluorescently labeled synaptic proteins. I'd appreciate discussing specific limitations to WormPsyQi related to image acquisitions. For instance, for neurons with 3D structures would WormPsyQi be able to handle z-stacks closer to coverslip and stacks that are deeper side in a similar manner? Would the users need to be aware of such limitations when comparing different genotypes?

To address the reviewer’s comment, we have elaborated the last paragraph in the limitations section to explicitly discuss where the user should exercise caution. The reviewer reasonably points out that the fluorescent signal away from the cover slip is typically dimmer, and neurite masking in this case is indeed compromised if dim to start with. In such cases, we recommend that the user either performs some preprocessing such as deconvolution, denoising, or contrast enhancement to boost the neurite signal, or segment synapses without the neurite mask if the puncta signal is brighter than that of the cytoplasmic marker. We hope that our additional figure supplements will clarify that WormPsyQi’s performance is contingent on reporter type and image quality, thus making it easier for the user to discern where automated quantification falls short and alternative reporters should be explored. In general, if puncta are not discernible to the user due to very poor S/N ratio, for instance, we do not recommend using WormPsyQi to process such datasets; this will be manifest in the results of the new “test all models” feature we added in the revised version.

Some Rab-3 fusion proteins are described as RAB-3::GFP(BFP). Do these represent the C-terminal fusion of the fluorescent proteins? RAB-3 is a small GTPase with a lipid modification site at its C-terminus essential for its localization and function. Is it possible that the diffuse signal of some RAB-3 markers is caused by c-terminal fusion of the fluorescent protein?

While we do have reporters with N- and C-terminal RAB-3 fusions for different neurons, we do not have both for the same neuron to perform a fair comparison. However, as noted in response to a previous comment by reviewer 2, RAB-3 and CLA-1 have distinct localization patterns at the synapse and this aligns with their distinct functions: while RAB-3 localizes at synaptic vesicles, CLA-1 is an active zone protein required for synaptic vesicle clustering. Accordingly, we have observed diffuse RAB-3 signal in reporters irrespective of where the protein is tagged, and while this is not problematic for ROIs with a low synapse density, it confounds quantification in synapse-dense regions. In contrast, CLA-1 puncta are typically easier to quantify more discretely, which is particularly relevant for features such synapse distribution, size, and intensity.

Author Response

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

Public Reviews:

Reviewer #1 (Public Review):

In this very strong and interesting paper the authors present a convincing series of experiments that reveal molecular mechanism of neuronal cell type diversification in the nervous system of Drosophila. The authors show that a homeodomain transcription factor, Bsh, fulfills several critical functions - repressing an alternative fate and inducing downstream homeodomain transcription factors with whom Bsh may collaborate to induce L4 and L5 fates (the author's accompanying paper reveals how Bsh can induce two distinct fates). The authors make elegant use of powerful genetic tools and an arsenal of satisfying cell identity markers.

Thanks!

I believe that this is an important study because it provides some fundamental insights into the conservation of neuronal diversification programs. It is very satisfying to see that similar organizational principles apply in different organisms to generate cell type diversity. The authors should also be commended for contextualizing their work very well, giving a broad, scholarly background to the problem of neuronal cell type diversification.

Thanks!

My one suggestion for the authors is to perhaps address in the Discussion (or experimentally address if they wish) how they reconcile that Bsh is on the one hand: (a) continuously expressed in L4/L4, (b) binding directly to a cohort of terminal effectors that are also continuously expressed but then, on the other hand, is not required for their maintaining L4 fate? A few questions: Is Bsh only NOT required for maintaining Ap expression or is it also NOT required for maintaining other terminal markers of L4? The former could be easily explained - Bsh simply kicks of Ap, Ap then autoregulates, but Bsh and Ap then continuously activate terminal effector genes. The second scenario would require a little more complex mechanism: Bsh binding of targets (with Notch) may open chromatin, but then once that's done, Bsh is no longer needed and Ap alone can continue to express genes. I feel that the authors should be at least discussing this. The postmitotic Bsh removal experiment in which they only checked Ap and depression of other markers is a little unsatisfying without further discussion (or experiments, such as testing terminal L4 markers). I hasten to add that this comment does not take away from my overall appreciation for the depth and quality of the data and the importance of their conclusions.

Great suggestions, we will discuss these two hypotheses as requested.

Bsh initiates Ap expression in L4 neurons which then maintain Ap expression independently of Bsh expression, likely through Ap autoregulation. During the synaptogenesis window, Ap expression becomes independent from Bsh expression, but Bsh and Ap are both still required to activate the synapse recognition molecule DIP-beta. Additionally, Bsh also shows putative binding to other L4 identity genes, e.g., those required for neurotransmitter choice, and electrophysiological properties, suggesting Bsh may initiate L4 identity genes as a suite of genes. The mechanism of maintaining identity features (e.g., morphology, synaptic connectivity, and functional properties) in the adult remains poorly understood. It is a great question whether primary HDTF Bsh maintains the expression of L4 identity genes in the adult. To test this, in our next project, we will specifically knock out Bsh in L4 neurons of the adult fly and examine the effect on L4 morphology, connectivity, and function properties.

Reviewer #2 (Public Review):

Summary:

In this paper, the authors explore the role of the Homeodomain Transcription Factor Bsh in the specification of Lamina neuronal types in the optic lobe of Drosophila. Using the framework of terminal selector genes and compelling data, they investigate whether the same factor that establishes early cell identity is responsible for the acquisition of terminal features of the neuron (i.e., cell connectivity and synaptogenesis).

Thanks for the positive words!

The authors convincingly describe the sequential expression and activity of Bsh, termed here as 'primary HDTF', and of Ap in L4 or Pdm3 in L5 as 'secondary HDTFs' during the specification of these two neurons. The study demonstrates the requirement of Bsh to activate either Ap and Pdm3, and therefore to generate the L4 and L5 fates. Moreover, the authors show that in the absence of Bsh, L4 and L5 fates are transformed into a L1 or L3-like fates.

Thanks!

Finally, the authors used DamID and Bsh:DamID to profile the open chromatin signature and the Bsh binding sites in L4 neurons at the synaptogenesis stage. This allows the identification of putative Bsh target genes in L4, many of which were also found to be upregulated in L4 in a previous single-cell transcriptomic analysis. Among these genes, the paper focuses on Dip-β, a known regulator of L4 connectivity. They demonstrate that both Bsh and Ap are required for Dip-β, forming a feed-forward loop. Indeed, the loss of Bsh causes abnormal L4 synaptogenesis and therefore defects in several visual behaviors. The authors also propose the intriguing hypothesis that the expression of Bsh expanded the diversity of Lamina neurons from a 3 cell-type state to the current 5 cell-type state in the optic lobe.

Thanks for the excellent summary of our findings!

Strengths:

Overall, this work presents a beautiful practical example of the framework of terminal selectors: Bsh acts hierarchically with Ap or Pdm3 to establish the L4 or L5 cell fates and, at least in L4, participates in the expression of terminal features of the neuron (i.e., synaptogenesis through Dip-β regulation).

Thanks!

The hierarchical interactions among Bsh and the activation of Ap and Pdm3 expression in L4 and L5, respectively, are well established experimentally. Using different genetic drivers, the authors show a window of competence during L4 neuron specification during which Bsh activates Ap expression. Later, as the neuron matures, Ap becomes independent of Bsh. This allows the authors to propose a coherent and well-supported model in which Bsh acts as a 'primary' selector that activates the expression of L4specific (Ap) and L5-specific (Pdm3) 'secondary' selector genes, that together establish neuronal fate.

Thanks again!

Importantly, the authors describe a striking cell fate change when Bsh is knocked down from L4/L5 progenitor cells. In such cases, L1 and L3 neurons are generated at the expense of L4 and L5. The paper demonstrates that Bsh in L4/L5 represses Zfh1, which in turn acts as the primary selector for L1/L3 fates. These results point to a model where the acquisition of Bsh during evolution might have provided the grounds for the generation of new cell types, L4 and L5, expanding lamina neuronal diversity for a more refined visual behaviors in flies. This is an intriguing and novel hypothesis that should be tested from an evo-devo standpoint, for instance by identifying a species when L4 and L5 do not exist and/or Bsh is not expressed in L neurons.

Thanks for the appreciation of our findings!

To gain insight into how Bsh regulates neuronal fate and terminal features, the authors have profiled the open chromatin landscape and Bsh binding sites in L4 neurons at mid-pupation using the DamID technique. The paper describes a number of genes that have Bsh binding peaks in their regulatory regions and that are differentially expressed in L4 neurons, based on available scRNAseq data. Although the manuscript does not explore this candidate list in depth, many of these genes belong to classes that might explain terminal features of L4 neurons, such as neurotransmitter identity, neuropeptides or cytoskeletal regulators. Interestingly, one of these upregulated genes with a Bsh peak is Dip-β, an immunoglobulin superfamily protein that has been described by previous work from the author's lab to be relevant to establish L4 proper connectivity. This work proves that Bsh and Ap work in a feed-forward loop to regulate Dip-β expression, and therefore to establish normal L4 synapses. Furthermore, Bsh loss of function in L4 causes impairs visual behaviors.<br /> Thanks for the excellent summary of our findings.

Weaknesses:

● The last paragraph of the introduction is written using rhetorical questions and does not read well. I suggest rewriting it in a more conventional direct style to improve readability.

We agree and have updated the text as suggested.

● A significant concern is the way in which information is conveyed in the Figures. Throughout the paper, understanding of the experimental results is hindered by the lack of information in the Figure headers. Specifically, the genetic driver used for each panel should be adequately noted, together with the age of the brain and the experimental condition. For example, R27G05-Gal4 drives early expression in LPCs and L4/L5, while the 31C06-AD, 34G07-DBD Split-Gal4 combination drives expression in older L4 neurons, and the use of one or the other to drive Bsh-KD has dramatic differences in Ap expression. The indication of the driver used in each panel will facilitate the reader's grasp of the experimental results.

We agree and have updated the figure annotation.

● Bsh role in L4/L5 cell fate: o It is not clear whether Tll+/Bsh+ LPCs are the precursors of L4/L5. Morphologically, these cells sit very close to L5, but are much more distant from L4.

Our current data show L4 and L5 neurons are generated by different LPCs. However, currently, we don’t have tools to demonstrate which subset of LPCs generate which lamina neuron type. We are currently working on a follow-up manuscript on LPC heterogeneity, but those experiments have just barely been started.

● Somatic CRISPR knockout of Bsh seems to have a weaker phenotype than the knockdown using RNAi. However, in several experiments down the line, the authors use CRISPR-KO rather than RNAi to knock down Bsh activity: it should be explained why the authors made this decision. Alternatively, a null mutant could be used to consolidate the loss of function phenotype, although this is not strictly necessary given that the RNAi is highly efficient and almost completely abolishes Bsh protein.

The reason we chose CRISPR-KO (L4-specific Gal4, uas-Cas9, and uas-Bsh-sgRNAs) is that it effectively removed Bsh expression from the majority of L4 neurons. However, it failed to knock down Bsh in L4 neurons using L4-split Gal4 and Bsh-RNAi because L4-split Gal4 expression depends on Bsh. We have updated this explanation in the text.

● Line 102: Rephrase "R27G05-Gal4 is expressed in all LPCs and turned off in lamina neurons" to "is turned off as lamina neurons mature", as it is kept on for a significant amount of time after the neurons have already been specified.

Thanks; we have made that change.

● Line 121: "(a) that all known lamina neuron markers become independent of Bsh regulation in neurons" is not an accurate statement, as the markers tested were not shown to be dependent on Bsh in the first place.

Good point. We have rephrased it as “that all known lamina neuron markers are independent of Bsh regulation in neurons”.

● Lines 129-134: Make explicit that the LPC-Gal4 was used in this experiment. This is especially important here, as these results are opposite to the Bsh Loss of Function in L4 neurons described in the previous section. This will help clarify the window of competence in which Bsh establishes L4/L5 neuronal identities through ap/pdm3 expression.

Thanks! We have updated Gal4 information in the text for every manipulation.

● DamID and Bsh binding profile:

● Figure 5 - figure supplement 1C-E: The genotype of the Control in (C) has to be described within the panel. As it is, it can be confused with a wild type brain, when it is in fact a Bsh-KO mutant.

Great point! Thank you for catching this and we have updated it.

● It Is not clear how L4-specific Differentially Expressed Genes were found. Are these genes DEG between Lamina neurons types, or are they upregulated genes with respect to all neuronal clusters? If the latter is the case, it could explain the discrepancy between scRNAseq DEGs and Bsh peaks in L4 neurons.

We did not use “L4-specific Differentially Expressed Genes”. Instead, we used all genes that are significantly transcribed in L4 neurons (line 209-213).

● Dip-β regulation:

● Line 234: It is not clear why CRISPR KO is used in this case, when Bsh-RNAi presents a stronger phenotype.

As we explained above, the reason we chose CRISPR-KO (L4-specific Gal4, uas-Cas9, and uas-BshsgRNAs) is that it effectively removed Bsh expression from the majority of L4 neurons. However, it failed to knock down Bsh in L4 neurons using L4-split Gal4 and Bsh-RNAi because L4-split Gal4 expression depends on Bsh. We have updated this explanation in the text.

● Figure 6N-R shows results using LPC-Gal4. It is not clear why this driver was used, as it makes a less accurate comparison with the other panels in the figure, which use L4-Split-Gal4. This discrepancy should be acknowledged and explained, or the experiment repeated with L4-Split-Gal4>Ap-RNAi.

I think you mean 6J-M shows results using LPC-Gal4. We first tried L4-Split-Gal4>Ap-RNAi but it failed to knock down Ap because L4-Split-Gal4 expression depends on Ap. We have added this to the text.

● Line 271: It is also possible that L4 activity is dispensable for motion detection and only L5 is required.

Thanks! Work from Tuthill et al, 2013 showed that L5 is not required for any motion detection. We have included this citation in the text.

● Discussion: It is necessary to de-emphasize the relevance of HDTFs, or at least acknowledge that other, non-homeodomain TFs, can act as selector genes to determine neuronal identity. By restricting the discussion to HDTFs, it is not mentioned that other classes of TFs could follow the same PrimarySecondary selector activation logic.

That is a great point, thank you! We have included this in the discussion.

Author Response:

We thank all reviewers for their comments and effort to improve our paper. We appreciate that the writing can be clarified overall, and some sections need more elaboration. We will provide these in the next revision within the coming months. Particularly, we will focus on some common themes identified by all reviewers:

1. We will clarify that the coarse-grained brain surfaces are an output of our algorithm alone and not to be directly/naively likened to actual brain surfaces, e.g. in terms of the location or shape of the folds. Our analysis purely focuses on the likeliness in terms of whole-brain morphometrics between actual brains and coarse-grained brains. Specifically on the point of “thickening” of the brain: this is anatomically well-founded, as less folded brains have a “thicker” cortex than more folded brains, when they are all normalised to the same size. This is fundamentally why the universal scaling law also applies to these coarse-grained brains. We will provide more detail to highlight this.

2. We will clarify the motivation behind our coarse-graining procedure better: mathematically, this is directly inspired by box-counting algorithms in fractal geometry; but this algorithm also has elegant parallels with other algorithms which we will highlight.

3. The age effects are demonstrated here in a small sample as a proof-of-principle, but we will update our latest results using ~100 subjects from the CamCAN data demonstrating the same effect. We have additionally described and verified these age effects in more detail in a separate preprint (https://arxiv.org/abs/2311.13501) with ~1500 subjects, and additionally showed that scale-dependent metrics substantially improve understanding and applications such as brain age prediction.

4. We have independently also received the feedback that we need to clarify how our method interacts with different resolution of the original MRI. We will add this as a new set of results, demonstrating that the MRI acquisition resolution (within a reasonable range) has a very small effect, as our method takes the reconstructed surfaces as a starting point.

5. We agree that it may be confusing to emphasise a constant K in the first set of results across species, and then later highlight a changing K in the human ageing results. We will clarify that in the first set of results, we find a “constant” K relative to a changing S: The range in K across melted primate brains is approx 0.1, whereas in S it is over 1.2. In other words, S changes are an order of magnitude higher than K changes. Hence, we described K as “constant” relative to S. Nevertheless, K shows subtle changes within individuals, which is what we are describing in the human ageing results. These changes are within the range of K values described in the across species results.

6. Finally, we will also make sure to summarise our specific contributions beyond existing work:

   (i) Showing for the first time that representative primate species follow the exact same fractal scaling – as opposed to previous work showing that they have a similar fractal dimension, i.e. slope, but not necessarily the same offset, as previous methods had no consistent way of comparing offsets.

   (ii) Previous work could also not show direct agreement in morphometrics between the coarse-grained brains of primate species and other non-primate mammalian species.

   (iii) Demonstrating in proof-of-principle that multiscale morphometrics, in practice, can have much larger effect sizes for classification applications. This moves beyond our previous work where we only showed the scaling law across and within species, but all on one (native) scale with comparable effect sizes for classification applications.

Author Response

Reviewer #2 (Public Review):

Weaknesses:

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

We will include the suggested data on the Cryo-EM analyses in a revised version of the preprint. We did not collect data on the sample used for the seeds in the cross seeding experiments because we had already confirmed in multiple datasets that the conditions in F1 and F2 reproducibly produce fibrils of Type 1 and Type 3, respectively. In a revised version we will include the analyses of several more datasets at the F1 and F2 conditions to support this statement.

Reviewer #3 (Public Review):

Weaknesses:

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

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

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

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

The pH 5.8 conditions that yield Type 3 fibrils has already been repeated several times in the original manuscript. The pH 7.4 conditions were only mentioned twice, once as an unseeded and once as a cross-seeded fibrilization. We solved a second Type 1 structure from a second dataset from the same protein batch fibrillized under similar conditions at pH 7.4 but with the addition of inositol trisphosphate in the hopes that we could replicate one of the in vivo polymorphs. However only the Type 1 polymorphs were observed and so we will add this data point to the revised manuscript. We are currently screening more fibrils produced at pH 7.0 and will include any replicates of Type 5 or the Type 1M polymorphs or of new structures that are obtained at these conditions… however, as noted in the original manuscript, reproducibility at this pH might be difficult because there appears to be a wider range of accessible polymorphs. As will be mentioned in the revised version, the Type 5 structure was solved from a manually picked set of fibers that represented 10-20% of the observed fibrils. The remaining fibers in the sample comprised polymorphs that could not be analyzed due to their inhomogeneity or lack of twist.

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

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

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

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

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

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

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

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

1. In the Method section of "Image processing", the authors describe the helical reconstruction procedure, without mentioning much detail about the 3D reconstruction and refinement process. For the benefit of reproducibility and to facilitate a deeper understanding among readers, the authors should enrich this part to include more comprehensive information, akin to the level of detail found in similar studies (refer to: https://doi.org/10.1038/nature23002).

As suggested by reviewer #2, we will add more comprehensive information on the 3D reconstruction and refinement process to a revised version.

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

That makes perfect sense and will be corrected in a revised version.

Reviewing Editor:

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

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

Author Response

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

Public Reviews:

Reviewer #1 (Public Review):

Strengths

This paper is well situated theoretically within the habit learning/OCD literature.

Daily training in a motor-learning task, delivered via smartphone, was innovative, ecologically valid and more likely to assay habitual behaviors specifically. Daily training is also more similar to studies with non-humans, making a better link with that literature. The use of a sequential-learning task (cf. tasks that require a single response) is also more ecologically valid.

The in-laboratory tests (after the 1 month of training) allowed the researchers to test if the OCD group preferred familiar, but more difficult, sequences over newer, simpler sequences.

The authors achieved their aims in that two groups of participants (patients with OCD and controls) engaged with the task over the course of 30 days. The repeated nature of the task meant that 'overtraining' was almost certainly established, and automaticity was demonstrated. This allowed the authors to test their hypotheses about habit learning. The results are supportive of the authors' conclusions.

Response: We truly appreciate the positive assessment of referee 1, particularly the consideration that our study is theoretically strong and that ‘the results are supportive of the authors' conclusions’. This is an important external endorsement of our conclusions, contrasting somewhat with the views of referee 2.

Weaknesses

The sample size was relatively small. Some potentially interesting individual differences within the OCD group could have been examined more thoroughly with a bigger sample (e.g., preference for familiar sequences). A larger sample may have allowed the statistical testing of any effects due to medication status. The authors were not able to test one criterion of habits, namely resistance to devaluation, due to the nature of the task

Response: We agree with the reviewer that the proof of principle established in our study opens new avenues for research into the psychological and behavioral determinants of the heterogeneity of this clinical population. However, considering the study timeline and the pandemic constraints, a bigger sample was not possible. Our sample can indeed be considered small if one compares it with current online studies, which do not require in-person/laboratory testing, thus being much easier to recruit and conduct. However, given the nature of our protocol (with 2 demanding test phases, 1-month engagement per participant and the inclusion of OCD patients without comorbidities only) and the fact that this study also involved laboratory testing, we consider our sample size reasonable and comparable to other laboratory studies (typically comprising on average between 30-50 participants in each group).

This article is likely to be impactful -- the delivery of a task across 30 days to a patient group is innovative and represents a new approach for the study of habit learning that is superior to an inlaboratory approach.

An interesting aspect of this manuscript is that it prompts a comparison with previous studies of goal-directed/habitual responding in OCD that used devaluation protocols, and which may have had their effects due to deficits in goal-directed behavior and not enhanced habit learning per se.

Response: Thank you for acknowledging the impact of our study, in particular the unique ability of our task to interrogate the habit system.

Reviewer #2 (Public Review):

In this study, the researchers employed a recently developed smartphone application to provide 30 days of training on action sequences to both OCD patients and healthy volunteers. The study tested learning and automaticity-related measures and investigated the effects of several factors on these measures. Upon training completion, the researchers conducted two preference tests comparing a learned and unlearned action sequences under different conditions. While the study provides some interesting findings, I have a few substantial concerns:

1. Throughout the entire paper, the authors' interpretations and claims revolve around the domain of habits and goal-directed behavior, despite the methods and evidence clearly focusing on motor sequence learning/procedural learning/skill learning. There is no evidence to support this framing and interpretation and thus I find them overreaching and hyperbolic, and I think they should be avoided. Although skills and habits share many characteristics, they are meaningfully distinguishable and should not be conflated or mixed up. Furthermore, if anything, the evidence in this study suggests that participants attained procedural learning, but these actions did not become habitual, as they remained deliberate actions that were not chosen to be performed when they were not in line with participants' current goals.

Response: We acknowledge that the research on habit learning is a topic of current controversy, especially when it comes to how to induce and measure habits in humans. Therefore, within this context referee’s 2 criticism could be expected. Across distinct fields of research, different methodologies have been used to measure habits, which represent relatively stereotyped and autonomous behavioral sequences enacted in response to a specific stimulus without consideration, at the time of initiation of the sequence, of the value of the outcome or any representation of the relationship that exists between the response and the outcome. Hence these are stimulus-bound responses which may or may not require the implementation of a skill during subsequent performance. Behavioral neuroscientists define habits similarly, as stimulus-response associations which are independent of reward or outcome, and use devaluation or contingency degradation strategies to probe habits (Dickinson and Weiskrantz, 1985; Tricomi et al., 2009). Others conceptualize habits as a form of procedural memory, along with skills, and use motor sequence learning paradigms to investigate and dissect different components of habit learning such as action selection, execution and consolidation (Abrahamse et al., 2013; Doyon et al., 2003; Squire et al., 1993). It is also generally agreed that the autonomous nature of habits and the fluid proficiency of skills are both usually achieved with many hours of training or practice, respectively (Haith and Krakauer, 2018).

We consider that Balleine and Dezfouli (2019) made an excellent attempt to bring all these different criteria within a single framework, which we have followed. We also consider that our discussion in fact followed a rather cautious approach to interpretation solely in terms of goaldirected versus habitual control.

Referee 2 does not actually specify criteria by which they define habits and skills, except for asserting that skilled behavior is goal-directed, without mentioning what the actual goal of the implantation of such skill is in the present study: the fulfillment of a habit? We assume that their definition of habit hinges on the effects of devaluation, as a single criterion of habit, but which according to Balleine and Dezfouli (2019) is only 1 of their 4 listed criteria. We carefully addressed this specific criterion in our manuscript: “We were not, however, able to test the fourth criterion, of resistance to devaluation. Therefore, we are unable to firmly conclude that the action sequences are habits rather than, for example, goal-directed skills. Regardless of whether the trained action sequences can be defined as habits or goal-directed motor skills, it has to be considered…”. Therefore, we took due care in our conclusions concerning habits and thus found the referee’s comment misleading and unfair.

We note that our trained motor sequences did in fact fulfil the other 3 criteria listed by Balleine and Dezfouli (2019), unlike many studies employing only devaluation (e.g. Tricomi et al 2009; Gillan et al 2011). Moreover, we cited a recent study using very similar methodology where the devaluation test was applied and shown to support the habit hypothesis (Gera et al., 2022).

Whether the initiation of the trained motor sequences in experiment 3 (arbitration) is underpinned by an action-outcome association (or not) has no bearing on whether those sequences were under stimulus-response control after training (experiment 1). Transitions between habitual and goal-directed control over behavior are quite well established in the experimental literature, especially when choice opportunities become available (Bouton et al (2021), Frölich et al (2023), or a new goal-directed schemata is recruited to fulfill a habit (Fouyssac et al, 2022). This switching between habits and goal-directed responding may reflect the coordination of these systems in producing effective behavior in the real world.

• Fouyssac M, Peña-Oliver Y, Puaud M, Lim NTY, Giuliano C, Everitt BJ, Belin D. (2021).Negative Urgency Exacerbates Relapse to Cocaine Seeking After Abstinence. Biological Psychiatry. doi: 10.1016/j.biopsych.2021.10.009

• Frölich S, Esmeyer M, Endrass T, Smolka MN and Kiebel SJ (2023) Interaction between habits as action sequences and goal-directed behavior under time pressure. Front. Neurosci. 16:996957. doi: 10.3389/fnins.2022.996957

• Bouton ME. 2021. Context, attention, and the switch between habit and goal-direction in behavior. Learn Behav 49:349– 362. doi:10.3758/s13420-021-00488-z

1. Some methodological aspects need more detail and clarification.

2. There are concerns regarding some of the analyses, which require addressing.

Response: We thank referee 2 for their detailed review of the methods and analyses of our study and for the helpful feedback, which clearly helps improve our manuscript. We will clarify the methodological aspects in detail and conduct the suggested analysis. Please see below our answers to the specific points raised.

Introduction:

1. It is stated that "extensive training of sequential actions would more rapidly engage the 'habit system' as compared to single-action instrumental learning". In an attempt to describe the rationale for this statement the authors describe the concept of action chunking, its benefits and relevance to habits but there is no explanation for why sequential actions would engage the habit system more rapidly than a single-action. Clarifying this would be helpful.

Response: We agree that there is no evidence that action sequences become habitual more readily than single actions, although action sequences clearly allow ‘chunking’ and thus likely engage neural networks including the putamen which are implicated in habit learning as well as skill. In our revised manuscript we will instead state: “we have recently postulated that extensive training of sequential actions could be a means for rapidly engaging the ‘habit system’ (Robbins et al., 2019)]”

DONE in page 2

1. In the Hypothesis section the authors state: “we expected that OCD patients... show enhanced habit attainment through a greater preference for performing familiar app sequences when given the choice to select any other, easier sequence”. I find it particularly difficult to interpret preference for familiar sequences as enhanced habit attainment.

Response: We agree that choice of the familiar response sequence should not be a necessary criterion for habitual control although choice for a familiar sequence is, in fact, not inconsistent with this hypothesis. In a recent study, Zmigrod et al (2022) found that 'aversion to novelty' was a relevant factor in the subjective measurement of habitual tendencies. It should also be noted that this preference was present in patients with OCD. If one assumes instead, like the referee, that the familiar sequence is goal-directed, then it contravenes the well-known 'egodystonia' of OCD which suggests that such tendencies are not goal-directed.

To clarify our hypothesis, we will amend the sentence to the following: “Finally, we expected that OCD patients would generally report greater habits, as well as attribute higher intrinsic value to the familiar app sequences manifested by a greater preference for performing them when given the choice to select any other, easier sequence”.

DONE in page 5. We have now rephrased it: “Additionally, we hypothesized that OCD patients would generally display stronger habits and assign greater intrinsic value to the familiar app sequences, evidenced by a marked preference for executing them even when presented with a simpler alternative sequence.”

A few notes on the task description and other task components:

1. It would be useful to give more details on the task. This includes more details on the time/condition of the gradual removal of visual and auditory stimuli and also on the within practice dynamic structure (i.e., different levels appear in the video).

Response: These details will be included in the revised manuscript. Thank you for pointing out the need for further clarification of the task design.

Done in page 7

1. Some more information on engagement-related exclusion criteria would be useful (what happened if participants did not use the app for more than one day, how many times were allowed to skip a day etc.).

Response: This additional information will be added to the revised manuscript. If participants omitted to train for more than 2 days, the researcher would send a reminder to the participant to request to catch up. If the participant would not react accordingly and a third day would be skipped, then the researcher would call to understand the reasons for the lack of engagement and gauge motivation. The participant would be excluded if more than 5 sequential days of training were missed. Only 2 participants were excluded given their lack of engagement.

Done in page 8

1. According to the (very useful) video demonstrating the task and the paper describing the task in detail (Banca et al., 2020), the task seems to include other relevant components that were not mentioned in this paper. I refer to the daily speed test, the daily random switch test, and daily ratings of each sequence's enjoyment and confidence of knowledge.

If these components were not included in this procedure, then the deviations from the procedure described in the video and Banca al. (2020) should be explicitly mentioned. If these components were included, at least some of them may be relevant, at least in part, to automaticity, habitual action control, formulation of participants' enjoyment from the app etc. I think these components should be mentioned and analyzed (or at least provide an explanation for why it has been decided not to analyze them).

This is also true for the reward removal (extinction) from the 21st day onwards which is potentially of particular relevance for the research questions.

Response: The task procedure was indeed the same as detailed in Banca et al., 2020. We did not include these extra components in this current manuscript for reasons of succinctness and because the manuscript was already rather longer than a common research article, given that we present three different, though highly inter-dependent, experiments in order to answer key interrelated questions in an optimal manner. However, since referee 2 considers this additional analysis to be important, we will be happy to include it in the supplementary material of the revised manuscript.

These additional components of the task as well as the respective analysis are now described in the Supplementary Materials.

Training engagement analysis:

1. I find referring to the number of trials including successful and unsuccessful trials as representing participants "commitment to training" (e.g. in Figure legend 2b) potentially inadequate. Given that participants need at least 20 successful trials to complete each practice, more errors would lead to more trials. Therefore, I think this measure may mostly represent weaker performance (of the OCD patients as shown in Figure 2b). Therefore, I find the number of performed practice runs, as used in Figure 2a (which should be perfectly aligned with the number of successful trials), a "clean" and proper measure of engagement/commitment to training.

Response: We acknowledge referee’s concern on this matter and agree to replace the y-axis variable of Figure 2b to the number of performed practices (thus aligning with Figure 2a). This amendment will remove any potential effect of weaker performance on the engagement measurement and will provide clearer results.

We have now decided to remove this figure as it does not add much to figure 2a. Instead, we replaced figure 2b and 2c for new plots, following new analysis linked to the next reviewer request (point 10)

1. Also, to provide stronger support for the claim about different diurnal training patterns (as presented in Figure 2c and the text) between patients and healthy individuals, it would be beneficial to conduct a statistical test comparing the two distributions. If the results of this test are not significant, I suggest emphasizing that this is a descriptive finding.

Response: Done, see revised Figure 2b and 2c. We have assessed the diurnal training patterns within each group using circular statistics, followed by independent-sample statistical testing of those circular distributions with the Watson’s U2 test ( Landler et al., 2021). While OCD participants have a group effect of practice with a significant peak at ~18:00, and HV participants have an earlier significant peak at ~15:00, the Watson’s U test did not find statistical betweengroup differences.

• Landler L, Ruxton GD, Malkemper EP. Advice on comparing two independent samples of circular data in biology. Scientific reports. 2021 Oct 13;11(1):20337.

Learning results:

1. When describing the Learning results (p10) I think it would be useful to provide the descriptive stats for the MT0 parameter (as done above for the other two parameters).

Response: Thank you for pointing this out. The descriptive stats for MT0 will be added to the revised version of the manuscript.

Done page 11

1. Sensitivity of sequence duration and IKI consistency (C) to reward:

I think it is important to add details on how incorrect trials were handled when calculating ∆MT (or C) and ∆R, specifically in cases where the trial preceding a successful trial was unsuccessful. If incorrect trials were simply ignored, this may not adequately represent trial-by-trial changes, particularly when testing the effect of a trial's outcome on performance change in the next trial.

Response: This is an important question. Our analysis protocol was designed to ensure that incorrect trials do not contaminate or confound the results. To estimate the trial-to-trial difference in ∆MT (or C) and ∆R, we exclusively included pairs of contiguous trials where participants achieved correct performance and received feedback scores for both trials. For example, if a participant made a performance error on trial 23, we did not include ∆R or ∆MT estimates for the pairs of trials 23-22 and 24-23. Instead of excluding incorrect trials from our analyses, we retained them in our time series but assigned them a NaN (not a number) value in Matlab. As a result, ∆R and ∆MT was not defined for those two pairs of trials. Similarly for C. This approach ensured that our analyses are not confounded by incremental or decremental feedback scores between noncontiguous trials. In the past, when assessing the timing of correct actions during skilled sequence performance, we also considered events that were preceded and followed by correct actions. This excluded effects such as post-error slowing from contaminating our results (Herrojo Ruiz et al., 2009, 2019). Therefore, we do not believe that any further reanalysis is required.

• Ruiz MH, Jabusch HC, Altenmüller E. Detecting wrong notes in advance: neuronal correlates of error monitoring in pianists. Cerebral cortex. 2009 Nov 1;19(11):2625-39.

• Bury G, García-Huéscar M, Bhattacharya J, Ruiz MH. Cardiac afferent activity modulates early neural signature of error detection during skilled performance. NeuroImage. 2019 Oct 1;199:704-17.

1. I have a serious concern with respect to how the sensitivity of sequence duration to reward is framed and analyzed. Since reward is proportional to performance, a reduction in reward essentially indicates a trial with poor performance, and thus even regression to the mean (along with a floor effect in performance [asymptote]) could explain the observed effects. It is possible that even occasional poor performance could lead to a participant demonstrating this effect, potentially regardless of the reward. Accordingly, the reduced improvement in performance following a reward decrease as a function of training length described in Figure 5b legend may reflect training-induced increased performance that leaves less room for improvement after poor trials, which are no longer as poor as before. To address this concern, controlling for performance (e.g., by taking into consideration the baseline MT for the previous trial) may be helpful. If the authors can conduct such an analysis and still show the observed effect, it would establish the validity of their findings."

Response: Thank you for raising this point. This has been done, see updated Figures 5 and 6. After normalizing the ∆MT(n+1) := MT(n+1) – MT(n) difference values by dividing them with the baseline MT(n) at trial n, we obtain the same results. Similar results are also obtained for IKI consistency (C).

See below our initial response from June 2023.

Thank you for raising this point. Figure 5b illustrates two distinct effects of reward changes on behavioral adaptation, which are expected based on previous research.

I. Practice effects: Firstly, we observe that as participants progress across bins of practice, the degree of improvement in behavior (reflected by faster movement time, MT) following a decrease in reward (∆R−) diminishes, consistent with our expectations based on previous work. Conversely, we found that ∆MT does not change across bins of practices following an increase in reward (∆R+).

We appreciate the reviewer’s suggestion regarding controlling for the reference movement time (MT) in the previous trial when examining the practice effect in the p(∆T|∆R−) and p(∆T|∆R+) distributions. In the revised manuscript, we will conduct the proposed control analysis to better understand whether the sensitivity of MT to score decrements changes across practice when normalising MT to the reference level on each trial. But see below for a preliminary control analysis.

II. Asymmetry of the effect of ∆R− and ∆R+ on performance: Figure 5b also depicts the distinct impact of score increments and decrements on behavioural changes. When aggregating data across practice bins, we consistently observed that the centre of the p(∆T|∆R−) distribution was smaller (more negative) than that of p(∆T|∆R+). This suggests that participants exhibited a greater acceleration following a drop in scores compared to a relative score increase, and this effect persisted throughout the practice sessions. Importantly, this enhanced sensitivity to losses or negative feedback (or relative drops in scores) aligns with previous research findings (Galea et al., 2015; Pekny et al., 2014; van Mastrigt et al., 2020).

We have conducted a preliminary control analysis to exclude the potential impact that reference movement time (MT) values could have on our analysis. We have assessed the asymmetry between behavioural responses to ∆R− and ∆R+ using the following analysis: We estimated the proportion of trials in which participants exhibited speed-up (∆T < 0) or slow-down (∆T > 0) behaviour following ∆R− and ∆R+ across different practice bins (bins 1 to 4). By discretising the series of behavioural changes (∆T) into binary values (+1 for slowing down, -1 for speeding up), we can assess the type of changes (speed-up, slow-down) without the absolute ∆T or T values contributing to our results. We obtained several key findings:

• Consistent with expectations (sanity check), participants exhibited more instances of speeding up than slowing down across all reward conditions.

• Participants demonstrated a higher frequency of speeding up following ∆R− compared to ∆R+, and this asymmetry persisted throughout the practice sessions (greater proportion of -1 events than +1 events). 53% events were speed-up events in the in the p(∆T|∆R+) distribution for the first bin of practices, and 55% for the last bin. Regarding p(∆T|∆R-), there were 63% speed-up events throughout each bin of practices, with this proportion exhibiting no change over time.

• Accordingly, the asymmetry of reward changes on behavioural adaptations, as revealed by this analysis, remained consistent across the practice bins.

Thus, these preliminary findings provide an initial response to referee 2 and offer valuable insights into the asymmetrical effects of positive/negative reward changes on behavioural adaptations. We plan to include these results in the revised manuscript, as well as the full control analysis suggested by the referee. We will further expand upon their interpretation and implications.

1. Another way to support the claim of reward change directionality effects on performance (rather than performance on performance), at least to some extent, would be to analyze the data from the last 10 days of the training, during which no rewards were given (pretending for analysis purposes that the reward was calculated and presented to participants). If the effect persists, it is less unlikely that the effect in question can be attributed to the reward dynamics.

Response: The reviewer’s concern is addressed in the previous quesQon. Also, this analysis would not be possible because our Gaussian fit analyses use the Qme series of conQnuous reward scores, in which ∆R− or ∆R+ are embedded. These events cannot be analyzed once reward feedback is removed because we do not have behavioral events following ∆R− or ∆R+ anymore.

Done

1. This concern is also relevant and should be considered with respect to the sensitivity of IKI consistency (C) to reward. While the relationship between previous reward/performance and future performance in terms of C is of a different structure, the similar potential confounding effects could still be present.

Response: We will conduct this analysis for the revised manuscript, similarly to the control analysis suggested by referee 2 on MT. Our preliminary control analysis, as explained above, suggests that the fundamental asymmetry in the effect of ∆R+ and ∆R+ on behavioral changes persists when excluding the impact of reference performance values in our Gaussian fit analysis.

Done. See updated Figure 6. The results are very similar once we normalize the IKI consistency index C with the IKI of the baseline performance at trial n.

1. Another related question (which is also of general interest) is whether the preferred app sequence (as indicated by the participants for Phase B) was consistently the one that yielded more reward? Was the continuous sequence the preferred one? This might tell something about the effectiveness of the reward in the task.

Response: We have now conducted this analysis. There is in fact no evidence to conclude that the continuously rewarded sequence was the preferred one. The result shows that 54.5% of HV and 29% of the OCD sample considered the continuous sequence to be their preferred one, a nonstatistically significant difference. Note that this preference may not necessarily be linked simply to programmed reward. The overall preference may be influenced by many other factors, such as, for example, the aesthetic appeal of particular combinations of finger movements.

Regarding both experiments 2 and 3:

1. The change in context in experiment 2 and 3 is substantial and include many different components. These changes should be mentioned in more detail in the Results section before describing the results of experiments 2 and 3.

Response: Following referee’s advice, we will move these details (currently written in the Methods section) to the Results section, when we introduce Phase B and before describing the results of experiments 2 and 3.

Done in page 21

Experiment 2:

1. In Experiment 2, the authors sometimes refer to the "explicit preference task" as testing for habitual and goal-seeking sequences. However, I do not think there is any justification for interpreting it as such. The other framings used by the authors - testing whether trained action sequences gain intrinsic/rewarding properties or value, and preference for familiar versus novel action sequences - are more suitable and justified. In support of the point I raised here, assigning intrinsic rewarding properties to the learned sequences and thereby preferring these sequences can be conceptually aligned with goal-directed behavior just as much as it could be with habit.

Response: We clearly defined the theoretical framing of experiment 2 as a test of whether trained action sequences gain intrinsic value and we are pleased to hear that the referee agrees with this framing. If the referee is referring to the paragraph below (in the Discussion), we actually do acknowledge within this paragraph that a preference for the trained sequences can either be conceptually aligned with a habit OR a goal-directed behavior.

“On the other hand, we are describing here two potential sources of evidence in favor of enhanced habit formation in OCD. First, OCD patients show a bias towards the previously trained, apparently disadvantageous, action sequences. In terms of the discussion above, this could possibly be reinterpreted as a narrowing of goals in OCD (Robbins et al., 2019) underlying compulsive behavior, in favor of its intrinsic outcomes”

This narrowing of goals model of OCD refers to a hypothetically transiQonal stage of compulsion development driven by behavior having an abnormally strong, goal-directed nature, typically linked to specific values and concerns.

If the referee is referring to the penulQmate sentence of hypothesis secQon, this has been amended in response to Q5. We cannot find any other possible instances in this manuscript stating that experiment 2 is a test of habitual or goal-directed behavior.

Experiment 3:

1. Similar to Experiment 2, I find the framing of arbitration between goal-directed/habitual behavior in Experiment 3 inadequate and unjustified. The results of the experiment suggest that participants were primarily goal-directed and there is no evidence to support the idea that this reevaluation led participants to switch from habitual to goal-directed behavior.

Also, given the explicit choice of the sequence to perform participants had to make prior to performing it, it is reasonable to assume that this experiment mainly tested bias towards familiar sequence/stimulus and/or towards intrinsic reward associated with the sequence in value-based decision making.

Response: This comment is aligned with (and follows) the referee’s criticism of experiment 1 not achieving automatic and habitual actions. We have addressed this matter above, in response 1 to Referee 2.

Mobile-app performance effect on symptomatology: exploratory analyses:

1. Maybe it would be worth testing if the patients with improved symptomatology (that contribute some of their symptom improvement to the app) also chose to play more during the training stage.

Response: We have conducted analysis to address this relevant question. There is no correlation between the YBOCS score change and the number of total practices, meaning that the patients who improved symptomatology post training did not necessarily chose to play the app more during the training stage (rs = 0.25, p = 0.15). Additionally, we have statistically compared the improvers (patients with reduced YBOCS scores post-training) and the non-improvers (patients with unchanged or increased YBOCS scores post-training) in their number of app completed practices during the training phase and no differences were observed (U = 169, p = 0.19).

The result from the correlational analysis has been added to the revised manuscript (page 28).

Discussion:

1. Based on my earlier comments highlighting the inadequacy and mis-framing of the work in terms of habit and goal-directed behavior, I suggest that the discussion section be substantially revised to reflect these concerns.

Response: We do not agree that the work is either "inadequate or mis-framed" and will not therefore be substantially revising the Discussion. We will however clarify further the interpretation we have made and make explicit the alternative viewpoint of the referee. For example, we will retitle experiment 3 as “Re-evaluation of the learned action sequence: possible test of goal/habit arbitration” to acknowledge the referee’s viewpoint as well as our own interpretation.

Done

1. In the sentence "Nevertheless, OCD patients disadvantageously preferred the previously trained/familiar action sequence under certain conditions" the term "disadvantageously" is not necessarily accurate. While there was potentially more effort required, considering the possible presence of intrinsic reward and chunking, this preference may not necessarily be disadvantageous. Therefore, a more cautious and accurate phrasing that better reflects the associated results would be useful.

Response: We recognize that the term "disadvantageously" may be semantically ambiguous for some readers and therefore we will remove it.

Done

Materials and Methods:

1. The authors mention: "The novel sequence (in condition 3) was a 6-move sequence of similar complexity and difficulty as the app sequences, but only learned on the day, before starting this task (therefore, not overtrained)." - for the sake of completeness, more details on the pre-training done on that day would be useful.

Response: Details of the learning procedure of the novel sequence (in condition 3, experiment 3) will be provided in the methods of the revised version of the manuscript.

Done in page 40

Minor comments:

1. In the section discussing the sensitivity of sequence duration to reward, the authors state that they only analyzed continuous reward trials because "a larger number of trials in each subsample were available to fit the Gaussian distributions, due to feedback being provided on all trials." However, feedback was also provided on all trials in the variable reward condition, even though the reward was not necessarily aligned with participants' performance. Therefore, it may be beneficial to rephrase this statement for clarity.

Response: We will follow this referee’s advice and will rephrase the sentence for clarity.

Done. See page 16.

1. With regard to experiment 2 (Preference for familiar versus novel action sequences) in the following statement "A positive correlation between COHS and the app sequence choice (Pearson r = 0.36, p = 0.005) further showed that those participants with greater habitual tendencies had a greater propensity to prefer the trained app sequence under this condition." I find the use of the word "further" here potentially misleading.

Response: The word "further" will be removed.

Done

Reviewer #1 (Recommendations For The Authors):

This is a very interesting manuscript, which was a pleasure to review. I have some minor comments you may wish to consider.

1. I believe that it is possible to include videos as elements in eLife articles - please consider if you can do this to demonstrate the action sequence on the smartphone. I followed the YouTube video, and it was very helpful to see exactly what participants did, but it would be better to attach the video directly, if possible.

Response: This is a great idea and we will definitely attach our video demonstrating the task to the revised manuscript (Version of Record) if the eLife editors allow.

We ask permission to the editor to add the video

1. The abstract states that the study uses a "novel smartphone app" but is the same one as described in Banca et al. Suggest writing simply "smartphone app".

Response: We will remove the word novel.

Done

1. Some of the hypotheses described in the second half of the Hypothesis section could be stated more explicitly. For example: "We also hypothesized that the acquisition of learning and automaticity would differ between the two action sequences based on their associated rewarded schedule (continuous versus variable) and reward valence (positive or negative)." The subsequent sentence explains the prediction for the schedule but what is the hypothesized direction for reward valence? More detail is subsequently given on p. 14, Results, but it would be better to bring these details up to the Introduction. "We additionally examined differential effects of positive and negative feedback changes on performance to build on previous work demonstrating enhanced sensitivity to negative feedback in patients with OCD (Apergis-Schoute et al 2023, Becker et al., 2014; Kanen et al., 2019)." In general, the second part of the Hypothesis section is a bit dense, sometimes with two predictions per sentence. It could be useful for the reader if hypotheses were enumerated and/or if a distinction was made among the hypotheses with respect to their importance.

We fully revised the hypothesis section, on page 5, following this reviewer’s suggestion. We think this section is much clearer now, in our revised manuscript.

Response: Thank you for pointing out the need for clarity in our hypothesis section. This is a very important point and we will carefully rewrite our hypothesis in the revised manuscript to make them as clear as possible.

1. Did medication status correlate with symptom severity in the OCD group (e.g., higher symptoms for the 6 participants on SSRI+antipsychotics?). Could this, or SSRI-only status, have impacted results in any way? I appreciate that there is no way to test medication status statistically but readers may be interested in your thoughts on this aspect.

Response: We have now conducted exploratory analysis to assess the potential effect of medication in the following output measures: app engagement (as measured by completed practices), explicit preference and YBOCS change post-training. The patients who were on combined therapy (SSRIs + antipsychotic) did not perform significantly different in these measures as compared to the remaining patients and no other effects of interest were observed. Their symptomatology was indeed slightly more severe but not statistically significant [Y-BOCS combined = 26.2 (6.5); Y-BOCS SSRI only = 23.8 (6.1); Y-BOCS No Med = 23.8 (2.2), mean(std)]. Only one patient showed symptom improvement after the app training, another became worse and the remaining patients on combined therapy remain stable during the month.

Palminteri et al (2011) found that unmedicated OCD patients exhibited instrumental learning deficits, which were fully alleviated with SSRI treatment. Therefore, it is possible that the SSRI medication (present in our sample) may have reduced habit formation and facilitated behavioral arbitration. However, since the effect goes against the habit hypothesis, it has is unlikely that it has confounded our measure of automaticity. If anything, medication rendered experiment 2 and 3 more goal-oriented. We agree that further studies are warranted to address the effect of SSRIs on these measures.

1. You could explain earlier why devaluation could not be tested here (it is only explained in the Limitations section near the end)

Response: The revised manuscript will be amended to account for this note.

Done in page 25.

1. Capitalize 'makey-makey', I didn't realize there was a product called Makey Makey until I Googled it.

Response: Sure. We will capitalize 'Makey-Makey'. Thank you for pointing this out!

Done

Reviewer #2 (Recommendations For The Authors):

Recommendations for the authors (ordered by the paper sections):

In the introduction

1. regarding this part "We used a period of 1-month's training to enable effective consolidation, required for habitual action control or skill retention to occur. This acknowledged previous studies showing that practice alone is insufficient for habit development as it also requires off-line consolidation computations, through longer periods of time (de Wit et al., 2018) and sleep (Nusbaum et al., 2018; Walker et al., 2003)." I advise the authors to re-check whether what is attributed here to de Wit et al. (2018) is indeed justified (if I remember correctly they have not mentioned anything about off-line consolidation computations).

Response: When we revise the manuscript, we will remove the de Wit et al. (2018) citation from this sentence.

Done

in the Outline paragraph

1. it stated: "We continuously collected data online, in real time, thus enabling measurements of procedural learning as well as automaticity development." I think this wording implies that the fact that the data was collected online in real time was advantageous in that it enabled to assess measurements of procedural learning and automaticity development, which in my understanding is not the case.

Response: To make this sentence clearer, we will change it to the following: ‘We continuously collected data online, to monitor engagement and performance in real time and to enable acquisition of sufficient data to analyze, à posteriori, procedural learning and automaticity development’.

Done in page 4: ‘We collected data online continuously to monitor engagement and performance in real-time. This approach ensured we acquired sufficient data for subsequent analysis of procedural learning and automaticity development’.

1. In the final sentence of this paragraph "or and" should be changed to "or/end".

Response: This was a typo. The word ‘and’ will be removed.

Done

1. In Figure 1c - Note that in the figure legend it says "Each sequence comprises 3 single press moves, 2 two-finger moves..." whereas in the example shown in the figure it's the other way around (2 single press moves and 3 two-finger moves).

Response: Thank you so much for spotting this! The example shown in the figure is incorrect. We apologize for the mistake. It should depict 3 single press moves, 2 two-finger moves and 1 three- finger move. The figure will be amended.

Done

In the results section:

1. Regarding the "were followed by a positive ring tone and the unsuccessful ones by a negative ring tone", I suggest mentioning that there was also a positive visual (rewarding) effect.

Response: Thank you. A mention to the visual effect will be added for both the positive (successful) and negative (unsuccessful) trials. Done in page 7

1. p 10. - Note a typo in the following sentence where the word "which" appears twice consecutively:

"Furthermore, both groups exhibited similar motor durations at asymptote which, which combined with the previous conclusion, indicates that OCD patients improved their motor learning more than controls, but to the same asymptote."

Response: Thank you for spotting this typo. The second word will be removed. Done

1. I have a few suggestions with respect to Figure 3:

2. keeping the y-axes scale similar in all subplots would be more visually informative.

Here we kept the y-axes scale similar in all subplots, except one of them, which was important to keep to capture all the data.

1. For the subplots in 3b I would recommend for the transparent regions, instead of the IQR, to use the median +/- 1.57 * IQR/sqrt(n) which is equivalent to how the notches are calculated in a box-plot figure (It is referred to as an approximate 95% confidence interval for the median). This should make the transparent area narrower and thus better communicate the results.

Done

1. I think the significant levels mentioned in figure legend 3b (which are referring to the group effect measured for each reward schedule type separately) is not mentioned in the text. While not crucial, maybe consider adding it in the text.

We don’t think this is necessary and may actually lead to confusion because in the text we report a Kruskal–Wallis H test (which is the most appropriate statistical test), including their H and p values for the group and reward effects. Since in the figure we separated the analysis and plots for variable and continuous reward schedules (for visual purposes) , we reported a U test separated for each reward schedule. Therefore, we consider that the correct statistics are reported in the appropriate places of the manuscript.

Response: Thank you for this very helpful suggestion. We will amend figure 3 accordingly.

1. In the Automaticity results (pp. 12 and 13) when describing the Descriptive stats the wrong parameter indicator are used (DL instead of CL and nD instead of nC.

Response: Thank you for noticing it. We will amend.

Done

1. In Sensitivity of IKI consistency (C) to reward results:

In Figure 6a legend: with respect to "... and for reward increments (∆R+, purple) and decrements (∆R-, green)" - note that there are also additional colors indicating these ∆Rs.

Response: Done. We had used a 2 x 2 color scheme: green hues for ∆R-, and purple hues for ∆R+. Then, OCD is denoted by dark colors, and HV by light colors. This represents all four colors used in the figure. For instance, OCD and ∆R- is dark green, whereas OCD and ∆R+ is denoted by dark purple.

1. p.21 - the YBOCS abbreviation appears before the full form is spelled out in the text.

Response: In the revised version, we will make sure the YBOCS abbreviation will be spelled out the first time it is mentioned.

Done in page 24

Experiments 2 and 3:

1. If there is a reason behind presenting the conditions sequentially rather than using intermixed trials in experiments 2 and 3, it would be useful to mention it in the text.

Response: Experiment 2 could have used intermixed trials. However, we were concerned that the use of intermixed trials in experiment 3 would increase excessively the memory load of the task, which could then be a confound.

Done in page 41

1. I wonder whether the presentation order of the conditions in experiments 2 and 3 affected participants' results? Maybe it is worth adding this factor to the analysis.

Response: As we mentioned both in the methods and results sections, we counterbalanced all the conditions across participants, in both experiments 2 and 3. This procedure ensures no order effects.

Experiment 2:

1. Regarding this sentence (pp. 21-22): "However, some participants still preferred the app sequence, specifically those with greater habitual tendencies, including patients who considered the app training beneficial." I think the part that mentions that there are "patients who considered the app training beneficial" appears below and it may confuse the reader. I suggest either providing a brief explanation or indicating that further details will be provided later in the text ("see below in...").

Response: We will clarify this section.

We added “see below exploratory analyses of “Mobile-app performance effect on symptomatology”” in the end of the sentence so that the reader knows this is further explained below. Page 25

1. Finally, in addition to subgrouping maybe it is worth testing whether there is a correlation between the YBOCS score change and the app-sequences preference (as to learn if the more they change their YBOCS the more they prefer the learned sequences and vice versa?)

Response: Thank you for suggesting this relevant correlational analysis, which we have now conducted. Indeed, there is a correlation between the YBOCS score change and the preference for the app-sequences, meaning that the higher the symptom improvement after the month training, the greater the preference for the familiar/learned sequence. This is particularly the case for the experimental condition 2, when subjects are required to choose between the trained app sequence and any 3-move sequence (rs = 0.35, p=0.04). A trend was observed for the correlation between the YBOCS score change and the preference for the app-sequences in experimental condition 1 (app preferred sequence versus any 6-move sequence): rs = 0.30, p=0.09.

This finding represents an additional corroboration of our conclusion that the app seems to be more beneficial to patients more prone to routine habits, who are somewhat more averse to novelty.

This analysis was added in page 24, 25 and page 35.

Experiment 3:

1. You mention "The task was conducted in a new context, which has been shown to promote reengagement of the goal system (Bouton, 2021)." In my understanding this observation is true also for experiment 2. In such case it should be stated earlier (probably under: "Phase B: Tests of actionsequence preference and goal/habit arbitration").

Response: As answered above in (Q17), we will follow this referee 2’s suggestion and describe the contextual details of experiments 2 and 3 in the Results section, when we introduce Phase B.

Done in page 21.

1. w.r.t this sentence - "...that sequence (Figure 8b, no group effects (p = 0.210 and BF = 0.742, anecdotal evidence)" I would add what the anecdotal evidence refers (as done in other parts of the paper), to prevent potential confusion.

Response: OK, this will be added.

Added on page 27

Discussion:

1. w.r.t. "Here we have trained a clinical population with moderately high baseline levels of stress and anxiety, with training sessions of a higher order of magnitude than in previous studies (de Wit et al., 2018, 2018; Gera et al., 2022) (30 days instead of 3 days)." The Gera et al. 2022 (was more than 3 days), you probably meant Gera et al. 2023 ("Characterizing habit learning in the human brain at the individual and group levels: a multi-modal MRI study", for which 3 days is true).

Response: Thank you for pointing this out. We will keep the citation to Gera et al 2022 given its relevance to the sentence but we will remove the information inside the parenthesis. This amendment will solve the issue raised here.

Done in page 32

1. w.r.t "to a simple 2-element sequence with less training (Gera et al., 2022)" - it's a 3-element sequence in practice.

Response: Thank you for this correction. We will amend this sentence accordingly.

Done in page 32

1. (p.30) w.r.t "and enhanced error-related negativity amplitudes in OCD" - a bit more context of what the negative amplitudes refer to would be useful (So the reader understands it refers to electrophysiology).

Response: We will add a sentence in our revised manuscript addressing this matter. This sentence has been removed in the revised manuscript

Supplementary materials:

1. under "Sample size for the reward sensitivity analysis":

It is stated "One practice corresponded to 20 correctly performed sequences. We therefore split the total number of correct sequences into four bins." I was not able to follow this reasoning here (20 correct trials in practice => splitting the data the 4 bins). More clarity here would be useful.

Response: We will clarify this procedure of our analysis in the revised version of the manuscript. Thanks.

Done. See Supplementary materials.

1. Also, maybe I am missing something, but I couldn't understand why the number of sequences available per bin is different for the calculation of ∆MT and C. Aren't any two consecutive sequences that are good for the calculation of one of these measures also good for the calculation of the other?

Response: Thank you for pointing this out. Indeed, the number of trials was the same for both analyses, ∆MT and C. We had saved an incorrect variable as number of trials. We will amend the text.

We have re-analyzed the trial number data. The average number of trials per bin both for the ∆MT and C analyses was 109 (9) in the HV and 127 (12) in OCD groups. Although the number was on average larger in the patient group, we did not find significant differences between groups (p = 0.47).

When assessing the p(∆T|∆R+) and p(∆T|∆R-) separately, more trials were available for p(∆T|∆R+), 107 (10) , than for p(∆T|∆R-), and 98 (8). These trial numbers differed significantly (p = 0.0046), but were identical for ∆MT and C analyses.

Done. Included in Supplementary materials.

Minor comments:

1. Not crucial, but maybe for the sake of consistency consider merging the "Self-reported habit tendencies" section and the "Other self-reported symptoms" section, preferably where the latter is currently placed.

Response: We fully understand the referee’s rationale underlying this suggestion. We indeed considered initially presenting the self-reported questionnaires all together, in a last, single section of the results, as suggested by the referee. However, we decided to report the higher habitual tendencies of OCD as an initial set of results, not only because it is a novel and important finding (which justifies it to be highlighted) but also because it is essential to the understanding of some of the remaining results presented.

1. In some figure legends the percentage of the interval of the mentioned confidence intervals (probably 95%) is missing. I suggest adding it.

Response: OK, this will be added.

Done

1. The NHS abbreviation appears without spelling out the full form.

Response: This will be amended accordingly.

I removed NHS as it is not relevant.

1. In p.38 the citation (Rouder et al., 2012) is duplicated (appears twice consecutively).

Response: Thank you for pointing this out. We will amend accordingly.

Done

In the results section:

1. The authors mention: "To promote motivation, the total points achieved on each daily training sessions were also shown, so participants could see how well they improved across days". Yet, if the score is based on the number of practices, it may not represent participants improvement in case in some days more practices are performed. I suggest to clarify this point.

Response: The goal of providing the scoring feedback was, as explained in the sentence, to gauge motivation and inform the subject about their performance. Having this goal in mind, it does not really matter if one day their scoring would be higher simply because they would have done more practice on that day. Participants could easily understand that the scoring reflected their performance on each practice so they would realize that the more practice, the greater their improvement and that the scoring would increase across days of practice. We will amend the sentence to the following: "To promote motivation, the total points achieved on each training session (i.e. practice) was also shown, so participants could see how well they improved across practice and across days".

Done in page 7 and 8.

Author Response

We thank the reviewers for their fair assessment of our work and will submit a revised version edited for clarity of presentation and precision of interpretations.

Author Response

Reviewer #3 (Public Review):

[...] Weaknesses:

The study produces a large amount of data that is in general cohesive and support the main conclusions, but more thorough considerations on some of their findings may be helpful, as exemplified by the following:

1) the effect of microglial ablation on chloral hydrate-induced RORR in Fig. 1B appears to be not the same as other anesthetics. what does this mean?

2) Macrophage ablation impedes anesthesia emergence from pentobarbital (Fig. 3C). how may this occur?

3) examination of the potential effect of microglial depletion on dendritic spine density is interesting but the experimental design does not seem to align well with the PPR and eEPSC data, which indicate a reduction in presynaptic release (Fig.10E) and increase of postsynaptic function (Fig. 10H), respectively. The PPR data seems to suggest a presynaptic effect of microglia; ablation.

This reviewer may confused the brain regions between our spine quantification (Figure 11) and patch-clamp recording (Figure 10). In our spine quantification, all evaluations were conducted in the mPFC. However, the patch-clamp recording were performed in SON (Figure 10 B-F) and LC (Figure 10 G-K), different brain regions from our spine quantification. As one of our conclusion, microglia differentially modulate the activity of neuronal network in a brain region-specific manner, neurons in different brain regions may exhibit different electrophysiological alterations upon microglial depletion. Therefore, this comment might be a factual error.

Author Response

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

Public Reviews:

Reviewer #1 (Public Review):

This is an interesting, timely and informative article. The authors used publicly available data (made available by a funding agency) to examine some of the academic characteristics of the individuals recipients of the National Institutes of Health (NIH) k99/R00 award program during the entire history of this funding mechanism (17 years, total ~ 4 billion US dollars (annual investment of ~230 million USD)). The analysis focuses on the pedigree and the NIH funding portfolio of the institutions hosting the k99 awardees as postdoctoral researchers and the institutions hiring these individuals. The authors also analyze the data by gender, by whether the R00 portion of the awards eventually gets activated and based on whether the awardees stayed/were hired as faculty at their k99 (postdoctoral) host institution or moved elsewhere. The authors further sought to examine the rates of funding for those in systematically marginalized groups by analyzing the patterns of receiving k99 awards and hiring k99 awardees at historically black colleges and universities.

The goals and analysis are reasonable and the limitations of the data are described adequately. It is worth noting that some of the observed funding and hiring traits are in line with the Matthew effect in science (https://www.science.org/doi/10.1126/science.159.3810.56) and in science funding (https://www.pnas.org/doi/10.1073/pnas.1719557115). Overall, the article is a valuable addition to the research culture literature examining the academic funding and hiring traits in the United States. The findings can provide further insights for the leadership at funding and hiring institutions and science policy makers for individual and large-scale improvements that can benefit the scientific community.

Thank you for these comments. We have incorporated the articles referenced on the Matthew effect into the first paragraph of the Discussion our revised preprint.

Reviewer #2 (Public Review):

Early career funding success has an immense impact on later funding success and faculty persistence, as evidenced by well-documented "rich-get-richer" or "Matthew effect" phenomena in science (e.g., Bol et al. 2018, PNAS). Woitowich et al. examined publicly available data on the distribution of the National Institutes of Health's K99/R00 awards - an early career postdoc-to-faculty transition funding mechanism - and showed that although 85% of K99 awardees successfully transitioned into faculty, disparities in subsequent R01 grant obtainment emerged along three characteristics: researcher mobility, gender, and institution. Men who moved to a top-25 NIH funded institution in their postdoc-to-faculty transition experienced the shortest median time to receiving a R01 award, 4.6 years, in contrast to the median 7.4 years for women working at less well-funded schools who remained at their postdoc institutions. This result is consistent with prior evidence of funding disparities by gender and institution type. The finding that researcher mobility has the largest effect on subsequent funding success is key and novel, and enhances previous work showing the relationship between mobility and ones' access to resources, collaborators, or research objects (e.g., Sugimoto and Larivière, 2023, Equity for Women in Science (Harvard University Press)).

These results empirically demonstrate that even after receiving a prestigious early career grant, researchers with less mobility belonging to disadvantaged groups at less-resourced institutions continue to experience barriers that delay them from receiving their next major grant. This result has important policy implications aimed at reducing funding disparities - mainly that interventions that focus solely on early career or early stage investigator funding alone will not achieve the desired outcome of improving faculty diversity.

The authors also highlight two incredible facts: No postdoc at a historically Black college or university (HBCU) has been awarded a K99 since the program's launch. And out of all 2,847 R00 awards given thus far, only two have been made to faculty at HBCUs. Given the track record of HBCUs for improving diversity in STEM contexts, this distribution of awards is a massive oversight that demands attention.

At no fault of the authors, the analysis is limited to only examining K99 awardees and not those who applied but did not receive the award. This limitation is solely due to the lack of data made publicly available by the NIH. If this data were available, this study would have been able to compare the trajectory of winners versus losers and therefore could potentially quantify the impact of the award itself on later funding success, much like the landmark Bol et al. (2018) paper that followed the careers of winners of an early career grant scheme in the Netherlands. Such an analysis would also provide new insights that would inform policy.

Although data on applications versus awards for the K99/R00 mechanism are limited, there exists data for applicant race and ethnicity for the 2007-2017 period, which were made available by a Freedom of Information Act request through the now defunct Rescuing Biomedical Research Initiative: https://web.archive.org/web/20180723171128/http://rescuingbiomedicalresearch.org/blog/examining-distribution-k99r00-awards-race/. These results are not presently discussed in the paper, but are highly relevant given the discussion of K99 award impacts on the sociodemographic composition of U.S. biomedical faculty. From 2007 to 2017, the K99 award rate for white applicants was 31.0% compared to 26.7% for Asian applicants and 16.2% for Black applicants. In terms of award totals, these funding rates amount to 1,384 awards to white applicants, 610 to Asian applicants, and 25 to Black applicants for the entire 2007-2017 period. And in terms of R00 awards, or successful faculty transitions: whereas 77.0% of white K99 awardees received an R00 award, the conversion rate for Asian and Black K99 awardees was lower, at 76.1% and 60.0%, respectively. Regarding this K99-to-R00 transition rate, Woitowich et al. found no difference by gender (Table 2). These results are consistent with a growing body of literature that shows that while there have been improvements to equity in funding outcomes by gender, similar improvements for achieving racial equity are lagging.

The conclusions are well-supported by the data, and limitations of the data and the name-gender matching algorithm are described satisfactorily.

One aspect that the authors should expand or comment on is the change in the rate of K99 to R00 conversions. Since 2016, while the absolute number of K99 and R00 awards has been increasing, the percentage of R00 conversions appears to be decreasing, especially in 2020 and 2021. This observation is not clearly stated or shown in Figure 1 but is an important point - if the effectiveness of the K99/R00 mechanism for postdoc-to-faculty transitions has been decreasing lately, then something is undermining the purpose of this mechanism. This result bears emphasis and potentially discussion for possible reasons for why this is happening.

Thank you for these insightful comments. We now calculate a rolling conversion rate for K99 to R00 awards which shows there is not as much of a decline in conversion from K99 to R00 (Fig 1B). We still see a slight decline in 2021 and 2022. 468 K99 awards are from 2020 or later so they may still convert to the R00 phase. Thus it is difficult to draw conclusions about 2021/2022 yet. As more time passes, we may better be able to determine whether or not significant alteration from normal occurred in these years, presumably due to pressures from the Covid-19 pandemic. We also thank you for providing the details of the FOIA request. We have included a discussion of these data in the discussion.

Reviewer #3 (Public Review):

The researchers aim add to the literature on faculty career pathways with particular attention to how gender disparities persist in the career and funding opportunities of researchers. The researchers also examine aspects of institutional prestige that can further amplify funding and career disparities. While some factors about individuals' pathways to faculty lines are known, including the prospects of certain K award recipients, the current study provides the only known examination of the K99/R00 awardees and their pathways.

Strengths:

The authors establish a clear overview of the institutional locations of K99 and R00 awardees and the pathways for K99-to-R00 researchers and the gendered and institutional patterns of such pathways. For example, there's a clear institutional hierarchy of hiring for K99/R00 researchers that echo previous research on the rigid faculty hiring networks across fields, and a pivotal difference in the time between awards that can impact faculty careers. Moreover, there's regional clusters of hiring in certain parts of the US where multiple research universities are located. Moreover, documenting the pathways of HBCU faculty is an important extension of the Wapman et al. study (among others from that research group), and provides a more nuanced look at the pathways of faculty beyond the oft-discussed high status institutions. (However, there is a need for more refinement in this segment of the analyses as discussed further below.). Also, the authors provide important caveats throughout the manuscript about the study's findings that show careful attention to the complexity of these patterns and attempting to limit misinterpretations of readers.

Weaknesses:

The authors reference institutional prestige in relation to some of the findings, but there's no specific measure of institutional prestige included in the analyses. If being identified as a top 25 NIH-funded institution is the proximate measure for prestige in the study, then more justification of how that relates to previous studies' measures of institutional prestige and status are needed to further clarify the interpretations offered in the manuscript.

The identification of institutional funding disparities impacting HBCUs is an important finding and highlights another aspect of how faculty at these institutions are under resourced and arguably undervalued in their research contributions. However, a lingering question exists: why compare HBCUs with Harvard? What are the theoretical and/or methodological justifications for such comparisons? This comparison lends itself to reifying the status hierarchy of institutions that perpetuate funding and career inequalities at the heart of the current manuscript. If aggregating all HBCU faculty together, then a comparable grouping for comparison is needed, not just one institution. Perhaps looking at the top 25 NIH funded institutions could be one way of providing a clearer comparison. Related to this point is the confusing inclusion of Gallaudet in Figure 6 as it is not an officially identified HBCU. Was this institution also included in the HBCU-related calculations?

Thank you for this comment. We agree this comparison perpetuates the perception of the prestige hierarchy and is problematic. We now compare all institutions in the top 25 NIH funding category to all HBCUs. Thank you also for identifying our error in mis-coding Gallaudet as an HBCU. We have corrected this in the current version.

There is a clear connection that is missed in the current iteration of the manuscript derived from the work of Robert Merton and others about cumulative advantages in science and the "Matthew effect." While aspects of this connection are noted in the manuscript such as well-resourced institutions (those with the most NIH funding in this circumstance) hire each others' K99/R00 awardees, elaborating on these connections are important for readers to understand the central processes of how a rigid hierarchy of funding and career opportunities exist around these pathways. The work the authors build on from Daniel Larremore, Aaron Clauset, and their colleagues have also incorporated these important theoretical connections from the sociology of knowledge and science, and it would provide a more interdisciplinary lens and further depth to understanding the faculty career inequalities documented in the current study.

Reviewer #1 (Recommendations For The Authors):

Comments to authors:

1. For the benefit of general reader, it would be informative to mention the amount of annual NIH investment in the k99 funding mechanism in the text (230 awards representing a ~ 230 million US dollars investment).

Thank you for this suggestion. We have added that this is ~$25 million investment annually.

1. It is worth noting that some of the observed funding and hiring traits resemble the Matthew effect, discussed in: The Matthew effect in science: https://www.science.org/doi/10.1126/science.159.3810.56

The Matthew effect in science funding: https://www.pnas.org/doi/10.1073/pnas.1719557115

It would be of value to cite these for further context for the readers.

Thank you for this suggestion. We have included these references and briefly discussed the Matthew effect in the first paragraph of the Discussion.

1. Figs 3, 6 and Fig S1 are hard to read without zooming in due to their format and don't work great within a letter size page but can work if they are also linked to a zoomable web version. It would make sense to have an online navigable/searchable/selectable version. But when the reader zooms out, there are patterns that reflect what points the authors are making (though those could be illustrated differently). These figures are really made for online webapp visualization (such as Shiny in R).

We agree with this comment and have used the “googleVis()” package in R to put together interactive Sankey diagrams. These can be found at: https://dantyrr.github.io/K99-R00-analysis/ and they are referenced in the manuscript.

1. The abstract states 85% of awardees get R00 awards. That appears to come from 198/234 (page 6) though it's not explicitly stated, and other ratios give different answers (e.g., 1-304/3475 = 91%) but the 85% seems to be the right one. That first paragraph of the results could be clearer. Also, in the middle of page three the number given is 90% so something is inconsistent. For Figure 1A, given the methodology it should be possible to calculate a rolling conversion rate as "R00(t) / K99(t-1)" (and a similarly-calculated cumulative rate).

Thank you for catching these errors. These were introduced because there are R00 awardees that did not have extramural K99 awards. These are intramural NIH K99 awardees but there is no public data on these awardees. The correct number is 78% of K99 awardees that transitioned to the R00 phase. We have also calculated the rolling conversion rate which is 89% if you exclude the first 2 years of the program (when the first awardees were within the 2-yr K99 period) and final 2 years (when most recent K99 awardees were still within their first 2 years of the K99 period).

1. Assuming that 85% is the correct number, is there any information/insight into why ~1/6 of awardees do not continue to R00, which seems high given that only two years passes - that's a lot of awardees not getting R00 positions.

We are unsure of why these don’t convert. In the revised version of the manuscript, we speculate on this in the 4th paragraph of the discussion:

The factors that prevented the other 302 K99 awardees from 2019 and earlier unable to convert their K99-R00 grants is cause for concern within our greater academic community. Possible explanations include leaving the biomedical workforce, accepting tenure-track positions or other positions abroad, or by simply not successfully securing a tenable tenure-track offer.

1. It looks like perhaps a non-zero number of K99s are just one year and not two (e.g., see 2006 in Fig 1A, which should not appear if all 2006 awards were 2 years). What is the typical percentage of K99s not activated for a second year, and is this a sizable % of the 15% not converting to R00?

This is an interesting question. We didn’t originally look into this and the dataset that we originally downloaded from NIH reporter included a significant number of duplicates for the grants because year 1 of the K99 was listed on its own line and year 2 was listed on a different line. The first step in curating the data was to delete the duplicate values so we only had one entry per person. Unfortunately based on sorting of the data tables, sometimes the year 1 appeared above year 2 and at other times year 2 appeared before year 1. Because none of the data we were interested in are benchmarked to K99 start date, we removed the duplicate values non-specifically. With the dataset we currently have, we would not be able to tell which individuals dropped out (didn’t convert to R00) during the first or second year of the K99. In order to do this we would have to download the raw data from NIH reporter again and curate it again. We may do this in the future but for the purpose of publishing the current manuscript we prefer to focus our efforts on other aspects of the revision.

1. Further down page 3, the authors state that "men typically experience 2-3% greater funding success rates" is ambiguous, as rates are themselves a percentage. So, is it 2-3% greater as in 23% vs 20%, or is it 2-3% greater as in 20.6% vs 20%? Please clarify the language.

Thank you for asking for this clarification. We have updated the text here to reflect that we mean “23% vs 20%”.

1. Metrics such as time to first R01 are compared internally within the study set, which yields interesting insights, but more could be done to benchmark these metrics to non-K99 scientists.

We agree with the reviewer that this would be ideal; however, we feel that it is out of the scope of this manuscript. We may examine this in the future.

1. In the text, several times percentages are being referred to when the figures cited do not show percentages. For example (page 6) 'proportion of awardees that stayed at the same institution declined to about 20% where it has remained consistent (Fig 1B)' - Figure 1B does not show percentages, instead the reader would need to work out from the raw numbers what the pattern of percentages might look like. It's fine (great even) to provide the raw numbers, but would be great to show the percentages as well. This happened for multiple graphs.

Thank you for this comment. We agree that showing the percentage would be beneficial so we have included the percentages in Figure 1 for the conversion rate. We also added a standalone figure panel for the rolling conversion rate for Figure 1. For Figure 4, we have also included a right Y-axis to better indicate the % women.

1. Figure 4 - putting the %women on a 0-250 scale makes it difficult to see the changes in that curve. Please replot it as a separate graph with an appropriate scale (30-50%? 30-70%?)

Thank you for this comment. We have made this edit.

1. Figure 5 - The table appears inconsistent - the Moved/Stayed HR is 1.411 suggesting that moving is better for reducing time to R01, but then Woman/Man is 1.208, so one of these pairs needs to be written in the opposite order to have the table make sense (intended to be listed as 'better/worse'?)

Thank you for noticing this. In the revised manuscript we have re-run the cox proportional hazard model using the R package “survival” and the function “coxph()”. There were minor differences in the hazard ratios using this package instead of Graphpad prism; however, the R package is much more widely used compared to prism for these types of analysis. We present the new data in the table in Figure 5B in the revised manuscript. We now present the “detrimental” cox hazard value for each variable (i.e. 0.7095 for the mobility [moved/stayed]). We also underlined the variable which was detrimental to receiving an R01 award earlier.

1. Figure 5's graph appears strange. All the lines have an appearance of stochasticity but are actually multiples of each other, rising exactly in sync. Are these actually modeled lines? If so, why not instead actually draw the lines based on the real data from the real groups depicted, and give the n for each group?

Thank you for picking this up. The software we originally used to plot the graphs did plot modeled lines instead of the actual data. We have re-run the cox proportional hazard model using the R “survival” package v3.5-5 and the coxph() and survfit() functions. The updated data are in Figure 5 of the revised manuscript.

1. Table 1 should note that each column sums to 100%.

This is a good suggestion. In the revised manuscript, we have added a row to the table to indicate the column total N and %.

1. The authors discuss how k99/R00 grant reviewing process may have to change but the k99 awards also impact the faculty hiring ecosystem as well. There are faculty hiring job ads explicitly requesting or indicating preference towards k99 holders and the results described in this article show that k99 awarding is biased towards particular demographics at select wealthy institutions. Of course, collective/central action is almost always more effective/impactful (especially in shorter time line) than individual elective action. In other words, NIH changing granting patterns would likely work better than encouraging faculty searches to change the weight they give to K99s, because there are many searches and just one NIH. But these are not mutually exclusive and individual action can still help when central action isn't done (if the NIH does not change the k99/R00 grant review process for more inclusive funding and does not increase the number of annual k99 awards hence the annual budget for this award mechanism) and it would be good to have this discussed in the manuscript.

Thank you for this comment and thoughtful insights. We have included additional discussion on this in the final paragraph of the discussion.

Reviewer #2 (Recommendations For The Authors):

Thank you for conducting this important work. On top of some thoughts I have described in the public review (in particular, Chris Pickett's FOIA data on K99/R00 outcomes by applicant race and ethnicity), I only have a few comments for potential improvements to this paper:

1. The comparison of K99-R00 transition rates by gender was interesting. However, I missed the analysis on the K99-R00 transition rates by institution (by type or by top-25 NIH funded institution versus not). I think this analysis may be buried somewhere in the more nuanced descriptions about faculty flows from one institution type to another, but I was not able to locate it. I wonder if the authors could consider dedicating a subsection to specifically describing the transition rate by institution type, creating a table equivalent to Table 2. This section would probably fit best somewhere before the authors dive into the nuances of self-hires and faculty flows.

Said another way: As I was reading, I felt I was missing an answer to a simple question - are there differences in conversion rates by institution type (however you define institution type, as an MSI or non MSI, or top-25 NIH funded versus not)?

Thank you for this suggestion. We have created the table (Table 3 and Table 4) in the revised manuscript. We also made a new figure (now figure 5 in the revised manuscript). This was an interesting way to look at the data and it is very clear that the number of K99 and R00 awards is heavily concentrated within the institutions that have the highest NIH funding. We have added a paragraph in the results in a new section entitled “K99 and R00 awards are concentrated within the highest funded institutions”.

1. Regarding the comparison of HBCUs and Harvard: this analysis was elucidating, but I am not sure if the framing of this analysis as pertaining to "systematically marginalized groups" - see second sentence in the section, "Faculty doctorates differ between Harvard and HBCUs" is appropriate. While it is true that proportionally more faculty at HBCUs are from marginalized groups, there are also many faculty at HBCUs who are from privileged or advantaged backgrounds (e.g., white, men, educated at elite institutions). It would be more accurate to rephrase the second sentence to say something along the lines of, "We sought to examine the rates of funding for those at historically under-funded institutions." I recommend that the authors comb the paper for any other potential places in the text that conflate systemic marginalization with institution type, and rephrase as needed for accuracy.

Thank you for pointing this out. This is an extremely important point and we have removed any instances we could find where we conflate systemically marginalized groups with institution type.

1. I strongly recommend Sugimoto and Larivière (2023)'s new book, Equity for Women in Science, which has an entire section dedicated to previous work investigating how researcher mobility impacts access to resources, collaborations, et cetera (Chapter 5 on Mobility; other chapters on Funding are also relevant but I hone in on Mobility since this is such a key result of this work). I think this chapter would provide significant food-for-thought and background that could strengthen the Discussion section of the paper.

Thank you for this suggestion. We have added some discussion of mobility in the first paragraph of the Discussion.

1. I appreciated the subsection headings that described key results (e.g., "Institutions with the most NIH funding tend to hire K99/R00 awardees from other institutions with the most funding"; "K99/R00 awardee self-hires are more common at institutions with the top NIH funding.") This paper structure made it easier for me to ensure that I was getting the intended takeaway from a figure or section. But partway through the paper, the subheadings changed to being less declarative and therefore less informative (e.g., "Gender of K99/R00 awardees"; "Factors influencing K99/R00 awardee future funding success"). It would be great to rephrase these boilerplate subsection headers to be more declarative, like earlier subsection headings. For example, maybe say "Men receive the majority of K99 awards" or "No gender difference in the rate of conversion from K99 to R00" or something to that effect, depending on what result the authors wish to emphasize.

Thank you for this comment. This is a very good point. We have re-worded the more generic headings in the revised version.

1. Lastly, I would like to share a question that came to my mind that involves an additional analysis, but is work that is (probably) out-of-the-scope of this paper, but could instead be a separate paper or product. Circling back to Chris Pickett's FOIA-ed data on K99/R00 funding outcomes by applicant race and ethnicity (https://web.archive.org/web/20180723171128/http://rescuingbiomedicalresearch.org/blog/examining-distribution-k99r00-awards-race/): Given that Pickett's numbers provide incontrovertible information on the number of awards to various racial and ethnic groups, I wonder if it is possible to use this information as an "answer key" to (1) check the accuracy of an algorithm that assigns race based on name for applications in your analysis but for 2007-2017 period, and, (2) if the results are reasonable, then examine the dataset with race and ethnicity information. Some recent papers performing large-scale bibliometric analyses have applied such algorithms (e.g., see Kozlowski et al. 2022 PNAS Intersectional inequalities in science) and I wonder if they could be useful, or at least tested, here. Again, Pickett's data would serve as the benchmark to see if the algorithm produces numbers that are consistent with the actual funding outcomes; if they're not wildly off, or perhaps accurate for some groups but not others, there might be something here.

This is a really insightful comment. We have discussed whether we could assign ethnicity based on an algorithm and check based on Chris Pickett’s data. We agree that it is beyond the scope of this article, but has potential for future research.

Reviewer #3 (Recommendations For The Authors):

-In the methods section, it would be helpful to provide an overview of the number of universities, departments, and faculty represented in the data analyzed in the study.

Thank you for this comment. We agree with the reviewer. We have added a section to the results discussing the distribution of different types of institutions. We also added Table 3 and Table 4 and a new Figure 5 describing these. Regarding the faculty, we have discussed the demographics of the K99 and R00 awardees as best as we could. We do not have data on which faculty laboratories the K99 awardees were in when they received their awards. This information is not available through NIH reporter.

-I would consider incorporating, or at least citing, Jeff Lockhart and colleagues' recent paper Nature Human Behavior article "Name-based demographic inference and the unequal distribution of misrecognition" about to provide readers with an additional resource and more information about the likelihood of misattribution and general cautionary notes about using gender and race/ethnicity ascription/imputation approaches and tools for research.

Thank you for bringing this reference to our attention. We have incorporated this into the methods section describing our name-based gender determination.

-In the next to last sentence under the final paragraph of the methods section, there looks to be a typo as it should read "K99 or R00," not "K00" as currently written.

Thank you for catching this. We have now corrected it.

-Clarifying some of the data and measures used are necessary to limit confusion and misinterpretations of the study's findings.

Thank you. We have significantly updated the revised manuscript and hope that it is more clear.

-Elaborating more on the gender inequality notable in the Cox proportional hazard model would strengthen the authors' point about persistent gender inequalities within the K99/R00 funding mechanism and pathways. In its current iteration, the findings are somewhat buried by the discussion of institutional differences, but when we look at the findings and the plot associated with the model, we notice that men have more advantages than women in funding and institutional location.

Thank you for highlighting this. This is true and we have elaborated on the gender inequality in the revised version of the manuscript.

-Also for the Cox proportional hazard model, I would consider exploring the inclusion of data that can further clarify the biomedical research infrastructure of institutions. For example, in the conversation about the differences between Princeton and other universities including other Ivies, it's important to note that Princeton does not have a medical school. Moreover, other institutions do not operate or are affiliated with a hospital. Adding more data to the model that can better contextualize the research infrastructure around researchers with NIH awards beyond the size of the NIH portfolio can shed light on possibly other important institutional differences that undergird these inequalities.

Thank you for this comment. We have added additional details about the institutional type; however, to examine whether institutions are attached to a hospital (or are themselves as hospital like MGH etc.) or whether institutions include a medical school may be difficult. We would have to manually code these and then determine whether or not the award recipient was affiliated with a department within that entity or not. We believe that this is a fascinating question but that it is out of the scope of the present manuscript. This is something that we will look into for potential future publications.

-Throughout the manuscript there's usage of "elite" and "prestigious" that are somewhat ambiguous regarding what exactly they are referring to about institutional characteristics. This is a common issue in the literature, but trying to clarify what these terms specifically mean for the current study and checking for consistent usage with limited interchangeability that can add confusion for readers about what is being referred to would give added strength to the conversation provided by the authors.

Thank you for this suggestion. Based on these comments and those by the other reviewers, in the revised version of the manuscript, we have limited the use of “elite” and “prestigious” to describe institutions in order not to perpetuate biases toward certain institutions.

-In relation to the discussion at the end of the manuscript of the longer time to award noted for researchers who stay at the same institutions, another possibility for the disparity could be their reliance for service work (e.g., hiring committees, departmental committees, supporting graduate students through mentoring and/or dissertation committee work, etc.) in their institutions given their knowledge of and experience within it.

Thank you for this suggestion. We have added 2 sentences to the discussion reflecting this possibility.

-Engaging with how STEM professional cultures can perpetuate these funding disparities and related hiring and career outcomes could enhance the contributions of the study. In relation to STEM professional cultures, engaging with the work of Mary Blair-Loy and Erin Cech in their recent book, Misconceiving Merit, could help provide additional insights for readers.

Thank you for these comments. We have incorporated edits to the revised manuscript reflecting the work of Erin Cech and Mary Blair-Loy.

Author Response

Reviewer #1 (Public Review):

Summary:

In this study, the authors showed that activation of RelA and Stat3 in hepatocytes of DSS-treated mice induced CYPs and thereby produced primary bile acids, particularly CDCA, which exacerbated intestinal inflammation.

Strengths:

This study reveals the RelA/Stat3-dependent gene program in the liver influences intestinal homeostasis.

Weaknesses:

Additional evidence will strengthen the conclusion.

1) In Fig. 1C, photos show that phosphorylation of RelA and Stat3 was induced in only a few hepatocytes. The authors conclude that activation of both RelA and Stat3 induces inflammatory pathways. Therefore, the authors should show that phosphorylation of RelA and Stat3 is induced in the same hepatocytes during DSS treatment.

Experiments in progress and data will be submitted in the revised manuscript- Co-staining of pRela and pStat3(727) on treated liver sections.

2) In Fig. 5, the authors treated mice with CDCA intraperitoneally. In this experiment, the concentration of CDCA in the colon of CDCA-treated mice should be shown.

Experiments in progress and data will be submitted in the revised manuscript - Supplementation of CDCA to knockout animals and estimation of CDCA in the colon of DSS treated and untreated animals.

Reviewer #2 (Public Review):

Singh and colleagues employ a methodic approach to reveal the function of the transcription factors Rela and Stat3 in the regulation of the inflammatory response in the intestine.

Strengths of the manuscript include the focus on the function of these transcription factors in hepatocytes and the discovery of their role in the systemic response to experimental colitis. While the systemic response to induce colitis is appreciated, the cellular and molecular mechanisms that drive such systemic response, especially those involving other organs beyond the intestine are an active area of research. As such, this study contributes to this conceptual advance. Additional strengths are the complementary biochemical and metabolomics approaches to describe the activation of these transcription factors in the liver and their requirement - specifically in hepatocytes - for the production of bile acids in response to colitis.

Some weaknesses are noted in the presentation of the data, including a lack of comprehensive representation of findings in all conditions and genotypes tested.

These will be incorporated in the revised version.

Reviewer #3 (Public Review):

Summary:

The authors try to elucidate the molecular mechanisms underlying the intra-organ crosstalks that perpetuate intestinal permeability and inflammation.

Strengths:

This study identifies a hepatocyte-specific rela/stat3 network as a potential therapeutic target for intestinal diseases via the gut-liver axis using both murine models and human samples.

Weaknesses:

1) The mechanism by which DSS administration induces the activation of the Rela and Stat3 pathways and subsequent modification of the bile acid pathway remains clear. As the authors state, intestinal bacteria are one candidate, and this needs to be clarified. I recommend the authors investigate whether gut sterilization by administration of antibiotics or germ-free condition affects 1. the activation of the Rela and Stat3 pathway in the liver by DSS-treated WT mice and 2. the reduction of colitis in DSS-treated relaΔhepstat3Δhep mice.

Experiments in progress and data will be submitted in the revised manuscript - Antibiotic treatment for 2/4 weeks, subsequently mice will be treated with DSS and the Rela and Stat3 phosphorylation will be tested using western blotting.

2) It has not been shown whether DSS administration causes an increase in primary bile acids, represented by CDCA, in the colon of WT mice following activation of the Rela and Stat3 pathways, as demonstrated in Figure 6.

We have demonstrated a enhanced level of CDCA in the colon following DSS treatment in the wild type animals in figure 4B.

3) The implications of these results for IBD treatment, especially in what ways they may lead to therapeutic intervention, need to be discussed.

These will be incorporated in the revised version.

Author Response

We decided to address the comments of the reviewers with additional experiments and modification of the text with the aim of submitting a new version of the report.

We would like to underline that the current study is an extension of the work published in eLife (Atze et al., 2021). For this reason, and in agreement with eLife guidelines, we did not repeat all the background information on the method used to identify PG subunit isotopologues using mass spectrometry.

Reviewer #1 (Public Review):

Summary:

Liang et. al., uses a previously devised full isotope labeling of peptidoglycan followed by mass spec to study the kinetics of Lpp tethering to PG and the hydrolysis of this bond by YafK.

Strengths:

-The labeling and mass spec analysis technique works very well to discern differentially labelled Tri-KR muropeptide containing new and old Lpp and PG.

Weaknesses:

-Only one line of experimentation using mass spec based analysis of labeled PG-Lpp is used to make all conclusions in the paper. The evidence is also not enough to fully deleanate the role of YafK.

Our approach based on heavy isotope labelling and mass spectrometry has the power to identify and kinetically characterize the specific products of the reaction leading to the tethering of Lpp to PG and the hydrolysis of the corresponding bond. We therefore advocate that our experimentation is sufficient to obtain meaningful results without combining other lines of experimentation.

-Only one mutant (YafK) is used to make the conclusion.

The aim of the study is to determine the effect of the hydrolysis of the PG→Lpp bond on the dynamics of the tethering of Lpp to PG. Since YafK is the only enzyme catalyzing this reaction, it is appropriate to compare the wild-type strain to an isogenic yafK deletion mutant. Nonetheless, we carefully consider this comment and will investigate the dynamics of the tethering of Lpp to PG in mutants deficient in the production of the L,D-transpeptidases responsible for tethering Lpp to PG.

-The paper makes a lot of 'implications' with minimal proof to support their hypothesis. Other lines of experimentations must be added to fully delineate their claims.

See our answer to the first comment.

-Time points to analyse Tri-KR isotopologues in Wt (0,10,20,40,60 min) and yafK mutant (0,15, 25, 40, 60 min) are not the same.

The purpose of the experiments is to compare the kinetics of formation and hydrolysis of the PG→Lpp bond in the WT versus ΔyafK strains. Comparison of the kinetics is therefore possible even though the kinetics are not based on the exact same time points. Nonetheless, we will reproduce the kinetics experiment (see also answers to Reviewer 2) and use the same time points in these additional experiments.

-Experiments to define physiological role of YafK are also missing

We will investigate the effect of the yafK deletion on the formation of outer membrane vesicles.

Reviewer #2 (Public Review):

Summary:

The authors of this study have sought to better understand the timing and location of the attachment of the lpp lipoprotein to the peptidoglycan in E. coli, and to determine whether YafK is the hydrolase that cleaves lpp from the peptidoglycan.

Strengths:

The method is relatively straightforward. The authors are able to draw some clear conclusions from their results, that lpp molecules get cleaved from the peptidoglycan and then re-attached, and that YafK is important for that cleavage.

Weaknesses:

However, the authors make a few other conclusions from their data which are harder to understand the logic of, or to feel confident in based on the existing data. They claim that their 5-time point kinetic data indicates that new lpp is not substantially added to lipidII before it is added to the peptidoglycan, and that instead lpp is attached primarily to old peptidoglycan. I believe that this conclusion comes from the comparison of Fig.s 3A and 3C, where it appears that new lpp is added to old peptidoglycan a few minutes before new lpp is added to new peptidoglycan. However, the very small difference in the timing of this result, the minimal number of time points and the complete lack of any presentation of calculated error in any of the data make this conclusion very tenuous. In addition, the authors conclude that lpp is not significantly attached to septal peptidoglycan. The logic behind this conclusion appears to be based on the same data, but the authors do not provide a quantitative model to support this idea.

The reviewer is correct in stating that we claim that Lpp is not substantially added to lipid II before incorporation of the disaccharide-pentapeptide subunit into the expanding PG network. This conclusion is based on the paucity of PG-Lpp covalent adducts containing light PG and Lpp moieties at the earliest time points. To substantiate more thoroughly this finding, we will reproduce the kinetic experiments with more early time points. The paucity of the new→new PG-Lpp isotopologues also implies that Lpp might not be extensively tethered to septal peptidoglycan since the latter is assembled from newly synthesized PG (see our previous publication Atze et al. 2021 and references therein). Quantitatively, septal synthesis roughly accounts for one third of the total PG synthesis. It is therefore expected that tethering of Lpp to septal PG would represent one third of the total number of newly synthesized Lpp molecules tethered to PG. We therefore proposed that the paucity of new→new PG- Lpp isotopologues at early time points of the kinetics implies that Lpp is preferentially tethered to the side wall. This is only one of several conclusions that we reach in the present study and we were very careful in the wording of our results.

-This work will have a moderate impact on the field of research in which the connections between the OM and are being studied in E. coli. Since lpp is not widely conserved in gram negatives, the impact across species is not clear. The authors do not discuss the impact of their work in depth.

We respectfully disagree with this reviewer’s comment. The work reported in this article for E. coli opens the way to the analysis and comparison of the mechanisms of the tethering of proteins to PG in various bacteria. In addition, we would like to stress that the Gram-negative bacteria that produce Lpp-related proteins and tether them to the PG include other major pathogens such as Pseudomonas aeruginosa (DOI: 10.1128/spectrum.05217-22).

Author Response

eLife assessment

The manuscript presents valuable evidence of temporal correlations during specific oscillatory activity between the prefrontal cortex, thalamic nucleus reuniens, and the hippocampus, in naturally sleeping animals. Such correlations represent solid evidence to support the notion that the thalamic nucleus reuniens participates in the hippocampal and prefrontal cortex dialogue subserving memory processes.

Thank you for your assessment.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In this study, Basha and colleagues aim to test whether the thalamic nucleus reuniens can facilitate the hippocampus/prefrontal cortex coupling during sleep. Considering the importance of sleep in memory consolidation, this study is important to understand the functional interaction between these three majorly involved regions. This work suggests that the thalamic nucleus reuniens has a functional role in synchronizing the hippocampus and prefrontal cortex.

Strengths:

The authors performed recordings in naturally sleeping cats, and analysed the correlation between the main slow wave sleep oscillatory hallmarks: slow waves, spindles, and hippocampal ripples, and with reuniens' neurons firing. They also associated intracellular recordings to assess the reuniens-prefrontal connectivity, and computational models of large networks in which they determined that the coupling of oscillations is modulated by the strength of hippocampal-thalamic connections.

Thank you for your positive evaluation.

Weaknesses:

The authors' main claim is made on slow waves and spindle coupling, which are recorded both in the prefrontal cortex and surprisingly in reuniens. Known to be generated in the cortex by cortico-thalamic mechanisms, the slow waves and spindles recorded in reuniens show no evidence of local generation in the reuniens, which is not anatomically equipped to generate such activities. Until shown differently, these oscillations recorded in reuniens are most likely volume-conducted from nearby cortices. Therefore, such a caveat is a major obstacle to analysing their correlation (in time or frequency domains) with oscillations in other regions.

1. We fully agree with the reviewer that reuniens likely does not generate neither slow waves nor spindles. We do not make such claim, which we clearly stated in the discussion (lines 319-324). We propose that Reuniens neurons mediate different forms of activity. In the model, we introduced MD nucleus only because without MD we were unable to generate spindles. While the slow waves and spindles are generated in other thalamocortical regions, the REU neurons show these rhythms due to long-range projections from these regions to REU as has been shown in the model.

2. Definitely, we cannot exclude some influence of volume conductance on obtained LFP recordings in REU nucleus. However, we show modulation of spiking activity within REU by spindles. Spike modulation cannot be explained by volume conductance but can be explained by either synaptic drive (likely the case here) or some intrinsic neuronal processes (like T-current).

3. In our REU recordings for spike identification we used tetrode recordings. If slow waves and spindles are volume conducted, then slow waves and spindles recorded with tetrodes should have identical shape. Following reviewer comment, we took these recordings and subtracted one channel from another. The difference in signal during slow waves is in the order 0.1 mV. Considering that the distance between electrodes is in the order of 20 um, such a difference in voltage is major and can only be explained by local extracellular currents, likely due to synaptic activities originating in afferent structures.

Finally, the choice of the animal model (cats) is the best suited one, as too few data, particularly anatomical ones regarding reuniens connectivity, are available to support functional results.

1. Thalamus of majority of mammals (definitely primates and carnivores, including cats) contain local circuit interneurons (about 30 % of all neurons). A vast majority of studies in rodents (except LGN nucleus) report either absence or extremally low (i.e. Jager P, Moore G, Calpin P, et al. Dual midbrain and forebrain origins of thalamic inhibitory interneurons. eLife. 2021; 10: e59272.) number of thalamic interneurons. Therefore, studies on other species than rodents are necessary, and bring new information, which is impossible to obtain in rodents.

2. Cats’ brain is much larger than the brain of mice or rats, therefore, the effects of volume conductance from cortex to REU are much smaller, if not negligible. The distance between REU and closest cortical structure (ectosylvian gyrus) in cats is about 15 mm.

3. Indeed, there is much less anatomical data on cats as opposed to rodents. This is why, we performed experiments shown in the figure 1. This figure contains functional anatomy data. Antidromic responses show that recorded structure projects to stimulated structure. Orthodromic responses show that stimulated structure projects to recorded structure.

Reviewer #2 (Public Review):

Summary:

The interplay between the medial prefrontal cortex and ventral hippocampal system is critical for many cognitive processes, including memory and its consolidation over time. A prominent idea in recent research is that this relationship is mediated at least in part by the midline nucleus reuniens with respect to consolidation in particular. Whereas the bulk of evidence has focused on neuroanatomy and the effects of temproary or permanent lesions of the nucleus reuniens, the current work examined the electrophysiology of these three structures and how they inter-relate, especially during sleep, which is anticipated to be critical for consolidation. They provide evidence from intercellular recordings of the bi-directional functional connectivity among these structures. There is an emphasis on the interactions between these regions during sleep, especially slow-wave sleep. They provide evidence, in cats, that cortical slow waves precede reuniens slow waves and hippocampal sharp-wave ripples, which may reflect prefrontal control of the timing of thalamic and hippocampal events, They also find evidence that hippocampal sharp wave ripples trigger thalamic firing and precede the onset of reuniens and medial prefrontal cortex spindles. The authors suggest that the effectiveness of bidirectional connections between the reuniens and the (ventral) CA1 is particularly strong during non-rapid eye movement sleep in the cat. This is a very interesting, complex study on a highly topical subject.

Strengths:

An excellent array of different electrophysiological techniques and analyses are conducted. The temporal relationships described are novel findings that suggest mechanisms behind the interactions between the key regions of interest. These may be of value for future experimental studies to test more directly their association with memory consolidation.

We thank this reviewer for very positive evaluation of our study.

Weaknesses:

Given the complexity and number of findings provided, clearer explanation(s) and organisation that directed the specific value and importance of different findings would improve the paper. Most readers may then find it easier to follow the specific relevance of key approaches and findings and their emphasis. For example, the fact that bidirectional connections exist in the model system is not new per se. How and why the specific findings add to existing literature would have more impact if this information was addressed more directly in the written text and in the figure legends.

Thank you for this comment. In the revised version, we will do our best to simplify presentation and more clearly explain our findings.

Author Response

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

Public Reviews:

Reviewer #1 (Public Review):

Activity has effects on the development of neural circuitry during almost any step of differentiation. In particular during specific time periods of circuit development, so-called critical periods (CP), altered neural activity can induce permanent changes in network excitability. In complex neural networks, it is often difficult to pinpoint the specific network components that are permanently altered by activity, and it often remains unclear how activity is integrated during the CP to set mature network excitability. This study combines electrophysiology with pharmacological and optogenetic manipulation in the Drosophila genetic model system to pinpoint the neural substrate that is influenced by altered activity during a critical period (CP) of larval locomotor circuit development. Moreover, it is then tested whether and how different manipulations of synaptic input are integrated during the CP to tune network excitability.

Strengths:

Based on previous work, during the CP, network activity is increased by feeding the GABA-AR antagonist PTX. This results in permanent network activity changes, as highly convincingly assayed by a prolonged recovery period following induced seizure and by altered intersegmental locomotor network coordination. This is then used to provide two important findings: First, compelling electro- and optophysiological experiments track the site of network change down to the level of single neurons and pre- versus postsynaptic specializations. In short, increased activity during the CP increases both the magnitude of excitatory and inhibitory synaptic transmission to the aCC motoneuron, but excitation is affected more strongly. This results in altered excitation inhibition ratios. Fine electrophysiology shows that excitatory synapse strengthening occurs postsynaptically. High-quality anatomy shows that dendrite size and numbers of synaptic contacts remain unaltered. It is a major accomplishment to track the tuning of network excitability during the CP down to the physiology of specific synapses to identified neurons.

Second, additional experiments with single neuron resolution demonstrate that during the CP different forms of activity manipulation are integrated so that opposing manipulations can rescue altered setpoints. This provides novel insight into how developing neural network excitability is tuned, and it indicates that during the CP, training can rescue the effects of hyperactivity.

Weaknesses:

There are no major weaknesses to the findings presented, but the molecular cause that underlies increased motoneuron postsynaptic responsiveness as well as the mechanism that integrates different forms of activity during the CP remain unknown. It is clear that addressing these experimentally is beyond the scope of this study, but some discussion about different candidates would be helpful.

We discuss likely mechanisms that underpin the increase in postsynaptic responsiveness below (Reviewer #1 (Recommendations For The Authors):, point 2). To address possible mechanisms that integrate different forms of activity we now include a new paragraph in the discussion.

Reviewer #2 (Public Review):

Summary:

In this study, the authors use the tractable Drosophila embryonic/larval motor circuit to determine how manipulations of activity during a critical period (CP) modify the circuit in ways that persist into later developmental stages. Previously, this group demonstrated that manipulations to the aCC/MN-Ib neuron in embryonic stages enhance (or can rescue) susceptibility to seizures at later larval stages. Here, the authors demonstrate that following enhanced excitatory drive (by PTX feeding), the aCC neuron acquires increased sensitivity to cholinergic excitatory transmission, presumably due to increased postsynaptic receptor abundance and/or sensitivity, although this is not clarified. Although locomotion is not altered at later developmental larval stages, the authors suggest there is reduced "robustness" to induced seizures. The second part of the study then goes on to enhance inhibition during the CP in an attempt to counteract the enhanced excitation, and show that many aspects of the CP plasticity are rescued. The authors conclude that "average" E/I activity is integrated during the CP to determine the excitability of the mature locomotor network.

Overall, this study provides compelling mechanistic insight into how a final motor output neuron changes in response to enhanced excitatory drive during a CP to change the functionality of the circuit at later mature developmental stages. The first part of this study is strong, clearly showing the changes in the aCC neuron that result from enhanced excitatory input. This includes very nice electrophysiology and imaging data that assess synaptic function and structure onto aCC neurons from pre-motor inputs resulting from PTX exposure during development. However, the later experiments in Figures 6 and 7 designed to counteract the CP plasticity are somewhat difficult to interpret. In particular, the specificity of the manipulations of the ch neuron intended to counteract the CP plasticity is unclear, given the complexities of how these changes impact the excitability of all neurons during development. It is clear that CP plasticity is largely rescued in later stages, but it is hard to know if downstream or secondary adaptations may be masking the PTX-induced plasticity normally observed. Nonetheless, this study provides an important advance in our understanding of what parameters change during CPs to calibrate network dynamics at later developmental stages.

Reviewer #3 (Public Review):

Summary:

In Hunter, Coulson et al, the authors seek to expand our understanding of how neural activity during developmental critical periods might control the function of the nervous system later in life. To achieve increased excitation, the authors build on their previous results and apply picrotoxin 17-19 hours after egg-laying, which is a critical period of nervous system development. This early enhancement of excitation leads to multiple effects in third-instar larvae, including prolonged recovery from electroshock, increased synchronization of motor neuron networks, and increased AP firing frequency. Using optogenetics and whole-cell patch clamp electrophysiology, the authors elegantly show that picrotoxin-induced over-excitation leads to increased strength of excitatory inputs and not loss of inhibitory inputs. To enhance inhibition, the authors chose an approach that involved the stimulation of mechanosensory neurons; this counteracts picrotoxin-induced signs of increased excitation. This approach to enhancing inhibition requires further control experiments and validation.

Strengths:

• The authors confirm their previous results and show that 17-19 hours after egg laying is a critical period of nervous system development.

• Using Ca2+/Sr2+ substitutions, the authors demonstrate that synaptic connections between A18a  aCC show increased mEPSP amplitudes. The authors show that this aCC input is what is driving enhanced excitation.

• The authors demonstrate that the effects of over-excitation attributed to picrotoxin exposure are generalizable and also occur in bss mutant flies.

Weaknesses:

• The authors build on their previous work and argue that the critical period (17-19h after egg-laying) is a uniquely sensitive period of development. Have the authors already demonstrated that exposure to picrotoxin at L1 or L2 (and even early L3 if experimentally possible) does not lead to changes in induced seizure at L3? This would further the authors' hypothesis of the uniqueness of the 17-19h AEL period. If this has already been established in prior publications, then this needs to be further explained. I do note in Gaicehllo and Baines (2015) that Fig 2E shows the identification of the 17-19h window.

This is a pertinent comment. We now have evidence that activity manipulation (in this instance by increasing temperature, which recapitulates the effect of PTX) is not effective at larval stages (L1 to L3) but remains effective between 17-19hrs AEL. This observation forms part of a separate study where we explore the role of circadian activity on embryonic and larval neuronal development. We include a brief statement to address this comment in the revision (first paragraph of Results).

• Regarding experiments in Fig 2, authors only report changes in AP firing frequency. Can the authors also report other metrics of excitability, including measures of intrinsic excitability with and without picrotoxin exposure (including RMP, Rm)? Was a different amount of current injection needed to evoke stable 5-10 Hz firing with and without picrotoxin? In the representative figure (Fig. 2A), it appears that the baseline firing frequencies are different prior to optogenetic stimulation.

No differences in RM, Rin or capacitance were observed due to PTX. This is now included in the revision along with an explanation that different levels of current injection were used to measure effects to excitatory vs inhibitory synaptic drive. We did not specifically monitor the amount of current required to maintain stable firing.

• The ch-related experiments require further controls and explanation. Regarding experiments in Fig 6, what is the effect of ch neuron stimulation alone on time lag and AP frequency? Can the authors further clarify what is known about connections between aCC and ch neurons? It is difficult for this reviewer to conceptualize how enhancing ch-mediated inhibition would worsen seizures. While the cited study (Carreira-Rosario et al 2021) convincingly shows that inhibition of mechanosensory input leads to excessive spontaneous network activity, has it been shown that the converse - stimulation of ch neurons - indeed enhances network inhibition?

• The interpretation of ch-related experiments is further complicated by the explanation in the Discussion that ch neuron stimulation depolarizes aCC neurons; this seems to undercut the authors' previous explanation that the increased E:I ratio is corrected by enhanced inhibition from ch neurons. The idea that ch neurons are placing neurons in a depolarized refractory state is not substantiated by data in the paper or citations.

To respond to these two points combined: The reviewer is correct in stating that additional experiments will be required to fully understand mechanism. We believe that cholinergic (excitatory) chordotonal input to aCC may be an important component for setting the rhythm of the locomotor CPG. Indeed, it may be that CPG rhythm is a key factor during the CP. Our observations suggest optogenetic stimulation of Ch neurons alone is sufficient to induce large, ~400-, currents that resemble endogenous spontaneous rhythmic currents (SRCs) associated with CPG activity. SRCs occur with a characteristic frequency of ~1Hz, and we have some unpublished data that suggests it is possible to change this frequency using ch stimulation. This data therefore unifies prior work (Carreira-Rosario et al., 2021 description of a brake) with our own (observation that ch depolarize aCC). However, we do not include this speculation in the Discussion because the experiments we have conducted were pilots. They may be expanded upon and included in future work.

• In the Discussion, the authors suggest that enhanced proprioception leading to seizures is reminiscent of neurological conditions. This seems to be an oversimplification. Connecting abnormal proprioception to seizures is quite different from connecting abnormal proprioception to disorders of coordination. This should be revised.

Because this is peripheral to our main study, we have deleted this from the revision.

Reviewer #1 (Recommendations For The Authors):

1. Although the authors have to be commended for the scrutiny with which they pinpoint a site of circuit change, it cannot be excluded that other parts of the circuit also undergo adjustments in response to activity manipulation during the CP, e.g. the membrane properties of the interneurons. This is not a problem but should be discussed.

We agree with this comment and have added the following text to the discussion……’However, we recognise that other parts of the locomotor network may also undergo change due to CP manipulation. The advantage of this system is that most of these elements are now open to specific manipulation through cell-specific genetic drivers’. (Discussion paragraph 3)

1. It is surprising that there is no discussion of the potential molecular cause for the observed increases in postsynaptic responses to SV release from cholinergic neurons. Given that there are no differences in postsynaptic structure, puncta number etc., the subunit composition of the nAChR seems an obvious guess. What is known about the nAChRs subunit composition on aCC, and when during development do the receptors/different subunits become expressed? A paragraph in the discussion on this issue would be highly relevant to the manuscript.

Our own work (unpublished) together with a recent paper from the Littleton lab (https://www.sciencedirect.com/science/article/pii/S0896627323005810?via%3Dihub#mmc2) suggests that aCC expresses the majority, if not all, of the 7 alpha and 3 beta subunits that compromise nAChRs. The situation is further complicated by the fact that these receptors are pentameric and are composed of various subunits – the composition significantly altering channel kinetics. Less is known about expression timelines for each receptor subunit, and certainly not in aCC. We already include the following sentence in the results text……’ A change in the frequency of mini excitatory postsynaptic potentials (mEPSPs, a.k.a. minis) would suggest the adaptation is primarily presynaptic (e.g. increased probability of release), whilst a change in distribution and/or amplitude of minis is more consistent with a mechanism acting postsynaptically (e.g. increased or altered receptor subunits).’ Given that we know next to nothing about the nAChR subunit composition in aCC and how this might change due to CP manipulation, we feel it better not to speculate further. To help the reader, we include the following sentence in the discussion……’The precise mechanism contributing to increased mini amplitude remains to be determined, but a plausible scenario may involve change in cholinergic subunit composition.’ (Discussion paragraph 3)

1. It would be important to provide the p-values for Figures 1B and C, especially because it seems that the inhibition also becomes stronger upon PTX treatment during the CP. There is no statistical testing mentioned, was no test done or was it not significant? It is agreed that the effect size is clearly stronger for the increased excitation than for the increased inhibition, but looking at the data suggests that the effect on excitation is not much more significant than the effect on inhibition.

The reviewer is referring to Fig 2B&C. P values have been added to both main text and to the figure legend.

1. Associated with the point above, in the discussion line 407 and below the authors come back to this point and reason that it is surprising that increased excitation is not compensated for by homeostatic mechanisms. It is concluded that homeostatic compensation brings the system back to a setpoint that is defined during the critical period, but the setpoint is set higher in this case. However, an alternative explanation is that GABA administration during the critical period causes the excitation set point to be too high, but this is then partially counteracted in a homeostatic manner by increasing inhibition. If the p-values in Figures 2B and C are rather similar, this might even be the favorable interpretation.

We believe the reviewer means ‘PTX administration’ and not GABA. This is an interesting idea and one we had not really considered. We address this comment by adding the following text………. ‘Alternatively, whilst the increased inhibition we observe is not statistically significant (p = 0.15), it is close and has a medium effect size (Cohen’s d = 0.78), and thus may be indicative of an attempt by the locomotor network to rebalance activity back towards a genetically pre-determined level. In this regard, it may just not have sufficient range to be able to counter the increase in excitation due to CP manipulation.’ (Discussion paragraph 5)

1. To asses the magnitudes of A18a-mediated excitation and A31k-mediated inhibition to aCC, changes in aCC firing frequency were measured. For this aCC was injected with current to fire at all. However, the current injections were chosen to cause firing at 5-10 Hz. During a crawling burst, aCC fires well above 100Hz (Kadas et al., 2017). Are the effects also visible at such firing frequencies, or at least across different firing frequencies? I am not asking for additional experiments, but maybe the data are there and can be referred to?

Spiking in aCC occurs as burst firing, evoked by cholinergic synaptic drive, that lasts for ~300ms and achieving firing frequencies of between 50-100Hz (Kadas et al., 2017 and our own unpublished data). We did not test for effects to excitation or inhibition at these higher frequencies. We now make this explicit in the discussion by adding the following sentence……’The firing frequencies that we imposed (1-10Hz) are also lower than seen during fictive locomotion (Kadas et al., 2017), which shows burst firing lasting for ~300 ms and achieving spike frequencies of up to 100Hz.’ (Discussion paragraph 3)

1. In Figure 3B some minis are demarked by green arrows and others are not. Were the non-marked ones not included in the analysis, and what were the criteria to mark some and others not? This is particularly important because the cumulative distribution of minis is analyzed in Figure 3D, and this depends crucially on what qualifies as mini and what does not.

All mini’s are marked by green arrows. The events not marked are not mini’s. Drosophila neurons are small and have an unfavourable dendritic structure for recording minis. Thus, we carefully analyse traces by eye taking only events that show very rapid rise times and slower, exponential decay (the typical mini shape). There are, however, other events which are most likely single/multiple channel openings, which due to filtering are rounded. We now include this same trace, greatly expanded, as Fig S1D to show how we identified minis from non-minis.

1. The asynchronous release experiment under Sr2+ seems an elegant way to analyze minis upon optogenetic stimulation of an identified presynaptic cholinergic neuron. I suggest being a little more conservative with the term asynchronous release (or replacing it), which is usually the release of many single vesicles that follow AP-mediated synaptic transmission and has nicely been demonstrated at the Drosophila NMJ (Besse et al., 2007). Also, please show the trace in Figure S2A under Sr2+ at a higher pA magnification, it is really hard to see the minis there.

We have adopted a previously published technique that, in our view, correctly uses the term ‘asynchronous release’. This is not to say that all asynchronous release occurs via the same mechanism. Indeed, the papers that report the technique we use predate Besse 2007. We also expand the trace in Fig S1A (not S2A as wrongly indicated).

Reviewer #2 (Recommendations For The Authors):

1. Can the authors explain what they think is the parameter of "activity" being measured in the locomotor circuit (mainly aCC) during the CP? Is the aCC neuron simply summing (perhaps through a proxy like Ca2+) total excitation/inhibition over time during the CP?

Reviewer #1 also requests that we discuss how activity is ‘measured’ and thus we now include a dedicated paragraph in the discussion to address this concern. Whether aCC sums ‘average’ activity or perhaps is influenced by activity extremes remains uncertain. Our data is consistent with the former but further work is required to validate our conclusion. This work will be published in due course.

Related to understanding this concept, could the authors' silence activity (using Kir2.1, TNT, or BoNT) from each of the monosynaptic premotor inputs in otherwise wildtype and following PTX exposure to determine how the circuit responds when each of the monosynaptic inputs are silenced? This might inform the role they play in instructing how activity is measured over time during the CP.

This is an excellent suggestion and, indeed, we have planned such experiments. Silencing specific neurons, whilst manipulating the CP, may well result in more significant network instability due to the setting of multiple (and physiologically inappropriate) homeostatic set points. Such studies go beyond the scope of the present study and thus we prefer not to speculate at this early stage, but to wait for experimental data.

On a related note, the authors focus on just 2 premotor inputs, presumably due to the availability of specific drivers. But do the authors know how many other inputs (other ACh, Gaba, and glutamate) onto aCC there are, and to what extent do the authors think these are changed in similar or distinct ways? Is it implied that all neurons are similarly altered by the manipulations?

The connectome details the number and types of neurons that directly contact the aCC motoneuron (Zarin et al., 2019). In terms of cholinergic excitors, the results present in Figure 3 suggest that most (all?) inputs are strengthened following embryonic PTX exposure. However, to conclude this would be highly speculative and thus we refrain from doing so in the manuscript. As other single-neuron driver lines become available, such expts will hopefully be possible.

1. If PTX treatment does indeed increase CPG synchronicity, shouldn't there be a readout of this effect on larval locomotion? While the speed of locomotion wasn't significantly impacted, perhaps another parameter was altered.

It is quite possible that other aspects of locomotion are being altered (turning, rearing, etc), but we have not analysed for these more subtle behaviours. Indeed, although not statistically significant, there is a modest reduction in average velocity in larvae derived from PTX-exposed embryos. We see similar reductions in characterised seizure mutants which also show increased synchronicity (Streit et al., 2016).

1. In Figure 2 and elsewhere, what is the baseline level of AP firing rate in each aCC neuron, before optogenetic stimulation? Is this informative about how PTX exposure alters excitability to begin with, perhaps by changing intrinsic excitability.

We now include this data in the relevant results section. Interestingly, following exposure to PTX, basal firing was significantly increased in A18a (excitatory premotor) but not in A31k (inhibitory premotor). This reflects our experiment in which we conclude that excitatory drive to aCC is increased relative to inhibitory synaptic drive. Thus, this measure seemingly validates our conclusion that E:I balance has been altered following activity-manipulation during the CP.

1. Figure 3: The apparent increase in mini amplitude is very small (4.1 vs 4.5 pA); is this physiologically meaningful? Although the authors say the decrease in mini freq is not significant in Fig. 3B after PTX, it does appear rather large, a 40% reduction (5 vs 3 Hz).

We must be guided by statistics in drawing conclusions, but the reader can interpret our data as they wish. Minis measure quantal release and thus to appreciate how small change can, when combined over the many receptors present, influence cell physiology, one needs to compare spiking activity. We show in Fig 2 that such change is sufficient to increase the excitatory synaptic drive provided by the A18a neuron. The seemingly larger reduction in mini frequency is intriguing and may reflect additional change, but without further experiments we cannot draw firm conclusions.

1. The clever vibration assay is a good one to induce the activation of mechanosensory neurons, but the specificity of the changes induced by this is difficult to ascertain. One possibility would be to silence the output of the ch neurons (by expression to tetanus or botulinum toxin) and still put the larvae through the same vibration during the CP to see if the rescue is lost.

We agree that further experiments are required to fully understand underlying mechanism(s). However, we will not be able to complete such follow-on expts in a timely manner and thus, these must wait and form the basis of future studies.

Minor points 1. Typos - there are numerous areas where it seems a comma is used inappropriately (e.g. lines 28, 69, 77, 104, 348, 365, etc). Suggest line editing the final "version of record".

Checked and corrected.

1. It would be of benefit to show the genotypes of the larvae in the various experimental manipulations in the relevant figure legends. This reviewer could not follow exactly how each experiment was done as it was not always clear which driver was being used to express which transgene in what genetic background.

Done

Reviewer #3 (Recommendations For The Authors):

• Please provide sample videos of electroshock-induced seizures (e.g. Fig 1B). Is it clear that the period of immobility after electroshock is a seizure (perhaps defined as hyperactivity originating from the brain)? I acknowledge the Baines group is quite skilled in this technique and perhaps there is a straightforward answer or citation to include.

We refer the reader to Marley and Baines 2011 which contains videos of seizure activity (first paragraph of Results).

• Seizures are generated in the brain and travel to the periphery. Do the authors think it is possible that the peripheral manipulations in this manuscript might be controlling the behavioral readout of seizures without affecting hypersynchronous activity in the brain?

We include the following statement (in methods) to provide our best understanding for how peripheral electroshock induces seizure………. ‘Strong peripheral stimulation likely causes excessive and synchronous synaptic excitation within the CNS resulting in seizure. However, the precise mechanism of this effect remains to be determined.’ Moreover, we feel it unlikely that manipulation of Ch neurons, by vibration, would suppress the effects we observe via peripheral mechanisms. Indeed, the Ch manipulation is limited to the embryonic CP, whilst our seizure assays are recorded many days later at L3.

• How might enhancement of inhibition lead to worsened seizures? Is the enhancement of ch-related inhibition selectively affecting inhibitory circuits, thereby leading to a net increase in excitation?

This is a difficult point to respond to at present. Enhanced inhibition per se might similarly disturb the encoding of an appropriate homeostatic setpoint(s) thus leaving a network open to being destabilized by a strong stimulus. Indeed, we have previously shown that increased inhibition during the CP results in the same effect (seizure) as increasing excitation (Giachello and Baines, 2015). Thus, presuming activation of Ch neurons during the CP translates to increased inhibition, then worsened seizure behaviour is a predictable effect. How this is achieved remains unknown and we prefer not to speculate here.

Author Response

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

We are pleased that Reviewers 1 and 3 have recommended that the revised paper be published.

Reviewer #2

For point A: Their preliminary simulation in 3D looks also nice, although it’s referenced in the discussion but not actually included in manuscript - I would advise adding it even under the mention of preliminary.

We appreciate the reviewer for liking our 3D results and suggesting to include them in the manuscript. However, these are preliminary results of our ongoing work. We are yet to establish the corresponding viscosity results quantitatively in the 3D simulations. Because the relationship between viscosity and relaxation time is not (always) linear in glass forming systems, we hesitate to report our results for publication. We hope to report the new results as part of a separate work.

For point B/C: I see some of the points of the authors - although not all of it made it in the main text. I still have some points that puzzle me. For instance, the authors mention that a single value of viscosity (from Green-Kubo) is ”valid for all time scales and amplitude”. This sounds very surprising to me for a complex fluid even at equilibrium: doesn’t it for instance assume linear response (hence small amplitudes)? Fast vs slow probing of a complex medium should also matter (see refs previously mentioned). Related to this, it’s not clear how can self-propulsion not matter if one would shear the system at a finite time scale, given past work on motility-driven unjamming and the mechanism of the authors from facilitation ( wouldn’t shearing at time scales larger vs smaller than the typical time for given cells to spontaneously rearrange from self-propulsion change drastically the effective complex modulus of the system?)

There might be a slight misunderstanding between the reviewer and us when

we say ‘single value of viscosity is valid for all time-scales and amplitude’. Let us explain this point more carefully. In our problem, we are studying the dynamics of a many body system which is undergoing Brownian dynamics where the fluctuation-dissipation theorem need not be valid (as the friction and the selfpropulsion noise strength are not related via Fluctuation-Dissipation Theorem). Now, for us to use the concepts of linear-response (which in the present study are the Green-Kubo relations for the transport coefficients in terms of timecorrelations functions), we need to show that the within the simulation time, the system has reached state that could be described using an “equilibrium” probability measure. This is the precise reason we calculated the ergodicity measure, which is a way to show that all the phase-space have been sampled uniformly under the given Brownian dynamics. This suggests (does not prove) that the system has attained a stationary probability measure (i.e, near equilibrium) for the value of self-propulsion used. Now for this value of self-propulsion, the Green-Kubo relations hold for ‘any time-scale of the simulations’ so that we can perform a time average over the trajectories of the particles (which is an alias of the stationary probability measure under the values of self-propulsion used). If we change the amplitude of the self-propulsion, we need to again compute the ergodicity measure and show the stationarity of the probability measure. If the system is ergodic with respect to the new self-propulsion, we can again use Green-Kubo for the simulations. Note that we will definitely get a different value of viscosity under the new self-propulsion as the shear-stresses generated will be different but the Green-Kubo holds. If the system is not ergodic, for the self-propulsion with the new amplitude, we cannot use Green-Kubo relations. Also a priori, one cannot say what is a large/small amplitude of self-propulsion because it has to be compared with the intrinsic energy scale, which is encoded in the energy function, which is difficult to say without explicit calculations.

This is what we meant when we said, ‘single value of viscosity is valid for all time-scales and amplitude’. It is valid for time-scales of the simulations for a given amplitude of self-propulsion only if the system is ergodic. Note that if the system is not ergodic, then the results of Ref. [14] (in the main text) could be questioned on theoretical grounds, because they were analyzed using 3 the equilibrium rigidity percolation theory. Nevertheless, the authors of Ref. [14] showed that equilibrium phase transition theory works in tissues. For these reasons, we have been, just like the Reviewer, puzzled that equilibrium ideas appear to be valid in the cell system. Additional theoretical work has to be done to clarify these links in tissues. Although this is not the last word, we hope this clarifies our view point.

For point D: I agree with the simplicity argument, although the added sentence from the discussion “Furthermore, the physics of the dynamics in glass forming materials does not change in systems with and without attractive forces” seems a bit strong given works like Lois et al., PRL, 2008 or Koeze et al, PRL, 2018 finding fundamentally different physics of jamming with or without adhesion. In the two cited papers the authors only consider equilibrium transitions in systems with attraction using computer simulations. Apparently, jamming properties depend on the strength of attraction. There are no attempts to characterize the dynamics, the focus of our work.

What we meant is that any universal relations, such as the Vogel-FulcherTammann relation, would still be valid. Of course, non-universal quantities such as glass transition temperature Tg or fragility will change. In our case, changing the adhesion strength would change ϕS, and the parameters in the VFT. However, our contention is that the overall finding that increase in viscosity followed by saturation is unlikely to change. We have added some clarifying statements in the manuscript to make this clear.

Author Response

We would like to thank the reviewers for their encouraging comments and useful feedback, which will enable us to improve the manuscript. We would like to briefly comment on some of the points they raised.

1. We agree this is a fairly specialized pipeline that has some requirements in terms of photographic setup. We are working hard to make these requirements as minimal as possible. However, given the huge variability in camera angles, backgrounds, arrangement of brain slices, etc., making the pipeline fully automated for unconstrained photos is extremely challenging.

2. In principle, it should be possible to extend our method to sagittal slices of the cerebellum or axial slices f the brainstem, but this would require collecting and labeling additional training data and thus remains as future work.

3. Producing accurate surfaces with sparse photographs is a very challenging problem and also remains as future work. We have a conference article producing surfaces on MRI scans with sparse slices (https://doi.org/10.1007/978-3-031-43993-3_4) but we haven’t gotten it to work well on photographs yet.

4. Another challenging issue that remains as future work is getting the pipeline to work well with nonlinear deformations, e.g., slices of fresh tissue. While incorporating nonlinear deformation into the model is trivial from the coding perspective, we have not been able to make it work at the level of robustness that we achieve with affine transformations. This is because the nonlinear model introduces huge ambiguity in the space of solutions: for example, if one adds identical small nonlinear deformations to every slice, the objective function barely changes.

5. As we acknowledge in the manuscript, the validation of the reconstruction error (in mm) with synthetic data is indeed optimistic, but informative in the sense that they reflect the trends of the error as a function of slice thickness and its variability (“jitter”).

6. Since we use a single central coronal slice in the direct evaluation, SAMSEG yields very high Dice scores for large structures with strong contrast (e.g., the lateral ventricles). However, Photo-SynthSeg provides better average results across the board, particularly when considering 3D analysis out of the coronal plane (see qualitative results in Figure 2 and results on volume correlations).

Author Response:

We would like to thank the editor and the three reviewers for their time and effort taken in reviewing our manuscript and providing constructive feedback. Unfortunately, the first author of this manuscript is no longer involved in academia, and does not wish to further revise this manuscript. However, we agree with the entirety of the feedback and critiques provided by the referees, and feel these points should be taken into account when interpreting our results and conclusions.

Author Response

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

Reviewer #1 (Public Review):

This work challenges previously published results regarding the presence and abundance of 6mA in the Drosophila genome, as well as the claim that the TET or DMAD enzyme serves as the "eraser" of this DNA methylation mark and its roles in development. This information is needed to clarify these questions in the field. I am less familiar with the biochemical approaches in this work, so my comments are mainly on the genetic analyses. Generally speaking, the methods for fly husbandry and treatment seem to be in accordance with those established in the field.

Response : We thank the reviewer for his/her work and positive assessment of our manuscript.

Reviewer #2 (Public Review):

DNA adenine methylation (6mA) is a rediscovered modification that has been described in a wide range of eukaryotes. However, 6mA presence in eukaryote remains controversial due to the low abundance of its modification in eukaryotic genome. In this manuscript, Boulet et al. re-investigate 6mA presence in drosophila using axenic or conventional fly to avoid contaminants from feeding bacteria. By using these flies, they find that 6mA is rare but present in the drosophila genome by performing LC/MS/MS. They also find that the loss of TET (also known as DMAD) does not impact 6mA levels in drosophila, contrary to previous studies. In addition, the authors find that TET is required for fly development in its enzymatic activity-independent manner.

The strength of this study is, that compared to previous studies of 6mA in drosophila, the authors employed axenic or conventional fly for 6mA analysis. These fly strains make it possible to analyze 6mA presence in drosophila without bacterial contaminant. Therefore, showing data of 6mA abundance in drosophila by performing LC-MS/MS in this manuscript is more convincing as compared with previous studies. Intriguingly, the authors find that the conserved iron-binding motif required for the catalytic activity of TET is dispensable for its function. This finding could be important to reveal TET function in organisms whose genomic 5mC levels are very low.

The manuscript in this paper is well written but some aspects of data analysis and discussion need to be clarified and extended.

1. It is convincing that an increase in 6mA levels is not observed in TETnull presented in Fig1. But it seems 6mA levels are altered in Ax.TET1/2 compared with Ax.TETwt and Ax.TETnull presented in Fig1f (and also WT vs TET1/2 presented in Fig1g). Is it sure that no statistically significant were not observed between Ax.TET1/2 and Ax.TETwt?

2. The representing data of in vitro demethylation assay presented in Fig.3 is convincing, but it is not well discussed and analyzed why these results are contrary to previous reports (Yao et al., 2018 and Zhang et al., 2015).

We thank the reviewer for his/her work and positive assessment of our manuscript.

(1) We repeated our statistical analyses and confirmed that there is no significant difference between wildtype and tet1/2 mutant embryos in axenic conditions (Welch two sample t-test : p=0.075).

(2) We added some elements in the revised manuscript to discuss the possible reasons for the discrepancies with previous reports. Notably both studies performed the in vitro demethylation assays over a much longer time course and with different sources of recombinant proteins. Zhang et al. purified TET catalytic domain from human cells (HEK293T) and observed around 2.5% of 6mA demethylation at 30 min and less than 25% after 10 hours of incubation as measured by HPLC-MS/MS analyses. Yao et al. incubated recombinant TET catalytic domain with 6mA DNA for 3h and observed a 25% decrease in 6mA levels as measured by dot blot. These results suggest that drosophila TET may oxidize 6mA, but with a much lower affinity than 5mC since with observed a near complete oxidation of 5mC after 1 minute and no decrease in 6mA levels after 30 minutes of reaction (for identical concentrations of substrate and enzyme). It is possible too that the preparation of TET catalytic domain in different systems changes its enzymatic activity, potentially in relation with distinct post-translational modifications. Still, as already mentioned in our manuscript, extensive biochemical analyses of the distant TET homolog from the fungus Coprinopsis cinerea (Mu et al., Nature Chem Biol 2022) strongly argue that TET enzymes do not harbor the residues required to serve as 6mA demethylase.

Reviewer #1 (Recommendations For The Authors):

Here are one comment (#1) and a couple of questions (#2-3) that could be addressed in the future, in order to understand the roles of 6mA and TET. Even though #2 and #3 are likely beyond the scope of this paper, #1 should be addressed within the scope of this work and compared with previous reports.

1. The phenotypic analyses in Fig. 4 should use tet_null/Deficiency and tet_CD/Deficiency for their potential phenotypes. This needs to be addressed since both the tet_null and the tet_CD were generated using the same starting fly line (GFP knock-in). Using a deficiency chromosome and testing these alleles in hemizygotes would be helpful to eliminate any secondary effects due to genetic background issues.

Thanks for this comment. Actually, tet_null and tet_CD were not generated using the same starting lines. Whereas tet_cd was generated (by CRISPR) using the tet-GFP knock-in line, tet_null was generated by FRT site recombination between two PBac insertions (Delatte et al. 2016). As for tet1 and tet2 (used in allelic combination in Fig 4 J-L), they correspond to two distinct mutant alleles generated by CRISPR (Zhang et al. 2015). We have clarified this in the M&M (page 9).

1. Regarding the estimated "200 to 400 methylated adenines per haplogenome", is there any insight into where are they located in the genome?

It is an interesting question and we initially used SMRT-seq sequencing to obtain this kind of information. As it turned out that this technique gives a high level of false positive, we should consider with caution the interpretation of these data and we decided not to include them in the manuscript. Still, we characterized the genomic features of the 6mA detected using stringent criteria (mQV>100, cov>25x in the fusion dataset and triplicated across samples of the same genotype). Both in wild type and tet_null, 6mA were dispersed along each chromosome although few of them were found on chromosome X. In both cases there appeared to be a higher accumulation of 6mAs on the histone locus and the transposon-rich tip of chromosome X, but 6mA density remained below 1.3/kb in other genomic regions. Comparisons with annotated genomic regions indicated that 6mA were enriched in long interspersed nuclear elements (LINEs) and satellite repeats, and depleted in 3’UTR and exons, but there was no significant difference in their repartition between the two genetic contexts. Besides, motif analyses showed similar enrichments in both conditions, with GAG triplet accounting for more than one quarter of all the sites. Whether this reflects the specificity of a putative adenine methylase or a technical bias associated the with SMTR-seq technology remains to be established.

1. The TET-GFP and TET-CD-GFP knock-in lines give proper nuclear localization and could be used to identify genomic regions bound with full-length TET and TET-CD using anti-GFP for ChIP-seq or CUT&RUN (or CUT&TAG).

Indeed, this is a line of research that we are following up and will be part of another study. Actually, our ChIP-seq experiments indicate that they bind on the same genomic regions.

Reviewer #2 (Recommendations For The Authors):

• I think the major findings of this paper are showing 6mA present in drosophila by using xenic or conventional breeding conditions and finding that TET function independently of its catalytic activity is essential for fly development. The authors could have been more precise in title and abstract to emphasize these findings.

We have now modified the abstract to try to emphasize these findings.

• The authors claim that any increase of 6mA levels was not observed in both TETnull and TET1/2, but it is not sufficiently convincing. Because it seems 6mA levels were increased in Ax. tet1/2 embryo as compared with in Ax.wt embryo (Fig.1). In this scenario, 6mA abundance in both TETnull and TET1/2 mutant are supposed to be the same. It would be better to re-analyze data carefully and discuss if 6mA levels were significantly increased in TET1/2, and why 6mA levels are different between TETnull and TET1/2. Additionally, the authors describe that the TET null mutant is pupal lethal, while the TET1/2 survivor is available. The text suggests that TET1/2 could have partial functionality on fly development (Fig.4). It would be better to check whether the N-terminus of TET is expressed in the TET1/2 mutant.

Indeed, the increase in 6mA levels in Ax. tet1/2 embryo seems consequent (although it is not statistically significant) and no increase was observed in Ax tet_null embryos. Thus, the putative effect on 6mA levels in tet1/2 embryos may not be directly due to the absence of TET function. We now mention in the revised manuscript (page 6) that “the apparent increase in 6mA levels in tet1/2 axenic embryos was not reproduced in tet_null embryos, suggesting that it does not simply reflect the tet loss of function, and that it was not statistically significant”. Besides, we do not have an antibody to check whether the N-terminus of TET is expressed in the tet1/2 mutants, but the western blot published by Zhang et al 2015 shows that tet2 mutation leads to the expression of TET N-terminal domain. This N-terminal domain could have partial TET functionality and/or interfere with the function of other factors (notably those implicated in 6mA metabolism).

• The authors show that SMRT-seq data did not reveal an increase in 6mA levels in loss of TET (Fig.2). It is convincing that total 6mA abundance was not altered by loss of TET. But were 6mA-accumulated locus/regions observed in WT not altered by loss of TET?

Please refer to our answer to reviewer 1 on that point.

• It remains unclear that the TET proteins the authors prepared do not exhibit 6mA demethylate activity in vitro, contrary to what was reported in previous papers (Fig.3). I think the preparation of recombinant proteins may make different results between this and previous papers. Yao et al., 2018 and Zhang et al., 2015 used recombinant proteins purified from Human cells or insect cells, while the author purified them from E.Coli. Additionally, it's mentioned that VK Rao et al., 2020 demonstrated cdk5-mediated phosphorylation of Tet3 increases its in catalytic activity in vitro. These previous reports suggest modification of TET could change demethylase activity. More analysis and discussion are needed to support the conclusion.

Thanks for your insights. This in an important point and we added the following elements in the revised manuscript to discuss possible reasons for the discrepancies with previous reports (pages 7-8): “Our results contrast with previous reports showing that recombinant drosophila TET demethylates 6mA on dsDNA in vitro (Yao et al. 2018; Zhang et al., 2015a). However, both studies ran much longer reactions (up to 10 hours) and used different sources of recombinant protein (drosophila TET catalytic domain purified from human HEK293T cells). Notably, Zhang et al. (2015a) only found around 2.5% of 6mA demethylation at 30 min and less than 25% after 10 hours of incubation as measured by HPLC-MS/MS analyses. These results suggest that drosophila TET may oxidize 6mA, but with a much lower affinity than 5mC since with observed a near complete oxidation of 5mC after 1 min. and no significant decrease in 6mA levels after 30 min. of reaction (for identical concentrations of substrate and enzyme). It is possible too that the preparation of TET catalytic domain in different systems changes its enzymatic activity, potentially in relation to distinct post-translational modifications.”

Author Response

1. Reviewer 1 raised the concern that the images shown in the figures seem inconsistent with the quantitative data.

Our provisional response: The quantitative data are based on many samples and the photographs are just supposed to show illustrations of example data. Because of the volume containing P1a cells, is impossible to present a single confocal image that covers all P1a neurons and would therefore correspond more closely to the quantitative data. We chose to illustrate the quantitative data using single confocal images which contain both Hr38+/GFP+ and Hr38-/GFP+ neurons, to demonstate that we can distinguish clearly which P1a neurons are positive or negative for for Hr38 expression. This can be clarified in the figure legends. If it is imperative to show images(s) to reflect the statistics, we can do that but will need to present multiple confocal images for each condition, which could be messy and confusing.

1. Reviewer 2 states: "the major weakness is the calibration of the temporal resolution of HI-CatFISH in Figure 4 and Figure Supplement 4. According to Figure Supplement 4C, close to 100% of the Hr38-positive cells are already labeled with the exonic probe 30min post-stimulation, which is not reflected in Figure 4B (there, the expression level of the exonic probe peaks 60min post-induction)”.

The confusion may arise because we drew the illustration diagram (Fig. 4B) based on the quantitative data in Fig.S4B, which plots the intensity of Hr38 exonic ISH signals, while the reviewer may be comparing the illustration to the time course based on Fig.S4C, which shows the % positive cells, a binary measure. In the illustration (fig.4B), we wrote 'Hr38 expression level', not '%Hr38 positive cells.’ We can clarify this in the figure legend. If the reviewers prefer, we can add a threshold line in the diagram corresponding to the % positive cells at maximum.

Author Response

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

Reviewer #1

The study provides a complete comparative interactome analysis of α-arrestin in both humans and drosophila. The authors have presented interactomes of six humans and twelve Drosophila α-arrestins using affinity purification/mass spectrometry (AP/MS). The constructed interactomes helped to find α-arrestins binding partners through common protein motifs. The authors have used bioinformatic tools and experimental data in human cells to identify the roles of TXNIP and ARRDC5: TXNIP-HADC2 interaction and ARRDC5-V-type ATPase interaction. The study reveals the PPI network for α-arrestins and examines the functions of α-arrestins in both humans and Drosophila.

Comments

I will like to congratulate the authors and the corresponding authors of this manuscript for bringing together such an elaborate study on α-arrestin and conducting a comparative study in drosophila and humans.

Introduction:

The introduction provides a rationale behind why the comparison between humans and Drosophila is carried out.

• Even though this is a research manuscript, including existing literature on similar comparison of α-arrestin from other articles will invite a wide readership.

Results:

The results cover all the necessary points concluded from the experiments and computational analysis.

1) The authors could point out the similarity of the α-arrestin in both humans and Drosophila. While comparing α-arrestin in both humans and Drosophila If percentage homology between α-arrestin of both Drosophila and humans needs to be calculated.

Thank you for your insightful feedback. As suggested by reviewer, we determined percentage homology of α-arrestin protein sequences from human and Drosophila using Clustal Omega. This homology is now illustrated as a heatmap in revised Figure S5. Please note that only the values with percentage homology of 40% or higher are selectively labeled.

• Citing the direct connecting genes from the network in the text will invite citations and a wider readership.

Figures:

The images are elaborate and well-made.

2) The authors could use a direct connected gene-gene network that pointing interactions. This can be used by other readers working on the same topic and ensure reproducibility and citations.

We appreciate your valuable comment. Based on the reviewer’s suggestion, we have developed a new website in which one can navigate the gene-gene networks of α-arrestins. These direct connected gene-gene networks are housed in the network data exchange (NDEx) project. Additionally, we have included gene ontology and protein class details for α-arrestins’ interactors in these set of networks, offering a more comprehensive view of α-arrestins’ interactomes.

On page 24 lines 15-18, we have revised the manuscript to introduce the newly developed website, as follows.

“Lastly, to assist the research community, we have made comprehensive α-arrestin interactome maps on our website (big.hanyang.ac.kr/alphaArrestin_PPIN). Researchers can search and download their interactomes of interest as well as access information on potential cellular functions and protein class associated with these interactomes.”  

3-1) The co-expression interactions represented as figures should reveal interaction among the α-arrestin and other genes. Which are the sub-network genes does the α- arrestin interact to/ with from the sub-network? The arrows are only pointing at the sub-networks. The figures do not reveal their interaction. Kindly reveal the interaction in the figure with the proper nodes in the figure.

3-2) Figure 2: the network attached in both human and drosophila is well represented. The green lines from α-arrestin indicate the strength of the interaction. Several smaller expression networks are seen. But "α-arrestin" in both organisms seems highly disconnected from all the genes. Connected genes have edges, not arrows. If α-arrestin can be shown connected to these gene-gene networks will help in identifying which genes connect with which gene through α-arrestin. This can be used by other readers working on the same topic and ensure reproducibility and citations.

Thank you for your valuable comment. In response to the reviewer’s recommendation, we’ve added supplementary figure, Figure S4, which illustrates direct interaction between α-arrestin and protein components of clustered complexes (or sub-networks) in addition to the associations shown between α-arrestins and the clustered complexes in Figure 2. We believe that this newly incorporated information regarding direct protein interactions will invite citations and wider readership as the reviewer pointed out.

On page 12 line 27 to page 13 line 5, we have revised the manuscript to cite the direction interactions between ARRDC3 and proteins involved in ubiquitination-dependent proteolysis, as follows.

“While the association of ARRDC3 with these ubiquitination-dependent proteolysis complexes is statistically insignificant, ARRDC3 does interact with individual components of these complexes such as NEDD4, NEDD4L, WWP1, and ITCH (Figure S4A). This suggest their functional relevance in this context, as previously reported in both literatures and databases (Nabhan et al., 2010; Shea et al., 2012; Szklarczyk et al., 2015; Warde-Farley et al., 2010) (Puca & Brou, 2014; Xiao et al., 2018).”

Direct interaction between α-arrestins and protein components of clustered complexes are illustrated in the newly added figure, Figure S4.

4-1) Figure 4. The Protein blot image was blurred. Kindly provide a higher-resolution image.

4-2) Figure 5. B. - The authors can provide images with higher resolution blot images. The bands were not visible.

We appreciate for valuable comment. Unfortunately, the protein blot image was scanned from the original film and the images we provided in the figure represent the highest resolution that we have obtained to date. Raw, uncropped images are shown in Author response image 1 and 2.

Author response image 1.

Raw image of Figure 4B

Author response image 2.

Raw image of Figure 5B

5) Figure: 5. A. - I see non-specific amplifications in the gel images. Are these blotting images? or the gel images that were changed to "Grayscale"? Non-specific amplification may imply that the experiment was not repeated and standardized. Was it gel images or blot images?

We appreciate your insightful comment. The images in Figure 5A represent western blot bands from co-immunoprecipitation assay for analysis of the interaction between TXNIP and HDAC2 proteins. Since immunoblotting using immunoprecipitates can usually detect some non-specific bands from heavy (~ 50 kDa) and light (~25 kDa) chains of the target antibody or from multiple co-immunoprecipitated proteins, we assume that the vague non-specific bands in Figure 5A might be a heavy chain of TXNIP or HDAC2 antibody or an unclear non-specific band. Because target bands showed strong intensity and very clear pattern compared to the non-specific bands in the co-immunoprecipitation assay, we believe that this data is sufficient to support the interaction of TXNIP with HDAC2. Finally, In the revised Figure 5A, we’ve modified the labeling for different experimental conditions, namely siCon and siTXNIP treatments, and added expected size of proteins (kDa), as shown below.

6) Figure 5. A. RT-PCR analysis: What was your expected size of the amplifications? the ladder indicated is in KDa. Is that right?

We appreciate your insightful questions. As mentioned above, Figure 5A shows the blotting images of co-immunoprecipitation analysis, and the ladder indicates the molecular weight (kDa) of protein markers. For clearer interpretation, the expected size of target proteins has been added in Figure 5A in the revised manuscript.

7) How were the band intensities determined?

Thank you for your question. For quantification of immunoblot results, the densities of target protein bands were analyzed with Image J, as we described in the Materials and Methods.

Discussion:

The authors have utilized and discussed the conclusion they draw from their study. But could highlight more on ARRDCs and why it was selected out of the other arrestins. The authors have provided future work directions associated with their work.

8) Why were only ARRDCs presented amongst all the arrestin in the main part of the manuscript?

We’re grateful for your valuable feedback. The reason we focused on α-arrestins was that α-arrestins have been discovered relatively recently, especially when compared to more established visual/ β-arrestin proteins in the same arrestin family but the biological functions of many α-arrestins remain largely unexplored, with notable exceptions in the budding yeast model and a few α-arrestins in mammals and invertebrate species. Most importantly, comparative study highlighting the shared or unique features of α-arrestins is yet to be undertaken. To gain a more comprehensive understanding of these unexplored α-arrestins across multiple species, we’ve centered our research on the ARRDCs within the arrestin protein family.

On page 21 lines 8-17, we’ve edited the manuscript to emphasize the importance of a comparative study on α-arrestins, as detailed below.

“According to a phylogenetic analysis of arrestin family proteins, α-arrestins were shown to be ubiquitously conserved from yeast to human (Alvarez, 2008). However, compared to the more established visual/ β-arrestin proteins, α-arrestins have been discovered more recently and much of their molecular mechanisms and functions remain mostly unexplored except for budding yeast model (Zbieralski & Wawrzycka, 2022). Based on the high-confidence interactomes of α-arrestins from human and Drosophila, we identified conserved and specific functions of these α-arrestins. Furthermore, we uncovered molecular functions of newly discovered function of human specific α-arrestins, TXNIP and ARRDC5. We anticipate that the discovery made here will enhance current understanding of α-arrestins.”

9) The discussion could be elaborated more by utilizing the data.

We appreciate your insightful feedback. Based on the reviewer’s suggestion, we’ve enhanced the discussion in the manuscript to provide a clearer interpretation of our results. First, we’ve added description of conserved protein complexes significantly associated with α-arrestins, stated on page 22 lines 5-12 and lines 23-26.

Page 22 lines 5-12: “The integrative map of protein complexes also highlighted both conserved and unique relationships between α-arrestins and diverse functional protein complexes. For instance, protein complexes involved in ubiquitination-dependent proteolysis, proteasome, RNA splicing, and intracellular transport (motor proteins) were prevalently linked with α-arrestins in both human and Drosophila. To more precisely identify conserved PPIs associated with α-arrestins, we undertook ortholog predictions within the α-arrestins’ interactomes. This revealed 58 orthologous interaction groups that were observed to be conserved between human and Drosophila (Figure 3).”

Page 22 lines 23-26: “Additionally, interaction between α-arrestins and entities like motor proteins, small GTPase, ATP binding proteins, and endosomal trafficking components were identified to be conserved. Further validation of these interactions could unveil molecular mechanisms consistently associated with these cellular functions.”

Secondly, we’ve added description of role of ARRDC5 in osteoclast maturation, as stated on page 23 lines 22-24.

“Conversely, depletion of ARRDC5 reduces osteoclast maturation, underscoring the pivotal role of ARRDC5 in osteoclast development and function (Figure S9A and B).”

Lastly, we examined the association between α-arrestins’ interactomes and human diseases, incorporating our findings into the discussion. The newly introduced figure based on the result is Figure S10.

On page 24 lines 10-14, we’ve added discussion on Figure S10 as follows.

“We further explored association between α-arrestins’ interactomes and disease pathways (Figure S10). Notably, the interactomes of α-arrestins in human showed clear links to specific diseases. For instance, ARRDC5 is closely associated with disease resulting from viral infection and cardiovascular conditions. ARRDC2, ARRDC4, and TXNIP share common association with certain neurodegenerative diseases, while ARRDC1 is implicated in cancer.”

Supplementary figures:

The authors have a rigorous amount of work added together for the success of this manuscript.

10) The reference section needs editing before publication. Maybe the arrangement was disturbed during compiling.

Thank you for your valuable comment. Based on the reviewer’s suggestion, we have rearranged the reference section to enhance its clarity. Below are excerpts from the update reference section in the manuscript.

“Adenuga, D., & Rahman, I. (2010). Protein kinase CK2-mediated phosphorylation of HDAC2 regulates co-repressor formation, deacetylase activity and acetylation of HDAC2 by cigarette smoke and aldehydes. Arch Biochem Biophys, 498(1), 62-73. doi:10.1016/j.abb.2010.04.002

Adenuga, D., Yao, H., March, T. H., Seagrave, J., & Rahman, I. (2009). Histone Deacetylase 2 Is Phosphorylated, Ubiquitinated, and Degraded by Cigarette Smoke. American Journal of Respiratory Cell and Molecular Biology, 40(4), 464-473. doi:10.1165/rcmb.2008-0255OC

Akalin, A., Franke, V., Vlahovicek, K., Mason, C. E., & Schubeler, D. (2015). Genomation: a toolkit to summarize, annotate and visualize genomic intervals. Bioinformatics, 31(7), 1127-1129. doi:10.1093/bioinformatics/btu775

Alvarez, C. E. (2008). On the origins of arrestin and rhodopsin. BMC Evol Biol, 8, 222. doi:10.1186/1471-2148-8-222”

11) many important references were missing.

We appreciate and agree with the reviewer’s comment. In response to the reviewer’s recommendation, we’ve thoroughly reviewed the manuscript and below are sections of the manuscript where around 20 new references have been added.

On page 8 lines 12-14:

“Utilizing the known affinities between short linear motifs in α-arrestins and protein domains in interactomes(El-Gebali et al., 2019; UniProt Consortium, 2018) “

On page 8 lines 19-22:

“One of the most well-known short-linear motifs in α-arrestin is PPxY, which is reported to bind with high affinity to the WW domain found in various proteins, including ubiquitin ligases (Ingham, Gish, & Pawson, 2004; Macias et al., 1996; Sudol, Chen, Bougeret, Einbond, & Bork, 1995)”

On page 9 lines 3-6:

“Next, we conducted enrichment analyses of Pfam proteins domains (El-Gebali et al., 2019; Huang da, Sherman, & Lempicki, 2009b) among interactome of each α-arrestin to investigate known and novel protein domains commonly or specifically associated (Figure S3A; Table S5).”

On page 9 lines 7-10:

“HECT and C2 domains are well known to be embedded in the E3 ubiquitin ligases such as NEDD4, HECW2, and ITCH along with WW domains (Ingham et al., 2004; Melino et al., 2008; Rotin & Kumar, 2009; Scheffner, Nuber, & Huibregtse, 1995; Weber, Polo, & Maspero, 2019)”

On page 10 lines 12-16:

“In fact, the known binding partners, NEDD4, WWP2, WWP1, and ITCH in human and CG42797, Su(dx), Nedd4, Yki, Smurf, and HERC2 in Drosophila, that were detected in our data are related to ubiquitin ligases and protein degradation (C. Chen & Matesic, 2007; Ingham et al., 2004; Y. Kwon et al., 2013; Marin, 2010; Melino et al., 2008; Rotin & Kumar, 2009) (Figure 1E; Figure S2F).”

On page 13 lines 20-21:

“Given that α-arrestins are widely conserved in metazoans (Alvarez, 2008; DeWire, Ahn, Lefkowitz, & Shenoy, 2007), “

On page 14 lines 12-17:

“The most prominent functional modules shared across both species were the ubiquitin-dependent proteolysis, endosomal trafficking, and small GTPase binding modules, which are in agreement with the well-described functions of α-arrestins in membrane receptor degradation through ubiquitination and vesicle trafficking (Dores et al., 2015; S. O. Han et al., 2013; Y. Kwon et al., 2013; Nabhan et al., 2012; Puca & Brou, 2014; Puca et al., 2013; Shea et al., 2012; Xiao et al., 2018; Zbieralski & Wawrzycka, 2022) (Figure 3).”  

Reviewer #2

In this manuscript, the authors present a novel interactome focused on human and fly alpha-arrestin family proteins and demonstrate its application in understanding the functions of these proteins. Initially, the authors employed AP/MS analysis, a popular method for mapping protein-protein interactions (PPIs) by isolating protein complexes. Through rigorous statistical and manual quality control procedures, they established two robust interactomes, consisting of 6 baits and 307 prey proteins for humans, and 12 baits and 467 prey proteins for flies. To gain insights into the gene function, the authors investigated the interactors of alpha-arrestin proteins through various functional analyses, such as gene set enrichment. Furthermore, by comparing the interactors between humans and flies, the authors described both conserved and species-specific functions of the alpha-arrestin proteins. To validate their findings, the authors performed several experimental validations for TXNIP and ARRDC5 using ATAC-seq, siRNA knockdown, and tissue staining assays. The experimental results strongly support the predicted functions of the alpha-arrestin proteins and underscore their importance. `

I would like to suggest the following analyses to further enhance the study:

1) It would be valuable if the authors could present a side-by-side comparison of the interactomes of alpha-arrestin proteins, both before and after this study. This visual summary network would demonstrate the extent to which this work expanded the existing interactome, emphasizing the overall contribution of this study to the investigation of the alpha-arrestin protein family.

We greatly appreciate your insightful feedback. In response to the reviewer’s suggestion, we’ve depicted a network of known PPIs associated with α-arrestins (Figure S2C and D). Furthermore, by comparing our high-confidence PPIs to these known sets, we found that the overlaps are statistically significant and the high-confidence PPIs of α-arrestins broaden the existing interactome (Figure S2E).

From page 7 line 26 to page 8 line 8, we’ve detailed this side-by-side comparisons of existing interactome and newly discovered high-confidence PPIs of α-arrestins, as outline below.

“As a result, we successfully identified many known interaction partners of α-arrestins such as NEDD4, WWP2, WWP1, ITCH and TSG101, previously documented in both literatures and PPI databases (Figure S2C-F) (Colland et al., 2004; Dotimas et al., 2016; Draheim et al., 2010; Mellacheruvu et al., 2013; Nabhan et al., 2012; Nishinaka et al., 2004; Puca & Brou, 2014; Szklarczyk et al., 2015; Warde-Farley et al., 2010; Wu et al., 2013). Additionally, we greatly expanded repertoire of PPIs associated with α-arrestins in human and Drosophila, resulting in 390 PPIs between six α-arrestins and 307 prey proteins in human, and 740 PPIs between twelve α-arrestins and 467 prey proteins in Drosophila (Figure S2E). These are subsequently referred to as ‘high-confidence PPIs’ (Table S3).”

2) While the authors conducted several analyses exploring protein function, there is a need to further explore the implications of the interactome in human diseases. For instance, it would be beneficial to investigate the association of the newly identified interactome members with specific human diseases. Including such investigations would strengthen the link between the interactome and human disease contexts.

Thank you for your valuable comment. As suggested by the reviewer, we examined the association between α-arrestins’ interactomes and human diseases, incorporating our findings into the discussion. The newly introduced figure based on the result is Figure S10.

On page 24 lines 10-14, we’ve added discussion on Figure S10 as follows.

“We further explored association between α-arrestins’ interactomes and disease pathways (Figure S10). Notably, the interactomes of α-arrestins in human showed clear links to specific diseases. For instance, ARRDC5 is closely associated with disease resulting from viral infection and cardiovascular conditions. ARRDC2, ARRDC4, and TXNIP share common association with certain neurodegenerative diseases, while ARRDC1 is implicated in cancer.”

Reviewer #3:

Lee, Kyungtae and colleagues have discovered and mapped out alpha-arrestin interactomes in both human and Drosophila through the affinity purification/mass spectrometry and the SAINTexpress method. They found the high confident interactomes, consisting of 390 protein-protein interactions (PPIs) between six human alpha-arrestins and 307 preproteins, as well as 740 PPIs between twelve Drosophila alpha-arrestins and 467 prey proteins. To define and characterize these identified alpha-arrestin interactomes, the team employed a variety of widely recognized bioinformatics tools. These included protein domain enrichment analysis, PANTHER for protein class enrichment, DAVID for subcellular localization analysis, COMPLEAT for the identification of functional complexes, and DIOPT to identify evolutionary conserved interactomes. Through these analyses, they confirmed known alpha-arrestin interactors' role and associated functions such as ubiquitin ligase and protease. Furthermore, they found unexpected biological functions in the newly discovered interactomes, including RNA splicing and helicase, GTPase-activating proteins, ATP synthase. The authors carried out further study into the role of human TXNIP in transcription and epigenetic regulation, as well as the role of ARRDC5 in osteoclast differentiation. This study holds important value as the newly identified alpha-arrestin interactomes are likely aiding functional studies of this group of proteins. Despite the overall support from data for the paper's conclusions, certain elements related to data quantification, interpretation, and presentation demand more detailed explanation and clarification.

1) In Figure 1B, it is shown that human alpha-arrestins were N-GFP tagged (N-terminal) and Drosophila alpha-arrestins were C-GFP (C-terminal). However, the rationale of why the authors used different tags for human and fly proteins was not explained in the main text and methods.

We appreciate your valuable comment. Both N- and C-terminally tagged α-arrestins have been used previously. Given that our study aims to increase the repertoire of α-arrestin interacting proteins, where GFP is added might not be a concern. We note that GFP is a relatively bulky tag, and tagging a protein with GFP can potentially abolish the interaction with some of the binding proteins. Follow-up studies utilizing different approaches for detecting protein-protein interactions, such as BioID and yeast two-hybrid, will allow us to build more comprehensive α-arrestin interactomes.

2) In Figure 2A, there seems to be an error for labeling the GAL4p/GAL80p complex that includes NOTCH2, NOTCH1 and TSC2.

Thank you for comment. We double-checked COMPLEAT (protein COMPLex Enrichment Analysis Tool) database for the name of protein complex consisting of NOTCH1, NOTCH2, AND TSC2. The database indeed labeled this complex as the “GAL4p/GAL80p complex”. However, given the potential for mis-annotation (since we could not ascertain the relevance of these proteins to the “GAL4p/GAL80p complex”), we chose to exclude this protein complex from the network. The update protein complex network is illustrated in the revised Figure 2A.

3) In Figure 5, given that knockdown of TXNIP did not affect the levels and nuclear localization of HDAC2, the authors suggest that TXNIP might modulate HDAC2 activity. However, the ChiP assay suggest a different model - TXNIP-HDAC2 interaction might inhibit the chromatin occupancy of HDAC2, reducing histone deacetylation and increasing global chromatin accessibly. The authors need to propose a model consistent with these sets of all data.

We greatly appreciate your detailed feedback. Our data indicates a global decrease in chromatin accessibility (Figure 4C-G) and a diminished interaction between TXNIP and HDAC2 under depletion of TXNIP (Figure 5A). Additionally, we observed an increased occupancy of HDAC2 and subsequent histone deacetylation at TXNIP-target promoter regions (Figure 5C) without any changes in the HDAC2 expression level (Figure 5A) in TXNIP- knockdown cells. From these observations, we infer that the interaction between TXNIP-HDAC2 might suppress the function of HDAC2, a major gene silencer affecting the formation of condensed or accessible chromatin by deacetylating activity. Although we checked whether TXNIP could induce cytosolic retention of HDAC2 to inhibit nuclear function of HDAC2, TNXIP knockdown did not alter its subcellular localization (Figure 5B).

To elucidate the mechanism by which TXNIP inhibits the function of HDAC2, we further investigated the effect of TXNIP on the levels of HDAC2 phosphorylation, which is known to be crucial for its deacetylase activity and the formation of transcriptional repressive complex. However, as shown in the Figure S8C and D, the knockdown of TXNIP did not affect the HDAC2 phosphorylation status, as well as the interaction between HDAC2 and other components in NuRD complex in the immunoblotting and co-IP assays, respectively. The results suggest that TXNIP may inhibit the function of HDAC2 independently of these factors.

Following the reviewer’s suggestion, we carefully provided a proposed model describing the possible role of TXNIP in transcriptional regulation through interaction with HDAC2 and co-repressor complex in Figure S8E.

Description of these newly added figures can be found in the revised manuscript from page 18 line 7 to 27, as outlined below.

“HDAC2 typically operates within the mammalian nucleus as part of co-repressor complexes as it lacks ability to bind to DNA directly (Hassig, Fleischer, Billin, Schreiber, & Ayer, 1997). The nucleosome remodeling and deacetylation (NuRD) complex is one of the well-recognized co-repressor complexes that contains HDAC2 (Kelly & Cowley, 2013; Seto & Yoshida, 2014) and we sought to determine if depletion of TXNIP affects interaction between HDAC2 and other components in this NuRD complex. While HDAC2 interacted with MBD3 and MTA1 under normal condition, the interaction between HDAC2 and MBD3 or MTA1 was not affected upon TXNIP depletion (Figure S8C). Next, given that HDAC2 phosphorylation is known to influence its enzymatic activity and stability (Adenuga & Rahman, 2010; Adenuga, Yao, March, Seagrave, & Rahman, 2009; Bahl & Seto, 2021; Tsai & Seto, 2002), we tested if TXNIP depletion alters phosphorylation status of HDAC2. The result indicated, however, that phosphorylation status of HDAC2 does not change upon TXNIP depletion (Figure S8D). In summary, our findings suggest a model where TXNIP plays a role in transcriptional regulation independent of these factors (Figure S8E). When TXNIP is present, it directly interacts with HDAC2, a key component of transcriptional co-repressor complex. This interaction suppresses the HDAC2 ‘s recruitment to target genomic regions, leading to the histone acetylation of target loci possibly through active complex including histone acetyltransferase (HAT). As a result, transcriptional activation of target gene occurs. In contrast, when TXNIP expression is diminished, the interaction between TXNIP and HDAC2 weakens. This restores histone deacetylating activity of HDAC2 in the co-repressor complex, leading to subsequent repression of target gene transcription.”

4) The authors showed that ectopic expression of ARRDC5 increased osteoclast differentiation and function. Does loss of ARDDC5 lead to defects in osteoclast function and fate determination?

We appreciate your valuable comment. We have confirmed the endogenous expression of ARRDC5 in osteoclasts and conducted a loss-of-function study using shARRDC5. As determined by qPCR, ARRDC5 was endogenously expressed very low in osteoclasts. Even during RANKL-induced osteoclast differentiation, the CT value (29-31) for ARRDC5 expression was high in osteoclasts compared to the CT value (17-24) for the expression of marker genes Cathepsin K, TRAP, and NFATc1. Even though its endogenous expression was very low, we generated ARRDC5 knockdown cells by infecting BMMs with lentivirus expressing shRNA of ARRDC5 and subsequently differentiated the cells into mature osteoclasts. After five days of differentiation, we observed a significant decrease in the total number of TRAP-positive multinucleated cells (No. of TRAP+ MNCs) in shARRDC5 cells compared to that in the control cells. This result indicates that the loss of ARRDC5 leads to defects in osteoclast differentiation. Result of this loss-of-function study using shARRDC5 is depicted in Figure S9A and B.

In the revised manuscript, following sentence explaining Figure S9A and B was added on page 19 lines 15-17 as follows.

“Depletion of ARRDC5 using short hairpin RNA (shRNA) impaired osteoclast differentiation, further affirming its crucial role in this differentiation process (Figure S9A and B).”

5) From Figure 6D, the authors argued that ARRDC5 overexpression resulted in more V-ATPase signals: however, there is no quantification. Quantification of the confocal images will foster the conclusion. Also, western blots for V-ATPase proteins will provide an alternative way to determine the effects of ARRDC5.

We appreciate your insightful feedback. As suggested by the reviewer, we quantified V-type ATPase signals using confocal images, which were shown in Figure 6D. The ImageJ program was employed for integrated density measurements, and the integrated density of GFP-GFP overexpressing osteoclasts was set to 1 for relative comparison. The result in the revised Figure 6D revealed a significant increase in V-type ATPase signals in GFP-ARRDC5 overexpressing osteoclasts compared to that in GFP-GFP overexpressing osteoclasts, as outlined below.

We also agree with the reviewer’s comment that Western blot for V-ATPase proteins will be an alternative way to determine the effects of ARRDC5 in osteoclast differentiation. We have confirmed no different expression of V-type ATPase between GFP-GFP and GFP-ARRDC5 overexpressing osteoclasts using qPCR and western blot analysis. The corresponding western blot result is shown in the revised Figure S9C.

In addition, the corresponding qPCR that measures the expression level of V-type ATPase between GFP-GFP and GFP-ARRDC5 overexpressing osteoclasts is shown in Author response image 3.

Author response image 3.

Moreover, based on the references, the V-type ATPase is localized at the plasma membrane during osteoclast differentiation (Toyomura et al., 2003). Although mRNA and protein expression levels were similar in both cells, localization of V-ATPase in plasma membrane was significantly increased in GFP-ARRDC5 overexpressing osteoclasts compared to that in GFP-GFP osteoclasts, as shown in the revised Figure 6D above.

6) The results from Figure 6D did not support the authors' argument that ARRDC5 might control the membrane localization of the V-ATPase, as bafilomycin is the V-ATPase inhibitor. ARRDC5 knockdown experiments will help to determine whether ARRDC5 can control the membrane localization of the V-ATPase in osteoclast.

Thank you for your insightful comment. V-type ATPase has been reported to play an important role in the differentiation and function of osteoclasts (Feng et al., 2009; Qin et al., 2012). Given that various subunits of the V-type ATPase interact with ARRDC5 (Figure 6A), we speculated that ARRDC5 might be involved in the function of this complex and play a role in osteoclast differentiation and function. As answered above, GFP-ARRDC5 overexpressing osteoclasts showed a similar expression level of V-type ATPase to GFP-GFP cells but exhibited increased V-type ATPase signals at the cell membrane compared to those in GFP-GFP cells (Figure 6D). Additionally, co-localization of ARRDC5 and V-type ATPase was observed in the osteoclast membrane (Figure 6D), as predicted by the human ARRDC5-centric PPI network. On the other side, bafilomycin A1, a V-type ATPase inhibitor, not only blocked localization of V-type ATPase to plasma membrane in GFP-ARRDC5 overexpressing osteoclasts, but also reduced ARRDC5 signals (Figure 6D). These results indicate that ARRDC5 plays a role in osteoclast differentiation and function by interacting with V-type ATPase and promoting the localization of V-type ATPase to plasma membrane in osteoclasts.

V-type ATPase present in osteoclast membrane is important to cell fusion, maturation, and function during osteoclast differentiation (Feng et al., 2009; Qin et al., 2012). GFP-ARRDC5 overexpressing osteoclasts showed a significant increase of V-type ATPase signals in the cell membrane compared to GFP-GFP cells (Figure 6D), and also significantly increased cell fusion (No. of TRAP+ MNCs in Figure 6B) and resorption activity (resorption pit formation in Figure 6C). However, ARRDC5 knockdown in osteoclasts (shARRDC5 cells) showed a significant decrease in No. of TRAP+ MNCs compared to that in the control cells, indicating that the loss of ARRDC5 leads to defects in cell fusion during osteoclast differentiation (Figure S9A and B). As described above, the endogenous expression of ARRDC5 was very low in osteoclasts and could be specifically expressed in a certain timepoint during the differentiation. Therefore, to better understand the interaction with V-type ATPase of ARRDC5 in osteoclasts, ARRDC5 overexpression is more suitable than its knockdown.

Part of the manuscript on page 19 line 21 to page 20 line 6 was edited to support our statement, as outlined below.

“The V-type ATPase is localized at the osteoclast plasma membrane (Toyomura et al., 2003) and its localization is important for cell fusion, maturation, and function during osteoclast differentiation (Feng et al., 2009; Qin et al., 2012). Furthermore, its localization is disrupted by bafilomycin A1, which is shown to attenuate the transport of the V-type ATPase to the membrane (Matsumoto & Nakanishi-Matsui, 2019). We analyzed changes in the expression level and localization of V-type ATPase, especially V-type ATPase V1 domain subunit (ATP6V1), in GFP-GFP and GFP-ARRDC5 overexpressing osteoclasts. The level of V-type ATPase expression did not change in osteoclasts regardless of ARRDC5 expression levels (Figure S9C). GFP signals were detected at the cell membrane when GFP-ARRDC5 was overexpressed, indicating that ARRDC5 might also localize to the osteoclast plasma membrane (Figure 6D; Figure S9D). In addition, we detected more V-type ATPase signals at the cell membrane in the GFP-ARRDC5 overexpressing osteoclasts, and ARRDC5 and V-type ATPase were co-localized at the osteoclast membrane (Figure 6D; Figure S9D).”

7) The tables (excel files) do not have proper names for each table S numbers. Please correct the name of excel files for readers.

We appreciate your valuable comments. In response to the reviewer’s suggestion, we’ve renamed excel files to more appropriate titles for easier readability. List of renamed tables (excel files) are shown below.

Table S1. List of α-arrestins from human and Drosophila Table S2. Evaluation sets of α-arrestins PPIs Table S3. Summary tables of SAINTexpress results Table S4. Protein domains and short linear motifs in the α-arrestin interactomes Table S5. Enriched Pfam domains in the α-arrestin interactomes Table S6. Subcellular localizations of α-arrestin interactomes Table S7. Summary of protein complexes and cellular components associated with α-arrestin Table S8. Orthologous relationship of α-arrestin interactomes between human and Drosophila Table S9. Summary of ATAC- and RNA-seq read counts before and after processing Table S10. Differential accessibility of ACRs and gene expression Table S11. Summary of ATAC-seq peaks located in promoters and gene expression level Table S12. List of primer sequences used in this study

8) http://big.hanyang.ac.kr/alphaArrestin_Fly link does not work. Please fix the link.

We appreciate your comment. In response to the reviewer’s comment, we have made comprehensive α-arrestin interactome maps on our new website (big.hanyang.ac.kr/alphaArrestin_PPIN) and confirmed that users can be re-directed to networks housed in NDEx.

Author response image 4.

Screen shot of the first page of the newly developed website.

Website address: big.hanyang.ac.kr/‌‌‌‌‌‍‍‍‌‌alphaArrestin_PPIN

Author response image 5.

Screen shot of the gene-gene network involving α-arrestin in human.

Author Response

eLife assessment

This study presents valuable insights into the epigenetic landscape in adult kidney podocytes. A series of solid experiments demonstrate that genes that are regulated by a key kidney transcription factor, Mafb, are essential for H3K4me3 methylation and recruitment of Wt1 to Nphs1 and Nphs2. This new information provides insights into the potential relationship and coordination of transcription factors in regulating target genes in podocytes in glomerular diseases, although the conclusion that MafB is generally required for Wt1 to bind to podocyte-specific promoters is incomplete and should be extended beyond two or three genes.

We thank the reviewers and editors for critically reading our manuscript and their insightful comments. We will strive to revise

Reviewer #1 (Public Review):

Summary:

In their manuscript, Massa and colleagues provide a map of the epigenetic landscape in podocytes and analyze the role of the transcription factor MafB in podocyte gene expression. They initially map the histone profile in adult podocytes of the mouse by assaying three different histone methylation marks, namely H3K4me3, H3K4me1, and H3K27me3 for active, primed, and repressed states. They then perform Wt1- and MafB-ChIP-Seq analysis to identify respective direct targets of those transcription factors. Subsequently, they employ an inducible MafB knockout model and show that homozygous knockout mice show proteinuria and FSGS, suggesting an important role for MafB in podocyte homeostasis. RNA-Seq analysis in mice two daysafter tamoxifen application identified direct and indirect MafB target genes. Finally, the authors turn to a constitutive MafB knockout model, carry out anti-H3K4me3 and anti-Wt1 ChIP experiments, and examine selected promoters. One main conclusion from this work is that MafB opens chromatin and thus facilitates the binding of other transcription factors like Wt1 to podocyte-specific genes.

Strengths and weaknesses:

The authors have performed an impressive number of experiments and generated very valuable data. They use state-of the-art technology and the data are presented well and are sound. This being said the manuscript contains significant novel data, but also experiments that are already available in some sort. The histone profile in adult mouse podocytes is novel and provides an interesting map of epigenetic marks in this particular cell type. It is maybe not too surprising that podocyte-differentiation genes have different chromatin accessibility than genes associated with general development. The Wt1-ChIP has been done before by several labs but is certainly an important control in this work. The MafB-ChIP is new. The inducible MafB knockout model including the identification of Tcf21 as a target gene has been published by others in 2020 (and is acknowledged by the authors). The experiments addressing the potential role of MafB in chromatin opening are new. I find that the data are certainly compatible with the model put forward by the authors, but they are not compelling.

We agree that additional data on changes in chromatin accessibility in the absence of Mafb would help to support our model and we will be working towards this data for a revised version of the manuscript.

Reviewer #2 (Public Review):

Summary:

The authors investigate the role of MafB in regulating podocyte genes. Mafb is required for podocyte differentiation and maintenance. Mutations of this gene cause FSGS in mice and humans. They profiled MafB binding genome-wide in isolated glomeruli and defined overlap with Wt1. They provide evidence that Mafb is required for Wt1 binding and H3K4me3 methylation at the promoters of two essential podocyte genes, Nphs1 and Nphs2 Understanding how the action of different transcription factors is coordinated to control gene expression - the main goal of this paper - is an important line of investigation.

While the main conclusion of the paper is supported by their data, the scope is limited. Additional ChIP-seq experiments and data analysis are needed to solidify and extend their conclusions.

Strengths:

1) Performing ChIP-seq for histone modifications on isolated podocytes provides valuable cell-type-specific information. Similarly, profiling Mafb and Wt1 in isolated glomeruli provides podocyte-specific binding patterns because these transcription factors (TFs) are not expressed in other cell types in glomeruli. The significant overlap of their Wt1 binding genome-wide withthat of prior published work is reassuring. RNA-seq on isolated podocytes provides the appropriate cell-type specific gene expression data to integrate with ChIP-seq data. Together, the RNA-seq and ChIP-seq data are valuable resources for other investigators examining gene regulation in mouse podocytes.

2) The phenotype analysis of their FSGS model is convincing and well done.

3) Testing how Wt1 binding is affected by loss of Mafb provides insight into how these key podocyte TFs may cooperate to regulate genes.

Weaknesses:

1) The conclusion that Mafb is required for Wt1 binding and H3K4me3 methylation is based solely on ChIP-PCR at two gene promoters (Nphs1, Nphs2). This result should be validated and extended by ChIP-seq. Mafb and Wt1 binding overlap at more than 200 sites. If their model is correct, it is likely that Wt1 binding would be affected at other genomic sites. This result would add strong support to their model of how Wt1 and Mafb cooperate to regulate genes in podocytes. Moreover, ChIP-seq would define whether the dependence of Wt1 on Mafb is also evident at distal regulatory regions (defined H3K4me1, which is typically found at predicted enhancers).

We agree that a genome wide analysis of chromatin accessibility would help corroborating our model and will work towards this data for a revised version.

2) The FSGS model generated by the authors involved conditional deletion of Mafb in podocytes at 8 weeks of age. They found that this resulted in reduced expression of Nphs1 and Nphs2 within 48 hours post-deletion. However, they investigated Wt1 binding and H3K4me3 genomic binding in Mafb homozygous null embryos. While this result provides information about podocyte differentiation, it does not address the maintenance of expression of these essential podocyte genes in the adult kidney. Because post-natal deletion of Mafb led to FSGS and reduced expression of Nphs1/2, ChIP-seq should be performed on the adult conditional mutants in order to provide mechanistic information about the disease.

The fact that the phenotype in Mafb conditional mutant animals is progressive means that epigenetic changes are also likely to be quantitative. Indeed, Nphs1/Nphs2 are still expressed 6 weeks after Mafb deletion, albeit at lower levels. Since ChIP-seq experiments are not necessarily quantitative, we believe it may be difficult to detect statistically significant changes in this model. We will discuss this limitation of our study in a revised version of our manuscript.

3) H3K4me1 binds enhancer regions. The authors performed ChIP-seq to profile H3K4me1 in isolated podocytes. However, there was no analysis reported of these results. It would be valuable to determine if Wt1 and Mafb co-localize at predicted enhancers in podocytes and if Wt1 binding is lost at these regions in Mafb mutant glomeruli.

We well reanalyse the data taking the reviewer’s comments into account.

Author Response

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

For the final Version of Record the following changes will be included: 1. Figure 4: Example traces replaced with a more representative simulation run that is more similar to the mean. 2. Methods: Description of the alignment procedure expanded to explain the algorithm steps better.

The following is the authors’ response to the previous reviews

We are grateful for the positive and insightful feedback from the editors and reviewers. These constructive comments have contributed to the enhancement of our work. We have revised the manuscript, addressing each of the comments raised. In addition, based on the commentary provided, we have introduced two new figures that offer a deeper understanding of our research findings:

In new Figure 7, we present the analysis of the difference in onset times between motion and flash responses. This figure also includes a simple illustration elucidating the origins of these differences, highlighting the varying engagement of receptive fields by these stimuli. The data presented in this figure were initially featured in the main text of the original manuscript. Figure 11 offers a detailed comparison of the temporal and spatial characteristics of the synthetic presynaptic signals driving optimal DS in SACs. We compare these characteristics with the properties extracted from recorded glutamate release. Our analysis suggests that the sluggish dynamics observed in biological signals impede effective directional integration. Below are the detailed point-by-point responses to reviewers comments.

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:

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

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:

• 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:

• 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.

The reviewer is correct that the proximal and more distal SAC dendrites sample from different IPL depths. It should be theoretically possible to match the functional clusters we measured with anatomical bipolar cell identities. However, the stratifications of these cells have significant overlaps (Figure 6-S2), and previous attempts to match iGluSnFR signals to anatomy proved to be challenging (Franke et al., 2017; Gaynes et al., 2022; Matsumoto et al., 2019; Srivastava et al., 2022; Strauss et al., 2022). In the revised version of the manuscript, we reorder the functional clusters based on their transiency, which has a higher correlation to stratification depth (Franke et al., 2017).

We have examined a scenario in which the presynaptic population comprises more than two clusters. We constructed synthetic models whose input structure was as in Figure 10 (old Figure 9). The optimal configuration for the most proximal and distal inputs closely resembled the proximal-distal model reported in Figure 2. However, we observed a nearly linear variation in the shape of the optimal mid-range inputs, transitioning from proximal-like to distal-like responses as the distance increased. We consider this outcome to be expected based on the structure of the space-time wiring model (Kim et al., 2014). Interestingly, this was not the case with models incorporating physiologically recorded signals. As we show in Figure 10, the most common optimal directional tuning was seen when the bipolar drive consisted of two main populations, both in the ON and OFF SACs.

Finally, we believe that uncovering additional mechanisms that underlie directional selectivity in SACs represents a crucial challenge for the field to tackle. It is highly probable that achieving directional selectivity involves a complex interplay of multiple factors. This includes the organization of the presynaptic circuit, which we have partially addressed in this study, as well as the influence of postsynaptic active conductances and feedback loops involving other SACs and presynaptic cells. We have expanded the discussion section to describe the possible mechanisms

• 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.

The reviewer's observation is accurate; the model depicted in Figure 1 did not include voltage-gated channels. Our goal was to study electrotonic isolation, which is often measured in passive models. However, while we did not incorporate voltage-gated potassium channels implicitly in the models, our simulations are rooted in previous models that were fine-tuned using empirical data. As potassium channels are expected to influence the experimentally recorded input resistance, we have indirectly accounted for their impact on the interdendritic signal propagation.

In subsequent model iterations, we have integrated voltage-gated calcium channels into our simulations to assess the signal responsible for driving synaptic release. We show that nonlinear voltage dependence of the calcium currents enhances compartmentalization of the local calcium levels (Figure 2), but did not significantly influence local voltages. Therefore, calcium channels do not appear to have a major impact on electrotonic distances.

• 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.

Indeed, we observed no discernible directional differences in bipolar responses. This phenomenon can be primarily attributed to the fact that the signals originating from the limited number of directionally-tuned release sites are overshadowed by the release from non-directionally-tuned units (Matsumoto et al., 2021). In the revised discussion, we have acknowledged this limitation in our recorded data.

• 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.

We have expanded the discussion section to address these points.

Reviewer #1 (Recommendations For The Authors):

Major comments

1. One of the main conclusions of this study is that diverse BC kinetics contribute to DS (Fig. 9). The authors nicely demonstrate using modeling that the experimentally measured BC kinetics are far from ideal. However, this conclusion is based on a model that almost exclusively relies on just two of the 7 putative BC types (e.g., C1 & C6 for On SACs) placed optimally along the dendrites, which raises two important caveats.

First, given that other BC types are likely to contribute, the effects of two distinct types are likely to be diluted. Thus, the contribution of BCs to DS is likely to be significantly overestimated. Second, given that the dendrites of 10-30 SACs cross each point in the honeycomb, for the given model to work, each BC would need to connect extremely selectively to SACs. i.e., at a given point, a sustained input must only connect to the more proximal dendritic segments, while avoiding entirely the distal segments of overlapping SAC dendrites. Thus, their model requires extremely selective wiring for which there is no evidence. In fact, there is evidence to the contrary provided by Ding et al. 2016, which showed that the type 7 (proximally biased) and type 5 (distally biased) populations had a substantial overlap (assuming these BC types correspond to kinetically diverse clusters).

We wholeheartedly concur with the reviewer's perspective that our findings have led to an overestimation of the space-time wiring mechanism's role in SAC directional selectivity (DS). We have adjusted our discussion to emphasize this point. In light of this, our assertion that, even with the most favorable distribution of synaptic inputs, the space-time wiring model still does not fully account for the experimentally-determined directional tuning in SAC, remains valid.

With regard to the model, it would also be worth comparing results to previous starburst models (e.g., Tukker et al,. 2004), which demonstrated a robust DS in SAC dendrites in the absence of kinetically diverse BC input. Why is the cell-intrinsic DS so weak in the present model?

We have directly explored this question in the synthetic model (Figures 2, 3). Despite variances in the anatomy of SACs and the distribution of bipolar inputs between our model and the study by (Tukker et al., 2004), we observed remarkably similar levels of directional selectivity index computed from the voltage response (approximately 10%, as shown in Figure 3, 'Identical BCs').

The primary distinction emerged in the degree of DS amplification mediated by calcium currents. Tukker et al., 2004 reported considerably higher DS compared to our findings, despite employing similar formulations for voltage-gated calcium channel models. The key factor driving this difference lies in the fact that Tukker et al., 2004 measured amplification in proximity to the threshold of calcium channel activation. Even minor variations in membrane potentials near this threshold can lead to substantial differences in calcium influx, especially when outward stimulation results in a calcium spike. In fact, recently, Robert Smith’s group revisited the threshold-based mechanism and concluded that it often fails to produce robust DS due to the heterogeneity of membrane potentials among different terminal dendrites (Wu et al., 2023).

Our models were trained on five different stimuli velocities whose synaptic integration produced substantially different peak amplitudes. Consequently, the spike threshold alone couldn't reliably distinguish between inward and outward directions across all five conditions, resulting in reduced directional performance in our simulations. In the revised Figure 2-S2 we directly explore the performance of the model with identical BC formulations, trained on a single velocity. We find a dramatic enhancement of calcium DS (DSI=66%) in this condition compared to an identical model trained on 5 velocities (DSI=17%). Thus, evolutionary search is capable of finding the threshold-based solution, but only when the training is performed on a single stimulus velocity (Figure 2-S2). This solution did not generalize to multiple stimuli speeds because, as mentioned above, they lead to different postsynaptic depolarization levels (Figure 2, 2-S1). Instead, the algorithm converged on a set of postsynaptic paraments leading to less nonlinear calcium channel activation over a broader voltage range, ensuring effective DS performance over multiple velocities and heterogenous local potentials (Wu et al., 2023).

1. Functionally distinct responses across different regions of interest (ROIs) were used to classify BC input. ROIs were obtained from multiple scan fields and retinas and combined into a single dataset for functional clustering. However, the consistency of the cluster distribution across these replicates has not been addressed. As BCs can exhibit different functional properties dependant on the state/health of the retina, it is important to know whether certain functional clusters may originate disproportionately from a particular experiment, as it implies that each cluster does not represent a different stable functional/anatomical population.

We acknowledge that the state of the preparation can significantly impact signal dynamics. In response to this important consideration, we have incorporated details about the distribution of functional clusters in various experiments in the revised version of the manuscript (Figure 6-S1, and discussion).

Other comments:

1. Interpreting iGluSnFR signals: Since the sensor is expressed uniformly across the SAC dendrite, it is important to clarify why the measured F signals are considered synaptic responses. Could spillover contribute to the generation of slower responses?

We do not believe spillover can explain slower responses because the sluggish clusters often responded significantly (up to 500ms) sooner to moving bars (Figures 6, 6-S3). We acknowledge and discuss this possibility of spillover in the revised discussion.

1. One striking finding is the diversity of BCs RF sizes (Fig. 7C). Some BCs have RF that are far larger than their dendritic fields. It will be useful to discuss the potential mechanisms that may underlie large BC RFs.

We changed the discussion to address this question.

1. SAC DS is independent of dendritic isolation: The authors claim that dendritic isolation does not significantly impact DS. However, while this might be true for a linear motion through the receptive field, dendritic isolation probably matters for more dynamic stimuli. For example, DSGCs can encode rapid changes in objection direction, as DS is computed over fine spatiotemporal scales relying on SACs (Murphy-Baum et al., 2022). This could not occur if SAC dendrites were not well electrically isolated from each other.

We believe that this is an accurate interpretation of our findings. Our research suggests that dendritic isolation is likely not a critical factor in the space-time wiring mechanism. However, as we demonstrate that this particular mechanism cannot fully account for the observed levels of DS in SACs, other mechanisms must be important. As previous studies revealed that dendritic isolation enhances SAC DS (for example, Koren et al., 2017), dendritic independence likely contributes to directional performance within SACs by these additional mechanisms.

1. Figure 4: From what I understand, the BC inputs for the electrotonic connectivity variations evolved much like they were for the original model without axial resistance constraints. This makes sense, since stronger/weaker inputs with different temporal kernels may be appropriate for each condition, hence why the axial resistance wasn't changed post-evolution, which would have likely caused the DS to drop. If that is the case, however, I wonder how the best DS attainable by the final model which is constrained to the radial arrangement of realistic BC inputs (without being able to fit much more optimal sustained-transient BCs to their circumstance) would be impacted. Is dendritic isolation similarly unimportant when the pre-synaptic story isn't ideal?

We have explored this question directly by allowing the evolutionary algorithm to modify the passive and active characteristics of the postsynaptic SAC. Our findings are summarized in Figure 9-S1. We observed a correlation between DSI levels and membrane/axial resistance values in SACs in the evolved models. Better DS was seen with leaky membranes (higher isolation) and lower axial resistance (lower isolation). While it is clear that postsynaptic parameters can influence synaptic integration, they can not fully compensate for inadequate presynaptic dynamics.

1. BC are shown to contribute to DS across velocities (Fig. 9), which contrasts with results from Srivastava et al., (2022) that showed BCs contribute to DS at lower velocities. However, this discrepancy can easily be explained by the choice of moving spots. In this study, the sweeping bars had dynamic width (targeting pixel dwell time of 2s), which means for higher velocities the bar is significantly wider. While in the previous study, the width of the stimulus was kept constant, and thus for higher velocities, the sustained/transient kinetic differences of BCs are less clear (Srivastava et al., 2021). The author's should discuss this explicitly, to avoid discrepancies between these two studies the reader might otherwise perceive.

We value reveiwer’s feedback, and in response, we have included an additional paragraph in the manuscript addressing the distinctions in directional tuning that arise from the space-time model presented in this work, in comparison to earlier studies.

1. Methods: It will be good to discuss how ROIs sizes and positions were selected (pixel correlations?)

We have included a more detailed explanation of the clustering procedure

• Lines 614 describe whole-cell patch clamp techniques, which are not used in this study.

We used patch-clamp to record the waveforms shown in Figure 2-S2

1. Figure 6: Diversity of Glut responses to motion in ON and OFF SACs, caption typos?

2. "Left:" without "Right:" to describe the population (I presume) viewed as an image

3. If there should still be A,C and B,D to group the ON and OFF halves, maybe it should be mentioned in the caption

Thank you for bringing this to our attention, the legends were fixed.

References:

Kim, J. S., Greene, M. J., Zlateski, A., Lee, K., Richardson, M., Turaga, S. C., Purcaro, M., Balkam, M., Robinson, A., Behabadi, B. F., Campos, M., Denk, W., Seung, H. S., & EyeWirers (2014). Space-time wiring specificity supports direction selectivity in the retina. Nature, 509(7500), 331-336. https://doi.org/10.1038/nature13240

Gaynes, J. A., Budoff, S. A., Grybko, M. J., Hunt, J. B., & Poleg-Polsky, A. (2022). Classical center-surround receptive fields facilitate novel object detection in retinal bipolar cells. Nature communications, 13(1), 5575. https://doi.org/10.1038/s41467-022-32761-8

Murphy-Baum B. and Awatramani GB (2022). Parallel processing in active dendrites during periods of intense spiking activity, Cell Reports, Volume 38, Issue 8,

Srivastava P, de Rosenroll G., MatsumotoA., Michaels T., Turple Z., Jain V, Sethuramanujam S, Murphy-Baum B, Yonehara K., Awatramani, G.B. (2022) Spatiotemporal properties of glutamate input support direction selectivity in the dendrites of retinal starburst amacrine cells eLife 11:e81533

Strauss, S., Korympidou, M. M., Ran, Y., Franke, K., Schubert, T., Baden, T., Berens, P., Euler, T., & Vlasits, A. L. (2022). Center-surround interactions underlie bipolar cell motion sensitivity in the mouse retina. Nature communications, 13(1), 5574. https://doi.org/10.1038/s41467-022-32762-7

Tukker, J. J., Taylor, W. R., & Smith, R. G. (2004). Direction selectivity in a model of the starburst amacrine cell. Visual neuroscience, 21(4), 611-625. https://doi.org/10.1017/S0952523804214109

Reviewer #2 (Recommendations For The Authors):

Specific comments

1. Line 223. The statement a model trained on only optimal DSI would produce "negligible absolute differences in calcium levels." is unclear. This needs to be better explained.

We have modified and expanded this paragraph to make it more clear

1. Figure 4. The authors use this model to test the hypothesis that space time wiring contribution to SAC process DS requires dendritic isolation. They do this by increasing axial resistance around the soma of their model neuron to isolate each dendrite. They found comparable DS was achieved in both conditions, indicating that the space-time wiring model works in two cases of high and low dendritic isolation. However, to test the claim that "specific details of postsynaptic integration appear to play a lesser role" (line 274) the authors may consider allowing the axial resistance to change as a part of the model rather than testing two extreme states.

Membrane and axial resistances (and active parameters) were allowed to change as part of model evolution in most simulations presented in this manuscript. We have added the information on the final resistance values reached in the evolved models in Figure 9-S1

1. Figure 6: To study glutamatergic input onto SACs, the authors expressed iGLuSnFR in ChAT-Cre mice and grouped similarly responding pixels into ROIs and separated these responses into functional groups based on cluster analysis (Figure 5). The alignment of the responses in Figure 6A was confusing. It appears that average responses for each cluster are aligned based on the peak observed during the stimulus in each direction, but it is unclear how they are aligned relative to each other or what this timing is relative to location of the stimulus (i.e. what is time 0 in 6A?).

The displayed traces represent the average responses to horizontally moving bars (speed = 0.5mm/s), either moving to the left or right. To achieve this alignment, we employed a procedure consistent with our recent publication (Gaynes et al., 2022), which we have now detailed more comprehensively. Here's the step-by-step process we followed:

1. Determination of half-maximum rise times: Initially, we calculated the half-maximum rise times for glutamate signals recorded in response to left and right-moving stimuli.

2. Calculation of mean rise time: We then computed the mean of these rise times, which served as a reference point for alignment.

3. Alignment procedure: To illustrate the alignment process, consider an example. Suppose the 50% rise time for responses to left-moving stimuli occurs at 3 seconds, while responses to right-moving stimuli occur 4 seconds after stimulation onset. This discrepancy suggests that the RF of the cell is shifted to the right from the center of the display (assuming a stimulation speed of 0.5mm/s on the retina, the RF's position would be approximately 250μm from the midline). To align these responses, we shifted both waveforms by 500ms so that their 50% rise times coincided at 3.5 seconds. Importantly, 3.5 seconds would represent the 50% rise time of the ROI if it were precisely centered on the display. This alignment effectively removed any spatial position dependence from the ROIs.

4. Comparative analysis and clustering: With the responses now aligned, we were able to compare their shapes and subsequently cluster the ROIs into distinct functional clusters. For clarity, we opted to highlight the time of response peak for cluster 1. Although this peak closely aligned with the calculated time of stimulus motion over the center of the 'shifted RF' in the adjusted time frame, it provided a more straightforward comparison between response dynamics.

1. The authors need to do a better job explaining how their results differ from Ezra-Tsur et al 2021, which uses the same sort of model to address the same question. The discussion about this study (lines 425-435) are based on how a more constrained version of these models work better but they do not directly address the difference in conclusion with regards to mechanisms that contribute to SAC process direction selectivity.

We have expanded the discussion related to mechanisms that contribute to DS in SACs and discuss the differences between our studies.

Minor point: The authors use the word "probe" to refer to visual stimulus. This is confusing because "probe" is also used to refer to sensors.

In the revised manuscript, we minimized the usage of ‘probe’ to reference visual stimuli

Reviewer #3 (Recommendations For The Authors):

Writing and figure presentations are excellent.

Thank you!

References:

Franke, K., Berens, P., Schubert, T., Bethge, M., Euler, T., & Baden, T. (2017). Inhibition decorrelates visual feature representations in the inner retina. Nature, 542(7642), 439-444. https://doi.org/10.1038/nature21394

Gaynes, J. A., Budoff, S. A., Grybko, M. J., Hunt, J. B., & Poleg-Polsky, A. (2022). Classical Center-Surround Receptive Fields Facilitate Novel Object Detection in Retinal Bipolar Cells. Nat Commun, 13(1), 5575. https://doi.org/https://doi.org/10.1038/s41467-022-32761-8

Kim, J. S., Greene, M. J., Zlateski, A., Lee, K., Richardson, M., Turaga, S. C., Purcaro, M., Balkam, M., Robinson, A., Behabadi, B. F., Campos, M., Denk, W., Seung, H. S., & EyeWirers. (2014). Space-time wiring specificity supports direction selectivity in the retina. Nature, 509(7500), 331-336. https://doi.org/10.1038/nature13240

Matsumoto, A., Agbariah, W., Nolte, S. S., Andrawos, R., Levi, H., Sabbah, S., & Yonehara, K. (2021). Direction selectivity in retinal bipolar cell axon terminals. Neuron. https://doi.org/10.1016/j.neuron.2021.07.008

Matsumoto, A., Briggman, K. L., & Yonehara, K. (2019). Spatiotemporally Asymmetric Excitation Supports Mammalian Retinal Motion Sensitivity. Curr Biol. https://doi.org/10.1016/j.cub.2019.08.048

Srivastava, P., de Rosenroll, G., Matsumoto, A., Michaels, T., Turple, Z., Jain, V., Sethuramanujam, S., Murphy-Baum, B. L., Yonehara, K., & Awatramani, G. B. (2022). Spatiotemporal properties of glutamate input support direction selectivity in the dendrites of retinal starburst amacrine cells. Elife, 11. https://doi.org/10.7554/eLife.81533

Strauss, S., Korympidou, M. M., Ran, Y., Franke, K., Schubert, T., Baden, T., Berens, P., Euler, T., & Vlasits, A. L. (2022). Center-surround interactions underlie bipolar cell motion sensing in the mouse retina. Nat Commun, 13(1), 5574. https://doi.org/https://doi.org/10.1038/s41467-022-32762-7

Tukker, J. J., Taylor, W. R., & Smith, R. G. (2004). Direction selectivity in a model of the starburst amacrine cell. Vis Neurosci, 21(4), 611-625. http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15579224

Wu, J., Kim, Y. J., Dacey, D. M., Troy, J. B., & Smith, R. G. (2023). Two mechanisms for direction selectivity in a model of the primate starburst amacrine cell. Vis Neurosci, 40, E003. https://doi.org/10.1017/S0952523823000019

Author Response

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

Public Reviews:

Reviewer #1 (Public Review):

The authors sought to understand the neurocomputational mechanisms of how acute stress impacts human effortful prosocial behavior. Functional neuroimaging during an effort-based decision task and computational modeling were employed. Two major results are reported: 1) Compared to controls, participants who experienced acute stress were less willing to exert effort for others, with a more prominent effect for those who were more selfish; 2) More stressed participants exhibited an increase in activation in the dorsal anterior cingulate cortex and anterior insula that are critical for self-benefiting behaviour. The authors conclude that their findings have important insights into how acute stress affects prosociality and its associated neural mechanisms.

Overall, there are several strengths in this well-written manuscript. The experimental design along with acute stress induction procedures were well controlled, the data analyses were reasonable and informative, and the results from the computational modeling provide important insights (e.g., subjective values). Despite these strengths, there were some weaknesses regarding potential confounding factors in both the experimental design and methodological approach, including selective reporting of only some aspects of this complex dataset, and the interpretation of the observations. These detract from from the overall impact of the manuscript. In particular, the stress manipulation and pro-social task are both effortful, raising the possibility that stressed participants were more fatigued. Other concerns include the opportunity for social dynamics or cues during task administration, the baseline social value orientation (SVO) in each group, and the possibility of a different SVO in individuals with selfish tendencies. Finally, Figure 4 should specify whether the depicted prosocial choices include all five levels of effort.

We thank the reviewer for their comments and suggestions. In our response to the recommendations for the author below, we have dealt with the reviewer’s concerns: - we added additional analysis on the role of fatigue and block effects to the supplementary materials. - we provided further information about the role of social cues and dynamics during task administration. - we showed there were no baseline group differences in SVO angle. - we clarified that Figure 4 refers to the proportion of prosocial choices across all effort levels.

Reviewer #2 (Public Review):

This manuscript describes an interesting study assessing the impact of acute stress on neural activity and helping behavior in young, healthy men. Strengths of the study include a combination of neuroimaging and psychoneuroendocrine measures, as well as computational modeling of prosocial behavior. Weaknesses include complex, difficult to understand 3-way interactions that the sample size may not be large enough to reliably test. Nonetheless, the study and results provide useful information for researchers seeking to better understand the influence of stress on the neural bases of complex behavior.

The stressor was effective at eliciting physiological and psychological stress responses as shown in Figure 2.

Higher perceived stress in more selfish participants (lower social value orientation (SVO) angle) was associated with lower prosocial responding (Figure 4). How can we reconcile this finding with the finding (presented on page 15) that those with a more prosocial SVO showed a significant decline in dACC activation to subjective value at increasing levels of perceived stress? This seems contrary to the behavioral response.

A larger issue with the study is that the power analysis presented on page 23 is based on a 2 (between: stress v. control) by 2 (within: self v. other) design. Most of the reported findings come from analyses of 3-way interactions. How can the readers have confidence in the reliability of results from 3-way interaction analyses, which were not powered to detect such effects?

We thank the reviewer for their comments and suggestions. When considering the influence of dACC activation on the behavioural response (i.e., proportion of prosocial choices), it is important to consider the difference in activation to SVself relative to SVother: - The difference in activation to SVself relative to SVother negatively predicted the proportion of prosocial choices, so more activation to SVself relative to SVother predicted a lower proportion of prosocial choices. - Similarly, SVO angle negatively predicted the difference in activation to SVself relative to SVother, so more activation to SVself relative to SVother was related to a lower (more individualistic) SVO angle (this is shown by the interaction between Recipient and SVO angle in Figure 4; right panel). In both cases, differences in prosociality (i.e. SVO angle or the proportion of prosocial choices) were related to differences in dACC activation to SVself relative to SVother.

Thus, we agree the finding that those participants with a more prosocial SVO showed a significant decline in dACC activation to SV overall (across SVself and SVother) at increasing levels of perceived stress is difficult to interpret. We expected a three-way interaction between Recipient, SVO angle and Perceived Stress to mirror the behavioural results, rather than a two-way interaction between SVO angle and Perceived Stress. We have now acknowledged this in the Discussion, whilst also highlighting the work of Schulreich et al. (2022) who report a related finding.

We have now added the following section to the results:

“When linking activation difference in dACC and AI to behaviour, we found that – independent of the stress manipulation – the difference in activation between SVself and SVother in the dACC predicted the proportion of prosocial choices. Thus, greater activation to SVself relative to SVother predicted a lower proportion of prosocial choices (B=-0.704, SE=0.339, P=0.041). This relationship was not present in the AI (B=-0.423, SE=0.332, P=0.205).”

And we have added the following to the discussion:

“Additionally, participants with a more prosocial SVO showed reduced responses in the dACC to SV (across both self and other trials) at greater levels of perceived stress (Figure 4; middle panel). This suggests that more prosocial individuals may become less sensitive to SV overall following stress, whilst the responses of more individualistic participants to SV do not change under stress. Trying to link these activation differences to changes in effortful prosocial behaviour is difficult given the absence of the three-way interaction between SVO angle, Perceived Stress and Recipient, which would have mirrored the behavioural results. Overall, differences in activation between SVself and SVother in the dACC predicted the proportion of prosocial choices, so greater activation to SVself relative to SVother predicted a lower proportion of prosocial choices. Thus, it remains unclear how activation differences to SV across both self trials and other trials relates to changes in prosocial behaviour under stress. Schulreich et al. (2022) found that a decline in charitable donations following increases in cortisol in high mentalisers was related to a reduced representation of value for donations in the right dlPFC. Whilst there are important differences between the present study and Schulriech et al. (2022), such as the way in which prosocial behaviour was measured, both studies suggest that existing differences in social preferences and abilities (i.e., mentalising, SVO) can have a detrimental effect on the neural representations of value following acute stress. Establishing how these changes in neural representations of value impact behaviour following acute stress is a challenge for future work.”

Concerning the power calculation, we have acknowledged this as a limitation in the discussion.

“Our power calculation was based on a 2 x 2 design (Group x Recipient), however, several of our key findings involved three-way interactions (e.g. between Group, Recipient and Effort). Thus, future studies should aim to replicate our effects with larger sample sizes to ensure the robustness of these effects.

Recommendations for the authors

Reviewer #1 (Recommendations For The Authors):

1. The authors employed an integrative approach on inducing acute stress by combining the strengths of MIST and TSST, as shown by a robust stress response in cortisol. However, some concerns regarding the stress manipulation and the effort-based task need to be addressed. The authors justified the order of deployment as necessary to maintain stress responses throughout the scanning period. It is unclear whether and how potential order effects were controlled, and whether the effort-task performance in the front and back of the line might have different effects in a 90-minute experiment.

Moreover, the stress manipulation itself involved a complex mental arithmetic task, which might have influenced participants' willingness to exert effort for others in the prosocial task. As shown in Figure 3, the proportion of participants working decreases as the effort levels increase for both self and other conditions in the stress and control groups. It is thus possible that participants could consider the prosocial task as an opportunity to take a break from the demanding arithmetic task. It would be helpful to present results from the different runs, particularly for the pre and post three runs.

We thank the reviewer for highlighting this potential issue. We have added several analyses to the supplementary analysis to explore potential block effects and fatigue effects. Here we provide a summary of the key findings.

Firstly, we investigated participants’ ratings of the effort levels, which they experienced immediately before and after the study, to investigate potential fatigue effects. We found that following the experiment compared to the before, participants in the stress group rated squeezing to the required effort levels as more physically demanding compared to the control group (p=.037). There were no group differences in how much more effort they reported exerting (p=.824) or how uncomfortable it was (p=.351) compared to before the experiment. Thus, overall the stress group found it more physically demanding to squeeze to the effort levels following the experiment. Crucially, however, increases in how physically demanding participants found it to squeeze to the required effort levels were not correlated with the number of effortful choices in the Self and Other condition in either group (all Ps >0.4). This suggests that whilst stressed participants rated squeezing to the required effort level as more physically demanding following the task relative to before, this was not related to how often participants exerted effort for self or other rewards.

Secondly, we investigated potential block effects. We repeated the mixed effects logistic regression reported in the manuscript but included the interaction between the factors Group, Recipient and Block (1:6) in the model. Although both groups showed a decline in the number of effortful choices during the experiment, the two-way interaction between Group and Block (p=.188) nor the three-way interaction between Group, Recipient and Block were significant (p=.138). This shows that whilst there was a decline in the number of effortful choices throughout the experiment, this was not more pronounced in the stress group, nor was it more pronounced in the stress group for self relative to other effortful choices compared to the control group. Additionally, the key three-way interaction between Group, Recipient and Block was unaffected when controlling for potential block effects. We now also plot the data by block in the supplementary materials (Figure S3).

Please see the section in the Supplementary Material and a summary of these analyses also appears in the manuscript in the Results section

“We conducted additional analyses to rule out the influence of potential fatigue and block effects (see Fatigue and block effects in the Supplementary Materials). In short, the stress group rated squeezing to the required effort level as more physically demanding immediately after the experiment compared to before, which was not seen in the control group (Figure S2). However, this was not related to the number of effortful choices for self or other rewards (Table S2). Moreover, when we conducted the same mixed effects logistic regression on participants’ choices but also included the interaction between Group, Recipient and Block, there was no significant three-way interaction between these factors, nor a significant two-way interaction between Group and Block (Figure S3). Additionally, the three-way interaction between Group, Recipient and Effort was unaffected when controlling for potential block effects (Type III Wald test χ2[4]=22.06, P<0.001). Thus, whilst the stress group rated squeezing to the required effort level as more physically demanding following the experiment, this was not related to the number of effortful choices (for self or other) and the effects of Block on effortful choices (for self or other) did not differ between the group. Thus, changes in how physically demanding participants rated squeezing to the effort levels did not influence decisions to exert effort.”

1. It would be useful to know whether the authors controlled for factors such as familiarity or gender among participants that might influence their choices on the task. If participants were able to interact or observe each other, it is possible that social dynamics played a role in their behavior, which could confound the interpretation of their results. It would be beneficial if the authors could provide further information on how the task was administered and whether any social cues were present.

For the experimental design, although salivary samples and subjective pressure were measured, did the authors measure participants' subjective ratings of other negative emotions?

Participants did not have the chance to see or interact with the participants in the “other” condition. Participants were told at the start of the experiment that they would be earning money for the next participant in the study, called Thomas. Thus, as all participants were men, the name of the participants was gender matched. Moreover, as they did not see or interact with the next participant, familiarity was controlled across participants.

We have now added a section p. 8 to clarify this:

“As all participants were men, the name of the next participant was gender matched (all participants were told he was called Thomas; see Methods). Moreover, as participants did not see or interact with the next participant, familiarity was controlled across participants.”

We have now added a plot to the supplementary materials (Figure S4) showing the changes in the ratings of the emotions. Apart from the emotions anxious and disgusted, all other emotions (calm, happy, bad, sad, surprised, angry) showed a significant sample timepoint (1:8) by group (stress, control) interaction, thus mirroring the results for the perceived stress ratings. We now refer to this figure in the manuscript on p. 8:

“for changes in other emotions during the experiment please see Figure S4”

1. Regarding the data analysis section, the authors' analysis is careful overall and the results about SVO are interesting. It would be interesting to know if baseline SVO was similar across both stress and control groups, and if there were any differences in SVO among participants with more individualistic or selfish tendencies. Regarding Figure 4, it would be helpful if the authors clarified whether the vertical coordinate "prosocial choices" is a combination of the five levels of effort or if it is specific to one level. Additionally, it would be useful to explore whether there is a correlation between SVO and prosocial choices and whether effort level could be used as a covariate to control for potential confounding effects. These suggestions could improve the clarity and strength of their contributions.

There were no differences in SVO angle between the control group and stress group (p=.956). There was also a significant correlation between SVO angle and the proportion of prosocial choices across the whole sample. This has now been reported in the manuscript on p. 13:

“There were no existing differences in SVO angle between the groups (control group mean = 19.33, SD = 8.67; stress group mean = 19.23, SD=8.14; p=0.956). We found that across the whole sample – independent of the stress manipulation – there was a significant correlation between SVO angle and the proportion of prosocial choices (r=0.225, P=0.032). So, as expected, those with a more prosocial SVO angle showed a higher proportion of prosocial choices in the task.

To clarify, the variable “% prosocial choices” is a combination of all the five effort levels. In other words, we took the total number of prosocial choices (‘work’ for other) across all effort levels relative to the total number of effortful choices. We have now clarified this in the manuscript on p. 13. As this was a combination of all effort levels (and reward levels), it was not possible to include effort level as a covariate.

“This measure combined all reward and effort levels.”

1. It is noteworthy that in the dACC, an effect was observed with regard to the interaction between perceived stress and SVO angle. Considering this observation, another suggestion would be for the authors to include visualization in Figure 4 to present the results of this interaction. This could help readers better comprehend the findings and provide a clearer representation of the results.

We have now updated Figure 4 so that it has three panels showing the behavioural and neural results concerning SVO angle as well as the relationship between SVO angle and activation to SVself and SVother in the dACC.

1. It would be helpful for readers if the authors could label all statistical plots with appropriate statistical values, effect sizes, and their respective significance levels. By doing so, readers would be able to quickly identify major findings of this study and gauge the degree of significance associated with each plot. The authors should consider including such information in their statistical plots to enhance the comprehensibility of the study results.

We have added statistical values (e.g., beta estimates), including indicators of significance to the plots.

1. The authors selected ROIs based on previous work on stress-related and effort-based decision making (i.e., AI and dACC). While other brain regions may also play a role in decision making and social cognition, the authors could choose to focus on these specific ROIs due to their relevance to the experimental question and hypotheses of this study such as prosocial, mentalizing and subjective values.

We agree that several other ROIs may have also been of interest. However, we decided to restrict our analysis to the dACC and the AI as these two ROIs were the focus of a previous study using the same prosocial effort paradigm (Lockwood et al. 2022) and multiple studies suggest these regions are sensitive to stress effects.

1. The authors chose to use one sample t-test with AUC as a covariate to examine brain activations across all participants regardless of their stress or control condition. This approach could identify brain regions that are associated with perceived stress. However, the authors didn't conduct a simple two sample t-test between stress and control groups since their research question and hypotheses focused on the neurocomputational mechanisms underlying prosocial decision-making during stress. Regarding the different stages of decision-making, such as offer, force, and outcome, the authors did not conduct specific analyses for each stage. Instead, they used the computational model to estimate the subjective value of each option at each stage, which allowed them to examine the neural correlates of different value-related parameters across the entire decision-making process. However, it would be interesting to examine the role of different stages as well.

Our design matrix modelled three events during each trial: the offer, force, and outcome phase (as per Lockwood et al. 2022). However, our hypotheses and research question for the effects of acute stress concerned the offer phase, i.e. when participants were deciding whether to exert effort or not (work vs. rest). Therefore, we decided to limit our reporting to this event. We have clarified this on p. 32 in the Methods:

“Our hypotheses and research questions concerning the effects of acute stress concerned the offer phase, i.e., when participants were deciding whether to exert effort or not (work vs. rest). Therefore, we limited our reporting to this event.”

1. The authors' findings pertaining to individual differences are intriguing, particularly for individuals with selfish tendencies to exhibit lower pro-social tendencies under stress. Additionally, group variations in effortful behavior related to benfitting others, relative to oneself, are more evident at lower effort levels rather than higher ones. The authors could dedicate more space in the discussion section to discuss the potential mechanisms involved and address the absence of pertinent theoretical support.

We have now extended the discussion to further outline potential mechanisms. Broadly, we interpret our findings in terms of compromised executive functioning under acute stress: “downregulation of the brain’s ‘executive control network’ (Hermans et al., 2014)”. In the original submission, we focused on changes in inhibition and shifts to habitual/automatic processing. We have now expanded this to include a section on cognitive flexibility (see below). Note that changes in executive functioning have been widely reported following stress (see Shields et al., 2016 for a meta-analyses). However, which specific executive functions influenced our observed changes in prosocial behaviour is an exciting avenue for future work.

We have added this section on p. 20-21 concerning cognitive flexibility:

“The dlPFC has also been implicated in cognitive flexibility under acute stress. For example, Kalia et al. (2018) used functional near infrared spectroscopy to show that reduced cognitive flexibility under stress was related to changes in activation in the dlPFC in men. In our study, participants in the control group were more likely to exert effort for self rewards compared to other rewards at higher, but not at lower, levels of effort. Whilst participants in the stress group favoured exerting effort for self rewards at every effort level (Figure 3). This consistent preference for self rewards compared to other rewards at all effort level suggests that stressed participants did not adapt their social behaviour in response to changing contextual information. This supports multiple studies showing reduced cognitive flexibility under stress (Goldfarb et al., 2017; Kalia et al., 2018; Raio et al., 2017; Shields et al., 2016). An exciting avenue for future work is to test whether individual differences in executive functions, such as inhibition and cognitive flexibility, predict changes in social behaviour following acute stress. This would be analogous to the finding in non-social domains, where greater working memory capacity protects against stress-induced changes in learning (Otto et al., 2013).

Reviewer #2 (Recommendations For The Authors):

The manuscript suggests that the stress group made more selfish responses than the control group at lower, but not higher, levels of effort (as shown in Figure 3). I recommend that Figure 3, showing these data, be modified for clarity. Currently, data for the between-subjects comparison (Control and Stress groups) are linked by a dashed line. This linkage (at least in my mind) connotes that these data points are from the same people at different times. In fact, the within-subjects data are not linked by a line, but are noted by different colored symbols. Please reconsider how these data are presented.

We have redrawn Figure 3. For each effort level, the self vs. other manipulation is shown on the x axis and the two groups (Control vs. Stress) are shown by black and grey lines. For each group, the lines are connected to show that the Self vs. Other manipulation is a within-subject manipulation.

Author Response

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

Thank you for the response and reviews of our manuscript eLife-RP-RA-2023-86638 “Energetics of the Microsporidian Polar Tube Invasion Machinery”. We are grateful for the comments and constructive criticism from all three reviewers, which have helped us to improve our manuscript.

As a summary to the editor, we here provide a list of the major revisions we have implemented to address all the comments provided by the referees.

1. We added Supplementary Section A.9 and Figure S4 to explain the details of calculation and have magnified sketches of flow fields.

2. We clarified the term "required pressure" to "required pressure differences", and explained that the same pressure differences can be achieved by either positive or negative pressure. We invoke the fact that the spore wall buckled inward to deduce that germination is a negative pressure process.

3. We only rank the hypotheses based on calculation of total energy requirement. The peak pressure and peak power requirement calculations are now just for quantitative reference. The ranking of hypotheses does not change.

4. We clarified the definition of topological connections in Section "Systematic evaluation of possible topological configurations of a spore," making it explicit that the topological questions listed only involved the "original PT content" (not PT space at all time).

Thank you again for the opportunity to revise our work. We attach a point-by-point response to the referees below.

Public Reviews:

Reviewer #1 (Public Review):

1. The authors used mathematical models to explore the mechanism(s) underlying the process of polar tube extrusion and the transport of the sporoplasm and nucleus through this structure. They combined this with experimental observations of the structure of the tube during extrusion using serial block face EM providing 3 dimensional data on this process. They also examined the effect of hyperosmolar media on this process to evaluate which model fit the predicted observed behavior of the polar tube in these various media solutions.

We thank the reviewer for their accurate summary of our work. One subtle point, however, is that we examine the effect of hyperviscous media on the polar tube extrusion process, rather than hyperosmolar media. In Supplementary Section A.6 of our updated manuscript, we have shown that the changes in osmolarity due to methylcellulose is negligible.

1. Overall, this work resulted in the authors arriving at a model of this process that fit the data (model 5, E-OE-PTPV-ExP). This model is consistent with other data in the literature and provides support for the concept that the polar tube functions by eversion (unfolding like a finger of a glove) and that the expanding polar vacuole is part of this process. Finally, the authors provide important new insights into the buckling of the spore wall (and possible cavitation) as providing force for the nucleus to be transported via the polar tube. This is an important observation that has not been in previous models of this process.

We thank the reviewer for acknowledging the novelty and importance of our study.

Reviewer #2 (Public Review):

1. Microsporidia has a special invasion mechanism, which the polar tube (PT) ejects from mature spores at ultra-fast speeds, to penetrate the host and transfer the cargo to host. This work generated models for the physical basis of polar tube firing and cargo transport through the polar tube. They also use a combination of experiments and theory to elucidate possible biophysical mechanisms of microsporidia. Moreover, their approach also provided the potential applications of such biophysical approaches to other cellular architecture.

We thank the reviewer for their accurate summary and acknowledging the potential applications on other organisms.

1. The conclusions of this paper are mostly well supported by data, but some analyses need to be clarified. According to the model 5 (E-OE-PTPV-ExP) in P42 Fig. 6, is the posterior vacuole connected with the polar tube? If yes, how does the nucleus unconnected with the posterior vacuole enter the polar tube?

As we mentioned in our glossary and detailed in Section "Systematic evaluation of possible topological configurations of a spore", Model 5 requires the "original PT content" (any material inside the PT prior to cargo entering the tube) to permit fluid flow to posterior vacuole and external environment post anchoring disc rupture, but cannot permit fluid flow to the sporoplasm that is transported through the tube. As the the germination process progresses, our model does not require the connection between PT and posterior vacuole to be maintained afterwards, and that creates space allowing sporoplasm (including nucleus) sporoplasm (including nucleus) to enter PT space through fluid entrainment. We have clarified the definitions in Section "Systematic evaluation of possible topological configurations of a spore" and have additional clarification in the caption of Fig. 6 in the updated manuscript.

1. In Fig. 6, would the posterior vacuole become two parts after spore germination? One part is transported via the polar tube, and the other is still in the spore. I recommend this process requires more experiments to prove.

According to our Model 5, the membrane connection between PT and posterior vacuole must be broken for the infectious cargo to extrude. However, our current data does not allow us to prove nor disprove the membrane fission event. In theory, the membrane content in PT can potentially be severed into multiple parts by Plateau-Rayleigh instability, an interfacial-tension-driven fluid thread breakup mechanism. Note that it is possible to have membrane fission at the time scale of germination process, as when the time scale of shearing is faster than the viscoelastic time of lipid membranes (roughly 10 msec), membrane fission can happen (Morlot & Roux 2013). For time scale longer than viscoelastic time of lipid membrane, protein complexes like dynamin would be required for membrane fission. Future cryo-EM study of the vacuole-PT connection at the anterior tip (and in the spore as a whole) is needed to clarify the physical process. We added this discussion in Section "Predictions and proposed future experiments".

Reviewer #3 (Public Review):

Abstract:

The paper follows a recent study by the same team (Jaroenlak et al Plos Pathogens 2020), which documented the dramatic ejection dynamics of the polar tube (PT) in microsporidia using live-imaging and scanning electron microscopy. Although several key observations were reported in this paper (the 3D architecture of the PT within the spore, the speed and extent of the ejection process, the translocation dynamics of the nucleus during germination), the precise geometry of the PT during ejection remain inaccessible to imaging, making it difficult to physically understand the phenomenon.

This paper aims to fill this gap with an indirect "data-driven" approach. By modeling the hydrodynamic dissipation for different unfolding mechanisms identified in the literature and by comparing the predictions with experiments of ejection in media of various viscosities, authors shows that data are compatible with an eversion (caterpillar-like) mechanism but not compatible with a "jack-in-the-box" scenario. In addition, the authors observe that most germinated spores exhibit an inward bulge, which they attribute to buckling due to internal negative pressure and which they suggest may be a mean of pushing the nucleus out of the PT during the final stage of ejection.

We thank the reviewer for their accurate summary of our work.

Major strengths:

Probably the most impressive aspect of the study is the experimental analysis of the ejection dynamics (velocity, ejection length) in medium of various viscosities over 3 orders of magnitudes, which, combined with a modeling of the viscous drag of the PT tube, provides very convincing evidence that the unfolding mechanism is not a global displacement of the tube but rather an apical extension mechanism, where the motion is localized at the end of the tube. The systematic classification of the different unfolding scenarios, consistent with the previous literature, and their confrontation with data in terms of energy, pressure and velocity also constitute an original approach in microbiology where in-situ and real time geometry is often difficult to access.

We thank the reviewer for acknowledging the novelty and importance of our study.

Major weaknesses:

1a. While the experimental part of the paper is clear, I had (and still have) a hard time understanding the modeling part. Overall, the different unfolding mechanisms should be much better explained, with much more informative sketches to justify the dissipation and pressure terms, magnifying the different areas where dissipation occurs, showing the velocity field and pressure field, etc.

We thank the reviewer for their comments and suggestions. In the Figure S4 and SI Section A.9 of the updated manuscript, we have magnified the sketches with flow field, and added a detailed explanation of the derivations of dissipation terms.

1b. In particular, a key parameter of eversion models is the geometry of the lubrication layers inside and outside the spore (h_sheath, h_slip). Where do the values of h_sheath and h_slip come from? What is the physical process that selects these parameters?

As we described in SI Section A.9, h_sheath was set to be 25 nm based on the observed translucent space around PT in activated spores (Lom 1972), and h_slip was set to be 6 nm based on the observed gap thickness between PT and cargo (Takovarian et al. 2020). Although we don't expect these numbers to be the same for each spore, the uncertainty in these two parameters are much less than the uncertainty in cytoplasmic viscosity (which varies several orders of magnitude) and boundary slip length. Our sensitivity testing on cytoplasmic viscosity and boundary slip length thus covers any uncertainty in h_sheath or h_slip already.

1c. For clarity, the figures showing the unfolding mechanics in the different scenarios should be in the main text, not in the supplemental materials.

We have added Figure S4 and SI Section A.9 to explain the details of our sketches. We believe, however, putting all the details of the mechanics and how each term is derived in the main text may detract from the flow of the manuscript, and result in it being less accessible to readers who are not as familiar with the physics. We therefore decided to keep this information in supplemental materials.

2a. The authors compute and discuss in several places "the pressure" required for ejection, but no pressure is indicated in the various sketches and no general "ejection mechanism" involving this pressure is mentioned in the paper.

In the updated manuscript, we have changed the term “pressure” to “pressure difference” or “required pressure difference”. We did not calculate the detailed pressure field around each structure, but only estimated the required pressure difference to overcome the drag force and drive fluid flow in various spaces. We also clarified this point in Section "Developing a mathematical model for PT energetics".

Also, as we mentioned in Section “Posterior vacuole expansion and the role of osmotic pressure”, we made no assumptions on how the pressure difference is generated in this paper. The unfolding mechanism of polar tube, how eversion is sustained, and the driving mechanism are ongoing research projects, and we decided not to make premature comments on that without strong support from experiments or simulation results.

2b. What is this "required pressure" and to what element does it apply?

The “required pressure” in the manuscript indicates the required pressure difference between the spore and the tip of the polar tube for it to push the tip forward and sustain the fluid flow within the polar tube. In the updated manuscript, we thus changed the term “required pressure” to “required pressure difference”. We also added this clarification to Section "Developing a mathematical model for PT energetics".

2c. I understand that the article focuses on the dissipation required to the deployment of the PT but I find it difficult to discuss the unfolding mechanism without having any idea on the driving mechanism of the movement. How could eversion be initiated and sustained?

As we mentioned in Section “Posterior vacuole expansion and the role of osmotic pressure”, we made no assumptions on how the energy, pressure or power is generated in this paper. We agree that the unfolding mechanism of the polar tube, how eversion is sustained, and the driving mechanism are important questions, and these are ongoing research projects. As no assumptions about this are required for our models, we decided not to comment on these aspects without strong support from experiments or simulation results. We have clarified this in Section “Posterior vacuole expansion and the role of osmotic pressure” of the updated manuscript.

1. Finally, the authors do not explain how pressure, which appears to be a positive, driving quantity at the beginning of the process, can become negative to induce buckling at the end of ejection. Although the hypothesis of rapid translocation induced by buckling is interesting, a much better mechanistic description of the process is needed to support it.

As discussed in Point 2-b above, the “required pressure” actually means “required pressure difference”. The same pressure difference can possibly be achieved by either positive pressure (the spore has a higher pressure than the ambient, pushing the fluid into PT) or negative pressure (the PT tip has a lower pressure than the ambient, sucking the fluid from the spore). Hydrodynamic dissipation analysis alone cannot tell the differences between positive or negative pressure, as it only tells you the required pressure differences between the spore and the polar tube tip. It will have to be inferred from the implied mechanisms or other evidence. We added these discussions in the 4th paragraph of Section "Developing a mathematical model for PT energetics" in the updated manuscript.

That being said, from our observations of buckled spore walls, it is still sufficient to deduce that the polar tube ejection process is a negative pressure driven process. For the spore wall to buckle inwards, the ambient pressure has to be higher than the pressure within the spore, but that would contradict with the positive pressure hypothesis as elaborated above. We added these clarifications in the 2nd paragraph of Section "Models for the driving force behind cargo expulsion".

References:

Lom, J. (1972). On the structure of the extruded microsporidian polar filament. Zeitschrift Für Parasitenkunde, 38(3), 200–213.

Takvorian, P. M., Han, B., Cali, A., Rice, W. J., Gunther, L., Macaluso, F., & Weiss, L. M. (2020). An Ultrastructural Study of the Extruded Polar Tube of Anncaliia algerae (Microsporidia). The Journal of Eukaryotic Microbiology, 67(1), 28–44.

Morlot, S., & Roux, A. (2013). Mechanics of dynamin-mediated membrane fission. Annual Review of Biophysics, 42, 629–649.

Reviewer #1 (Recommendations For The Authors):

The work is solid and supported by the experimental data presented, the literature and the biophysical modeling.

1. The model (Model 5) indicates that the polar tube is connected to the posterior vacuole and that the contents of this vacuole may be transported by the polar tube before the sporoplasm. This needs experimental validation in the future, which will require the identification of posterior vacuole markers (i.e. proteins specific to this structure). I find the topology of this idea difficult to understand. If the polar tube is outside of the sporoplasm membrane then how does it connect to the posterior vacuole? If the expanded posterior vacuole is still in the spore at the end of germination then how does the sporoplasm get out?

Model 5 requires the "original PT content" (any material inside the PT prior to cargo entering the tube) to permit fluid flow to posterior vacuole and external environment post anchoring disc rupture, but cannot permit fluid flow to sporoplasm. As the germination process progresses, our model does not require the connection between PT and posterior vacuole to be maintained afterwards, and that creates space allowing sporoplasm (including nucleus) to enter PT space through fluid entrainment.

We agree with the reviewer that the specific predictions from Model 5 need to be experimentally validated in the future, and identification of posterior vacuole markers is a good direction. We have mentioned this in Section "Predictions and proposed future experiments".

1. I have always thought that the polaroplast was the initial cargo in the polar tube and that this formed the limiting membrane of the sporoplasm and nucleus after passage through the polar tube (i.e., the limiting membrane of the sporont).

In this manuscript, we only analyze the possible topology of the organelles that are relevant for energy dissipation calculations. Our final hypothesis (E-OE-PTPV-ExP) indicates that there is a limiting membrane of the infectious cargo as they pass through PT, but the energy calculation cannot tell you where this membrane comes from. That being said, our final hypothesis is consistent with the common belief that polaroplast provides the limiting membrane of the sporoplasm, even though our analysis neither proved nor disproved it.

1. I understand that the model indicates that during eversion the end of the PT moves away from the posterior vacuole allowing the sporoplasm access to the PT lumen, however, I am not clear how this process occurs (although I understand the reason that this model was the best fit for the available data). Does the model distinguish between connected (as in the PV is in the polar tube lumen) to the idea of it being in proximity (i.e. the PT is at the PV at the start of eversion)?

As we mentioned in our reply to Point 1 of the same reviewer above, "connectivity" simply means whether fluid flow is permitted across the end connections among organelles and sub-spaces within the spores. For Model 5, the content of posterior vacuole can pass to the original PT content and to the external environment post anchoring disc disruption through fluid flow, but not to sporoplasm. However, as the germination progresses, the PT does not have to maintain its spatial proximity or membrane connection to posterior vacuole, as the topological connectivity questions are pertaining to the "original PT content". We clarified this point in Section "Systematic evaluation of possible topological configurations of a spore" in the updated manuscript.

Reviewer #2 (Recommendations For The Authors):

1. The connection of polar tube and posterior vacuole need to be analyzed by Cryo -EM.

We thank the reviewer for their comments. This work is underway.

Reviewer #3 (Recommendations For The Authors):

1a. As stated in the public review, the explanation and description of the unfolding mechanism should be much better described and associated with clear sketches, magnifying all the areas where the flow shear rate is concentrated (surrounding zone, lubrication inside and outside the spore, etc) and drawing the velocity field, the boundary solid motion and pressure distribution in order to clearly understand, for each model, the dissipation and pressure terms given in figs. S2 and S3.

In the updated manuscript, we added Figure S4 to enlarge all the regions where fluid shear is considered, with sketches of velocity fields.

1b. This is particularly important for explaining the eversion models (see comment in the Public Review) but even the "jack-in-the-box" model sketched in Fig. S2 is confusing: Why does the blue tube disappear outside the spore? What happens to the tube in this case?

The blue tube in the sketch of Model 1 in Fig. S2 is the fluid between the two outermost layers of PT, not the PT itself. We have clarified that in the newly added Fig. S4.

1. Many ejection mechanisms based on the deployment of invaginated appendages have been described in the literature (e.g. Zuckerkandl Biol. Bull. 1950, Karabulut et al Nat. Com. 2022) and also mimicked for robotic applications (e.g. Hawkes et al Science Robotics 2017). Although this is not the main topic of the paper, it would be very useful if the authors could discuss in the introduction the most acceptable theory for motion generation (eversion driven by an overpressure in the spore?). In the current version, this comes too late in the discussion.

As we discussed in Section “Lack of biophysical models explaining the microsporidian infection process”, PT eversion is the most widely accepted hypothesis because of experimental evidence (e.g. microscopic observations of PT extrusions, and pulse-labeling of half-ejected tubes). However, whether or not it is driven by an overpressure in the spore remains controversial. In fact, our observations of inwardly buckled spores indicates that the ejection process likely involves negative pressure.

In our work, we thus take a data-driven approach to generate models for the physical basis of PT extrusion process, without immediately assuming that eversion is the correct hypothesis. It would therefore not make sense to have elaborated discussion on other eversion mechanisms in Introduction.

1. About the physical constraints, I understand that the stored energy must be the same when the viscosity is changed (by conservation of energy), but what physical basis do you have for requiring that the power and pressure also be the same (lines 295-298)? For e.g. when a spring is stretched and released in a very viscous fluid without inertia, the total energy dissipated is the same whatever the viscosity but the power is not the same. The formulation of the chosen physical constraints should be better justified.

We thank the reviewer for their feedback. In our updated manuscript, we only use total energy requirement for the ranking, and the peak pressure difference requirement and peak power requirements are calculated just for quantitative reference. The ranking of the 5 hypotheses does not change.

1. About the mechanism for cargo translocation, authors should explain the physical origin of the hypothetical negative pressure. How could the initial positive pressure become negative?

As we mentioned in our reply to Point 3 of the same reviewer in the public review, the “required pressure” actually means “required pressure difference”. The same pressure difference can possibly be achieved by either positive pressure (the spore has a higher pressure than the ambient, pushing the fluid into PT) or negative pressure (the PT tip has a lower pressure than the ambient, sucking the fluid from the spore). Hydrodynamic dissipation analysis alone cannot tell the differences between positive or negative pressure, as it only tells you the required pressure differences between the spore and the polar tube tip. It will have to be inferred from the implied mechanisms or other evidence. We added these discussions in the 4th paragraph of Section "Developing a mathematical model for PT energetics" in the updated manuscript.

That being said, from our observations of buckled spore walls, it is still sufficient to deduce that the polar tube ejection process is a negative pressure driven process. For the spore wall to buckle inwards, the ambient pressure has to be higher than the pressure within the spore, but that would contradict with the positive pressure hypothesis as elaborated above. We added these clarifications in the 2nd paragraph of Section "Models for the driving force behind cargo expulsion".

More minor comments:

1. The videos are amazing but it is not clear if the PT is ejected through a bulk fluid or if the spores (and ejected PT) are in contact with a solid.

As described in Supplementary Section A.6, purified spores were spotted on a coverslip and let water evaporate. 2.0 μL of germination buffer (10 mM Glycine-NaOH buffer pH 9.0 and 100 mM KCl) with different concentration (0%, 0.5%, 1%, 2%, 3%, 4%) of methylcellulose was added to the slide and place the coverslip on top. So the spore is attached to the coverslip and ejected through a bulk liquid of germination buffer.

1. S2 caption: please be precise that H is the Heaviside step function.

We have updated the captions for both Figure S2 and S3 to make it explicit.

1. Line 233 a pi is missing, no?

We thank the reviewer for their careful read. We have corrected that.

1. The notations are quite unfortunate and confusing. In fluid mechanics capital D usually refers to the dissipation, capital C to the drag coefficient. It would be much clearer to call D the dissipation power (in Watt) and P the pressure requirement (in Pa), whatever the mechanism and put the different contribution (drag, lubrication, cytoplasm flow) in subscript.

We thank the reviewer for their feedback. The notation of this paper is challenging as there are many symbols while keeping everything relatively intuitive to both people with biology background and physics background. We will keep these feedback in mind in our future work.

1. Fig S2: what is D (in the formula of the total dissipation power)? Why not use R instead?

D is the PT diameter, as we mentioned in the caption. We keep that as it is used in the definition of the shape factor.

1. Fig S3 why the pressure requirement for the "jack-in-the-box" hypothesis is 2\mu (vLf(epsilon)/R^2)?

We have now elaborated the calculation in SI Section A.9.

1. Lines 486-497: Although shear thinning fluids have their viscosity that decreases with the shear rate, in most cases the resistance (stress) still increases with speed with these fluids. Is mucin a "velocity-weakening" fluid, i.e. a fluid in which stress decreases when shear rate increases.

We agree that stress still increases with speed for most shear thinning fluids. The mechanical properties of mucin solution strongly depend on its compositions and buffers. In our discussion, we thus simply mention this possibility without claiming whether mucin (or other biopolymer environment that microsporidia species actually experience in vivo) is a velocity-weakening fluid or not.

Author Response

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

Reviewer #1 (Public Review):

In this study, the authors investigated the role of MAM and the Notch signaling pathway in the onset of the atrophic phenotype in both in vivo and in vitro models. The rationale used to obtain the data is one of the main strengths of the study. Already from the reading, the reasoning scheme used by the authors in setting up the study and evaluating the data obtained is clear. Using both cellular and mouse models in vivo consolidates the data obtained. The authors also methodologically described all the choices made in the supplementary section. A weakness, on the other hand, is the failure to include averages and statistical data in the results that would give a quantifiable idea of the data obtained. To complete the picture, the authors could also investigate the possible involvement of the intrinsic apoptosis pathway as well as describe probable metabolic shifts to muscle cells in atrophic conditions. The rationale used by the authors to obtain the result is linear. The data obtained are useful for understanding the onset and characterization of the atrophic phenotype under disuse and microgravity conditions. The methods used are in line with those used in the field and can be a starting point for other studies. The cellular models are well described in the Materials and methods section. The selected mouse models followed a logical rationale and were in line with the intended aim.

We thank this reviewer for comments that have led us to clarify several points.

Reviewer #1 (Recommendations For The Authors):

• In order to reinforce and justify the results obtained, I would suggest that the authors include numerical and statistical data in the results obtained.

Answer) As the reviewer suggested, we have incorporated actual numerical and statistical data into each graph in all figures.

• With the aim of better framing the picture of an atrophic muscle phenotype caused by microgravity or disuse, I would advise the authors to also focus on the possible involvement of the intrinsic apoptosis pathway. To this end, it would be interesting to assess a possible relationship between MAM and apoptosis. It would be useful to integrate this part into the discussion.

Answer) It has been shown that suppression of Mfn2 expression attenuates calcium influx into mitochondria during apoptosis-inducing stimuli, thereby inhibiting apoptosis (Martins de Brito & Scorrano, Nature 2008), however, in our study, we found that apoptotic pathways, including Caspase3 or p-AKT were not significantly altered in differentiated human myocytes by microgravity for 7 days in culture, suggesting that microgravity-induced apoptosis is not an initial pathway to MAM. We have added these data in the new supplementary file 3 and mentioned it in the results.

• In addition to TA, did the authors investigate what was seen in other muscles impacted by microgravity? If so, I would recommend supplementing what is available or, on the contrary, justifying the exclusivity of the choice of TA.

Answer) It has been reported that the soleus, a slow-type muscle is more susceptible than the fast-type tibialis anterior muscle during gravity changes, so it makes more sense for the content of this study to analyze the soleus muscle. However, we chose the tibialis anterior muscle as our target because it provides the most stable results as a site for stem cell transplantation to observe muscle regeneration.

• The authors affirm that there is an altered distribution and morphology of mitochondria under microgravity conditions. To corroborate this assertion, I would recommend including a morphological image that confirms it.

Answer) The morphology of mitochondria in cultured myotubes, as observed by mitotracker staining in Figure 4G, varied widely, from finely divided to fused even within a single fiber compared to MFN2-mutated human iPS cells, making it difficult to conclude whether these changes were brought about by microgravity. Therefore, in this study, we have shown that they are reduced in microgravity by the difference in fluorescence intensity of mitotracker, which is directly proportional to mitochondrial activity.

• It would be interesting if the authors would show whether there are changes in myosin expression or metabolic changes in cells subjected to microgravity and in the cell model with Mnf2 deletion. It would also be interesting to evaluate this in the presence of DAPT.

Answer) As the reviewer’s suggestion, we have checked MYH1, MYH3, and MYH7 transcripts in differentiated myotubes under microgravity, with or without DAPT in the new supplementary file 12. We have added the data showing that not MYH1 but MYH7 transcript was partially recovered in the Results.

A detailed description of the metabolic analyses with myogenic cells cultured in microgravity conditions will be published elsewhere (Sugiura et al., “Mitochondria aconitase is a main target for unloading-mediated mitochondria dysfunction toward muscle atrophy”, in preparation). We have described it in the Materials and methods of the manuscript.  

Reviewer #2 (Public Review):

In this study, the authors examined how the maintenance of mitochondrial-associated endoplasmic reticulum membranes (MAM) is critical for the prevention of muscle atrophy under microgravity conditions. They observed, a reduction in MAM in myotubes placed in a microgravity condition; in addition, MFN2-deficient human iPS cells showed a decrease in the number of MAM, similar to in myotubes differentiated under microgravity conditions, in addition to the activation of the Notch signaling pathway. The authors, moreover, observed that treatment with the gamma-secretase inhibitor with DAPT preserved the atrophic phenotype of differentiated myotubes in microgravity and improve the regenerative capacity of Mfn2-deficient muscle stem cells in dystrophic mice. The entire study was well conducted, bringing an interesting analysis in vitro and in vivo of aging conditions. In my opinion, it is necessary to improve the analysis of both genes and proteins to better support the conclusions

The study can contribute to a better understanding of one of the major problems of aging, such as muscle atrophy and inhibition of muscle regeneration, emphasizing the importance of the NOTCH pathway in these pathological situations. The work will be of interest to all scientists working on aging

We thank this reviewer for the positive comments and remarks that we have attempted to address.

Reviewer #2 (Recommendations For The Authors):

Results:

In Figure 1b authors observed an increase in the transcripts of MuRF1 and FBXO32 after 7 days of microgravity condition. I suggest to investigate the protein expression of these genes to give more validation to this data.

Answer) As the reviewer’s suggestion, we have investigated the western blotting with atrophic markers in microgravity samples. These data have been added in Figure 1D.

Moreover, I suggest investigating not only Myogenin as an earlier gene of myotubes formation but also MRF4.

Methods:

I suggest when doing real-time PCR not to use a single gene as housekeeping but the average of three genes, to avoid the influence of a single housekeeping gene affecting the results.

Answer) As the reviewer’s suggestion, we have investigated MRF4 expression by qPCR experiments with 3 different housekeeping genes (RPL13a, GAPDH, and ACTB). Our experiments showed no significant differences among these three housekeeping genes. We have added these data to Figure 1C and Methods in the manuscript.

Author Response

We thank the reviewers and editor for their careful evaluation of our manuscript, and we appreciate their favorable assessment of our work. Below, we clarify a few points concerning the relationship between our study and previous studies evaluating ligand docking to protein models.

As reviewer 2 correctly notes, several previous assessments of AF2 models have simply excluded templates above a sequence identity cutoff when using AF2 to predict structures. Such AF2 predictions are still informed by all structures in the PDB before April 30, 2018, because these structures were used to train AF2—that is, to determine the tens of millions of parameters (“weights”) in the AF2 neural network. Machine learning methods nearly always perform better when evaluated on the data used to train them than when evaluated on other data. For this reason, we consider AF2 models only for proteins whose structures were not used to train AF2—that is, for proteins whose structures were not available in the PDB before April 30, 2018.

Previous papers (including Beuming and Sherman, 2012, https://doi.org/10.1021/ci300411b) have shown a clear correlation between the binding pocket RMSD of a protein model and pose prediction accuracy based on that model. Our main findings are unexpected in light of these previous reports: we find that AF2 models yield pose prediction accuracy similar to that of traditional homology models despite having much better binding pocket RMSDs, and that AF2 models yield substantially worse pose prediction accuracy than experimentally determined structures with different ligands bound despite having similar binding pocket RMSDs.

Reviewer 2 also correctly notes that previous papers have described AF2 models as “apo models,” because these models do not include coordinates for bound ligands. As noted by the AF2 developers (e.g., https://alphafold.ebi.ac.uk/faq), however, AF2 is designed to predict coordinates of protein atoms as they might appear in the PDB, and AF2 models are thus frequently consistent with structures in the presence of ligands even though those ligands are not included in the models. GPCR structures in the PDB, including those used to train AF2, nearly always contain a ligand in the orthosteric binding pocket. An AF2 model of a GPCR should thus not be viewed as an attempt to predict the GPCR’s structure in the unliganded (apo) state.

Finally, we did not apply flexible docking in this study because previous work has found that standard flexible docking protocols typically improve pose prediction performance only when given prior information on which amino acid residues to treat as flexible. For example, previous studies that performed successful flexible docking to AF2 models generally used prior knowledge of the ligand’s experimentally determined binding pose to identify the residues to treat as flexible.

Author Response

Reviewer #3 (Public Review):

Summary:

The manuscript from Tariq and Maurici et al. presents important biochemical and biophysical data linking protein phosphorylation to phase separation behavior in the repressive arm of the Neurospora circadian clock. This is an important topic that contributes to what is likely a conceptual shift in the field. While I find the connection to the in vivo physiology of the clock to be still unclear, this can be a topic handled in future studies.

Strengths: The ability to prepare purified versions of unphosphorylated FRQ and P-FRQ phosphorylated by CK-1 is a major advance that allowed the authors to characterize the role of phosphorylation in structural changes in FRQ and its impact on phase separation in vitro.

Weaknesses: The major question that remains unanswered from my perspective is whether phase separation plays a key role in the feedback loop that sustains oscillation (for example by creating a nonlinear dependence on overall FRQ phosphorylation) or whether it has a distinct physiological role that is not required for sustained oscillation.

The reviewer raises the key question regarding data suggesting LLPS and phase separated regions in circadian systems. To date condensates have been seen in cyanobacteria (Cohen et al, 2014, Pattanayak et al, 2020) where there are foci containing KaiA/C during the night, in Drosophila (Xiao et al, 2021) where PER and dCLK colocalize in nuclear foci near the periphery during the repressive phase, and in Neurospora (Bartholomai et al, 2022) where the RNA binding protein PRD-2 sequesters frq and ck1a transcripts in perinuclear phase separated regions. Because the proteins responsible for the phase separation in cyanobacteria and Drosophila are not known, it is not possible to seamlessly disrupt the separation to test its biological significance (Yuan et al, 2022), so only in Neurospora has it been possible to associate loss of phase separation with clock effects. There, loss of PRD-2, or mutation of its RNA-binding domains, results in a ~3 hr period lengthening as well as loss of perinuclear localization of frq transcripts. A very recent manuscript (Xie et al., 2024) calls into question both the importance and very existence of LLPS of clock proteins at least as regards to mammalian cells, noting that it may be an artefact of overexpression in some places where it is seen, and that at normal levels of expression there is no evidence for elevated levels at the nuclear periphery. Artefacts resulting from overexpression plainly cannot be a problem for our study nor for Xiao et al. 2021 as in both cases the relevant clock protein, FRQ or PER, was labeled at the endogenous locus and expressed under its native promoter. Also, it may be worth noting that although we called attention to enrichment of FRQ[NeonGreen] at the nuclear periphery, there remained abundant FRQ within the core of the nucleus in our live-cell imaging.

Cohen SE, et al.: Dynamic localization of the cyanobacterial circadian clock proteins. Curr Biol 2014, 24:1836–1844, https://doi.org/10.1016/j.cub.2014.07.036.

Pattanayak GK, et al.: Daily cycles of reversible protein condensation in cyanobacteria. Cell Rep 2020, 32:108032, https://doi.org/10.1016/j.celrep.2020.108032.

Xiao Y, Yuan Y, Jimenez M, Soni N, Yadlapalli S: Clock proteins regulate spatiotemporal organization of clock genes to control circadian rhythms. Proc Natl Acad Sci U S A 2021, 118, https://doi.org/10.1073/pnas.2019756118.

Bartholomai BM, Gladfelter AS, Loros JJ, Dunlap JC. 2022 PRD-2 mediates clock-regulated perinuclear localization of clock gene RNAs within the circadian cycle of Neurospora. Proc Natl Acad Sci U S A. 119(31):e2203078119. doi: 10.1073/pnas.2203078119.

Yuan et al., Curr Biol 78: 102129, 2022. https://doi.org/10.1016/j.ceb.2022.102129

Pancheng Xie, Xiaowen Xie, Congrong Ye, Kevin M. Dean, Isara Laothamatas , S K Tahajjul Taufique, Joseph Takahashi, Shin Yamazaki, Ying Xu, and Yi Liu (2024). Mammalian circadian clock proteins form dynamic interacting microbodies distinct from phase separation. Proc. Nat. Acad. Sci. USA. In press.

Author Response

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

We thank the reviewers and editors for their time and careful consideration of this study. Nearly every comment proved to be highly constructive and thoughtful, and as a result, the manuscript has undergone major revisions including the title, all figures, associated conclusions and web app. We feel that the revised resource provides a more systematic and comprehensive approach to correlating inter-individual transcript patterns across tissues for analysis of organ cross-talk. Moreover, the manuscript has been restructured to highlight utility of the web tool for queries of genes and pathways, as opposed to focused discrete examples of cherry-picked mechanisms. A few key revisions include:

• Manuscript: All figures have been revised to place to explore broad pathway representation. These analyses have replaced the previous circadian and muscle-hippocampal figures to emphasize ability to recapitulate known physiology and remove the discovery portion which has not been validate experimentally.

• Manuscript: The term “genetic correlation” or “genetically-derived” has been replaced throughout with “transcriptional”, “inter-individual”, or mostly just “correlations”.

• Manuscript: A new figure (revised fig 2) has been added to evaluate the innate correlation structure of data used for common metabolic pathways, in addition an exploration of which tissues generally show more co-correlation and centrality among correlations.

• Manuscript: A new figure (revised fig 4) has been added to highlight the utility of exploring gene ~ trait correlations in mouse populations, where controlled diets can be compared directly. These highlight sex hormone receptor correlations with the large amount of available clinical traits, which differ entirely depending on the tissue of expression and/or diet in mouse populations.

• Web tool: Addition of a mouse section to query expression correlations among diverse inbred strains and associated traits from chow or HFHS diet within the hybrid mouse diversity panel.

• Web tool: Overrepresentation analysis for pathway enrichments have been replaced with score-based gene set enrichment analyses and including network topology views for GSEA outputs.

• Web tool: Associated github repository containing scripts for apps now include a detailed walk-through of the interface and definitions for each query and term.

Public Reviews:

Reviewer #1 (Public Review):

Zhou et al. have set up a study to examine how metabolism is regulated across the organism by taking a combined approach looking at gene expression in multiple tissues, as well as analysis of the blood. Specifically, they have created a tool for easily analyzing data from GTEx across 18 tissues in 310 people. In principle, this approach should be expandable to any dataset where multiple tissues of data were collected from the same individuals. While not necessary, it would also raise my interest to see the "Mouse(coming soon)" selection functional, given that the authors have good access to multi-tissue transcriptomics done in similarly large mouse cohorts.

Summary

The authors have assembled a web tool that helps analyze multiple tissues' datasets together, with the aim of identifying how metabolic pathways and gene regulation are connected across tissues. This makes sense conceptually and the web tool is easy to use and runs reasonably quickly, considering the size of the data. I like the tool and I think the approach is necessary and surprisingly under-served; there is a lot of focus on multi-omics recently, but much less on doing a good job of integrating multi-tissue datasets even within a single omics layer.

What I am less convinced about is the "Research Article" aspect of this paper. Studying circadian rhythm in GTEx data seems risky to me, given the huge range in circadian clock in the sample collection. I also wonder (although this is not even remotely in my expertise) whether the circadian rhythm also gets rather desynchronized in people dying of natural causes - although I suppose this could be said for any gene expression pathway. Similarly for looking at secreted proteins in Figure 4 looking at muscle-hippocampus transcript levels for ADAMTS17 doesn't make sense to me - of all tissue pairs to make a vignette about to demonstrate the method, this is not an intuitive choice to me. The "within muscle" results look fine but panels C-E-G look like noise to me...especially panel C and G are almost certainly noise, since those are pathways with gene counts of 2 and 1 respectively.

I think this is an important effort and a good basis but a significant revision is necessary. This can devote more time and space to explaining the methodology and for ensuring that the results shown are actually significant. This could be done by checking a mix of negative controls (e.g. by shuffling gene labels and data) and a more comprehensive look at "positive" genes, so that it can be clearly shown that the genes shown in Fig 1 and 2 are not cherry-picked. For Figure 3, I suspect you would get almost an identical figure if instead of showing pan-tissue circadian clock correlations, you instead selected the electron transport chain, or the ribosome, or any other pathway that has genes that are expressed across all tissues. You show that colon and heart have relatively high connectivity to other tissues, but this may be common to other pathways as well.

Response: We are thankful to the reviewer in their detailed assessment of the manuscript. The comments raised in both the public and suggested reviews clearly improved the revised study and helped to identify limitations. In general, we have removed data suggesting “discovery” using these generalized analyses, such as removing figures evaluating circadian rhythm genes and muscle-hippocampus correlations. These have been replaced with more thorough investigations of tissue correlation structure and potentially identified regions of data sparsity which are important for users to consider. Also, we have added a similar full detailed pipeline of mouse (HMDP) data and highlighted in the manuscript by showing transcript ~ trait correlations of sex hormone receptor genes which differ between organs and diets. Further responses to individual points are also provided below.

Reviewer #2 (Public Review):

Summary:

Zhou et al. use publicly available GTEx data of 18 metabolic tissues from 310 individuals to explore gene expression correlation patterns within-tissue and across-tissues. They detect signatures of known metabolic signaling biology, such as ADIPOQ's role in fatty acid metabolism in adipose tissue. They also emphasize that their approach can help generate new hypotheses, such as the colon playing an important role in circadian clock maintenance. To aid researchers in querying their own genes of interest in metabolic tissues, they have developed an easy-to-use webtool (GD-CAT).

This study makes reasonable conclusions from its data, and the webtool would be useful to researchers focused on metabolic signaling. However, some misconceptions need to be corrected, as well as greater clarification of the methodology used.

Strengths:

GTEx is a very powerful resource for many areas of biomedicine, and this study represents a valid use of gene co-expression network methodology. The authors do a good job of providing examples confirming known signaling biology as well as the potential to discover promising signatures of novel biology for follow-up and future studies. The webtool, GD-CAT, is easy to use and allows researchers with genes and tissues of interest to perform the same analyses in the same GTEx data.

Weaknesses:

A key weakness of the paper is that this study does not involve genetic correlations, which is used in the title and throughout the manuscript, but rather gene co-expression networks. The authors do mention the classic limitation that correlation does not imply causation, but this caveat is even more important given that these are not genetic correlations. Given that the goal of their study aligns closely with multi-tissue WGCNA, which is not a new idea (e.g., Talukdar et al. 2016; https://doi.org/10.1016/j.cels.2016.02.002), it is surprising that the authors only use WGCNA for its robust correlation estimation (bicor), but not its latent factor/module estimation, which could potentially capture cross-tissue signaling patterns. It is possible that the biological signals of interest would be drowned out by all the other variation in the data but given that this is a conventional step in WGCNA, it is a weakness that the authors do not use it or discuss it.

Response: Thank you for the helpful and detailed suggestions regarding the study. The review raised some important points regarding methodological interpretations (ex. bicor-exclusive application as opposed to module-based approaches), as well as clarification of “genetic” inferences throughout the study. The comparison to module-based approaches has also now been discussed directly, pointing our considerations and advantages to each. We hope that the reviewer with our corrections to the misconceptions posed, many of which we feel were due to our insufficient description of methodological details and underlying interpretations. The revised manuscript, web portal and associated github provide much more detail and many more responses to specific points are provided below.

Reviewer #3 (Public Review):

Summary: A useful and potentially powerful analysis of gene expression correlations across major organ and tissue systems that exploits a subset of 310 humans from the GTEx collection (subjects for whom there are uniformly processed postmortem RNA-seq data for 18 tissues or organs). The analysis is complemented by a Shiny R application web service.

The need for more multisystems analysis of transcript correlation is very well motivated by the authors. Their work should be contrasted with more simple comparisons of correlation structure within different organs and tissues, rather than actual correlations across organs and tissues.

Strengths and Weaknesses: The strengths and limitations of this work trace back to the nature of the GTEx data set itself. The authors refer to the correlations of transcripts as "gene" and "genetic" correlations throughout. In fact, they name their web service "Genetically-Derived Correlations Across Tissues". But all GTEx subjects had strong exposure to unique environments and all correlations will be driven by developmental and environmental factors, age, sex differences, and shared and unshared pre- and postmortem technical artifacts. In fact we know that the heritability of transcript levels is generally low, often well under 25%, even studies of animals with tight environmental control.

This criticism does not comment materially detract for the importance and utility of the correlations-whether genetic, GXE, or purely environmental-but it does mean that the authors should ideally restructure and reword text so as to NOT claim so much for "genetics". It may be possible to incorporate estimates of chip heritability of transcripts into this work if the genetic component of correlations is regarded as critical (all GTEx cases have genotypes).

Appraisal of Work on the Field: There are two parts to this paper: 1. "case studies" of cross-tissue/organ correlations and 2. the creation of an R/Shiny application to make this type of analysis much more practical for any biologist. Both parts of the work are of high potential value, but neither is fully developed. My own opinion is that the R/Shiny component is the more important immediate contribution and that the "case studies" could be placed in the context of a more complete primer. Or Alternatively, the case studies could be their own independent contributions with more validation.

Response: We thank the reviewer for their supportive and helpful comments. The discussion of usage of the term “genetic” has been removed entirely from the manuscript as this point was made by all reviewers. Further, we have revised the previous study to focus on more detailed investigations of why transcript isoforms seemed correlated between tissues and areas where datasets are insufficient to provide sufficient information (ex. Kidney in GTEx). As the reviewer points out, the previous “case studies” were unvalidated and incomplete and as a result, have been replaced. Additional points below have been revised to present a more comprehensive analyses of transcript correlations across tissues and improved web tool.

(Recommendations For The Authors):

As this manuscript is focused on the analytical process rather than the biological findings, the reviewer concerns are not a fundamental issue to subsequent acceptance of the paper, but some of the examples will need to be replaced or double-checked to ensure their biological and statistical relevance. To raise the scope and interest of the method developed, it would be seen very positively to include additional datasets, as the authors seem to have intended to have done, with a non-functional (and highlighted as such) selection for mouse data. Establishing that the authors can easily - and will easily - add additional datasets into their tool would greatly raise the reviewers' confidence in the methodology/resource aspect of this paper. This may also help address the significant concerns that all three reviewers raised with the biological examples, e.g. that GTEx data is so uncontrolled that studying environmentally-influenced traits such as circadian rhythm may be challenging or even impossible to do properly. Adding in a more highly controlled set of cross-tissue mouse data may be able to address both these concerns at once, i.e. the resource concern (can the website easily be updated with new data) and the biological concern (are the results from these vignettes actually statistically significant).

Reviewer #1 (Recommendations For The Authors):

Comments, in approximately reverse order of importance

1. Some figure panels are not referenced in the text, e.g. Fig 1B and Figure 2E. Response: Thank you for pointing this out. We have revised every figure in the manuscript and additionally gone through to make sure every panel is referenced in the text.

2. The authors mention "genetic data" several times but I don't see anything about DNA. By "genetic data" do you mean "transcriptome expression data," or something else?

Response: This is an important point, also raised by all 3 reviewers. We have clarified in the abstract, results and discussion that correlations are between transcripts. As a result, all mentions of “genetics” or “genetic data” has been removed, with the exception of introducing mouse genetic reference panels.

1. For Figure 3, the authors look at circadian clock data, but the GTEx data is from all sorts of different times of day from across the patient cohort depending on when the donor died, and I don't see this metadata actually mentioned anywhere. I see Arntl Clock and all the other circadian genes are highly coexpressed in each tissue (except not so strong in liver) but correlation across tissue seems more random. Also hypothalamus seems to be very strongly negatively correlated with spleen, but this large green block doesn't have significance? That is surprising to me, since the sample sizes are all equivalent I would expect any correlation remotely close to -1.0 to be highly significant.

Response: The reviewer raises several important points with regard to the source of data and underlying interpretations. We have added a revised Fig 2, suggesting that representation of gene expression between tissues can be strongly biased by nature of samples (ex. differences in data that is available for each tissue) and also discussed considerations of the nature of sample origin in the limitations section. We have also used some of these points when introducing rationale for using mouse population data. As a result of comments from this reviewer and others, we have removed the circadian rhythm analysis and muscle-hippocampal figures from the revised study; however, specifically mentioned these cohort differences in the discussion section (lines 294-298). Circadian rhythm terms are also evaluated in Fig 2 and consistent with the reviewers concerns, less overall correlations are observed between transcripts across tissues when compared to other common GO terms assessed.

1. Figure 4, this is all transcript-level data, so it is confusing to see protein nomenclature used, e.g. "expression of muscle ADAMTS17" should be "expression of muscle ADAMTS17" (ADAMTS17 the transcript should be in italics, in case the formatting is removed by the eLife portal). Same for FNDC5. In the figures you do have those in italics, so it is just an issue in the manuscript text. In general please look through the text and make sure whether you are referring really to a "gene," "transcript," or "protein." For instance, Figure 1 legend I think should be "A, All transcripts across the ... with local subcutaneous and muscle transcript expression." I know people still sometimes use "gene expression" to refer to transcripts, but now that proteomics is pretty mainstream, I would push for more careful vocabulary here.

Response: Thank you for pointing these out. While we have replaced Fig 4 entirely as to limit the unvalidated discovery or research aspects of the paper, we have gone through the text and figures to check that the correct formatting is used for references to human genes (capitalized italics) or the newly-included mouse genes (lower-case italics).

1. "Briefly, these data were filtered to retain genes which were detected across individuals where individuals were required to show counts > 0 in 1.2e6 gene-tissue combinations across all data." I don't quite understand the filtering metric here - what is 1.2 million gene-tissue combinations referring to? 20k genes times 18 tissues times 310 people is ~100 million measurements, but for a given gene across 310 people * 18 tissues that is only ~6000 quantifications per gene.

Response: We apologize for this oversight, as the numbers were derived from the whole GTEx dataset in total and not the tissues used for the current study. We have clarified this point in the revised manuscript (methods section in Datasets used) and also removed confusing references to specific numbers of transcripts and tissues unless made clear.

1. Generally I think your approach makes sense conceptually but... for the specific example used in e.g. figure 4, this only makes sense to me if applied to proteins and not to transcripts. Looking at the transcript levels per tissue for genes which are secreted could be interesting but this specific example is confusing, as is the tissue selected. I would not really expect much crosstalk between the hippocampus and the muscle, especially not in terms of secreted proteins.

Response: This is a valid point, also raised by other reviewers. While we wanted to highlight the one potentially-new (ADAMTS7) and two established proteins (FNDC5 and ERFE) and their correlations, the fact that this direct circuit remains to be validated led us to replace the figure entirely. The point raised about inference of protein secretion compared to action; however, has been expanded upon in the results and discussion. We now show that complexities arise when using this approach to infer mechanisms of proteins which are primarily regulated post-transcriptionally. We provide a revised Supplemental Fig 4 showing that this general framework, when applied to expression of INS (insulin), almost exclusively captured pathways leading to its secretion and not action.

1. It's not clear to me how correction for multiple testing is working in the analyses used in this manuscript. You mention q-values so I am sure it was done, I just don't see the precise method mentioned in the Methods section.

Response: We apologize for this oversight and have included a specific mention of qvalue adjustment using BH methods, where our reasoning was the efficiency in run-time (compared to other qvalue methods). In addition, we provide a revised Fig 2 which suggests that innate correlation structure exists between tissues for a variety of pathways which should be considered. We also compare several empirical bicor pvalues and qvalue adjustments directly between these large pathways where much of the innate tissue correlation structure does appear present when BH qvalue adjustments are applied (revised Fig 2A).

1. The piecharts in Figure 1 are interesting - I would actually be curious which tissues generally have closer coexpression. This would be an absolutely massive number of pairwise correlations to test, but maybe there is a smarter way to do it? For instance, for ADIPOQ, skeletal muscle has the best typical correlation, but would that be generally true just that many adipose genes have closer relationship between the two tissues?

Response: This comment inspired us to perform a more systematic query of global gene-gene correlation structures, which is now shown as the revised Fig 2A. With respect to ADIPOQ, the reviewer is correct in that there does appear to be a general pattern of muscle genes showing stronger correlation with adipose genes. We emphasize and discuss there in the revised manuscript to point out that global trends of tissue correlation structure should be taken into account when looking at specific genes. Much of this innate co-correlation structure could be normalized by the BH qvalue adjustment (above); however, strongly correlated pathways like mitochondria showed selective patterns throughout thresholds (revised Fig 2A). Further, we analyze KEGG terms and general correlation structures (revised Fig 2B) to point out the converse, that some tissues are just poorly represented. Interpretation of correlated genes from these organ and pathway combinations should be especially considered in the framework that their poor representation in the dataset clearly impacted the global correlation structures. We have added these points to both results and discussion. In sum, we feel that this was a critical point to explore and attempted to provide a framework to identify/consider in the revised manuscript.

1. The pathway enrichments in Figure 1 are more difficult for me to interpret, e.g. for ADIPOQ, the scWAT pathways make sense, but the enriched skeletal muscle pathways are less clearly relevant (rRNA processing?? Not impossible but no clear relevance either). What are the significances for these pathway enrichments? Is it even possible to select a gene that has no peripheral pathway enrichment, e.g. if you take some random Gm#### or olfactory receptor gene and run the analysis, are you also going to see significant pathways selected, as pathway enrichment often has a trend to overfit? The "within organ" does seem to make sense, but I am also just looking at 4 anecdotes here and it is unclear whether they are cherry picked because they did make sense. That is, it's unclear why you selected ADIPOQ and not APOE or HMGCR or etc. I also don't figure out how I can make these pathway enrichment plots using your website. I do get the pie chart but when I try the enrichment analysis block (NB: typo on your website, it says "Enrich-E-ment Analysis" with an extra E) I always get that "the selected tissue do not contain enough genes to generate positive the enrichment." (Also two typos in that phrase; authors should check and review extensively for improvements to the use of English.) After trying several genes I eventually got it to work. I think there is some significant overfitting here, as I am pretty sure that XIST expression in the white adipose tissue has nothing to do with olfactory signalling pathways, which are the top positive network (but with an n = 4 genes).

Response: Several good points within this comment. 1) the pathway enrichments have been revised completely. The reviewer provided a helpful suggestion of a rank-based approach to query pathways, as opposed to the previous over-representation tests. After evaluating several different pathway enrichment tools based on correlated tissue expression transcripts, a rank- and weight-based test (GSEA) captured the most physiologic pathways observed from known actions of select secreted proteins. Therefore, revised pathway enrichments and web-tool queries unitize a GSEA approach which accounts for the rank and weight determined by correlation coefficient. In implementing these new pathway approaches, we feel that pathway terms perform significantly better at capturing mechanisms. 2) With respect to the selection genes, we wanted to provide a framework for investigating genes which encode secreted proteins that signal as a result of the abundance of the protein alone. This is a group-bias; however, and not necessarily reflective of trying to tackle the most important physiologic mechanisms underlying human disease. We agree with the reviewer in those evaluating genes such as APOE and cholesterol synthesis enzymes present an exciting opportunity, our expertise in interpretation and mechanistic confirmation is limited. 3) We have gone through the revised manuscript and attempted to correct all grammatical and/or spelling mistakes.

1. The network figures I get on your website look actually more interesting than the ones you have in Figure 2, which only stay within a tissue. Making networks within a tissue is pretty easy I think for any biologist today, but the cross-tissue analysis is still fairly hard due to the size of the datasets and correlation matrices.

Response: We greatly appreciate the reviewer’s enthusiasm for the network model generation aspect. We have tried to improve the figure generation and expanded the gene size selection for network generation in the web tool, both within and across tissues. We are working toward allowing users to select specific pathway terms and/or tissue genes to include in these networks as well, but will need more time to implement.

1. I get a bug with making networks for certain genes, e.g. XIST - Liver does not work for plotting network graphs. Maybe XIST is a suppressed gene because it has zero expression in males? It is an interesting gene to look at as a "positive control" for many analyses, since it shows that sample sexing is done correctly for all samples.

Response: The reviewer recognized a key consideration in underlying data structure for GTEx. In the revised manuscript, we evaluated tissue representation (or lack thereof) being a crucial factor in driving where significant relationships cannot be observed in tissues such as kidney, liver and spleen (Fig 2). Moreover, the representation of females (self-reported) in GTEx is less-than half of males (100 compared to 210 individuals). We have emphasized this point in the discussion where we specifically pointed out the lack of XIST Liver correlation being a product of data structure/availability and not reflecting real biologic mechanisms. We expanded on this point by highlighting the clear sex-bias in terms of representation.

1. On the network diagram on your website, there doesn't seem to be any way to zoom in on the website itself? You can make a PDF which is nice but the text is often very small and hard to read.

Response: We have revised the web interface plot parameters to create a more uniform graph.

1. On a related note, is it possible to output the raw data and gene lists for the network graph? I would want to know what are those genes and their correlation coefficient.

Response: We have enabled explore as .pdf or .svg graphics for the network and all plots. In addition, following pie chart generation at the top of the web app, users now have the ability to download a .csv file containing the bicor coefficients, regression pvalues and adjusted qvalues for all other gene-tissue combinations.

1. Some functionality issues, e.g. on the "Scatter plot" block, I input a gene name again here. Shouldn't this use the same gene selected already at the top of the page? It seems confusing to again select the gene and tissue here, but maybe there is a reason for that.

Response: It would be more intuitive to only display genes from a given selected tissue for scatterplots; however, we chose to keep all possible combinations with the [perhaps unnecessary] option of reselecting a tissue to allow users to query any specific gene without having to wait to run the pathways for all that correspond to a given tissues.

1. Figure 4H should also probably be Figure 1A.

Response: Good point, the revised Fig 1A is now a summary of the web tool

I realize I have written a fairly critical review that will require most of the figures to be redone, but I think the underlying method is sound and the implementation by and end-user is quite simple, so I think your group should have no trouble addressing these points.

Response: Your comments were really helpful and we feel that the tool has significantly improved as a result. So, we are thankful to the time and effort put toward helping here.

Reviewer #2 (Recommendations For The Authors)

Comments on the use of "genetic correlation"

• The use of "genetic correlation" in title and throughout the manuscript is misleading. Should broadly be replaced with "gene expression correlation". Within genetics, "genetic correlation" generally refers to the correlation between traits due to genetic variation, as would be expected under pleiotropy (genetic variation that affects multiple traits). Here, I think the authors are somewhat conflating "genetic" (normally referring to genetic variation) with "gene" (because the data are gene expression phenotypes). I don't think they perform any genetic analysis in the manuscript. I hope I don't sound too harsh. I think the paper still has merit and value, but it is important to correct the terminology.

Response: This was an important clarification raised by all reviewers. We apologize for the oversight. As a result, all mentions of “genetics” or “genetic data” has been removed, with the exception of introducing mouse genetic reference panels. These have generally been replaced with “transcript correlations”, “correlations” or “correlations across individuals” to avoid confusion.

• The authors note an important limitation in the Discussion that correlations don't imply a specific causal model between two genes, and furthermore note that statistical procedures (mediation and Mendelian randomization) are dependent on assumptions and really only a well-designed experiment can completely determine the relationship. This is a very important point that I greatly appreciate. I think they could even further expand this discussion. The potential relationships between gene A and gene B are more complex than causal and reactive. For example, a genetic variant or environmental exposure could regulate a gene that then has a cascade of effects on other genes, including A and B. They belong to a shared causal pathway (and are potentially biologically interesting), but it's good to emphasize that correlations can reflect many underlying causal relationships, some more or less interesting biologically.

Response: We thank the reviewer for pointing this out. We have expanded both the results and discussion sections to mention specifically how correlation between two genes can be due to a variety of parameters, often and not just encompassing their relationship. We mention the importance of considering genetic and environmental variables in these relationships as well which we feel will be an important “take-home message” for the reader. These points were also explored in the revised Fig 2 in terms of investigating broad pathway gene-gene correlation structures. As noted by the reviewer, contexts such as circadian rhythm or other variables in the data which are not fixed show much less overall significance in terms of broad relationships across organs.

• It would be good for the authors to provide more context for the methods they use, even when they are fully published. For example, stating that biweight midcorrelation (bicor) is an approach for comparing to variables that is more robust to outliers than traditional correlations and is commonly used with gene co-expression correlation.

Response: Thank you for pointing this out. A lack of method description was also an important reason for lack of clarity on other aspects so we have done our best to detail what exact approaches are being implemented and why. In the revised manuscript, we mention the usage if bicor values to limit influence of outlier individuals in driving regressions, but also point out that it is still a generalized linear model to assess relationships. We hope that the revised methods and expanded git repositories which detail each analysis provide much more transparency on what is being implemented.

• Performing a similar analysis based on genetic correlation is an interesting idea, as it would potentially simplify the underlying causal models (removing variation that doesn't stem from genetic variants). I don't expect the authors to do this for this paper because it would be a significant amount of work (fitting and testing genetic correlations are not as straightforward). But still, an interesting idea to think about, and individuals in GTEx are genotyped I believe. Could be mentioned in the Discussion.

Response: Absolutely. While we did not implement and models of genetic correlation (despite misusing the term) in this analysis. We have added to the discussion on how when genetic data is available, these approaches offer another way to tease out potentially causal interactions among the large amount of correlated data occurring for a variety of reasons.

Comments on use of the term "local" and "regression"

• "Local" is largely used to mean within-tissue, so how correlated gene X in tissue Y is with other genes in tissue Y. I think this needs to be defined explicitly early in the manuscript or possibly replaced with something like "within-tissue".

Response: We have replaced al “local” mentions with “within-tissue” or simply name the tissue that the gene is expressed to avoid confusion with other terms of local (ex a transcript in proximity to where it is encoded on the genome).

• "Regression" is also used frequently throughout, often when I think "correlation" would be more accurate. It's true that the regression coefficient is a function of the correlation between X and Y, but I don't think actual regression (the procedure) applies here. The coefficients being used are bicor, which I don't think relates as cleanly to linear regression.

Response: Thank you for pointing this out. A lack of method description was also an important reason for lack of clarity on other aspects so we have done our best to detail what exact approaches are being implemented and why. In the revised manuscript, we mention the usage if bicor values to limit influence of outlier individuals in driving correlations, but also point out that it is still a generalized linear model to assess relationships. Further, we have removed usage of “regression” when referencing bicor values. We hope that the revised methods and expanded git repositories which detail each analysis provide much more transparency on what is being implemented.

• "Further, pan-tissue correlations tend to be dominated by local regressions where a given gene is expressed. This is due to the fact that within-tissue correlations could capture both the regulatory and putative consequences of gene regulation, and distinguishing between the two presents a significant challenge" (lines 219-223). This sentence includes both "local" and "regressions" (and would be improved by my suggested changes I think), but I also don't fully understand the argument of "regulatory and putative consequences". I think the authors should elaborate further. In the examples, the within-tissue correlations do look stronger, suggesting within-tissue regulation that is quite strong and potentially secondary inter-tissue regulation. If that's the idea, I think it can be stated more clearly.

Response: Thank you for pointing this out. We have revised the sentence to state the following:

Further, many correlations tend to be dominated by genes expressed within the same organ. This could be due to the fact that, within-tissue correlations could capture both the pathways regulating expression of a gene, as well as potential consequences of changes in expression/function, and distinguishing between the two presents a significant challenge. For example, a GD-CAT query of insulin (INS) expression in pancreas shows exclusive enrichments in pancreas and corresponding pathway terms reflect regulatory mechanisms such as secretion and ion transport (Supplemental Fig 4).

We feel that this point might not be intuitive, so have included a new figure (Supplemental Fig 4) which contains the tissue correlations and pathways for INS expression in pancreas. These analyses show an example where co-correlation structure seems almost entirely dominated by genes within the same organ (pancreas) and GSEA enrichments highlight many known pathways which are involved in regulating the expression/secretion of the gene/protein. We hope that this makes the point more clearly to the reader.

Additional comments on Results:

• I would break the titled Results sections into multiple paragraphs. For example, the first section (lines 84-129) has a few natural breakpoints that I noticed that would potentially make it feel less over-whelming to the reader.

Response: We have broken up the results section into separate paragraphs in the revised manuscript. In addition, we have gone through to try and make sure that the amount of information per block/sentence focuses on key points.

• "Expression of a gene and its corresponding protein can show substantial discordances depending on the dataset used" (line 224 of Results). This is a good point, and the authors could include citations here of studies that show discordance between transcripts and proteins, of which there are a good number. They could also add some biological context, such as saying differences could reflect post-translational regulation, etc.

Response: Thank you for the supportive comment. We have referenced several comprehensive reviews of the topic, each of which contain tables summarizing details of mRNA-protein correlation. The revised discussion sentence is as follows:

Expression of a gene and its corresponding protein can show substantial discordances depending on the dataset used. These have been discussed in detail39–41, but ranges of co-correlation can vary widely depending on the datasets used and approaches taken. We note that for genes encoding proteins where actions from acute secretion grossly outweigh patterns of gene expression, such as insulin, caution should be taken when interpreting results. As the depth and availability of tissue-specific proteomic levels across diverse individuals continues to increase, an exciting opportunity is presented to explore the applicability of these analyses and identify areas when gene expression is not a sufficient measure.

1. Liu, Y., Beyer, A. & Aebersold, R. On the Dependency of Cellular Protein Levels on mRNA Abundance. Cell 165, 535–550 (2016).

2. Maier, T., Güell, M. & Serrano, L. Correlation of mRNA and protein in complex biological samples. FEBS Letters 583, 3966–3973 (2009).

3. Buccitelli, C. & Selbach, M. mRNAs, proteins and the emerging principles of gene expression control. Nat Rev Genet 21, 630–644 (2020).

• In many ways, this work has similar goals to many studies that have performed multi-tissue WGCNA (e.g., Talukdar et al. 2016; https://doi.org/10.1016/j.cels.2016.02.002). In this manuscript, WGCNA's conventional approach to estimating robust correlations (bicor) is used, but they do not use WGCNA's data reduction/clustering functionality to estimate modules. Perhaps the modules would miss the signaling relationships of interest, being sort of lost in the presence of stronger signals that aren't relevant to the biological questions here. But I think it would be good for the authors to explain why they didn't use the full WGCNA approach.

Response: This is an important point and we also feel that the previous lack of methodological details and discussion did a poor job at distinguishing why module-based approaches were not used. We wanted to be careful not to emphasize one approach being superior/inferior to another, rather point out the different considerations and when a direct correlation might inform a given question. As the reviewer points out, our general feeling is that adopting a simple gene-focused correlation approach allows users to view mechanisms through the lens of a single gene; however, this is limited in that these could be influenced by cumulative patterns of correlation structure (for example mitochondria in revised Fig 2A) which would be much more apparent in a module-based approach. This comment, in combination with the other listed above, was our motivation in exploring cumulative patterns of gene-gene correlations in the revised Fig 2. In the revised manuscript, we expanded on the results and discussion section to highlight utility of these types of approaches compared to module-based methods:

The queries provided in GD-CAT use fairly simple linear models to infer organ-organ signaling; however, more sophisticated methods can also be applied in an informative fashion. For example, Koplev et al generated co-expression modules from 9 tissues in the STARNET dataset, where construction of a massive Bayesian network uncovered interactions between correlated modules6. These approaches expanded on analysis of STAGE data to construct network models using WGCNA across tissues and relating these resulting eigenvectors to outcomes42. The generalized approach of constructing cross-tissue gene regulatory modules presents appeal in that genes are able to be viewed in the context of a network with respect to all other gene-tissue combinations. In searching through these types of expanded networks, individuals can identify where the most compelling global relationships occur. One challenge with this type of approach; however, is that coregulated pathways and module members are highly subjective to parameters used to construct GRNs (for example reassignment threshold in WGCNA) and can be difficult in arriving at a “ground truth” for parameter selection. We note that the WGCNA package is also implemented in these analyses, but solely to perform gene-focused correlations using biweight midcorrelation to limit outlier inflation. While the midweight bicorrelation approach to calculate correlations could also be replaced with more sophisticated models, one consideration would be a concern of overfitting models and thus, biasing outcomes.

Additional comments on Discussion:

• In the second paragraph of the Discussion (lines 231-244), the authors mention that GD-CAT uses linear models to compare data between organs and point to other methods that use more complex or elaborate models. It's good to cite these methods, but I think they could more directly state that there are limitations to high complexity models, such as over-fitting.

Response: Thank you for this suggestion. We have added a line (above) mentioning the overfitting concern.

Comments on Methods:

• The described gene filtration in the Methods of including genes with non-zero expression for 1.2e6 gene-tissue combinations is confusing. If there are 310 individuals and 18 tissues, for a given gene, aren't there only 5,580 possible data points? Might be helpful to contextualize the cut-off in terms of like the average number of individuals with non-zero expression within a tissue.

Response: We apologize for this error. This number was pasted from a previous dataset used and not appropriate for this manuscript. In general, we have removed specific mentions of total number of gene_tissue correlation combinations, as these numbers reflect large but almost meaningless quantifications. Instead, we expanded the methods in terms of how individuals and genes filtered.

• More details should be given about the gene ontology/pathway enrichment analysis. I suspect that a set-based approach (e.g., hypergeometric test) was used, rather than a score-based approach. The authors don't state what universe of genes were used, i.e., the overall set of genes that the reduced set of interest is compared to. Seems like this could or should vary with the tissues that are being compared. A score-based approach could be interesting to consider (https://www.biorxiv.org/content/10.1101/060012v3), using the genetic correlations as the score, as this would remove the unappealing feature of sets being dependent on correlation thresholds. This isn't something that I would demand of the published paper, but it could be an appealing approach for the authors to consider and confirm similar results to the set-based analysis.

Response: This is an important point. Following this suggestion, we evaluated several different rank- and weight-based pathway enrichment tools, including FGSEA and others. Ultimately, we concluded that GSEA performed significantly better at 1) recapitulating known biology of select secreted protein genes and 2) leveraging the large numbers of genes occurring at qvalue cutoffs without having to further refine (ex. in the previous overrepresentation tests). For this reason, all pathway enrichments in the web tools and manuscripts not contain GSEA outputs and corresponding pathway enrichments or network graph visualizations. Thank you for this suggestion.

Comments on figures:

• I think there is a bit of a missed opportunity to use the figures to introduce and build up the story for readers. For example, in Figure 1, plotting ADIPOQ expression against a correlated gene in adipose (local) as well as peripheral tissues. This doesn't need to be done for every example, but I think it would help readers understand what the data are, and what's being detected before jumping into higher level summaries.

Response: Thank you, this point also builds on others which recommended to restructure the manuscript and figures. In the revised manuscript, we first introduce the web tool (which was last previously), and immediately highlight comparisons of within- and across-organ correlations, such as ADIPOQ. We feel that the revised manuscript presents a superior structure in terms of demonstrating the key points and utility of looking at gene-gene correlations across tissues.

• Figures 1 and 4 are missing the color scale legend for the bar plots, so it's impossible to tell how significant the enrichments are.

Response: We apologize for the oversight. The pathways in the revised Fig 1 detail pathway network graphs among the top pathways which should make interpretation more intuitive. We have also gone through and made sure that GSEA enrichment pvalues are now present for all figures including pathways (revised Fig 1, Fig 3 and supplemental Fig 4).

• The Figure 2 caption says that edges are colored based on correlation sign? Are there any negative correlations (red)? They all look blue to me. The caption could also state that edge weight reflects correlation magnitude (I assume). It would be ideal to include a legend that links a range of the depicted edge weights to their genetic correlation, though I don't know how feasible that may be depending on the package being used to plot the networks.

Response: Good catch. We included in the revised manuscript the network edge parameters: Network edges represent positive (blue) and negative (red) correlations and the thicknesses are determined by coefficients. They are set for a range of bicor=0.6 (minimum to include) to bicor=0.99

Related to seeing a dominant pattern of positive correlations, we agree that this observation is fascinating and gene-gene correlations being dominated by positive coefficients will be the topic of a closely-following manuscript from the lab

• Figure 4A would be more informative as boxplots, which could still include Ssec score. This would allow the reader to get a sense of the variation in correlation p-value across all hippocampus transcripts.

Response: Related to comments from this reviewer and others, we have removed the previous Fig 4 entirely from the manuscript to emphasize the ability of these gene-gene correlations to capture known biology and limit the extend of unvalidated “suggested” new mechanisms.

Comments on GD-CAT

• The online webtool worked nicely for me. It was easy to use and produce figures like in the manuscript. One suggestion is show data points in the scatter plot rather than just the regression line (if that's possible currently, I didn't figure it out). A regression line isn't that interesting to look at, but seeing how noisy the data look around it is something humans can usually interpret intuitively.

Response: Thank you so much. We are excited that the web tool works sufficiently. We have also revised the individual gene-gene correlation tab to show individual data points instead of simple regression lines.

Minor comments:

Response: Thank you for these detailed improvements

• This sentence is awkwardly constructed: "Here, we surveyed gene-gene genetic correlation structure for ~6.1x10^12 gene pairs across 18 metabolic tissues in 310 individuals where variation of genes such as FGF21, ADIPOQ, GCG and IL6 showed enrichments which recapitulate experimental observations" (lines 68-70). It's an important sentence because it's where in the Abstract/Introduction the authors succinctly state what they did, thus I would re-work it to something like: "Here, we surveyed gene expression correlation structure..., identifying genes, such as FGF21, ADIPOQ, GCG and IL6, that possess correlation networks that recapitulate known biological pathways."

Response: The numbers of pairs examined and dataset size have been removed for clarity and we have revised this statement and results as a whole

• Prefer swapping "signal" for "signaling" in line 53 of Abstract/Introduction.

Response: Done

• Remove extra period in line 208 of Results.

Response: Removed

• Change "well-establish" to "well-established" in line 247 of Discussion.

Response: Replaced

• Missing commas in line 302 of Methods.

Response: added

• Missing comma in line 485 of Figure 3 caption.

Response: The previous Fig 3 has been removed

• Typo in title of Figure 3E (change "Perihperal" to "Peripheral")

Response: Thank you, changed

• Add y-axis label to y-axis labels (relative cell proportions) to Supplemental Figures 1-3.

Response: These labels have been added

Reviewer #3 (Recommendations For The Authors):

Minor technical comment: The authors refer to correlations between genes when they actually mean correlations between GTEX transcript isoform models. It is exceedingly important to keep this distinction clear in the reader's mind, a fact that is emphasized by the authors themselves when they comment on the potential value of similar proteomic assays to evaluate multiorgan system communication. GTEx has tried to do proteomics but I do not know of any open data yet.

Response: Thank you for this point. We have gone through the manuscript and replaced “gene correlations” with “transcript” or other similar mentions. Related to the comment on GTEx proteomics, this is an important point as well. As the reviewer mentions, proteomics has been performed on GTEx data; however, given that this dataset contains only 6 sparsely-represented individuals, analyses such as the ones highlighted in our study remain highly limited. We have added the following to the discussion: As the depth and availability of tissue-specific proteomic levels across diverse individuals continues to increase, an exciting opportunity is presented to explore the applicability of these analyses and identify areas when gene expression is not a sufficient measure. For example, mass-spec proteomics was recently performed on GTEx42; however, given that these data represent 6 individuals, analyses utilizing well-powered inter-individual correlations such as ours which contain 310 individuals remain limited n applications.

The R/Shiny companion application: The community utility of this application would be greatly improved by a link to a primer and more basic functionality. The Github site is a "work in progress" and does not include a readme file or explanation (that I could find) on the license.

Response: Thank you, we are excited that the apps operate sufficiently. We have revised the github repository entirely to contain a full walk-through of app details and parameter selections. These are meant to walk users through each step of the pipeline and discuss what is being done at each step. We agree that this updated github repository allows users to understand the details of the R/Shiny app in much more detail. We also made all the app scripts, datasets, markdown/walkthrough files and docker image fully available to enhance accessibility.

Author Response

We appreciate the reviewers’ and editors’ advice on further improving this manuscript. We have provided point by point responses to the reviewers’ comments mentioned below. A revised version of this manuscript will be uploaded within a few weeks.

Authors’ response to Reviewer 1 comments:

• We appreciate the reviewer’s time in highlighting the strengths and weaknesses of this manuscript.

• Per the reviewer’s advice, we will provide further description of the methods in a revised version of this manuscript.

• The interpretation about the biological threat in response to elevated glycosuria in renal Glut2 KO mice is based on our observation that these mice exhibit changes in acute phase proteins measured using plasma proteomics. We will further discuss this in a revised version of this manuscript.

• We acknowledge that this manuscript provides a resource for future mechanistic studies. Because multiple secreted proteins are changed between the control and experimental groups, some of them could be causal and others corelative in the context of enhancing compensatory glucose production in response to elevated glycosuria. Through future studies we will determine the causal proteins that trigger the increase in glucose production and identify the tissues that secrete these proteins.

• We have shown previously (Cordeiro et al., Diabetologia 2022) that renal Glut2 deficiency doesn’t change insulin sensitivity (i.e. renal Glut2 KO mice don’t exhibit insulin resistance despite the activation of the HPA axis). It is likely that the massive glycosuria in renal Glut2 KO mice may overcome or mask the phenotype of insulin resistance potentially induced by an increase in the stress hormones.

• In this manuscript, our major goal was to determine how elevated glycosuria leads to an increase in compensatory glucose production. We are not suggesting renal Glut2 as a therapeutic in this manuscript (that was already demonstrated in our previously published manuscript, Cordeiro et al., Diabetologia 2022).

Authors’ response to Reviewer 2 comments:

1) Renal Glut2 KO mice didn’t exhibit sex differences for the variables reported in our previous manuscript (Cordeiro et al., Diabetologia 2022). Therefore, in the present manuscript we decided to use male or female mice depending on their availability for each reported experiment. Per the reviewer’s advice, we will describe these details including age and sexes in each figure legend.

2) For the method description, we have cited previous publications and mentioned ‘as described previously’. Based on the reviewer’s suggestion we will further describe the methods in detail to clarify the reviewer’s concerns. In addition, we will include age and sexes in the legends of each figure.

3) For littermate controls, we had used Glut2loxp/loxp mice (which are like WT controls as described in Cordeiro et al., Diabetologia 2022) that were injected with tamoxifen exactly in the same way as the experimental mice. Het mice for Cre were not used as controls because they would have confounded the results as pointed out by the reviewer.

4) Because elevated HPA activity is known to increase blood glucose levels, we suggested ‘the HPA axis may…..’. Given the nature of this manuscript, we agree the secreted proteins identified using plasma proteomics could contribute to enhanced glucose production directly or through secondary mechanisms. Afferent renal denervation using capsaicin reduced blood glucose levels concomitant with the suppression of the HPA axis in renal Glut2 KO mice. Based on these findings we speculated that the HPA axis may be partly responsible for increasing glucose production in renal Glut2 KO mice.

We had considered using CRF antagonist and glucocorticoid receptor antagonists to determine the causal role of the HPA axis in contributing to the increase in glucose production in renal Glut2 KO mice. However, these drugs activate compensatory mechanisms including changes in insulin sensitivity. Therefore, use of these drugs would further confound the results instead of providing a clarity on the causal role of the HPA axis in enhancing glucose production in renal Glut2 KO mice.

5) We understand the reviewer’s concerns whether the results reported here are translatable to humans. Please note that expression of SGLT2 is not kidney-specific; therefore, pleiotropic effects of SGLT2 inhibition in tissues other than the kidney cannot be excluded in animal models and humans. In contrast, the mouse model reported in this manuscript is kidney-specific Glut2 KO mice. Therefore, phenotype produced in renal Glut2 KO mice cannot be directly compared with that produced after SGLT2 inhibition. It may be too early to speculate whether the results reported in this manuscript are translatable to humans.

In the referred research papers by the reviewer, the authors have used either models of different types of diabetes or included individuals with diabetes in their study. Notedly, diabetes itself affects the HPA axis independently of SGLT2 or GLUT2 inhibition. Therefore, it may not be appropriate to compare results obtained from animals or individuals with diabetes with that reported in this manuscript from renal Glut2 KO mice.

6) Yes, we are currently performing mechanistic studies including assessment of mitochondrial function in renal Glut2 KO mice to determine whether and how the kidneys sense loss of glucose in urine.

7) We apologize for the lack of methods description. We will provide additional method details in a revised version of this manuscript. All the assays were performed as per manufacturer’s instructions. Aliquots of the same samples were used for analyses of the hormones and for consistency across different assays.

Author Response

We highly appreciate the constructive feedback provided by the reviewers, which we believe will greatly improve the quality of our work. We were encouraged to see that our manuscript was considered to be “important”, of “great interest” as well as to “yield valuable results”.

We also highly appreciate the overall positive eLife assessment. However, we were surprised to read that our “results range from solid from inadequate”. This especially applies given the positive and engaging nature of the reviews which seem to mainly concern the results interpretation being “inadequate” rather than the results themselves. Hence, we kindly request a reconsideration of this aspect of the assessment.

Moreover, there is one Reviewer comment we would like to address directly. Reviewer #3 pointed out that “this study did not conduct a direct association analysis between MetS and cognitive levels without considering subgroup comparisons.” and that “After a thor-ough review of the methods and results sections” she/he “found no direct or strong evidence supporting the authors' claim that the identified latent variables were related to more severe MetS to worse cognitive performance. While a sub-group comparison was conducted, it did not adequately account for confounding factors such as educational level.”.

We appreciate the observations of Reviewer #3 regarding the absence of a direct association analysis between Metabolic Syndrome (MetS) and cognitive levels without subgroup comparisons, and the lack of evidence linking latent variables to MetS severity and cognitive performance. Our apologies for any confusion caused by unclear presentation. Our study incorporated association analyses between MetS, brain structure, and cognition using MetS components, regional cortical thickness, and cognitive performance data in a PLS. These analyses were separately performed on the UK Biobank and HCHS datasets, due to their distinct cognitive assessments. We adjusted for age, sex, and education in the subgroup analyses by removing their effects from the input variables. The primary latent variables demonstrated significant associations with MetS components, cortical thickness, and cognitive scores, indicating that higher obesity, blood pressure, lipidemia, and glycemia levels correlate with lower cognitive performance. These relationships are detailed in supplementary figures S15b and S16b, with negligible loadings for age, sex, and education, confirming effective deconfounding. We acknowledge the reviewer's constructive feedback and will enhance the clarity of the Methods and Results sections, including conducting a mediation analysis.

Furthermore, we strive to incorporate the Reviewers’ other suggestions into the analysis. The revision will include major changes to the manuscript.

In response to Reviewer #1:

• We will revise considering non-fasting plasma glucose as a surrogate marker of insuline resistance.

• We will report Field IDs of the used UK Biobank variables.

• We aim to moderate causal interpretations and reword the indicated passages for clarity.

In response to Reviewer #2:

• We will reconsider claims of binarizing vascular dementia and Alzheimer’s dementia pathophysiology.

• We will further explore the cell type associations of the other latent variables.

• We will expand the discussion regarding conclusions from our results and the future outlook.

In response to Reviewer #3.

• We will add an additional flowchart to detail the virtual histology analysis.

• We will add a discussion of the second latent variable.

• We will conduct a mediation analysis to statistically assess the mediation effect of brain structure on the relationship between MetS and cognitive performance.

We are convinced that with these revisions, our manuscript will align even more closely with the high standards of eLife and make a strong contribution to its distinguished portfolio. We thank you for your consideration.

Author Response

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

We are grateful to the reviewers for their remarks, which significantly improved the paper. We repeated the biochemical assay concerning SIRT6 activity on H3-K27Ac and quantified the results as requested. Please find our detailed answers bellow each recommendation of the reviewers.

Major recommendations:

1. Grammatical errors are still common; the authors may need to consider an external editing service if they intend to fix the problems as they indicate that they believe the errors have been removed. The Results section is relatively clean, but parts of the Abstract, Introduction, and Discussion are more difficult to understand, and errors are especially common in the Methods section and those parts of the manuscript that are new in this revision.

We corrected the grammatical errors.

1. The introduction doesn't mention the other structures published; this is considered to be a serious deficiency as it prevents the reader from understanding the context for the contributions described here. Withholding the comparison with (or mention of) the previously published work to the last sentence of the Discussion seems misleading and does not give the reader adequate ability to judge the novelty of the results presented in this manuscript.

A paragraph comparing our paper to the other structures published appear at the end of the discussion. We feel this is still the right place for such a paragraph.

1. The addition of the assay for deacetylation is a significant improvement over the initial submission. This is important both for validating the importance of the acidic patch contacts and for helping to resolve the conflicting reports regarding activity on H3-K27Ac. Given the importance of this assay for the impact of the manuscript, it is not clear why the authors chose to 1) put the data in the supplement instead of in the main manuscript, and 2) provide only single samples without quantitation. These both seem to be significant limitations.

We repeated the experiment and provided quantification of the results. We placed the figure in the main manuscript.

1. The authors should add text or a table to the Methods section explaining which maps were used for each figure. By our count, there are 8 maps and 5 models (plus MD models) based on two datasets, but the relationships among them are not clearly stated, and the names of the maps (such as "Zn-finger focused" and "Rossman-Fold-Focused") might be changed to be more helpful to the reader (for example, the latter includes more than the Rossman fold and might be renamed "Sirt6-focused"). The authors should also explain how the maps were validated, which data were deposited in public repositories, and why some data were not deposited. For example, no statistics or methods regarding how particles were separated into integrated vs. non-integrated motion are provided for the CryoDRGN models. Further, the "two principle movements" described are depicted in 4 maps from two CryoDRGN runs using two separate sets of particles, but the relationships among them are not defined clearly. Finally, the connectivity of densities in Fig 8 are not obvious in the submitted maps. Until these points are addressed, the work is considered incomplete.

AND

1. The PDB model provided for review and submitted to the PDB database shows loosely bound DNA at the nucleosomal entry/exit points near the binding site of SIRT6, but the maps provided for review and submitted to the EMDB show stronger density for the canonical location of the DNA expected at these sites. The CryoDRGN maps support a more extended conformation, but these maps were not deposited or provided for review so their validity cannot be assessed.

We added a section to the methods listing the different maps used for the figures. We deposited the map we used to trance the H2A N-terminal tail (EMD-18497). Unfortunately, we couldn’t deposit the cryoDRGN maps as the deposition system either accepts composite maps, where the consensus should be deposited too or experimental maps, where the deposition of half maps are mandatory. Nevertheless, the cryoDRGN maps are available upon request. We also added a supplementary figure (Supplementary Fig 6) to show how the cryoDRGN analyses were performed.

1. The orientation, angle and threshold used in Fig 1 make it difficult to see the multiple DNA orientations that are visible in the deposited consensus map. Examination of the map suggests that the DNA model submitted to PDB corresponds to a weaker DNA conformation than is present in the map where both DNA conformations are visible. The authors should consider modeling both conformations in their deposited model to provide a more complete, accurate representation of the data. It is concerning that a key conclusion of the manuscript is that the DNA conformation changes upon SIRT6 binding, but density for the canonical position is observable in Fig 8a.

Figure 1 is showing the overall representation of the SIRT6 bound nucleosome structure. We show the DNA linker orientations in the subsequent figure. Figure 8 (now Figure 9) shows the rearrangement of the SIRT6 Rossmann fold domain not the DNA linker.

1. Figure 4 needs a more complete legend, indicating that it is a hybrid of the consensus structure (one color) and the MD simulations (another color). In general, the colors used in the figure should be changed to make the main points more accessible.

As there is a color code for the histones, changing colors might be confusing. The figure legend mentions that panels c, d and e are from MD simulations.

Minor recommendations:

1. Figures 2c, e, and f are not referenced in the text.

We now referenced all figure panels in the text.

1. Consider moving Supp. 5C to Fig. 2 as the models in that figure come from the CryoDRGN maps and not the consensus map.

Supplemental Figure 5c show the DNA linker deviation upon SIRT6 binding from another angle. We prefer to keep it there.

1.) Supp Fig 3 is labeled "ZnF-nucleosome" refinement, but this appears to come from Data Set #2 processing. The map might be labeled ZnF-nucleosome but then a mask should be shown that excludes the Rossman Fold. It is not clear if this is a focused refinement or just a 2.9 A map that was merged with the "Rossman-fold" map.

We changed both supplemental figures accordingly.

1. The orientation of Fig 2 b and e do not show the differences in these models as well as panels c and f. Panels b and e could be replaced with the 4 CryoDRGN maps.

The models reflect the cryoDRGN maps and panels c and f were added to clarify the movement.

1. The MD description should emphasize that the H3 tails are moving with respect to the active site, as it currently suggests the active site is moving.

In the results and in the discussion section we mention that we observe new conformations of the H3 tail, not of the active site.

1. The authors refer to the "flexibility of the Rossmann fold domain," but the Rossman Fold domain isn't flexible, the linkage to the ZnF is flexible. Perhaps "observed conformational space" or "dynamic Rossman-fold domain position" are meant.

The text was changed accordingly.

1. The H2A C-terminal tail present in Fig 1 (bottom right) and Figure 3e is not present in the model in Fig 4a,b.

The H2A tails conformation was not resolved in the cryoDRGN maps so we didn’t model it.

1. The crosslinking agent used is not specified.

The crosslinking agent used is specified more clearly in the methods.

1. Supp Table 1 and EM methods do not agree on the magnification for Dataset #1. Verify nominal versus binned magnification and reported pixel size.<br /> The magnification in the methods was changed.

2. Fig 3F showing the difference between affinity for H2A and H2A.Z-containing nucleosomes would be more convincing with a titration rather than the current comparison of a single concentration.

We agree with this remark however, we find single concentration comparison is convincing enough for the purposes of this paper as it is not a central finding.

1. Fig S1 legend; both the Zn-finger and helix bundle are stated to be shown in green.

Figure S1 legend was changed.

Author Response

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

Response to Reviewers:

Thank you for taking the time to review our manuscript and provide us with helpful comments. Your comments enabled us to improve the clarity of the manuscript, in particular:

1. We improved the organization of the figures by associating each supplemental figure with a main-text figure using the eLife “figure supplements” format.

2. We reduced the length of figure captions where possible.

3. We improved organizational clarity by adding a brief organizational summary statement at the beginning of the results section which outlines the contents of the results subsections in the context of the introduction. We took particular care to use the same language, so the parallelism is clearer.

4. In addition, we made various modifications to the main text to improve clarity for the reader. For this we asked specific help of our biologist co-authors to indicate which aspects would benefit from further clarification to enable the broad biology readership of eLife to comprehend our research better.

Reviewer #1 (Public Review):

The authors sought to resolve the coordinated functions of the two muscles that primarily power flight in birds (supracoracoideus and pectoralis), with particular focus on the pectoralis. Technology has limited the ability to resolve some details of pectoralis function, so the authors developed a model that can make accurate predictions about this muscle's function during flight. The authors first measured aerodynamic forces, wing shape changes, and pectoralis muscle activity in flying doves. They used cutting-edge techniques for the aerodynamic and wing shape measurements and they used well-established methods to measure activity and length of the pectoralis muscle. The authors then developed two mathematical models to estimate the instantaneous force vector produced by the pectoralis throughout the wing stroke. Finally, the authors applied their mathematical models to other-sized birds in order to compare muscle physiology across species.

The strength of the methods is that they smoothly incorporate techniques from many complementary fields to generate a comprehensive model of pectoralis muscle function during flight. The high-speed structured-light technique for quantifying surface area during flight is novel and cutting-edge, as is the aerodynamic force platform used. These methods push the boundaries of what has historically been used to quantify their respective aspects of bird flight and their use here is exciting. The methods used for measuring muscle activation and length are standard in the field. Together, these provide both a strong conceptual foundation for the model and highlight its novelty. This model allows for estimations of muscle function that are not feasible to measure in live birds during flight at present. The weakness of this approach is that it relies heavily on a series of assumptions. While the research presented in this paper makes use of powerful methods from multiple fields, those methods each have assumptions inherent to them that simplify the biological system of study. This reduction in the complexity of phenomena allows the specific measurements to be made. In joining the techniques of multiple fields to study the greater complexity of the phenomenon of interest, the assumptions are all incorporated also. Furthermore, assumptions are inherent to mathematical modeling of biological phenomena. That being said, the authors acknowledge and justify their assumptions at each step and their model seems to be quite good at predicting muscle function.

Indeed, the authors achieve their aims. They effectively integrate methods from multiple disciplines to explore the coordination and function of the pectoralis and supracoracoideus muscles during flight. The conclusions that the authors derive from their model address the intended research aim.

The authors demonstrate the value of such interdisciplinary research, especially in studying complex behaviors that are difficult or infeasible to measure in living animals. Additionally, this work provides predictions for muscle function that can be tested empirically. These methods are certainly valuable for understanding flight but also have implications for biologists studying movement and muscle function more generally.

Thank you for your thorough and positive review. We appreciate that you read our manuscript carefully and gave detailed feedback.

Recommendations For The Authors:

I thought that your manuscript was very interesting and your integration of techniques from multiple fields was effective. You address the weaknesses I highlighted in the public review well throughout the manuscript.

Thank you for your well-measured feedback on this weakness and how we addressed it.

I sometimes found that the manuscript was difficult to follow. With the interdisciplinary nature of your work, your manuscript has a lot of complexity. Your introduction is clear and I think that the last paragraph outlines your study very well. In the subsequent sections, the sub-headings are helpful, but I think your manuscript could be improved by indicating where those subsections fit into the phases you outline in your introduction (namely, muscle function, kinematics and aerodynamics, and mathematical modeling).

Complied: throughout the manuscript we made modifications to improve the clarity. We also added a brief organizational summary statement at the beginning of the results section which outlines the contents of the results section in the context of the language introduced in the introduction. Finally, we reorganized the supplemental figures into eLife’s favored format of “figure supplements”, so that each extra figure is now associated with a figure in the main text. This should help the reader access information in an easier, hierarchical manner.

Reviewer #2 (Public Review):

In this work, the authors investigated the pectoralis work loop and the function of the supracoracoideus muscle in the down stroke during slow flight in doves. The aim of this study was to determine how aerodynamic force is generated, using simultaneous high-speed measurements of the wings' kinematics, aerodynamics, and activation and strain of pectoralis muscles during slow flight. The measurements show a reduction in the angle of attack during mid-downstroke, which induces a peak power factor and facilitates the tensioning of the supracoracoideus tendon with pectoralis power, which then can be released in the up-stroke. By combining the data with a muscle mechanics model, the timely tuning of elastic storage in the supracoracoideus tendon was examined and showed an improvement of the pectoralis work loop shape factor. Finally, other bird species were integrated into the model for a comparative investigation.

The major strength of the methods is the simultaneous application of four high-speed techniques - to quantify kinematics, aerodynamics and muscle activation and strain - as well as the implementation of the time-resolved data into a muscle mechanics model. With a thorough analysis which supports the conclusions convincingly, the authors achieved their goal of reaching an improved understanding of the interplay of the pectoralis and supracoracoideus muscles during slow flight and the resulting energetic benefits.

Thank you for your helpful and positive review. We appreciate that you summarized our manuscript accurately in a way that can help the reader.

Recommendations For The Authors:

The manuscript is very detailed and appears a bit long, including all the supplementary materials. It seems that the manuscript could easily have been separated into several publications, especially the comparative investigation including other extant bird species into the new model could have been a separate publication. This would have reduced the length of the supplements.

Thank you for your feedback on our manuscript; we made numerous improvements to improve the readability. Hence, we decided to not cut the supplement short or split it into more papers. We chose eLife because we wanted to publish this study in one complete manuscript. This has three benefits: (1) The reader can find all information in one well-edited paper at one publisher that is open-access and high-quality. (2) The first author works in industry and gets no benefits from publishing multiple papers, and hence he opted to publish one with support of the author team. (3) The senior author is not interested in fragmented publishing. Rather, he writes fewer, more comprehensive integrative papers because that is ultimately more informative for the reader: one trusted published source has all that is important to know based on this completed research project. Overall, we weren’t able to find technical information that shouldn't go in the paper using the lens of reproducibility, so the supplement is relatively long. Combining three methods (kinematics, forces, muscles), of which two are only available in the senior author’s lab, and extensive math (two new integrative models plus scaling laws) requires sharing the information needed for replication for all approaches we combine.

Also, some figure captions are very long and some of the content might have been included in the main text.

Complied: thank you for helping us streamline the captions. We reviewed all the figure captions and removed material that is repeated in the main text, but not essential to understanding the figures. However, because of the length of the manuscript and our desire to make the manuscript readable and clear, we left all other text in the captions intact so they remain readable independently of the main text. This way, the reader does not have to go searching for information in the main text just to make sense of the figures. This is especially important because readers often read the figures first before deciding if they want to read the main text completely. In addition, we moved two panels from Figure 2 into its associated figure supplement, because it was not a main point in the text, and hence this helped reduce the length of the caption in figure 2.

Author Response

The authors wish to thank the Reviewers for valuable and constructive comments that will help up improve the paper’s quality.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

This manuscript builds upon the authors' previous work on the cross-talk between transcription initiation and post-transcriptional events in yeast gene expression. These prior studies identified an mRNA 'imprinting' phenomenon linked to genes activated by the Rap1 transcription factor (TF), a surprising role for the Sfp1 TF in promoting RNA polymerase II (RNAPII) backtracking, and a role for the non-essential RNAPII subunits Rpb4/7 in the regulation of mRNA decay and translation. Here the authors aimed to extend these observations to provide a more coherent picture of the role of Sfp1 in transcription initiation and subsequent steps in gene expression. They provide evidence for (1) a physical interaction between Sfp1 and Rpb4, (2) Sfp1 binding and stabilization of mRNAs derived from genes whose promoters are bound by both Rap1 and Sfp1 and (3) an effect of Sfp1 on Rpb4 binding or conformation during transcription elongation.

Strengths:

This study provides evidence that a TF (yeast Sfp1), in addition to stimulating transcription initiation, can at some target genes interact with their mRNA transcripts and promote their stability. Sfp1 thus has a positive effect on two distinct regulatory steps. Furthermore, evidence is presented indicating that strong Sfp1 mRNA association requires both Rap1 and Sfp1 promoter binding and is increased at a sequence motif near the polyA track of many target mRNAs. Finally, they provide compelling evidence that Sfp1-bound mRNAs have higher levels of RNAPII backtracking and altered Rpb4 association or conformation compared to those not bound by Sfp1.

Weaknesses:

The Sfp1-Rpb4 association is supported only by a two-hybrid assay that is poorly described and lacks an important control. Furthermore, there is no evidence that this interaction is direct, nor are the interaction domains on either protein identified (or mutated to address function).

Indeed, our two hybrid, immunoprecipitation and imaging results do not allow us to conclusively discern whether the interaction between Rpb4 and Sfp1 is direct or indirect. While the interaction holds significance, we consider the direct versus indirect distinction to be of secondary importance in the context of this paper. We intend to give more attention to this matter in our revised paper. In addition, we will make an effort to investigate an in vitro interaction between Sfp1 and Rpb4 by employing purified Sfp1 and Rpb4 proteins.

The contention that Sfp1 nuclear export to the cytoplasm is transcription-dependent is not well supported by the experiments shown, which are not properly described in the text and are not accompanied by any primary data.

We note that this assay has been developed and published in prior research by Lee, M. S., M. Henry, and P. A. Silver. (G&D, 1996) and was reported in a number of subsequent papers. Reassuringly, our conclusion is supported by the observation that Sfp1 binds to Pol II transcripts co-transcriptionally suggesting that Sfp1 is exported in the context of the mRNA.

The presence of Sfp1 in P-bodies is of unclear relevance and the authors do not ask whether Sfp1-bound mRNAs are also present in these condensates.

In the revised paper, we will indicate that we do not know whether RP mRNAs are present in the actual foci shown in Fig. 1B.

Further analysis of Sfp1-bound mRNAs would be of interest, particularly to address the question of whether those from ribosomal protein genes and other growth-related genes that are known to display Sfp1 binding in their promoters are regulated (either stabilized or destabilized) by Sfp1.

Fig. 4A, C and D show that RP mRNAs become destabilized in sfp1Δ cells.

The authors need to discuss, and ideally address, the apparent paradox that their previous findings showed that Rap1 acts to destabilize its downstream transcripts, i.e. that it has the opposite effect of Sfp1 shown here.

We would like to thank Reviewer 1 for this valuable comment. In the revised paper, we will delve into our hypothesis suggesting that Rap1 is likely responsible for regulating the imprinting of other proteins, that, in turn, lead to the destabilization of mRNAs, such as Rpb4.

Finally, recent studies indicate that the drugs used here to measure mRNA stability induce a strong stress response accompanied by rapid and complex effects on transcription. Their relevance to mRNA stability in unstressed cells is questionable.

Half-lives were determined mainly by the GRO analysis of optimally proliferating cells. This method does not requires any drug or stressful treatment. The results obtained by this method were consistent with the those obtained after thiolutin addition. Nevertheless, in our revised manuscript, we plan to supplement the half-life data with results obtained by subjecting cells to a temperature shift to 42°C, a natural method to block transcription in wild-type (WT) cells. This approach to determine half-lives has been previously reported in our publications, such as Lotan et al. (2005, 2007) and Goler Baron et al. (2008). This may rule out effects of the drug on halfe-life.

Reviewer #2 (Public Review):

Summary:

The manuscript by Kelbert et al. presents results on the involvement of the yeast transcription factor Sfp1 in the stabilisation of transcripts whose synthesis it stimulates. Sfp1 is known to affect the synthesis of a number of important cellular transcripts, such as many of those that code for ribosomal proteins. The hypothesis that a transcription factor can remain bound to the nascent transcript and affect its cytoplasmic half-life is attractive, but the methods used to demonstrate the half-life effects and the association of Sfp1 with cytoplasmic transcripts remain to be fully validated, as explained in my comments on the results below:

Comments on methodology and results:

1. A two-hybrid-based assay for protein-protein interactions identified Sfp1, a transcription factor known for its effects on ribosomal protein gene expression, as interacting with Rpb4, a subunit of RNA polymerase II. Classical two-hybrid experiments depend on the presence of the tested proteins in the nucleus of yeast cells, suggesting that the observed interaction occurs in the nucleus. Unfortunately, the two-hybrid method cannot determine whether the interaction is direct or mediated by nucleic acids.

Please see our response to comment 1 of Reviewer 1.

1. Inactivation of nup49, a component of the nuclear pore complex, resulted in the redistribution of GFP-Sfp1 into the cytoplasm at the temperature non-permissive for the nup49-313 strain, suggesting that GFP-Sfp1 is a nucleo-cytoplasmic shuttling protein. This observation confirmed the dynamic nature of the nucleo-cytoplasmic distribution of Sfp1. For example, a similar redistribution to the cytoplasm was previously reported following rapamycin treatment and under starvation (Marion et al., PNAS 2004). In conjunction with the observation of an interaction with Rpb4, the authors observed slower nuclear import kinetics for GFP-Sfp1 in the absence of Rpb4 when cells were transferred to a glucose-containing medium after a period of starvation. Since the redistribution of GFP-Sfp1 was abolished in an rpb1-1/nup49-313 double mutant, the authors concluded that Sfp1 localisation to the cytoplasm depends on transcription. The double mutant yeast cells may show a variety of non-specific effects at the restrictive temperature, and whether transcription is required for Sfp1 cytoplasmic localisation remains incompletely demonstrated.

We concur with Reviewer 2 that any heat inactivation of a temperature-sensitive (ts) protein can result in non-specific effects. In the instance of rpb1-1, these non-specific effects are anticipated because of the transcriptional arrest, which can eventually lead to a reduction in protein content. However, it is worth noting that this process takes some time, whereas the impact on export is more rapid. We note that that this assay has been developed and published in prior research by Pam Silver (op. cit.) and was reported in a number of subsequent papers. Reassuringly, our conclusion is supported by the observation that Sfp1 binds to Pol II transcripts co-transcriptionally.

1. Under starvation conditions, which led to the presence of Sfp1 in the cytoplasm and have previously been correlated with a decrease in the transcription of Sfp1 target genes, the authors observed that a plasmid-based expressed GFP-Sfp1 accumulated in cytoplasmic foci. These foci were also labelled by P-body markers such as Dcp2 and Lsm1. The quality of the microscopic images provided does not allow to determine whether Rpb4-RFP colocalises with GFP-Sfp1.

The submitted PDF figure is of low quality. We believe that high quality figure will be convincing.

1. To understand to which RNA Sfp1 might bind, the authors used an N-terminally tagged fusion protein in a cross-linking and purification experiment. This method identified 264 transcripts for which the CRAC signal was considered positive and which mostly correspond to abundant mRNAs, including 74 ribosomal protein mRNAs or metabolic enzyme-abundant mRNAs such as PGK1. The authors did not provide evidence for the specificity of the observed CRAC signal, in particular, what would be the background of a similar experiment performed without UV cross-linking. In a validation experiment, the presence of several mRNAs in a purified SFP1 fraction was measured at levels that reflect the relative levels of RNA in a total RNA extract. Negative controls showing that abundant mRNAs not found in the CRAC experiment were clearly depleted from the purified fraction with Sfp1 would be crucial to assessing the specificity of the observed protein-RNA interactions. The CRAC-selected mRNAs were enriched for genes whose expression was previously shown to be upregulated upon Sfp1 overexpression (Albert et al., 2019). The presence of unspliced RPL30 pre-mRNA in the Sfp1 purification was interpreted as a sign of co-transcriptional assembly of Sfp1 into mRNA, but in the absence of valid negative controls, this hypothesis would require further experimental validation.

We argue that the 264 CRAC+ genes represent a distinct group with many unique features. Moreover, many CRAC+ genes do not fall into the category of highly transcribed genes.

The biological significance of the 264 CRAC+ mRNAs was demonstrated by various experiments; all are inconsistent with technical flaws. Some examples are:

1. Fig. 2a and B show that most reads of CRAC+ mRNA were mapped to specific location – close the pA sites.

2. Fig. 2C shows that most reads of CRAC+ mRNA were mapped to specific RNA motif.

3. Most RiBi CRAC+ promoter contain Rap1 binding sites (p= 1.9x10-22), whereas the vast majority of RiBi CRAC- promoters do not contain Rap1 binding site. (Fig. 3C).

4. Fig. 4A shows that RiBi CRAC+ mRNAs become destabilized due to Sfp1 deletion, whereas RiBi CRAC- mRNAs do not. Fig. 4B shows similar results due to

5. Fig. 6B shows that the impact of Sfp1 on backtracking is substantially higher for CRAC+ than for CRAC- genes. This is most clearly visible in RiBi genes.

6. Fig. 7A shows that the Sfp1-dependent changes along the transcription units is substantially more rigorous for CRAC+ than for CRAC-.

7. Fig. S4B Shows that chromatin binding profile of Sfp1 is different for CRAC+ and CRAC- genes

Moreover, only a portion of the RiBi mRNAs binds Sfp1, despite similar expression of all RiBi.

Most importantly, these genes do not all fall into the category of highly transcribed genes. On the contrary, as depicted in Figure 6A (green dots), it is evident that CRAC+ genes exhibit a diverse range of Rpb3 ChIP and GRO signals. Furthermore, as illustrated in Figure 7A, when comparing CRAC+ to Q1 (the most highly transcribed genes), it becomes evident that the Rpb4/Rpb3 profile of CRAC+ genes is not a result of high transcription levels. In our revised paper, we will give increased attention to this matter in the Discussion section.

1. To address the important question of whether co-transcriptional assembly of Spf1 with transcripts could alter their stability, the authors first used a reporter system in which the RPL30 transcription unit is transferred to vectors under different transcriptional contexts, as previously described by the Choder laboratory (Bregman et al. 2011). While RPL30 expressed under an ACT1 promoter was barely detectable, the highest levels of RNA were observed in the context of the native upstream RPL30 sequence when Rap1 binding sites were also present. Sfp1 showed better association with reporter mRNAs containing Rap1 binding sites in the promoter region. However, removal of the Rap1 binding sites from the reporter vector also led to a drastic decrease in reporter mRNA levels. Whether the fraction of co-purified RNA is nuclear and co-transcriptional or not cannot be inferred from these results.

The proposed co-transcriptional binding of Sfp1 is based on the findings presented in Figure 5C and Figure S2D, as well as the observed binding of Sfp1 to transcripts containing introns, as shown in Figures 2D and 3B. Our conclusion, which we still uphold, was drawn from the results presented in Figure 3. These results led us to the assertion that the "RNA-binding capacity of Sfp1 is regulated by Rap1-binding sites located at the promoter." We maintain our stance on this conclusion. Indeed, the Rap1 binding site does impact mRNA levels, as highlighted by Reviewer 2. However, "construct E," which possesses a promoter with a Rap1 binding site, exhibits lower transcript levels compared to "construct F," which lacks such a binding site in its promoter. Despite this difference in transcript levels, Sfp1 was able to pull down the former transcript but not the latter, even though expression of the former gene is relatively low. Thus, the results appear to be more reliant on the specific capacity of Sfp1 to interact with the transcript rather than on the transcript's expression level.

1. To complement the biochemical data presented in the first part of the manuscript, the authors turned to the deletion or rapid depletion of SFP1 and used labelling experiments to assess changes in the rate of synthesis, abundance, and decay of mRNAs under these conditions. An important observation was that in the absence of Sfp1, mRNAs encoding ribosomal protein genes not only had a reduced synthesis rate but also an increased degradation rate. This important observation needs careful validation, as genomic run-on experiments were used to measure half-lives, and this particular method was found to give results that correlated poorly with other measures of half-life in yeast (e.g. Chappelboim et al., 2022 for a comparison). Similarly, the use of thiolutin to block transcription as a method of assessing mRNA half-life has been reported to be problematic, as thiolutin can specifically inhibit the degradation of ribosomal protein mRNA (Pelechano & Perez-Ortin, 2008). Specific repressible reporters, such as those used by Baudrimont et al. (2017), would need to be tested to validate the effect of Sfp1 on the half-life of specific mRNAs. Also, it would be very difficult to infer from the images presented whether the rate of deadenylation is altered by Sfp1.

Various methods exist for assessing mRNA half-lives (HLs), and each of them carries its own set of challenges and biases. Consequently, it becomes problematic to directly compare HL values of a specific mRNA when different methods are employed. The superiority of one particular method over others remains unclear. However, they all exhibit a high degree of reliability when it comes to comparing different strains under the identical conditions using a single method.

Estimating half-lives through the GRO approach is a non-invasive method, applied on optimally proliferating cells, which has been employed in numerous publications. While no method is without its limitations, we consider this approach to be among the most dependable. Our HL determination using thiolutin to block transcription provided results that were consistent with the values obtained by the GRO approach.

Nevertheless, in our revised manuscript, we plan to supplement the HL data, obtain by thiolutin, with results obtained by subjecting cells to a temperature shift to 42°C, a natural method to block transcription in wild-type (WT) cells. This approach to determine HLs has been previously reported in our publications, such as Lotan et al. (2005, 2007) and Goler Baron et al. (2008).

1. The effects of SFP1 on transcription were investigated by chromatin purification with Rpb3, a subunit of RNA polymerase, and the results were compared with synthesis rates determined by genomic run-on experiments. The decrease in polII presence on transcripts in the absence of SFP1 was not accompanied by a marked decrease in transcript output, suggesting an effect of Sfp1 in ensuring robust transcription and avoiding RNA polymerase backtracking. To further investigate the phenotypes associated with the depletion or absence of Sfp1, the authors examined the presence of Rpb4 along transcription units compared to Rpb3. One effect of spf1 deficiency was that this ratio, which decreased from the start of transcription towards the end of transcripts, increased slightly. The results presented are largely correlative and could arise from the focus on very specific types of mRNAs, such as those of ribosomal protein genes, which are sensitive to stress and are targeted by very active RNA degradation mechanisms activated, for example, under heat stress (Bresson et al., 2020).

Figure 7A illustrates a significant reduction in Rpb4/Rpb3 ratios along the transcription unit in WT cells. This reduction is notably more pronounced in CRAC+ genes compared to the highly transcribed quartile (Q1), which includes all ribosomal protein (RP) genes, and it is completely absent in sfp1∆ cells. Furthermore, it's important to highlight that the CRAC+ gene group displays a wide range of transcription rates, as measured by either Rpb3 ChIP or GRO (Figure 6A). Given these observations, it is challenging to reconcile how the heightened sensitivity of RP mRNA degradation in response to stress could account for the more pronounced differences in the configuration of the Pol II elongation complex that are detected in CRAC+ genes under standard culture conditions in wt cells.

Correlative studies are particularly informative when a gene mutation eliminates a correlation, and this is precisely the type of study depicted in Figure 7B-C. The configuration of elongating Pol II (as reflected by Rpb4/Rpb3 ratios) and the backtracking index are both transcriptional outputs. It is difficult to envision how stress-induced destabilization of RP mRNAs could explain the twofold higher correlation between these two parameters observed in CRAC+ genes under non-stressful conditions in WT cells (Figure 7B).

Furthermore, it's worth noting that in WT cells, CRAC+ genes did not display any apparent unusual destabilization, but rather exhibited higher (not lower) mRNA stability compared to CRAC- genes (Figure 7C).

Strengths: - Diversity of experimental approaches used - Validation of large-scale results with appropriate reporters

Weaknesses: - Choice of evaluation method to test mRNA half-life - Lack of controls for the CRAC results

Author Response

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

Reviewer #1 (Recommendations For The Authors):

Weaknesses: One minor weakness in this study is the conclusion that the guide RNAs didn't seem to have unique effects on GnRH cFos expression or the reproductive phenotypes. Though the data indicate a 60-70% knockdown for both gRNA2 and gRNA3, 3 of the 4 gRNA2 mice had no cFos expression in GnRH neurons during the time of the LH surge, whereas all mice receiving gRNA3 had at least some cFos/GnRH co-expression. In addition, when mice were re-categorized based on reduction (>75%) in kisspeptin expression, most of the mice in the unilateral or bilateral groups received gRNA2, whereas many of the mice that received gRNA3 were in the "normal" group with no disruption in kisspeptin expression. Thus, additional experiments with increased sample sizes are needed, even if the efficacy of the ESR1 knockdown was comparable before concluding these 2 gRNAs don't result in unique reproductive effects.

Response: A draw back of the CRISPR approach is the substantial mosaicism in gene knockdown that is unavoidable due to the nature of DNA repair in each cell relying on several competing pathways. As such, variable knockdown occurs in each mouse as shown in Fig.1C. In the case of the correlation between RP3V ESR1 knockdown and cFos in GnRH neurons (Fig.4C), three gRNA3 and four 4 gRNA2 mice look to be very similar with two gRNA3 mice having knockdown but normal cFos activation. The reasons for this are not known and it is very likely chance that these two (of nine) mice happened to have received gRNA3. This issue becomes exacerbated when animal group numbers unintentionally become smaller with the re-grouping on the basis of kisspeptin expression. The key point here is that each “kisspeptin grouping” remains mixed in terms of gRNA2 and gRNA3 mice so that gRNA3 mice did contribute to the “bilateral group” even if it was only one of four mice. The practicalities of repeating this work are substantial and we do not think justified. We would note that we have previously used Kiss-Cre mice to undertake CRISPR knockdown of ESR1 in RP3V kisspeptin neurons but this failed to target sufficient cells with Cas9 to be experimentally useful.

In Figure 2B (gRNA2), there appear to be 4 mice (4 lines) that have a normal cycle length and then drop to 0 for the cycle length. However, in the Figure legend, it states that there were 3 gRNA2 mice that had a cycle length of 0. Can the authors clarify if it was 4 mice (as indicated in Figure 2B) or 3 mice (as indicated in the legend) that received gRNA2 and exhibited constant estrus?

Response: We have now clarified in the text that 3 gRNA2 mice went into constant estrus, the other mouse was in constant diestrus, also scored as “0” cycles.

In Figure 3H, there is one green data point that has an LH level of around 0.15 and % VGAT with ESR1 around 10%. However, that data point does not appear in Figures 3I and 3J, when you would expect it to be in a similar place (~10%) on the x-axis in those Figures. Was it excluded? If so, please elaborate on the justification for excluding that data point. Response: This was one of the three mice that exhibited no LH pulses so we were only able to report on mean LH levels.

Similarly, in Figure 3K, there is a blue data point that is almost at 0 for both the x-axis and the y-axis. However, that data point does not show up in Figures 3L and 3M around 0 on the x-axis as you would expect. Can the authors clarify where this data point went in Figures 3L and 3M?

Response: This was one of the three mice that exhibited no LH pulses so we were only able to report on mean LH levels.

Reviewer #2 (Recommendations For The Authors):

Finally, the study leaves unanswered the role of GABA itself. As there was no evident phenotype for the ESR1 knockdown in GABA neurons that do not coexpress kisspeptin, this suggests that GABA neurotransmission in the preoptic area is not involved in the estrogen regulation of LH secretion.

Response: The current evidence for no substantial role of GABA from RP3V neurons in the LH surge agrees with our prior in vivo work showing that low frequency optogenetic stimulation of RP3V kisspeptin neurons (only GABA release) has no impact on LH secretion (doi: 10.1523/JNEUROSCI.0658-18.2018).

1. Title. The present data do not clearly demonstrate the blockade of the LH surge. Thus, the statement that "abolishes the preovulatory surge" is an overinterpretation of the findings.

Response: We agree and now use “suppresses the preovulatory surge”.

1. Fig. 3. The numbers of individual data points per group change for the different LH pulse parameters, but they should not (Fig. 3 E-G).

Response: This occurs because one mouse in each group had no LH pulses so that only a mean value was available for these mice.

1. Fig. 4. (4B) The use of only one terminal blood collection (4B) is insufficient to comprehensively characterize the LH surge. It is not possible to conclude what was the actual effect on the LH surge, whether a blockade or altered amplitude or timing. Serial blood samples at 30- or 60-minute intervals should be used. For comparative purposes, the pulsatile LH secretion, which does not seem to be a major outcome in the study, was fully characterized (Fig. 3). (4C) The linear correlation between c-Fos/GnRH and RP3V/ESR1 appears to be well-fitted for gRNA2 (blue) but not gRNA3 (green). Although this is interpreted as an important result of the study, its description and consistency are not so clear. Authors should perform an Anova/ Kruskal-Wallis analysis of these data as a column graph (as in Fig. 4A, B) and discuss the discrepancies between gRNA2 and gRNA3.

Response: As noted in the manuscript, we agree that a single point LH measurement is a relatively inaccurate assessment of the LH surge and very likely underlies much of the substantial variability between mice. However, the extended duration of cFos expression in GnRH neurons at the time of the surge is a much more accurate “single point” indicator and we feel that these results better reflect the state of surge activation. This was noted in the original manuscript.

The linear correlations for the different preoptic regions are undertaken on the complete data set not on individual gRNA groups due to low N numbers in the sub-divided groups. However, column graphs of the RP3V and MPN look the same as Fig.4A and would not change the current interpretation. Please see comments to Reviewer 1 on discrepancies between gRNA2 and 3.

1. Table. It is unclear why the % VGAT with ESR1 was not statistically reduced in the "bilateral" animals. Would this mean that the ESR1 knockdown was not effective in this subgroup with the more consistent effects?

Response: Yes, this would be a reasonable interpretation suggesting that mice with kisspeptin ablation may have had a slightly different overall impact on ESR1 in VGAT neurons. However, this was not discernable from examining the anatomical distribution of AAV.

1. Discussion 1st paragraph. It is interpreted that mice lacking kisspeptin expression "failed to exhibit an LH surge". This should be revised.

Response: We believe that this is a correct statement. Mice lacking kisspeptin had LH surge values between 0.8 and 2.1 ng/ml that we would not consider consistent with being a surge.

1. Immunohistochemistry. It is not clear in the text how a cross-reaction between goat antirabbit 568 (ERa) and goat antirabbit/streptavidin 647 (mChery) was avoided when used in the same reaction.

Response: We were forced into this option due to the lack of different primary antisera to ESR1 and mCherry. We first stained for rabbit ESR1 detected by biotin anti-rabbit/ strep647 which resulted in confined nuclear staining (pseudo-blue; far red). The subsequent staining for rabbit mCherry was detected by goat anti-rabbit 568 that will indeed cross-react by binding to any free epitopes on the rabbit ESR1 primary antibody. However, this would not compromise interpretation as additional 568 labelling to the nucleus is essentially irrelevant when examining far red 647 nm emission and only mCherry cytoplasmic immunoreactivity was used to define the anatomical locations of the AAV spread. This is now clearly explained in the Methods section.

1. Statistical analysis. It is unclear when repeated measures Wilcoxon tests were used in the manuscript.

Response: Thank you for pointing this out. Only Wilcoxon paired test were used. Amended.

1. Data Availability. Further reference to supplementary information files was not found in the manuscript.

Response: A supplementary file with individual data for each mouse is now attached.

Reviewer #3 (Recommendations For The Authors):

Weaknesses:

One aspect for which I have ambiguous feelings is the minimal level of detail regarding the HPG axis and its regulation by estrogens. This limited amount of detail allows for an easy read with the well-articulated introduction quickly presenting the framework of the study. Although not presenting the axis itself nor mentioning the position of GnRH neurons in this axis or its lack of ERα expression is not detrimental to the understanding of the study, presenting at least the position of GnRH neurons in the axis and their critical role for fertility would likely broaden the impact of this work beyond a rather specialist audience.

Response: We agree that this would provide a more complete picture and have modified the Introduction.

The expression of kisspeptin constitutes a key element for the analysis and conclusion of the present work. However, the quality of the kisspeptin immunostaining seems suboptimal based on the representative images. The staining primarily consists of light punctuated structures and it is very difficult to delineate cytoplasmic immunoreactive material defining the shape of neurons in LacZ animals. For some of the cells marked by an arrow, it is also sometimes difficult to determine whether the staining for ESR1 and Kp are in the same focal plane and thus belong to the same neurons. Although this co-expression is not critical for the conclusions of the study, this begs the question of whether Kp expression was determined directly at the microscope (where the focal plan can be adjusted) or on the picture (without possible focal adjustment). Moreover, in the representative image of Kp loss, several nuclei stained for fos (black) show superimposed brown staining looking like a dense nucleus (but smaller than an actual nucleus). This suggests some sort of condensed accumulation of Kp immunoproduct in the nucleus which is not commented. Given the critical importance of this reported change in Kp expression for the interpretation of the present results, it is important to provide strong evidence of the quality/nature of this staining and its analysis which may help interpret the observed functional phenotype.

Response: The kisspeptin immunoreactivity represents both fiber and cytoplasmic staining that can be difficult to discern in some cases. The reviewer can be assured that all counts were undertaken “live” on the microscope so that the plane of focus was adjusted to establish co-labelling. Please note that the nuclear immunoreactivity is for ESR1 and not cFos. Regardless, we struggle to see condensed brown staining over the black nuclei as suggested by the Reviewer. The kisspeptin staining is light brown and confined to just a few fibers in Fig.5B.

As acknowledged in the introduction, this study is not the first to use in vivo Crisp-Cas editing to demonstrate the role of kisspeptin neurons in the control of positive feedback. Although the present work achieved this indirectly by targeting VGAT neurons, I was surprised that the paper did not include more comparison of their results with those of Wang et al., 2019. In particular, why was the present approach more successful in achieving both lack of surge and complete acyclicity?

Response: Wang et al., reported an ~60% reduction in ESR1 expression in Kiss1-Cre (Elias) driven Cas9-expressing cells in the AVPV. As they did not examine kisspeptin expression itself it is unknown to what degree their editing impacted upon kisspeptin neurons. The other differentiating factor was that Wang focussed on the AVPV that only contains a minority of the preoptic kisspeptin population whereas we targeted the AVPV and PeN together. Thus, we suspect that the Wang phenotype arises from insufficient ESR1 knockdown in just the AVPV sub-population of preoptic kisspeptin neurons. We have added a comment to the Discussion as requested.

Moreover, why is it that targeting ESR1 in a selected fraction of GABAergic neurons can lead to a near-complete absence of Kp expression in this region? This is briefly discussed in the penultimate paragraph but mostly focuses on the non-kisspeptinergic GABA neurons rather than those co-expressing the two markers.

Response: We have modified this section to try and make it clear that it is very likely that all RP3V kisspeptin neurons would have been targeted to express Cas9 in this mouse model. Our very recent unpublished RNA scope data show that >80% of RP3V kisspeptin neurons express Vgat mRNA in adult mice.

• Unless I have missed it, the target sequence of the guide RNAs is not mentioned. For reproducibility purposes and to allow comparison with Wang et al., 2019, this information should be provided.

Response: The target sequences for gRNA2 and gRNA3 were around exon 3 and are provided in the Supplementary files of McQuillan et al., 2022 (https://doi.org/10.1038/s41467-022-35243-z). The Wang et al study used the unusual strategy of designing sense and antisense gRNAs against the same sequence in Exon1.

• The first result section is devoted to the design and validation of the guide RNA reports data that were recently published (McQuillan et al., 2022). It is actually acknowledged that the design was reported previously but as written it is not clear whether the actual validation was already reported. This should be said more clearly.

Response: Clarified as requested.

• What was the rationale for choosing gRNA 2 and 3 and not 3 and 6 like in the McQuillan study?

Response: As all three gRNAs worked equally well, the choice of 2 and 3 was entirely pragmatic and only based upon quantities of packaged AAVs that we had produced and were available at the time.

• Introduction, 4th paragraph: It would be clearer if GABAa receptor dynamics was replaced by GABAa receptors mediated neurotransmission or any other verbiage avoiding possible confusion with receptor mobility.

Response: Clarified as requested.

• The section reporting the location of ESR1 knockdown is really clear about the number of animals included in the functional analyses. This is less clear for the number of mice involved in the evaluation of the extent of ESR1 knockdown in the previous section. Specifically, the text reports that 8 and 9 mice received gRNA3 in PVpo and MPN respectively, but the figure shows 7 and 8. This is likely explained by the mouse that was excluded due to normal ESR1 despite the correct positioning of the injection site. It is thus unclear whether this mouse was included in the calculation of the mean percentage of neurons reported in the previous page. Logically, this mouse should have been removed from this analysis and it is assumed that the sample size reported in the text is incorrect.

Response: thank you for picking this up - you are correct. In reviewing this point we realized that the gRNA-lacZ RP3V N numbers also were incorrect and have re-analyzed the data set completely resulting in even stronger significance levels.

• In the section « CRISPR knockdown ESR1 in RP3V GABA-kisspeptin neurons », the extent of ESR1 knockdown is expressed in a counterintuitive manner as « <20% » which is thought to represent the percentage of cells expressing ESR1 rather than the actual knockdown (>80%). This should be clarified.

Response: Corrected as noted.

• Page 6, 3rd line before the last paragraph, there is a mismatch between the highest p value reported in the text (0.242) and the value reported in the table (0.0242).

Response: Corrected thank you.

• Similar to presenting F values for ANOVAs, H values should also be presented for Kruskal Wallis tests.

Response: Values have been added.

• Immunohistochemistry : Origin and reference numbers of all primary antibodies should be reported as well as citation of studies where they have been validated. Although these protocols are standard, information regarding the duration of incubation is necessary to allow replication or for comparison purposes.

Response: We have included the RRID numbers for each of these antisera and added information on incubation times.

• The section on data availability mentions the existence of supplementary files, but I see none.

Response: These have now been attached.

• There are several typos or redundancies to be corrected. Here are a few examples but the manuscript should be carefully double-checked.

Introduction, 3rd paragraph, line 4: upregulated

Introduction, 4th paragraph, 4th line: « to » or « through » not both.

Page 7, line 11 : Kruskal

Page 7, 6th line to the end: does this indicate 'the' general utility?

Page 8, 2nd paragraph, line 13: Crispr

Response: Thank you for these edits.

Author Response

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

Reviewer #1 (Recommendations for the Authors):

The authors provide their data and code via Github, and that shiny apps allow easy access to their data. However, spending a few minutes with the snRNAseq app I could not figure out how to search for individual genes (e.g. DBH) on their web interface. Some changes could help to make this app more user-friendly.

While it was not possible to easily modify the user interface of the snRNA-seq app itself, we have instead added two additional supplementary figures displaying screenshots and schematics with sequential instructions that provide a short tutorial showing how to search for individual genes and display either spatial gene expression (for the Visium SRT data) or gene expression by cluster or population (for the snRNA-seq data) in each interactive web app (Figure 3-figure supplement 20-21). We hope this makes the apps more accessible and assists users to more easily query specific genes that they are interested in.

The first sentence of the abstract and line 70 on page 2 need to be revised for language / grammar / clarity.

We have revised these two sentences. Line 70 on page 2 contained a typo / copy-paste error. Thank you for pointing this out.

Reviewer #2 (Recommendations For The Authors):

While the efforts of the authors to identify NE neurons in the LC is appreciated, the data fall a little short of conclusively calling these neurons solely noradrenergic as there is an apparent lack of overlap between TH and SLC6A2 in the spots. Undoubtedly, some spots contain both which is consistent with the RNA scope results, but there is clearly a pattern that shows spots that don't contain both. It would be worth testing the presence of other catecholamines in some of these certain spots particularly dopamine (Kempadoo et al. 2016, Takeuchi et al., 2016, Devoto et al. 2005).

We agree this is an important point. To more rigorously investigate whether TH is co-expressed within cells that produce other catecholamines, particularly dopamine (DA) in addition to norepinephrine (NE), we have included additional analyses of the snRNA-seq and Visium data, as well as generated additional RNAscope data in the revised manuscript, as follows.

(i) We investigated the spatial expression of DA neuron marker genes besides TH, including SLC6A3 (encoding the dopamine transporter), ALDH1A1, and SLC26A7 in the Visium samples (Figure 3-figure supplement 15), which shows that these genes are not strongly expressed within the manually annotated LC regions in the Visium samples (see Figure 2-figure supplement 1).

(ii) We investigated expression of DA neuron marker genes SLC6A3, ALDH1A1, and SLC26A7 in the snRNA-seq clustering (updated heatmap in Figure 3-figure supplement 8), which shows minimal expression of these genes within the NE neuron cluster (cluster 6).

(iii) Despite the data above suggesting little expression of markers for DA neurons within the human LC, we wanted to investigate this question more thoroughly with an orthogonal method given that relatively lower coverage in the sequencing approaches may miss expression, particularly for more lowly expressed transcripts. We generated new high-resolution RNAscope smFISH images at 40x magnification for samples from 3 additional donors (Br8689, Br5529, and Br5426) showing expression of NE neuron marker genes (DBH and TH), a 5-HT neuron marker gene (TPH2), and a DA neuron marker gene (SLC6A3) within individual cells within the LC regions in these samples. Expression of SLC6A3 within individual NE neurons (identified by co-expression of DBH and TH) was not apparent in these RNAscope images (Figure 3-figure supplement 16).

Together with the previous high-magnification RNAscope images showing co-expression of NE neuron marker genes (DBH, TH, and SLC6A2) within individual NE neurons (Figure 3-figure supplement 4), these new results further strengthen the conclusion that the observed TH+ cells we profiled in the LC are NE-producing neurons. In our view, the lack of observed co-expression of TH and SLC6A2 within some individual Visium spots is likely due to sampling variability and relatively lower sequencing coverage in the Visium data, rather than a true lack of co-expression. We have included additional text in the Results and Discussion further discussing this issue.

Likewise, given the low throughput of RNA scope, and the fact that it was not done in a systematic manner, it does not conclusively identify the cell types in the region. It might be worth a systematic survey of the cells in the region with both NE and DA markers. Otherwise, it is suggested that the authors be more conservative with their annotations.

As discussed above, we have now generated additional high-magnification RNAscope images for 3 independent donors (Br8689, Br5529, and Br5426), visualizing expression of two NE neuron marker genes (DBH and TH), one 5-HT neuron marker gene (TPH2), and one DA neuron marker gene (SLC6A3, encoding the dopamine transporter) within individual cells within the LC region in each sample (Figure 3-figure supplement 16). Expression of the DA neuron marker gene (SLC6A3) within individual NE neuron cell bodies (identified by co-expression of DBH and TH) was not apparent in these RNAscope images. Together with our previous RNAscope images showing co-expression of DBH, TH, and SLC6A2 within individual cells (Figure 3-figure supplement 4), in our view, these results provide strong evidence that the observed TH+ cells in the LC are NE-producing neurons, and the data do not provide supporting evidence for the existence of DA-synthesizing neurons in the human LC.

For the manual annotation, it would be useful to include HE tissue images to better understand how the annotations were derived especially because the annotations are not well corroborated by the clustering.

We have now included the H&E stained histology images for the Visium samples in Figure 2-figure supplement 2A, which can be compared with the previous figures showing the manual annotations for the LC regions (Figure 2-figure supplement 1). The histology images can also be viewed at higher resolution through the Shiny web app (https://libd.shinyapps.io/locus-c_Visium/).

The unsupervised clustering is certainly contingent on the number of genes detected, which is in turn dependent on the quality of the material and the success of the experiment. It is unclear from the methods whether the samples were pooled for clustering. If they were pooled, the author might consider using only the samples with UMIs > 500. The low UMI may represent free-floating RNA, suggesting issues with tissue permeabilization in turn influencing the ability to confidently associate genes with spots. Sticking with the higher quality sample may improve the ability to perform unsupervised clustering.

For the spot-level unsupervised clustering using BayesSpace, our aim was to demonstrate whether it is feasible to segment the LC and non-LC regions in the Visium samples in a data-driven manner using a spatial clustering algorithm, instead of relying on manual annotations. We performed clustering across samples (i.e. pooled) -- we have included additional wording in the text and figure caption to clarify this. We agree with the reviewer there may be further optimizations possible, such as filtering out spots or samples with low UMI counts. However, filtering out low-UMI spots may also confound the clustering if low-UMI spots are associated with biological signal (e.g. preferentially located in white matter regions).

Overall, we found that applying data-driven methods such as BayesSpace to segment the LC and non-LC regions did not perform sufficiently to rely on for our downstream analyses (Figure 2-figure supplement 6), and, in our view, further incremental optimizations were unlikely to reach sufficient performance and robustness, so we chose to rely on the manual annotations instead. In addition, as noted in the Results, this avoids potentially inflated false discoveries due to issues of circularity when performing differential gene expression testing between regions defined by unsupervised clustering on the same sets of genes (Gao et al. 2022). We included the BayesSpace results (Figure 2-figure supplement 6) to provide information and ideas to method developers interested in using this dataset as a test case for further development of spatial clustering algorithms. However, further adapting or optimizing these spatial clustering algorithms ourselves was not within the scope of our current work.

It is not entirely clear why the authors used FANS, especially with the scored tissue. Do the authors think this could have negatively influenced the capture of the desired cell type since FANS can compromise the integrity of the nuclei? In other words, have the authors considered that this may have resulted in a loss rather than enrichment? The proportion of "NE" neurons in the snRNA-Seq data is less than 2% in all cases and at its lowest in sample 6522 which does not correspond well with the proportion of tissue that was manually annotated as containing NE cells, even when taken into consideration the potential size difference of cells. In the same vein, in some samples, there are more "5-HT" neurons in the region than "NE" according to the numbers.

As noted in our initial response to reviewers (“Response to Public Review Comments”), we used FANS to enrich for neurons based on our previous success with this approach to identify relatively rare neuronal populations in other brain regions (e.g. nucleus accumbens and amygdala; Tran and Maynard et al. 2021). Based on this previous work, our rationale was that without neuronal enrichment, we could potentially miss the LC-NE population, given the relative scarcity and low absolute number of this neuronal population (e.g. estimates of ~50K total in the entire human LC).

We do not have a definitive answer to the question of whether our use of FANS to enrich for neurons may have led to damage and contributed to the low recovery rate of LC-NE neurons (as well as the relatively increased levels of mitochondrial contamination compared to other brain regions / preparations in the human brain in our hands). Due to our limited tissue resources for this study, we did not have sufficient tissue to perform a direct comparison with non-sorted data. However, we agree with the reviewer that this is plausible, and warrants further investigation in future work. In particular, the relatively large size and fragility of LC-NE neurons, as well as our use of a standard cell straining approach (70 µm, which may not be ideal for this population), may also be contributing factors.

Systematically optimizing the preparation to attempt to increase recovery rate (and decrease mitochondrial contamination) are important avenues for future work, and we have decided to share our data and experiences now to assist other groups performing related work. We have included additional wording in the Discussion to further highlight these issues.

The majority of the snRNA-seq remained unannotated "ambiguous" neurons. It would be highly advantageous to include an annotation for these numerous cells.

These nuclei were unidentifiable due to ambiguous marker gene expression profiles, i.e. expression of pan-neuronal marker genes without clear expression of either excitatory or inhibitory neuronal marker genes (see Figure 3A and Figure 3-figure supplement 8). Since we were not able to clearly identify these clusters, and due to our additional concerns regarding the data quality (e.g. low recovery rate of the NE neuron population of interest, potential cell damage, and mitochondrial contamination), we decided to label these neuronal clusters as “ambiguous” instead of assigning low-confidence cluster labels. We have included additional wording in the Results section to explain this issue.

The most likely explanation for identifying serotonergic neurons in these samples is the inclusion of the Raphe Nucleus within the dissection, especially since these cells do not map to the LC per se. As such, is there a way to neuroanatomically define the potential inclusion of this region from these tissue blocks used? Or to the contrary, definitively demonstrate the exclusion of the Raphe?

As noted in our initial response to reviewers (“Response to Public Review Comments”), our dissection strategy in this initial study precluded the ability to keep track of the exact orientation of the tissue sections on the Visium arrays with respect to their location within the brainstem. Therefore, it is not possible to definitively answer the question of whether the dissections included the raphe nucleus, and if so, which portion of it, based on neuroanatomy from the tissue blocks.

However, during the course of this study and in parallel, ongoing work for other small, challenging brain regions, we developed a number of specialized technical and logistical strategies for keeping track of orientation and mounting serial sections from the same tissue block onto a single spatial array, which is extremely technically challenging. We are now well-prepared for addressing these issues in future studies, e.g. keeping track of the orientation of the dissections and potential inclusion of adjacent neuroanatomical structures. We have included additional details on this issue in the Discussion.

Given that one sample (Visium capture area) was excluded as it did not seem to contain a representation of the LC for the profiling of "NE" cells, does it make sense to include this sample in the analysis of 5HT cells given the authors are trying to make claims about the cell composition in and around the LC? Since there appears to be little 5HT contribution from this sample and its inclusion results in inconsistency across experiments and not any notable advantages, the authors might want to reconsider its inclusion in the results.

We identified a cluster of 5-HT neurons in the snRNA-seq data (Figure 3) and used the Visium samples to further investigate the spatial distribution of this population (Figure 3-figure supplement 9). For the enrichment analyses in the Visium data (Figure 3-figure supplement 9C), we used only the 8 Visium samples that passed quality control (QC). We included the 9th sample (which did not pass QC) in the spot plot visualizations (Figure 3-figure supplement 9A-B) for completeness, but did not base our main conclusions on this sample (in this sample, the tissue resource was likely depleted during earlier sections, so the section for the Visium sample was taken slightly past the extent of the LC within this tissue block). We have included additional wording in the Results section and figure captions to clarify this issue.

For the RNAscope images, it would be useful to include (draw) the manual annotation of the LC to facilitate interpretation. This is especially useful for demonstrating the separate populations of 5HT and "NE" cells. In general, it would be useful to keep a hashed line perimeter for all sections processed by Visium.

We have now added a dashed outline indicating the manually annotated LC region in the RNAscope image showing the full tissue section (Figure 3-figure supplement 11). The high-magnification RNAscope images (Figure 3-figure supplement 4, 16, and 17) show regions entirely within the LC regions -- we have included additional wording to note this in the figure captions. For the Visium spot

plots, we either labeled spots within the annotated regions within the figures or included additional wording in the figure captions to refer to the figures showing the annotations (Figure 2-figure supplement 1).

The authors state that they successfully mapped the NE neuron population from snRNA-seq to the manually annotated regions on the Visium slides. Based on the color-coded map, these results are not very convincing since the abundance of the given transcript profile is extremely low. Here again, it would help to draw a hashed line perimeter on the slide to denote the manually annotated region. Perhaps the authors could try a different strategy for mapping snRNA signal to the slide? However, it appears that the mapping worked better for the capture areas with higher UMI/genes counts. Perhaps the authors should consider using only the slides with high gene/UMI counts.

We agree that the performance of these analyses (Figure 3-figure supplement 14) was not clearly described in the previous version of the manuscript. We have rewritten the corresponding paragraph in the Results section to make it more clear that the mapping (spot-level deconvolution) performance was relatively poor overall, and that we did not use these results for further downstream analyses. We did however want to include these results from the cell2location algorithm to provide information and data for method developers on the challenges of these types of analyses in our dataset (e.g. due to the presence of rare populations, relatively subtle differences in expression profiles between neuronal subpopulations, and potential issues due to large nuclei size and high transcriptional activity for NE neurons). While further approaches for these types of analyses exist, and additional optimizations such as subsetting samples or spots with high UMI counts could also be investigated, in our view, these further optimizations lie outside the scope of our current work. We have also added wording in the figure caption to refer to Figure 2-figure supplement 1, which displays the corresponding annotated LC regions per sample.

It is hard to see if the RNA scope image Supplementary Figure 11 shows co-localization of SLC6A2, TH, and DBH. Having the individual image from each microscope filter along with the merged image is required to properly assess the colocalization of the signals.

We updated the multi-channel RNAscope images to show both the merged channels and individual channels in separate panels (Figure 3-figure supplement 4, 16, and 17), which makes the visualization more clear. Thank you for this suggestion. (Note that the previous Supplementary Figure 11 has been re-numbered to Figure 3-figure supplement 4.)

The heatmap showing the level of marker transcripts shows a much lower expression of specific markers, TH, DBH, SLC6A2 in NE vs other clusters looks surprisingly low (particularly TH), while the much broader marker SLC18A2 (monoamine transporter) is considerably more differential. What do the authors make of this finding?

This is correct. In the snRNA-seq data, we observed that SLC18A2 is one of the most highly differentially expressed (DE) genes in the NE neuron cluster vs. other neuronal clusters, with a high level of expression in the NE neuron cluster (Figure 3C). Note that this heatmap shows the top 70 DE genes (excluding mitochondrial genes) out of the full list of 327 statistically significant DE genes with elevated expression in the NE neuron cluster (the full list of 327 genes is provided in Supplementary File 2C). While all four of these genes (DBH, TH, SLC6A2, and SLC18A2) are identified as statistically significant DE genes, SLC18A2 is the most highly DE out of these and has an especially high level of expression in the NE neuron cluster, as noted by the reviewer (Figure 3C). This could be due to the fact that SLC18A2 transcripts are expressed at higher absolute levels in these neurons than the transcripts that are more specific to LC-NE neurons. While it is true that SLC18A2 is a “broader” marker in the sense that it is found in more cell types -- e.g. cell types within brain nuclei that contain monoaminergic as well as brain nuclei that contain catecholaminergic cells -- expression of SLC18A2 within the LC is highly specific to the catecholaminergic LC-NE neurons given its specialized functional role within monoamine and catecholamine neurons in packaging amine neurotransmitters into synaptic vesicles. We note that SLC18A2 plays a specialized role that is critical to the core function of LC-NE neurons, and hence we are not particularly surprised with this finding and think that one possibility is that this differential expression appears more robustly due to higher absolute levels of the marker.

While it is understandable that the authors decided to include cells/nuclei with high mitochondrial reads, further work is needed to ensure these cells are of sufficient quality to use in an unbiased way knowing that a high percentage of mitochondrial reads in nuclei sequencing is usually indicative of low-quality nuclei. This can be assessed by evaluating the quality of the nuclei with GWA, which stains an intact nuclear membrane acting as a measure of the integrity of the nuclei.

To further investigate these results, we added additional analyses evaluating quality control (QC) metrics for the NE neuron cluster in the snRNA-seq data, which had an unusually high proportion of mitochondrial reads (Figure 3-figure supplement 2, shown also below in comments for Reviewer 3) (see also related Figure 3-figure supplement 1, 3, which were included in the manuscript previously). These additional QC analyses do not show any other problematic values for this cluster, other than the high mitochondrial proportion, so we do not believe this is purely a data quality issue. We are aware that this is an unexpected result -- in most cell populations, a high proportion of mitochondrial reads would be indicative of cell damage and poor data quality. However, we have recently also observed high mitochondrial proportions in other relatively rare neuronal populations characterized by large size and high metabolic demand. As discussed below for Reviewer 3, we believe that this is mitochondrial “contamination”, as there should be no mitochondrial reads per se within the nuclear compartment.

However, it may be possible that in cell populations that have abundant levels of mitochondria and high transcript expression of mitochondrial transcripts in the cell body, that the likelihood of ambient RNA capture of mitochondrial transcripts during nuclear preparation may be higher than for other cell types that have lower expression of mitochondrial transcripts. Hence, we believe that our interpretation is likely correct, i.e. that a combination of technical and biological factors contributes to the inclusion of a relatively high amount of mitochondrial RNA within the droplets for these nuclei. We agree with the reviewer that this finding warrants further investigation in future work. However, in our current study, the tissue resource is depleted for any further experimental validation of this question, so we preferred to provide our data to the community in its current form, while transparently noting this unexpected finding in our results. We have included additional text in the Results section describing the new QC analyses shown in Figure 3-figure supplement 2.

Minor comments:

Line 319-321 could be written more clearly to indicate that due to the lack of resolution in a given spot, there are "contaminating reads" that reduce the precision of the cell profile. This reduced precision is likely what results in the "lack of conservation" across species.

We have added additional wording to this sentence to clarify this point.

In the discussion, the authors write that the analyses "unbiasedly identified a number of genes enriched in human LC", however, given the manual annotation of the region for each capture area, this resulted in a biased assessment of the spots.

We have replaced this wording to refer to “untargeted, transcriptome-wide” analyses (i.e. analyses that are not based on a targeted panel of genes) instead of “unbiased”. We agree that the meaning of “unbiased” is ambiguous in this context.

Reviewer #3 (Recommendations For The Authors):

Major points:

Overall, the discovery of some cells in the LC region that express serotonergic markers is intriguing. However, no evidence is presented that these neurons actually produce 5-HT. Perhaps more conservative language would be appropriate (i.e. "cells that possess mRNA signatures of serotonergic neurons" or something like that). Did these cells co-express other markers one would expect in 5-HT neurons like 5-HT autoreceptors and SLC6A18? Also would be useful to compare expression profiles of these putative 5-HT neurons with any published material on bona fide dorsal raphe 5-HT neurons. For the RNAscope confirmation in the supplementary material, it would be helpful to show each marker separately as well as the overlay, and to include representative higher magnification images like were provided for the ACH markers.

Thank you for this comment. In order to further investigate the identity of these cells, we have investigated the expression of several additional genes including SLC6A18, 5-HT autoreceptor genes (HTR1A, HTR1B), marker genes for 5-HT neurons (SLC18A2, FEV), and marker genes for 5-HT neuronal subpopulations within the dorsal and median raphe nuclei from the literature (Ren et al. 2019), in both the Visium and the snRNA-seq data.

We observed some expression of SLC18A2 and FEV within the same areas as SLC6A4 and TPH2 in the Visium samples (Figure 3-figure supplement 10A-B, reproduced below; note that SLC18A2 is also a marker gene for NE neurons located within the LC regions), consistent with Ren et al. (2019). However, we did not observe a strong or consistent expression signal for the 5-HT autoreceptors (HTR1A, HTR1B) (Figure 3-figure supplement 10C-D, reproduced below), and we observed zero expression of SLC6A18 in the Visium samples. In the snRNA-seq data, within the cluster identified as 5-HT neurons, we observed some expression of SLC18A2, low expression of FEV, and almost zero expression of SLC6A18 (Figure 3-figure supplement 8, reproduced below; note that SLC6A18 is not shown since it was removed during filtering for low-expressed genes). Similarly, we observed very low expression of the 5-HT autoreceptors (HTR1A, HTR1B) and the additional marker genes for 5-HT neuronal subpopulations from Ren et al. (2019) -- with the possible exception of the neuropeptide receptor gene HCRTR2, which was identified by Ren et al. (2019) within several clusters in both the dorsal and median raphe in mice (Figure 3-figure supplement 8, reproduced below).

Overall, these additional results give us some further confidence that these are likely 5-HT neurons (due to expression of SLC18A2 and FEV), while also raising further questions (due to the absence of 5-HT autoreceptor genes HTR1A, HTR1B and 5-HT neuronal subpopulation marker genes). While we believe that the most likely explanation is the inclusion of 5-HT neurons from the edges of the adjacent dorsal raphe nuclei in our samples, we acknowledge that the evidence presented is not fully conclusive and does not identify specific subpopulations of 5-HT neurons. In addition, the limited size of our dataset (number of samples and cells) and the lack of information on sample orientation precludes any definitive identification of subpopulations based on their association with specific anatomical regions within the dorsal raphe nuclei. We have updated the manuscript by (i) adjusting our language in the Results and Discussion, (ii) including the additional analyses, supplementary figures, and reference to the literature (Ren et al. 2019) discussed above, and (iii) including additional wording in the Discussion on improvements to the dissection strategy that would allow these questions to be addressed in future studies via a focused molecular profiling of the dorsal raphe nuclei across the rostral-caudal axis.

Regarding the RNAscope images, we have included additional images showing channels side-by-side and higher magnification, as suggested (and also discussed above for Reviewers 1 and 2). In addition, we have added an outline highlighting the LC region in Figure 3-figure supplement 11 (as suggested above by Reviewer 2), and included an additional high-magnification RNAscope image demonstrating co-expression of 5-HT neuron marker genes (TPH2 and SLC6A4) within individual cells (Figure 3-figure supplement 12).

Concerning the snRNA-seq experiments, why were only 3 of the 5 donors used, particularly given the low number of LC-NE nuclear transcriptomes obtained? How were the 3 donors chosen from the 5 total donors and how many 100 um sections were used from each donor? Are the 295 nuclei obtained truly representative of the LC population or are they just the most resilient LC nuclei? How many LC nuclei would be estimated to be captured from staining the 100 um tissue sections?

As discussed in our previous response to reviewers (“Response to Public Review Comments”), the reason we included only 3 of the 5 donors for the snRNA-seq assays was due to tissue availability on the tissue blocks. In this study, we were working with a finite tissue resource. Due to the logistics and thickness of the required tissue sections for Visium (10 μm) and snRNA-seq (100 μm), running Visium first allowed us to ensure that we could collect data from both assays -- if we ran snRNA-seq first and captured no neurons, the tissue block would be depleted. Due to resource depletion, we did not have sufficient available tissue remaining on all tissue blocks to run the snRNA-seq assay for all donors. We have conducted extensive piloting in other brain regions on the amount (mg) of tissue that is needed from various sized cryosections, and the LC is particularly difficult since these are small tissue blocks and the extent of the structure is small. Hence, in some of the subjects, we did not have sufficient tissue available for the snRNA-seq assay.

We have included details on the number of 100 μm sections used for each donor in Methods -- this varied between 10-15 sections per donor, approximating 50-80 mg of tissue per donor.

Regarding the question about the representativeness / resilience of the LC nuclei -- as discussed in our previous response to reviewers (“Response to Public Review Comments”) and above for Reviewer 2, we agree that this is a concern. As discussed above for Reviewer 2, it is plausible that our use of FANS may have contributed to cell damage and the low recovery rate of LC-NE neurons. The relatively large size and fragility of LC-NE neurons, as well as our use of a standard cell straining approach (70 µm, which may not be ideal for this population), may also be contributing factors. Due to our limited tissue resource, we did not have sufficient tissue to perform a direct comparison with non-sorted data.

Systematically optimizing the preparation to attempt to increase recovery rate is an important avenue for future work. We have included additional discussion of this issue in the Discussion.

Regarding the question about the number of expected nuclei, we have now included estimates of the number of cells per spot within the LC regions in the Visium data (see also related point below, and Figure 2-figure supplement 2B reproduced below), based on the H&E stained histology images and use of cell segmentation software (VistoSeg; Tippani et al. 2022). While we do not have any confident estimates of the number of expected nuclei in the snRNA-seq data, these estimates of cell density from the Visium data could, together with information on additional factors such as the accuracy of the tissue scoring and the effectiveness of FANS, be used to help derive an an expected number of nuclei in future studies. We have included additional wording in the Discussion to note that these estimates could be used in this manner during future studies.

The LC displays rostral/caudal and dorsal/ventral differences, including where they project, which functions they regulate, and which parts are vulnerable in neurodegenerative disease (e.g. Loughlin et al., Neuroscience 18:291-306, 1986; Dahl et al., Nat Hum Behav 3:1203-14, 2019; Beardmore et al., J Alzheimer's Dis 83:5-22, 2021; Gilvesy et al., Acta Neuropathol 144:651-76, 2022; Madelung et al., Mov Disord 37:479-89, 2022). Which part(s) of the LC was captured for the SRT and snRNAseq experiments?

As discussed in our previous response to reviewers (“Response to Public Review Comments”), a limitation of this study was that we did not record the orientation of the anatomy of the tissue sections, precluding our ability to annotate the tissue sections with the rostral/caudal and dorsal/ventral axis labels. We agree with the reviewer that additional spatial studies, in future work, could offer needed and important information about expression profiles across the spatial axes (rostral/caudal, ventral/dorsal) of the LC. Our study provides us with insight about optimizing the dissections for spatial assays, as well as bringing to light a number of technical and logistical issues that we had not initially foreseen. For example, during the course of this study and parallel, ongoing work in other, small, challenging regions, we have now developed a number of specialized technical and logistical strategies for keeping track of orientation and mounting serial sections from the same tissue block onto a single spatial array, which is extremely technically challenging. We are now well-prepared for addressing these issues in future studies with larger numbers of donors and samples in order to make these types of insights. We have included additional details in the Discussion to further discuss this point.

The authors mention that in other human SRT studies, there are typically between 1-10 cells per expression spot. I imagine that this depends heavily on the part of the brain being studied and neuronal density. In this specific case, can the authors estimate how many LC cells were contained in each expression spot?

We have now performed additional analyses to provide an estimate of the number of cells per spot in the Visium data (Figure 2-figure supplement 2B), based on the application of cell segmentation software (VistoSeg; Tippani et al. 2022) to identify cell bodies in the H&E stained histology images. We applied this methodology and calculated summary statistics within the annotated LC regions for 6 samples (see Methods), and found that the median number of cells per spot within the LC regions ranged from 2 to 5 per sample. We note that these estimates include both NE neurons and other cell types within the LC regions, and that applying cell segmentation software in this brain region is particularly challenging due to the wide range in cell body sizes, with NE neurons being especially large. We have included these updated estimates in the Results and Discussion, and additional details in Methods.

Regarding comparison of human LC-associated genes with rat or mouse LC-associated genes (Fig. 2D-F), the authors speculate that the modest degree of overlap may be due to species differences between rodent and human and/or methodological differences (SRT vs microarray vs TRAP). Was there greater overlap between mouse and rat than between mouse/rat and human? If so, that is evidence for the former. If not, that is evidence for the latter. Also would be useful for more in-depth comparison with snRNA-seq data from mouse LC. https://www.biorxiv.org/content/10.1101/2022.06.30.498327v1

Our comparisons with the mouse (Mulvey et al. 2018) and rat (Grimm et al. 2004) data showed that we observed a relatively higher overlap between the human vs. mouse data than the human vs. rat data (Figures 2F-G and 3D-E). However, we note that the substantially different technologies used (TRAP-seq in mouse vs. laser capture microdissection and microarrays in rat) make it difficult to confidently interpret the degree of overlap between the two studies, and a direct comparison of these alternative platforms (TRAP-seq vs. LCM / microarray) or species (mouse vs. rat) lies outside the scope of our study. We have included updated wording in the Results and Discussion to explain this issue and help interpret these results.

Regarding the newer mouse study using snRNA-seq (Luskin and Li et al. 2022), we have extended our analyses to perform a more in-depth comparison with this study. Specifically, we have evaluated the expression of an additional set of GABAergic neuron marker genes from this study within our secondary clustering of inhibitory neurons in the snRNA-seq data (Figure 3-figure supplement 13B). We observe some evidence of cluster-specific expression of several genes, including CCK, PCSK1, PCSK2, PCSK1N, PENK, PNOC, SST, and TAC1. We have also included additional text describing these results in the Results section.

The finding of ACHE expression in LC neurons is intriguing. Susan Greenfield has published a series of papers suggesting that ACHE has functions independent of ACH metabolism that contributes to cellular vulnerability in neurodegenerative disease. This might be worth mentioning.

We thank the reviewer for pointing this out. We were very surprised too by the observed expression of SLC5A7 and ACHE in the LC regions (Visium data) and within the LC-NE neuron cluster (snRNA-seq data), coupled with absence of other typical cholinergic marker genes (e.g. CHAT, SLC18A3), and we do not have a compelling explanation or theory for this. Hence, the work of Susan Greenfield and colleagues suggesting non-cholinergic actions of ACHE, particularly in other catecholaminergic neuron populations (e.g. dopaminergic neurons in the substantia nigra) is very interesting. We have included references to this work and how it could inform interpretation of this expression (Greenfield 1991; Halliday and Greenfield 2012) in the Discussion.

High mitochondrial reads from snRNA-seq can indicate lower quality. Can the authors comment on this and explain why they are confident in the snRNA-seq data from presumptive LC-NE neurons?

As mentioned above for Reviewer 2, we have included additional analyses to further compare quality control (QC) metrics for the NE neuron cluster (which had an unusually high proportion of mitochondrial reads) against other neuronal and non-neuronal clusters and nuclei in the snRNA-seq data (Figure 3-figure supplement 2). These additional QC analyses do not show any other problematic values for this cluster. Specifically, we show that the QC metric values for sum UMIs and detected genes per droplet for the NE neuron cluster fall within the range for (A) other neurons and (B) all other nuclei (excluding droplets with ambiguous / unidentifiable neuronal signatures). In addition, we observe that the droplets with the highest mitochondrial percentages (>75%) (C-D), which also have unusually low number of detected genes (D), tend to be from the ambiguous category (droplets with ambiguous / unidentifiable neuronal signatures), suggesting that true low-quality droplets are correctly identified and included within the ambiguous category (e.g. consisting of a mixture of debris from partial damaged nuclei) instead of as NE neurons. Since our QC analyses for the NE neuron cluster do not show any problems other than the high mitochondrial percentage, we do not believe these are simply mis-classified low-quality droplets. We also note that we have recently observed high mitochondrial proportions in other relatively rare neuronal populations characterized by large size and high metabolic demand in human data. We believe that our interpretation is correct -- i.e. that a combination of technical and biological factors has led to the inclusion of a relatively high amount of mitochondrial RNA within the droplets for these nuclei. We have included these additional QC analyses (Figure 3-figure supplement 2) and further discussion of this issue in the Results section.

The Discussion could be expanded. Because there is a lot known and/or assumed about the LC, discussing all of it is certainly beyond the scope of this manuscript. However, perhaps the authors could pick a few more for confirmation and hypothesis generation. For example, one of the most well studied and important aspects of the LC is its regulation by neuromodulatory inputs. It would be interesting for the authors to discuss the expression of receptors for CRF, cannabinoids, orexin, galanin, 5-HT, etc, particularly when compared with the available rodent TRAP and snRNA-seq data (https://www.biorxiv.org/content/10.1101/2022.06.30.498327v1) contained some surprises, such as very low expression of CRF1 in LC-NE neurons, suggesting that the powerful activation of LC cells by CRF is indirect. Does this hold up in humans?

We have expanded the Discussion to include additional discussion and references on several points, as discussed also above. Indeed these are interesting questions and these neuromodulatory systems are all of interest in the context of signaling within the LC in terms of function of the LC-NE system. We note that the manuscript serves primarily as a data resource and will be useful in many different ways depending on the different goals and interests of the readers. This is precisely why we wanted to take the time to make accessible and easy to use tools to interrogate and visualize the data. We have provided screenshots in Author response image 1-4 from the Shiny visualization app for the Visium data (https://libd.shinyapps.io/locus-c_Visium/) querying several main receptors of the neuromodulatory systems that this reviewer is particularly interested in to illustrate how the visualization apps can readily be used to query specific genes and systems of interest.

Author response image 1.

CRHR1:

Author response image 2.

CNR1:

Author response image 3.

OXR1:

Author response image 4.

GALR1:

Minor points:

Line 46 add stress responses to the key functions of LC neurons

We have added this point and included additional references to support the findings.

Line 47 add that the LC was so named "blue spot" because of its signature production of neuromelanin pigment