caterpillar

English Explanation

A caterpillar is the larval stage of an insect, particularly of butterflies and moths, which belong to the order Lepidoptera. Caterpillars are known for their elongated, worm-like bodies and are often highly voracious eaters, primarily consuming leaves and other plant material. They are typically characterized by having a segmented body with a head, thorax, and abdomen, and they may have many small legs.

Caterpillars undergo a transformation process known as metamorphosis, which involves several stages: the egg from which they hatch, the larval stage (caterpillar), the pupal stage (chrysalis or cocoon), and finally, the adult stage (butterfly or moth). This process is a remarkable transformation, allowing the caterpillar to evolve into a completely different form. During the caterpillar stage, they grow significantly before forming a pupa, where they undergo a radical transformation into their adult form. Many caterpillars are also camouflaged or possess defensive mechanisms to protect themselves from predators.

中文解释

毛虫 是一种昆虫的幼虫阶段，特别是属于鳞翅目（蜻蜓和蛾类）的昆虫。毛虫以其修长、像虫一样的身体而闻名，通常是非常贪食的主要以树叶和其他植物材料为食。毛虫通常具有分段的身体，分为头、胸、腹，并且可能有许多小腿。

毛虫经历一种称为变态发育的转变过程，这个过程包括几个阶段：首先是它们从卵中孵化出来自幼虫阶段（毛虫），然后是蛹阶段（蛹或蚕茧），最后是成年阶段（蝴蝶或蛾）。这个过程是一个显著的变革，使得毛虫能够演变为一种完全不同的形态。在毛虫阶段，它们会显著生长，然后形成蛹，在蛹的阶段中经历一种彻底的转变，最终变成它们的成年形态。许多毛虫也通过伪装或具有防御机制来保护自己免受捕食者的攻击。

Mantises, commonly known as mantids or praying mantises, belong to the order Mantodea. They are characterized by their distinctive posture, which involves holding their front limbs up in a position that resembles prayer. Here are some key points about mantises:

Physical Characteristics:

• Appearance: Mantises have elongated bodies and a triangular head. Their large, compound eyes provide excellent vision.
• Limbs: They are known for their raptorial forelegs, which are adapted for grasping and capturing prey.

Behavior:

• Predatory Nature: Mantises are carnivorous insects that primarily feed on other insects, although larger species can also prey on small vertebrates.
• Camouflage: Many mantises can blend into their surroundings, making it easier for them to ambush prey.

Reproduction:

• Mating: The mating process can be risky for male mantises, as females of some species may eat the males after or during mating.
• Egg Cases: Female mantises can produce egg cases known as oothecae, which contain multiple eggs and are often protected by a hard, frothy exterior.

Habitat:

• Mantises can be found in a variety of environments, including gardens, forests, and grasslands, and they are distributed across most of the world except for extreme cold regions.

Importance:

• Mantises play a crucial role in the ecosystem as pest controllers, helping to manage populations of agricultural pests.

If you have specific questions about mantises or want to know about a particular aspect, feel free to ask!

his trails do not fade

head over heels.

The phrase "head over heels" refers to someone who is deeply in love or infatuated, often to the point of being irrational. In the context provided, it suggests that George has strong feelings for someone, which could explain why he hasn't shared the news previously.

在这个上下文中，“head over heels”指的是一个人深深地爱上或迷恋某人，常常达到一种非理性的程度。该短语在对话中暗示，乔治对某人有强烈的感情，这也解释了他为何之前没有分享这个消息。

I'm not 100% sure but I think the way how Google and Wikipedia would use it is different.

The proposed use here in Wikipedia directly summarizes the content of the article where you are reading the summary.

Whereas in the Google case it may be using knowledge embedded in the model itself, plus summarizing some choice among the search results.

Reviewer #2 (Public review):

This paper introduces a framework for modeling individual differences in decision-making by learning a low-dimensional representation (the "individuality index") from one task and using it to predict behaviour in a different task. The approach is evaluated on two types of tasks: a sequential value-based decision-making task and a perceptual decision task (MNIST). The model shows improved prediction accuracy when incorporating this learned representation compared to baseline models.

The motivation is solid, and the modelling approach is interesting, especially the use of individual embeddings to enable cross-task generalization. That said, several aspects of the evaluation and analysis could be strengthened.

(1) The MNIST SX baseline appears weak. RTNet isn't directly comparable in structure or training. A stronger baseline would involve training the GRU directly on the task without using the individuality index-e.g., by fixing the decoder head. This would provide a clearer picture of what the index contributes.

(2) Although the focus is on prediction, the framework could offer more insight into how behaviour in one task generalizes to another. For example, simulating predicted behaviours while varying the individuality index might help reveal what behavioural traits it encodes.

(3) It's not clear whether the model can reproduce human behaviour when acting on-policy. Simulating behaviour using the trained task solver and comparing it with actual participant data would help assess how well the model captures individual decision tendencies.

(4) Figures 3 and S1 aim to show that individuality indices from the same participant are closer together than those from different participants. However, this isn't fully convincing from the visualizations alone. Including a quantitative presentation would help support the claim.

(5) The transfer scenarios are often between very similar task conditions (e.g., different versions of MNIST or two-step vs three-step MDP). This limits the strength of the generalization claims. In particular, the effects in the MNIST experiment appear relatively modest, and the transfer is between experimental conditions within the same perceptual task. To better support the idea of generalizing behavioural traits across tasks, it would be valuable to include transfers across more structurally distinct tasks.

(6) For both experiments, it would help to show basic summaries of participants' behavioural performance. For example, in the MDP task, first-stage choice proportions based on transition types are commonly reported. These kinds of benchmarks provide useful context.

(7) For the MDP task, consider reporting the number or proportion of correct choices in addition to negative log-likelihood. This would make the results more interpretable.

(8) In Figure 5, what is the difference between the "% correct" and "% match to behaviour"? If so, it would help to clarify the distinction in the text or figure captions.

(9) For the cognitive model, it would be useful to report the fitted parameters (e.g., learning rate, inverse temperature) per individual. This can offer insight into what kinds of behavioural variability the individuality index might be capturing.

(10) A few of the terms and labels in the paper could be made more intuitive. For example, the name "individuality index" might give the impression of a scalar value rather than a latent vector, and the labels "SX" and "SY" are somewhat arbitrary. You might consider whether clearer or more descriptive alternatives would help readers follow the paper more easily.

(11) Please consider including training and validation curves for your models. These would help readers assess convergence, overfitting, and general training stability, especially given the complexity of the encoder-decoder architecture.

We now know that Emily had in fact killed Homer. In a very twisted way I understand her. She wanted nothing more than to be loved and made sure she had the one she wanted. Got to love a girl who knows what she wants.

I can relate to this from personal experience!

Author response:

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

Reviewer #1 (Public review):

Summary:

The authors report a study on how stimulation of receptive-field surround of V1 and LGN neurons affects their firing rates. Specifically, they examine stimuli in which a grey patch covers the classical RF of the cell and a stimulus appears in the surround. Using a number of different stimulus paradigms they find a long latency response in V1 (but not the LGN) which does not depend strongly on the characteristics of the surround grating (drifting vs static, continuous vs discontinuous, predictable grating vs unpredictable pink noise). They find that population responses to simple achromatic stimuli have a different structure that does not distinguish so clearly between the grey patch and other conditions and the latency of the response was similar regardless of whether the center or surround was stimulated by the achromatic surface. Taken together they propose that the surround-response is related to the representation of the grey surface itself. They relate their findings to previous studies that have put forward the concept of an ’inverse RF’ based on strong responses to small grey patches on a full-screen grating. They also discuss their results in the context of studies that suggest that surround responses are related to predictions of the RF content or figure-ground segregation. Strengths:

I find the study to be an interesting extension of the work on surround stimulation and the addition of the LGN data is useful showing that the surround-induced responses are not present in the feedforward path. The conclusions appear solid, being based on large numbers of neurons obtained through Neuropixels recordings. The use of many different stimulus combinations provides a rich view of the nature of the surround-induced responses.

Weaknesses:

The statistics are pooled across animals, which is less appropriate for hierarchical data. There is no histological confirmation of placement of the electrode in the LGN and there is no analysis of eye or face movements which may have contributed to the surround-induced responses. There are also some missing statistics and methods details which make interpretation more difficult.

We thank the reviewer for their positive and constructive comments, and have addressed these specific issues in response to the minor comments. For the statistics across animals, we refer to “Reviewer 1 recommendations” point 1. For the histological analysis, we refer to “Reviewer 1 recommendations point 2”. For the eye and facial movements, we refer to “Reviewer 1 recommendations point 5”. Concerning missing statistics and methods details, we refer to various responses to “Reviewer 1 recommendations”. We thoroughly reviewed the manuscript and included all missing statistical and methodological details.

Reviewer #2 (Public review):

Cuevas et al. investigate the stimulus selectivity of surround-induced responses in the mouse primary visual cortex (V1). While classical experiments in non-human primates and cats have generally demonstrated that stimuli in the surround receptive field (RF) of V1 neurons only modulate activity to stimuli presented in the center RF, without eliciting responses when presented in isolation, recent studies in mouse V1 have indicated the presence of purely surround-induced responses. These have been linked to prediction error signals. In this study, the authors build on these previous findings by systematically examining the stimulus selectivity of surround-induced responses.

Using neuropixels recordings in V1 and the dorsal lateral geniculate nucleus (dLGN) of head-fixed, awake mice, the authors presented various stimulus types (gratings, noise, surfaces) to the center and surround, as well as to the surround only, while also varying the size of the stimuli. Their results confirm the existence of surround-induced responses in mouse V1 neurons, demonstrating that these responses do not require spatial or temporal coherence across the surround, as would be expected if they were linked to prediction error signals. Instead, they suggest that surround-induced responses primarily reflect the representation of the achromatic surface itself.

The literature on center-surround effects in V1 is extensive and sometimes confusing, likely due to the use of different species, stimulus configurations, contrast levels, and stimulus sizes across different studies. It is plausible that surround modulation serves multiple functions depending on these parameters. Within this context, the study by Cuevas et al. makes a significant contribution by exploring the relationship between surround-induced responses in mouse V1 and stimulus statistics. The research is meticulously conducted and incorporates a wide range of experimental stimulus conditions, providing valuable new insights regarding center-surround interactions.

However, the current manuscript presents challenges in readability for both non-experts and experts. Some conclusions are difficult to follow or not clearly justified.

I recommend the following improvements to enhance clarity and comprehension:

(1) Clearly state the hypotheses being tested at the beginning of the manuscript.

(2) Always specify the species used in referenced studies to avoid confusion (esp. Introduction and Discussion).

(3) Briefly summarize the main findings at the beginning of each section to provide context.

(4) Clearly define important terms such as “surface stimulus” and “early vs. late stimulus period” to ensure understanding.

(5) Provide a rationale for each result section, explaining the significance of the findings.

(6) Offer a detailed explanation of why the results do not support the prediction error signal hypothesis but instead suggest an encoding of the achromatic surface.

These adjustments will help make the manuscript more accessible and its conclusions more compelling.

We thank the reviewer for their constructive feedback and for highlighting the need for improved clarity regarding the hypotheses and their relation to the experimental findings.

• We have strongly improved the Introduction and Discussion section, explaining the different hypotheses and their relation to the performed experiments.

• In the Introduction, we have clearly outlined each hypothesis and its predictions, providing a structured framework for understanding the rationale behind our experimental design. • In the Discussion, we have been more explicit in explaining how the experimental findings inform these hypotheses.

• We explicitly mentioned the species used in the referenced studies.

• We provided a clearer rationale for each experiment in the Results section.

We have also always clearly stated the species that previous studies used, both in the Introduction and Discussion section.

Reviewer #3 (Public review):

Summary:

This paper explores the phenomenon whereby some V1 neurons can respond to stimuli presented far outside their receptive field. It introduces three possible explanations for this phenomenon and it presents experiments that it argues favor the third explanation, based on figure/ground segregation.

Strengths:

I found it useful to see that there are three possible interpretations of this finding (prediction error, interpolation, and figure/ground). I also found it useful to see a comparison with LGN responses and to see that the effect there is not only absent but actually the opposite: stimuli presented far outside the receptive field suppress rather than drive the neurons. Other experiments presented here may also be of interest to the field.

Weaknesses:

The paper is not particularly clear. I came out of it rather confused as to which hypotheses were still standing and which hypotheses were ruled out. There are numerous ways to make it clearer.

We thank the reviewer for their constructive feedback and for highlighting the need for improved clarity regarding the hypotheses and their relation to the experimental findings.

• We have strongly improved the Introduction and Discussion section, explaining the different hypotheses and their relation to the performed experiments.

• In the Introduction, we have clearly outlined each hypothesis and its predictions, providing a structured framework for understanding the rationale behind our experimental design. • In the Discussion, we have been more explicit in explaining how the experimental findings inform these hypotheses.

** Recommendations for the Authors:**

Reviewer #1 (Recommendations for the Authors):

(1) Given the data is hierarchical with neurons clustered within 6 mice (how many recording sessions per animal?) I would recommend the use of Linear Mixed Effects models. Simply pooling all neurons increases the risk of false alarms.

To clarify: We used the standard method for analyzing single-unit recordings, by comparing the responses of a population of single neurons between two different conditions. This means that the responses of each single neuron were measured in the different conditions, and the statistics were therefore based on the pairwise differences computed for each neuron separately. This is a common and standard procedure in systems neuroscience, and was also used in the previous studies on this topic (Keller et al., 2020; Kirchberger et al., 2023). We were not concerned with comparing two groups of animals, for which hierarchical analyses are recommended. To address the reviewer’s concern, we did examine whether differences between baseline and the gray/drift condition, as well as the gray/drift compared to the grating condition, were consistent across sessions, which was indeed the case. These findings are presented in Supplementary Figure 6.

(2) Line 432: “The study utilized three to eight-month-old mice of both genders”. This is confusing, I assume they mean six mice in total, please restate. What about the LGN recordings, were these done in the same mice? Can the authors please clarify how many animals, how many total units, how many included units, how many recording sessions per animal, and whether the same units were recorded in all experiments?

We have now clarified the information regarding the animals used in the Methods section.

• We state that “We included female and male mice (C57BL/6), a total of six animals for V1 recordings between three and eight months old. In two of those animals, we recorded simultaneously from LGN and V1.”

• We state that“For each animal, we recorded around 2-3 sessions from each hemisphere, and we recorded from both hemispheres.”

• We noted that the number of neurons was not mentioned for each figure caption. We apologize for this omission. We have now added the number for all of the figures and protocols to the revised manuscript. We note that the same neurons were recorded for the different conditions within each protocol, however because a few sessions were short we recorded more units for the grating protocol. Note that we did not make statistical comparisons between protocols.

(3) I see no histology for confirmation of placement of the electrode in the LGN, how can they be sure they were recording from the LGN? There is also little description of the LGN experiments in the methods.

For better clarity, we have included a reconstruction of the electrode track from histological sections of one animal post-experiment (Figure S4). The LGN was targeted via stereotactical surgery, and the visual responses in this area are highly distinct. In addition, we used a flash protocol to identify the early-latency responses typical for the LGN, which is described in the Methods section: “A flash stimulus was employed to confirm the locations of LGN at the beginning of the recording sessions, similar to our previous work in which we recorded from LGN and V1 simultaneously (Schneider et al., 2023). This stimulus consisted of a 100 ms white screen and a 2 s gray screen as the inter-stimulus interval, designed to identify visually responsive areas. The responses of multi-unit activity (MUA) to the flash stimulus were extracted and a CSD analysis was then performed on the MUA, sampling every two channels. The resulting CSD profiles were plotted to identify channels corresponding to the LGN. During LGN recordings, simultaneous recordings were made from V1, revealing visually responsive areas interspersed with non-responsive channels.”

(4) Many statements are not backed up by statistics, for example, each time the authors report that the response at 90degree sign is higher than baseline (Line 121 amongst other places) there is no test to support this. Also Line 140 (negative correlation), Line 145, Line 180.

For comparison purposes, we only presented statistical analyses across conditions. However, we have now added information to the figure captions stating that all conditions show values higher than the baseline.

(5) As far as I can see there is no analysis of eye movements or facial movements. This could be an issue, for example, if the onset of the far surround stimuli induces movements this may lead to spurious activations in V1 that would be interpreted as surround-induced responses.

To address this point, we have included a supplementary figure analyzing facial movements across different sessions and comparing them between conditions (Supplementary Figure 5). A detailed explanation of this analysis has been added to the Methods section. Overall, we observed no significant differences in face movements between trials with gratings, trials with the gray patch, and trials with the gray screen presented during baseline. Animals exhibited similar face movements across all three conditions, supporting the conclusion that the observed neural firing rate increases for the gray-patch condition are not related to face movements.

(6) The experiments with the rectangular patch (Figure 3) seem to give a slightly different result as the responses for large sizes (75, 90) don’t appear to be above baseline. This condition is also perceptually the least consistent with a grey surface in the RF, the grey patch doesn’t appear to occlude the surface in this condition. I think this is largely consistent with their conclusions and it could merit some discussion in the results/discussion section.

While the effect is maybe a bit weaker, the total surround stimulated also covers a smaller area because of the large rectangular gray patch. Furthermore, the early responses are clearly elevated above baseline, and the responses up to 70 degrees are still higher than baseline. Hence we think this data point for 90 degrees does not warrant a strong interpretation.

Minor points:

(1) Figure 1h: What is the statistical test reported in the panel (I guess a signed rank based on later figures)? Figure 4d doesn’t appear to be significantly different but is reported as so. Perhaps the median can be indicated on the distribution?

We explained that we used a signed rank test for Figure 1h and now included the median of the distributions in Figure 4d.

(2) What was the reason for having the gratings only extend to half the x-axis of the screen, rather than being full-screen? This creates a percept (in humans at least) that is more consistent with the grey patch being a hole in the grating as the grey patch has the same luminance as the background outside the grating.

We explained in the Methods section that “We presented only half of the x-axis due to the large size of our monitor, in order to avoid over-stimulation of the animals with very large grating stimuli.”. Perceptually speaking, the gray patch appears as something occluding the grating, not as a “hole”.

(3) Line 103: “and, importantly, had less than 10degree sign (absolute) distance to the grating stimulus’ RF center.” Re-phrase, a stimulus doesn’t have an RF center.

We corrected this to “We included only single units into the analysis that met several criteria in terms of visual responses (see Methods) and, importantly, the RF center had less than 10(absolute) distance to the grating stimulus’ center. ”.

(4) Line 143: “We recorded single neurons LGN” - should be “single LGN neurons”.

We corrected this to “we recorded single LGN neurons”.

(5) Line 200: They could spell out here that the latency is consistent with the latency observed for the grey patch conditions in the previous experiments. (6) Line 465: This is very brief. What criteria did they use for single-unit assignation? Were all units well-isolated or were multi-units included?

We clarified in the Methods section that “We isolated single units with Kilosort 2.5 (Steinmetz et al., 2021) and manually curated them with Phy2 (Rossant et al., 2021). We included only single units with a maximum contamination of 10 percent.”

(7) Line 469: “The experiment was run on a Windows 10”. Typo.

We corrected this to “The experiment was run on Windows 10”.

(9) Line 481: “We averaged the response over all trials and positions of the screen”. What do they mean by ’positions of the screen’?

We changed this to “We computed the response for each position separately right, by averaging the response across all the trials where a square was presented at a given position.”

(9) Line 483: “We fitted an ellipse in the center of the response”. How?

We additionally explain how we preferred the detection of the RF using an ellipse fitting: “A heatmap of the response was computed. This heatmap was then smoothed, and we calculated the location of the peak response. From the heatmap we calculated the centroid of the response using the function regionprops.m that finds unique objects, we then selected the biggest area detected. Using the centroids provided as output. We then fitted an ellipse centered on this peak response location to the smoothed heatmap using the MATLAB function ellipse.m.“

(10) Line 485 “...and positioned the stimulus at the response peak previously found”. Unclear wording, do you mean the center of the ellipse fit to the MUA response averaged across channels or something else? (11) Line 487: “We performed a permutation test of the responses inside the RF detected vs a circle from the same area where the screen was gray for the same trials.”. The wording is a bit unclear here, can they clarify what they mean by the ’same trials’, what is being compared to what here?

We used a permutation test to compare the neuron’s responses to black and white squares inside the RF to the condition where there was no square in the RF (i.e. the RF was covered by the gray background).

(12) Was the pink noise background regenerated on each trial or as the same noise pattern shown on each trial?

We explain that “We randomly presented one of two different pink noise images”

(13) Line 552: “...used a time window of the Gaussian smoothing kernel from-.05 to .05”. Missing units.

We explained that “we used a time window of the Gaussian smoothing kernel from -.05 s to .05 s, with a standard deviation of 0.0125 s.”

(14) Line 565: “Additionally, for the occluded stimulus, we included patch sizes of 70 degree sign and larger.”. Not sure what they’re referring to here.

We changed this to: “For the population analyses, we analyzed the conditions in which the gray patch sizes were 70 degrees and 90 degrees”.

(15) Line 569: What is perplexity, and how does changing it affect the t-SNE embeddings?

Note that t-SNE is only used for visualization purposes. In the revised manuscript, we have expanded our explanation regarding the use of t-SNE and the choice of perplexity values. Specifically, we have clarified that we used a perplexity value of 20 for the Gratings with circular and rectangular occluders and 100 for the black-and-white condition. These values were empirically selected to ensure that the groups in the data were clearly separable while maintaining the balance between local and global relationships in the projected space. This choice allowed us to visually distinguish the different groups while preserving the meaningful structure encoded in the dissimilarity matrices. In particular, varying the perplexity values would not alter the conclusions drawn from the visualization, as t-SNE does not affect the underlying analytical steps of our study.

(16) Line 572: “We trained a C-Support Vector Classifier based on dissimilarity matrices”. This is overly brief, please describe the construction of the dissimilarity matrices and how the training was implemented. Was this binary, multi-class? What conditions were compared exactly?

In the revised manuscript, we have expanded our explanation regarding the construction of the dissimilarity matrices and the implementation of the C-Support Vector Classification (C-SVC) model (See Methods section).

The dissimilarity matrices were calculated using the Euclidean distance between firing rate vectors for all pairs of trials (as shown in Figure 6a-b). These matrices were used directly as input for the classifier. It is important to note that t-SNE was not used for classification but only for visualization purposes. The classifier was binary, distinguishing between two classes (e.g., Dr vs St). We trained the model using 60% of the data for training and used 40% for testing. The C-SVC was implemented using sklearn, and the classification score corresponds to the average accuracy across 20 repetitions.

Reviewer #2 (Recommendations for the Authors):

The relationship between the current paper and Keller et al. is challenging to understand. It seems like the study is critiquing the previous study but rather implicitly and not directly. I would suggest either directly stating the criticism or presenting the current study as a follow-up investigation that further explores the observed effect or provides an alternative function. Additionally, defining the inverse RF versus surround-induced responses earlier than in the discussion would be beneficial. Some suggestions:

(1) The introduction is well-written, but it would be helpful to clearly define the hypotheses regarding the function of surround-induced responses and revisit these hypotheses one by one in the results section.

Indeed, we have generally improved the Introduction of the manuscript, and stated the hypotheses and their relationships to the Experiments more clearly.

(2) Explicitly mention how you compare classic grating stimuli of varying sizes with gray patch stimuli. Do the patch stimuli all come with a full-field grating? For the full-field grating, you have one size parameter, while for the patch stimuli, you have two (size of the patch and size of the grating).

We now clearly describe how we compare grating stimuli of varying sizes with gray patch stimuli.

(3) The third paragraph in the introduction reads more like a discussion and might be better placed there.

We have moved content from the third paragraph of the Introduction to the Discussion, where it fits more naturally.

(4) Include 1-2 sentences explaining how you center RFs and detail the resolution of your method.

We have added an explanation to the Methods: “To center the visual stimuli during the recording session, we averaged the multiunit activity across the responsive channels and positioned the stimulus at the center of the ellipse fit to the MUA response averaged across channels.”.

(5) Motivate the use of achromatic stimuli. This section is generally quite hard to understand, so try to simplify it.

We explained better in the Introduction why we performed this particular experiment.

(6) The decoding analysis is great, but it is somewhat difficult to understand the most important results. Consider summarizing the key findings at the beginning of this section.

We now provide a clearer motivation at the start of the Decoding section.

Reviewer #3 (Recommendations for the Authors):

I have a few suggestions to improve the clarity of the presentation.

Abstract: it lists a series of observations and it ends with a conclusion (“based on these findings...”). However, it provides little explanation for how this conclusion would arise from the observations. It would be more helpful to introduce the reasoning at the top and show what is consistent with it.

We have improved the abstract of the paper incorporating this feedback.

To some extent, this applies to Results too. Sometimes we are shown the results of some experiment just because others have done a similar experiment. Would it be better to tell us which hypotheses it tests and whether the results are consistent with all 3 hypotheses or might rule one or more out? I came out of the paper rather confused as to which hypotheses were still standing and which hypotheses were ruled out.

We have strongly improved our explanation of the hypotheses and the relationships to the experiments in the Introduction.

It would be best if the Results section focused on the results of the study, without much emphasis on what previous studies did or did not measure. Here, instead, in the middle of Results we are told multiple times what Keller et al. (2020) did or did not measure, and what they did or did not find. Please focus on the questions and on the results. Where they agree or disagree with previous papers, tell us briefly that this is the case.

We have revised the Results section in the revised manuscript, and ensured that there is much less focus on what previous studies did in the Results. Differences to previous work are now discussed in the Discussion section.

The notation is extremely awkward. For instance “Gc” stands for two words (Gray center) but “Gr” stands for a single word (Grating). The double meaning of G is one of many sources of confusion.

This notation needs to be revised. Here is one way to make it simpler: choose one word for each type of stimulus (e.g. Gray, White, Black, Drift, Stat, Noise) and use it without abbreviations. To indicate the configuration, combine two of those words (e.g. Gray/Drift for Gray in the center and Drift in the surround).

We have corrected the notation in the figures and text to enhance readability and improve the reader’s understanding.

Figure 1e and many subsequent ones: it is not clear why the firing rate is shown in a logarithmic scale. Why not show it in a linear scale? Anyway, if the logarithmic scale is preferred for some reason, then please give us ticks at numbers that we can interpret, like 0.1,1,10,100... or 0.5,1,2,4... Also, please use the same y-scale across figures so we can compare.

To clarify: it is necessary to normalize the firing rates relative to baseline, in order to pool across neurons. However such a divisive normalization would be by itself problematic, as e.g. a change from 1 to 2 is the same as a change from 1 to 0.5, on a linear scale. Furthermore such division is highly outlier sensitive. For this reason taking the logarithm (base 10) of the ratio is an appropriate transformation. We changed the tick labels to 1, 2, 4 like the reviewer suggested.

Figure 3: it is not clear what “size” refers to in the stimuli where there is no gray center. Is it the horizontal size of the overall stimulus? Some cartoons might help. Or just some words to explain.

Figure 3: if my understanding of “size” above is correct, the results are remarkable: there is no effect whatsoever of replacing the center stimulus with a gray rectangle. Shouldn’t this be remarked upon?

We have added a paragraph under figure 3 and in the Methods section explaining that the sizes represent the varying horizontal dimensions of the rectangular patch. In this protocol, the classical condition (i.e. without gray patch) was shown only as full-field gratings, which is depicted in the plot as size 0, indicating no rectangular patch was present.

DETAILS The word “achromatic” appears many times in the paper and is essentially uninformative (all stimuli in this study are achromatic, including the gratings). It could be removed in most places except a few, where it is actually used to mean “uniform”. In those cases, it should be replaced by “uniform”.

Ditto for the word “luminous”, which appears twice and has no apparent meaning. Please replace it with “uniform”.

We have replaced the words achromatic and luminous with “uniform” stimuli to improve the clarity when we refer to only black or white stimuli.

Page 3, line 70: “We raise some important factors to consider when describing responses to only surround stimulation.” This sentence might belong in the Discussion but not in the middle of a paragraph of Results.

We removed this sentence.

Neuropixel - Neuropixels (plural)

“area LGN” - LGN

We corrected for misspellings.

References

Keller, A.J., Roth, M.M., Scanziani, M., 2020. Feedback generates a second receptive field in neurons of the visual cortex. Nature 582, 545–549. doi:10.1038/s41586-020-2319-4.

Kirchberger, L., Mukherjee, S., Self, M.W., Roelfsema, P.R., 2023. Contextual drive of neuronal responses in mouse V1 in the absence of feedforward input. Science Advances 9, eadd2498. doi:10. 1126/sciadv.add2498.

Rossant, C., et al., 2021. phy: Interactive analysis of large-scale electrophysiological data. https://github.com/cortex-lab/phy.

Schneider, M., Tzanou, A., Uran, C., Vinck, M., 2023. Cell-type-specific propagation of visual flicker. Cell Reports 42.

Steinmetz, N.A., Aydin, C., Lebedeva, A., Okun, M., Pachitariu, M., Bauza, M., Beau, M., Bhagat, J., B¨ohm, C., Broux, M., Chen, S., Colonell, J., Gardner, R.J., Karsh, B., Kloosterman, F., Kostadinov, D., Mora-Lopez, C., O’Callaghan, J., Park, J., Putzeys, J., Sauerbrei, B., van Daal,R.J.J., Vollan, A.Z., Wang, S., Welkenhuysen, M., Ye, Z., Dudman, J.T., Dutta, B., Hantman, A.W., Harris, K.D., Lee, A.K., Moser, E.I., O’Keefe, J., Renart, A., Svoboda, K., H¨ausser, M., Haesler, S., Carandini, M., Harris, T.D., 2021. Neuropixels 2.0: A miniaturized high-density probe for stable, long-term brain recordings. Science 372, eabf4588. doi:10.1126/science.abf4588.

for - metaphor - Mr Potatohead toy - Picasso tadpole - Michael Levin

I still can’t wrap my head around the universe having a start. I always wondered what it was before then if there even was anything.

Reviewer #1 (Public review):

This study is part of an ongoing effort to clarify the effects of cochlear neural degeneration (CND) on auditory processing in listeners with normal audiograms. This effort is important because ~10% of people who seek help for hearing difficulties have normal audiograms and current hearing healthcare has nothing to offer them.

The authors identify two shortcomings in previous work that they intend to fix. The first is a lack of cross-species studies that make direct comparisons between animal models in which CND can be confirmed and humans for which CND must be inferred indirectly. The second is the low sensitivity of purely perceptual measures to subtle changes in auditory processing. To fix these shortcomings, the authors measure envelope following responses (EFRs) in gerbils and humans using the same sounds, while also performing histological analysis of the gerbil cochleae, and testing speech perception while measuring pupil size in the humans.

The study begins with a comprehensive assessment of the hearing status of the human listeners. The only differences found between the young adult (YA) and middle aged (MA) groups are in thresholds at frequencies > 10 kHz and DPOAE amplitudes at frequencies > 5 kHz. The authors then present the EFR results, first for the humans and then for the gerbils, showing that amplitudes decrease more rapidly with increasing envelope frequency for MA than for YA in both species. The histological analysis of the gerbil cochleae shows that there were, on average, 20% fewer IHC-AN synapses at the 3 kHz place in MA relative to YA, and the number of synapses per IHC was correlated with the EFR amplitude at 1024 Hz.

The study then returns to the humans to report the results of the speech perception tests and pupillometry. The correct understanding of keywords decreased more rapidly with decreasing SNR in MA than in YA, with a noticeable difference at 0 dB, while pupillary slope (a proxy for listening effort) increased more rapidly with decreasing SNR for MA than for YA, with the largest differences at SNRs between 5 and 15 dB. Finally, the authors report that a linear combination of audiometric threshold, EFR amplitude at 1024 Hz, and a few measures of pupillary slope is predictive of speech perception at 0 dB SNR.

I only have two questions/concerns about the specific methodologies used:

(1) Synapse counts were made only at the 3 kHz place on the cochlea. But the EFR sounds were presented at 85 dB SPL, which means that a rather large section of the cochlea will actually be excited. Do we know how much of the EFR actually reflects AN fibers coming from the 3 kHz place? And are we sure that this is the same for gerbils and humans given the differences in cochlear geometry, head size, etc.?

[Note added after revision: the authors have added new data, references, and discussion that have answered my initial questions].

(2) Unless I misunderstood, the predictive power of the final model was not tested on held out data. The standard way to fit and test such model would be to split the data into two segments, one for training and hyperparameter optimization, and one for testing. But it seems that the only spilt was for training and hyperparameter optimization.

[Note added after revision: the authors now make it clear in their response that the modeling tells us how much of the current data can be explained but not necessary about generalization to other datasets.]

While I find the study to be generally well executed, I am left wondering what to make of it all. The purpose of the study with respect to fixing previous methodological shortcomings was clear, but exactly how fixings these shortcomings has allowed us to advance is not. I think we can be more confident than before that EFR amplitude is sensitive to CND, and we now know that measures of listening effort may also be sensitive to CND. But where is this leading us?

I think what this line of work is eventually aiming for is to develop a clinical tool that can be used to infer someone's CND profile. That seems like a worthwhile goal but getting there will require going beyond exploratory association studies. I think we're ready to start being explicit about what properties a CND inference tool would need to be practically useful. I have no idea whether the associations reported in this study are encouraging or not because I have no idea what level of inferential power is ultimately required.

[Note added after revision: the authors have added to the Discussion to put their work into a broader perspective.]

That brings me to my final comment: there is an inappropriate emphasis on statistical significance. The sample size was chosen arbitrarily. What if the sample had been half the size? Then few, if any, of the observed effects would have been significant. What if the sample had been twice the size? Then many more of the observed effects would have been significant (particularly for the pupillometry). I hope that future studies will follow a more principled approach in which relevant effect sizes are pre-specified (ideally as the strength of association that would be practically useful) and sample sizes are determined accordingly.

[Note added after revision: my intention with this comment was not to make a philosophical or nitty-gritty point about statistics. It was more of a follow on to the previous point. Because I don't know what sort of effect size is big enough to matter (for whatever purpose), I don't find the statistical significance (or lack thereof) of the effect size observed to be informative. But I don't think there is anything more that the authors can or should do in this regard.]

So, in summary, I think this study is a valuable but limited advance. The results increase my confidence that non-invasive measures can be used to infer underlying CND, but I am unsure how much closer we are to anything that is practically useful.

Author response:

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

Reviewer #1 (Public review):

Wang et al. investigated how sexual failure influences sweet taste perception in male Drosophila. The study revealed that courtship failure leads to decreased sweet sensitivity and feeding behavior via dopaminergic signaling. Specifically, the authors identified a group of dopaminergic neurons projecting to the suboesophageal zone that interacts with sweet-sensing Gr5a+ neurons. These dopaminergic neurons positively regulate the sweet sensitivity of Gr5a+ neurons via DopR1 and Dop2R receptors. Sexual failure diminishes the activity of these dopaminergic neurons, leading to reduced sweet-taste sensitivity and sugar-feeding behavior in male flies. These findings highlight the role of dopaminergic neurons in integrating reproductive experiences to modulate appetitive sensory responses.

Previous studies have explored the dopaminergic-to-Gr5a+ neuronal pathways in regulating sugar feeding under hunger conditions. Starvation has been shown to increase dopamine release from a subset of TH-GAL4 labeled neurons, known as TH-VUM, in the suboesophageal zone. This enhanced dopamine release activates dopamine receptors in Gr5a+ neurons, heightening their sensitivity to sugar and promoting sucrose acceptance in flies. Since the function of the dopaminergic-to-Gr5a+ circuit motif has been well established, the primary contribution of Wang et al. is to show that mating failure in male flies can also engage this circuit to modulate sugar-feeding behavior. This contribution is valuable because it highlights the role of dopaminergic neurons in integrating diverse internal state signals to inform behavioral decisions.

An intriguing discrepancy between Wang et al. and earlier studies lies in the involvement of dopamine receptors in Gr5a+ neurons. Prior research has shown that Dop2R and DopEcR, but not DopR1, mediate starvation-induced enhancement of sugar sensitivity in Gr5a+ neurons. In contrast, Wang et al. found that DopR1 and Dop2R, but not DopEcR, are involved in the sexual failure-induced decrease in sugar sensitivity in these neurons. I wish the authors had further explored or discussed this discrepancy, as it is unclear how dopamine release selectively engages different receptors to modulate neuronal sensitivity in a context-dependent manner.

Our immunostaining experiments showed that three dopamine receptors, Dop1R1, Dop2R, and DopEcR were expressed in Gr5a⁺ neurons in the proboscis, which was consistent with previous findings by using RT-PCR (Inagaki et al 2012). As the reviewer pointed out, we found that Dop1R1 and Dop2R were required for courtship failure-induced suppression of sugar sensitivity, whereas Marella et al 2012 and Inagaki et al 2012 found that Dop2R and DopEcR were required for starvation-induced enhancement of sugar sensitivity. These results may suggest that different internal states (courtship failure vs. starvation) modulate the peripheral sensory system via different signaling pathways (e.g. different subsets of dopaminergic neurons; different dopamine release mechanisms; and different dopamine receptors). We have discussed these possibilities in the revised manuscript.

The data presented by Wang et al. are solid and effectively support their conclusions. However, certain aspects of their experimental design, data analysis, and interpretation warrant further review, as outlined below.

(1) The authors did not explicitly indicate the feeding status of the flies, but it appears they were not starved. However, the naive and satisfied flies in this study displayed high feeding and PER baselines, similar to those observed in starved flies in other studies. This raises the concern that sexually failed flies may have consumed additional food during the 4.5-hour conditioning period, potentially lowering their baseline hunger levels and subsequently reducing PER responses. This alternative explanation is worth considering, as an earlier study demonstrated that sexually deprived males consumed more alcohol, and both alcohol and food are known rewards for flies. To address this concern, the authors could remove food during the conditioning phase to rule out its influence on the results.

This is an important consideration. To rule out potential confound from food intake during courtship conditioning, we have now also conducted courtship conditioning in vials absent of food. In the absence of any feeding opportunity over the 4.5-hour courtship conditioning period, sexually rejected males still exhibited a robust decrease in sweet taste sensitivity compared with Naïve and Satisfied controls (Figure 1-supplement 1C). These data confirm that the suppression of PER is driven by courtship failure per se, rather than by differences in feeding during the conditioning phase.

(2) Figure 1B reveals that approximately half of the males in the Failed group did not consume sucrose yet Figure 1-S1A suggests that the total volume consumed remained unchanged. Were the flies that did not consume sucrose omitted from the dataset presented in Figure 1-S1A? If so, does this imply that only half of the male flies experience sexual failure, or that sexual failure affects only half of males while the others remain unaffected? The authors should clarify this point.

Our initial description of the experimental setup might be a bit confusing. Here is a brief clarification of our experimental design and we have further clarified the details in the revised manuscript, which should resolve the reviewer’s concerns:

After the behavioral conditioning, male flies were divided for two assays. On the one hand, we quantified PER responses of individual flies. As shown in Figure 1C, Failed males exhibited decreased sweet sensitivity (as demonstrated by the right shift of the dose-response curve). On the other hand, we sought to quantify food consumption of individual flies by using the MAFE assay (Qi et al 2005).

In the initial submission, we used 400 mM sucrose for the MAFE assay. When presented with 400 mM sucrose, approximately 100% of the flies in the Naïve and Satisfied groups, and 50% of the flies in the Failed group, extended their proboscis and started feeding, as a natural consequence of decreased sugar sensitivity (Figure 1B). We were able to quantify the actual volume of food consumed of these flies showing PER responses towards 400 mM sucrose and observed no change (Figure 1-supplement 1A, left). To avoid potential confusion, we have now repeated the MAFE assay with 800 mM sucrose, which elicited feeding in ~100% of flies among all three groups, as shown in Figure 1C. Again, we observed no change in food intake (Figure 1-supplement 1A, right).

These experiments in combination suggest that sexual failure suppresses sweet sensitivity of the Failed males. Meanwhile, as long as they still responded to a certain food stimulus and initiated feeding, the volume of food consumption remained unchanged. These results led us to focus on the modulatory effect of sexual failure on the sensory system, the main topic of this present study.

(3) The evidence linking TH-GAL4 labeled dopaminergic neurons to reduced sugar sensitivity in Gr5a+ neurons in sexually failed males could be further strengthened. Ideally, the authors would have activated TH-GAL4 neurons and observed whether this restored GCaMP responses in Gr5a+ neurons in sexually failed males. Instead, the authors performed a less direct experiment, shown in Figures 3-S1C and D. The manuscript does not describe the condition of the flies used in this experiment, but it appears that they were not sexually conditioned. I have two concerns with this experiment. First, no statistical analysis was provided to support the enhancement of sucrose responses following activation of TH-GAL4 neurons. Second, without performing this experiment in sexually failed males, the authors lack direct evidence to confirm that the dampened response of Gr5a+ neurons to sucrose results from decreased activity in TH-GAL4 neurons.

We have now quantified the effect of TH⁺ neuron activation on Gr5a⁺ neuron calcium responses. in Naïve males, dTRPA1-mediated activation of TH⁺ cells significantly enhanced sucrose-induced calcium responses (Figure 3-supplement 1C); while in Failed males, the baseline activity of Gr5a⁺ neurons was lower (Figure 3C), the same activation also produced significant (even slightly larger) effect on the calcium responses of Gr5a⁺ neurons (Figure 3-supplement 1D).

Taken together, we would argue that these experiments using both Naïve and Failed males were adequate to show a functional link between TH⁺ neurons and Gr5a⁺ neurons. Combining with the results that these neurons form active synapses (Figure 3-supplement 1B) and that the activity of TH⁺ neurons was dampened in sexually failed males (Figure 3G-I), our data support the notion that sexual failure suppresses sweet sensitivity via TH-Gr5a circuitry.

(4) The statistical methods used in this study are poorly described, making it unclear which method was used for each experiment. I suggest that the authors include a clear description of the statistical methods used for each experiment in the figure legends. Furthermore, as I have pointed out, there is a lack of statistical comparisons in Figures 3-S1C and D, a similar problem exists for Figures 6E and F.

We have added detailed information of statistical analysis in each figure legend.

(5) The experiments in Figure 5 lack specificity. The target neurons in this study are Gr5a+ neurons, which are directly involved in sugar sensing. However, the authors used the less specific Dop1R1- and Dop2R-GAL4 lines for their manipulations. Using Gr5a-GAL4 to specifically target Gr5a+ neurons would provide greater precision and ensure that the observed effects are directly attributable to the modulation of Gr5a+ neurons, rather than being influenced by potential off-target effects from other neuronal populations expressing these dopamine receptors.

We agree with the reviewer that manipulating Dop1R1 and Dop2R genes (Figure 4) and the neurons expressing them (Figure 5) might have broader impacts. For specificity, we have also tested the role of Dop1R1 and Dop2R in Gr5a⁺ neurons by RNAi experiments (Figure 6). As shown by both behavioral and calcium imaging experiments, knocking down Dop1R1 and Dop2R in Gr5a⁺ neurons both eliminated the effect of sexual failure to dampen sweet sensitivity, further confirming the role of these two receptors in Gr5a⁺ neurons.

(6) I found the results presented in Fig. 6F puzzling. The knockdown of Dop2R in Gr5a+ neurons would be expected to decrease sucrose responses in naive and satisfied flies, given the role of Dop2R in enhancing sweet sensitivity. However, the figure shows an apparent increase in responses across all three groups, which contradicts this expectation. The authors may want to provide an explanation for this unexpected result.

We agree that there might be some potential discrepancies. We have now addressed the issues by re-conducting these calcium imaging experiments again with a head-to-head comparison with the controls (Gr5a-GCaMP, +/- Dop1R1 and Dop2R RNAi).

In these new experiments, Dop1R1 or Dop2R knockdown completely prevented the suppression of Gr5a⁺ neuron responsiveness by courtship failure (Figure 6E), whereas the activities of Gr5a⁺ neurons in Naïve/Satisfied groups were not altered. These results demonstrate that Dop1R1 and Dop2R are specifically required to mediate the decrease in sweet sensitivity following courtship failure.

(7) In several instances in the manuscript, the authors described the effects of silencing dopamine signaling pathways or knocking down dopamine receptors in Gr5a neurons with phrases such as 'no longer exhibited reduced sweet sensitivity' (e.g., L269 and L288), 'prevent the reduction of sweet sensitivity' (e.g., L292), or 'this suppression was reversed' (e.g. L299). I found these descriptions misleading, as they suggest that sweet sensitivity in naive and satisfied groups remains normal while the reduction in failed flies is specifically prevented or reversed. However, this is not the case. The data indicate that these manipulations result in an overall decrease in sweet sensitivity across all groups, such that a further reduction in failed flies is not observed. I recommend revising these descriptions to accurately reflect the observed phenotypes and avoid any confusion regarding the effects of these manipulations.

We have changed the wording in the revised manuscript. In brief, we think that these manipulations have two consequences: suppressing the overall sweet sensitivity, and eliminating the effect of sexual failure on sweet sensitivity.

Reviewer #2 (Public review):

Summary:

The authors exposed naïve male flies to different groups of females, either mated or virgin. Male flies can successfully copulate with virgin females; however, they are rejected by mated females. This rejection reduces sugar preference and sensitivity in males. Investigating the underlying neural circuits, the authors show that dopamine signaling onto GR5a sensory neurons is required for reduced sugar preference. GR5a sensory neurons respond less to sugar exposure when they lack dopamine receptors.

Strengths:

The findings add another strong phenotype to the existing dataset about brain-wide neuromodulatory effects of mating. The authors use several state-of-the-art methods, such as activity-dependent GRASP to decipher the underlying neural circuitry. They further perform rigorous behavioral tests and provide convincing evidence for the local labellar circuit.

Weaknesses:

The authors focus on the circuit connection between dopamine and gustatory sensory neurons in the male SEZ. Therefore, it is still unknown how mating modulates dopamine signaling and what possible implications on other behaviors might result from a reduced sugar preference.

We agree with the reviewer that in the current study, we did not examine the exact mechanism of how mating experience suppressed the activity of dopaminergic neurons in the SEZ. The current study mainly focused on the behavioral characterization (sexual failure suppresses sweet sensitivity) and the downstream mechanism (TH-Gr5a pathway). We think that examining the upstream modulatory mechanism may be more suitable for a separate future study.

We believe that a sustained reduction in sweet sensitivity (not limited to sucrose but extend to other sweet compounds Figure 1-supplement 1D-E) upon courtship failure suggests a generalized and sustained consequence on reward-related behaviors. Sexual failure may thus resemble a state of “primitive emotion” in fruit flies. We have further discussed this possibility in the revised manuscript.

Reviewer #3 (Public review):

Summary

In this work, the authors asked how mating experience impacts reward perception and processing. For this, they employ fruit flies as a model, with a combination of behavioral, immunostaining, and live calcium imaging approaches.

Their study allowed them to demonstrate that courtship failure decreases the fraction of flies motivated to eat sweet compounds, revealing a link between reproductive stress and reward-related behaviors. This effect is mediated by a small group of dopaminergic neurons projecting to the SEZ. After courtship failure, these dopaminergic neurons exhibit reduced activity, leading to decreased Gr5a+ neuron activity via Dop1R1 and Dop2R signaling, and leading to reduced sweet sensitivity. The authors therefore showed how mating failure influences broader behavioral outputs through suppression of the dopamine-mediated reward system and underscores the interactions between reproductive and reward pathways.

Concern

My main concern regarding this study lies in the way the authors chose to present their results. If I understood correctly, they provided evidence that mating failure induces a decrease in the fraction of flies exhibiting PER. However, they also showed that food consumption was not affected (Fig. 1, supplement), suggesting that individuals who did eat consumed more. This raises questions about the analysis and interpretation of the results. Should we consider the group as a whole, with a reduced sensitivity to sweetness, or should we focus on individuals, with each one eating more? I am also concerned about how this could influence the results obtained using live imaging approaches, as the flies being imaged might or might not have been motivated to eat during the feeding assays. I would like the authors to clarify their choice of analysis and discuss this critical point, as the interpretation of the results could potentially be the opposite of what is presented in the manuscript.

Please refer to our responses to the Public Review (Reviewer 1, Point 2) for details.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) The label for the y-axis in Figure 1B should be "fraction", not "percentage".

We have revised the figure as suggested.

(2) I suggest that the authors indicate the ROIs they used to quantify the signal intensity in Figure 3E and G.

We have revised the figures as suggested.

(3) There is a typo in Figure 4A: it should be "Wilde type", not "Wide type".

We have revised the figure as suggested.

(4) The elav-GAL4/+ data in Figure 4-S1B, C, and D appears to be reused across these panels. However, the number of asterisks indicating significance in the MAT plots differs between them (three in panels B and C, and four in panel D). Is this a typo?

It is indeed a typo, and we have revised the figure accordingly.

Reviewer #2 (Recommendations for the authors):

Additional comments:

The authors should add this missing literature about dopamine and neuromodulation in courtship:

Boehm et al., 2022 (eLife) - this study shows that mating affects olfactory behavior in females.

Cazalé-Debat et al., 2024 (Nature) - Mating proximity blinds threat perception.

Gautham et al., 2024 (Nature) - A dopamine-gated learning circuit underpins reproductive state-dependent odor preference in Drosophila females.

We have added these references in the introduction section.

Has the mating behavior been quantified? How often did males copulate with mated and virgin females?

We tried to examine the copulation behavior based on our video recordings. In the “Failed” group (males paired with mated females), we observed virtually no successful copulation events at all, confirming that nearly 100% of those males experienced sexual failure. In contrast, males in the “Satisfied” group (paired with virgin females) mated on average 2-3 times during the 4.5-hour conditioning period. We have added some explanations in the manuscript.

Do the rejected males live shorter? Is the effect also visible when they are fed with normal fly food, or is it only working with sugar?

We did not directly measure the lifespan of these males. But we conducted a relevant assay (starvation resistance), in which “Failed” males died significantly faster than both Naïve and Satisfied controls, indicating a clear reduction in their ability to endure food deprivation (Figure 1-supplement 1B). Since sweet taste is a primary cue for food detection in Drosophila, and sugar makes up a large portion of their standard diet, the drop in sugar sensitivity we observed in Failed males could likewise impair their perception and consumption of regular fly food, hence their resistance to starvation.

Also, the authors mention that the reward pathway is affected, this is probably the case as sugar sensation is impaired. One interesting experiment would be (and maybe has been done?) to test rejected males in normal odor-fructose conditioning. The data would suggest that they would do worse.

We have already measured how courtship failure affected fructose sensitivity (Figure 1 supplement 1D), and we found that the reduction in fructose perception was even more profound than for sucrose. We have not yet tested whether Failed males showed deficits in odor-fructose associative conditioning. That was indeed a very interesting direction to explore. But olfactory reward learning relies on molecular and circuit mechanisms distinct from those governing taste. We therefore argue such experiments would be more suitable in a separate, follow up study.

The authors could have added another group where males are exposed to other males. It would be interesting if this is also a "stressful" context and if it would also reduce sugar preference - probably beyond the scope of this paper.

In our experiments, all flies, including those in the Naïve, Failed, and Satisfied groups, were housed in groups of 25 males per vial before the conditioning period (and the Naïve group remained in the same group housing until PER testing). This means every cohort experienced the same level of “social stress” from male-male interactions. While it would indeed be interesting to compare that to solitary housing or other male-only exposures, isolation itself imposes a different kind of stress, and disentangling these effects on sugar preference would require a separate, dedicated study beyond the scope of the present work.

Would the behavior effect also show up with experienced males? Maybe this has been tested before. Does mating rejection in formerly successful males have the same impact?

As suggested by the reviewer, we performed an additional experiment in which males that had previously mated successfully were subsequently subjected to courtship rejection. As shown in Figure 1 supplement 1F, prior successful mating did not prevent the decline in sweet sensitivity induced by subsequent mating failure, indicating that even experienced males exhibit the reduction in sugar sensitivity after rejection.

Is the same circuit present and functioning in females? Does manipulating dopamine receptors in GR5a neurons in females lead to the same phenotype? This would suggest that different internal states in males and females could lead to the same phenotype and circuit modulations.

This is indeed a very interesting suggestion. In male flies, Gr5a-specific knockdown of dopamine receptors did not alter baseline sweet sensitivity, but it selectively prevented the reduction in sugar perception that followed mating failure (Figure 6C-D), indicating that this dopaminergic pathway is engaged only in the context of courtship rejection. By extension, knocking down the same receptors in female GR5a neurons would likewise be expected to leave their basal sugar sensitivity unchanged. Moreover, because there is currently no established paradigm for inducing mating failure in female flies, we cannot yet test whether sexual rejection similarly modulates sweet taste in females, or whether it operates via the same circuit.

Reviewer #3 (Recommendations for the authors):

Suggestions to the authors:

Introduction, line 61. I suggest the authors add references in fruit flies concerning the rewarding nature of mating. For example, the paper from Zhang et al, 2016 "Dopaminergic Circuitry Underlying Mating Drive" demonstrates the role of the dopamine rewarding system in mating drive. There is a large body of literature showing the link between dopamine and mating.

We have added this literature in the introduction section.

Figure 1B and Figure Supplement 1: If I understood correctly, Figure Supplement 1A shows that the total food consumption across all tested flies remains unchanged. However, fewer flies that failed to mate consumed sucrose. I would be curious to see the results for sucrose consumption per individual fly that did eat. According to their results, individual flies that failed to mate should consume more sucrose. This would change the conclusion. The authors currently show that a group of flies that failed to mate consumed less sucrose overall, but since fewer males actually ate, those that failed to mate and did eat consumed more sucrose. The authors should distinguish between failed and satisfied flies in two groups: those that ate and those that did not.

Please see our responses to the Public Review for details (Reviewer 1, Point 2).

Figure 1C, right: For a better understanding of all the "MAT" figures, I suggest the authors start the Y axis with the unit 25 and increase it to 400. This would match better the text (line 114) saying that it was significantly elevated in the failed group. As it is, we have the impression of a decrease in the graph.

We have revised the figures accordingly.

Line 103: When suggesting a reduced likelihood of meal initiation of these males, do these males take longer to eat when they did it? In other words, is the latency to eat increased in failed males? That would be a good measure of motivational state.

We tried to analyze feeding latency in the MAFE assay by measuring the time from sucrose presentation to the first proboscis extension, but it was too short to be accurately accounted. Nevertheless, when conducting the experiments, we did not feel/observe any significant difference in the feeding latency between Failed males and Naïve or Satisfied controls.

Line 117. I don't understand which results the authors refer to when writing "an overall elevation in the threshold to initiate feeding upon appetitive cues". Please specify.

This phrase refers to the fact that for every sweet tastant we tested, including sucrose (Figure 1C), fructose and glucose (Figure 1 supplement 1D-E), the concentration-response curve in Failed males shifted to the right, and the Mean Acceptance Threshold (MAT) was significantly higher. In other words, for these different appetitive cues, mating failure raised the concentration of sugar required to trigger a proboscis extension, indicating a general elevation in the threshold to initiate feeding upon an appetitive cue.

Figure 1D. Please specify the time for the satisfied group.

For clarity, the Naïve and Satisfied groups in Figure 1D each represent pooled data from 0 to 72 hours post-treatment, as their sweet sensitivity remained stable throughout this period. Only the Failed group was shown with time-resolved data, since it was the only group exhibiting a dynamic change in sugar sensitivity over time. We have now specified this in the figure legend.

Figure 1F. The phenotype was not totally reversed in failed-re-copulated males. Could it be due to the timing between failure and re-copulation? I suggest the authors mention in the figure or in the text, the time interval between failure and re-copulation.

We’d like to clarify that the interval between the initial treatment (“Failed”) and the opportunity for re copulation was within 30 minutes. The incomplete reversal in the Failed-re-copulated group indeed raised interesting questions. One possible explanation is that mating failure reduces synaptic transmissions between the SEZ dopaminergic neurons and Gr5a⁺ sweet sensory neurons (Figure 3), and the regeneration of these transmissions takes a longer time. We have added this information to the figure legend and the Method section.

Line 227-228 and Figure 3E. The authors showed that the synaptic connections between dopaminergic neurons and Gr5a+ GRNs were significantly weakened. I am wondering about the delay between mating failure and the GFP observation. It would be informative to know this timing to interpret this decrease in synaptic connections. If the timing is relatively long, it is possible that we can observe a neuronal plasticity. However, if this timing is very short, I would not expect such synaptic plasticity.

The interval between the behavioral treatment and the GRASP-GFP experiment was approximately 20 hours. We chose this time window because it was sufficient for both GFP expression and accumulation. Therefore, the observed reduction in synaptic connections between dopaminergic neurons and Gr5a⁺ GRNs likely reflects a genuine, experience-induced structural and functional change rather than an immediate, transient effect. We have added this information to the revised manuscript for clarity in the Method section.

Line 240-243: The authors demonstrated that there is a reduction of CaLexA-mediated GFP signals in dopaminergic neurons in the SEZ after mating failure, but not a reduction in Gr5a+ GRNs. I suggest replacing "indicate" with "suggest' in line 240.

We have made the change accordingly. Meanwhile, we would like to clarify that while we observed a reduction of NFAT signal in SEZ dopaminergic neurons (Figure 3G), we did not directly test NFAT signal in Gr5a⁺ neurons. Notably, the results that the synaptic transmissions from SEZ dopaminergic neurons to Gr5a⁺ neurons were weakened (Figure 3E-F), and the reduction of NFAT signal in SEZ dopaminergic neurons (Figure 3G-I), were in line with a reduction in sweet sensitivity of Gr5a⁺ neurons upon courtship failure (Figure 3B-D).

Line 243: replace "consecutive" with "constitutive".

We have revised it accordingly.

Figure 5: I have trouble understanding the results obtained in Figure 5. Both constitutive activation and inhibition of Dop1R1 and Dop2R neurons lead to the same results, knowing that males who failed mating no longer exhibit decreased sweet sensitivity. I would have expected contrary results for both experimental conditions. I suggest the author to discuss their results.

Both activation and inhibition of Dop1R1 and Dop2R neurons eliminated the effect of courtship failure on sweet sensitivity (Figure 5). These results are in line with our hypothesis that courtship failure leads to changes in dopamine signaling and hence sweet sensitivity. If dopamine signaling via Dop1R1 and Dop2R was locked, either to a silenced or a constitutively activated state, the effect of courtship failure on sweet sensitivity was eliminated.

Nevertheless, as the reviewer pointed out, constitutive activation/inhibition should in principle lead to the opposite effect on Naïve flies. In fact, when Dop1R1⁺/Dop2R⁺ neurons were silenced in Naïve flies, PER to sucrose was significantly reduced (Figure 5C-D), confirming that these neurons normally facilitate sweet sensation. Meanwhile, while neuronal activation by NaChBac did show a trend towards enhanced PER compared to the GAL4/+ controls, it did not exhibit a difference compared to +>UAS-NaChBac controls that showed a high PER level, likely due to a potential ceiling effect. We have added the discussions to the manuscript.

Figure 7: I suggest the authors modify their figure a bit. It is not clear why in failed mating, the red arrow in "behavioral modulation" goes to the fly. The authors should find another way to show that mating failure decreased the percentage of flies that are motivated to eat sugar.

We have modified the figure as suggested.

Overall, I would suggest the authors be precautious with their conclusion. For example, line 337= "sexual failure suppressed feeding behavior". This is not what is shown by this study. Here, the study shows that mating failure decreases the fraction of flies to eat sucrose. Unless the authors demonstrate that this decrease is generalizable to other metabolites, I suggest the authors modify their conclusion.

While we primarily used sucrose as the stimulant in our experiments, we also tested responses to two other sugars: fructose and glucose (Figure 1 supplement 1D-E). In all three cases, mating failure led to a significant reduction in sweet perception, suggesting that the effect of courtship failure is not limited to a single metabolite but rather reflects a general decrease in sweet sensitivity. Meanwhile, reduced sweet sensitivity indeed led to a reduction of feeding initiation (Figure 1).

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

In their comprehensive analysis Diallo et al. deorphanise the first olfactory receptor of a nonhymenopteran eusocial insect - a termite and identified the well-established trail pheromone neocembrene as the receptor's best ligand. By using a large set of odorants the authors convincingly show that, as expected for a pheromone receptor, PsimOR14 is very narrowly tuned. While the authors first make use of an ectopic expression system, the empty neuron of Drosophila melanogaster, to characterise the receptor's responses, they next perform single sensillum recordings with different sensilla types on the termite antenna. By that, they are able to identify a sensillum that houses three neurons, of which the B neuron exhibits the narrow responses described for PsimOR14. Hence the authors do not only identify the first pheromone receptor in a termite but can even localize its expression on the antenna. The authors in addition perform a structural analysis to explain the binding properties of the receptor and its major and minor ligands (as this is beyond my expertise, I cannot judge this part of the manuscript). Finally, they compare expression patterns of ORs in different castes and find that PsimOR14 is more strongly expressed in workers than in soldier termites, which corresponds well with stronger antennal responses in the worker caste.

Strengths:

The manuscript is well-written and a pleasure to read. The figures are beautiful and clear. I actually had a hard time coming up with suggestions.

We thank the reviewer for the positive comments.

Weaknesses:

Whenever it comes to the deorphanization of a receptor and its potential role in behaviour (in the case of the manuscript it would be trail-following of the termite) one thinks immediately of knocking out the receptor to check whether it is necessary for the behaviour. However, I definitely do not want to ask for this (especially as the establishment of CRISPR Cas-9 in eusocial insects usually turns out to be a nightmare). I also do not know either, whether knockdowns via RNAi have been established in termites, but maybe the authors could consider some speculation on this in the discussion.

We agree that a functional proof of the PsimOR14 function using reverse genetics would be a valuable addition to the study to firmly establish its role in trail pheromone sensing. Nevertheless, such a functional proof is difficult to obtain. Due to the very slow ontogenetic development inherent to termites (several months from an egg to the worker stage) the CRISPR Cas-9 is not a useful technique for this taxon. By contrast, termites are quite responsive to RNAimediated silencing and RNAi has previously been used for the silencing of the ORCo co-receptor in termites resulting in impairment of the trail-following behavior (DOI: 10.1093/jee/toaa248). Likewise, our previous experiments showed a decreased ORCo transcript abundance, lower sensitivity to neocembrene and reduced neocembrene trail following upon dsPsimORCo administration to P. simplex workers, while we did not succeed in reducing the transcript abundance of PsimOR14 upon dsPsimOR14 injection. We do not report these negative results in the present manuscript so as not to dilute the main message. In parallel, we are currently developing an alternative way of dsRNA delivery using nanoparticle coating, which may improve the RNAi experiments with ORs in termites.

Reviewer #2 (Public review):

Summary:

In this manuscript, the authors performed the functional analysis of odorant receptors (ORs) of the termite Prorhinotermes simplex to identify the receptor of trail-following pheromone. The authors performed single-sensillum recording (SSR) using the transgenic Drosophila flies expressing a candidate of the pheromone receptor and revealed that PsimOR14 strongly responds to neocembrene, the major component of the pheromone. Also, the authors found that one sensillum type (S I) detects neocembrene and also performed SSR for S I in wild termite workers. Furthermore, the authors revealed the gene, transcript, and protein structures of PsimOR14, predicted the 3D model and ligand docking of PsimOR14, and demonstrated that PsimOR14 is higher expressed in workers than soldiers using RNA-seq for heads of workers and soldiers of P. simplex and that EAG response to neocembrene is higher in workers than soldiers. I consider that this study will contribute to further understanding of the molecular and evolutionary mechanisms of the chemoreception system in termites.

Strength:

The manuscript is well written. As far as I know, this study is the first study that identified a pheromone receptor in termites. The authors not only present a methodology for analyzing the function of termite pheromone receptors but also provide important insights in terms of the evolution of ligand selectivity of termite pheromone receptors.

We thank the reviewer for the overall positive evaluation of the manuscript.

Weakness:

As you can see in the "Recommendations to the Authors" section below, there are several things in this paper that are not fully explained about experimental methods. Except for this point, this paper appears to me to have no major weaknesses.

We address point by point the specific comments listed in the Recommendation to the authors chapter below.

Reviewer #3 (Public review):

Summary:

Chemical communication is essential for the organization of eusocial insect societies. It is used in various important contexts, such as foraging and recruiting colony members to food sources. While such pheromones have been chemically identified and their function demonstrated in bioassays, little is known about their perception. Excellent candidates are the odorant receptors that have been shown to be involved in pheromone perception in other insects including ants and bees but not termites. The authors investigated the function of the odorant receptor PsimOR14, which was one of four target odorant receptors based on gene sequences and phylogenetic analyses. They used the Drosophila empty neuron system to demonstrate that the receptor was narrowly tuned to the trail pheromone neocembrene. Similar responses to the odor panel and neocembrene in antennal recordings suggested that one specific antennal sensillum expresses PsimOR14. Additional protein modeling approaches characterized the properties of the ligand binding pocket in the receptor. Finally, PsimOR14 transcripts were found to be significantly higher in worker antennae compared to soldier antennae, which corresponds to the worker's higher sensitivity to neocembrene.

Strengths:

The study presents an excellent characterization of a trail pheromone receptor in a termite species. The integration of receptor phylogeny, receptor functional characterization, antennal sensilla responses, receptor structure modeling, and transcriptomic analysis is especially powerful. All parts build on each other and are well supported with a good sample size.

We thank the reviewer for these positive comments.

Weaknesses:

The manuscript would benefit from a more detailed explanation of the research advances this work provides. Stating that this is the first deorphanization of an odorant receptor in a clade is insufficient. The introduction primarily reviews termite chemical communication and deorphanization of olfactory receptors previously performed. Although this is essential background, it lacks a good integration into explaining what problem the current study solves.

We understand the comment about the lack of an intelligible cue to highlight the motivation and importance of the present study. In the current version of the manuscript the introduction has been reworked. As suggested by Reviewer 3 in the Recommendations section below, the introduction now integrates some parts of the original discussion, especially the part discussing the OR evolution and emergence of eusociality in hymenopteran social insects and in termites, while underscoring the need of data from termites to compare the commonalities and idiosyncrasies in neurophysiological (pre)adaptations potentially linked with the independent eusociality evolution in the two main social insect clades.

Selecting target ORs for deorphanization is an essential step in the approach. Unfortunately, the process of choosing these ORs has not been described. Were the authors just lucky that they found the correct OR out of the 50, or was there a specific selection process that increased the probability of success?

Indeed, we were extremely lucky. Our strategy was to first select a modest set of ORs to confirm the feasibility of the Empty Neuron Drosophila system and newly established SSR setup, while taking advantage of having a set of termite pheromones, including those previously identified in the P. simplex model, some of them de novo synthesized for this project. The selection criteria for the first set of four receptors were (i) to have full-length ORF and at least 6 unambiguously predicted transmembrane regions, and (ii) to be represented on different branches (subbranches) of the phylogenetic tree. Then it was a matter of a good luck to hit the PsimOR14 selectively responding to the genuine P. simplex trail-following pheromone main component. In the revised version, we state these selection criteria in the results section (Phylogenetic reconstruction and candidate OR selection).

The deorphanization attempts of additional P. simplex ORs are currently running.

The authors assigned antennal sensilla into five categories. Unfortunately, they did not support their categories well. It is not clear how they were able to differentiate SI and SII in their antennal recordings.

We agree that the classification of multiporous sensilla into five categories lacks robust discrimination cues. The identification of the neocembrene-responding sensillum was initially carried out by SSR measurements on individual olfactory sensilla of P. simplex workers one-by-one and the topology of each tested sensillum was recorded on optical microscope photographs taken during the SSR experiment. Subsequently, the SEM and HR-SEM were performed in which we localized the neocembrene sensillum and tried to find distinguishing characters. We admit that these are not robust. Therefore, in the revised version of the manuscript we decided to abandon the attempt of sensilla classification and only report the observations about the specific sensillum in which we consistently recorded the response to neocembrene (and geranylgeraniol). The modifications affect Fig. 4, its legend and the corresponding part of the results section (Identification of P. simplex olfactory sensillum responding to neocembrene).

The authors used a large odorant panel to determine receptor tuning. The panel included volatile polar compounds and non-volatile non-polar hydrocarbons. Usually, some heat is applied to such non-volatile odorants to increase volatility for receptor testing. It is unclear how it is possible that these non-volatile compounds can reach the tested sensilla without heat application.

The reviewer points at an important methodological error we made while designing the experiments. Indeed, the inclusion of long-chain hydrocarbons into Panel 1 without additional heat applied to the odor cartridges was inappropriate, even though the experiments were performed at 25–26 °C. We carefully considered the best solution to correct the mistake and finally decided to remove all tested ligands beyond C22 from Panel 1, i.e. altogether five compounds. These changes did not affect the remaining Panels 2-4 (containing compounds with sufficient volatility), nor did they affect the message of the manuscript on highly selective response of PsimOR14 to neocembrene (and geranylgeryniol). In consequence, Figures 2, 3 and 5 were updated, along with the supplementary tables containing the raw data on SSR measurements. In addition, the tuning curve for PsimOR14 was re-built and receptor lifetime sparseness value re-calculated (without any important change). We also exchanged squalene for limonene in the docking and molecular dynamics analysis and made new calculations.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) L 208: "than" instead of "that"

Corrected.

(2) L 527+527 strange squares (•) before dimensions

Apparently an error upon file conversion, corrected.

(3) L553 "reconstructing" instead of "reconstruct"

Corrected.

(4) Two references (Chahda et al. and Chang et al. appear too late in the alphabet.

Corrected. Thank you for spotting this mistake. Due to our mistake the author list was ordered according to the alphabet in Czech language, which ranks CH after H.

Reviewer #2 (Recommendations for the authors):

(1) L148: Why did the authors select only four ORs (PsimOR9, 14, 30, and 31) though there are 50 ORs in P. simplex? I would like you to explain why you chose them.

Our strategy was to first select a modest set of ORs to confirm the feasibility of the Empty Neuron Drosophila system and newly established SSR setup, while taking advantage of having a set of termite pheromones, including those previously identified in the P. simplex model, some of them de novo synthesized for this project. Then, it was a matter of a good luck to hit the PsimOR14 selectively responding to the genuine P. simplex trail-following pheromone main component, while the deorphanization attempts of a set of additional P. simplex ORs is currently running. In the revised version of the manuscript, we state the selection criteria for the four ORs studied in the Results section (Phylogenetic reconstruction and candidate OR selection).

(2) L149: Where is Figure 1A? Does this mean Figure 1?

Thank you for spotting this mistake. Fig. 1 is now properly labelled as Fig. 1A and 1B in the figure itself and in the legend. Also the text now either refers to either 1A or 1B.

(3) Figure 1: The authors also showed the transcription abundance of all 50 ORs of P. simplex in the right bottom of Figure 1, but there is no explanation about it in the main text.

The heatmap reporting the transcript abundances is now labelled as Fig. 1B and is referred to in the discussion section (in the original manuscript it was referred to on the same place as Fig. 1).

(4) L260-265: The authors confirmed higher expression of PsimOR14 in workers than soldiers by using RNA-seq data and stronger EAG responses of PsimOR14 to neocembrene in workers than soldiers, but I think that confirming the expression levels of PsimOR14 in workers and soldiers by RT-qPCR would strengthen the authors' argument (it is optional).

qPCR validation is a suitable complement to read count comparison of RNA Seq data, especially when the data comes from one-sample transcriptomes and/or low coverage sequencing. Yet, our RNA Seq analysis is based on sequencing of three independent biological replicates per phenotype (worker heads vs. soldier heads) with ~20 millions of reads per sample. Thus, the resulting differential gene expression analysis is a sufficient and powerful technique in terms of detection limit and dynamic range.

We admit that the replicate numbers and origin of the RNA seq data should be better specified since the Methods section only referred to the GenBank accession numbers in the original manuscript. Therefore, we added more information in the Methods section (Bioinformatics) and make clear in the Methods that this data comes from our previous research and related bioproject.

(5) L491: I think that "The synthetic processes of these fatty alcohols are ..." is better.

We replaced the sentence with “The de novo organic synthesis of these fatty alcohols is described …”

(6) L525 and 527: There are white squares between the number and the unit. Perhaps some characters have been garbled.

Apparently an error upon file conversion, corrected.

(7) L795: ORCo?

Corrected.

(8) L829-830 & Figure 4: Where is Figure 4D?

Thank you for spotting this mistake from the older version of Figure 4. The SSR traces referred to in the legend are in fact a part of Figure 5. Moreover, Figure 4 is now reworked based on the comments by Reviewer 3.

(9) L860-864: Why did the authors select the result of edgeR for the volcano plot in Figure 7 although the authors use both DESeq2 and edgeR? An explanation would be needed.

Both algorithms, DESeq2 and EdgeR, are routinely used for differential gene expression analysis. Since they differ in read count normalization method and statistical testing we decided to use both of them independently in order to reduce false positives. Because the resulting fold changes were practically identical in both algorithms (results for both analyses are listed in Supplementary table S15), we only reported in Fig. 7 the outputs for edgeR to avoid redundancies. We added in the Results section the information that both techniques listed PsimOR14 among the most upregulated in workers.

Reviewer #3 (Recommendations for the authors):

The discussion contains many descriptions that would fit better into the introduction, where they could be used to hint at the study's importance (e.g., 292-311, 381-412). The remaining parts often lack a detailed discussion of the results that integrates details from other insect studies. Although references were provided, no details were usually outlined. It would be helpful to see a stronger emphasis on what we learn from this study.

Along with rewriting the introduction, we also modified the discussion. As suggested, the lines 292-311 were rewritten and placed in the introduction. By contrast, we preferred to keep the two paragraphs 381-412 in the discussion, since both of them outline the potential future interesting targets of research on termite ORs.

As suggested, the discussion has been enriched and now includes comparative examples and relevant references about the broad/narrow selectivity of insect ORs, about the expected breadth of tuning of pheromone receptors vs. ORs detecting environmental cues, about the potential role of additional neurons housed in the neocembrene-detecting sensillum of P. simplex workers, etc. From both introduction and discussion the redundant details on the chemistry of termite communication have been removed.

This includes explanations of the advantages of the specific methodologies the authors used and how they helped solve the manuscript's problem. What does the phylogeny solve? Was it used to select the ORs tested? It would be helpful to discuss what the phylogeny shows in comparison to other well-studied OR phylogenies, like those from the social Hymenoptera.

We understand the comment. In fact, our motivation to include the phylogenetic tree of termite ORs was essentially to demonstrate (i) the orthologous nature of OR diversity with few expansions on low taxonomic levels, and (ii) to demonstrate graphically the relationship among the four selected sequences. We do not attempt here for a comprehensive phylogenetic analysis, because it would be redundant given that we recently published a large OR phylogeny which includes all sequences used in the present manuscript and analysed them in the proper context of related (cockroaches) and unrelated insect taxa (Johny et al., 2023). This paper also discusses the termite phylogenetic pattern with those observed in other Insecta. This paper is repeatedly cited on appropriate places of the present manuscript and its main observations are provided in the Introduction section. Therefore, we feel that thorough discussion on termite phylogeny would be redundant in the present paper.

The authors categorized the sensilla types. Potential problems in the categorization aside, it would be helpful to know if it is expected that you have sensilla specialized in perceiving one specific pheromone. What is known about sensilla in other insects?

We understand. In the discussion of the revised version, we develop more about the features typical/expected for a pheromone receptor and the sensillum housing this receptor together with two other olfactory sensory neurons, including examples from other insects.

As the manuscript currently stands, specialist readers with their respective background knowledge would find this study very interesting. In contrast, the general reader would probably fail to appreciate the importance of the results.

We hope that the re-organized and simplified introduction may now be more intelligible even for non-specialist readers.

(1) L35: Should "workers" be replaced with "worker antennae"?

Corrected.

(2) L62: Should "conservativeness" be replaced by "conservation"?

Replaced with “parsimony”.

(3) L129: How and why did the authors choose four candidate ORs? I could not find any information about this in the manuscript. I wondered why they did not pick the more highly expressed PsimOr20 and 26 (Figure 7).

As already replied above in the Weaknesses section, we selected for the first deorphanization attempts only a modest set of four ORs, while an additional set is currently being tested. We also explained above the inclusion criteria, i.e. (i) full-length ORF and at least 6 unambiguously predicted transmembrane regions, and (ii) presence on different branches (subbranches) of the OR phylogeny. For these reasons, we did not primarily consider the expression patterns of different ORs. As for Fig. 7, it shows differential expression between soldiers and workers, which was not the primary guideline either and the data was obtained only after having the ORs tested by SSR. Yet, even though we had data on P. simplex ORs expression (Fig. 1B), we did not presume that pheromone receptors should be among the most expressed ORs, given the richness of chemical cues detected by worker termites and unlike, e.g., male moths, where ORs for sex pheromones are intuitively highly expressed.

The strategy of OR selection is specified in the results section of the revised manuscript under “Phylogenetic reconstruction and candidate OR selection”.

(4) 198 to 200: SI, II, and III look very similar. Additional measurements rather than qualitative descriptions are required to consider them distinct sensilla. The bending of SIII could be an artifact of preparation. I do not see how the authors could distinguish between SI and SII under the optical microscope for recordings. A detailed explanation is required.

As we responded above in “Weaknesses” chapter, we admit that the sensilla classification is not intelligible. Therefore, we decided in the revised version to abandon the classification of sensilla types and only focus on the observations made on the neocembreneresponding sensillum. To recognize the specific sensillum, we used its topology on the last antennal segment. Because termite antennae are not densely populated with sensilla, it is relatively easy to distinguish individual sensilla based on their topology on the antenna, both in optical microscope and SEM photographs. The modifications affect Fig. 4, its legend and the corresponding part of the results section (Identification of P. simplex olfactory sensillum responding to neocembrene).

(5) 208: "Than" instead of "that"

Corrected.

(6) 280: I suggest replacing "demand" with "capabilities"

Corrected.

(7) 312: Why "nevertheless? It sounds as if the authors suggest that there is evidence that ORs are not important for communication. This should be reworded.

We removed “Nevertheless” from the beginning of the sentence.

(8) 321 to 323: This sentence sounds as if something is missing. I suggest rewriting it.

This sentence simply says that empty neuron Drosophila is a good tool for termite OR deorphanization and that termite ORs work well Drosophila ORCo. We reworded the sentence.

(9) 323: I suggest starting a new paragraph.

Corrected.

(10) 421: How many colonies were used for each of the analyses?

The data for this manuscript were collected from three different colonies collected in Cuba. We now describe in the Materials and Methods section which analyses were conducted with each of the colonies.

(11) 430: Did the termites originate from one or multiple colonies and did the authors sample from the Florida and Cuba population?

The data for this manuscript were collected from three different colonies collected in Cuba. We now describe in the Materials and Methods section which analyses were conducted with each of the colonies.

(12) 501: How was the termite antenna fixated? The authors refer to the Drosophila methods, but given the large antennal differences between these species, more specific information would be helpful.

Understood. We added the following information into the Methods section under “Electrophysiology”: “The grounding electrode was carefully inserted into the clypeus and the antenna was fixed on a microscope slide using a glass electrode. To avoid the antennal movement, the microscope slide was covered with double-sided tape and the three distal antennal segments were attached to the slide.”

(13)509: I want to confirm that the authors indicate that the outlet of the glass tube with the airstream and odorant is 4 cm away from the Drosophila or termite antenna. The distance seems to be very large.

Thank you for spotting this obvious mistake. The 4 cm distance applies for the distance between the opening for Pasteur pipette insertion into the delivery tube, the outlet itself is situated approx. 1 cm from the antenna. This information is now corrected.

(14) 510/527: It looks like all odor panels were equally applied onto the filter paper despite the difference in solvent (hexane and paraffin oil). How was the solvent difference addressed?

In our study we combine two types of odorant panels. First, we test on all four studied receptors a panel containing several compounds relevant for termite chemical communication including the C12 unsaturated alcohols, the diterpene neocembrene, the sesquiterpene (3R,6E)-nerolidol and other compounds. These compounds are stored in the laboratory as hexane solutions to prevent the oxidation/polymerization and it is not advisable to transfer them to another solvent. In the second step we used three additional panels of frequently occurring insect semiochemicals, which are stored as paraffin oil solutions, so as to address the breadth of PsimOR14 tuning. We are aware that the evaporation dynamics differ between the two solvents but we did not have any suitable option how to solve this problem. We believe that the use of the two solvents does not compromise the general message on the receptor specificity. For each panel, the corresponding solvent is used as a control. Similarly, the use of two different solvents for SSR can be encountered in other studies, e.g. 10.1016/j.celrep.2015.07.031.

(15) 518: delta spikes/sec works for all tables except for the wild type in Table S5. I could not figure out how the authors get to delta spikes/sec in that table.

Thank you for your sharp eye. Due to our mistake, the values of Δ spikes per second reported in Table S5 for W1118 were erroneously calculated using the formula for 0.5 sec stimulation instead of 1 sec. We corrected this mistake which does not impact the results interpretation in Table S5 and Fig. 2.

522: Did the workers and soldiers originate from different colonies or different populations?

We now clearly describe in the Material and Methods section the origin of termites for different experiments. EAG measurements were made using individuals (workers, soldiers) from one Cuban colony.

(16) Figure 6C/D: I suggest matching colors between the two figures. For example, instead of using an orange circle in C and a green coloration of the intracellular flap in D, I recommend using blue, which is not used for something else. In addition, the binding pocket could be separated better from anything else in a different color.

We agree that the color match for the intracellular flap was missing. This figure is now reworked and the colors should have a better match and the binding region is better delineated.

(17) Figure 7/Table S15: It is unclear where the transcriptome data originate and what they are based on. Are these antennal transcriptomes or head transcriptomes? Do these data come from previous data sets or data generated in this study? Figure 7 refers to heads, Table S15 to workers and soldiers, and the methods only refer to antennal extractions. This should be clarified in the text, the figure, and the table.

We admit that the replicate numbers and origin of the RNA seq data should be better specified and that the information that the RNASeq originated from samples of heads+antennae of workers and soldiers should be provided at appropriate places. Therefore, we added more information on replicates and origin of the data in the Methods section (Bioinformatics) and make clear that this data comes from our previous research and refer to the corresponding bioproject. Likewise, the Figure 7 legend and Table S15 heading have been updated.

Reviewer #1 (Public review):

SMC5/6 is a highly conserved complex able to dynamically alter chromatin structure, playing in this way critical roles in genome stability and integrity that include homologous recombination and telomere maintenance. In the last years, a number of studies have revealed the importance of SMC5/6 in restricting viral expression, which is in part related to its ability to repress transcription from circular DNA. In this context, Oravcova and colleagues recently reported how SMC5/6 is recruited by two mutually exclusive complexes (orthologs of yeast Nse5/6) to SV40 LT-induced PML nuclear bodies (SIMC/SLF2) and DNA lesions (SLF1/2). In this current work, the authors extend this study, providing some new results. However, as a whole, the story lacks unity and does not delve into the molecular mechanisms responsible for the silencing process. One has the feeling that the story is somewhat incomplete, putting together not directly connected results.

(1) In the first part of the work, the authors confirm previous conclusions about the relevance of a conserved domain defined by the interaction of SIMC and SLF2 for their binding to SMC6, and extend the structural analysis to the modelling of the SIMC/SLF2/SMC complex by AlphaFold. Their data support a model where this conserved surface of SIMC/SLF2 interacts with SMC at the backside of SMC6's head domain, confirming the relevance of this interaction site with specific mutations. These results are interesting but confirmatory of a previous and more complete structural analysis in yeast (Li et al. NSMB 2024). In any case, they reveal the conservation of the interaction. My major concern is the lack of connection with the rest of the article. This structure does not help to understand the process of transcriptional silencing reported later beyond its relevance to recruit SMC5/6 to its targets, which was already demonstrated in the previous study.

(2) In the second part of the work, the authors focus on the functionality of the different complexes. The authors demonstrate that SMC5/6's role in transcription silencing is specific to its interaction with SIMC/SLF2, whereas SMC5/6's role in DNA repair depends on SLF1/2. These results are quite expected according to previous results. The authors already demonstrated that SLF1/2, but not SIMC/SLF2, are recruited to DNA lesions. Accordingly, they observe here that SMC5/6 recruitment to DNA lesions requires SLF1/2 but not SIMC/SLF2. Likewise, the authors already demonstrated that SIMC/SLF2, but not SLF1/2, targets SMC5/6 to PML NBs. Taking into account the evidence that connects SMC5/6's viral resistance at PML NBs with transcription repression, the observed requirement of SIMC/SLF2 but not SLF1/2 in plasmid silencing is somehow expected. This does not mean the expectation has not to be experimentally confirmed. However, the study falls short in advancing the mechanistic process, despite some interesting results as the dispensability of the PML NBs or the antagonistic role of the SV40 large T antigen. It had been interesting to explore how LT overcomes SMC5/6-mediated repression: Does LT prevent SIMC/SLF2 from interacting with SMC5/6? Or does it prevent SMC5/6 from binding the plasmid? Is the transcription-dependent plasmid topology altered in cells lacking SIMC/SLF2? And in cells expressing LT? In its current form, the study is confirmatory and preliminary. In agreement with this, the cartoons modelling results here and in the previous work look basically the same.

(3) There are some points about the presented data that need to be clarified.

Author response:

This study builds on, extends, and experimentally validates results/models from our previous study. Our and others’ data implicated SMC5/6, PML nuclear bodies (PML NBs), and SUMOylation in the transcriptional repression of extrachromosomal circular DNA (ecDNA). Moreover, multiple viruses were found to express early genes that combat SMC5/6-based repression through targeted proteasomal degradation (e.g. Hepatitis B virus HBx and HIV-1 Vpr). Thus, our analysis of the roles of the foregoing in plasmid repression yields a coherent set of results for the field to build on.

In our previous study we presented a model, but no supportive ecDNA silencing data, suggesting that distinct SMC5/6 subcomplexes, SIMC1-SLF2 and SLF1/2, separately control its transcriptional repression and DNA repair activities. In this study we experimentally validate that prediction using an ecDNA silencing assay and SMC5/6 localization analysis following DNA damage.

Our study further reveals the unexpected dispensability of PML NBs in the silencing of simple plasmid DNA, a departure from current dogma. This raises important questions for the field to address in terms of the silencing mechanisms for different ecDNAs across different cell types. Despite the dispensability of SUMO-rich PML NBs, SUMOylation is required for ecDNA repression. Lastly, the SV40 LT antigen early gene product counteracts ecDNA silencing. These results used genetic epistasis arguments to implicate SUMO and LT in SMC5/6-based transcriptional silencing. We provide provisional responses for some of the reviewer’s general comments below.

Public Reviews:

Reviewer #1 (Public review):

SMC5/6 is a highly conserved complex able to dynamically alter chromatin structure, playing in this way critical roles in genome stability and integrity that include homologous recombination and telomere maintenance. In the last years, a number of studies have revealed the importance of SMC5/6 in restricting viral expression, which is in part related to its ability to repress transcription from circular DNA. In this context, Oravcova and colleagues recently reported how SMC5/6 is recruited by two mutually exclusive complexes (orthologs of yeast Nse5/6) to SV40 LT-induced PML nuclear bodies (SIMC/SLF2) and DNA lesions (SLF1/2). In this current work, the authors extend this study, providing some new results. However, as a whole, the story lacks unity and does not delve into the molecular mechanisms responsible for the silencing process. One has the feeling that the story is somewhat incomplete, putting together not directly connected results.

Please see the introductory overview above.

(1) In the first part of the work, the authors confirm previous conclusions about the relevance of a conserved domain defined by the interaction of SIMC and SLF2 for their binding to SMC6, and extend the structural analysis to the modelling of the SIMC/SLF2/SMC complex by AlphaFold. Their data support a model where this conserved surface of SIMC/SLF2 interacts with SMC at the backside of SMC6's head domain, confirming the relevance of this interaction site with specific mutations. These results are interesting but confirmatory of a previous and more complete structural analysis in yeast (Li et al. NSMB 2024). In any case, they reveal the conservation of the interaction. My major concern is the lack of connection with the rest of the article. This structure does not help to understand the process of transcriptional silencing reported later beyond its relevance to recruit SMC5/6 to its targets, which was already demonstrated in the previous study.

Demonstrating the existence of a conserved interface between the Nse5/6-like complexes and SMC6 in both yeast and human is foundationally important and was not revealed in our previous study. It remains unclear how this interface regulates SMC5/6 function, but yeast studies suggest a potential role in inhibiting the SMC5/6 ATPase cycle. Nevertheless, the precise function of Nse5/6 and its human orthologs in SMC5/6 regulation remain undefined, largely due to technical limitations in available in vivo analyses. The SIMC1/SLF2/SMC6 complex structure likely extends to the SLF1/2/SMC6 complex, suggesting a unifying function of the Nse5/6-like complexes in SMC5/6 regulation, albeit in the distinct processes of ecDNA silencing and DNA repair. There have been no studies to date (including this one) showing that SIMC1-SLF2 is required for SMC5/6 recruitment to ecDNA. Our previous study showed that SIMC1 was needed for SMC5/6 to colocalize with SV40 LT antigen at PML NBs. Here we show that SIMC1 is required for ecDNA repression, in the absence of PML NBs, which was not anticipated.

(2) In the second part of the work, the authors focus on the functionality of the different complexes. The authors demonstrate that SMC5/6's role in transcription silencing is specific to its interaction with SIMC/SLF2, whereas SMC5/6's role in DNA repair depends on SLF1/2. These results are quite expected according to previous results. The authors already demonstrated that SLF1/2, but not SIMC/SLF2, are recruited to DNA lesions. Accordingly, they observe here that SMC5/6 recruitment to DNA lesions requires SLF1/2 but not SIMC/SLF2.

Our previous study only examined the localization of SLF1 and SIMC1 at DNA lesions. The localization of these subcomplexes alone should not be used to define their roles in SMC5/6 localization. Indeed, the field is split in terms of whether Nse5/6-like complexes are required for ecDNA binding/loading, or regulation of SMC5/6 once bound.

Likewise, the authors already demonstrated that SIMC/SLF2, but not SLF1/2, targets SMC5/6 to PML NBs. Taking into account the evidence that connects SMC5/6's viral resistance at PML NBs with transcription repression, the observed requirement of SIMC/SLF2 but not SLF1/2 in plasmid silencing is somehow expected. This does not mean the expectation has not to be experimentally confirmed. However, the study falls short in advancing the mechanistic process, despite some interesting results as the dispensability of the PML NBs or the antagonistic role of the SV40 large T antigen. It had been interesting to explore how LT overcomes SMC5/6-mediated repression: Does LT prevent SIMC/SLF2 from interacting with SMC5/6? Or does it prevent SMC5/6 from binding the plasmid? Is the transcription-dependent plasmid topology altered in cells lacking SIMC/SLF2? And in cells expressing LT? In its current form, the study is confirmatory and preliminary. In agreement with this, the cartoons modelling results here and in the previous work look basically the same.

We agree, determining the potential mechanism of action of LT in overcoming SMC5/6-based repression is an important next step. It will require the identification of any direct interactions with SMC5/6 subunits, and better methods for assessing SMC5/6 loading and activity on ecDNAs. Unlike HBx, Vpr, and BNRF1 it does not appear to induce degradation of SMC5/6, making it a more complex and interesting challenge. Also, the dispensability of PML NBs in plasmid silencing versus viral silencing raises multiple important questions about SMC5/6’s repression mechanism.

(3) There are some points about the presented data that need to be clarified.

Reviewer #2 (Public review):

Oracová et al. present data supporting a role for SIMC1/SLF2 in silencing plasmid DNA via the SMC5/6 complex. Their findings are of interest, and they provide further mechanistic detail of how the SMC5/6 complex is recruited to disparate DNA elements. In essence, the present report builds on the author's previous paper in eLife in 2022 (PMID: 36373674, "The Nse5/6-like SIMC1-SLF2 complex localizes SMC5/6 to viral replication centers") by showing the role of SIMC1/SLF2 in localisation of the SMC5/6 complex to plasmid DNA, and the distinct requirements as compared to recruitment to DNA damage foci. Although the findings of the manuscript are of interest, we are not yet convinced that the new data presented here represents a compelling new body of work and would better fit the format of a "research advance" article. In their previous paper, Oracová et al. show that the recruitment of SMC5/6 to SV40 replication centres is dependent on SIMC1, and specifically, that it is dependent on SIMC1 residues adjacent to neighbouring SLF2.

We agree, this manuscript fits the Research Advance model, which is the format that this manuscript was submitted in.

Reviewer #3 (Public review):

Summary:

This study by the Boddy and Otomo laboratories further characterizes the roles of SMC5/6 loader proteins and related factors in SMC5/6-mediated repression of extrachromosomal circular DNA. The work shows that mutations engineered at an AlphaFold-predicted protein-protein interface formed between the loader SLF2/SIMC1 and SMC6 (similar to the interface in the yeast counterparts observed by cryo-EM) prevent co-IP of the respective proteins. The mutations in SLF2 also hinder plasmid DNA silencing when expressed in SLF2-/- cell lines, suggesting that this interface is needed for silencing. SIMC1 is dispensable for recruitment of SMC5/6 to sites of DNA damage, while SLF1 is required, thus separating the functions of the two loader complexes. Preventing SUMOylation (with a chemical inhibitor) increases transcription from plasmids but does not in SLF2-deleted cell lines, indicating the SMC5/6 silences plasmids in a SUMOylation dependent manner. Expression of LT is sufficient for increased expression, and again, not additive or synergistic with SIMC1 or SLF2 deletion, indicating that LT prevents silencing by directly inhibiting 5/6. In contrast, PML bodies appear dispensable for plasmid silencing.

Strengths:

The manuscript defines the requirements for plasmid silencing by SMC5/6 (an interaction of Smc6 with the loader complex SLF2/SIMC1, SUMOylation activity) and shows that SLF1 and PML bodies are dispensable for silencing. Furthermore, the authors show that LT can overcome silencing, likely by directly binding to (but not degrading) SMC5/6.

Weaknesses:

(1) Many of the findings were expected based on recent publications.

Please see introductory paragraphs above.

Describes the poet as a normal person with not much as people would think, showing the multi tasking of having to do the best to get everything needed for a show. Like that the rich had to contribute more to help the less fortunate and the city as a whole with entertainment from a financial perspective.Like how the wealthy were tasked to do more because they were able to so the citizens as a whole and the city were able to grow and still do more together especially in entertainment. The success of the furnishers towards the end could of had a more probable success if they had aeschylus or sophicles and I like how they added him next to aeschylus and added the in this to show they're greatness in this field.

① With conventional RT, xerostomia is permanent. ① Konvansiyonel radyoterapide, ağız kuruluğu (xerostomia) kalıcıdır.

② Salivary gland‑sparing techniques using IMRT (intensity‑modulated irradiation technique - yoğunluk ayarlı radyoterapi) have been now in use. ② Şu anda, tükürük bezlerini koruyan yöntemler olarak IMRT (yoğunluk ayarlı radyoterapi) kullanılmaktadır.

③ IMRT is rapidly emerging as the standard of care for head and neck cancer. ③ IMRT, baş ve boyun kanserleri için hızla standart tedavi yöntemi haline gelmektedir.

④ Salivary gland‑sparing IMRT is associated with a gradual recovery of salivary flow over time and improved quality‑of‑life as compared to conventional RT. ④ Tükürük bezlerini koruyan IMRT, konvansiyonel radyoterapiye kıyasla zamanla tükürük akışında kademeli iyileşme ve yaşam kalitesinde artış ile ilişkilidir.

⑤ A team including speech therapist, dietitian, dental specialist, and psychologist along with radiation oncology is required to deal with those complications and prevent to morbidity and mortality of head and neck cancer patients during and after radiotherapy. ⑤ Konuşma terapisti, diyetisyen, diş hekimi, psikolog ve radyasyon onkolojisi uzmanlarından oluşan bir ekip, baş ve boyun kanseri hastalarının radyoterapi sırasında ve sonrasında ortaya çıkan komplikasyonları yönetmek ve hastalık yükünü azaltmak için gereklidir.

❶ To minimize patient discomfort and morbidity, an understanding of the deleterious effects of radiotherapy is required. ❶ Hasta rahatsızlığını ve hastalık yükünü en aza indirmek için, radyoterapinin zararlı etkilerinin anlaşılması gereklidir.

❷ Introducing good oral home care and more frequent oral prophylaxis visits to the dentists before radiotherapy will allow for continuing care during and after therapy. ❷ Radyoterapiden önce iyi ağız hijyeni uygulamalarının başlatılması ve diş hekimine daha sık profilaktik ziyaretlerin yapılması, tedavi sırasında ve sonrasında devam eden bakımın sağlanmasına olanak tanır.

❸ The cancer patient who is to receive or has received curative doses of radiation to the head and neck cancer presents a challenge for the dentist. ❸ Baş ve boyun kanseri için küratif dozlarda radyasyon alacak veya alan kanser hastası, diş hekimi için zorlu bir durum oluşturur.

❹ The importance of patient compliance should be emphasized. ❹ Hasta uyumunun önemi vurgulanmalıdır.

❶ Osteoradionecrosis is a condition of a nonvital bone in the site of radiation injury. ❶ Osteoradiyonekroz, radyasyon hasarı olan bölgede canlılığını yitirmiş kemik durumudur.

❷ ORN can be spontaneous but it most related to hypovascular, hypocellular, and hypoxic conditions that exist in bone. ❷ ORN kendiliğinden ortaya çıkabilir ancak daha çok kemikte bulunan az damar, az hücre ve oksijen yetersizliği koşullarıyla ilişkilidir.

❸ Induced deficient cellular turnover and collagen synthesis in a hypoxic, hypovascular, and hypocellular environment in which tissue breakdown exceeds the repair capabilities of the wounded tissue. ❸ Oksijen yetersizliği, az damar ve az hücre içeren ortamda hücre yenilenmesi ve kolajen sentezindeki yetersizlik, doku yıkımının yaralı dokunun onarım kapasitesini aşmasıyla oluşur.

❹ Clinically, ORN may initially present as bone lysis under gingiva and mucosa. ❹ Klinik olarak ORN, başlangıçta diş eti ve mukoza altında kemik çözünmesi şeklinde görülebilir.

❺ This process is self‑limiting because the damaged bone sequestrates then is shed with subsequent healing. ❺ Bu süreç kendi kendini sınırlar çünkü hasarlı kemik ayrılır ve ardından iyileşme gerçekleşir.

❻ If the soft tissue breakdown, the bone becomes exposed to saliva and secondary contamination occurs. ❻ Yumuşak dokunun yıkılması halinde, kemik tükürüğe maruz kalır ve ikincil kontaminasyon gelişir.

❼ Sepsis may also be introduced by dental extraction or surgery producing a more aggressive form. ❼ Diş çekimi veya cerrahi işlemler sepsise neden olarak daha agresif bir form oluşturabilir.

❽ This progressive form may produce severe pain or fracture and require extensive resection. ❽ Bu ilerleyici form şiddetli ağrıya veya kırığa yol açabilir ve geniş rezeksiyon gerektirebilir.

❾ The reported incidence of ORN ranges from 0.92% of all head and neck cancer patients receiving radiotherapy. ❾ ORN bildirilen insidansı, radyoterapi alan tüm baş ve boyun kanseri hastalarının %0,92’si arasında değişmektedir.

❶ Patients treated for head and neck cancer have a high risk of malnutrition. ❶ Baş ve boyun kanseri tedavisi gören hastalarda malnütrisyon riski yüksektir.

❷ Patients may lose one desire to eat because of soreness of the mouth, trouble swallowing or dry mouth. ❷ Hastalar, ağızda ağrı, yutma güçlüğü veya kuru ağız nedeniyle yemek yeme isteğini kaybedebilirler.

❸ When eating causes discomfort or pain, the patients quality of life and nutritional of wellbeing suffer. ❸ Yemek yemek rahatsızlık veya ağrıya neden olduğunda, hastaların yaşam kalitesi ve beslenme durumu olumsuz etkilenir.

❹ Nutritional support may include liquid diet and tube feeding ❹ Beslenme desteği sıvı diyet ve beslenme tüpü ile olabilir.

❺ High calorie, high protein liquid to meet their needs ❺ İhtiyaçlarını karşılamak için yüksek kalorili, yüksek proteinli sıvılar.

❻ Intravenous infusion of nutritional supplements. ❻ Besin takviyelerinin damar yoluyla verilmesi.

❼ Swallowing problems are managed by a team of experts ❼ Yutma problemleri bir uzman ekibi tarafından yönetilir.

❽ Speech therapist ❽ Konuşma terapisti

❾ Dietician ❾ Diyetisyen

❿ Dental specialist ❿ Diş hekimi uzmanı

⓫ Psychologist ⓫ Psikolog

❶ Radiation‑induced dry mouth is a common and significant consequences of head and neck radiotherapy, oral dryness reflects the progressive radiation‑induced salivary gland acinar cell inflammation, fibrosis, and degeneration. ❶ Radyasyon kaynaklı kuru ağız, baş ve boyun radyoterapisinin yaygın ve önemli bir sonucudur; ağız kuruluğu, radyasyona bağlı tükürük bezi asinar hücrelerinde ilerleyici iltihaplanma, fibrozis ve dejenerasyonu yansıtır.

❷ The salivary glands are very sensitive to radiation. There is a sharp decrease in the salivary flow rate during the 1st week of RT with conventional fractionation (2 Gy/day). ❷ Tükürük bezleri radyasyona karşı çok hassastır. Konvansiyonel fraksiyonlamada (günde 2 Gy) radyoterapinin ilk haftasında tükürük akış hızında keskin bir azalma olur.

❸ The decrease in flow rate is continuous throughout the treatment period especially when both parotids are irradiated. This correlates to the dose and duration of RT. ❸ Akış hızındaki azalma, özellikle her iki parotis bezi de ışınlandığında, tedavi süresi boyunca devam eder. Bu durum radyasyon dozu ve süresiyle ilişkilidir.

❹ There is immediate serous cell death accomplished by inflammatory cell infiltration and then continuous reduction of salivary flow rates. ❹ İnflamatuar hücre infiltrasyonuyla birlikte hemen seröz hücre ölümü meydana gelir ve ardından tükürük akış hızlarında sürekli azalma olur.

❺ Patients often complain of thick, ropy, saliva, and a sensation that there is too much saliva because it is difficult to swallow. ❺ Hastalar genellikle yoğun, yapışkan tükürükten ve yutkunmanın zor olması nedeniyle aşırı tükürük varmış hissinden şikayet ederler.

❶ Damage to the lining of the mouth and weakened immune system makes it easy for the infection to occur. ❶ Ağız astarının (mukozasının) zarar görmesi ve bağışıklık sisteminin zayıflaması, enfeksiyonun oluşmasını kolaylaştırır.

❷ Oral mucositis breaks down the lining of the mouth which lets, bacteria, viruses, and fungal get into the blood. ❷ Oral mukozit, ağız astarını bozar ve bu durum bakteri, virüs ve mantarların kana karışmasına neden olabilir.

❸ Dry mouth which is common during radiotherapy to head and neck may also raise the risk of infection in the mouth. ❸ Baş ve boyun radyoterapisi sırasında yaygın olan ağız kuruluğu da ağızda enfeksiyon riskini artırabilir.

❹ Infection may be caused by bacteria, virus or fungal. ❹ Enfeksiyon; bakteri, virüs veya mantar kaynaklı olabilir.

❺ A systemic review indicated that the weighted mean prevalence of clinical oral candidiasis during head and neck RT is 37.4%. ❺ Sistematik bir derleme, baş ve boyun radyoterapisi sırasında klinik oral kandidiyazis görülme sıklığının ağırlıklı ortalama %37.4 olduğunu göstermiştir.

❶ The oral complications of head and neck radiation can be divided into two groups on the basis of the usual time of their occurrence. ❶ Baş ve boyun radyasyonunun ağız komplikasyonları, genellikle ortaya çıkış zamanlarına göre iki gruba ayrılabilir.

❷ Acute complications ❷ Akut komplikasyonlar

❸ A therapeutic dose of radiation in head and neck cancer usually comprises a total of 64 Gy to 70 Gy in 32–35 fractions with the daily dose of 1.8–2.0 Gy/fraction. ❸ Baş ve boyun kanserinde terapötik radyasyon dozu genellikle toplam 64 Gy ile 70 Gy arasında, 32–35 fraksiyonda ve günlük 1.8–2.0 Gy/fraksiyon şeklindedir.

❹ Acute complication appears 1–2 weeks after radiation starts; it depends on dose and site of radiation also. ❹ Akut komplikasyonlar radyasyon başladıktan 1–2 hafta sonra ortaya çıkar; doz ve radyasyon bölgesine bağlıdır.

❺ Common acute complications include: ❺ Yaygın akut komplikasyonlar şunlardır:

Oropharyngeal mucositis Orofaringeal mukozit

Change in salivary composition Tükürük bileşiminde değişiklik

Alteration of taste (Dysgeusia) Tat değişikliği (Dizgezi)

Infection (bacterial, fungal and viral) Enfeksiyon (bakteriyel, fungal ve viral)

Periodontium pain Periodonsiyum ağrısı

❶ Oral complications of head and neck radiation are more predictable, and are often more severe and can lead to permanent tissue changes that put the patient at risk for serious chronic complications. ❶ Baş ve boyun radyasyonuna bağlı oral komplikasyonlar daha öngörülebilirdir, genellikle daha şiddetlidir ve hastayı ciddi kronik komplikasyon riskine sokabilecek kalıcı doku değişikliklerine yol açabilir.

❷ Patients should go for routine dental evaluation, preventive oral care, and close follow-up before, during, and after radiation therapy. ❷ Hastalar radyoterapi öncesinde, sırasında ve sonrasında düzenli diş muayenesi, koruyucu ağız bakımı ve yakın takip yaptırmalıdır.

Reviewer #1 (Public review):

Summary:

The authors note that it is challenging to perform diffusion MRI tractography consistently in both humans and macaques, particularly when deep subcortical structures are involved. The scientific advance described in this paper is effectively an update to the tracts that the XTRACT software supports. The claims of robustness are based on a very small selection of subjects from a very atypical dMRI acquisition (n=50 from HCP-Adult) and an even smaller selection of subjects from a more typical study (n=10 from ON-Harmony).

Strengths:

The changes to XTRACT are soundly motivated in theory (based on anatomical tracer studies) and practice (changes in seeding/masking for tractography), and I think the value added by these changes to XTRACT should be shared with the field. While other bundle segmentation software typically includes these types of changes in release notes, I think papers are more appropriate.

Weaknesses:

The demonstration of the new tracts does not include a large number of carefully selected scans and is only compared to the prior methods in XTRACT. The small n and limited statistical comparisons are insufficient to claim that they are better than an alternative. Qualitatively, this method looks sound.

Subject selection at each stage is unclear in this manuscript. On page 5 the data are described as "Using dMRI data from the macaque (𝑁 = 6) and human brain (𝑁 = 50)". Were the 50 HCP subjects selected to cover a range of noise levels or subject head motion? Figure 4 describes 72 pairs for each of monozygotic, dizygotic, non-twin siblings, and unrelated pairs - are these treated separately? Similarly, NH had 10 subjects, but each was scanned 5 times. How was this represented in the sample construction?

In the paper, the authors state "the mean agreement between HCP and NH reconstructions was lower for the new tracts, compared to the original protocols (𝑝 < 10^−10). This was due to occasionally reconstructing a sparser path distribution, i.e., slightly higher false negative rate," - how can we know this is a false negative rate without knowing the ground truth?

Joint Public Review:

Summary:

The authors have conducted the largest to date Mendelian Randomization (MR) analysis of the association between genetically predicted measures of adiposity and risk of head and neck cancer (HNC) overall and by subsites within HNC. MR uses genetic predictors of an exposure, such as gene variants associated with high BMI or tobacco use, rather than data from individual physical exams or questionnaires, and if it can be done in its idealized state, there should be no problems with confounding. Traditional epidemiologic studies have reported a variety of associations between BMI (and a few other measures of adiposity) and risk of HNC that typically differ by the smoking status of the subjects. Those findings are controversial given the complex relationship between tobacco and both BMI and HNC risk. Tobacco smokers are often thinner than non-smokers, so this could create an artificial ('confounded') association that may not be fully adjusted away in risk models. The findings of a BMI-HNC association are often attributed to residual confounding, and this seems ripe for an MR approach if suitable genetic instrumental variables can be created. Here, the authors built a variety of genetic instrumental variables for BMI and other measures of adiposity, as well as two instrumental variables for smoking habits, and then tested their hypotheses in a large case-control set of HNC and controls with genetic data.

The authors found that the genetic model for BMI was associated with HNC risk in simple models, but this association disappeared when using models that better accounted for pleiotropy, the condition when genetic variants are associated with more than one trait, such as both BMI and tobacco use. When they used both adiposity and tobacco use genetic instruments in a single model, there was a strong association with genetically predicted tobacco use (as is expected), but there was no remaining association with genetic predictors of adiposity. They conclude that high BMI/adiposity is not a risk factor for HNC.

Strengths:

The primary strength was the expansive use of a variety of different genetic instruments for BMI/adiposity/body size, along with employing a variety of MR model types, several of which are known to be less sensitive to pleiotropy. They also used the largest case-control sample size to date.

Weaknesses:

The lack of pleiotropy is an unconfirmable assumption of MR, and the addition of those models is therefore quite important, as this is a primary weakness of the MR approach. Given that concern, I read the sensitivity analyses using pleiotropy-robust models as the main result, and in that case, they can't test their hypotheses as these models do not show a BMI instrumental variable association. The other weakness, which might be remedied, is that the power of the tests here is not described. When a hypothesis is tested with an under-powered model, the apparent lack of association could be due to inadequate sample size rather than a true null. Typically, when a statistically significant association is reported, power concerns are discounted as long as the study is not so small as to create spurious findings. That is the case with their primary BMI instrumental variable model - they find an association so we can presume it was adequately powered. But the primary models they share are not the pleiotropy-robust methods MR-Egger, weighted median, and weighted mode. The tests for these models are null, and that could mean a couple of things: (1) the original primary significant association between the BMI genetic instrument was due to pleiotropy, and they therefore don't have a robust model to explore the effects of the tobacco genetic instrument. (2) The power for the sensitivity analysis models (the pleiotropy-robust methods) is inadequate, and the authors share no discussion about the relative power of the different MR approaches. If they do have adequate power, then again, there is no need to explore the tobacco instrument.

Reviewing Editor Comments:

We suggest that the authors add power estimates to assess whether the sample size is sufficient, given the strength and variability of the genetic instruments. It would also be helpful to present effect estimates for the tobacco instruments alone, to clarify their independent contribution and improve the interpretation of the joint models. In addition, the role of pleiotropy should be addressed more clearly, including which model is considered primary. Stratified analyses by smoking status are encouraged, as prior studies indicate that BMI-HNC associations may differ between smokers and non-smokers. Finally, the comparison with previous studies should be revised, as most reported null findings without accounting for tobacco instruments. If this study finds an association, it should not be framed as a replication.

Author response:

Our response aims to address the following:

The lack of pleiotropy is an unconfirmable assumption of MR, and the addition of those models is therefore quite important, as this is a primary weakness of the MR approach. Given that concern, I read the sensitivity analyses using pleiotropy-robust models as the main result, and in that case, they can't test their hypotheses as these models do not show a BMI instrumental variable association. The other weakness, which might be remedied, is that the power of the tests here is not described. When a hypothesis is tested with an under-powered model, the apparent lack of association could be due to inadequate sample size rather than a true null. Typically, when a statistically significant association is reported, power concerns are discounted as long as the study is not so small as to create spurious findings. That is the case with their primary BMI instrumental variable model - they find an association so we can presume it was adequately powered. But the primary models they share are not the pleiotropy-robust methods MR-Egger, weighted median, and weighted mode. The tests for these models are null, and that could mean a couple of things: (1) the original primary significant association between the BMI genetic instrument was due to pleiotropy, and they therefore don't have a robust model to explore the effects of the tobacco genetic instrument. (2) The power for the sensitivity analysis models (the pleiotropy-robust methods) is inadequate, and the authors share no discussion about the relative power of the different MR approaches. If they do have adequate power, then again, there is no need to explore the tobacco instrument.

We would like to highlight that post-hoc power calculations are often considered redundant since the statistical power estimated for an observed association is directly related to its p-value[1]. In other words, the uncertainty of the association is already reflected in its 95% confidence interval. However, we understand power calculations may still be of interest to the reader, so we will incorporate them in the revised manuscript.

The reason we use inverse variance weighted (IVW) Mendelian randomization (MR) to obtain our main results rather than the pleiotropy-robust methods mentioned by the reviewer/editors (i.e., MR-Egger, weighted median and weighted mode) is that the former has greater statistical power than the latter[2]. Hence, instead of focussing on the statistical significance of the pleiotropy-robust analyses, we consider it is of more value to compare the consistency of the effect sizes and direction of the effect estimates across methods. Any evidence of such consistency increases our confidence in our main findings, since each method relies on different assumptions. As we cannot be sure about the presence and nature of horizontal pleiotropy, it is useful to compare results across methods even though they are not equally powered. It is true that our results for the genetically predicted effects of body mass index (BMI) on the risk of head and neck cancer (HNC) differ across methods. This is precisely what led us to question the validity of our main finding (suggesting a positive effect of BMI on HNC risk). We will clarify this in the discussion section of the revised manuscript as advised.

We understand that the reviewer/editors are concerned that we do not have a robust model to explore the role of tobacco consumption in the link between BMI and HNC. However, we have a different perspective on the matter. If indeed, the main IVW finding for BMI and HNC is due to pleiotropy (since some of the pleiotropy-robust methods suggest conflicting results), then the IVW multivariable MR method is a way to explore the potential source of this bias[3]. We were particularly interested in exploring the role of smoking in the observed association because smoking and adiposity are known to influence each other [4-9] and share a genetic basis[10, 11].

References:

(1) Heinsberg LW, Weeks DE: Post hoc power is not informative. Genet Epidemiol 2022, 46(7):390-394.

(2) Burgess S, Butterworth A, Thompson SG: Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol 2013, 37(7):658-665.

(3) Burgess S, Davey Smith G, Davies NM, Dudbridge F, Gill D, Glymour MM, Hartwig FP, Kutalik Z, Holmes MV, Minelli C et al: Guidelines for performing Mendelian randomization investigations: update for summer 2023. Wellcome Open Res 2019, 4:186.

(4) Morris RW, Taylor AE, Fluharty ME, Bjorngaard JH, Asvold BO, Elvestad Gabrielsen M, Campbell A, Marioni R, Kumari M, Korhonen T et al: Heavier smoking may lead to a relative increase in waist circumference: evidence for a causal relationship from a Mendelian randomisation meta-analysis. The CARTA consortium. BMJ Open 2015, 5(8):e008808.

(5) Taylor AE, Morris RW, Fluharty ME, Bjorngaard JH, Asvold BO, Gabrielsen ME, Campbell A, Marioni R, Kumari M, Hallfors J et al: Stratification by smoking status reveals an association of CHRNA5-A3-B4 genotype with body mass index in never smokers. PLoS Genet 2014, 10(12):e1004799.

(6) Taylor AE, Richmond RC, Palviainen T, Loukola A, Wootton RE, Kaprio J, Relton CL, Davey Smith G, Munafo MR: The effect of body mass index on smoking behaviour and nicotine metabolism: a Mendelian randomization study. Hum Mol Genet 2019, 28(8):1322-1330.

(7) Asvold BO, Bjorngaard JH, Carslake D, Gabrielsen ME, Skorpen F, Smith GD, Romundstad PR: Causal associations of tobacco smoking with cardiovascular risk factors: a Mendelian randomization analysis of the HUNT Study in Norway. Int J Epidemiol 2014, 43(5):1458-1470.

(8) Carreras-Torres R, Johansson M, Haycock PC, Relton CL, Davey Smith G, Brennan P, Martin RM: Role of obesity in smoking behaviour: Mendelian randomisation study in UK Biobank. BMJ 2018, 361:k1767.

(9) Freathy RM, Kazeem GR, Morris RW, Johnson PC, Paternoster L, Ebrahim S, Hattersley AT, Hill A, Hingorani AD, Holst C et al: Genetic variation at CHRNA5-CHRNA3-CHRNB4 interacts with smoking status to influence body mass index. Int J Epidemiol 2011, 40(6):1617-1628.

(10) Thorgeirsson TE, Gudbjartsson DF, Sulem P, Besenbacher S, Styrkarsdottir U, Thorleifsson G, Walters GB, Consortium TAG, Oxford GSKC, consortium E et al: A common biological basis of obesity and nicotine addiction. Transl Psychiatry 2013, 3(10):e308.

(11) Wills AG, Hopfer C: Phenotypic and genetic relationship between BMI and cigarette smoking in a sample of UK adults. Addict Behav 2019, 89:98-103.

eLife Assessment

The findings represent an important contribution to understanding whether BMI influences head and neck cancer (HNC) risk after accounting for tobacco use. Within the context of the Mendelian Randomization (MR) field, the strength of evidence appears convincing, supported by rigorous methods and a thorough exploration of multiple genetic models of adiposity using diverse MR approaches. Limitations include the absence of associations in sensitivity models designed to better account for pleiotropy, which prevents evaluation of whether incorporating an instrumental variable for tobacco use would alter the findings. Additionally, the lack of a formal power assessment for detecting associations with the instrumental variables employed limits the interpretability and reach of the results.

① Be aware of ergonomic problems in the operatory. ① Klinik ortamındaki ergonomik sorunların farkında olun.

② Chairside stretching is an important strategy to perform throughout the workday to prevent microtrauma and muscle imbalances. Strengthen specific stabilizing muscles (like shoulder and back). Physical therapists, neuromuscular therapist should be consulted for musculoskeletal disorders. ② Gün boyunca yapılacak sandalye başı esneme hareketleri, mikrotravmaları ve kas dengesizliklerini önlemek için önemli bir stratejidir. Omuz ve sırt gibi belirli dengeleyici kaslar güçlendirilmelidir. Kas-iskelet sistemi rahatsızlıklarında fizyoterapistlere veya nöromüsküler terapistlere danışılmalıdır.

③ Patient should be seated so that all his body parts are well supported. The patient’s head should always be supported by adjustable/articulated. ③ Hastanın tüm vücut bölgeleri iyi bir şekilde desteklenecek biçimde oturtulmalıdır. Hastanın başı her zaman ayarlanabilir/eklemli bir baş desteği ile desteklenmelidir.

④ Upright Position is the initial position of chair from which further adjustments are made. Almost Supine position is such that patient’s head, knees and feet are approximately at same level, patient is almost in a lying position. Patient’s head should not be lower than feet. In reclined 45 degree, chair is reclined at 45°, and the mandibular occlusal surfaces are almost at 45° to the floor. ④ Dik pozisyon, koltuğun başlangıç konumudur ve buradan sonra ayarlamalar yapılır. Neredeyse sırtüstü pozisyonda hastanın başı, dizleri ve ayakları yaklaşık aynı seviyededir; hasta neredeyse yatar durumdadır. Hastanın başı, ayaklarından daha aşağıda olmamalıdır. 45 derece yatırılmış pozisyonda ise koltuk 45° eğimle yatırılmıştır ve mandibular oklüzal yüzeyler zemine yaklaşık 45° açıyla durur.

⑤ For better understanding, sitting positions of operator are related to a clock. In this clock concept, an imaginary circle is drawn over the dental chair, keeping the patient’s head at the center of the circle. Then the numbering to circle is given similar to a clock with the top of the circle at 12 o’clock. ⑤ Daha iyi anlaşılması için operatörün oturma pozisyonları bir saat ile ilişkilendirilir. Bu saat kavramında, hastanın başı merkeze alınarak diş üniti üzerine hayali bir daire çizilir. Dairenin üst kısmı 12 yönü olacak şekilde saat numaraları verilir.

⑥ Accordingly the operator’s positions (right handed operator) can be 7 o’clock, 9 o’clock, 11 o’clock, and 12 o’clock and for left handed operator, it can be 5 o’clock, 3 o’clock and 1 o’clock. ⑥ Buna göre, sağ elini kullanan bir operatör için pozisyonlar 7, 9, 11 ve 12 yönleri olabilirken; sol elini kullanan operatör için bu pozisyonlar 5, 3 ve 1 yönleri olabilir.

⑦ Stool height of assistant should be 4 to 6 inches above the dentist’s eye level. ⑦ Asistanın oturduğu taburenin yüksekliği, diş hekiminin göz hizasından 4 ila 6 inç daha yüksek olmalıdır.

① Nowadays, stools having backrest with curved extensions that provide additional body support are also available. ① Günümüzde, ilave vücut desteği sağlayan kıvrımlı uzantılara sahip sırt dayanaklı tabureler de mevcuttur.

② If seat is positioned too high, the edges will cut off supply to user’s legs resulting in more fatigue and stress. ② Eğer oturma yeri çok yüksek ayarlanırsa, kenarları bacaklardaki kan dolaşımını keserek daha fazla yorgunluk ve strese neden olur.

③ Assistant should sit as close as possible to the back of the patient’s chair with feet directed towards the head of the chair. ③ Asistan, hastanın sandalyesinin arkasına mümkün olduğunca yakın oturmalı ve ayakları sandalyenin baş kısmına doğru yönlendirilmelidir.

④ Stool height of assistant should be 4 to 6 inches above the dentist’s eye level. ④ Asistanın taburesinin yüksekliği, diş hekiminin göz seviyesinden 4 ila 6 inç daha yüksek olmalıdır.

⑤ The assistant should sit in an erect position with feet firmly placed on the foot-support ring at the base of the assistant chair. ⑤ Asistan, dik bir pozisyonda oturmalı ve ayakları asistan sandalyesinin tabanındaki ayak destek halkasına sağlam şekilde yerleştirilmelidir.

⑥ The instrument tray should be placed towards the head of the patient’s chair, and positioned to allow easy access to the instruments and materials. ⑥ Alet tepsisi, hastanın sandalyesinin baş kısmına yerleştirilmeli ve aletlere ve malzemelere kolay erişim sağlayacak şekilde konumlandırılmalıdır.

⑦ Above positions can be adjusted according to specific needs. ⑦ Yukarıdaki pozisyonlar, özel ihtiyaçlara göre ayarlanabilir.

① CHAIR AND PATIENT POSITIONS Dental chair and patient positions are important aspect in restorative dentistry. ⓵ Sandalye ve hasta pozisyonları, restoratif diş hekimliğinde önemli bir unsurdur.

② Modern dental chairs are properly designed so as to provide total body support and comfort in any position. ⓶ Modern diş hekimi koltukları, her pozisyonda tam vücut desteği ve konfor sağlamak üzere uygun şekilde tasarlanmıştır.

③ Patient should be seated so that all his body parts are well supported. ⓷ Hasta, tüm vücut bölümleri iyi desteklenecek şekilde oturtulmalıdır.

④ The patient’s head should always be supported by adjustable/articulated headrest. ⓸ Hastanın başı her zaman ayarlanabilir veya eklemli baş desteği ile desteklenmelidir.

⑤ Preferably the patient’s head should be in line with his back, whether the dental chair base is parallel or slightly at an angle to the floor. ⓹ Tercihen, hastanın başı sırtıyla aynı hizada olmalıdır; diş hekimi koltuğunun tabanı yere paralel ya da hafif açılı olsa bile.

⑥ The dental chair should be designed in such a way that it should provide maximum working area to the operator. ⓺ Diş ünitesi, operatöre maksimum çalışma alanı sağlayacak şekilde tasarlanmalıdır.

⑦ The foot switches are preferred than hand switches so as to improve infection control. ⓻ Enfeksiyon kontrolünü artırmak için ayak pedalları, el anahtarlarına göre daha fazla tercih edilmelidir.

⑧ And the adjustable control switches should be conveniently located. ⓼ Ayarlanabilir kontrol düğmeleri, kolay erişilebilir bir konumda yer almalıdır.

⑨ The chair height should be kept low, backrest should be upright and armrest should be adjustable while making the patient to seat in the dental chair. ⓽ Hastayı koltuğa oturturken, koltuk yüksekliği düşük, sırt desteği dik ve kol destekleri ayarlanabilir olmalıdır.

⑩ Now, the chair can be adjusted to place the patient in reclining position. ⓾ Bundan sonra, hasta yatırılabilir pozisyona getirilmek üzere koltuk ayarlanabilir.

⑪ Patient position can vary with operator, type of procedure and area of the oral cavity. ⓫ Hasta pozisyonu, operatöre, yapılan işleme ve ağız boşluğundaki bölgeye göre değişkenlik gösterebilir.

① The goal of overhead lighting is to produce even, shadow-free, color-corrected illumination that is concentrated on the operating field. ⓵ Üstten aydınlatmanın amacı, operasyon alanına odaklanmış, eşit, gölgesiz ve renk düzeltmeli aydınlatma sağlamaktır.

② In general, the intensity ratio between task lighting (the dental operating light) and ambient room lighting should be no greater than 3 to 1.6. ⓶ Genel olarak, görev aydınlatması (diş operasyon ışığı) ile ortam ışığı arasındaki yoğunluk oranı 3’e 1.6’dan fazla olmamalıdır.

③ Furthermore, the light source should be in the patient’s mid-sagittal plane; directly above and slightly behind the patient’s oral cavity, and 5° toward the head of the operator in the 12 o’clock position. ⓷ Ayrıca, ışık kaynağı hastanın orta-sagital düzleminde olmalı; hastanın ağız boşluğunun tam üstünde ve hafifçe arkasında, operatörün başına doğru 12 saat pozisyonunda 5° açıyla konumlandırılmalıdır.

① Various delivery systems have advantages and disadvantages. ⓵ Çeşitli teslimat sistemlerinin avantajları ve dezavantajları vardır.

② When working in four-handed dentistry the dentist maintains a position around the operating field with limited hand, arm and body movement, and should best confine eye focus to the working field. ⓶ Dört elli diş hekimliği çalışılırken, diş hekimi ellerini, kollarını ve vücudunu sınırlı hareket ettirerek operasyona yakın bir pozisyonda kalmalı ve gözlerini en iyi şekilde çalışma alanına odaklamalıdır.

③ Additionally, the dental equipment and instruments should be centered on the dental assistant. ⓷ Ayrıca, diş ekipmanları ve aletleri diş asistanının erişiminde ve merkezinde olmalıdır.

④ From an ergonomic viewpoint, over-the-head and over-the-patient delivery systems better allow the dental assistant to access the handpieces for bur changes or other operations. ⓸ Ergonomik açıdan bakıldığında, baş üstü ve hasta üstü teslimat sistemleri, diş asistanının bur değiştirme veya diğer işlemler için handpiece'lere daha kolay erişmesini sağlar.

Author response:

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

We wanted to clarify Reviewer #1’s latest comment in the last round of review, “Furthermore, the referee appreciates that the authors have echoed the concern regarding the limited statistical robustness of the observed scrambling events.” We appreciate the follow up information provided from Reviewer #1 that their comment is specifically about the low count alternative pathway events that we view at the dimer interface, and not the statistics of the manuscript overall as they believe that “the study presents a statistically rigorous analysis of lipid scrambling events across multiple structures and conformations (Reviewer #1)”. We agree with the Reviewer and acknowledge that overall our coarse-grained study represents the most comprehensive single manuscript of the entire TMEM16 family to date.

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

Public Review:

Reviewer #1 (Public review):

Summary:

The manuscript investigates lipid scrambling mechanisms across TMEM16 family members using coarse-grained molecular dynamics (MD) simulations. While the study presents a statistically rigorous analysis of lipid scrambling events across multiple structures and conformations, several critical issues undermine its novelty, impact, and alignment with experimental observations.

Critical issues:

(1) Lack of Novelty:

The phenomenon of lipid scrambling via an open hydrophilic groove is already well-established in the literature, including through atomistic MD simulations. The authors themselves acknowledge this fact in their introduction and discussion. By employing coarse-grained simulations, the study essentially reiterates previously known findings with limited additional mechanistic insight. The repeated observation of scrambling occurring predominantly via the groove does not offer significant advancement beyond prior work.

We agree with the reviewer’s statement regarding the lack of novelty when it comes to our observations of scrambling in the groove of open Ca2+-bound TMEM16 structures. However, we feel that the inclusion of closed structures in this study, which attempts to address the yet unanswered question of how scrambling by TMEM16s occurs in the absence of Ca2+, offers new observations for the field. In our study we specifically address to what extent the induced membrane deformation, which has been theorized to aid lipids cross the bilayer especially in the absence of Ca2+, contributes to the rate of scrambling (see references 36, 59, and 66). There are also several TMEM16F structures solved under activating conditions (bound to Ca2+ and in the presence of PIP2) which feature structural rearrangements to TM6 that may be indicative of an open state (PDB 6P48) and had not been tested in simulations. We show that these structures do not scramble and thereby present evidence against an out-of-the-groove scrambling mechanism for these states. Although we find a handful of examples of lipids being scrambled by Ca2+-free structures of TMEM16 scramblases, none of our simulations suggest that these events are related to the degree of deformation.

(2) Redundancy Across Systems:

The manuscript explores multiple TMEM16 family members in activating and non-activating conformations, but the conclusions remain largely confirmatory. The extensive dataset generated through coarse-grained MD simulations primarily reinforces established mechanistic models rather than uncovering fundamentally new insights. The effort, while statistically robust, feels excessive given the incremental nature of the findings.

Again, we agree with the reviewer’s statement that our results largely confirm those published by other groups and our own. We think there is however value in comparing the scrambling competence of these TMEM16 structures in a consistent manner in a single study to reduce inconsistencies that may be introduced by different simulation methods, parameters, environmental variables such as lipid composition as used in other published works of single family members. The consistency across our simulations and high number of observed scrambling events have allowed us to confirm that the mechanism of scrambling is shared by multiple family members and relies most obviously on groove dilation.

(3) Discrepancy with Experimental Observations:

The use of coarse-grained simulations introduces inherent limitations in accurately representing lipid scrambling dynamics at the atomistic level. Experimental studies have highlighted nuances in lipid permeation that are not fully captured by coarse-grained models. This discrepancy raises questions about the biological relevance of the reported scrambling events, especially those occurring outside the canonical groove.

We thank the reviewer for bringing up the possible inaccuracies introduced by coarse graining our simulations. This is also a concern for us, and we address this issue extensively in our discussion. As the reviewer pointed out above, our CG simulations have largely confirmed existing evidence in the field which we think speaks well to the transferability of observations from atomistic simulations to the coarse-grained level of detail. We have made both qualitative and quantitative comparisons between atomistic and coarse-grained simulations of nhTMEM16 and TMEM16F (Figure 1, Figure 4-figure supplement 1, Figure 4-figure supplement 5) showing the two methods give similar answers for where lipids interact with the protein, including outside of the canonical groove. We do not dispute the possible discrepancy between our simulations and experiment, but our goal is to share new nuanced ideas for the predicted TMEM16 scrambling mechanism that we hope will be tested by future experimental studies.

(4) Alternative Scrambling Sites:

The manuscript reports scrambling events at the dimer-dimer interface as a novel mechanism. While this observation is intriguing, it is not explored in sufficient detail to establish its functional significance. Furthermore, the low frequency of these events (relative to groove-mediated scrambling) suggests they may be artifacts of the simulation model rather than biologically meaningful pathways.

We agree with the reviewer that our observed number of scrambling events in the dimer interface is too low to present it as strong evidence for it being the alternative mechanism for Ca2+-independent scrambling. This will require additional experiments and computational studies which we plan to do in future research. However, we are less certain that these are artifacts of the coarse-grained simulation system as we observed a similar event in an atomistic simulation of TMEM16F.

Conclusion:

Overall, while the study is technically sound and presents a large dataset of lipid scrambling events across multiple TMEM16 structures, it falls short in terms of novelty and mechanistic advancement. The findings are largely confirmatory and do not bridge the gap between coarse-grained simulations and experimental observations. Future efforts should focus on resolving these limitations, possibly through atomistic simulations or experimental validation of the alternative scrambling pathways.

Reviewer #2 (Public review):

Summary:

Stephens et al. present a comprehensive study of TMEM16-members via coarse-grained MD simulations (CGMD). They particularly focus on the scramblase ability of these proteins and aim to characterize the "energetics of scrambling". Through their simulations, the authors interestingly relate protein conformational states to the membrane's thickness and link those to the scrambling ability of TMEM members, measured as the trespassing tendency of lipids across leaflets. They validate their simulation with a direct qualitative comparison with Cryo-EM maps.

Strengths:

The study demonstrates an efficient use of CGMD simulations to explore lipid scrambling across various TMEM16 family members. By leveraging this approach, the authors are able to bypass some of the sampling limitations inherent in all-atom simulations, providing a more comprehensive and high-throughput analysis of lipid scrambling. Their comparison of different protein conformations, including open and closed groove states, presents a detailed exploration of how structural features influence scrambling activity, adding significant value to the field. A key contribution of this study is the finding that groove dilation plays a central role in lipid scrambling. The authors observe that for scrambling-competent TMEM16 structures, there is substantial membrane thinning and groove widening. The open Ca2+-bound nhTMEM16 structure (PDB ID 4WIS) was identified as the fastest scrambler in their simulations, with scrambling rates as high as 24.4 {plus minus} 5.2 events per μs. This structure also shows significant membrane thinning (up to 18 Å), which supports the hypothesis that groove dilation lowers the energetic barrier for lipid translocation, facilitating scrambling.

The study also establishes a correlation between structural features and scrambling competence, though analyses often lack statistical robustness and quantitative comparisons. The simulations differentiate between open and closed conformations of TMEM16 structures, with open-groove structures exhibiting increased scrambling activity, while closed-groove structures do not. This finding aligns with previous research suggesting that the structural dynamics of the groove are critical for scrambling. Furthermore, the authors explore how the physical dimensions of the groove qualitatively correlate with observed scrambling rates. For example, TMEM16K induces increased membrane thinning in its open form, suggesting that membrane properties, along with structural features, play a role in modulating scrambling activity.

Another significant finding is the concept of "out-of-the-groove" scrambling, where lipid translocation occurs outside the protein's groove. This observation introduces the possibility of alternate scrambling mechanisms that do not follow the traditional "credit-card model" of groove-mediated lipid scrambling. In their simulations, the authors note that these out-of-the-groove events predominantly occur at the dimer interface between TM3 and TM10, especially in mammalian TMEM16 structures. While these events were not observed in fungal TMEM16s, they may provide insight into Ca2+-independent scrambling mechanisms, as they do not require groove opening.

Weaknesses:

A significant challenge of the study is the discrepancy between the scrambling rates observed in CGMD simulations and those reported experimentally. Despite the authors' claim that the rates are in line experimentally, the observed differences can mean large energetic discrepancies in describing scrambling (larger than 1kT barrier in reality). For instance, the authors report scrambling rates of 10.7 events per μs for TMEM16F and 24.4 events per μs for nhTMEM16, which are several orders of magnitude faster than experimental rates. While the authors suggest that this discrepancy could be due to the Martini 3 force field's faster diffusion dynamics, this explanation does not fully account for the large difference in rates. A more thorough discussion on how the choice of force field and simulation parameters influence the results, and how these discrepancies can be reconciled with experimental data, would strengthen the conclusions. Likewise, rate calculations in the study are based on 10 μs simulations, while experimental scrambling rates occur over seconds. This timescale discrepancy limits the study's accuracy, as the simulations may not capture rare or slow scrambling events that are observed experimentally and therefore might underestimate the kinetics of scrambling. It's however important to recognize that it's hard (borderline unachievable) to pinpoint reasonable kinetics for systems like this using the currently available computational power and force field accuracy. The faster diffusion in simulations may lead to overestimated scrambling rates, making the simulation results less comparable to real-world observations. Thus, I would therefore read the findings qualitatively rather than quantitatively. An interesting observation is the asymmetry observed in the scrambling rates of the two monomers. Since MARTINI is known to be limited in correctly sampling protein dynamics, the authors - in order to preserve the fold - have applied a strong (500 kJ mol-1 nm-2) elastic network. However, I am wondering how the ENM applies across the dimer and if any asymmetry can be noticed in the application of restraints for each monomer and at the dimer interface. How can this have potentially biased the asymmetry in the scrambling rates observed between the monomers? Is this artificially obtained from restraining the initial structure, or is the asymmetry somehow gatekeeping the scrambling mechanism to occur majorly across a single monomer? Answering this question would have far-reaching implications to better describe the mechanism of scrambling.

The main aim of our computational survey was to directly compare all relevant published TMEM16 structures in both open and closed states using the Martini 3 CGMD force field. Our standardized simulation and analysis protocol allowed us to quantitatively compare scrambling rates across the TMEM16 family, something that has never been done before. We do acknowledge that direct comparison between simulated versus experimental scrambling rates is complicated and is best to be interpreted qualitatively. In line with other reports (e.g., Li et al, PNAS 2024), lipid scrambling in CGMD is 2-3 orders of magnitude faster than typical experimental findings. In the CG simulation field, these increased dynamics due to the smoother energy landscape are a well known phenomenon. In our view, this is a valuable trade-off for being able to capture statistically robust scrambling dynamics and gain mechanistic understanding in the first place, since these are currently challenging to obtain otherwise. For example, with all-atom MD it would have been near-impossible to conclude that groove openness and high scrambling rates are closely related, simply because one would only measure a handful of scrambling events in (at most) a handful of structures.

Considering the elastic network: the reviewer is correct in that the elastic network restrains the overall structure to the experimental conformation. This is necessary because the Martini 3 force field does not accurately model changes in secondary (and tertiary) structure. In fact, by retaining the structural information from the experimental structures, we argue that the elastic network helped us arrive at the conclusion that groove openness is the major contributing factor in determining a protein’s scrambling rate. This is best exemplified by the asymmetric X-ray structure of TMEM16K (5OC9), in which the groove of one subunit is more dilated than the other. In our simulation, this information was stored in the elastic network, yielding a 4x higher rate in the open groove than in the closed groove, within the same trajectory.

Notably, the manuscript does not explore the impact of membrane composition on scrambling rates. While the authors use a specific lipid composition (DOPC) in their simulations, they acknowledge that membrane composition can influence scrambling activity. However, the study does not explore how different lipids or membrane environments or varying membrane curvature and tension, could alter scrambling behaviour. I appreciate that this might have been beyond the scope of this particular paper and the authors plan to further chase these questions, as this work sets a strong protocol for this study. Contextualizing scrambling in the context of membrane composition is particularly relevant since the authors note that TMEM16K's scrambling rate increases tenfold in thinner membranes, suggesting that lipid-specific or membrane-thickness-dependent effects could play a role.

Considering different membrane compositions: for this study, we chose to keep the membranes as simple as possible. We opted for pure DOPC membranes, because it has (1) negligible intrinsic curvature, (2) forms fluid membranes, and (3) was used previously by others (Li et al, PNAS 2024). As mentioned by the reviewer, we believe our current study defines a good, standardized protocol and solid baseline for future efforts looking into the additional effects of membrane composition, tension, and curvature that could all affect TMEM16-mediated lipid scrambling.

Reviewer #3 (Public review):

Strengths:

The strength of this study emerges from a comparative analysis of multiple structural starting points and understanding global/local motions of the protein with respect to lipid movement. Although the protein is well-studied, both experimentally and computationally, the understanding of conformational events in different family members, especially membrane thickness less compared to fungal scramblases offers good insights.

We appreciate the reviewer recognizing the value of the comparative study. In addition to valuable insights from previous experimental and computational work, we hope to put forward a unifying framework that highlights various TMEM16 structural features and membrane properties that underlie scrambling function.

Weaknesses:

The weakness of the work is to fully reconcile with experimental evidence of Ca²⁺-independent scrambling rates observed in prior studies, but this part is also challenging using coarse-grain molecular simulations. Previous reports have identified lipid crossing, packing defects, and other associated events, so it is difficult to place this paper in that context. However, the absence of validation leaves certain claims, like alternative scrambling pathways, speculative.

Answer: It is generally difficult to quantitatively compare bulk measurements of scrambling phenomena with simulation results. The advantage of simulations is to directly observe the transient scrambling events at a spatial and temporal resolution that is currently unattainable for experiments. The current experimental evidence for the precise mechanism of Ca2+-independent scrambling is still under debate. We therefore hope to leverage the strength of MD and statistical rigor of coarse-grained simulations to generate testable hypotheses for further structural, biochemical, and computational studies.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

The findings are largely confirmatory and do not bridge the gap between coarse-grained simulations and experimental observations. Future efforts should focus on resolving these limitations, possibly through atomistic simulations or experimental validation of the alternative scrambling pathways.

While we agree with what the reviewer may be hinting at regarding limitations of coarse-grained MD simulations, we believe that our study holds much more merit than this comment suggests. We have provided something that has yet to be done in the field: a comprehensive study that directly compares the scrambling rates of multiple TMEM16 family members in different conformations using identical simulation conditions. Our work clearly shows that a sufficiently dilated grooves is the major structural feature that enables robust scrambling for all TMEM16 scramblases members with solved structures. While all TMEM16s cause significant distortion and thinning of the membrane, we assert that the extreme thinning observed around open grooves is significantly enhanced by the lipid scrambling itself as the two leaflets merge through lipid exchange.  We saw no evidence that membrane thinning/distortion alone, in the absence of an open groove, could support scrambling at the rates observed under activating conditions or even the low rates observed in Ca2+-independent scrambling. Moreover, our handful of observations of scrambling events outside of the groove, which has not yet been reported in any study, opens an exciting new direction for studying alternative scrambling mechanisms. That said, we are currently following up on many of the observations reported here such as: scrambling events outside the groove, the kinetics of scrambling, the possibility that lipids line the groove of non-scramblers like TMEM16A, etc. This is being done experimentally with our collaborators through site directed mutagenesis and with all-atom MD in our lab. Unfortunately, it is well beyond the scope of the current study to include all of this in the current paper.

Reviewer #2 (Recommendations for the authors):

Major comments and questions:

(1) Line 214 and Figure 1- Figure Supplement 1: why have you only compared the final frame of the trajectory to the cryo-EM structure? Even if these comparisons are qualitative, they should be representative of the entire trajectory, not a single frame.

We thank the reviewer for this suggestion and replaced the single-frame snapshots in Figure 1-figure supplement 1 for ensemble-averaged head groups densities. The overall agreement between membrane shapes in CGMD and cryo-EM was not affected by this change.

(2) Lines 228-231: You comment 'Residues in this site on nhTMEM16 and TMEMF also seem to play a role in scrambling but the mechanism by which they do so is unclear.' This is something you could attempt to quantify in the simulations by calculating the correlation between scrambling and protein-membrane interactions/contacts in this site. Can you speculate on a mechanism that might be a contributing factor?

We probed the correlation between these residues and scrambling lipids, as suggested by the reviewer, and interestingly not all scrambling lipids interact with these residues. Yet there is strong lipid density in this vicinity (see insets in Figure 1 and Figure 4-figure supplement 2). These observations lead us to suspect these residues impact scrambling indirectly through influencing the conformation of the protein or flexibility and shape of the membrane. This interpretation fits with mutagenesis studies highlighting a role for these residues in scrambling (see refs 59, 62, and 67). Specifically, Falzone et al. 2022 (ref 59) suggested that they may thin the membrane near the groove, but this has not been tested via structure determination and a detailed model of how they impact scrambling is missing. We could address this question with in silico mutations; however, CG simulation is not an appropriate method to study large scale protein dynamics, and AA simulations are likely best, but beyond the scope of this paper.

(3) Lines 240-245 and Figure 1B: This section discusses the coupling between membrane distortions and the sinusoidal curve around the protein, however, Figure 1B only shows snapshots of the membrane distortions. Is it possible to understand how these two collective variables are correlated quantitatively (as opposed to the current qualitative analysis)?

We believe that it may be possible to quantitatively capture these two key features of the membrane, as we did previously with nhTMEM16 using our continuum elasticity-based model of the membrane (Bethel and Grabe 2016). Our model agreed with all atom MD surfaces to within ~1 Å, hence showing good quantitative agreement throughout the entire membrane. However, we doubt that we could distill the essence of our model down to a simple functional relationship between the sinusoidal wave and pinching, which we think the reviewer is asking. Rather, we believe that the large-scale sinusoidal distortion (collective variable 1) and pinching/distortion (collective variable 2) near the groove arise from the interplay of the specific protein surface chemistry for each protein (patterning of polar and non-polar residues) and the membrane. This is why we chose to simply report the distinct patterns that the family members impose on the surrounding membrane, which we think is fascinating. Specifically, Fig. 1B shows that different TMEM16 family members distort the membrane in different ways. Most notably, fungal TMEM16s feature a more pronounced sinusoidal deformation, whereas the mammalian members primarily produce local pinching. Then, in Fig. 3A we show that the thinning at the groove happens in all structures and is more pronounced in open, scrambling-competent conformations. In other words, proteins can show very strong thinning (e.g. TMEM16K, 5OC9) even though the membrane generally remains flat.

(4) Lines 257-258: Authors comment that TMEM16A lacks scramblase activity yet can achieve a fully lipid-lined groove (note the typo - should be lipid-lined, not lipid-line). Is a fully lipid-lined groove a prerequisite for scramblase activity? Are lipid-lined grooves the only requirement for scramblase activity? Could the authors clarify exactly what the prerequisite for scramblase activity is to avoid any confusion; this will be useful for later descriptions (i.e. line 295) where scrambling competence is again referred to. Additionally, the associated figure panel (Figure 1D) shows a snapshot of this finding but lacks any statistical quantifications - is a fully lipid-lined groove a single event? Perhaps the additional analyses, such as the groove-lipid contacts, may be useful here.

The definition of lipid scrambling is that a lipid fully transitions from one membrane leaflet to the other. While a single lipid could transition through the groove on its own, it is well documented in both atomistic and CG MD simulations, that lipid scrambling typically happens through a lipid-lined groove, as shown in Fig. 1A-B. The lipids tend to form strong choline-to-phosphate interactions with nearest neighbors that make this energetically favorable. That said, lipid-lined grooves are not sufficient for robust scrambling, which is what we show in Fig. 1D where the non-scrambler TMEM16A did in fact feature a lipid-lined groove. As suggested, we performed contact analysis and found that residue K645 on TM6 in the middle of the groove contacts lipids in 9.2% of the simulation frames.

To get a better understanding of how populated the TM4-TM6 pathway is with lipids across all simulated structures, we determined for every simulation frame how many headgroup beads resided in the groove. This indicates that the ion-conductive state of TMEM16A (5OYB*, Fig. 1D) only had 1 lipid in the pathway, on average, meaning that the configuration shown Fig. 1D is indeed exceptional. As a reference, our strongest scrambler nhTMEM16 4WIS, had an average of 2.8 lipids in the groove. We added a table containing the means and standard deviations that resulted from this analysis as Figure 1-Table supplement 1.

(5) Lines 295-298 : The scrambling rates of the Ca²⁺-bound and Ca²⁺-free structures fall within overlapping error margins, it becomes difficult to definitively state that Ca²⁺ binding significantly enhances scrambling activity. This undermines the claim that the Ca²⁺-bound structure is the strongest scrambler. The authors should conduct statistical analyses to determine if the difference between the two conditions is statistically significant.

In contrast to the reviewer’s comment, we do not claim that Ca2+-binding itself enhances lipid scrambling. Instead, what we show is that WT structures that are solved in an open confirmation (all of which are Ca2+-bound, except 6QM6) are robust scramblers. For nhTMEM16, we did not observe any scrambling events for the closed-groove proteins, making further statistical analysis redundant.

(6) The authors claim that the scrambling rates derived from their MD simulations are in "excellent agreement" with experimental findings (lines 294-295), despite significant discrepancy between simulated and experimentally measured rates. For example, the simulated rate of 24.4 {plus minus} 5.2 events/µs for the open, Ca²⁺-bound fungal nhTMEM16 (PDB ID 4WIS) corresponds to approximately 24 million events per second, which is vastly higher than experimental rates. Experimental studies have reported scrambling rate constants of ~0.003 s⁻¹ for TMEM16 family members in the absence of Ca²⁺, measured under physiological conditions (https://doi.org/10.1038/s41467-019-11753-1 ). Even with Ca²⁺ activation, scrambling rates remain several orders of magnitude lower than the rates observed in simulations. Moreover, this highlights a larger problem: lipid scrambling rates occur over timescales that are not captured by these simulations. While the authors elude to these discrepancies (lines 605-606), they should be emphasised in the text, as opposed to the table caption. These should also be reconducted to differences between the membrane compositions of different studies.

We agree with the spirit of the reviewer’s comment, and because of that, we were very careful not to claim that we reproduce experimental scrambling rates, just that the trends (scrambling-competent, or not) are correct. On lines 294-295, we actually said that the scrambling rates in our simulations excellently agree with “the presumed scrambling competence of each experimental structure”, which is true. 

As explained extensively in the discussion section of our paper (and by many others), direct comparison between MD (e.g., Martini 3, but also atomistic force fields) dynamics and experimental measurements is challenging. The primary goal of our paper is to quantify and compare the scrambling capacity of different TMEM16 family members and different states, within a CGMD context.

That said, we agree with the reviewer that we may have missed rare or long-timescale events (as is the case in any MD experiment) and added this point to the discussion.

(7) To address these discrepancies, the authors should: i) emphasize that simulated rates serve as qualitative indicators of scrambling competence rather than absolute values comparable to experimental findings and ii) discuss potential reasons for the divergence, such as simulation timescale limitations or lipid bilayer compositions that may favor scrambling and force field inaccuracies.

Please see our answer to question 6. Within the context of our CGMD survey, we confidently call our results quantitative. However, we agree with the reviewer that comparison with experimental scrambling rates is qualitative and should be interpreted with caution. To reflect this, we rewrote the first sentence of the relevant paragraph in the discussion section.

(8) Line 310: Can the authors provide a rationale as to why one monomer has a wider groove than the other? Perhaps a contact analysis could be useful. See the comment above about ENM.

The simulation of Ca2+-bound TMEM16K was initiated from an asymmetric X-ray structure in which chain B features a more dilated groove than chain A (PDB 5OC9). The backbones of TM4 and TM6 in the closed groove (A) are close enough together to be directly interconnected by the elastic network. In contrast, TM4 and TM6 in the more dilated subunit (B) are not restricted by the elastic network and, as a consequence, display some “breathing” behavior (Fig. 3B and Fig. 3-Suppl. 6A), giving rise to a ~4x higher scrambling rate. We explicitly added the word “cryo-EM” and the PDB ID to the sentence to emphasize that the asymmetry stems from the original experimental structure.

When answering this question, we also corrected a mislabeled chain identifier which was in the original manuscript ‘chain A’ when it is actually ‘chain B’ in Fig.2-Suppl. 3A.

(9) Line 312: Authors speculate that increased groove width likely accounts for increased scrambling rates. For statistical significance, authors should attempt to correlate scrambling rates and groove width over the simulation period.

The Reviewer is referring to our description of scrambling rates we measured for TMEM16K where we noted that on average the groove with the highest scrambling rate is also on average wider than the opposite subunit which is below 6 Å. We do not suggest that the correlation between scrambling and groove width is continuous, as the Reviewer may have interpreted from our original submission, but we think it is a binary outcome – lipids cannot easily enter narrow grooves (< 6 Å) and hence scrambling can only occur once this threshold is reached at which point it occurs at a near constant rate. We showed this for 4 different family members in the original Fig. 3B, where scrambling events (black dots) were much more likely during, or right after, groove dilation to distances > 6 Å. 

(10) Line 359: Authors have plotted the minimum distance between residues TM4 and TM6 in Fig. 3A/B, claiming that a wide groove is required for scrambling. Upon closer examination, it is clear that several of these distributions overlap, reducing the statistical significance of these claims. Statistical tests (i.e. KS-tests) should be performed to determine whether the differences in distributions are significant.

The Reviewer appears to be asking for a statistical test between the six distance distributions represented by the data in Fig. 3A for the scrambling competent structures (6QP6*, 8B8J, 6QM6, 7RXG, 4WIS, 5OC9), and we think this is being asked because it is believed that we are making a claim that the greater the distance, the greater the scrambling rate. If we have interpreted this comment correctly, we are not making this claim. Rather, we are simply stating that we only observe robust scrambling when the groove width regularly separates beyond 6 Å. The full distance distributions can now be found in Figure 3-figure supplement 6B, and we agree there is significant overlap between some of these distributions. However, the distinguishing characteristic of the 6 distributions from scrambling competent proteins is that they all access large distances, while the others do not. Notably, TMEM16F proteins (6QP6*, 8B8J) are below the 6 Å threshold on average, but they have wide standard deviations and spend well over ¼ of their time in the permissive regime (the upper error bar in the whisker plots in Fig. 3A is the 75% boundary).

(11) Line 363-364: The authors state that all TMEM16 structures thin the membrane. Could the authors include a description of how membrane thinning is calculated, for instance, is the entire membrane considered, or is thinning calculated on a membrane patch close to the protein? Do membrane patches closer to the transmembrane protein increase or decrease thickness due to hydrophobic packing interactions? The latter question is of particular concern since Martini3 has been shown to induce local thinning of the membrane close to transmembrane helices, yielding thicknesses 2-3 Å thinner than those reported experimentally (https://doi.org/10.1016/j.cplett.2023.140436). This could be an important consideration in the authors' comparison to the bulk membrane thickness (line 364). Finally, how is the 'bulk membrane thickness' measured (i.e., from the CG simulations, from AA simulations, or from experiments)?

Regarding the calculation of thinning and bulk membrane thickness, as described in Method “Quantification of membrane deformations”, the minimal membrane thickness, or thinning, is defined as the shortest distance between any two points from the interpolated upper and lower leaflet surfaces constructed using the glycerol beads (GL1 and GL2). Bulk membrane thickness is calculated by taking the vertical distance between the averaged glycerol surfaces at the membrane edge.

The concern of localized membrane deformation due to force field artifacts is well-founded. However, the sinusoidal deformations shown here are much greater than 2-3 Å Martini3 imperfections, and they extend for up to 10 Å radially away from the protein into the bulk membrane (see Figure 3-figure supplement 1-5 for more of a description). Most importantly, the sinusoidal wave patterns set up by the proteins is very similar to those described in the previous continuum calculation and all-atom MD for nhTMEM16 (https://www.pnas.org/doi/full/10.1073/pnas.1607574113).

(12) Line 374: The authors state a 'positive correlation' between membrane thinning/groove opening and scrambling rates. To support this claim, the authors should report. the correlation coefficients.

We have removed any discussion concerning correlations between the magnitude of the scrambling rate and the degree of membrane thinning/groove opening. Rather we simply state that opening beyond a threshold distance is required for robust scrambling, as shown in our analysis in Fig. 3A.

Concerning the relation between thinning and scrambling: Instantaneous membrane thinning is poorly defined (because it is governed by fluctuations of single lipids), and therefore difficult to correlate with the timing of individual scrambling events in a meaningful way.  Moreover, as we state later in that same section, “we argue that the extremely thin membranes are likely correlated with groove opening, rather than being an independent contributing factor to lipid scrambling”.

(13) Line 396: It is stated that TMEM16A is not a scramblase but the simulating scrambling activity is not zero. How can you be sure that you are monitoring the correct collective variable if you are getting a false positive with respect to experiments?

We only observe 2 scrambling events in 10 ms, which is a very small rate compared to the scrambling competent states. In a previous large survey Martini CG simulation study that inspired our protocol (Li et al, PNAS 2024), they employed a 1 event/ms cut-off to distinguish scramblers from non-scramblers. Hence, they would have called TMEM16A a non-scrambler as well. We expect that false negatives in this context might be an artifact of the CG forcefield, or it could be that TMEM16A can scramble but too slowly to be experimentally detected. Regarding the collective variable for lipid flipping, it is correct, and we know that this lipid actually flipped.

(14) Line 402: Distance distributions for the electrostatic interactions between E633 and K645 should be included in the manuscript. This is also the case for the interactions between E843-K850 (lines 491-492).

Our description of interactions between lipid headgroups and E633 and K645 in TMEM16A (5OYB*) are based on qualitative observations of the MD trajectory, and we highlight an example of this interaction in Figure 3-video 4. The video clearly shows that the lipid headgroups in the center of the groove orient themselves such that the phosphate bead (red) rests just above K645 (blue) and at other times the choline bead (blue) rests just below E633 (red). We do not think an additional plot with the distance distributions between lipids and these residues will add to our understanding of how lipids interact residues in the TMEM16A pore.

We made a similar qualitative observation for the interaction between the POPC choline to E843 and POPC phosphate to K850 while watching the AAMD simulation trajectory of TMEM16F (PDB ID 6QP6). Given that this was a single observation, and the same interactions does not appear in CG simulation of the same structure (see simulation snapshots in Figure 4-figure supplement 5) we do not think additional analysis would add significantly to our understanding of which residues may stabilize lipids in the dimer interface.

(15) Lines 450-451: 'As the groove opens, water is exposed to the membrane core and lipid headgroups insert themselves into the water-filled groove to bridge the leaflets.' Is this a qualitative observation? Could the authors report the correlation between groove dilation and the number of water permeation events?

Yes, this is qualitative, and it sketches the order of events during scrambling, and we revised the main text starting at line 450 to indicate this. As illustrated by the density isosurfaces in Appendix 1-Figure 2A, the amount of water found in the closed versus open grooves is striking – there is a significant flood of water that connects the upper and lower solutions upon groove opening. Moreover, Appendix 1-Figure 2B shows much greater water permeation for open structures (4WIS, 7RXG, 5OC9, 8B8J, …) compared to closed structures (6QMB, 6QMA, 8B8Q, and many of the non-labeled data in the figure that all have closed grooves and near 0 water permeation). A notable exception is TMEM16A (7ZK3*8), which has water permeation but a closed groove and little-to-no lipid scrambling.

Minor Comments:

(1) Inconsistent use of '10' and 'ten' throughout.

We like to kindly point out that we do not find examples of inconsistent use.

(2) Line 32: 'TM6 along with 3, 4 and 5...' should be 'TM6 along with TM3, TM4 and TM5...'. Same in line 142. Naming should stay consistent.

Changes are reflected in the updated manuscript.

(3) Line 141: do you mean traverse (i.e. to travel across)? Or transverse (i.e. to extend across the membrane)?

This is a typo. We meant “traverse”. Thanks for pointing it out.

(4) Line 142: 'greasy' should be 'strongly hydrophobic'.

Changes are reflected in the updated manuscript.

(5) Line 143-144: "credit card mechanism" requires quotation marks.

Changes are reflected in the updated manuscript.

(6) Line 144: state if Nectria haematococca is mammalian or fungal, this is not obvious for all readers.

Changes are reflected in the updated manuscript.

(7) Line 147-148: Is TMEM16A/TMEM16K fungal or mammalian? What was the residue before the mutation and which residue is mutated? Perhaps the nomenclature should read as TMEM16X10Y where X=the residue prior to the mutation, 10 is a placeholder for the residue number that is mutated and Y=the new residue following mutation.

“TMEM16” is the protein family. “A” denotes the specific homolog rather than residue.  

(8) Lines 157-158: same as 10, it is unclear if these are fungal or mammalian.

Clarifications added.

(9) Line 184: "...CGMD simulation" should be "...CGMD simulations".

Changes made.

(10) Line 191-192: It would help to create a table of all of the mutants (including if they are mammalian or fungal) summarizing the salt concentrations, lipid and detergent environments, the presence of modulators/activators, etc.

We added this information to Appendix 1-Table 1 in the supplemental information. We did not specify NaCl concentrations, because they all experimental procedures used standard physiological values for this (100-150 mM).

(11) Line 210: inconsistencies with 'CG' and 'coarse-grain'.

Changes made.

(12) Figure 1 caption: '...totaling ~2μs (B)...' is missing the fullstop after 2μs.

Changes made.

(13) Figure 1B: it may be useful to label where the Ca2+ ion binds or include a schematic.

We updated Fig. 1A to illustrate where Ca2+ binds.

(14) Line 311: Are these mean distances? The authors should add standard deviations.

Yes, they are. We added the standard deviations to the text.

(15) Line 321-322: Perhaps a schematic in Figure 2 would be useful to visualize the structural features described here.

We would kindly refer interested readers to reference [60].

(16) Line 377: '...are likely a correlate of groove opening...' should read as: '...are likely correlated to groove opening...'.

Thank you for pointing it out. Changes made.

(17) Line 398: the '...empirically determined 6Å threshold for scrambling.' Was this determined from the simulations or from experiments? What does "empirically" mean here? Please state this.

This value was determined from the simulations. Based on our analysis of the correlation between scrambling rate and groove dilation, we found that the minimal TM4/6 distance of 6 Å can distinguish between the high and low activity scramblers. The exact numerical value is somewhat arbitrary as there is a range of values around 6 Å that serve to distinguish scramblers from non-scramblers.

(18) Figure 4: This figure should be labelled as A, B, C and D, with the figure caption updated accordingly.

We updated Figure 4 and its caption.

Reviewer #3 (Recommendations for Authors):

The authors must do additional simulations to further validate their claim with different lipids and further substantiate dimer interface independent of Ca2+ ions.

Thank you for the suggestion. We completely agree that studying scrambling in the context of a diverse lipid environment is an exciting area to explore. We are indeed actively working on a project that shares the similar idea. We decided not to include that study because we think the additional discussion involved would be excessive for the current manuscript. We, however, look forward to publishing our findings in a separate manuscript in the near future. In terms of Ca2+-independent scrambling, we are planning with our experimental collaborator for mutagenesis studies that target the residues we identified along the dimer interface.

Since calcium ions are critical for the stability of these structures, authors should show that they were placed throughout the simulations consistently.

As stated in the method section “Coarse-grained system preparation and simulation detail”, all Ca2+ ions are manually placed into the coarse-grained structure from the beginning of the simulation at their identical corresponding position in the experimental structure and harmonically bonded to adjacent acidic residues throughout the duration of simulation. We have also added a label to Fig 1A to indicate where the two Ca2+ ions are located.

The comparison with experimental structures should be consistent with complete simulation, and not the last structure of the trajectory. Depending on the conformational variability, this might be misleading.

We agree and updated Fig. 1-supplement figure 1 accordingly. The overall agreement between membrane shapes in CGMD and cryo-EM was not affected by this change.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public review):

Wang et al., recorded concurrent EEG-fMRI in 107 participants during nocturnal NREM sleep to investigate brain activity and connectivity related to slow oscillations (SO), sleep spindles, and in particular their co-occurrence. The authors found SO-spindle coupling to be correlated with increased thalamic and hippocampal activity, and with increased functional connectivity from the hippocampus to the thalamus and from the thalamus to the neocortex, especially the medial prefrontal cortex (mPFC). They concluded the brain-wide activation pattern to resemble episodic memory processing, but to be dissociated from task-related processing and suggest that the thalamus plays a crucial role in coordinating the hippocampal-cortical dialogue during sleep.

The paper offers an impressively large and highly valuable dataset that provides the opportunity for gaining important new insights into the network substrate involved in SOs, spindles, and their coupling. However, the paper does unfortunately not exploit the full potential of this dataset with the analyses currently provided, and the interpretation of the results is often not backed up by the results presented. I have the following specific comments.

Thank you for your thoughtful and constructive feedback. We greatly appreciate your recognition of the strengths of our dataset and findings Below, we address your specific comments and provide responses to each point you raised to ensure our methods and results are as transparent and comprehensible as possible. We hope these revisions address your comments and further strengthen our manuscript. Thank you again for the constructive feedback.

(1) The introduction is lacking sufficient review of the already existing literature on EEG-fMRI during sleep and the BOLD-correlates of slow oscillations and spindles in particular (Laufs et al., 2007; Schabus et al., 2007; Horovitz et al., 2008; Laufs, 2008; Czisch et al., 2009; Picchioni et al., 2010; Spoormaker et al., 2010; Caporro et al., 2011; Bergmann et al., 2012; Hale et al., 2016; Fogel et al., 2017; Moehlman et al., 2018; Ilhan-Bayrakci et al., 2022). The few studies mentioned are not discussed in terms of the methods used or insights gained.

We acknowledge the need for a more comprehensive review of prior EEG-fMRI studies investigating BOLD correlates of slow oscillations and spindles. However, these articles are not all related to sleep SO or spindle. Articles (Hale et al., 2016; Horovitz et al., 2008; Laufs, 2008; Laufs, Walker, & Lund, 2007; Spoormaker et al., 2010) mainly focus on methodology for EEG-fMRI, sleep stages, or brain networks, which are not the focus of our study. Thank you again for your attention to the comprehensiveness of our literature review, and we will expand the introduction to include a more detailed discussion of the existing literature, ensuring that the contributions of previous EEG-fMRI sleep studies are adequately acknowledged.  

Introduction, Page 4 Lines 62-76

“Investigating these sleep-related neural processes in humans is challenging because it requires tracking transient sleep rhythms while simultaneously assessing their widespread brain activation. Recent advances in simultaneous EEG-fMRI techniques provide a unique opportunity to explore these processes. EEG allows for precise event-based detection of neural signal, while fMRI provides insight into the broader spatial patterns of brain activation and functional connectivity (Horovitz et al., 2008; Huang et al., 2024; Laufs, 2008; Laufs, Walker, & Lund, 2007; Schabus et al., 2007; Spoormaker et al., 2010). Previous EEG-fMRI studies on sleep have focused on classifying sleep stages or examining the neural correlates of specific waves (Bergmann et al., 2012; Caporro et al., 2012; Czisch et al., 2009; Fogel et al., 2017; Hale et al., 2016; Ilhan-Bayrakcı et al., 2022; Moehlman et al., 2019; Picchioni et al., 2011). These studies have generally reported that slow oscillations are associated with widespread cortical and subcortical BOLD changes, whereas spindles elicit activation in the thalamus, as well as in several cortical and paralimbic regions. Although these findings provide valuable insights into the BOLD correlates of sleep rhythms, they often do not employ sophisticated temporal modeling (Huang et al., 2024), to capture the dynamic interactions between different oscillatory events, e.g., the coupling between SOs and spindles.”

(2) The paper falls short in discussing the specific insights gained into the neurobiological substrate of the investigated slow oscillations, spindles, and their interactions. The validity of the inverse inference approach ("Open ended cognitive state decoding"), assuming certain cognitive functions to be related to these oscillations because of the brain regions/networks activated in temporal association with these events, is debatable at best. It is also unclear why eventually only episodic memory processing-like brain-wide activation is discussed further, despite the activity of 16 of 50 feature terms from the NeuroSynth v3 dataset were significant (episodic memory, declarative memory, working memory, task representation, language, learning, faces, visuospatial processing, category recognition, cognitive control, reading, cued attention, inhibition, and action).

Thank you for pointing this out, particularly regarding the use of inverse inference approaches such as “open-ended cognitive state decoding.” Given the concerns about the indirectness of this approach, we decided to remove its related content and results from Figure 3 in the main text and include it in Supplementary Figure 7. We will refocus the main text on direct neurobiological insights gained from our EEG-fMRI analyses, particularly emphasizing the hippocampal-thalamocortical network dynamics underlying SO-spindle coupling, and we will acknowledge the exploratory nature of these findings and highlight their limitations.

Discussion, Page 17-18 Lines 323-332

“To explore functional relevance, we employed an open-ended cognitive state decoding approach using meta-analytic data (NeuroSynth: Yarkoni et al. (2011)). Although this method usefully generates hypotheses about potential cognitive processes, particularly in the absence of a pre- and post-sleep memory task, it is inherently indirect. Many cognitive terms showed significant associations (16 of 50), such as “episodic memory,” “declarative memory,” and “working memory.” We focused on episodic/declarative memory given the known link with hippocampal reactivation (Diekelmann & Born, 2010; Staresina et al., 2015; Staresina et al., 2023). Nonetheless, these inferences regarding memory reactivation should be interpreted cautiously without direct behavioral measures. Future research incorporating explicit tasks before and after sleep would more rigorously validate these potential functional claims.”

(3) Hippocampal activation during SO-spindles is stated as a main hypothesis of the paper - for good reasons - however, other regions (e.g., several cortical as well as thalamic) would be equally expected given the known origin of both oscillations and the existing sleep-EEG-fMRI literature. However, this focus on the hippocampus contrasts with the focus on investigating the key role of the thalamus instead in the Results section.

We appreciate your insight regarding the relative emphasis on hippocampal and thalamic activation in our study. We recognize that the manuscript may currently present an inconsistency between our initial hypothesis and the main focus of the results. To address this concern, we will ensure that our Introduction and Discussion section explicitly discusses both regions, highlighting the complementary roles of the hippocampus (memory processing and reactivation) and the thalamus (spindle generation and cortico-hippocampal coordination) in SO-spindle dynamics.

Introduction, Page 5 Lines 87-103

“To address this gap, our study investigates brain-wide activation and functional connectivity patterns associated with SO-spindle coupling, and employs a cognitive state decoding approach (Margulies et al., 2016; Yarkoni et al., 2011)—albeit indirectly—to infer potential cognitive functions. In the current study, we used simultaneous EEG-fMRI recordings during nocturnal naps (detailed sleep staging results are provided in the Methods and Table S1) in 107 participants. Although directly detecting hippocampal ripples using scalp EEG or fMRI is challenging, we expected that hippocampal activation in fMRI would coincide with SO-spindle coupling detected by EEG, given that SOs, spindles, and ripples frequently co-occur during NREM sleep. We also anticipated a critical role of the thalamus, particularly thalamic spindles, in coordinating hippocampal-cortical communication.

We found significant coupling between SOs and spindles during NREM sleep (N2/3), with spindle peaks occurring slightly before the SO peak. This coupling was associated with increased activation in both the thalamus and hippocampus, with functional connectivity patterns suggesting thalamic coordination of hippocampal-cortical communication. These findings highlight the key role of the thalamus in coordinating hippocampal-cortical interactions during human sleep and provide new insights into the neural mechanisms underlying sleep-dependent brain communication. A deeper understanding of these mechanisms may contribute to future neuromodulation approaches aimed at enhancing sleep-dependent cognitive function and treating sleep-related disorders.”

Discussion, Page 16-17 Lines 292-307

“When modeling the timing of these sleep rhythms in the fMRI, we observed hippocampal activation selectively during SO-spindle events. This suggests the possibility of triple coupling (SOs–spindles–ripples), even though our scalp EEG was not sufficiently sensitive to detect hippocampal ripples—key markers of memory replay (Buzsáki, 2015). Recent iEEG evidence indicates that ripples often co-occur with both spindles (Ngo, Fell, & Staresina, 2020) and SOs (Staresina et al., 2015; Staresina et al., 2023). Therefore, the hippocampal involvement during SO-spindle events in our study may reflect memory replay from the hippocampus, propagated via thalamic spindles to distributed cortical regions.

The thalamus, known to generate spindles (Halassa et al., 2011), plays a key role in producing and coordinating sleep rhythms (Coulon, Budde, & Pape, 2012; Crunelli et al., 2018), while the hippocampus is found essential for memory consolidation (Buzsáki, 2015; Diba & Buzsá ki, 2007; Singh, Norman, & Schapiro, 2022). The increased hippocampal and thalamic activity, along with strengthened connectivity between these regions and the mPFC during SO-spindle events, underscores a hippocampal-thalamic-neocortical information flow. This aligns with recent findings suggesting the thalamus orchestrates neocortical oscillations during sleep (Schreiner et al., 2022). The thalamus and hippocampus thus appear central to memory consolidation during sleep, guiding information transfer to the neocortex, e.g., mPFC.”

(4) The study included an impressive number of 107 subjects. It is surprising though that only 31 subjects had to be excluded under these difficult recording conditions, especially since no adaptation night was performed. Since only subjects were excluded who slept less than 10 min (or had excessive head movements) there are likely several datasets included with comparably short durations and only a small number of SOs and spindles and even less combined SO-spindle events. A comprehensive table should be provided (supplement) including for each subject (included and excluded) the duration of included NREM sleep, number of SOs, spindles, and SO+spindle events. Also, some descriptive statistics (mean/SD/range) would be helpful.

We appreciate your recognition of our sample size and the challenges associated with simultaneous EEG-fMRI sleep recordings. We acknowledge the importance of transparently reporting individual subject data, particularly regarding sleep duration and the number of detected SOs, spindles, and SO-spindle events. To address this, we will provide comprehensive tables in the supplementary materials, contains descriptive information about sleep-related characteristics (Table S1), as well as detailed information about sleep waves at each sleep stage for all 107 subjects(Table S2-S4), listing for each subject:(1)Different sleep stage duration; (2)Number of detected SOs; (3)Number of detected spindles; (4)Number of detected SO-spindle coupling events; (5)Density of detected SOs; (6)Density of detected spindles; (7)Density of detected SO-spindle coupling events.

However, most of the excluded participants were unable to fall asleep or had too short a sleep duration, so they basically had no NREM sleep period, so it was impossible to count the NREM sleep duration, SO, spindle, and coupling numbers.

Supplementary Materials, Page 42-54, Table S1-S4

(5) Was the 20-channel head coil dedicated for EEG-fMRI measurements? How were the electrode cables guided through/out of the head coil? Usually, the 64-channel head coil is used for EEG-fMRI measurements in a Siemens PRISMA 3T scanner, which has a cable duct at the back that allows to guide the cables straight out of the head coil (to minimize MR-related artifacts). The choice for the 20-channel head coil should be motivated. Photos of the recording setup would also be helpful.

Thank you for your comment regarding our choice of the 20-channel head coil for EEG-fMRI measurements. We acknowledge that the 64-channel head coil is commonly used in Siemens PRISMA 3T scanners; however, the 20-channel coil was selected due to specific practical and technical considerations in our study. In particular, the 20-channel head coil was compatible with our EEG system and ensured sufficient signal-to-noise ratio (SNR) for both EEG and fMRI acquisition. The EEG electrode cables were guided through the lateral and posterior openings of the head coil, secured with foam padding to reduce motion and minimize MR-related artifacts. Moreover, given the extended nature of nocturnal sleep recordings, the 20-channel coil allowed us to maintain participant comfort while still achieving high-quality simultaneous EEG-fMRI data.

We have made this clearer in the revised manuscript. 

Methods, Page 20 Lines 385-392

“All MRI data were acquired using a 20-channel head coil on a research-dedicated 3-Tesla Siemens Magnetom Prisma MRI scanner. Earplugs and cushions were provided for noise protection and head motion restriction. We chose the 20-channel head coil because it was compatible with our EEG system and ensured sufficient signal-to-noise ratio (SNR) for both EEG and fMRI acquisition. The EEG electrode cables were guided through the lateral and posterior openings of the head coil, secured with foam padding to reduce motion and minimize MR-related artifacts. Moreover, given the extended nature of nocturnal sleep recordings, the 20-channel coil helped maintain participant comfort while still achieving high-quality simultaneous EEG-fMRI data.”

(6) Was the EEG sampling synchronized to the MR scanner (gradient system) clock (the 10 MHz signal; not referring to the volume TTL triggers here)? This is a requirement for stable gradient artifact shape over time and thus accurate gradient noise removal.

Thank you for raising this important point. We confirm that the EEG sampling was synchronized to the MR scanner’s 10 MHz gradient system clock, ensuring a stable gradient artifact shape over time and enabling accurate artifact removal. This synchronization was achieved using the standard clock synchronization interface of the EEG amplifier, minimizing timing jitter and drift. As a result, the gradient artifact waveform remained stable across volumes, allowing for more effective artifact correction during preprocessing. We appreciate your attention to this critical aspect of EEG-fMRI data acquisition.

We have made this clearer in the revised manuscript. 

Methods, Page 19-20 Lines 371-383

“EEG was recorded simultaneously with fMRI data using an MR-compatible EEG amplifier system (BrainAmps MR-Plus, Brain Products, Germany), along with a specialized electrode cap. The recording was done using 64 channels in the international 10/20 system, with the reference channel positioned at FCz. In order to adhere to polysomnography (PSG) recording standards, six electrodes were removed from the EEG cap: one for electrocardiogram (ECG) recording, two for electrooculogram (EOG) recording, and three for electromyogram (EMG) recording. EEG data was recorded at a sample rate of 5000 Hz, the resistance of the reference and ground channels was kept below 10 kΩ, and the resistance of the other channels was kept below 20 kΩ. To synchronize the EEG and fMRI recordings, the BrainVision recording software (BrainProducts, Germany) was utilized to capture triggers from the MRI scanner. The EEG sampling was synchronized to the MR scanner’s 10 MHz gradient system clock, ensuring a stable gradient artifact shape over time and enabling accurate artifact removal. This was achieved via the standard clock synchronization interface of the EEG amplifier, minimizing timing jitter and drift.”

(7) The TR is quite long and the voxel size is quite large in comparison to state-of-the-art EPI sequences. What was the rationale behind choosing a sequence with relatively low temporal and spatial resolution?

We acknowledge that our chosen TR and voxel size are relatively long and large compared to state-of-the-art EPI sequences. This decision was made to optimize the signal-to-noise ratio (SNR) and reduce susceptibility-related distortions, which are particularly critical in EEG-fMRI sleep studies where head motion and physiological noise can be substantial. A longer TR allowed us to sample whole-brain activity with sufficient coverage, while a larger voxel size helped enhance BOLD sensitivity and minimize partial volume effects in deep brain structures such as the thalamus and hippocampus, which are key regions of interest in our study. We appreciate your concern and hope this clarification provides sufficient rationale for our sequence parameters.

We have made this clearer in the revised manuscript. 

Methods, Page 20-21 Lines 398-408

“Then, the “sleep” session began after the participants were instructed to try and fall asleep. For the functional scans, whole-brain images were acquired using k-space and steady-state T2*-weighted gradient echo-planar imaging (EPI) sequence that is sensitive to the BOLD contrast. This measures local magnetic changes caused by changes in blood oxygenation that accompany neural activity (sequence specification: 33 slices in interleaved ascending order, TR = 2000 ms, TE = 30 ms, voxel size = 3.5 × 3.5 × 4.2 mm3, FA = 90°, matrix = 64 × 64, gap = 0.7 mm). A relatively long TR and larger voxel size were chosen to optimize SNR and reduce susceptibility-related distortions, which are critical in EEG-fMRI sleep studies where head motion and physiological noise can be substantial. The longer TR allowed whole-brain coverage with sufficient temporal resolution, while the larger voxel size helped enhance BOLD sensitivity and minimize partial volume effects in deep brain structures (e.g., the thalamus and hippocampus), which are key regions of interest in this study.”

(8) The anatomically defined ROIs are quite large. It should be elaborated on how this might reduce sensitivity to sleep rhythm-specific activity within sub-regions, especially for the thalamus, which has distinct nuclei involved in sleep functions.

We appreciate your insight regarding the use of anatomically defined ROIs and their potential limitations in detecting sleep rhythm-specific activity within sub-regions, particularly in the thalamus. Given the distinct functional roles of thalamic nuclei in sleep processes, we acknowledge that using a single, large thalamic ROI may reduce sensitivity to localized activity patterns. To address this, we will discuss this limitation in the revised manuscript, acknowledging that our approach prioritizes whole-structure effects but may not fully capture nucleus-specific contributions.

Discussion, Page 18 Lines 333-341

“Despite providing new insights, our study has several limitations. First, our scalp EEG did not directly capture hippocampal ripples, preventing us from conclusively demonstrating triple coupling. Second, the combination of EEG-fMRI and the lack of a memory task limit our ability to parse fine-grained BOLD responses at the DOWN- vs. UP-states of SOs and link observed activations to behavioral outcomes. Third, the use of large anatomical ROIs may mask subregional contributions of specific thalamic nuclei or hippocampal subfields. Finally, without a memory task, we cannot establish a direct behavioral link between sleep-rhythm-locked activation and memory consolidation. Future studies combining techniques such as ultra-high-field fMRI or iEEG with cognitive tasks may refine our understanding of subregional network dynamics and functional significance during sleep.”

(9) The study reports SO & spindle amplitudes & densities, as well as SO+spindle coupling, to be larger during N2/3 sleep compared to N1 and REM sleep, which is trivial but can be seen as a sanity check of the data. However, the amount of SOs and spindles reported for N1 and REM sleep is concerning, as per definition there should be hardly any (if SOs or spindles occur in N1 it becomes by definition N2, and the interval between spindles has to be considerably large in REM to still be scored as such). Thus, on the one hand, the report of these comparisons takes too much space in the main manuscript as it is trivial, but on the other hand, it raises concerns about the validity of the scoring.

We appreciate your concern regarding the reported presence of SOs and spindles in N1 and REM sleep and the potential implications. Our detection method for detecting SO, spindle, and coupling were originally designed only for N2&N3 sleep data based on the characteristics of the data itself, and this method is widely recognized and used in the sleep research (Hahn et al., 2020; Helfrich et al., 2019; Helfrich et al., 2018; Ngo, Fell, & Staresina, 2020; Schreiner et al., 2022; Schreiner et al., 2021; Staresina et al., 2015; Staresina et al., 2023). While, because the detection methods for SO and spindle are based on percentiles, this method will always detect a certain number of events when used for other stages (N1 and REM) sleep data, but the differences between these events and those detected in stage N23 remain unclear. We will acknowledge the reasons for these results in the Methods section and emphasize that they are used only for sanity checks.

Methods, Page 25 Lines 515-524

“We note that the above methods for detecting SOs, spindles, and their couplings were originally developed for N2 and N3 sleep data, based on the specific characteristics of these stages. These methods are widely recognized in sleep research (Hahn et al., 2020; Helfrich et al., 2019; Helfrich et al., 2018; Ngo, Fell, & Staresina, 2020; Schreiner et al., 2022; Schreiner et al., 2021; Staresina et al., 2015; Staresina et al., 2023). However, because this percentile-based detection approach will inherently identify a certain number of events if applied to other stages (e.g., N1 and REM), the nature of these events in those stages remains unclear compared to N2/N3. We nevertheless identified and reported the detailed descriptive statistics of these sleep rhythms in all sleep stages, under the same operational definitions, both for completeness and as a sanity check. Within the same subject, there should be more SOs, spindles, and their couplings in N2/N3 than in N1 or REM (see also Figure S2-S4, Table S1-S4).”

(10) Why was electrode F3 used to quantify the occurrence of SOs and spindles? Why not a midline frontal electrode like Fz (or a number of frontal electrodes for SOs) and Cz (or a number of centroparietal electrodes) for spindles to be closer to their maximum topography?

We appreciate your suggestion regarding electrode selection for SO and spindle quantification. Our choice of F3 was primarily based on previous studies (Massimini et al., 2004; Molle et al., 2011), where bilateral frontal electrodes are commonly used for detecting SOs and spindles. Additionally, we considered the impact of MRI-related noise and, after a comprehensive evaluation, determined that F3 provided an optimal balance between signal quality and artifact minimization. We also acknowledge that alternative electrode choices, such as Fz for SOs and Cz for spindles, could provide additional insights into their topographical distributions.

(11) Functional connectivity (hippocampus -> thalamus -> cortex (mPFC)) is reported to be increased during SO-spindle coupling and interpreted as evidence for coordination of hippocampo-neocortical communication likely by thalamic spindles. However, functional connectivity was only analysed during coupled SO+spindle events, not during isolated SOs or isolated spindles. Without the direct comparison of the connectivity patterns between these three events, it remains unclear whether this is specific for coupled SO+spindle events or rather associated with one or both of the other isolated events. The PPIs need to be conducted for those isolated events as well and compared statistically to the coupled events.

We appreciate your critical perspective on our functional connectivity analysis and the interpretation of hippocampus-thalamus-cortex (mPFC) interactions during SO-spindle coupling. We acknowledge that, in the current analysis, functional connectivity was only examined during coupled SO-spindle events, without direct comparison to isolated SOs or isolated spindles. To address this concern, we have conducted PPI analyses for all three ROIs(Hippocampus, Thalamus, mPFC) and all three event types (SO-spindle couplings, isolated SOs, and isolated spindles). Our results indicate that neither isolated SOs nor isolated Spindles yielded significant connectivity changes in all three ROIs, as all failed to survive multiple comparison corrections. This suggests that the observed connectivity increase is specific to SO-spindle coupling, rather than being independently driven by either SOs or spindles alone.

Results, Page 14 Lines 248-255

“Crucially, the interaction between FC and SO-spindle coupling revealed that only the functional connectivity of hippocampus -> thalamus (ROI analysis, t(106) = 1.86, p = 0.0328) and thalamus -> mPFC (ROI analysis, t(106) = 1.98, p = 0.0251) significantly increased during SO-spindle coupling, with no significant changes in all other pathways (Fig. 4e). We also conducted PPI analyses for the other two events (SOs and spindles), and neither yielded significant connectivity changes in the three ROIs, as all failed to survive whole-brain FWE correction at the cluster level (p < 0.05). Together, these findings suggest that the thalamus, likely via spindles, coordinates hippocampal-cortical communication selectively during SO-spindle coupling, but not isolated SOs or spindle events alone.”

(12) The limited temporal resolution of fMRI does indeed not allow for easily distinguishing between fMRI activation patterns related to SO-up- vs. SO-down-states. For this, one could try to extract the amplitudes of SO-up- and SO-down-states separately for each SO event and model them as two separate parametric modulators (with the risk of collinearity as they are likely correlated).

We appreciate your insightful comment regarding the challenge of distinguishing fMRI activation patterns related to SO-up vs. SO-down states due to the limited temporal resolution of fMRI. While our current analysis does not differentiate between these two phases, we acknowledge that separately modeling SO-up and SO-down states using parametric modulators could provide a more refined understanding of their distinct neural correlates. However, as you notes, this approach carries the risk of collinearity, and there is indeed a high correlation between the two amplitudes across all subjects in our results (r=0.98). Future studies could explore more on leveraging high-temporal-resolution techniques. While implementing this in the current study is beyond our scope, we will acknowledge this limitation in the Discussion section.

Discussion, Page 17 Lines 308-322

“An intriguing aspect of our findings is the reduced DMN activity during SOs when modeled at the SO trough (DOWN-state). This reduced DMN activity may reflect large-scale neural inhibition characteristic of the SO trough. The DMN is typically active during internally oriented cognition (e.g., self-referential processing or mind-wandering) and is suppressed during external stimuli processing (Yeshurun, Nguyen, & Hasson, 2021). It is unlikely, however, that this suppression of DMN during SO events is related to a shift from internal cognition to external responses given it is during deep sleep time. Instead, it could be driven by the inherent rhythmic pattern of SOs, which makes it difficult to separate UP- from DOWN-states (the two temporal regressors were highly correlated, and similar brain activation during SOs events was obtained if modelled at the SO peak instead, Fig. S5). Since the amplitude at the SO trough is consistently larger than that at the SO peak, the neural activation we detected may primarily capture the large-scale inhibition from DOWN-state. Interestingly, no such DMN reduction was found during SO-spindle coupling, implying that coupling may involve distinct neural dynamics that partially re-engage DMN-related processes, possibly reflecting memory-related reactivation. Future research using high-temporal-resolution techniques like iEEG could clarify these possibilities.”

Discussion, Page 18 Lines 333-341

“Despite providing new insights, our study has several limitations. First, our scalp EEG did not directly capture hippocampal ripples, preventing us from conclusively demonstrating triple coupling. Second, the combination of EEG-fMRI and the lack of a memory task limit our ability to parse fine-grained BOLD responses at the DOWN- vs. UP-states of SOs and link observed activations to behavioral outcomes. Third, the use of large anatomical ROIs may mask subregional contributions of specific thalamic nuclei or hippocampal subfields. Finally, without a memory task, we cannot establish a direct behavioral link between sleep-rhythm-locked activation and memory consolidation. Future studies combining techniques such as ultra-high-field fMRI or iEEG with cognitive tasks may refine our understanding of subregional network dynamics and functional significance during sleep.”

(13) L327: "It is likely that our findings of diminished DMN activity reflect brain activity during the SO DOWN-state, as this state consistently shows higher amplitude compared to the UP-state within subjects, which is why we modelled the SO trough as its onset in the fMRI analysis." This conclusion is not justified as the fact that SO down-states are larger in amplitude does not mean their impact on the BOLD response is larger.

We appreciate your concern regarding our interpretation of diminished DMN activity reflecting the SO down-state. We acknowledge that the current expression is somewhat misleading, and our interpretation of it is: it could be driven by the inherent rhythmic pattern of SOs, which makes it difficult to separate UP- from DOWN-states (the two temporal regressors were highly correlated, and similar brain activation during SOs events was obtained if modelled at the SO peak instead). Since the amplitude at the SO trough is consistently larger than that at the SO peak, the neural activation we detected may primarily capture the large-scale inhibition from DOWN-state. And we will make this clear in the Discussion section.

Discussion, Page 17 Lines 308-322

“An intriguing aspect of our findings is the reduced DMN activity during SOs when modeled at the SO trough (DOWN-state). This reduced DMN activity may reflect large-scale neural inhibition characteristic of the SO trough. The DMN is typically active during internally oriented cognition (e.g., self-referential processing or mind-wandering) and is suppressed during external stimuli processing (Yeshurun, Nguyen, & Hasson, 2021). It is unlikely, however, that this suppression of DMN during SO events is related to a shift from internal cognition to external responses given it is during deep sleep time. Instead, it could be driven by the inherent rhythmic pattern of SOs, which makes it difficult to separate UP- from DOWN-states (the two temporal regressors were highly correlated, and similar brain activation during SOs events was obtained if modelled at the SO peak instead, Fig. S5). Since the amplitude at the SO trough is consistently larger than that at the SO peak, the neural activation we detected may primarily capture the large-scale inhibition from DOWN-state. Interestingly, no such DMN reduction was found during SO-spindle coupling, implying that coupling may involve distinct neural dynamics that partially re-engage DMN-related processes, possibly reflecting memory-related reactivation. Future research using high-temporal-resolution techniques like iEEG could clarify these possibilities.”

(14) Line 77: "In the current study, while directly capturing hippocampal ripples with scalp EEG or fMRI is difficult, we expect to observe hippocampal activation in fMRI whenever SOs-spindles coupling is detected by EEG, if SOs- spindles-ripples triple coupling occurs during human NREM sleep". Not all SO-spindle events are associated with ripples (Staresina et al., 2015), but hippocampal activation may also be expected based on the occurrence of spindles alone (Bergmann et al., 2012).

We appreciate your clarification regarding the relationship between SO-spindle coupling and hippocampal ripples. We acknowledge that not all SO-spindle events are necessarily accompanied by ripples (Staresina et al., 2015). However, based on previous research, we found that hippocampal ripples are significantly more likely to occur during SO-spindle coupling events. This suggests that while ripple occurrence is not guaranteed, SO-spindle coupling creates a favorable network state for ripple generation and potential hippocampal activation. To ensure accuracy, we will revise the manuscript to delete this misleading sentence in the Introduction section and acknowledge in the Discussion that our results cannot conclusively directly observe the triple coupling of SO, spindle, and hippocampal ripples.

Discussion, Page 18 Lines 333-341

“Despite providing new insights, our study has several limitations. First, our scalp EEG did not directly capture hippocampal ripples, preventing us from conclusively demonstrating triple coupling. Second, the combination of EEG-fMRI and the lack of a memory task limit our ability to parse fine-grained BOLD responses at the DOWN- vs. UP-states of SOs and link observed activations to behavioral outcomes. Third, the use of large anatomical ROIs may mask subregional contributions of specific thalamic nuclei or hippocampal subfields. Finally, without a memory task, we cannot establish a direct behavioral link between sleep-rhythm-locked activation and memory consolidation. Future studies combining techniques such as ultra-high-field fMRI or iEEG with cognitive tasks may refine our understanding of subregional network dynamics and functional significance during sleep.”

Reviewer #2 (Public review):

In this study, Wang and colleagues aimed to explore brain-wide activation patterns associated with NREM sleep oscillations, including slow oscillations (SOs), spindles, and SO-spindle coupling events. Their findings reveal that SO-spindle events corresponded with increased activation in both the thalamus and hippocampus. Additionally, they observed that SO-spindle coupling was linked to heightened functional connectivity from the hippocampus to the thalamus, and from the thalamus to the medial prefrontal cortex-three key regions involved in memory consolidation and episodic memory processes.

This study's findings are timely and highly relevant to the field. The authors' extensive data collection, involving 107 participants sleeping in an fMRI while undergoing simultaneous EEG recording, deserves special recognition. If shared, this unique dataset could lead to further valuable insights. While the conclusions of the data seem overall well supported by the data, some aspects with regard to the detection of sleep oscillations need clarification.

The authors report that coupled SO-spindle events were most frequent during NREM sleep (2.46 [plus minus] 0.06 events/min), but they also observed a surprisingly high occurrence of these events during N1 and REM sleep (2.23 [plus minus] 0.09 and 2.32 [plus minus] 0.09 events/min, respectively), where SO-spindle coupling would not typically be expected. Combined with the relatively modest SO amplitudes reported (~25 µV, whereas >75 µV would be expected when using mastoids as reference electrodes), this raises the possibility that the parameters used for event detection may not have been conservative enough - or that sleep staging was inaccurately performed. This issue could present a significant challenge, as the fMRI findings are largely dependent on the reliability of these detected events.

Thank you very much for your thorough and encouraging review. We appreciate your recognition of the significance and relevance of our study and dataset, particularly in highlighting how simultaneous EEG-fMRI recordings can provide complementary insights into the temporal dynamics of neural oscillations and their associated spatial activation patterns during sleep. In the sections that follow, we address each of your comments in detail. We have revised the text and conducted additional analyses wherever possible to strengthen our argument, clarify our methodological choices. We believe these revisions improve the clarity and rigor of our work, and we thank you for helping us refine it.

We appreciate your insightful comments regarding the detection of sleep oscillations. Our methods for detecting SOs, spindles, and their couplings were originally developed for N2 and N3 sleep data, based on the specific characteristics of these stages. These methods are widely recognized in sleep research (Hahn et al., 2020; Helfrich et al., 2019; Helfrich et al., 2018; Ngo, Fell, & Staresina, 2020; Schreiner et al., 2022; Schreiner et al., 2021; Staresina et al., 2015; Staresina et al., 2023). However, because this percentile-based detection approach will inherently identify a certain number of events if applied to other stages (e.g., N1 and REM), the nature of these events in those stages remains unclear compared to N2/N3. We nevertheless identified and reported the detailed descriptive statistics of these sleep rhythms in all sleep stages, under the same operational definitions, both for completeness and as a sanity check. Within the same subject, there should be more SOs, spindles, and their couplings in N2/N3 than in N1 or REM. We will acknowledge the reasons for these results in the Methods section and emphasize that they are used only for sanity checks.

Regarding the reported SO amplitudes (~25 µV), during preprocessing, we applied the Signal Space Projection (SSP) method to more effectively remove MRI gradient artifacts and cardiac pulse noise. While this approach enhances data quality, it also reduces overall signal power, leading to systematically lower reported amplitudes. Despite this, our SO detection in NREM sleep (especially N2/N3) remain physiologically meaningful and are consistent with previous fMRI studies using similar artifact removal techniques. We appreciate your careful evaluation and valuable suggestions.

In addition, we will provide comprehensive tables in the supplementary materials, contains descriptive information about sleep-related characteristics (Table S1), as well as detailed information about sleep waves at each sleep stage for all 107 subjects(Table S2-S4), listing for each subject:(1)Different sleep stage duration; (2)Number of detected SOs; (3)Number of detected spindles; (4)Number of detected SO-spindle coupling events; (2)Density of detected SOs; (3)Density of detected spindles; (4)Density of detected SO-spindle coupling events.

Methods, Page 25 Lines 515-524

“We note that the above methods for detecting SOs, spindles, and their couplings were originally developed for N2 and N3 sleep data, based on the specific characteristics of these stages. These methods are widely recognized in sleep research (Hahn et al., 2020; Helfrich et al., 2019; Helfrich et al., 2018; Ngo, Fell, & Staresina, 2020; Schreiner et al., 2022; Schreiner et al., 2021; Staresina et al., 2015; Staresina et al., 2023). However, because this percentile-based detection approach will inherently identify a certain number of events if applied to other stages (e.g., N1 and REM), the nature of these events in those stages remains unclear compared to N2/N3. We nevertheless identified and reported the detailed descriptive statistics of these sleep rhythms in all sleep stages, under the same operational definitions, both for completeness and as a sanity check. Within the same subject, there should be more SOs, spindles, and their couplings in N2/N3 than in N1 or REM (see also Figure S2-S4, Table S1-S4).”

Supplementary Materials, Page 42-54, Table S1-S4

Reviewer #3 (Public review):

Summary:

Wang et al., examined the brain activity patterns during sleep, especially when locked to those canonical sleep rhythms such as SO, spindle, and their coupling. Analyzing data from a large sample, the authors found significant coupling between spindles and SOs, particularly during the upstate of the SO. Moreover, the authors examined the patterns of whole-brain activity locked to these sleep rhythms. To understand the functional significance of these brain activities, the authors further conducted open-ended cognitive state decoding and found a variety of cognitive processing may be involved during SO-spindle coupling and during other sleep events. The authors next investigated the functional connectivity analyses and found enhanced connectivity between the hippocampus, the thalamus, and the medial PFC. These results reinforced the theoretical model of sleep-dependent memory consolidation, such that SO-spindle coupling is conducive to systems-level memory reactivation and consolidation.

Strengths:

There are obvious strengths in this work, including the large sample size, state-of-the-art neuroimaging and neural oscillation analyses, and the richness of results.

Weaknesses:

Despite these strengths and the insights gained, there are weaknesses in the design, the analyses, and inferences.

Thank you for your detailed and thoughtful review of our manuscript. We are delighted that you recognize our advanced analysis methods and rich results of neuroimaging and neural oscillations as well as the large sample size data. In the following sections, we provide detailed responses to each of your comments. And we have revised the text and conducted additional analyses to strengthen our arguments and clarify our methodological choices. We believe these revisions enhance the clarity and rigor of our work, and we sincerely appreciate your thoughtful feedback in helping us refine the manuscript.

(1) A repeating statement in the manuscript is that brain activity could indicate memory reactivation and thus consolidation. This is indeed a highly relevant question that could be informed by the current data/results. However, an inherent weakness of the design is that there is no memory task before and after sleep. Thus, it is difficult (if not impossible) to make a strong argument linking SO/spindle/coupling-locked brain activity with memory reactivation or consolidation.

We appreciate your suggestion regarding the lack of a pre- and post-sleep memory task in our study design. We acknowledge that, in the absence of behavioral measures, it is hard to directly link SO-spindle coupling to memory consolidation in an outcome-driven manner. Our interpretation is instead based on the well-established role of these oscillations in memory processes, as demonstrated in previous studies. We sincerely appreciate this feedback and will adjust our Discussion accordingly to reflect a more precise interpretation of our findings.

Discussion, Page 18 Lines 333-341

“Despite providing new insights, our study has several limitations. First, our scalp EEG did not directly capture hippocampal ripples, preventing us from conclusively demonstrating triple coupling. Second, the combination of EEG-fMRI and the lack of a memory task limit our ability to parse fine-grained BOLD responses at the DOWN- vs. UP-states of SOs and link observed activations to behavioral outcomes. Third, the use of large anatomical ROIs may mask subregional contributions of specific thalamic nuclei or hippocampal subfields. Finally, without a memory task, we cannot establish a direct behavioral link between sleep-rhythm-locked activation and memory consolidation. Future studies combining techniques such as ultra-high-field fMRI or iEEG with cognitive tasks may refine our understanding of subregional network dynamics and functional significance during sleep.”

(2) Relatedly, to understand the functional implications of the sleep rhythm-locked brain activity, the authors employed the "open-ended cognitive state decoding" method. While this method is interesting, it is rather indirect given that there were no behavioral indices in the manuscript. Thus, discussions based on these analyses are speculative at best. Please either tone down the language or find additional evidence to support these claims.

Moreover, the results from this method are difficult to understand. Figure 3e showed that for all three types of sleep events (SO, spindle, SO-spindle), the same mental states (e.g., working memory, episodic memory, declarative memory) showed opposite directions of activation (left and right panels showed negative and positive activation, respectively). How to interpret these conflicting results? This ambiguity is also reflected by the term used: declarative memory and episodic memories are both indexed in the results. Yet these two processes can be largely overlapped. So which specific memory processes do these brain activity patterns reflect? The Discussion shall discuss these results and the limitations of this method.

We appreciate your critical assessment of the open-ended cognitive state decoding method and its interpretational challenges. Given the concerns about the indirectness of this approach, we decided to remove its related content and results from Figure 3 in the main text and include it in Supplementary Figure 7. 

Due to the complexity of memory-related processes, we acknowledge that distinguishing between episodic and declarative memory based solely on this approach is not straightforward. We will revise the Supplementary Materials to explicitly discuss these limitations and clarify that our findings do not isolate specific cognitive processes but rather suggest general associations with memory-related networks.

Discussion, Page 17-18 Lines 323-332

“To explore functional relevance, we employed an open-ended cognitive state decoding approach using meta-analytic data (NeuroSynth: Yarkoni et al. (2011)). Although this method usefully generates hypotheses about potential cognitive processes, particularly in the absence of a pre- and post-sleep memory task, it is inherently indirect. Many cognitive terms showed significant associations (16 of 50), such as “episodic memory,” “declarative memory,” and “working memory.” We focused on episodic/declarative memory given the known link with hippocampal reactivation (Diekelmann & Born, 2010; Staresina et al., 2015; Staresina et al., 2023). Nonetheless, these inferences regarding memory reactivation should be interpreted cautiously without direct behavioral measures. Future research incorporating explicit tasks before and after sleep would more rigorously validate these potenial functional claims.”

(3) The coupling strength is somehow inconsistent with prior results (Hahn et al., 2020, eLife, Helfrich et al., 2018, Neuron). Specifically, Helfrich et al. showed that among young adults, the spindle is coupled to the peak of the SO. Here, the authors reported that the spindles were coupled to down-to-up transitions of SO and before the SO peak. It is possible that participants' age may influence the coupling (see Helfrich et al., 2018). Please discuss the findings in the context of previous research on SO-spindle coupling.

We appreciate your concern regarding the temporal characteristics of SO-spindle coupling. We acknowledge that the SO-spindle coupling phase results in our study are not identical to those reported by Hahn et al. (2020); Helfrich et al. (2018). However, these differences may arise due to slight variations in event detection parameters, which can influence the precise phase estimation of coupling. Notably, Hahn et al. (2020) also reported slight discrepancies in their group-level coupling phase results, highlighting that methodological differences can contribute to variability across studies. Furthermore, our findings are consistent with those of Schreiner et al. (2021), further supporting the robustness of our observations.  

That said, we acknowledge that our original description of SO-spindle coupling as occurring at the "transition from the lower state to the upper state" was not entirely precise. The -π/2 phase represents the true transition point, while our observed coupling phase is actually closer to the SO peak rather than strictly at the transition. We will revise this statement in the manuscript to ensure clarity and accuracy in describing the coupling phase.  

Discussion, Page 16 Lines 283-291

“Our data provide insights into the neurobiological underpinnings of these sleep rhythms. SOs, originating mainly in neocortical areas such as the mPFC, alternate between DOWN- and UP-states. The thalamus generates sleep spindles, which in turn couple with SOs. Our finding that spindle peaks consistently occurred slightly before the UP-state peak of SOs (in 83 out of 107 participants), concurs with prior studies, including Schreiner et al. (2021). Yet it differs from some results suggesting spindles might peak right at the SO UP-state (Hahn et al., 2020; Helfrich et al., 2018). Such discrepancies could arise from differences in detection algorithms, participant age (Helfrich et al., 2018), or subtle variations in cortical-thalamic timing. Nonetheless, these results underscore the importance of coordinated SO-spindle interplay in supporting sleep-dependent processes.”

(4) The discussion is rather superficial with only two pages, without delving into many important arguments regarding the possible functional significance of these results. For example, the author wrote, "This internal processing contrasts with the brain patterns associated with external tasks, such as working memory." Without any references to working memory, and without delineating why WM is considered as an external task even working memory operations can be internal. Similarly, for the interesting results on SO and reduced DMN activity, the authors wrote "The DMN is typically active during wakeful rest and is associated with self-referential processes like mind-wandering, daydreaming, and task representation (Yeshurun, Nguyen, & Hasson, 2021). Its reduced activity during SOs may signal a shift towards endogenous processes such as memory consolidation." This argument is flawed. DMN is active during self-referential processing and mind-wandering, i.e., when the brain shifts from external stimuli processing to internal mental processing. During sleep, endogenous memory reactivation and consolidation are also part of the internal mental processing given the lack of external environmental stimulation. So why during SO or during memory consolidation, the DMN activity would be reduced? Were there differences in DMN activity between SO and SO-spindle coupling events?

We appreciate your concerns regarding the brevity of the discussion and the need for clearer theoretical arguments. We will expand this section to provide more in-depth interpretations of our findings in the context of prior literature. Regarding working memory (WM), we acknowledge that our phrasing was ambiguous. We will modify this statement in the Discussion section.

For the SO-related reduction in DMN activity, we recognize the need for a more precise explanation. This reduced DMN activity may reflect large-scale neural inhibition characteristic of the SO trough. The DMN is typically active during internally oriented cognition (e.g., self-referential processing or mind-wandering) and is suppressed during external stimuli processing (Yeshurun, Nguyen, & Hasson, 2021). It is unlikely, however, that this suppression of DMN during SO events is related to a shift from internal cognition to external responses given it is during deep sleep time. Instead, it could be driven by the inherent rhythmic pattern of SOs, which makes it difficult to separate UP- from DOWN-states (the two temporal regressors were highly correlated, and similar brain activation during SOs events was obtained if modelled at the SO peak instead). Since the amplitude at the SO trough is consistently larger than that at the SO peak, the neural activation we detected may primarily capture the large-scale inhibition from DOWN-state.

To address your final question, we have conducted the additional post hoc comparison of DMN activity between isolated SOs and SO-spindle coupling events. Our results indicate that

DMN activation during SOs was significantly lower than during SO-spindle coupling (t(106) = -4.17, p < 1e-4). This suggests that SO-spindle coupling may involve distinct neural dynamics that partially re-engage DMN-related processes, possibly reflecting memory-related reactivation. We appreciate your constructive feedback and will integrate these expanded analyses and discussions into our revised manuscript.

Results, Page 11 Lines 199-208

“Spindles were correlated with positive activation in the thalamus (ROI analysis, t(106) = 15.39, p < 1e-4), the anterior cingulate cortex (ACC), and the putamen, alongside deactivation in the DMN (Fig. 3c). Notably, SO-spindle coupling was linked to significant activation in both the thalamus (ROI analysis, t(106) \= 3.38, p = 0.0005) and the hippocampus (ROI analysis, t(106) \= 2.50, p = 0.0070, Fig. 3d). However, no decrease in DMN activity was found during SO-spindle coupling, and DMN activity during SO was significantly lower than during coupling (ROI analysis, t(106) \= -4.17, p < 1e-4). For more detailed activation patterns, see Table S5-S7. We also varied the threshold used to detect SO events to assess its effect on hippocampal activation during SO-spindle coupling and observed that hippocampal activation remained significant when the percentile thresholds for SO detection ranged between 71% and 80% (see Fig. S6).”

Discussion, Page 17-18 Lines 308-332

“An intriguing aspect of our findings is the reduced DMN activity during SOs when modeled at the SO trough (DOWN-state). This reduced DMN activity may reflect large-scale neural inhibition characteristic of the SO trough. The DMN is typically active during internally oriented cognition (e.g., self-referential processing or mind-wandering) and is suppressed during external stimuli processing (Yeshurun, Nguyen, & Hasson, 2021). It is unlikely, however, that this suppression of DMN during SO events is related to a shift from internal cognition to external responses given it is during deep sleep time. Instead, it could be driven by the inherent rhythmic pattern of SOs, which makes it difficult to separate UP- from DOWN-states (the two temporal regressors were highly correlated, and similar brain activation during SOs events was obtained if modelled at the SO peak instead, Fig. S5). Since the amplitude at the SO trough is consistently larger than that at the SO peak, the neural activation we detected may primarily capture the large-scale inhibition from DOWN-state. Interestingly, no such DMN reduction was found during SO-spindle coupling, implying that coupling may involve distinct neural dynamics that partially re-engage DMN-related processes, possibly reflecting memory-related reactivation. Future research using high-temporal-resolution techniques like iEEG could clarify these possibilities.

To explore functional relevance, we employed an open-ended cognitive state decoding approach using meta-analytic data (NeuroSynth: Yarkoni et al. (2011)). Although this method usefully generates hypotheses about potential cognitive processes, particularly in the absence of a pre- and post-sleep memory task, it is inherently indirect. Many cognitive terms showed significant associations (16 of 50), such as “episodic memory,” “declarative memory,” and “working memory.” We focused on episodic/declarative memory given the known link with hippocampal reactivation (Diekelmann & Born, 2010; Staresina et al., 2015; Staresina et al., 2023). Nonetheless, these inferences regarding memory reactivation should be interpreted cautiously without direct behavioral measures. Future research incorporating explicit tasks before and after sleep would more rigorously validate these potential functional claims.”

Recommendations for the authors:

Reviewing Editor Comment:

The reviewers think that you are working on a relevant and important topic. They are praising the large sample size used in the study. The reviewers are not all in line regarding the overall significance of the findings, but they all agree the paper would strongly benefit from some extra work, as all reviewers raise various critical points that need serious consideration.

We appreciate your recognition of the relevance and importance of our study, as well as your acknowledgment of the large sample size as a strength of our work. We understand that there are differing perspectives regarding the overall significance of our findings, and we value the constructive critiques provided. We are committed to addressing the key concerns raised by all reviewers, including refining our analyses, clarifying our interpretations, and incorporating additional discussions to strengthen the manuscript. Below, we address your specific recommendations and provide responses to each point you raised to ensure our methods and results are as transparent and comprehensible as possible. We believe that these revisions will significantly enhance the rigor and impact of our study, and we sincerely appreciate your thoughtful feedback in helping us improve our work.

Reviewer #1 (Recommendations for the authors):

(1) The phrase "overnight sleep" suggests an entire night, while these were rather "nocturnal naps". Please rephrase.

Response: Thank you for pointing this out. We have revised the phrasing in our manuscript to "nocturnal naps" instead of "overnight sleep" to more accurately reflect the duration of the sleep recordings.

(2) Sleep staging results (macroscopic sleep architecture) should be provided in more detail (at least min and % of the different sleep stages, sleep onset latency, total sleep duration, total recording duration), at least mean/SD/range.

Thank you for this suggestion. We will provide comprehensive tables in the supplementary materials, contains descriptive information about sleep-related characteristics. This information will help provide a clearer overview of the macroscopic sleep architecture in our dataset.

Reviewer #2 (Recommendations for the authors):

In order to allow for a better estimation of the reliability of the detected sleep events, please:

(1) Provide densities and absolute numbers of all detected SOs and spindles (N1, NREM, and REM sleep).

Thank you for pointing this out. We will provide comprehensive tables in the supplementary materials, contains detailed information about sleep waves at each sleep stage for all 107 subjects (Table S2-S4), listing for each subject:1) Different sleep stage duration; 2) Number of detected SOs; 3) Number of detected spindles; 4) Number of detected SO-spindle coupling events; 5) Density of detected SOs; 6) Density of detected spindles; 7) Density of detected SO-spindle coupling events.

Supplementary Materials, Page 43-54, Table S2-S4

(2) Show ERPs for all detected SOs and spindles (per sleep stage).

Thank you for the suggestion. We will provide ERPs for all detected SOs and spindles, separated by sleep stage (N1, N2&N3, and REM) in supplementary Fig. S2-S4. These ERP waveforms will help illustrate the characteristic temporal profiles of SOs and spindles across different sleep stages.

Methods, Page 25, Line 525-532

“Event-related potentials (ERP) analysis. After completing the detection of each sleep rhythm event, we performed ERP analyses for SOs, spindles, and coupling events in different sleep stages. Specifically, for SO events, we took the trough of the DOWN-state of each SO as the zero-time point, then extracted data in a [-2 s to 2 s] window from the broadband (0.1–30 Hz) EEG and used [-2 s to -0.5 s] for baseline correction; the results were then averaged across 107 subjects (see Fig. S2a). For spindle events, we used the peak of each spindle as the zero-time point and applied the same data extraction window and baseline correction before averaging across 107 subjects (see Fig. S2b). Finally, for SO-spindle coupling events, we followed the same procedure used for SO events (see Fig. 2a, Figs. S3–S4).”

(3) Provide detailed info concerning sleep characteristics (time spent in each sleep stage etc.).

Thank you for this suggestion. Same as the response above, we will provide comprehensive tables in the supplementary materials, contains descriptive information about sleep-related characteristics.

Supplementary Materials, Page 42, Table S1 (same as above)

(4) What would happen if more stringent parameters were used for event detection? Would the authors still observe a significant number of SO spindles during N1 and REM? Would this affect the fMRI-related results?

Thank you for this suggestion. Our methods for detecting SOs, spindles, and their couplings were originally developed for N2 and N3 sleep data, based on the specific characteristics of these stages. These methods are widely recognized in sleep research (Hahn et al., 2020; Helfrich et al., 2019; Helfrich et al., 2018; Ngo, Fell, & Staresina, 2020; Schreiner et al., 2022; Schreiner et al., 2021; Staresina et al., 2015; Staresina et al., 2023). However, because this percentile-based detection approach will inherently identify a certain number of events if applied to other stages (e.g., N1 and REM), the nature of these events in those stages remains unclear compared to N2/N3. We nevertheless identified and reported the detailed descriptive statistics of these sleep rhythms in all sleep stages, under the same operational definitions, both for completeness and as a sanity check. Within the same subject, there should be more SOs, spindles, and their couplings in N2/N3 than in N1 or REM (see also Figure S2-S4, Table S1-S4).

Furthermore, in order to explore the impact of this on our fMRI results, we conducted an additional sensitivity analysis by applying different detection parameters for SOs. Specifically, we adjusted amplitude percentile thresholds for SO detection (the parameter that has the greatest impact on the results). We used the hippocampal activation value during N2&N3 stage SO-spindle coupling as an anchor value and found that when the parameters gradually became stricter, the results were similar to or even better than the current results. However, when we continued to increase the threshold, the results began to gradually decrease until the threshold was increased to 80%, and the results were no longer significant. This indicates that our results are robust within a specific range of parameters, but as the threshold increases, the number of trials decreases, ultimately weakening the statistical power of the fMRI analysis.

Thank you again for your suggestions on sleep rhythm event detection. We will add the results in Supplementary and revise our manuscript accordingly.

Results, Page 11, Line 199-208

“Spindles were correlated with positive activation in the thalamus (ROI analysis, t(106) = 15.39, p < 1e-4), the anterior cingulate cortex (ACC), and the putamen, alongside deactivation in the DMN (Fig. 3c). Notably, SO-spindle coupling was linked to significant activation in both the thalamus (ROI analysis, t(106) \= 3.38, p = 0.0005) and the hippocampus (ROI analysis, t(106) \= 2.50, p = 0.0070, Fig. 3d). However, no decrease in DMN activity was found during SO-spindle coupling, and DMN activity during SO was significantly lower than during coupling (ROI analysis, t(106) \= -4.17, p < 1e-4). For more detailed activation patterns, see Table S5-S7. We also varied the threshold used to detect SO events to assess its effect on hippocampal activation during SO-spindle coupling and observed that hippocampal activation remained significant when the percentile thresholds for SO detection ranged between 71% and 80% (see Fig. S6).”

Finally, we sincerely thank all again for your thoughtful and constructive feedback. Your insights have been invaluable in refining our analyses, strengthening our interpretations, and improving the clarity and rigor of our manuscript. We appreciate the time and effort you have dedicated to reviewing our work, and we are grateful for the opportunity to enhance our study based on your recommendations.  

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It would be incredibly difficult to be open to even listening to your friends vent about their day if you can't even calm down the storm in your head to hear your own thoughts ! A great example of why it's so important to take care of yourself so you have the capacity to be there for your community.

Author response:

We thank the reviewers for the valuable and constructive reviews. Thanks to these, we believe the article will be considerably improved. We have organized our response to address points that are relevant to both reviewers first, after which we address the unique concerns of each individual reviewer separately. We briefly paraphrase each concern and provide comments for clarification, outlining the precise changes that we will make to the text.

Common Concerns (Reviewer 1 & Reviewer 2):

Can you clarify how NREM and REM sleep relate to the oneirogen hypothesis?

Within the submission draft we tried to stay agnostic as to whether mechanistically similar replay events occur during NREM or REM sleep; however, upon a more thorough literature review, we think that there is moderately greater evidence in favor of Wake-Sleep-type replay occurring during REM sleep which is related to classical psychedelic drug mechanisms of action.

First, we should clarify that replay has been observed during both REM and NREM sleep, and dreams have been documented during both sleep stages, though the characteristics of dreams differ across stages, with NREM dreams being more closely tied to recent episodic experience and REM dreams being more bizarre/hallucinatory (see Stickgold et al., 2001 for a review). Replay during sleep has been studied most thoroughly during NREM sharp-wave ripple events, in which significant cortical-hippocampal coupling has been observed (Ji & Wilson, 2007). However, it is critical to note that the quantification methods used to identify replay events in the hippocampal literature usually focus on identifying what we term ‘episodic replay,’ which involves a near-identical recapitulation of neural trajectories that were recently experienced during waking experimental recordings (Tingley & Peyrach, 2020). In contrast, our model focuses on ‘generative replay,’ where one expects only a statistically similar reproduction of neural activity, without any particular bias towards recent or experimentally controlled experience. This latter form of replay may look closer to the ‘reactivation’ observed in cortex by many studies (e.g. Nguyen et al., 2024), where correlation structures of neural activity similar to those observed during stimulus-driven experience are recapitulated. Under experimental conditions in which an animal is experiencing highly stereotyped activity repeatedly, over extended periods of time, these two forms of replay may be difficult to dissociate.

Interestingly, though NREM replay has been shown to couple hippocampal and cortical activity, a similar study in waking animals administered psychedelics found hippocampal replay without any obvious coupling to cortical activity (Domenico et al., 2021). This could be because the coupling was not strong enough to produce full trajectories in the cortex (psychedelic administration did not increase ‘alpha’ enough), and that a causal manipulation of apical/basal influence in the cortex may be necessary to observe the increased coupling. Alternatively, as Reviewer 1 noted, it may be that psychedelics induce a form of hippocampus-decoupled replay, as one would expect from the REM stage of a recently proposed complementary learning systems model (Singh et al., 2022). 

Evidence in favor of a similarity between the mechanism of action of classical psychedelics and the mechanism of action of memory consolidation/learning during REM sleep is actually quite strong. In particular, studies have shown that REM sleep increases the activity of soma-targeting parvalbumin (PV) interneurons and decreases the activity of apical dendrite-targeting somatostatin (SOM) interneurons (Niethard et al., 2021), that this shift in balance is controlled by higher-order thalamic nuclei, and that this shift in balance is critical for synaptic consolidation of both monocular deprivation effects in early visual cortex (Zhou et al., 2020) and for the consolidation of auditory fear conditioning in the dorsal prefrontal cortex (Aime et al., 2022). These last studies were not discussed in the present manuscript–we will add them, in addition to a more nuanced description of the evidence connecting our model to NREM and REM replay.

Can you explain how synaptic plasticity induced by psychedelics within your model relates to learning at a behavioral level?

While the Wake-Sleep algorithm is a useful model for unsupervised statistical learning, it is not a model of reward or fear-based conditioning, which likely occur via different mechanisms in the brain (e.g. dopamine-dependent reinforcement learning or serotonin-dependent emotional learning). The Wake-Sleep algorithm is a ‘normative plasticity algorithm,’ that connects synaptic plasticity to the formation of structured neural representations, but it is not the case that all synaptic plasticity induced by psychedelic administration within our model should induce beneficial learning effects. According to the Wake-Sleep algorithm, plasticity at apical synapses is enhanced during the Wake phase, and plasticity at basal synapses is enhanced during the Sleep phase; under the oneirogen hypothesis, hallucinatory conditions (increased ‘alpha’) cause an increase in plasticity at both apical and basal sites. Because neural activity is in a fundamentally aberrant state when ‘alpha’ is increased, there are no theoretical guarantees that plasticity will improve performance on any objective: psychedelic-induced plasticity within our model could perhaps better be thought of as ‘noise’ that may have a positive or negative effect depending on the context.

In particular, such ‘noise’ may be beneficial for individuals or networks whose synapses have become locked in a suboptimal local minimum. The addition of large amounts of random plasticity could allow a system to extricate itself from such local minima over subsequent learning (or with careful selection of stimuli during psychedelic experience), similar to simulated annealing optimization approaches. If our model were fully validated, this view of psychedelic-induced plasticity as ‘noise’ could have relevance for efforts to alleviate the adverse effects of PTSD, early life trauma, or sensory deprivation; it may also provide a cautionary note against repeated use of psychedelic drugs within a short time frame, as the plasticity changes induced by psychedelic administration under our model are not guaranteed to be good or useful in-and-of themselves without subsequent re-learning and compensation.

We should also note that we have deliberately avoided connecting the oneirogen hypothesis model to fear extinction experimental results that have been observed through recordings of the hippocampus or the amygdala (Bombardi & Giovanni, 2013; Jiang et al., 2009; Kelly et al., 2024; Tiwari et al., 2024). Both regions receive extensive innervation directly from serotonergic synapses originating in the dorsal raphe nucleus, which have been shown to play an important role in emotional learning (Lesch & Waider, 2012); because classical psychedelics may play a more direct role in modulating this serotonergic innervation, it is possible that fear conditioning results (in addition to the anxiolytic effects of psychedelics) cannot be attributed to a shift in balance between apical and basal synapses induced by psychedelic administration. We will provide a more detailed review of these results in the text, as well as more clarity regarding their relation to our model.

Reviewer 1 Concerns:

Is it reasonable to assign a scalar parameter ‘alpha’ to the effects of classical psychedelics? And is your proposed mechanism of action unique to classical psychedelics? E.g. Could this idea also apply to kappa opioid agonists, ketamine, or the neural mechanisms of hallucination disorders?

We will clarify that within our model ‘alpha’ is a parameter that reflects the balance between apical and basal synapses in determining the activity of neurons in the network. For the sake of simplicity we used a single ‘alpha’ parameter, but realistically, each neuron would have its own ‘alpha’ parameter, and different layers or individual neurons could be affected differentially by the administration of any particular drug; therefore, our scalar ‘alpha’ value can be thought of as a mean parameter for all neurons, disregarding heterogeneity across individual neurons.

There are many different mechanisms that could theoretically affect this ‘alpha’ parameter, including: 5-HT2a receptor agonism, kappa opioid receptor binding, ketamine administration, or possibly the effects of genetic mutations underlying the pathophysiology of complex developmental hallucination disorders. We focused exclusively on 5-HT2a receptor agonism for this study because the mechanism is comparatively simple and extensively characterized, but similar mechanisms may well be responsible for the hallucinatory symptoms of a variety of drugs and disorders.

Can you clarify the role of 5-HT2a receptor expression on interneurons within your model?

While we mostly focused on the effects of 5-HT2a receptors on the apical dendrites of pyramidal neurons, these receptors are also expressed on soma-targeting parvalbumin (PV) interneurons. This expression on PV interneurons is consistent with our proposed psychedelic mechanism of action, because it could lead to a coordinated decrease in the influence of somatic and proximal dendritic inputs while increasing the influence of apical dendritic inputs. We will elaborate on this point, and will move the discussion earlier in the text.

Discussions of indigenous use of psychedelics over millenia may amount to over-romanticization.

We will take great care to conduct a more thorough literature review to reevaluate our statement regarding indigenous psychedelic use (including the citations you suggested), and will either provide a more careful statement or remove this discussion from our introduction entirely, as it has little bearing on the rest of the text. The Ethics Statement will also be modified accordingly.

You isolate the 5-HT2a agonism as the mechanism of action underlying ‘alpha’ in your model, but there exist 5-HT2a agonists that do not have hallucinatory effects (e.g. lisuride). How do you explain this?

Lisuride has much-reduced hallucinatory effects compared to other psychedelic drugs at clinical doses (though it does indeed induce hallucinations at high doses; Marona-Lewicka et al., 2002), and we should note that serotonin (5-HT) itself is pervasive in the cortex without inducing hallucinatory effects during natural function. Similarly, MDMA is a partial agonist for 5-HT2a receptors, but it has much-reduced perceptual hallucination effects relative to classical psychedelics (Green et al., 2003) in addition to many other effects not induced by classical psychedelics.

Therefore, while we argue that 5-HT2a agonism induces an increase in influence of apical dendritic compartments and a decrease in influence of basal/somatic compartments, and that this change induces hallucinations, we also note that there are many other factors that control whether or not hallucinations are ultimately produced, so that not all 5-HT2a agonists are hallucinogenic. We will discuss two such factors in our revision: 5-HT receptor binding affinity and cellular membrane permeability.

Importantly, many 5-HT2a receptor agonists are also 5-HT1a receptor agonists (e.g. serotonin itself and lisuride), while MDMA has also been shown to increase serotonin, norepinephrine, and dopamine release (Green et al., 2003). While 5-HT2a receptor agonism has been shown to reduce sensory stimulus responses (Michaiel et al., 2019), 5-HT1a receptor agonism inhibits spontaneous cortical activity (Azimi et al., 2020); thus one might expect the net effect of administering serotonin or a nonselective 5-HT receptor agonist to be widespread inhibition of a circuit, as has been observed in visual cortex (Azimi et al., 2020). Therefore, selective 5-HT2a agonism is critical for the induction of hallucinations according to our model, though any intervention that jointly excites pyramidal neurons’ apical dendrites and inhibits their basal/somatic compartments across a broad enough area of cortex would be predicted to have a similar effect. Lisuride has a much higher binding affinity for 5-HT1a receptors than, for instance, LSD (Marona-Lewicka et al., 2002).

Secondly, it has recently been shown that both the head-twitch effect (a coarse behavioral readout of hallucinations in animals) and the plasticity effects of psychedelics are abolished when administering 5-HT2a agonists that are impermeable to the cellular membrane because of high polarity, and that these effects can be rescued by temporarily rendering the cellular membrane permeable (Vargas et al., 2023). This suggests that the critical hallucinatory effects of psychedelics (apical excitation according to our model) may be mediated by intracellular 5-HT2a receptors. Notably, serotonin itself is not membrane permeable in the cortex.

Therefore, either of these two properties could play a role in whether a given 5-HT2a agonist induces hallucinatory effects. We will provide a considerably extended discussion of these nuances in our revision.

Your model proposes that an increase in top-down influence on neural activity underlies the hallucinatory effects of psychedelics. How do you explain experimental results that show increases in bottom-up functional connectivity (either from early sensory areas or the thalamus)?

Firstly, we should note that our proposed increase in top-down influence is a causal, biophysical property, not necessarily a statistical/correlative one. As such, we will stress that the best way to test our model is via direct intervention in cortical microcircuitry, as opposed to correlative approaches taken by most fMRI studies, which have shown mixed results with regard to this particular question. Correlative approaches can be misleading due to dense recurrent coupling in the system, and due to the coarse temporal and spatial resolution provided by noninvasive recording technologies (changes in statistical/functional connectivity do not necessarily correspond to changes in causal/mechanistic connectivity, i.e. correlation does not imply causation).

There are two experimental results that appear to contradict our hypothesis that deserve special consideration in our revision. The first shows an increase in directional thalamic influence on the distributed cortical networks after psychedelic administration (Preller et al., 2018). To explain this, we note that this study does not distinguish between lower-order sensory thalamic nuclei (e.g. the lateral and medial geniculate nuclei receiving visual and auditory stimuli respectively) and the higher-order thalamic nuclei that participate in thalamocortical connectivity loops (Whyte et al., 2024). Subsequent more fine-grained studies have noted an increase in influence of higher order thalamic nuclei on the cortex (Pizzi et al., 2023; Gaddis et al., 2022), and in fact extensive causal intervention research has shown that classical psychedelics (and 5-HT2a agonism) decrease the influence of incoming sensory stimuli on the activity of early sensory cortical areas, indicating decoupling from the sensory thalamus (Evarts et al., 1955; Azimi et al., 2020; Michaiel et al. 2019). The increased influence of higher-order thalamic nuclei is consistent with both the cortico-striatal-thalamo-cortical (CTSC) model of psychedelic action as well as the oneirogen hypothesis, since higher-order thalamic inputs modulate the apical dendrites of pyramidal neurons in cortex (Whyte et al., 2024).

The second experimental result notes that DMT induces traveling waves during resting state activity that propagate from early visual cortex to deeper cortical layers (Alamia et al., 2020). There are several possibilities that could explain this phenomenon: 1) it could be due to the aforementioned difficulties associated with directed functional connectivity analyses, 2) it could be due to a possible high binding affinity for DMT in the visual cortex relative to other brain areas, or 3) it could be due to increases in apical influence on activity caused by local recurrent connectivity within the visual cortex which, in the absence of sensory input, could lead to propagation of neural activity from the visual cortex to the rest of the brain. This last possibility is closest to the model proposed by (Ermentrout & Cowan, 1979), and which we believe would be best explained within our framework by a topographically connected recurrent network architecture trained on video data; a potentially fruitful direction for future research.

Shouldn’t the hallucinations generated by your model look more ‘psychedelic,’ like those produced by the DeepDream algorithm?

We believe that the differences in hallucination visualization quality between our algorithm and DeepDream are mostly due to differences in the scale and power of the models used across these two studies. We are confident that with more resources (and potentially theoretical innovations to improve the Wake-Sleep algorithm’s performance) the produced hallucination visualizations could become more realistic, but we believe this falls outside the scope of the present study.

We note that more powerful generative models trained with backpropagation are able to produce surreal images of comparable quality (Rezende et al., 2014; Goodfellow et al., 2020; Vahdat & Kautz, 2020), though these have not yet been used as a model of psychedelic hallucinations. However, the DeepDream model operates on top of large pretrained image processing models, and does not provide a biologically mechanistic/testable interpretation of its hallucination effects. When training smaller models with a local synaptic plasticity rule (as opposed to backpropagation), the hallucination effects are less visually striking due to the reduced quality of our trained generative model, though they are still strongly tied to the statistics of sensory inputs, as quantified by our correlation similarity metric (Fig. 5b). We will provide a more detailed explanation of this phenomenon when we discuss our model limitations in our revised manuscript.

Your model assumes domination by entirely bottom-up activity during the ‘wake’ phase, and domination entirely by top-down activity during ‘sleep,’ despite experimental evidence indicating that a mixture of top-down and bottom-up inputs influence neural activity during both stages in the brain. How do you explain this?

Our use of the Wake-Sleep algorithm, in which top-down inputs (Sleep) or bottom-up inputs (Wake) dominate network activity is an over-simplification made within our model for computational and theoretical reasons. Models that receive a mixture of top-down and bottom-up inputs during ‘Wake’ activity do exist (in particular the closely related Boltzmann machine (Ackley et al., 1985)), but these models are considerably more computationally costly to train due to a need to run extensive recurrent network relaxation dynamics for each input stimulus. Further, these models do not generalize as cleanly to processing temporal inputs. For this reason, we focused on the Wake-Sleep algorithm, at the cost of some biological realism, though we note that our model should certainly be extended to support mixed apical-basal waking regimes. We will make sure to discuss this in our ‘Model Limitations’ section.

Your model proposes that 5-HT2a agonism enhances glutamatergic transmission, but this is not true in the hippocampus, which shows decreases in glutamate after psychedelic administration.

We should note that our model suggests only compartment specific increases in glutamatergic transmission; as such, our model does not predict any particular directionality for measures of glutamatergic transmission that includes signaling at both apical and basal compartments in aggregate, as was measured in the provided study (Mason et al., 2020).

You claim that your model is consistent with the Entropic Brain theory, but you report increases in variance, not entropy. In fact, it has been shown that variance decreases while entropy increases under psychedelic administration. How do you explain this discrepancy?

Unfortunately, ‘entropy’ and ‘variance’ are heavily overloaded terms in the noninvasive imaging literature, and the particularities of the method employed can exert a strong influence on the reported effects. The reduction in variance reported by (Carhart-Harris et al., 2016) is a very particular measure: they are reporting the variance of resting state synchronous activity, averaged across a functional subnetwork that spans many voxels; as such, the reduction in variance in this case is a reduction in broad, synchronous activity. We do not have any resting state synchronous activity in our network due to the simplified nature of our model (particularly an absence of recurrent temporal dynamics), so we see no reduction in variance in our model due to these effects.

Other studies estimate ‘entropy’ or network state disorder via three different methods that we have been able to identify. 1) (Carhart-Harris et al., 2014) uses a different measure of variance: in this case, they subtract out synchronous activity within functional subnetworks, and calculate variability across units in the network. This measure reports increases in variance (Fig. 6), and is the closest measure to the one we employ in this study. 2) (Lebedev et al., 2016) uses sample entropy, which is a measure of temporal sequence predictability. It is specifically designed to disregard highly predictable signals, and so one might imagine that it is a measure that is robust to shared synchronous activity (e.g. resting state oscillations). 3) (Mediano et al., 2024) uses Lempel-Ziv complexity, which is, similar to sample entropy, a measure of sequence diversity; in this case the signal is binarized before calculation, which makes this method considerably different from ours. All three of the preceding methods report increases in sequence diversity, in agreement with our quantification method. Our strongest explanation for why the variance calculation in (Carhart-Harris et al., 2016) produces a variance reduction is therefore due to a reduction in low-rank synchronous activity in subnetworks during resting state.

As for whether the entropy increase is meaningful: we share Reviewer 1’s concern that increases in entropy could simply be due to a higher degree of cognitive engagement during resting state recordings, due to the presence of sensory hallucinations or due to an inability to fall asleep. This could explain why entropy increases are much more minimal relative to non-hallucinating conditions during audiovisual task performance (Siegel et al., 2024; Mediano et al., 2024). However, we can say that our model is consistent with the Entropic Brain Theory without including any form of ‘cognitive processing’: we observe increases in variability during resting state in our model, but we observe highly similar distributions of activity when averaging over a wide variety of sensory stimulus presentations (Fig. 5b-c). This is because variability in our model is not due to unstructured noise: it corresponds to an exploration of network states that would ordinarily be visited by some stimulus. Therefore, when averaging across a wide variety of stimuli, the distribution of network states under hallucinating or non-hallucinating conditions should be highly similar.

One final point of clarification: here we are distinguishing Entropic Brain Theory from the REBUS model–the oneirogen hypothesis is consistent with the increase in entropy observed experimentally, but in our model this entropy increase is not due to increased influence of bottom-up inputs (it is due instead to an increase in top-down influence). Therefore, one could view the oneirogen hypothesis as consistent with EBT, but inconsistent with REBUS.

You relate your plasticity rule to behavioral-timescale plasticity (BTSP) in the hippocampus, but plasticity has been shown to be reduced in the hippocampus after psychedelic administration. Could you elaborate on this connection?

When we were establishing a connection between our ‘Wake-Sleep’ plasticity rule and BTSP learning, the intended connection was exclusively to the mathematical form of the plasticity rule, in which activity in the apical dendrites of pyramidal neurons functions as an instructive signal for plasticity in basal synapses (and vice versa): we will clarify this in the text. Similarly, we point out that such a plasticity rule tends to result in correlated tuning between apical and basal dendritic compartments, which has been observed in hippocampus and cortex: this is intended as a sanity check of our mapping of the Wake-Sleep algorithm to cortical microcircuitry, and has limited further bearing on the effects of psychedelics specifically.

Reduction in plasticity in the hippocampus after psychedelic administration could be due to a complementary learning systems-type model, in which the hippocampus becomes partly decoupled from the cortex during REM sleep (Singh et al., 2022); were this to be the case, it would not be incompatible with our model, which is mostly focused on the cortex. Notably, potentiating 5HT-2a receptors in the ventral hippocampus does not induce the head-twitch response, though it does produce anxiolytic effects (Tiwari et al., 2024), indicating that the hallucinatory and anxiolytic effects of classical psychedelics may be partly decoupled. 

Reviewer 2 Concerns:

Could you provide visualizations of the ‘ripple’ phenomenon that you’re referring to?

We will do this! For now, you can get a decent understanding of what the ‘ripple effect’ looks like from the ‘eyes closed’ hallucination condition for networks trained on CIFAR10 (Fig. 2d). The ripple effect that we are referring to is very similar, except it is superimposed on a naturalistic image under ordinary viewing conditions; to give a higher quality visualization of the ripple phenomenon itself, we will subtract out the static contribution of the image itself, leaving only the ripple phenomenon.

Could you provide a more nuanced description of alternative roles for top-down feedback, beyond being used exclusively for learning as depicted in your model?

For the sake of simplicity, we only treat top-down inputs in our model as a source of an instructive teaching signal, the originator of generative replay events during the Sleep phase, and as the mechanism of hallucination generation. However, as discussed in a response to a previous question, in the cortex pyramidal neurons receive and respond to a mixture of top-down and bottom-up processing.

There are a variety of theories for what role top-down inputs could play in determining network activity. To name several, top-down input could function as: 1) a denoising/pattern completion signal (Kadkhodaie & Simoncelli, 2021), 2) a feedback control signal (Podlaski & Machens, 2020), 3) an attention signal (Lindsay, 2020), 4) ordinary inputs for dynamic recurrent processing that play no specialized role distinct from bottom-up or lateral inputs except to provide inputs from higher-order association areas or other sensory modalities (Kar et al., 2019; Tugsbayar et al., 2025). Though our model does not include these features, they are perfectly consistent with our approach.

In particular, denoising/pattern completion signals in the predictive coding framework (closely related to the Wake-Sleep algorithm) also play a role as an instructive learning signal (Salvatori et al., 2021); and top-down control signals can play a similar role in some models (Gilra & Gerstner, 2017; Meulemans et al., 2021). Thus, options 1 and 2 are heavily overlapping with our approach, and are a natural consequence of many biologically plausible learning algorithms that minimize a variational free energy loss (Rao & Ballard, 1997; Ackley et al., 1985). Similarly, top-down attentional signals can exist alongside top-down learning signals, and some models have argued that such signals can be heavily overlapping or mutually interchangeable (Roelfsema & van Ooyen, 2005). Lastly, generic recurrent connectivity (from any source) can be incorporated into the Wake-Sleep algorithm (Dayan & Hinton, 1996), though we avoided doing this in the present study due to an absence of empirical architecture exploration in the literature and the computational complexity associated with training on time series data.

To conclude, there are a variety of alternative functions proposed for top-down inputs onto pyramidal neurons in the cortex, and we view these additional features as mutually compatible with our approach; for simplicity we did not include them in our model, but we believe that these features are unlikely to interfere with our testable predictions or empirical results.

A hint at knowledge of the old rhetoric version of inventio?

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public review):

Summary:

Recent work has demonstrated that the hummingbird hawkmoth, Macroglossum stellatarum, like many other flying insects, use ventrolateral optic flow cues for flight control. However, unlike other flying insects, the same stimulus presented in the dorsal visual field elicits a directional response. Bigge et al., use behavioral flight experiments to set these two pathways in conflict in order to understand whether these two pathways (ventrolateral and dorsal) work together to direct flight and if so, how. The authors characterize the visual environment (the amount of contrast and translational optic flow) of the hawkmoth and find that different regions of the visual field are matched to relevant visual cues in their natural environment and that the integration of the two pathways reflects a priortiziation for generating behavior that supports hawkmoth safety rather than than the prevalence for a particular visual cue that is more prevalent in the environment.

Strengths:

This study creatively utilizes previous findings that the hawkmoth partitions their visual field as a way to examine parallel processing. The behavioral assay is well-established and the authors take the extra steps to characterize the visual ecology of the hawkmoth habitat to draw exciting conclusions about the hierarchy of each pathway as it contributes to flight control.

Weaknesses:

The work would be further clarified and strengthened by additional explanation included in the main text, figure legends, and methods that would permit the reader to draw their own conclusions more feasibly. It would be helpful to have all figure panels referenced in the text and referenced in order, as they are currently not. In addition, it seems that sometimes the incorrect figure panel is referenced in the text, Figure S2 is mislabeled with D-E instead of A-C and Table S1 is not referenced in the main text at all. Table S1 is extremely important for understanding the figures in the main text and eliminating acronyms here would support reader comprehension, especially as there is no legend provided for Table S1. For example, a reader that does not specialize in vision may not know that OF stands for optic flow. Further detail in figure legends would also support the reader in drawing their own conclusions. For example, dashed red lines in Figures 3 and 4 A and B are not described and the letters representing statistical significance could be further explained either in the figure legend or materials to help the reader draw their own conclusions.

We appreciate the suggestions to improve the clarity of the manuscript. We have extensively re-structured the entire manuscript. Among others, we have referenced all figure panels in the text in the order they appear. To do so, we combined the optic flow and contrast measurements of our setup with the methods description of the behavioural experiments (formerly Figs. 5 and 2, respectively). This new figure 2 now introduces the methods of the study, while the remainder of Fig. 2, which presented the experiments that investigated the vetrolateral and dorsal response in more detail, is now a separate figure (Fig. 3). This arrangement also balances the amount of information contained  in each figure better.

Reviewer #2 (Public review):

Summary:

Bigge and colleagues use a sophisticated free-flight setup to study visuo-motor responses elicited in different parts of the visual field in the hummingbird hawkmoth. Hawkmoths have been previously shown to rely on translational optic flow information for flight control exclusively in the ventral and lateral parts of their visual field. Dorsally presented patterns, elicit a formerly completely unknown response - instead of using dorsal patterns to maintain straight flight paths, hawkmoths fly, more often, in a direction aligned with the main axis of the pattern presented (Bigge et al, 2021). Here, the authors go further and put ventral/lateral and dorsal visual cues into conflict. They found that the different visuomotor pathways act in parallel, and they identified a 'hierarchy': the avoidance of dorsal patterns had the strongest weight and optic flow-based speed regulation the lowest weight.

Strengths:

The data are very interesting, unique, and compelling. The manuscript provides a thorough analysis of free-flight behavior in a non-model organism that is extremely interesting for comparative reasons (and on its own). These data are both difficult to obtain and very valuable to the field.

Weaknesses:

While the present manuscript clearly goes beyond Bigge et al, 2021, the advance could have perhaps been even stronger with a more fine-grained investigation of the visual responses in the dorsal visual field. Do hawkmoths, for example, show optomotor responses to rotational optic flow in the dorsal visual field?

We thank the reviewer for the feedback, and the suggestions for improvement of the manuscript (our implementations are detailed below). We fully agree that this study raises several intriguing questions regarding the dorsal visual response, including how the animals perceive and respond to rotational optic flow in their dorsal visual field, particularly since rotational optic flow may be processed separately from translational optic flow.

In our free-flight setup, it was not possible to generate rotational optic flow in a controlled manner. To explore this aspect more systematically, a tethered-flight setup would be ideal, or alternatively, a free-flight setup integrated with virtual reality. This would be a compelling direction for a follow-up study.

Reviewer #3 (Public review):

The central goal of this paper as I understand it is to extract the "integration hierarchy" of stimulus in the dorsal and ventrolateral visual fields. The segregation of these responses is different from what is thought to occur in bees and flies and was established in the authors' prior work. Showing how the stimuli combine and are prioritized goes beyond the authors' prior conclusions that separated the response into two visual regions. The data presented do indeed support the hierarchy reported in Figure 5 and that is a nice summary of the authors' work. The moths respond to combinations of dorsal and lateral cues in a mixed way but also seem to strongly prioritize avoiding dorsal optic flow which the authors interpret as a closed and potentially dangerous ecological context for these animals. The authors use clever combinations of stimuli to put cues into conflict to reveal the response hierarchy.

My most significant concern is that this hierarchy of stimulus responses might be limited to the specific parameters chosen in this study. Presumably, there are parameters of these stimuli that modulate the response (spatial frequency, different amounts of optic flow, contrast, color, etc). While I agree that the hierarchy in Figure 5 is consistent for the particular stimuli given, this may not extend to other parameter combinations of the same cues. For example, as the contrast of the dorsal stimuli is reduced, the inequality may shift. This does not preclude the authors' conclusions but it does mean that they may not generalize, even within this species. For example, other cue conflict studies have quantified the responses to ranges of the parameters (e.g. frequency) and shown that one cue might be prioritized or up-weighted in one frequency band but not in others. I could imagine ecological signatures of dorsal clutter and translational positioning cues could depend on the dynamic range of the optic flow, or even having spatial-temporal frequency-dependent integration independent of net optic flow.

We absolutely agree that in principle, an observed integration hierarchy is only valid for the stimuli tested. Yet, we do believe that we provide good evidence that our key observations are robust also for related stimuli to the ones tested:

Most importantly, we found that both pathways act in parallel (and are not mutually exclusive, or winner-takes-all, for example), when the animals can enact the locomotion induced by the dorsal and ventrolateral pathway. We tested this with the same dorsal cue (the line switching direction), but different behavioural paradigms (centring vs unilateral avoidance), and different ventrolateral stimuli (red gratings of one spatial frequency, and 100% nominal contrast black-and-white checkerboard stimuli which comprised a range of spatial frequencies) – and found the same integration strategy.

Certainly, if the contrast of the visual cues was reduced to the point that the dorsal or ventrolateral responses became weaker, we would expect this to be visible in the combined responses, with the respective reduction in response strength for either pathway, to the same degree as they would be reduced when stimuli were shown independently in the dorsal and ventrolateral visual field.

For testing whether the animals would show a weighting of responses when it was not possible to enact locomotion to both pathways, we felt it was important to use similar external stimuli to be able to compare the responses. So we can confidently interpret their responses in terms of integration. Indeed, how this is translated to responses in the two pathways depends a) on the spatiotemporal tuning, contrast sensitivity and exact receptive fields of the two systems, b) the geometry of the setup and stimulus coverage, and therefore the ability of the animals to enact responses to both pathways independently and c) on the integration weights.

It would indeed be fascinating to obtain this tuning and the receptive fields, and having these, test a large array of combinations of stimuli and presentation geometries, so that one could extract integration weights for different presentation scenarios from the resulting flight responses in a future study.

We also expanded the respective discussion section to reflect these points: l. 391-417. We also updated the former Fig. 5, now Fig. 6 to reflect this discussion.

The second part of this concern is that there seems to be a missed opportunity to quantify the integration, especially when the optic flow magnitude is already calculated. The discussion even highlights that an advantage of the conflict paradigm is that the weights of the integration hierarchy can be compared. But these weights, which I would interpret as stimulus-responses gains, are not reported. What is the ratio of moth response to optic flow in the different regions? When the moth balances responses in the dorsal and ventrolateral region, is it a simple weighted average of the two? When it prioritizes one over the other is the response gain unchanged? This plays into the first concern because such gain responses could strongly depend on the specific stimulus parameters rather than being constant.

Indeed, we set up stimuli that are comparable, as they are all in the visual domain, and since we can calculate their external optic flow and contrast magnitudes, to control for imbalances in stimulus presentation, which is important for the interpretation of the resulting data.

As we discussed above, we are confident that we are observing general principles of the integration of the two parallel pathways. However, we refrained from calculating integration weights, because these might be misleading for several reasons:

(1) In situations where the animals can enact responses to both pathways, we show that they do so at the full original magnitudes. So there are no “weights” of the hierarchy in this case.

(2) Only when responses to both systems are not possible in parallel, do we see a hierarchy. However, combined with point (1), this hierarchy likely depends on the geometry of the moths’ environment: it will be more pronounced the less both systems can be enacted in parallel.

(3) The hierarchy also does not affect all features of the dorsal or ventrolateral pathway equally. The hawkmoths still regulate their perpendicular distance to ventral gratings with dorsal gratings present, to same degree as with only ventral grating - because perpendicular distance regulation is not a feature of the dorsal response. And while the hawkmoths show a significant reduction in their position adjustment to dorsal contrast when it is in conflict with lateral gratings (Fig. 4C), they show exactly the same amount of lateral movement and speed adjustment as for dorsal gratings alone, when not combined with lateral ones (Fig. 4D and Fig. S3A). So even for one particular setup geometry and stimulus combination, there clearly is not one integration weight for all features of the responses.

We extended the discussion section to clarify these points “The benefit of our study system is that the same cues activate different control pathways in different regions of the visual field, so that the resulting behaviour can directly be interpreted in terms of integration weights” (l. 448-451)

l. 391-417, we also updated the former Fig. 5, now Fig. 6 to reflect this discussion.

The authors do explain the choice of specific stimuli in the context of their very nice natural scene analysis in Fig. 1 and there is an excellent discussion of the ecological context for the behaviors. However, I struggled to directly map the results from the natural scenes to the conclusions of the paper. How do they directly inform the methods and conclusions for the laboratory experiments? Most important is the discussion in the middle paragraph of page 12, which suggests a relationship with Figure 1B, but seems provocative but lacking a quantification with respect to the laboratory stimuli.

We show that contrast cues and translational optic flow are not homogeneously distributed in the natural environments of hawkmoths. This directly related to our laboratory findings, when it comes to responses to these stimuli in different parts of their visual field. In order to interpret the results of these behavioural experiments with respect to the visual stimuli, we did perform measurements of translational optic flow and contrast cues in the laboratory setup. As a result, we make several predictions about the animals’ use of translational optic flow and contrast cues in natural settings:

a) Hawkmoths in the lab responded strongest to ventral optic flow, even though it was not stronger in magnitude, given our measurements, than lateral optic flow. Thus, we propose that the stronger response to ventral optic flow might be an evolutionary adaptation to the natural distribution of translational optic flow cues.

b) In the natural habitats of hawkmoths, dorsal coverage is much less frequent that ventrolateral structures generating translational optic flow, yet the hawkmoths responded with a much higher weight to the former. Moreover, in our flight tunnel experiments, the animals responded with the same or higher weights to dorsal cues, which had a lower magnitude of translational optic flow and contrast than the same cues in the ventrolateral visual field. So we showed, combining behavioural experiments and stimulus measurements in the lab that the weighting of dorsal and ventrolateral cues did not follow their stimulus magnitude in the lab. Moreover, comparing to the natural cue distributions, we suggest that the integration weights also did not evolve to match the prevalence of these cues in natural habitats.

We integrated the measurements of natural visual scene statistics in the new Fig. 6, to relate the behavioural findings to the natural context also in the figure structure, and sequence logic of the text, as they are discussed here.

The central conclusion of the first section of the results is that there are likely two different pathways mediating the dorsal and the ventrolateral response. This seems reasonable given the data, however, this was also the message that I got from the authors' prior paper (ref 11). There are certainly more comparisons being done here than in that paper and it is perfectly reasonable to reinforce the conclusion from that study but I think what is new about these results needs to be highlighted in this section and differentiated from prior results. Perhaps one way to help would be to be more explicit with the open hypotheses that remain from that prior paper.

We appreciate the suggestion to highlight more clearly what the open questions that are addressed in this study are. As a result, we have entirely restructured the introduction, added sections to the discussion and fundamentally changed the graphical result summary in Fig. 6, to reflect the following new findings (and differences to the previous paper):

The previous paper demonstrated that there are two different pathways in hummingbird hawkmoths that mediate visual flight guidance, and newly described one of them, the dorsal response. This established flight guidance in hummingbird hawkmoths as a model for the questions asked in the current study, which are very different in nature from the previous paper.  

The main question addressed in the current study is how these two flight guidance pathways interact to generate consistent behaviour? Throughout the literature of parallel sensory and motor pathways guiding behaviour, there are different solutions – from winner-takes-all to equal mixed responses. We tested this fundamental question using the hummingbird hawkmoth flight guidance systems as a model.

This is the main question addressed in the various conflict experiments in this study, and we show that indeed, the two systems operate in parallel. As long as the animals can enact both dorsal and optic-flow responses, they do so at the original strengths of the responses. Only when this is not possible, hierarchies become visible. We carefully measured the optic flow and contrast cues generated by the different stimuli to ensure that the hierarchies we observed were not generated by imbalances of the external stimuli.

- Does the interaction hierarchy of the two pathways follow the statistics of natural environments?  We did show qualitatively previously how optic flow and contrast cues are distributed across the visual field in natural habitats of the hummingbird hawkmoth. In this study, we quantitatively analysed the natural image data, including a new analysis for the contrast edges, and statistically compared the results across conditions. This quantitative analysis supported the previous qualitative assessment that the prevalence of translational optic flow was highest in the ventral and lowest in the dorsal visual field in all natural habitat types. The distribution of contrast edges across the visual field did depend on habitat type much stronger than visible in the qualitative analysis in the previous paper. When compared to the magnitude of the behavioural responses, and considering that the hummingbird hawkmoth is predominantly found in open and semi-open habitats, the natural distributions of optic flow and contrast edges did not align with the response hierarchy observed in our laboratory experiments. Dorsal cues elicited much stronger responses relative to ventrolateral optic flow responses than would be expected.

To provide a more complete picture of the dorsal pathway, which will be important to understand its nature, and also compare to other species, we conducted additional experiments that were specifically set up to test for response features known from the translational optic flow response. To compare and contrast the two systems. These experiments here allowed us to show that the dorsal response is not simply a translational optic flow reduction response that creates much stronger output than the ventrolateral optic flow response. We particularly show that the dorsal response was lacking the perpendicular distance regulation of the optic flow response, while it did provide alignment with prominent contrasts (possibly to reduce the perceived translational optic flow), which is not observed in the ventrolateral optic flow response. The strong avoidance of any dorsal contrast cues, not just those inducing translational optic flow, is another feature not found in the ventrolateral pathway.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Many comparisons between visual conditions are made and it was confusing at times to know which conditions the authors were comparing. Thinking of a way to label each condition with a letter or number so that the authors could specify which conditions are specifically being compared would greatly enhance comprehension and readability.

We appreciate this concern. To be able to refer to the individual stimulus conditions in the analysis and results description, we gave each stimulus a unique identifier (see table S1), and provided these identifiers in the respective figures and throughout the text. We hope that this makes the identification of the individual stimuli easier.

Consider adding in descriptive words to the y-axis labels for the position graphs that would help the reader quickly understand what a positive or negative value means with respect to the visual condition.

We did now change the viewpoint on the example tracks in Figs. 2-5, to take a virtual viewpoint from the top, not as the camera recorded from below, which requires some mental rotation to reconcile the left and right sides. Moreover, we noticed that the example track axes were labelled in mm, while the axes for the plots showing median position in the tunnel were labelled in cm. We reconciled the units as well. This will make it easier to see the direct equivalent of the axis (as well as positive and negative values) in the example tracks in those figures, and the median positions, as well as the cross-index.

There are no line numbers provided so it is a bit challenging to provide feedback on specific sentences but there are a handful of typos in the manuscript, a few examples:

(1) Cue conflict section, first paragraph: "When both cues were presented to in combination, ..." (remove to)

(2) The ecological relevance section, first paragraph, first sentence: "would is not to fly"

(3) Figure S3 legend: explanation for C is labeled as B and B is not included with A

We apologise for the missing line numbers. We added these and resolved the issues 1-3.

Reviewer #2 (Recommendations for the authors):

- The pictograms in Fig. 1a were at first glance not clear to me, maybe adding l, r, d, v to the first pictogram could make the figure more immediately accessible.

We added these labels to make it more accessible.

- I would suggest noting in the main text that the red patterns were chosen for technical reasons (see Methods), if this is correct.

We added this information and a reference to the methods in the main text (lines 100-102).

- "Thus, hawkmoths are currently the only insect species for which a partitioning of the visual field has been demonstrated in terms of optic-flow-based flight control [33-35]." I think that is a bit too strong and maybe it would be more interesting to connect the current data to connected data in other insects to perhaps discuss important similarities. Ref 32 for example shows that fruit flies weigh ventral translational optic flow considerably more than dorsal translational optic flow. Reichardt 1983 (Naturwissenschaften) showed that stripe fixation in large flies (a behaviour relying in part on the motion pathway) is confined to the ventral visual field, etc...

We have changed this sentence to acknowledge partitioning in other insects, and motivating the use of our model species for this study: While fruit flies weight ventral translational optic flow stronger than dorsal optic flow, the most extreme partitioning of the visual field in terms of  optic-flow-based flight control has been observed in hawkmoths [33-35]. (lines 60-62)

- I think the statistical differences group mean differences could be described in more detail at least in Fig. 2 (to me the description was not immediately clear, in particular with the double letters).

We added an explanation of the letter nomenclature to all respective figure legends:

Black letters show statistically significant differences in group means or median, depending on the normality of the test residuals (see Methods, confidence level: 5%). The red letters represent statistically significant differences in group variance from pairwise Brown–Forsythe tests (significance level 5%). Conditions with different letters were significantly different from each other. The white boxplots depict the median and 25% to 75% range, the whiskers represent the data exceeding the box by more than 1.5 interquartile ranges, and the violin plots indicate the distribution of the individual data points shown in black.

- "When translational optic flow was presented laterally" I would use a more wordy description, since it is the hawkmoth that is controlling the optic flow and in addition to translational optic flow, there might also be rotational components, retinal expansion etc.

We extended the description to explain that the moths were generating the optic flow percept based on stationary gratings in different orientations, by way of their flight through the tunnel. Lines 127-129

- While it is clearly stated that the measure of the perpendicular distance from the ventral and dorsal pattern via the size of the insect as seen by the camera is indirect, I would suggest to determine the measurement uncertainty of distance estimate.

- Connected to above - is the hawkmoth area averaged over the entire flight and is the variance across frames similar in all the stimuli conditions? Is it, in principle, conceivable that the hawkmoths' pitch (up or down) is different across conditions, e.g. with moths rising and falling more frequently in a certain condition, which could influence the area in addition to distance?

There are a number of sources that generate variance in the distance estimate (which was based on the size of the moth in each video frame, after background subtraction): the size of the animal, the contrast with which the animal was filmed (which also depended on the type of pattern in the tunnel – it was lower with ventral or dorsal patterns as a background than with lateral ones), and the speed of the animal, as motion blur could impact the moth’s image on the video. The latter is hard to calibrate, but the uncertainty related to animal size and pattern types could theoretically be estimated. However, since we moved between finishing the data acquisition for this study and publishing the paper, the original setup has been dismantled. We could attempt to recreate it as faithfully as possible, but would be worried to introduce further noise. We therefore decided to not attempt to characterise the uncertainty, to not give a false impression of quantifiability of this measure. For the purpose of this study, it will have to remain a qualitative, rather than a quantitative measure. If we should use a similar measure again, we will make sure to quantify all sources of uncertainty that we have access to.

The variance in area is different between conditions. Most likely, the animals vary their flight height different for different dorsal and ventral patterns, as they vary their lateral flight straightness with different lateral visual input. For the reasons mentioned above, we cannot disentangle the effects of variations in flight height and other sources of uncertainty relating to animal size in the video frames. We therefore averaged the extracted area across the entire flight, to obtain a coarse measure of their flight height. Future studies focusing specifically on the vertical component or filming in 3D will be required to determine the exact amount of vertical flight variation.

- Results second paragraph, suggestion: pattern wavelength or spatial frequency instead of spatial resolution.

- Same paragraph, suggestion: For an optimal wavelength/spatial frequency of XX

We corrected these to spatial frequency.

- Above Fig 3- "this strongly suggests a different visual pathway". In my opinion it would be better to say sensory-motor /visuomotor pathway or to more clearly define visual pathway? Could one in principle imagine a uniform set of local motion sensitive neurons across the entire visual field that connect differentially to descending/motor neurons.

We appreciate this point and changed this, and further instances in the manuscript to visuomotor pathway.

- If I understood correctly, you calculated the magnitude of optic flow in the different tunnel conditions based on the image of a fisheye camera moving centrally in the tunnel, equidistant from all walls. I did not understand why the magnitude of optic flow should differ between the four quadrants showing the same squarewave patterns. Apologies if I missed something, but maybe it is worth explaining this in more detail in the manuscript.

We recognize that this point may not have been immediately clear and have therefore provided additional clarification in the Methods and results section (lines 106-111, 543-549). We anticipated differences in the magnitude of optic flow due to potential contrast variations arising from the way the stimuli were generated—being mounted on the inner surfaces of different tunnel walls while the light source was positioned above. On the dorsal wall, light from the overhead lamps passed through the red material. For laterally mounted patterns, the animals perceived mainly reflected light, as these tunnel walls were not transparent.

A similar principle applied to the background, which consisted of a white diffuser allowing light to pass through dorsally, but white non-transmissive paper laterally, with a 5% contrast random checkerboard patterns. The ventral side presented a more complex scenario, as it needed to be partially transparent for the ventrally mounted camera. Consequently, the animals perceived a combination of light reflections from the red patterns and the white gauze covering the ventral tunnel side, against the much darker background of the surrounding room.

To ensure that the observed flight responses were not artifacts of deviations in visual stimulation from an ideal homogeneous environment, we used the camera to quantify the magnitude of optic flow and contrast patterns under these real experimental conditions. This approach also allowed us to directly relate the optic flow measurements taken indoors to those recorded outdoors, as we employed the same camera and analytical procedures for both datasets.

Reviewer #3 (Recommendations for the authors):

In addition to the considerations above I had a few minor points:

There are so many different directions of stimuli and response that it is quite challenging to parse the results. Can this be made a little easier for the reader?

We appreciate this concern. To be able to refer to the individual stimulus conditions in the analysis and results description, we gave each stimulus a unique identifier (see table S1), and provided these identifiers in the respective figures and throughout the text. We hope that this makes the identification of the individual stimuli easier.

One suggestion (only a suggestion): I found myself continuously rotating the violin plots in my head so that the lateral position axis lined up with the lateral position of the tunnel icons below. Consider if rotating the plots 90 degs would help interpretability. It was challenging to keep track of which side was side.

We did discuss this with a number of test-readers, and tried multiple configurations. They all have advantages and drawbacks, but we decided that the current configuration for the majority of testers was the current one. To help the mental transformations from the example flight tracks in the figures, we now present the example flight tracks in Figs. 2-5 in the same reference frame as the figures showing median position (so positive and negative values on those axes correspond directly), and changed the view from a below the tunnel to an above the tunnel view, as this is the more typical depiction. We hope that this enhances readability.

Are height measurements sensitive to the roll and pitch of the animal? I suspect this is likely small but worth acknowledging.

They are indeed. These effects are likely small but contribute to the overall inaccuracy, which we could not quantify in this particular setup (see also response to reviewer 2 on that point), which is why the height measurements have to be considered a qualitative approximation rather than a quantification of flight height. We added text to acknowledge the effects of roll and pitch specifically (lines 657-658)

The Brown-Forsythe test was reported as paired but this seems odd because the same moths were not used in each condition. Maybe the authors meant something different by "paired" than a paired statistical design?

Indeed, the data was not paired in the sense that we could attribute individual datapoints to individual moths across conditions. We applied the Brown-Forsythe test in a pairwise manner, comparing the variance of each condition with another one in pairs each, to test if the variance in position differed across conditions. We did phrase this misleadingly, and have corrected it to „The variance in the median lateral position (in other words, the spread of the median flight position) was statistically compared between the groups using the pairwise Brown–Forsythe tests“ l. 187-188

There is some concern about individual moth preferences and bias due to repeated measures. I appreciate that the individual moth's identity was not likely known in most cases, but can the authors provide an approximate breakdown of how many individual moths provided the N sample trajectories?

This is a very valid concern, and indeed one we did investigate in a previous study with this setup. We confirmed that the majority of animals (70%, 68% and 53% out of 40 hawkmoths, measured on three consecutive days) crossed the tunnel within a randomly picked window of 3h (Stöckl et al. 2019). We now state this explicitly in the methods section (lines 594-597). Thus, for the sample sizes in our study, statistically, each moth would have contributed a small number of tracks compared to the overall number of tracks sampled.

The statistics section of the methods said that both Tukey-Kramer (post-hoc corrected means) and Kruskal-Wallis (non-parametric medians) were done. It is sometimes not clear which test was done for which figure, and where the Kruskal-Wallis test was done there does not seem to be a corrected statistical significance threshold for the many multiple comparisons (Fig. 2). It is quite possible I am just missing the details and they need to be clarified. I think there also needs to be a correction for the Brown-Forsythe tests but I don't know this method well.

We first performed an ANOVA, and if the test residuals were not normally distributed, we used a Kruskal-Wallis test instead. For the post-hoc tests of both we used Tukey-Kramer to correct for multiple comparisons. The figure legends did indeed miss this information. We added it to clarify our statistical analysis strategy and refer to the methods section for more details (i.e. l. 185-186). All statistical results, including the type of statistical test used, have been uploaded to the data repository as well.

The connection to stimulus reliability in the discussion seems to conflate reliability with prevalence or magnitude.

We have rephrased the respective discussion sections to clearly separate the prevalence and magnitude of stimuli, which was measured, from an implied or hypothesized reliability (lines 510-511).

Line numbers would be helpful for future review.

We apologize for missing the line numbers and have added them to the revised manuscript.

This whole paragraph really hit the nail right on the head in my opinion. Those four terms cannot be used interchangeably because they all mean different things entirely. I believe that everyone loves to learn naturally, it's the vector through which their education is brought to them in that plays a key role in determining if the training or education was valuable.

Steve Jobs is an inventor who co-founded the Apple industry and is at the head of modern tech. The introduction of the iPhone completely reshaped the smartphone industry and developments of the iPad and MacBook continue to impact the market today. I do feel he is slightly overshadowed by Microsoft in recent years and should still be remembered as one of the great pioneers of a new era of tech.

Try a toothbrush with a long, thin handle and a small head.

The brushes sold with many metal and rubber straws or thin bottle brushes are also excellent for reaching into places like this. Sometimes you can find similar thin brushes in the baby bottle section of big box retailers or specialty stores doing baby goods.

Similarly a plastic oiler with mineral spirits in combination with an air compressor/blow gun or canned air is also a solid way to go.

Long handle cotton swabs can also be used if necessary.

reply to u/General-Writing-1764 at https://old.reddit.com/r/typewriters/comments/1l05a17/i_dont_think_that_superficial_dust_should_be_a/

The king of Tenochtitlan and the incoming Spanish are having a friendly conversation showing no hatred or fear or longing to conquer. The king even treats the Spanish as gods coming to their land and praises them.

Contacted LATAM head, close to the founder. Asked to follow up in 2 weeks. Interested in talking.

They are Not selling now, its majority-owned by Russian partners and are Considering a minority funding round to become a top 2 player by year-end.

Will talk just to see the possibilities

This part seems similar to the travel narratives of Smith and De Vaca, as Franklin is talking about his own struggles travelling to Philadelphia

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Reply to the reviewers

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

In this manuscript the authors have done cryo-electron tomography of the manchette, a microtubule-based structure important for proper sperm head formation during spermatogenesis. They also did mass-spectrometry of the isolated structures. Vesicles, actin and their linkers to microtubules within the structure are shown.

__We thank the reviewer for the critical reading of our manuscript; we have implemented the suggestions as detailed below, which we believe indeed improved the manuscript. __

Major:

The data the conclusions are based on seem very limited and sometimes overinterpreted. For example, only one connection between actin and microtubules was observed, and this is thought to be MACF1 simply based on its presence in the MS.

__We regret giving the impression that the data is limited. We in fact collected >100 tilt series from 3 biological replicas for the isolated manchette. __

__In the revised version, we added data from in-situ studies showing vesicles interacting with the manchette (as requested below, new Fig. 1). __

Specifically, for the interaction of actin with microtubule we added more examples (Revised Fig. 6) and we toned down the discussion related to the relevance of this interaction (lines 193-194, 253-255). MACF1 is mentioned only as a possible candidate in the discussion (line 254).

Another, and larger concern, is that the authors do a structural study on something that has been purified out of the cell, a process which is extremely disruptive. Vesicles, actin and other cellular components could easily be trapped in this cytoskeletal sieve during the purification process and as such, not be bona fide manchette components. This could create both misleading proteomics and imaging. Therefore, an approach not requiring extraction such as high-pressure freezing, sectioning and room-temperature electron tomography and/or immunoEM on sections to set aside this concern is strongly recommended. As an additional bonus, it would show if the vesicles containing ATP synthase are deformed mitochondria.

__We recognise the concern raised by the reviewer. __

__To alleviate this concern, we added imaging data of manchettes in-situ that show vesicles, mitochondria and filaments interacting with the manchette (new Fig. 1), essentially confirming the observations that were made on the isolated manchette. __

__The benefits of imaging the isolated manchette were better throughput (being able to collect more data) and reaching higher resolution allowing to resolve unequivocally the dynein/dynactin and actin filaments. __

Minor: Line 99: "to study IMT with cryo-ET, manchettes were isolated ...(insert from which organism)..."

__Added in line 102 in the revised version. __

Line 102 "...demonstrating that they can be used to study IMT".. can the authors please clarify?

This paragraph was revised (lines 131-137), we hope it is now more clear.

Line 111 "densities face towards the MT plus-end" How can a density "face" anywhere? For this, it needs to have a defined front and back.

Microtubule motor proteins (kinesin and dynein) are often attached to the microtubules with an angle and dynactin and cargo on one side (plus end). We rephrased this part and removed the word “face” in the revised version to make it more clear (lines 161-162).

Line 137: is the "perinuclear ring" the same as the manchette?

The perinuclear ring is the apical part of the manchette that connects it to the nucleus. We added to the revised version imaging of the perinuclear ring with observations on how it changes when the manchette elongates (new Fig. 2).

Figure 2B: How did the authors decide not to model the electron density found between the vesicle and the MT at 3 O'clock? Is there no other proteins with a similar lollipop structure as ATP synthase, so that this can be said to be this protein with such certainty?

__The densities connecting the vesicles to the microtubules shown in (now) Fig. 4D are not consistent enough to be averaged. __

__The densities resembling ATP synthase are inside the vesicles. Nevertheless, we have decided to remove the averaging of the ATP synthases from the revised manuscipt as they are not of great importance for this manuscript. Instead, the new in-situ data clearly show mitochondria (with their characteristic double membrane and cristae) interacting with manchette microtubule (new Fig 1C). __

Line 189: "F-actin formed organized bundles running parallel to mMTs" - this observation needs confirming in a less disrupted sample.

__Phalloidin (actin marker) was shown before to stain the manchette (PMID: 36734600). As actin filaments are very thin (7 nm) they are very hard to observe in plastic embedded EM. __

In the in-situ data we added to the revised manuscipt (new Fig 1D), we observe filaments with a diameter corresponding to actin. In addition, we added more examples of microtubules interacting with actin in isolated manchette (new Fig. 6 E-K).

Line 242 remove first comma sign.

Removed.

Line 363 "a total of 2 datasets" - is this manuscript based on only two tilt-series? Or two datasets from each of the 4 grids? In any case, this is very limited data.

We apologise for not clearly providing the information about the data size in the original manuscipt. The data is based on three biological replicas (3 animals). We collected more than 100 tomograms of different regions of the manchettes. As such, we would argue that the data is not limited per se.

Reviewer #1 (Significance (Required)):

The article is very interesting, and if presented together with the suggested controls, would be informative to both microtubule/motorprotein researchers as well as those trying studying spermatogenesis.

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

The manchette appears as a shield-like structure surrounding the flagellar basal body upon spermiogenesis. It consists of a number of microtubules like a comb, but actin (Mochida et al. 1998 Dev. Biol. 200, 46) and myosin (Hayasaka et al. 2008 Asian J. Androl. 10, 561) were found, suggesting transportation inside the manchette. Detailed structural information and functional insight into the manchette was still awaited. There is a hypothesis called IMT (intra-machette transport) based on the fact that machette and IFT (intraflagellar transport) share common components (or homologues) and on their transition along the stages of spermiogenesis. While IMT is considered as a potential hypothesis to explain delivery of centrosomal and flagellar components, no one has witnessed IMT at the same level as IFT. IMT has never been purified, visualized in motion or at high resolution. This study for the first time visualized manchette using high-end cryo-electron tomography of isolated manchettes, addressing structural characterization of IMT. The authors successfully microtubular bundles, vesicles located between microtubules and a linker-like structure connecting the vesicle and the microtubule. On multilamellar membranes in the vesicles they found particles and assigned them to ATPase complexes, based on intermediate (~60A) resolution structure. They further identified interesting structures, such as (1) particles on microtubules, which resemble dynein and (2) filaments which shows symmetry of F-actin. All the molecular assignments are consistent with their proteomics of manchettes.

__We thank the reviewer for highlighting the novelty of our study.____ __

Their assignment of ATPase will be strengthened by MS data, if it proves absence of other possible proteins forming such a membrane protein complex.

All the ATPase components were indeed found in our proteomics data. Nevertheless, we have decided to remove the averaging of the ATPase as it does not directly relate to IMT, the focus of this manuscript.

They discussed possible role of various motor proteins based on their abundance (Line 134-151, Line 200). This makes sense only with a control. Absolute abundance of proteins would not necessarily present their local importance or roles. This reviewer would suggest quantitative proteomics of other organelles, or whole cells, or other fractions obtained during manchette isolation, to demonstrate unique abundance of KIF27 and other proteins of their interest.

We agree with the reviewer that absolute abundance does not necessarily indicate importance or a role. As such, we removed this part of the discussion from the revised manuscript.

A single image from a tomogram, Fig.6B, is not enough to prove actin-MT interaction. A gallery and a number (how many such junctions were found from how many MTs) will be necessary.

We agree that one example is not enough. In the new Fig. 6E-K, we provide a gallery of more examples. We have revised the text to reflect the point that these observations are still rare and more data will be needed to quantify this interaction (Lines 253-254).

Minor points: Their manchette purification is based on Mochida et al., which showed (their Fig.2) similarity to the in vivo structure (for example, Fig.1 of Kierszenbaum 2001 Mol. Reproduc. Dev. 59, 347). Nevertheless, since this is not a very common prep, it is helpful to show the isolated manchette’s wide view (low mag cryo-EM or ET) to prove its intactness.

We thank the reviewer for this suggestion, in the revised version, new Fig. 2 provides a cryo-EM overview of purified manchette from different developmental stages.

Line 81: Myosin -> myosin (to be consistent with other protein names)

Corrected.

This work is a significant step toward the understanding of manchettes. While the molecular assignment of dynein and ATPase is not fully decisive, due to limitation of resolution (this reviewer thinks the assignment of actin filament is convincing, based on its helical symmetry), their speculative model still deserves publication.

Reviewer #2 (Significance (Required)):

This work is a significant step toward the understanding of manchettes. While the molecular assignment of dynein and ATPase is not fully decisive, due to limitation of resolution (this reviewer thinks the assignment of actin filament is convincing, based on its helical symmetry), their speculative model still deserves publication.

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

->Summary:

The manchette is a temporary microtubule (MT)-based structure essential for the development of the highly polarised sperm cell. In this study, the authors employed cryo-electron tomography (cryo-ET) and proteomics to investigate the intra-manchette transport system. Cryo-EM analysis of purified rat manchette revealed a high density of MTs interspersed with actin filaments, which appeared either bundled or as single filaments. Vesicles were observed among the MTs, connected by stick-like densities that, based on their orientation relative to MT polarity, were inferred to be kinesins. Subtomogram averaging (STA) confirmed the presence of dynein motor proteins. Proteomic analysis further validated the presence of dynein and kinesins and showed the presence of actin crosslinkers that could bundle actin filaments. Proteomics data also indicated the involvement of actin-based transport mediated by myosin. Importantly, the data indicated that the intraflagellar transport (IFT) system is not part of the intra-manchette transport mechanism. The visualisation of motor proteins directly from a biological sample represents a notable technical advancement, providing new insights into the organisation of the intra-manchette transport system in developing sperm.

We thank the reviewer for summarising the novelty of our observations.

-> Are the key conclusions convincing? Below we comment on three main conclusions. MT and F-actin bundles are both constituents of the manchette While the data convincingly shows that MT and F-actin are part of the manchette, one cannot conclude from it that F-actin is an integral part of the manchette. The authors would need to rephrase so that it is clear that they are speculating.

We have rephrased our statements and replaced “integral” with ‘actin filaments are associated’. Of note previous studies suggested actin are part of the manchette including staining with phalloidin (PMID: 36734600, PMID: 9698455, PMID: 18478159) and we here visualised the actin in high resolution.

The transport system employs different transport machinery on these MTs Proteomics data indicates the presence of multiple motor proteins in the manchette, while cryo-EM data corroborates this by revealing morphologically distinct densities associated with the MTs. However, the nature of only one of these MT-associated densities has been confirmed-specifically, dynein, as identified through STA. The presence of kinesin or myosin in the EM data remains unconfirmed based on just the cryo-ET density, and therefore it is unclear whether these proteins are actively involved in cargo transport, as this cannot be supported by just the proteomics data. In summary, we recommend that the authors rephrase this conclusion and avoid using the term "employ".

We agree that our cryo-ET only confirmed the motor protein dynein. As such, we removed the term employ and rephrased our claims regarding the active transport and accordingly changed the title.

Dynein mediated transport (Line 225-227) The data shows that dynein is present in the manchette; however, whether it plays and active role in transport cannot be determined from the cryo-ET data provided in the manuscript, as it does not clearly display a dynein-dynactin complex attached to cargo. The attachment to cargo is also not revealed via proteomics as no adaptor proteins that link dynein-dynactin to its cargo have been shown.

A list of cargo adaptor proteins were found in our proteomics data but we agree that cryo-ET and proteomics alone cannot prove active transport. As such we toned down the discussion about active transport (lines 212-220).

-> Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?

F-actin • In the abstract, the authors state that F-actin provides tracks for transport as well as having structural and mechanical roles. However, the manuscript does not include experiments demonstrating a mechanical role. The authors appear to base this statement on literature where actin bundles have been shown to play a mechanical role in other model systems. We suggest they clarify that the mechanical role the authors suggest is speculative and add references if appropriate.

__ ____We removed the claim about the mechanical role of the actin from the abstract and rephrased this in the discussion to suggest this role for the F-actin (lines 242-243).__

• Lines 15,92, 180 and 255: The statement "Filamentous actin is an integral part of the manchette" is misleading. While the authors show that F-actin is present in their purified manchette structures, whether it is integral has not been tested. Authors should rephrase the sentence.

We removed the word integral.

• To support the claim that F-actin plays a role in transport within the manchette, the authors present only one instance where an unidentified density is attached to an actin filament. This is insufficient evidence to claim that it is myosin actively transporting cargo. Although the proteomics data show the presence of myosin, we suggest the authors exercise more caution with this claim.

We agree that our data do not demonstrate active transport as such we removed that claim. We mention the possibility of cargo transport in the discussion (lines 250-255).

• The authors mention the presence of F-actin bundles but do not show direct crosslinking between the F-actin filaments. They could in principle just be closely packed F-actin filaments that are not necessarily linked, so the term "bundle" should be used more cautiously.

We do not assume that a bundle means that the F-actin filaments are crosslinked. A bundle simply indicates the presence of multiple F-actin filaments together. We rephrased it to call them actin clusters.

Observations of dynein • Relating to Figure 2B: From the provided image it is not clear whether the density corresponds to a dynein complex, as it does not exhibit the characteristic morphological features of dynein or dynactin molecules.

We indeed do not claim that the densities in this figure are dynein or dynactin. __We revised this paragraph and hope that it is now more clear (lines 135-137). __

• Lines 171-172 and Figure 4: It is well established that dynein is a dimer and should always possess two motor domains. The authors have incorrectly assumed they observed single motor heads, except possibly in Figure 4A (marked by an arrow). In all other instances, the dynein complexes show two motor domains in proximity, but these have not been segmented accurately. Furthermore, the "cargos" shown in grey are more likely to represent dynein tails or the dynactin molecule, based on comparisons with in vitro structures of these complexes (see references 1-3).

We thank the reviewer for this correction. We improved the annotations in the figure and revised the text to clarify that we identified dimers of dynein motor heads (lines 140-144). We further added a projection of a dynein dynactin complex to compare to the observation on the manchette (new Fig. 5E). We further changed claims on the presence of protein cargo to the presence of dynein/dynactin that allows cargo tethering based on the presence of cargo adaptors in the proteomics data.

• Lines 21, 173, and 233 mention cargos, but as noted above, it seems to be parts of the dynein complex the authors are referring to.

This was corrected as mentioned above.

• Panel 4B appears to show a dynein-dynactin complex, but whether there is a cargo is unclear and if there is it should be labelled accordingly. To assessment of whether there is any cargo bound to the dynein-dynactin complex a larger crop of the panel would be helpful In summary, we recommend that the authors revisit their segmentations in Figures 2B and 4, revise their text based on these observations, and perform quantification of the data (as suggested in the next section).

We thank the reviewers for sharing their expertise on dynein-dynactin complexes. We have revised the text as detailed above and excluded the assignment of any cargo, as we cannot (even from larger panels) see a clear association of cargo. We have made clear that we only refer to dynein dynactin with the capability of linking cargo based on the presence of proteomics data. We have removed claims on active transport with dynein.

Dynein versus kinesin-based transport The calculation presented in lines 147-151 does not account for the fact that both the dynein-dynactin complex and kinesin proteins require cargo adaptors to transport cargo. Additionally, the authors overlook the possibility that multiple motors could be attached to a single cargo. If the authors did not observe this, they should explicitly mention it to support their argument. In short, the calculations are based on an incorrect premise, rendering the comparison inaccurate. Unless the authors have identified any dynein-dynactin or kinesin cargo adaptors in their proteomics data which could be used for such a comparison, we believe the authors lack sufficient data to accurately estimate the "active transport ratio" between dynein and kinesin.

Even though we detect cargo adaptors in our proteomics, we agree that calculating relative transport based only on the proteomics can be inaccurate as such we removed absolute quantification and comparison between dynein and kinesin-based IMT.

• Would additional experiments be essential to support the claims of the paper?

F-actin distance and length distribution • To support the claim that F-actin is bundled (line 189), could the authors provide the distance between each F-actin filament and its neighbours? Additionally, could they compare the average distance to the length of actin crosslinkers found in their proteomics data, or compare it to the distances between crosslinked F-actin observed in other research studies?

We measured distances between the actin filaments and added a plot to new Fig 6.

• While showing that F-actin is important for the manchette would require cellular experiments, authors could provide quantification of how frequently these actin structures are observed in comparison to MTs to support their claims that these actin filaments could be important for the manchette structure.

We agree that claims on the role and function of actin in the manchette require cellular experiments that are beyond the scope of this study. Absolute quantification of the ratio between MTs and actin from cryoET is very hard and will be inaccurate as the manchette cannot be imaged as a whole due to its size and thickness. The ratio we have is based on the relative abundance provided by the proteomics (Fig. 5F).

• In line 193, the authors claim that the F-actin in bundles appears too short for transport. Could they provide length distributions for these filaments? This might provide further support to their claim that individual F-actin filaments can serve as transport tracks (line 266).

__In addition to the limitation mentioned in the previous point, quantification of length from high magnification imaging will likely be inaccurate as the length of the actin in most cases is bigger than the field of view that is captured. Nevertheless, we removed the claim about the actin being too short for transport. __

• Could the authors also quantify the abundance of individual F-actin filaments observed, compared to MTs and F-actin bundles, to support the idea that they could play a role in transport?

As explained for the above points absolute quantification of the ratio between MTs and actin is not feasible from cryoET data that cannot capture all of the manchette in high enough resolution to resolve the actin.

• In the discussion, the authors mention "interactions between F-actin singlets and mMTs" (line 269), yet they report observing only one instance of this interaction (lines 210 and 211). Given the limited data, they should refer to this as a single interaction in the discussion. The scarcity of data raises questions about how representative this event truly is.

We agree that one example is not enough. In the new Fig. 6E-K, we provide a gallery of more examples as also requested by reviewers 1 and 2. We have also revised the text to reflect the point that these observations are still rare (Lines 190-194).

Quantifications for judgement of representativity The authors should quantify how often they observed vesicles with a stick-like connection to MTs (lines 106-107); this would strengthen the interpretation of the density, as currently only one example is shown in the manuscript (Figure 4A). If possible, they could show how many of them are facing towards the MT plus end.

__As mentioned in the text (lines 135-137), the linkers connecting vesicles to MTs were irregular and so we could not interpret them further this is in contrast to dynein that were easily recognisable but were not associated with vesicles. __

Dynein quantifications • The authors are recommended to quantify how many dynein molecules per micron of MT they observe and how often they are angled with their MT binding domain towards the minus-end.

As the manchette is large and highly dense any quantification will likely be biased towards parts of the manchette that are easier to image, for example the periphery. As such we do not think quantifying the dynein density will yield meaningful insight.

• Could the authors quantify how many dynein densities they found to be attached to a (vesicle) cargo, if any (line 175)? They could show these observations in a supplementary figure.

We did not observe any case of a connection between a vesicle and dynein motors, we edited this sentence to be more clear on that.

• For densities that match the size and location of dynein but lack clear dynein morphology (as seen in Figure 2B), could the authors quantify how many are oriented towards the MT minus end?

We had many cases where the connection did not have a clear dynein morphology, and as the morphology is not clear, it is impossible to make a claim about whether they are oriented towards the minus end.

Artefacts due to purification: Authors should discuss if the purification could have effects on visualizing components of the manchette. For example, if it has effect on the MTs and actin structure or the abundance/structure of the motor protein complexes (bound to cargo or isolated).

We have followed a protocol that was published before and showed the overall integrity of the manchette. Nevertheless, losing connections between manchette and other cellular organelles are expected. To address this point, we added in-situ data (new Fig 1) showing manchette in intact spermatids interacting with vesicles and mitochondria, as well as overviews of manchettes (new Fig 2), the text was revised accordingly.

• Are the experiments adequately replicated and statistical analysis adequate? The cryo-ET data presented in the manuscript is collected using two separate sample preparations. Along with the quantifications of the different observations suggested above which will help the reader assess how abundant and representative these observations are, the authors could further strengthen their claims by acquiring data from a third sample preparation and then analysing how consistent their observations are between different purifications. This however could be time consuming so it is not a major requirement but recommended if possible within a short time frame.

We regret not explicitly mentioning our data set size, it was added now to the revised version. In essence, the data is based on three biological replicas (3 animals). We collected more than 100 tomograms of different regions of the manchettes. We provided in the revised version more observations (new Fig 1, 2, 4B-C and 6E-K).

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

Most of the comments deal with either modifying the text or analysing the data already presented, so the revision could be done with 1-3 months.

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Minor comments: - Specific experimental issues that are easily addressable. 1) Could the authors state how many tilt series were collected for each dataset/independent sample preparation? We recommend that they upload their raw data or tomograms to EMPAIR.

We added this information in the material and methods.

2) It is not clear to me if the same sample was used for cryo-ET and proteomics. Could the authors clarify how comparable the sample preparation for the cryo-ET and proteomics data is or if the same sample was used for both. If there is a discrepancy between these preparations, they would need to discuss how this can affect comparing observations from cryo-ET and mass spectrometry. Ideally both samples should be the same.

After sample preparation the manchettes were directly frozen on grids. The rest of the samples was used for proteomics. Consequently, EM and MS data were acquired on the same samples. We clarified this in the text (lines 327-328).

• Are prior studies referenced appropriately? We recommend including additional references to support the claim that F-actin has a mechanical role (line 242). Could the authors compare their proteomics data to other mass spectrometry studies conducted on the Manchette (for example, see reference 4)?

We added the comparison but it is important to point out that in reference 4 the manchettes were isolated from mice testes.

• Are the text and figures clear and accurate? Text: We do not see the necessity of specifying the microtubules (MTs) in the data as "manchette MTs" or "mMTs" rather than simply "MTs". However, we recommend that the authors use either "MT" or "mMT" consistently throughout the manuscript.

We changed to only MTs.

The authors appear to refer to both dynein-1 (cytoplasmic dynein) and dynein-2 (axonemal dynein or IFT dynein). To avoid confusion, it is important that the authors clearly specify which dynein they are referring to throughout the text. This is particularly relevant as the study aims to demonstrate that IFT is not part of the manchette transport system.

• Introduction: In the third paragraph (lines 59-75), the authors should specify that they are referring to dynein-2, which is distinct from cytoplasmic dynein discussed in the previous paragraph (lines 44-58).

We specify the respective dyneins in the text (line 66,140-141,145).

• Figure 4D: The authors could fit a dynein-1 motor domain instead of a dynein-2 into the density to stay consistent with the fact that the density belongs to cytoplasmic dynein-1.

__We changed the figure and fitted a cytosolic dynein-1 structure (5nvu) instead. __

Figures: • Figure 2B: The legend mentions a large linker complex; however, this may correspond to two or three separate densities.

We have addressed this and changed the wording.

• Figure 4: please revisit the segmentation of this whole figure based on previous comments.

__We revised as suggested. __

• Figures 1, 2, 4, 5, and 6: It would be helpful to state in the legends that the tomograms are denoised. There are stripe-like densities visible in the images (e.g., in the vesicle in Figure 2B). Do these artefacts also appear in the raw data?

As stated in the Methods section, tomograms were generally denoised with CryoCare for visualisation purposes. The “stripe-like densities” are artefacts of the gold fiducials used for tomogram alignment and appear in the raw data (before denoising).

• Do you have suggestions that would help the authors improve the presentation of their data and conclusions? We suggest revising the paragraph title "Dynein-mediated cargo along the manchette" (line 165) to "Dynein-mediated cargo transport along the manchette".

__We have changed this in the revised version. __

We recommend that the authors provide additional evidence to support the interpretation that the observed EM densities correspond to motor proteins. Specifically: • Include scale bars or reference lines indicating the known dimensions of motor proteins, based on previous data, to demonstrate that the observed densities match the expected size.

The dynein structure is provided for reference. We also added the cytosolic dynein–dynactin as a reference (Fig 5E).

• Make direct comparisons to existing EM data and highlight morphological similarities.

We have added a comparison to existing data (Fig 5E).

In the discussion (lines 249-254), the authors could speculate on alternative roles for the IFT components in the manchette, particularly if they are not part of the IFT trains. We also suggest rephrasing the claim in line 266 to make it more speculative in tone.

__We have addressed this in the revised version (lines 221-230). __

Finally, a schematic overview of the manchette ultrastructure in a spermatid would greatly aid the reader in understanding the material presented.

We now include a graphical abstract and overviews of isolated manchettes on cryo-EM grids.

References: 1. Chowdhury, S., Ketcham, S., Schroer, T. et al. Structural organization of the dynein-dynactin complex bound to microtubules. Nat Struct Mol Biol 22, 345-347 (2015). https://doi.org/10.1038/nsmb.2996

1. Grotjahn, D.A., Chowdhury, S., Xu, Y. et al. Cryo-electron tomography reveals that dynactin recruits a team of dyneins for processive motility. Nat Struct Mol Biol 25, 203-207 (2018). https://doi.org/10.1038/s41594-018-0027-7

2. Chaaban, S., Carter, A.P. Structure of dynein-dynactin on microtubules shows tandem adaptor binding. Nature 610, 212-216 (2022).https://doi.org/10.1038/s41586-022-05186-y

3. W. Hu, R. Zhang, H. Xu, Y. Li, X. Yang, Z. Zhou, X. Huang, Y. Wang, W. Ji, F. Gao, W. Meng, CAMSAP1 role in orchestrating structure and dynamics of manchette microtubule minus-ends impacts male fertility during spermiogenesis, Proc. Natl. Acad. Sci. U.S.A. 120 (45) e2313787120, https://doi.org/10.1073/pnas.2313787120 (2023).

Reviewer #3 (Significance (Required)):

This study employs cryo-electron tomography (cryo-ET) and proteomics to elucidate the architecture of the manchette. It advances our understanding of the components involved in intracellular transport within the manchette and introduces the following technical and conceptual innovations:

a) Technical Advances: The authors have visualized the manchette at high resolution using cryo-ET. They optimized a purification pipeline capable of retaining, at least partially, the transport machinery of the manchette. Notably, they observed dynein and putative kinesin motors attached to microtubules-a significant achievement that, to our knowledge, has not been reported previously.

b) Conceptual Advances: This study provides novel insights into spermatogenesis. The findings suggest that intraflagellar transport (IFT) is unlikely to play a role at this stage of sperm development while shedding light on alternative transport systems. Importantly, the authors demonstrate that actin filaments organize in two distinct ways: clustering parallel to microtubules or forming single filaments.

This work is likely to be of considerable interest to researchers in sperm development and structural biology. Additionally, it may appeal to scientists studying motor proteins and the cytoskeleton.

We thank the reviewers for appreciating the significance and novelty of our study.

The reviewers possess extensive expertise in in situ cryo-electron tomography and single-particle microscopy, including work on dynein-based complexes. Collectively, they have significant experience in the field of cytoskeleton-based transport.

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

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Referee #1

Evidence, reproducibility and clarity

In this manuscript the authors have done cryo-electron tomography of the manchette, a microtubule-based structure important for proper sperm head formation during spermatogenesis. They also did mass-spectrometry of the isolated structures. Vesicles, actin and their linkers to microtubules within the structure are shown.

Major:

The data the conclusions are based on seem very limited and sometimes overinterpreted. For example, only one connection between actin and microtubules was observed, and this is thought to be MACF1 simply based on its presence in the MS.

Another, and larger concern, is that the authors do a structural study on something that has been purified out of the cell, a process which is extremely disruptive. Vesicles, actin and other cellular components could easily be trapped in this cytoskeletal sieve during the purification process and as such, not be bona fide manchette components. This could create both misleading proteomics and imaging. Therefore, an approach not requiring extraction such as high-pressure freezing, sectioning and room-temperature electron tomography and/or immunoEM on sections to set aside this concern is strongly recommended. As an additional bonus, it would show if the vesicles containing ATP synthase are deformed mitochondria.

Minor:

Line 99: "to study IMT with cryo-ET, manchettes were isolated ...(insert from which organism)..."

Line 102 "...demonstrating that they can be used to study IMT".. can the authors please clarify?

Line 111 "densities face towards the MT plus-end" How can a density "face" anywhere? For this, it needs to have a defined front and back.

Line 137: is the "perinuclear ring" the same as the manchette?

Figure 2B: How did the authors decide to not model the electron density found between the vesicle and the MT at 3 O'clock? Is there no other proteins with a similar lollipop structure as ATP synthase, so that this can be said to be this protein with such certainty?

Line 189: "F-actin formed organized bundles running parallel to mMTs" - this observation needs confirming in a less disrupted sample.

Line 242 remove first comma sign

Line 363 "a total of 2 datasets" - is this manuscript based on only two tilt-series? Or two datasets from each of the 4 grids? In any case, this is very limited data.

Significance

The article is very interesting, and if presented together with the suggested controls, would be informative to both microtubule/motorprotein researchers as well as those trying studying spermatogenesis.

eLife Assessment

This useful study presents a virtual reality-based contextual fear conditioning paradigm for head-fixed mice. Solid evidence supports the claim that the reported methods provide a reliable paradigm for studying contextual fear conditioning in head-fixed mice. The approach provides a way to perform multiphoton imaging of neural circuits, and other techniques that are typically performed in head-fixed animals, during behaviors that have traditionally been studied in freely moving animals.

Reviewer #1 (Public review):

The authors have developed a contextual fear learning (CFC) paradigm in head-fixed mice that produces freezing as the conditioned response. Typically, lick suppression is the conditioned response in such designs, but this 1) introduces a potential confounding influence of reward learning on neural assessments of aversion learning and 2) does not easily allow comparison of head-fixed studies with extensive previous work in freely moving animals, which use freezing as the primary conditioned response. This report describes 3 versions of this virtual reality CFC paradigm, its validation using place-cell remapping, and provides suggestions for further refinement and application.

The first part of this study is a report on the development and outcomes of 3 variations of the CFC paradigm in a virtual reality environment. The fundamental design is strong, with head-fixed mice required to run down a linear virtual track to obtain a water reward. Once trained, the water reward is no longer necessary and mice will navigate virtual reality environments. There are rigorous performance criteria to ensure that mice that make it to the experimental stage show very low levels of inactivity prior to fear conditioning. These criteria do result in only 40% of the mice making it to the experimental stage, but high rates of activity in the VR environment is crucial for detecting learning-related freezing. It is possible that further adjustments to the procedure could improve attrition rates.

Paradigm versions 1 and 2 vary the familiarity of the control context while paradigm versions 2 and 3 vary the inter-shock interval. Version 1 is the most promising, showing the greatest increase in conditioned freezing (~40%) and good discrimination between contexts (delta ~15-20%). Version 2 showed no clear evidence of learning - average freezing at recall day 1 was not different than pre-shock freezing. First lap freezing showed a difference, but this single lap effect is not useful for many of the neural circuit questions for which this paradigm is meant to facilitate. Version 3 produces greater freezing and slower extinction than version 2. While the magnitude of the context discrimination is less than that in version 1, further optimization of the VR CFC is likely to produce robust learning and extinction. The authors discuss several options for further optimization.

The second part of the study is a validation of the head-fixed CFC VR protocol through demonstration that fear conditioning leads to remapping of dorsal CA1 place fields, similar to that observed in freely moving subjects. The results support this aim and largely replicate previous findings in freely moving subjects. One difference from previous work of note is that VR CFC led to remapping of the control environment, not just the conditioning context. The authors present several possible explanations for this lack of specificity to the shock context. While this experiment examined place cell remapping after fear conditioning, it did not attempt to link neural activity to the learned association or freezing behavior.

In summary, this is an important methodological innovation and this study sets the initial parameters and neuronal validation needed to further optimize a head-fixed CFC paradigm that produces freezing. In the discussion, the authors note the limitations of this study, suggest next steps in refinement, and point to several future directions using this protocol to significantly advance our understanding of the neural circuits of threat-related learning and behavior.

Comments on revisions:

The manuscript is much stronger with the additions and revisions the authors provided in their revised submission.

Reviewer #2 (Public review):

Summary:

In this manuscript, Krishnan et al devised three paradigms to perform contextual fear conditioning in head-fixed mice. Each of the paradigms relied on head-fixed mice running on a treadmill through virtual reality arenas. The authors tested the validity of three versions of the paradigms by using various parameters. The authors have addressed some of my initial concerns in their revised manuscript.

Strengths:

The authors have devised three new contextual fear conditioning paradigms in head-fixed mice. The authors tested a number of parameters towards optimization of this approach.

Weaknesses:

While some experimental parameters were tested in the manuscript, it appears that a large amount of additional testing and optimization will be required before reliable behavioral responses can be acquired and ultimately for the paradigm(s) to be useful for answering biological questions. One major factor will be optimizing parameters such that head-fixed mice in this paradigm can (largely) recapitulate what is observed in freely behaving mice. This may be challenging however, as they have previously published one of the three paradigms and the extensive additional testing they did in this current manuscript did not greatly improve the experimental setup. This may indicate limited immediate usefulness for the community as significant work likely remains for optimization.

Achievement of Aims:

The authors have put a significant amount of work in testing the paradigms, and as a result, progress has been made towards their usefulness in the field. However, a significant amount of optimization likely exists.

Impact on the field:

The development of a reliable paradigm for studying contextual fear in head-fixed animals would be a strong contribution to the field as it would enable sophisticated cell and circuit imaging analyses. This study is a good start towards this goal, but significant optimization is required for the paradigm(s) to fully benefit the field - especially to allow those who may have less experience in these approaches to use it in their own research.

Reviewer #3 (Public review):

Summary:

Krishnan et al. present a novel contextual fear conditioning (CFC) paradigm using a virtual reality (VR) apparatus to evaluate whether conditioned context-induced freezing can be elicited in head-fixed mice. By combining this approach with two-photon imaging, the authors aim to provide high-resolution insights into the neural mechanisms underlying learning, memory, and fear. Their experiments demonstrate that head-fixed mice can discriminate between threat and non-threat contexts, exhibit fear-related behavior in VR, and show context-dependent variability during extinction. Supplemental analyses further explore alternative behaviors and the influence of experimental parameters, while hippocampal neuron remapping is tracked throughout the experiments, showcasing the paradigm's potential for studying memory formation and extinction processes.

Strengths:

Methodological Innovation: The integration of a VR-based CFC paradigm with real-time two-photon imaging offers a powerful, high-resolution tool for investigating the neural circuits underlying fear, learning, and memory.

Versatility and Utility: The paradigm provides a controlled and reproducible environment for studying contextual fear learning, addressing challenges associated with freely moving paradigms.

Potential for Broader Applications: By demonstrating hippocampal neuron remapping during fear learning and extinction, the study highlights the paradigm's utility for exploring memory dynamics, providing a strong foundation for future studies in behavioral neuroscience.

Comprehensive Data Presentation: The inclusion of supplemental figures and behavioral analyses (e.g., licking behaviors and variability in extinction) strengthens the manuscript by addressing additional dimensions of the experimental outcomes.

Weaknesses:

Optimization: many parameters remain to be tested in the VR fear conditioning paradigm.

Extended training and attrition rate: the paradigm requires weeks of training and only 40% of mice reach criteria.

Author response:

The following is the authors’ response to the original reviews

We thank all the reviewers for their time and valuable feedback, which helped us improve our manuscript. Based on the comments, we have made several critical changes to the revised manuscript.

(1) We have changed our threshold for detecting freezing epochs from 1 cm/s to 0 cm/s in this revised manuscript. This change allows us to capture periods when animals are completely still on the treadmill, better matching the "true freezing" behavior seen in freely moving set-ups. We have added a new supplementary video (Supplementary Video 2) that better demonstrates the freezing response we observe. All results and figures in the revised manuscript reflect this updated threshold (Figure 2-6, Supplementary Figures 16, Tables 1-6). Our main findings remain robust, demonstrating that freezing serves as a reliable conditioned response in our paradigms, comparable to freely moving animals. Specifically, freezing behavior increased reliably in the fear-conditioned environment following CFC across all paradigms. We have also added data from a no-shock control group (Supplementary Figure 2) which, when compared to the conditioned group, shows that freezing responses in the conditioned group result from fear conditioning rather than immobility. We do observe other avoidance behaviors unique to our treadmill-based task— such as hesitation, backward movement, and slow crawls. These conditioned behaviors are captured through a separate metric: the time taken to complete a lap.

(2) As suggested by the reviewers, we have separately analyzed fear discrimination and extinction dynamics across recall days (Supplementary Figures 2, 5 and 6, Table 1-6). To assess fear discrimination, we use within-group comparisons to evaluate how well animals differentiate between the two VRs across days. For extinction, we use within-VR comparisons to examine freezing dynamics over time. Freezing across recall days is compared to baseline freezing (pre-conditioning) using a Linear Mixed Effects model (Tables 1-6), with recall days as fixed effects and mouse as a random effect, using baseline freezing as the reference.

(3) We have expanded the behavioral dataset in Paradigm 1 to investigate the effect of shock amplitude on the conditioned fear response (Supplementary Figure 2 C-E). Consistent with findings in freely moving animals, our data show that increasing shock intensity from 0.6 mA to 1.0 mA leads to stronger freezing. For the revised manuscript, we specifically increased the sample size in the 0.6 mA group (n = 8) in Paradigm 1, as this intensity is used in Paradigm 3. These additional data demonstrate that combining a lower shock amplitude with shorter inter-shock intervals and retaining the tail-coat during recall can enhance freezing, suggesting that these parameters help compensate for lower shock intensity.

(4) We have added more sample sizes to the imaging dataset (now n = 8, Figures 7-8).

Finally, we acknowledge that many aspects of this paradigm still require optimization. The headfixed CFC paradigm is in its early stages compared to the decades of research dedicated to understanding fear learning parameters in freely moving CFC paradigms. While there are numerous parameters that could be tested—both those identified through our own discussions and those raised by the reviewers—it is not feasible for a single lab to conduct a full evaluation of all the possible factors that could influence CFC in the head-fixed prep. A key limitation is that our approach requires robust navigation behavior in the VR without rewards, which requires weeks of training per mouse. It also necessitates larger sample sizes at the outset as not all animals will make it through our behavioral criteria required for CFC. Another important consideration is scalability. Unlike freely moving CFC paradigms, which allow parallel testing of many animals with minimal pre-training, the VR-CFC setup requires several weeks of behavior training and involves a more complex integration of hardware and software to accurately track behavior in virtual space. The number of VR rigs that can be operated simultaneously in a single lab is often limited, making high-throughput testing more challenging. These factors mean that the testing of a single parameter in a group of animals requires approximately 3–4 months to complete. Despite these constraints, we are committed to continue refining this paradigm over time. With this manuscript, our main aim was to provide a detailed framework, initial parameters, and evidence for conditioned behavior in the head-fixed preparation. By doing so, we hope to facilitate the adoption of this paradigm by researchers interested in studying the neural correlates of learning and memory using multiphoton imaging and stimulation techniques. This approach enables investigations that are not possible in freely moving animals, while the presence of freezing as a conditioned response allows for direct comparisons to the extensive body of work done in freely moving paradigms. Moving forward, we anticipate that optimizing this paradigm and identifying the key parameters that drive learning will be a collaborative, community-led effort.

Public Reviews:

Reviewer #1 (Public review):

The authors set out to develop a contextual fear learning (CFC) paradigm in head-fixed mice that would produce freezing as the conditioned response. Typically, lick suppression is the conditioned response in such designs, but this (1) introduces a potential confounding influence of reward learning on neural assessments of aversion learning and (2) does not easily allow comparison of head-fixed studies with extensive previous work in freely moving animals, which use freezing as the primary conditioned response.

The first part of this study is a report on the development and outcomes of 3 variations of the CFC paradigm in a virtual reality environment. The fundamental design is strong, with headfixed mice required to run down a linear virtual track to obtain a water reward. Once trained, the water reward is no longer necessary and mice will navigate virtual reality environments. There are rigorous performance criteria to ensure that mice that make it to the experimental stage show very low levels of inactivity prior to fear conditioning. These criteria do result in only 40% of the mice making it to the experimental stage, but high rates of activity in the VR environment are crucial for detecting learning-related freezing. It is possible that further adjustments to the procedure could improve attrition rates.

We acknowledge that further adjustments to the procedure could improve attrition rates, and we will continue to work on improving the paradigm.

Paradigm versions 1 and 2 vary the familiarity of the control context while paradigm versions 2 and 3 vary the inter-shock interval. Paradigm version 1 is the most promising, showing the greatest increase in conditioned freezing (~40%) and good discrimination between contexts (delta ~15-20%). Paradigm version 2 showed no clear evidence of learning - average freezing at recall day 1 was not different than pre-shock freezing. First-lap freezing showed a difference, but this single-lap effect is not useful for many of the neural circuit questions for which this paradigm is meant to facilitate. Also, the claim that mice extinguished first-lap freezing after 1 day is weak. Extinction is determined here by the loss of context discrimination, but this was not strong to begin with. First-lap freezing does not appear to be different between Recall Day 1 and 2, but this analysis was not done.

This is an important point. Following reviewer suggestions, we have replotted our figures for all paradigms to show within-VR freezing (see Supplementary Figures 2, 5 and 6) as the appropriate method for quantifying fear extinction across days. Using an LME model (Tables 16), we quantify freezing during recall days against baseline freezing levels measured before fear conditioning within each VR. In Paradigm 2, while some fear discrimination persists across days, extinction does occur rapidly. After the first lap in the CFC VR, we observed no significant differences in freezing compared to the baseline. These results are shown in the revised Supplementary Figure 5, and the revised text is in lines 393-399.

Paradigm version 3 has some promise, but the magnitude of the context discrimination is modest (~10% difference in freezing). Thus, further optimization of the VR CFC will be needed to achieve robust learning and extinction. This could include factors not thoroughly tested in this study, including context pre-exposure timing and duration and shock intensity and frequency.

We acknowledge that many aspects of this paradigm still need optimization, as virtual reality CFC is in its early stages, and we have not explored all of the parameter space. We describe above the reasoning for this. However, for this revised version of the paper we have added new behavioral data (Supplementary Figure 2 C-E) showing that increasing shock intensities from 0.6 mA to 1 mA enhances freezing, both in the first lap and on average. There are of course many other parameters that are likely important, like the ones pointed out here by the reviewer, but exploring the entire parameter space will take many years and will likely require many labs. The purpose of this paper is to show that VR-CFC fundamentally works and is a starting point from which the field can build on. We have now pointed out in the introduction (lines 54-58) and discussion (lines 730-737, 810-814) that there remains significant scope for improving this paradigm and optimizing parameters in the future.

The second part of the study is a validation of the head-fixed CFC VR protocol through the demonstration that fear conditioning leads to the remapping of dorsal CA1 place fields, similar to that observed in freely moving subjects. The results support this aim and largely replicate previous findings in freely moving subjects. One difference from previous work of note is that VR CFC led to the remapping of the control environment, not just the conditioning context. The authors present several possible explanations for this lack of specificity to the shock context, further underscoring the need for further refinement of the CFC protocol before it can be widely applied. While this experiment examined place cell remapping after fear conditioning, it did not attempt to link neural activity to the learned association or freezing behavior.

This is an interesting observation. We think that the remapping observed in the control context likely occurred due to the absence of reward in a previously rewarded environment. Our prior work has demonstrated that removal of reward causes increased remapping (Krishnan et al., 2022, Krishnan and Sheffield, 2023). In other words, the continued presence of reward within an environment stabilizes CA1 place fields. The Moita et al. (2004) paper, which showed remapping only in the fear conditioned context and not in the control context, provided rats with food pellets throughout the experimental session in both the control and conditioned context— likely to increase exploration necessary for identifying place cells. The presence of reward in the Moita et al experiment could explain the minimal remapping observed in their control context compared to our control context which lacked reward. Another possibility could lie in the differences in the intervals between place cell activity recordings in our study and that of Moita et al. While Moita et al. separated their recordings by just one hour, our recordings were separated by a full day, with a sleep period in between. The absence of sleep and the shorter time interval between conditioning and retrieval sessions in their study could explain the minimal remapping observed by Moita et al. compared to our findings. We have now addressed this discrepancy explicitly in lines 596-606.

Although we agree with the reviewer that it would be informative to perform analysis of how neural activity correlates with freezing responses, we think this warrants its own stand-alone manuscript as the neural dynamics and methods to appropriately analyze them are complicated. We are in the midst of analyzing this data further and will present these findings in a separate publication.

In summary, this is an important study that sets the initial parameters and neuronal validation needed to establish a head-fixed CFC paradigm that produces freezing behaviors. In the discussion, the authors note the limitations of this study, suggest the next steps in refinement, and point to several future directions using this protocol to significantly advance our understanding of the neural circuits of threat-related learning and behavior.

Reviewer #2 (Public review):

Summary:

In this manuscript, Krishnan et al devised three paradigms to perform contextual fear conditioning in head-fixed mice. Each of the paradigms relied on head-fixed mice running on a treadmill through virtual reality arenas. The authors tested the validity of three versions of the paradigms by using various parameters. As described below, I think there are several issues with the way the paradigms are designed and how the data are interpreted. Moreover, as Paradigm 3 was published previously in a study by the same group, it is unclear to me what this manuscript offers beyond the validations of parameters used for the previous publication. Below, I list my concerns point-by-point, which I believe need to be addressed to strengthen the manuscript.

Major comments

(1) In the analysis using the LME model (Tables 1 and 2), I am left wondering why the mice had increased freezing across recall days as well as increased generalization (increased freezing to the familiar context, where shock was never delivered). Would the authors expect freezing to decrease across recall days, since repeated exposure to the shock context should drive some extinction? This is complicated by the analysis showing that freeing was increased only on retrieval day 1 when analyzing data from the first lap only. Since reward (e.g., motivation to run) is removed during the conditioning and retrieval tests, I wonder if what the authors are observing is related to decreased motivation to perform the task (mice will just sit, immobile, not necessarily freezing per se). I think that these aspects need to be teased out.

This is an important point and we agree teasing out a lack of motivation versus fearful freezing would be useful. To address the possibility that reduced motivation to run without reward could contribute to the observed freezing behavior, we have now included a no-shock control group in the revised manuscript (n = 7; Supplementary Figure 2A-B, H–I). These control mice experienced the same protocol, including the wearing of a tail coat, but did not receive any shocks. We observed no increases in freezing across days in these controls, confirming that the increased freezing in the Familiar context of our experimental group stems from fear conditioning rather than the removal of reward from a previously rewarded context. If reduced motivation from reward removal were the primary driver, similar freezing patterns would have emerged in the no-shock controls. We have added lines 248-261 in the revised manuscript, discussing this point, and we thank the reviewer for motivating us to do this experiment and analysis.

That said, the precise mechanisms underlying the fear generalization observed in the nonconditioned context—particularly its emergence during later recall days—remain unclear. Studies in freely moving animals have shown that fear memories initially specific to the conditioned context can become generalized with repeated exposures, which may be occurring here (Biedenkapp & Rudy, 2007; Wiltgen & Silva, 2007). Alternatively, it is possible that the combination of fear conditioning and the removal of expected reward contributes to a delayed generalization effect. This may reflect a limitation of our approach, which relies on reward to motivate initial training. As noted by another reviewer, we have now addressed this potential drawback of reward-based training in the discussion (see lines 809-817). Clearly, unique factors specific to the head-fixed VR paradigm may contribute to this phenomenon. Understanding the mechanisms underlying fear generalization in the head-fixed VR CFC paradigm will be a valuable direction for future research.

(2) Related to point 1, the authors actually point out that these changes could be due to the loss of the water reward. So, in line 304, is it appropriate to call this freezing? I think it will be very important for the authors to exactly define and delineate what they consider as freezing in this task, versus mice just simply sitting around, immobile, and taking a break from performing the task when they realize there is no reward at the end.

As noted in point 1 above, we have added a no-shock control group (n = 7; Supplementary Figure 2A-B, H–I) to determine whether the observed freezing was driven by fear conditioning or by reduced motivation to run in the absence of reward. The absence of increased freezing in these controls supports the interpretation that the behavior in the conditioned group is fearrelated. In future studies, incorporating additional physiological measures—such as heart rate monitoring—could further help distinguish fear-related freezing from other forms of immobility.

(3) In the second paradigm, mice are exposed to both novel and (at the time before conditioning) neutral environments just before fear conditioning. There is a big chance that the mice are 'linking' the memories (Cai et al 2016) of the two contexts such that there is no difference in freezing in the shock context compared to the neutral context, which is what the authors observe (Lines 333-335). The experiment should be repeated such that exposure to the contexts does not occur on the conditioning day.

This is an interesting idea. However, if memory linking were driving the observed freezing patterns, we would expect to see similarly reduced fear discrimination across all three paradigms, as mice experience both contexts sequentially in each case. However, this effect appears to be specific to Paradigm 2, suggesting this may be due to other factors. We agree it would be informative to eliminate pre-conditioning exposure to both environments—to assess whether this improves fear discrimination and helps clarify the potential contribution of memory linking. This is something we plan to do in future studies that are beyond the scope of this initial paper on VR-CFC.

(4) On lines 360-361, the authors conclude that extinction happens rapidly, within the first lap of the VR trial. To my understanding, that would mean that extinction would happen within the first 5-10 seconds of the test (according to Figure S1E). That seems far too fast for extinction to occur, as this never occurs in freely behaving mice this quickly.

We agree with the reviewer that extinction in Paradigm 2 appears to occur relatively rapidly.

However, the average time to complete the first lap in the fear-conditioned context in Paradigm 2 is 25.68 ± 5.55 seconds (as stated in line 384), indicating that extinction occurs within approximately the first 30 seconds of context exposure—not within 5–10 seconds. This is specific to Paradigm 2 and does not happen in either of the other paradigms, as shown in Supplementary Figure 4. For clarification, Figure S1E pertains to baseline running in Paradigm 1 and does not apply to Paradigm 2.

As the reviewer points out, even at 30 seconds, extinction seems to be happening more quickly in Paradigm 2 than seen in freely moving setups. This may be due to a key structural difference in our setup. The VR-CFC task is organized into discrete trials, with mice being teleported back to the start after reaching the end of the virtual track. Completing a full lap without receiving a shock could serve as a clear signal that the threat is no longer present within the environment as the completion of a lap means that the animals have surveyed all locations within the environment. This structure could accelerate extinction compared to freely moving setups, where animals take longer to explore their complete environment due to the lack of discrete trials. Although this is true for all our paradigms, the accelerated extinction seen in paradigm 2 versus 1 and 3 may be driven by other factors. As noted by the reviewers, other task parameters—such as context pre-exposure timing, shock intensity, and conditioning duration— are likely to play a role in shaping extinction dynamics. These factors warrant further investigation, and we plan to explore them in future studies to better understand the conditions influencing extinction in the VR-CFC paradigm.

(5) Throughout the different paradigms, the authors are using different shock intensities. This can lead to differences in fear memory encoding as well as in levels of fear memory generalization. I don't think that comparisons can be made across the different paradigms as too many variables (including shock intensity - 0.5/0.6mA can be very different from 1.0 mA) are different. How can the authors pinpoint which works best? Indeed, they find Paradigm 3 'works' better than Paradigm 2 because mice discriminate better between the neutral and shock contexts. This can definitely be driven by decreased generalization from using a 0.6mA shock in Paradigm 3 compared to 1.0 mA shock in Paradigm 2.

The reviewer brings up important points here. We have now added new data evaluating 0.6 mA shocks in Paradigm 1 (Supplementary Figure 2A–E, n=8). These data show that 1.0 mA shocks produced stronger conditioned responses and greater fear discrimination compared to 0.6 mA. Our goal in Paradigm 3 was to begin with a lower shock intensity and assess whether additional modifications—specifically the shorter ISI and retention of the tail-coat during recall—could enhance fear conditioning. Surprisingly, despite the weaker shock intensity, Paradigm 3 resulted in improved discrimination and freezing behavior relative to Paradigm 2. We have now clarified this point in the manuscript (lines 466-470), and we interpret this outcome as evidence that the shorter ISIs and contextual cue continuity (tail-coat) likely play a more significant role in enhancing learning and recall. However, as noted in the text (lines 511-514), further testing is needed to determine the individual contributions of each parameter to successful VR-CFC. Fully optimizing the parameter settings will take additional time and resources, and we aim to continually refine the parameter space in the future, as has been done over the years for freely moving animals.

(6) There are some differences in the calcium imaging dataset compared to other studies, and the authors should perform additional testing to determine why. This will be integral to validating their head-fixed paradigm(s) and showing they are useful for modeling circuit dynamics/behaviors observed in freely behaving mice. Moreover, the sample size (number of mice) seems low.

The one notable difference between our imaging study and that done in freely moving animals is that we observed remapping of place cells in the control context. In contrast, Moita et al. (2004) reported more stable place fields in the control context. A key distinction is that their study included rewards in the control context, which may have contributed to the spatial stability. We now discuss this difference in the manuscript (lines 599-605).

It should be noted that there are many key distinctions among paradigms that study neural activity during fear conditioning in freely moving animals. These include varying exposure times to environments (1–6 days), the time interval between neural activity recordings, and the use of food rewards during the experiment stages in freely moving animals to encourage exploration for place cell identification. Although freely moving paradigms that investigate fear conditioning and place cells are heterogeneous, we were encouraged by the replication of several key findings. This validates VR-based CFC as a viable tool for neural circuit investigations. While future work will include more thorough analyses, our current findings demonstrate the paradigm's effectiveness for modeling circuit dynamics and behavior. We have now expanded our dataset, which includes four additional mice, further corroborating these original findings.

(7) It appears that the authors have already published a paper using Paradigm 3 (Ratigan et al 2023). If they already found a paradigm that is published and works, it is unclear to me what the current manuscript offers beyond that initial manuscript.

The reviewer is correct that we have published a paper using Paradigm 3. However, this manuscript goes beyond that one and provides a much more comprehensive description and fundamental analysis of the behavior and experimental parameters regarding VR-CFC, allowing the research community to adapt our paradigm reproducibly. While Ratigan et al. (2023) offered only a minimal description of behavior and included just Paradigm 3, we present two additional paradigms along with neuronal validation using hippocampal place cells. We have now explicitly stated this in the introduction (lines 50-55).

(8) As written, the manuscript is really difficult to follow with the averages and standard error reported throughout the text. This reporting in the text occurred heterogeneously throughout the text, as sometimes it was reported and other times it was not. Cleaning this reporting up throughout the paper would greatly improve the flow of the text and qualitative description of the results.

We completely agree with this point and have now cleaned up the text, leaving details only in a few places we felt were important.

Reviewer #3 (Public review):

Summary:

Krishnan et al. present a novel contextual fear conditioning (CFC) paradigm using a virtual reality (VR) apparatus to evaluate whether conditioned context-induced freezing can be elicited in head-fixed mice. By combining this approach with two-photon imaging, the authors aim to provide high-resolution insights into the neural mechanisms underlying learning, memory, and fear. Their experiments demonstrate that head-fixed mice can discriminate between threat and non-threat contexts, exhibit fear-related behavior in VR, and show context-dependent variability during extinction. Supplemental analyses further explore alternative behaviors and the influence of experimental parameters, while hippocampal neuron remapping is tracked throughout the experiments, showcasing the paradigm's potential for studying memory formation and extinction processes.

Strengths:

Methodological Innovation: The integration of a VR-based CFC paradigm with real-time twophoton imaging offers a powerful, high-resolution tool for investigating the neural circuits underlying fear, learning, and memory.

Versatility and Utility: The paradigm provides a controlled and reproducible environment for studying contextual fear learning, addressing challenges associated with freely moving paradigms.

Potential for Broader Applications: By demonstrating hippocampal neuron remapping during fear learning and extinction, the study highlights the paradigm's utility for exploring memory dynamics, providing a strong foundation for future studies in behavioral neuroscience.

Comprehensive Data Presentation: The inclusion of supplemental figures and behavioral analyses (e.g., licking behaviors and variability in extinction) strengthens the manuscript by addressing additional dimensions of the experimental outcomes.

Weaknesses:

Characterization of Freezing Behavior: The evidence supporting freezing behavior as the primary defensive response in VR is unclear. Supplementary videos suggest the observed behaviors may include avoidance-like actions (e.g., backing away or stopping locomotion) rather than true freezing. Additional physiological measurements, such as EMG or heart rate, are necessary to substantiate the claim that freezing is elicited in the paradigm.

To strengthen our claim that freezing is a conditioned response in this task, we have taken three key steps:

(1) We adjusted our freezing detection threshold from 1 cm/s to near 0 cm/s to capture only periods where the animal is virtually motionless on the treadmill. We validated this approach in Figure 2, particularly in the zoomed-in track position trace in Figure 2A, which clearly shows that the identified freezing epochs correspond to no change in track position. All analyses and figures have been updated to reflect this more stringent threshold.

(2) We have added a no-shock control group in the revised manuscript (n = 7; Supplementary Figure 2A-B, H–I) where mice experienced the same protocol, including wearing a tail-coat, but received no shocks. These mice showed no increases in freezing behavior, which further demonstrates that the increased freezing we observe is a result of fear conditioning.

(3) We have added a new supplementary video (Supplementary Video 2) that better illustrates the freezing behavior in our task.

That said, we fully agree with the reviewer that freezing is not the only defensive response observed. Other behaviors—such as hesitation, backward movement, and slowing down—also emerge that are unique to our treadmill-based paradigm. We chose to focus on freezing in this manuscript to align with convention in freely moving fear conditioning studies and to facilitate direct comparisons. We agree that additional physiological measurements (e.g., EMG or heart rate) would provide further validation and could help distinguish between different forms of defensive responses. We view this as an important future direction and plan to incorporate such measures in upcoming studies. We highlight this in the results section (lines 175-179, 262-268) and in the discussion (lines 739-750).

Analysis of Extinction: Extinction dynamics are only analyzed through between-group comparisons within each Recall day, without addressing within-group changes in behavior across days. Statistical comparisons within groups would provide a more robust demonstration of extinction processes.

This is an important distinction and we have now added figures (Supplementary Figures 2H-I, 5C-D, 6C-D) showing within-VR behavior across Recall days, along with statistical comparisons and a description of the extinction process based on these results.

Low Sample Sizes: Paradigm 1 includes conditions with very low sample sizes (N=1-3), limiting the reliability of statistical comparisons regarding the effects of shock number and intensity.

Increasing sample sizes or excluding data from mice that do not match the conditions used in Paradigms 2 and 3 would improve the rigor of the analysis.

While we included all conditions in Figure 2 for completeness, we have separated these conditions in Supplementary Figure 2 to ensure clarity. This allows researchers interested in this paradigm to see the approximate range of conditioned responses observed across different parameters. When comparing Paradigm 1 with Paradigms 2 and 3, we have only used data from 1mA, 6 shocks condition.

Potential Confound of Water Reward: The authors critique the use of reward in conjunction with fear conditioning in prior studies but do not fully address the potential confound introduced by using water reward during the training phase in their own paradigm.

We agree this is a point that needs discussion. We have now noted the limitation of using water rewards during training in the discussion section, particularly its effect on the animal’s motivation in the long term and on place cell activity (lines 814-820).

Recommendations for the authors

Reviewer #1 (Recommendations for the authors):

I suggest changing "3 paradigms" to "3 versions of a CFC paradigm," as the paradigm is fundamentally the same, but parameters were adjusted towards finding an optimal protocol.

We have changed this phrasing where applicable.

Figure S2: There appear to be different sets of shock parameters for different mice, most with an n of 1 or 2. This is not reliable for making a decision for optimal shock parameters and should not be discussed in that way until a full-powered comparison is completed. Also, the N adds up to 19, yet only 18 are described as being included in the study.

We thank the reviewer for this important point. We agree that the current study is not powered to definitively identify optimal parameter settings. We have been careful not to interpret it in that way in the text. Rather, we adopted a commonly used starting point from the freely moving literature—1 mA with six shocks—as our initial condition (lines 196-199). To provide context for others interested in pursuing this work, we have presented a range of conditioned responses from different parameter combinations to illustrate potential variability. In most cases, these data are intended for illustrative purposes only and are not meant to support firm conclusions. We agree that a systematic and fully powered investigation of each parameter would be highly valuable, and we plan to pursue this in future work (and hope other labs contribute to this goal, too), much like the iterative optimizations performed in freely moving paradigms over time.

We thank the reviewer for catching the sample size discrepancy and have now corrected it.

The number of animals for the no-shock condition should be included.

Thank you. We have now included this.

A possible explanation for the lower fear and poorer discrimination in versions 2 and 3 could be that 10 min pre-exposure to the CFC context on day -1 led to latent inhibition. Shorter (or eliminated) pre-exposure may improve outcomes.

We agree that the exposure time is a parameter that we should explore. We have highlighted this in the discussion (lines 729-736) as a parameter that is worth testing in the future.

For analysis of extinction, it is best to establish this within condition - is freezing to the CFC context significantly reduced compared with initial recall and similar to pre-training freezing? By using discrimination as your index of extinction, increases in control context freezing/inactivity can eliminate context discrimination without the conditioned response of freezing actually undergoing extinction.

This is a good point, and we have now included analysis and conclusions based on a within-VR comparison for the analysis of fear extinction (Supplementary Figures 2H-I, 5C-D, 6C-D).

Reviewer #3 (Recommendations for the authors):

Clarification of Treadmill Shape: The manuscript describes the treadmill as "spherical" throughout. However, based on representative images and videos, the treadmill appears cylindrical. This discrepancy should be clarified to ensure consistency between the text and visuals.

The reviewer is correct that the treadmill is cylindrical, and this was an error on our part. We have corrected it throughout.

Figure and Legend Labeling: To improve clarity, all figures and their legends should be explicitly labeled with the corresponding paradigm (1, 2, or 3) to facilitate interpretation.

We have now added a label on all figures that clarifies which Paradigm the figures are referring to. We have also explicitly added this to the figure legends.

Objective Language: Subjective language, such as "since we wanted animals to" (Line 850), should be revised to reflect an objective tone (e.g., "to allow animals to"). Similarly, phrases like "We believe" (Line 896) should be avoided to maintain an unbiased presentation.

We have removed subjective language from our text.

Placement of Future Directions: Speculations on future experimental plans, such as the use of sex as a biological variable (Lines 895-903), should be included in the Discussion section rather than the Methods. Additionally, remarks about the responsiveness of female mice to tail shocks should be moved to the main text for proper contextualization.

We have moved these lines as suggested by the reviewer.

• Whether the mean is the best summary metric depends on the distribution of the variable.
• If it is normal distribution, then mean is a good summary metric, otherwise, median is better.
• we can use function shapiro.test() to test the Normality of a variable
• since shapiro.test() limits the maximum length of a vector of 5000, we can subset 5000 elements of gss_cat$tvhours to perform this test.

gss_cat$tvhours %>% head(5000) %>% shapiro.test()

results: p-value < 0.01, which means the tvhours variable is not normal distribution, therefore, mean is not a good summary metric for this variable, we'd better use median.

Sarah Everett suggests using a saw to turn Phillips head screws into a flat head screw if they've been stripped.

CHECK_COMPATIBILITY

add a space after the period

maybe just snap?

① The anatomy and physiology of this region are unique and complex. ① Bu bölgenin anatomi ve fizyolojisi benzersiz ve karmaşıktır.

② Function and appearance are critical to patients images quality‑of‑life, ② İşlev ve görünüm, hastaların yaşam kalitesi algısı açısından kritik öneme sahiptir.

③ most patients with head and neck squamous cell carcinoma are middle‑aged, adult males in lower socioeconomic classes who are chronic tobacco chewer and alcohol consumers have advanced tumors. ③ Baş ve boyun yassı hücreli karsinomu olan hastaların çoğu, düşük sosyoekonomik sınıfa mensup, orta yaşlı, erişkin erkeklerdir ve bu kişiler genellikle kronik tütün çiğneyicisi ve alkol tüketicisidir; tümörleri de ileri evrededir.

④ These patients tend to less conscious and to have less social support then most cancer patients. ④ Bu hastalar, çoğu kanser hastasına kıyasla genellikle daha az bilinçli olma ve daha az sosyal desteğe sahip olma eğilimindedir.

① Head and neck cancer is a term used to define cancer that develops in the mouth, throat, nose, salivary glands, oral cancers or other areas of the head and neck. ① Baş ve boyun kanseri, ağız, boğaz, burun, tükürük bezleri, ağız kanserleri veya baş ve boynun diğer bölgelerinde gelişen kanserleri tanımlamak için kullanılan bir terimdir.

② Most of these cancers are squamous cell carcinomas, or cancers that begin in the lining of the mouth, nose and throat. ② Bu kanserlerin çoğu, ağız, burun ve boğazın yüzeyini döşeyen hücrelerde başlayan yassı hücreli karsinomlardır.

③ Eighty-five percent of head and neck cancers are linked to tobacco use, ③ Baş ve boyun kanserlerinin %85’i tütün kullanımıyla ilişkilidir,

④ and 75 percent are associated with a combination of tobacco and alcohol use (tobacco and alcohol are strong synergistic effects of oral cancer). ④ ve %75’i tütün ve alkol kullanımının birleşimiyle ilişkilidir (tütün ve alkol, ağız kanseri üzerinde güçlü sinerjistik etkiye sahiptir).

⑤ Human papilloma virus especially types 16 and 18 are known risk factors (there are over 100 variables) and independent causative factor for oral cancer. ⑤ İnsan papilloma virüsü, özellikle 16 ve 18 tipleri, bilinen risk faktörleri olup (100’den fazla varyantı vardır), ağız kanseri için bağımsız bir neden olarak kabul edilmektedir.

⑥ Because of their location, head and neck tumors and treatment-related side effects may impair patients’ ability to eat, swallow and breathe. ⑥ Bulundukları konum nedeniyle, baş ve boyun tümörleri ile tedaviye bağlı yan etkiler, hastaların yeme, yutma ve nefes alma yetilerini bozabilir.

This passage reveals the deeply spiritual and symbolic nature of Mongol domestic life, with each dwelling containing felt figures that represent both personal and household guardians. It’s notable that Rubruck focuses on these figures not as mere decorations, but as part of daily ritual life, emphasizing how the Mongols integrated their belief system into the structure of their homes. This challenges Western stereotypes of the Mongols as purely nomadic warriors, showing instead a rich and organized spiritual culture that informed their everyday routines.

It is once again how schools pull us away from uncomfortable truths — by neglecting or performing, as they might describe it, the “sanitary” action of removing complex pieces of history. Explaining that the fear of naming powerfully interprets why racism and other forms of injustice are so rarely tackled head-on in classrooms — they’re too disruptive. But Kozol's book adds another layer: while the inclusions themselves might be significant, they’re stripped of their radical and inspirational qualities to become more “acceptable.” It has all the effect of erasing their true presence. But along with the small events that do happen, taking this detour means depriving students of its deeper, more empowering understanding — making not to mention that, once they begin it, a tragedy.

Author response:

The following is the authors’ response to the original reviews

Reviewer #1 (Public Review):

Summary:

In this manuscript, Ning et al. reported that Bcas2 played an indispensable role in zebrafish primitive hematopoiesis via sequestering β-catenin in the nucleus. The authors showed that loss of Bcas2 caused primitive hematopoietic defects in zebrafish. They unraveled that Bcas2 deficiency promoted β-catenin nuclear export via a CRM1-dependent manner in vivo and in vitro. They further validated that BCAS2 directly interacted with β-catenin in the nucleus and enhanced β-catenin accumulation through its CC domains. They unveil a novel insight into Bcas2, which is critical for zebrafish primitive hematopoiesis via regulating nuclear β-catenin stabilization rather than its canonical pre-mRNA splicing functions. Overall, the study is impressive and well-performed, although there are also some issues to address.

Strengths:

The study unveils a novel function of Bcas2, which is critical for zebrafish primitive hematopoiesis by sequestering β-catenin. The authors validated the results in vivo and in vitro. Most of the figures are clear and convincing. This study nicely complements the function of Bcas2 in primitive hematopoiesis.

Weaknesses:

A portion of the figures were over-exposed.

Thank you for the time reviewing our manuscript. We agree with your suggestion and the exposure of Figure 5C and Figure 7E has been reduced. We hope that the revisions will meet your expectation.

Reviewer #2 (Public Review):

Summary:

Ning and colleagues present studies supporting a role for breast carcinoma amplified sequence 2 (Bcas2) in positively regulating primitive wave hematopoiesis through amplification of beta-catenin-dependent (canonical) Wnt signaling. The authors present compelling evidence that zebrafish bcas2 is expressed at the right time and place to be involved in primitive hematopoiesis, that there are primitive hematopoietic defects in hetero- and homozygous mutant and knockdown embryos, that Bcas2 mechanistically positively regulates canonical Wnt signaling, and that Bcas2 is required for nuclear retention of B-cat through physical interaction involving armadillo repeats 9-12 of B-cat and the coiled-coil domains of Bcas2. Overall, the data and writing are clean, clear, and compelling. This study is a first-rate analysis of a strong phenotype with highly supportive mechanistic data. The findings shed light on the controversial question of whether, when, and how canonical Wnt signaling may be involved in hematopoietic development. We detail some minor concerns and questions below, which if answered, we believe would strengthen the overall story and resolve some puzzling features of the phenotype. Notwithstanding these minor concerns, we believe this is an exceptionally well-executed and interesting manuscript that is likely suitable for publication with minor additional experimental detail and commentary.

Strengths:

(1) The study features clear and compelling phenotypes and results.

(2) The manuscript narrative exposition and writing are clear and compelling.

(3) The authors have attended to important technical nuances sometimes overlooked, for example, focusing on different pools of cytosolic or nuclear b-catenin.

(4) The study sheds light on a controversial subject: regulation of hematopoietic development by canonical Wnt signaling and presents clear evidence of a role.

(5) The authors present evidence of phylogenetic conservation of the pathway.

Weaknesses:

(1) The authors present compelling data that Bcas2 regulates nuclear retention of B-cat through physical association involving binding between the Bcas2 CC domains and B-cat arm repeats 9-12. Transcriptional activation of Wnt target genes by B-cat requires physical association between B-cat and Tcf/Lef family DNA binding factors involving key interactions in Arm repeats 2-9 (Graham et al., Cell 2000). Mutually exclusive binding by B-cat regulatory factors, such as ICAT that prevent Tcf-binding is a documented mechanism (e.g. Graham et al., Mol Cell 2002). It would appear - based on the arm repeat usage by Bcas2 (repeats 9-12)-that Bcas2 and Tcf binding might not be mutually exclusive, which would support their model that Bcas2 physical association with B-cat to retain it in the nucleus would be compatible with co-activation of genes by allowing association with Tcf. It might be nice to attempt a three-way co-IP of these factors showing that B-cat can still bind Tcf in the presence of Bcas2, or at least speculate on the plausibility of the three-way interaction.

We appreciate your assessment and generous comments for the manuscript. As you mentioned, the binding sites for TCF on β-catenin almost do not overlap with those for BCAS2. It is likely that BCAS2-mediated nuclear sequestration of β-catenin would be compatible with the initiation of gene transcription by allowing TCF to associate with β-catenin. To test this possibility, we have taken your suggestion and performed co-IP assays. The results showed that β-catenin still bound with TCF4 in the presence of BCAS2 (Supplemental Figure 12), confirming that the binding of BCAS2 to β-catenin would not interfere with the formation of β-catenin/TCF complex.

(2) A major way that canonical Wnt signaling regulates hematopoietic development is through regulation of the LPM hematopoietic competence territories by activating expression of cdx1a, cdx4, and their downstream targets hoxb5a and hoxa9a (Davidson et al., Nature 2003; Davidson et al., Dev Biol 2006; Pilon et al., Dev Biol 2006; Wang et al., PNAS 2008). Could the authors assess (in situ) the expression of cdx1a, cdx4, hoxb5a, and hoxa9a in the bcas2 mutants?

We agree with your suggestion and have examined the expression of cdx4 and hoxa9a by performing WISH. Diminished expression of cdx4 and hoxa9a was detected in the lateral plate mesoderm of bcas2^+/- embryos at the 6-somite stage (Supplemental Figure 7).

(3) The authors show compellingly that even heterozygous loss of bcas2 has strong Wnt-inhibitory effects. If Bcas2 is required for canonical Wnt signaling and bcas2 is expressed ubiquitously from the 1-cell stage through at least the beginning of gastrulation, why do bcas2 KO embryos not have morphological axis specification defects consistent with loss of early Wnt signaling, like loss of head (early), or brain anteriorization (later)? Could the authors provide some comments on this puzzle? Or if they do see any canonical Wnt signaling patterning defects in het- or homozygous embryos, could they describe and/or present them?

You have raised an interesting question. In fact, we did not observe ventralization or axis determination defects in the early embryos of bcas2^+/- mutants. Even in the very small number of homozygous mutant embryos, we did not find such morphological defects. Given that the homozygous and heterozygous mutant embryos were derived from crossing bcas2^+/- males with bcas2^+/- females, maternal Bcas2 might still remain and function in these embryos during gastrulation when axis determination and neural patterning took place. Accordingly, we have expanded our discussion to incorporate these insights (Line 565-572).

Reviewer #3 (Public Review):

Summary:

This manuscript utilized zebrafish bcas2 mutants to study the role of bcas2 in primitive hematopoiesis and further confirms that it has a similar function in mice. Moreover, they showed that bcas2 regulates the transition of hematopoietic differentiation from angioblasts via activating Wnt signaling. By performing a series of biochemical experiments, they also showed that bcas2 accomplishes this by sequestering b-catenin within the nucleus, rather than through its known function in pre-mRNA splicing.

Strengths:

The work is well-performed, and the manuscript is well-written.

Weaknesses:

Several issues need to be clarified.

(1) Is wnt signaling also required during hematopoietic differentiation from angioblasts? Can the authors test angioblast and endothelial markers in embryos with wnt inhibition? Also, can the authors add export inhibitor LMB to the mouse mutants to test if sequestering of b-catenin by bcas2 is conserved during primitive hematopoiesis in mice?

Thank you very much for your appreciation and detailed assessment. To test whether Wnt signaling is also required during hematopoietic differentiation from angioblasts, wild-type embryos were exposed to 10 µM CCT036477, a small molecule β-catenin antagonist, from 9 hpf and then collected for WISH experiments. As shown in Supplemental Figure 8, the expression of hemangioblast markers npas4l, scl, and gata2 and endothelial marker fli1a remained unchanged, but the expression of erythroid progenitor marker gata1 was significantly reduced. These results suggest that canonical Wnt pathway may not be required for the generation of hemangioblasts or their endothelial differentiation, but is pivotal for their hematopoietic differentiation.

It is quite difficult to validate the conserve role of BCAS2 during primitive hematopoiesis in mice, because the toxicity of LMB may cause severe adverse effects in mice.[1,2]

(2) Bcas2 is required for primitive myelopoiesis in ALM. Does bcas2 play a similar function in primitive myelopoiesis, or is bcas2/b-catenin interaction more important for hematopoietic differentiation in PLM?

You have raised an important question. In our study, we have demonstrated that the expression of myeloid progenitor marker pu.1 was significantly decreased in bcas2 mutants, hinting that Bcas2 is pivotal for primitive myelopoiesis. To further clarify the function of Bcas2 in primitive myelopoiesis, we injected 8 ng of bcas2 morpholino into Tg(coro1a:GFP) embryos at the 1-cell stage and examined β-catenin distribution at 17 hpf via immunostaining. We observed a significant decline of nuclear β-catenin in primitive myeloid cells (Supplemental Figure 9), indicating that Bcas2 is highly likely to play a similar role in sequestering β-catenin within the nucleus during primitive myelopoiesis.

(3) Is it possible that CC1-2 fragment sequester b-catenin? The different phenotypes between this manuscript and the previous article (Yu, 2019) may be due to different mutations in bcas2. Is it possible that the bcas2 mutation in Yu's article produces a complete CC1-2 fragment, which might sequester b-catenin?

This is an interesting perspective. To test the possibility that CC1-2 sequesters β-catenin, mRNA expressing the CC domains of BCAS2 has been co-injected with bcas2 morpholino into Tg(gata1:GFP) embryo at the one-cell stage. Increased nuclear β-catenin levels were detected in the GFP-positive hematopoietic progenitor cells at 16 hpf (Supplemental Figure 11). Our findings support that CC1-2 fragment of BCAS2 can sequester β-catenin within the nucleus.

In the previous article (Yu, 2019), a deletion 5 bases mutation in the third exon of BCAS2 was produced by TALEN, therefore the CC domains of this mutant should be affected. It is difficult to conclude that the mutant BCAS2 protein in Yu’s study still remains association with β-catenin.

(4) Can the author clarify what embryos the arrows point to in SI Figure 2D? In SI Figure 6B and B', can the author clarify how the nucleus and cytoplasm are bleached? In B, the nucleus also appears to be bleached.

Thank you for your query and suggestion. In our revisions, the corresponding clarifications have been supplemented (Line 239-242; Line 978-979).

We acknowledge that the nuclei in both the BCAS2 overexpression group and control group were slightly bleached. Given that we have performed real-time analysis for fluorescent recovery after photobleaching, and we have observed a much slower recovery of cytoplasmic fluorescence in BCAS2 overexpressed cells, the conclusion that BCAS2 inhibits the nuclear export of β-catenin but not its nuclear import, remains changed.

Reviewer #1 (Recommendations For The Authors):

Major concerns:

(1) In this study, the authors detected β-catenin distribution in erythrocytes (gata1-GFP+ cells). Estimating the β-catenin distribution in the myeloid cells is recommended.

Thank you for your assessment and we have taken your suggestion. Tg(coro1a:GFP) embryos, which is commonly used to track both macrophages and neutrophils,[3] were injected with 8 ng of bcas2 morpholino into at the 1-cell stage and collected for immunostaining to examine the β-catenin distribution at 17 hpf. We observed a significant decline of nuclear β-catenin in primitive myeloid cells (Supplemental Figure 9). This result indicates that Bcas2 is highly likely to play a similar role in sequestering β-catenin within the nucleus during primitive myelopoiesis.

(2) The reduced nuclear localization of β-catenin in Figure 3H required further evidence. It would be helpful if the authors quantified the fluorescence intensity in the cell nucleus and cytoplasm. Meanwhile, the figures (Figure 5C, Figure 7E) were over-exposed. Please validate these figures.

Thank you for your suggestions. We agree with you that the fluorescence intensity of β-catenin in the nucleus and cytoplasm should be quantified. However, as the nucleus comprises a large part of the cell, we believe it would be more appropriate to quantify the relative fluorescence intensity by dividing the fluorescence intensity of nuclear β-catenin by the fluorescence intensity of DAPI.

Such quantifications have been added for Figure 3G, 5C, 7E, S9A, and S13A. In addition, we have reduced the exposure of Figure 5C and Figure 7E. We hope that you will be satisfied with the revisions.

(3) The authors used cKO mice to validate that the erythrocytes were eliminated. It would be interesting to detect β-catenin distribution by immunofluorescent staining in primitive hematopoietic cells in cKO mice. Addressing this issue can provide further evidence to support the conservation of Bcas2.

We appreciate your suggestion. However, we found that red blood cells were almost eliminated in the yolk sac of Bcas2^F/F;Flk1-Cre mice at E12.5. It is difficult to further detect β-catenin distribution in primitive erythroid cells in these mice.

(4) The authors discovered that Bcas2 mediated β-catenin nuclear export in a CRM1-dependent manner. CRM1 is a key regulator involved in the majority of factors of nuclear export via recognizing specific nuclear export signals (NES). Validating the NES of Bcas2 is recommended. Furthermore, I wonder about the relationship between Bcas2 and CRM1 in regulating β-catenin nuclear export. One possibility is that Bcas2 covers the NES to inhibit the interaction between CRM1 and β-catenin, thus leading to β-catenin accumulation in the cell nucleus. The authors should discuss this possibility accordingly.

Thank you for providing an interesting perspective. CRM1-mediated nuclear export of β-catenin usually requires CRM1 recognition and binding with the NES sequences in chaperon proteins, such as APC, Axin and Chibby.[4-6] Moreover, CRM1 can bind directly to and function as an efficient nuclear exporter for β-catenin.[7] Since BCAS2 has not been reported to contain any recognizable NES sequences, it will be interesting to investigate whether BCAS2 competitively inhibits β-catenin from associating with CRM1, or with the chaperone proteins. We have rewritten the discussion on CRM1-dependent nuclear export of β-catenin in line with your comments (Line 572-578).

(5) It would be interesting if the authors could answer the specificity in Bcas2-mediated protein nuclear export pathway. The authors should detect other classical factors (CRM1 mediated) distribution when loss of Bcas2.

Thank you for bringing up this point. To test whether BCAS2 specifically regulates CRM1-mediated nuclear export of β-catenin, we have investigated the nucleocytoplasmic distribution of other known CRM1 cargoes, such as ATG3 and CDC37L.[8] BCAS2 overexpression in HeLa cells slightly enhanced the nuclear localization of CDC37L, and had no significant impact on that of ATG3 (Supplemental Figure 11), indicating the specificity of BCAS2 in the regulation of CRM1-dependent nuclear export of β-catenin.

Minor concerns:

(1) The name "bcas2Δ7+/- and bcas2Δ14+/-" should be changed into "bcas2+/Δ7 and bcas2+/Δ14"(+/Δ7 or +/Δ14 should be superior on the right).

Thank you for your suggestion. We have changed the names of the mutants throughout the manuscript.

(2) The scale bar position in the figures should be unified.

We agree with your suggestion and have unified the scale bar position in all figures.

(3) In Figure 4E, "Nuclear" should be changed into "Nucleus".

We apologize for the mistake and Figure 4E has been revised.

(4) There are some unaesthetic issues in the figures. The figures need to be further edited. Figure 3H "β-catenin and Merge", Figure 4D "Merge". All these words should be centered in the figures.

Thank you. We have edited all the figures to ensure that the text is centered.

Reviewer #2 (Recommendations For The Authors):

(1) It would be nice to have whole blot images for the Westerns in Supplementary Info.

Thank you for your suggestion. Whole images for immunoblotting have been supplemented as Source data.

(2) Line 292 change 5 hpf to 5 dpf.

(3) Line 301 change "primary" to "primitive"?

We apologize for the mistakes. We have incorporated these suggestions in the revised manuscript and reexamined spelling throughout the paper.

(4) Figure S2C: is "Maker" a typographical error? Change to "ladder"?

We apologize for this typographical error and we have revised it in Figure S2C.

Reference

(1) Ishizawa J, Kojima K, Hail N, Tabe Y, Andreeff M. Expression, function, and targeting of the nuclear exporter chromosome region maintenance 1 (CRM1) protein. Pharmacology & Therapeutics. 2015;153:25-35.

(2) Li X, Feng Y, Yan MF, et al. Inhibition of Autism-Related Crm1 Disrupts Mitosis and Induces Apoptosis of the Cortical Neural Progenitors. Cerebral Cortex. 2020;30(7):3960-3976.

(3) Li L, Yan B, Shi YQ, Zhang WQ, Wen ZL. Live Imaging Reveals Differing Roles of Macrophages and Neutrophils during Zebrafish Tail Fin Regeneration. Journal of Biological Chemistry. 2012;287(30):25353-25360.

(4) Neufeld KL, Nix DA, Bogerd H, et al. Adenomatous polyposis coli protein contains two nuclear export signals and shuttles between the nucleus and cytoplasm. Proceedings of the National Academy of Sciences of the United States of America. 2000;97(22):12085-12090.

(5) Li FQ, Mofunanya A, Harris K, Takemaru KI. Chibby cooperates with 14-3-3 to regulate β-catenin subcellular distribution and signaling activity. Journal of Cell Biology. 2008;181(7):1141-1154.

(6) Cong F, Varmus H. Nuclear-cytoplasmic shuttling of Axin regulates subcellular localization of β-catenin. Proceedings of the National Academy of Sciences of the United States of America. 2004;101(9):2882-2887.

(7) Ki H, Oh M, Chung SW, Kim K. β-Catenin can bind directly to CRM1 independently of adenomatous polyposis coli, which affects its nuclear localization and LEF-1/β-catenin-dependent gene expression. Cell Biology International. 2008;32(4):394-400.

(8) Kirli K, Karaca S, Dehne HJ, et al. A deep proteomics perspective on CRM1-mediated nuclear export and nucleocytoplasmic partitioning. Elife. 2015;4.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

Using a cross-modal sensory selection task in head-fixed mice, the authors attempted to characterize how different rules reconfigured representations of sensory stimuli and behavioral reports in sensory (S1, S2) and premotor cortical areas (medial motor cortex or MM, and ALM). They used silicon probe recordings during behavior, a combination of single-cell and population-level analyses of neural data, and optogenetic inhibition during the task.

Strengths:

A major strength of the manuscript was the clarity of the writing and motivation for experiments and analyses. The behavioral paradigm is somewhat simple but well-designed and wellcontrolled. The neural analyses were sophisticated, clearly presented, and generally supported the authors' interpretations. The statistics are clearly reported and easy to interpret. In general, my view is that the authors achieved their aims. They found that different rules affected preparatory activity in premotor areas, but not sensory areas, consistent with dynamical systems perspectives in the field that hold that initial conditions are important for determining trial-based dynamics.

Weaknesses:

The manuscript was generally strong. The main weakness in my view was in interpreting the optogenetic results. While the simplicity of the task was helpful for analyzing the neural data, I think it limited the informativeness of the perturbation experiments. The behavioral read-out was low dimensional -a change in hit rate or false alarm rate- but it was unclear what perceptual or cognitive process was disrupted that led to changes in these read-outs. This is a challenge for the field, and not just this paper, but was the main weakness in my view. I have some minor technical comments in the recommendations for authors that might address other minor weaknesses.

I think this is a well-performed, well-written, and interesting study that shows differences in rule representations in sensory and premotor areas and finds that rules reconfigure preparatory activity in the motor cortex to support flexible behavior.

Reviewer #2 (Public Review):

Summary:

Chang et al. investigate neuronal activity firing patterns across various cortical regions in an interesting context-dependent tactile vs visual detection task, developed previously by the authors (Chevee et al., 2021; doi: 10.1016/j.neuron.2021.11.013). The authors report the important involvement of a medial frontal cortical region (MM, probably a similar location to wM2 as described in Esmaeili et al., 2021 & 2022; doi: 10.1016/j.neuron.2021.05.005; doi: 10.1371/journal.pbio.3001667) in mice for determining task rules.

Strengths:

The experiments appear to have been well carried out and the data well analysed. The manuscript clearly describes the motivation for the analyses and reaches clear and well-justified conclusions. I find the manuscript interesting and exciting!

Weaknesses:

I did not find any major weaknesses.

Reviewer #3 (Public Review):

This study examines context-dependent stimulus selection by recording neural activity from several sensory and motor cortical areas along a sensorimotor pathway, including S1, S2, MM, and ALM. Mice are trained to either withhold licking or perform directional licking in response to visual or tactile stimulus. Depending on the task rule, the mice have to respond to one stimulus modality while ignoring the other. Neural activity to the same tactile stimulus is modulated by task in all the areas recorded, with significant activity changes in a subset of neurons and population activity occupying distinct activity subspaces. Recordings further reveal a contextual signal in the pre-stimulus baseline activity that differentiates task context. This signal is correlated with subsequent task modulation of stimulus activity. Comparison across brain areas shows that this contextual signal is stronger in frontal cortical regions than in sensory regions. Analyses link this signal to behavior by showing that it tracks the behavioral performance switch during task rule transitions. Silencing activity in frontal cortical regions during the baseline period impairs behavioral performance.

Overall, this is a superb study with solid results and thorough controls. The results are relevant for context-specific neural computation and provide a neural substrate that will surely inspire follow-up mechanistic investigations. We only have a couple of suggestions to help the authors further improve the paper.

(1) We have a comment regarding the calculation of the choice CD in Fig S3. The text on page 7 concludes that "Choice coding dimensions change with task rule". However, the motor choice response is different across blocks, i.e. lick right vs. no lick for one task and lick left vs. no lick for the other task. Therefore, the differences in the choice CD may be simply due to the motor response being different across the tasks and not due to the task rule per se. The authors may consider adding this caveat in their interpretation. This should not affect their main conclusion.

We thank the Reviewer for the suggestion. We have discussed this caveat and performed a new analysis to calculate the choice coding dimensions using right-lick and left-lick trials (Fig. S3h) on page 8. 

“Choice coding dimensions were obtained from left-lick and no-lick trials in respond-to-touch blocks and right-lick and no-lick trials in respond-to-light blocks. Because the required lick directions differed between the block types, the difference in choice CDs across task rules (Fig. S4f) could have been affected by the different motor responses. To rule out this possibility, we did a new version of this analysis using right-lick and left-lick trials to calculate the choice coding dimensions for both task rules. We found that the orientation of the choice coding dimension in a respond-to-touch block was still not aligned well with that in a respond-to-light block (Fig. S4h;  magnitude of dot product between the respond-to-touch choice CD and the respond-to-light choice CD, mean ± 95% CI for true vs shuffled data: S1: 0.39 ± [0.23, 0.55] vs 0.2 ± [0.1, 0.31], 10 sessions; S2: 0.32 ± [0.18, 0.46] vs 0.2 ± [0.11, 0.3], 8 sessions; MM: 0.35 ± [0.21, 0.48] vs 0.18 ± [0.11, 0.26], 9 sessions; ALM: 0.28 ± [0.17, 0.39] vs 0.21 ± [0.12, 0.31], 13 sessions).”

We also have included the caveats for using right-lick and left-lick trials to calculate choice coding dimensions on page 13.

“However, we also calculated choice coding dimensions using only right- and left-lick trials. In S1, S2, MM and ALM, the choice CDs calculated this way were also not aligned well across task rules (Fig. S4h), consistent with the results calculated from lick and no-lick trials (Fig. S4f). Data were limited for this analysis, however, because mice rarely licked to the unrewarded water port (# of licksunrewarded port  / # of lickstotal , respond-to-touch: 0.13, respond-to-light: 0.11). These trials usually came from rule transitions (Fig. 5a) and, in some cases, were potentially caused by exploratory behaviors. These factors could affect choice CDs.”

(2) We have a couple of questions about the effect size on single neurons vs. population dynamics. From Fig 1, about 20% of neurons in frontal cortical regions show task rule modulation in their stimulus activity. This seems like a small effect in terms of population dynamics. There is somewhat of a disconnect from Figs 4 and S3 (for stimulus CD), which show remarkably low subspace overlap in population activity across tasks. Can the authors help bridge this disconnect? Is this because the neurons showing a difference in Fig 1 are disproportionally stimulus selective neurons?

We thank the Reviewer for the insightful comment and agree that it is important to link the single-unit and population results. We have addressed these questions by (1) improving our analysis of task modulation of single neurons  (tHit-tCR selectivity) and (2) examining the relationship between tHit-tCR selective neurons and tHit-tCR subspace overlaps.  

Previously, we averaged the AUC values of time bins within the stimulus window (0-150 ms, 10 ms bins). If the 95% CI on this averaged AUC value did not include 0.5, this unit was considered to show significant selectivity. This approach was highly conservative and may underestimate the percentage of units showing significant selectivity, particularly any units showing transient selectivity. In the revised manuscript, we now define a unit as showing significant tHit-tCR selectivity when three consecutive time bins (>30 ms, 10ms bins) of AUC values were significant. Using this new criterion, the percentage of tHittCR selective neurons increased compared with the previous analysis. We have updated Figure 1h and the results on page 4:

“We found that 18-33% of neurons in these cortical areas had area under the receiver-operating curve (AUC) values significantly different from 0.5, and therefore discriminated between tHit and tCR trials (Fig. 1h; S1: 28.8%, 177 neurons; S2: 17.9%, 162 neurons; MM: 32.9%, 140 neurons; ALM: 23.4%, 256 neurons; criterion to be considered significant: Bonferroni corrected 95% CI on AUC did not include 0.5 for at least 3 consecutive 10-ms time bins).”

Next, we have checked how tHit-tCR selective neurons were distributed across sessions. We found that the percentage of tHit-tCR selective neurons in each session varied (S1: 9-46%, S2: 0-36%, MM:25-55%, ALM:0-50%). We examined the relationship between the numbers of tHit-tCR selective neurons and tHit-tCR subspace overlaps. Sessions with more neurons showing task rule modulation tended to show lower subspace overlap, but this correlation was modest and only marginally significant (r= -0.32, p= 0.08, Pearson correlation, n= 31 sessions). While we report the percentage of neurons showing significant selectivity as a simple way to summarize single-neuron effects, this does neglect the magnitude of task rule modulation of individual neurons, which may also be relevant. 

In summary, the apparent disconnect between the effect sizes of task modulation of single neurons and of population dynamics could be explained by (1) the percentages of tHit-tCR selective neurons were underestimated in our old analysis, (2) tHit-tCR selective neurons were not uniformly distributed among sessions, and (3) the percentages of tHit-tCR selective neurons were weakly correlated with tHit-tCR subspace overlaps. 

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

For the analysis of choice coding dimensions, it seems that the authors are somewhat data limited in that they cannot compare lick-right/lick-left within a block. So instead, they compare lick/no lick trials. But given that the mice are unable to initiate trials, the interpretation of the no lick trials is a bit complicated. It is not clear that the no lick trials reflect a perceptual judgment about the stimulus (i.e., a choice), or that the mice are just zoning out and not paying attention. If it's the latter case, what the authors are calling choice coding is more of an attentional or task engagement signal, which may still be interesting, but has a somewhat different interpretation than a choice coding dimension. It might be worth clarifying this point somewhere, or if I'm totally off-base, then being more clear about why lick/no lick is more consistent with choice than task engagement.

We thank the Reviewer for raising this point. We have added a new paragraph on page 13 to clarify why we used lick/no-lick trials to calculate choice coding dimensions, and we now discuss the caveat regarding task engagement.  

“No-lick trials included misses, which could be caused by mice not being engaged in the task. While the majority of no-lick trials were correct rejections (respond-to-touch: 75%; respond-to-light: 76%), we treated no-licks as one of the available choices in our task and included them to calculate choice coding dimensions (Fig. S4c,d,f). To ensure stable and balanced task engagement across task rules, we removed the last 20 trials of each session and used stimulus parameters that achieved similar behavioral performance for both task rules (Fig. 1d; ~75% correct for both rules).”

In addition, to address a point made by Reviewer 3 as well as this point, we performed a new analysis to calculate choice coding dimensions using right-lick vs left-lick trials. We report this new analysis on page 8:

“Choice coding dimensions were obtained from left-lick and no-lick trials in respond-to-touch blocks and right-lick and no-lick trials in respond-to-light blocks. Because the required lick directions differed between the block types, the difference in choice CDs across task rules (Fig. S4f) could have been affected by the different motor responses. To rule out this possibility, we did a new version of this analysis using right-lick and left-lick trials to calculate the choice coding dimensions for both task rules. We found that the orientation of the choice coding dimension in a respond-to-touch block was still not aligned well with that in a respond-to-light block (Fig. S4h;  magnitude of dot product between the respond-to-touch choice CD and the respond-to-light choice CD, mean ± 95% CI for true vs shuffled data: S1: 0.39 ± [0.23, 0.55] vs 0.2 ± [0.1, 0.31], 10 sessions; S2: 0.32 ± [0.18, 0.46] vs 0.2 ± [0.11, 0.3], 8 sessions; MM: 0.35 ± [0.21, 0.48] vs 0.18 ± [0.11, 0.26], 9 sessions; ALM: 0.28 ± [0.17, 0.39] vs 0.21 ± [0.12, 0.31], 13 sessions).” 

We added discussion of the limitations of this new analysis on page 13:

“However, we also calculated choice coding dimensions using only right- and left-lick trials. In S1, S2, MM and ALM, the choice CDs calculated this way were also not aligned well across task rules (Fig. S4h), consistent with the results calculated from lick and no-lick trials (Fig. S4f). Data were limited for this analysis, however, because mice rarely licked to the unrewarded water port (# of licksunrewarded port  / # of lickstotal , respond-to-touch: 0.13, respond-to-light: 0.11). These trials usually came from rule transitions (Fig. 5a) and, in some cases, were potentially caused by exploratory behaviors. These factors could affect choice CDs.”

The authors find that the stimulus coding direction in most areas (S1, S2, and MM) was significantly aligned between the block types. How do the authors interpret that finding? That there is no major change in stimulus coding dimension, despite the change in subspace? I think I'm missing the big picture interpretation of this result.

That there is no significant change in stimulus coding dimensions but a change in subspace suggests that the subspace change largely reflects a change in the choice coding dimensions.

As I mentioned in the public review, I thought there was a weakness with interpretation of the optogenetic experiments, which the authors generally interpret as reflecting rule sensitivity. However, given that they are inhibiting premotor areas including ALM, one might imagine that there might also be an effect on lick production or kinematics. To rule this out, the authors compare the change in lick rate relative to licks during the ITI. What is the ITI lick rate? I assume pretty low, once the animal is welltrained, in which case there may be a floor effect that could obscure meaningful effects on lick production. In addition, based on the reported CI on delta p(lick), it looks like MM and AM did suppress lick rate. I think in the future, a task with richer behavioral read-outs (or including other measurements of behavior like video), or perhaps something like a psychological process model with parameters that reflect different perceptual or cognitive processes could help resolve the effects of perturbations more precisely.

Eighteen and ten percent of trials had at least one lick in the ITI in respond-to-touch and  respond-tolight blocks, respectively. These relatively low rates of ITI licking could indeed make an effect of optogenetics on lick production harder to observe. We agree that future work would benefit from more complex tasks and measurements, and have added the following to make this point (page 14):

“To more precisely dissect the effects of perturbations on different cognitive processes in rule-dependent sensory detection, more complex behavioral tasks and richer behavioral measurements are needed in the future.”

Reviewer #2 (Recommendations For The Authors):

I have the following minor suggestions that the authors might consider in revising this already excellent manuscript :

(1) In addition to showing normalised z-score firing rates (e.g. Fig 1g), I think it is important to show the grand-average mean firing rates in Hz.

We thank the Reviewer for the suggestion and have added the grand-average mean firing rates as a new supplementary figure (Fig. S2a). To provide more details about the firing rates of individual neurons, we have also added to this new figure the distribution of peak responses during the tactile stimulus period (Fig. S2b).

(2) I think the authors could report more quantitative data in the main text. As a very basic example, I could not easily find how many neurons, sessions, and mice were used in various analyses.

We have added relevant numbers at various points throughout the Results, including within the following examples:

Page 3: “To examine how the task rules influenced the sensorimotor transformation occurring in the tactile processing stream, we performed single-unit recordings from sensory and motor cortical areas including S1, S2, MM and ALM (Fig. 1e-g, Fig. S1a-h, and Fig. S2a; S1: 6 mice, 10 sessions, 177 neurons, S2: 5 mice, 8 sessions, 162 neurons, MM: 7 mice, 9 sessions, 140 neurons, ALM: 8 mice, 13 sessions, 256 neurons).”

Page 5: “As expected, single-unit activity before stimulus onset did not discriminate between tactile and visual trials (Fig. 2d; S1: 0%, 177 neurons; S2: 0%, 162 neurons; MM: 0%, 140 neurons; ALM: 0.8%, 256 neurons). After stimulus onset, more than 35% of neurons in the sensory cortical areas and approximately 15% of neurons in the motor cortical areas showed significant stimulus discriminability (Fig. 2e; S1: 37.3%, 177 neurons; S2: 35.2%, 162 neurons; MM: 15%, 140 neurons; ALM: 14.1%, 256 neurons).”

Page 6: “Support vector machine (SVM) and Random Forest classifiers showed similar decoding abilities

(Fig. S3a,b; medians of classification accuracy [true vs shuffled]; SVM: S1 [0.6 vs 0.53], 10 sessions, S2

[0.61 vs 0.51], 8 sessions, MM [0.71 vs 0.51], 9 sessions, ALM [0.65 vs 0.52], 13 sessions; Random

Forests: S1 [0.59 vs 0.52], 10 sessions, S2 [0.6 vs 0.52], 8 sessions, MM [0.65 vs 0.49], 9 sessions, ALM [0.7 vs 0.5], 13 sessions).”

Page 6: “To assess this for the four cortical areas, we quantified how the tHit and tCR trajectories diverged from each other by calculating the Euclidean distance between matching time points for all possible pairs of tHit and tCR trajectories for a given session and then averaging these for the session (Fig. 4a,b; S1: 10 sessions, S2: 8 sessions, MM: 9 sessions, ALM: 13 sessions, individual sessions in gray and averages across sessions in black; window of analysis: -100 to 150 ms relative to stimulus onset; 10 ms bins; using the top 3 PCs; Methods).” 

Page 8: “In contrast, we found that S1, S2 and MM had stimulus CDs that were significantly aligned between the two block types (Fig. S4e; magnitude of dot product between the respond-to-touch stimulus CDs and the respond-to-light stimulus CDs, mean ± 95% CI for true vs shuffled data: S1: 0.5 ± [0.34, 0.66] vs 0.21 ± [0.12, 0.34], 10 sessions; S2: 0.62 ± [0.43, 0.78] vs 0.22 ± [0.13, 0.31], 8 sessions; MM: 0.48 ± [0.38, 0.59] vs 0.24 ± [0.16, 0.33], 9 sessions; ALM: 0.33 ± [0.2, 0.47] vs 0.21 ± [0.13, 0.31], 13 sessions).”  Page 9: “For respond-to-touch to respond-to-light block transitions, the fractions of trials classified as respond-to-touch for MM and ALM decreased progressively over the course of the transition (Fig. 5d; rank correlation of the fractions calculated for each of the separate periods spanning the transition, Kendall’s tau, mean ± 95% CI: MM: -0.39 ± [-0.67, -0.11], 9 sessions, ALM: -0.29 ± [-0.54, -0.04], 13 sessions; criterion to be considered significant: 95% CI on Kendall’s tau did not include 0).

Page 11: “Lick probability was unaffected during S1, S2, MM and ALM experiments for both tasks, indicating that the behavioral effects were not due to an inability to lick (Fig. 6i, j; 95% CI on Δ lick probability for cross-modal selection task: S1/S2 [-0.18, 0.24], 4 mice, 10 sessions; MM [-0.31, 0.03], 4 mice, 11 sessions; ALM [-0.24, 0.16], 4 mice, 10 sessions; Δ lick probability for simple tactile detection task: S1/S2 [-0.13, 0.31], 3 mice, 3 sessions; MM [-0.06, 0.45], 3 mice, 5 sessions; ALM [-0.18, 0.34], 3 mice, 4 sessions).”

(3) Please include a clearer description of trial timing. Perhaps a schematic timeline of when stimuli are delivered and when licking would be rewarded. I may have missed it, but I did not find explicit mention of the timing of the reward window or if there was any delay period.

We have added the following (page 3): 

“For each trial, the stimulus duration was 0.15 s and an answer period extended from 0.1 to 2 s from stimulus onset.”

(4) Please include a clear description of statistical tests in each figure legend as needed (for example please check Fig 4e legend).

We have added details about statistical tests in the figure legends:

Fig. 2f: “Relationship between block-type discriminability before stimulus onset and tHit-tCR discriminability after stimulus onset for units showing significant block-type discriminability prior to the stimulus. Pearson correlation: S1: r = 0.69, p = 0.056, 8 neurons; S2: r = 0.91, p = 0.093, 4 neurons; MM: r = 0.93, p < 0.001, 30 neurons; ALM: r = 0.83, p < 0.001, 26 neurons.” 

Fig. 4e: “Subspace overlap for control tHit (gray) and tCR (purple) trials in the somatosensory and motor cortical areas. Each circle is a subspace overlap of a session. Paired t-test, tCR – control tHit: S1: -0.23, 8 sessions, p = 0.0016; S2: -0.23, 7 sessions, p = 0.0086; MM: -0.36, 5 sessions, p = <0.001; ALM: -0.35, 11 sessions, p < 0.001; significance: ** for p<0.01, *** for p<0.001.”  

Fig. 5d,e: “Fraction of trials classified as coming from a respond-to-touch block based on the pre-stimulus population state, for trials occurring in different periods (see c) relative to respond-to-touch → respondto-light transitions. For MM (top row) and ALM (bottom row), progressively fewer trials were classified as coming from the respond-to-touch block as analysis windows shifted later relative to the rule transition. Kendall’s tau (rank correlation): MM: -0.39, 9 sessions; ALM: -0.29, 13 sessions. Left panels: individual sessions, right panels: mean ± 95% CI. Dash lines are chance levels (0.5). e, Same as d but for respond-to-light → respond-to-touch transitions. Kendall’s tau: MM: 0.37, 9 sessions; ALM: 0.27, 13 sessions.”

Fig. 6: “Error bars show bootstrap 95% CI. Criterion to be considered significant: 95% CI did not include 0.”

(5) P. 3 - "To examine how the task rules influenced the sensorimotor transformation occurring in the tactile processing stream, we performed single-unit recordings from sensory and motor cortical areas including S1, S2, MM, and ALM using 64-channel silicon probes (Fig. 1e-g and Fig. S1a-h)." Please specify if these areas were recorded simultaneously or not.

We have added “We recorded from one of these cortical areas per session, using 64-channel silicon probes.”  on page 3.  

(6) Figure 4b - Please describe what gray and black lines show.

The gray traces are the distance between tHit and tCR trajectories in individual sessions and the black traces are the averages across sessions in different cortical areas. We have added this information on page 6 and in the Figure 4b legend. 

Page 6: “To assess this for the four cortical areas, we quantified how the tHit and tCR trajectories diverged from each other by calculating the Euclidean distance between matching time points for all possible pairs of tHit and tCR trajectories for a given session and then averaging these for the session (Fig. 4a,b; S1: 10 sessions, S2: 8 sessions, MM: 9 sessions, ALM: 13 sessions, individual sessions in gray and averages across sessions in black; window of analysis: -100 to 150 ms relative to stimulus onset; 10 ms bins; using the top 3 PCs; Methods).

Fig. 4b: “Distance between tHit and tCR trajectories in S1, S2, MM and ALM. Gray traces show the time varying tHit-tCR distance in individual sessions and black traces are session-averaged tHit-tCR distance (S1:10 sessions; S2: 8 sessions; MM: 9 sessions; ALM: 13 sessions).”

(7) In addition to the analyses shown in Figure 5a, when investigating the timing of the rule switch, I think the authors should plot the left and right lick probabilities aligned to the timing of the rule switch time on a trial-by-trial basis averaged across mice.

We thank the Reviewer for suggesting this addition. We have added a new figure panel to show the probabilities of right- and left-licks during rule transitions (Fig. 5a).

Page 8: “The probabilities of right-licks and left-licks showed that the mice switched their motor responses during block transitions depending on task rules (Fig. 5a, mean ± 95% CI across 12 mice).” 

(8) P. 12 - "Moreover, in a separate study using the same task (Finkel et al., unpublished), high-speed video analysis demonstrated no significant differences in whisker motion between respond-to-touch and respond-to-light blocks in most (12 of 14) behavioral sessions.". Such behavioral data is important and ideally would be included in the current analysis. Was high-speed videography carried out during electrophysiology in the current study?

Finkel et al. has been accepted in principle for publication and will be available online shortly. Unfortunately we have not yet carried out simultaneous high-speed whisker video and electrophysiology in our cross-modal sensory selection task.

Reviewer #3 (Recommendations For The Authors):

(1) Minor point. For subspace overlap calculation of pre-stimulus activity in Fig 4e (light purple datapoints), please clarify whether the PCs for that condition were constructed in matched time windows. If the PCs are calculated from the stimulus period 0-150ms, the poor alignment could be due to mismatched time windows.

We thank the Reviewer for the comment and clarify our analysis here. We previously used timematched windows to calculate subspace overlaps. However, the pre-stimulus activity was much weaker than the activity during the stimulus period, so the subspaces of reference tHit were subject to noise and we were not able to obtain reliable PCs. This caused the subspace overlap values between the reference tHit and control tHit to be low and variable (mean ± SD, S1:  0.46± 0.26, n = 8 sessions, S2: 0.46± 0.18, n = 7 sessions, MM: 0.44± 0.16, n = 5 sessions, ALM: 0.38± 0.22, n = 11 sessions).  Therefore, we used the tHit activity during the stimulus window to obtain PCs and projected pre-stimulus and stimulus activity in tCR trials onto these PCs. We have now added a more detailed description of this analysis in the Methods (page 32). 

“To calculate the separation of subspaces prior to stimulus delivery, pre-stimulus activity in tCR trials (100 to 0 ms from stimulus onset) was projected to the PC space of the tHit reference group and the subspace overlap was calculated. In this analysis, we used tHit activity during stimulus delivery (0 to 150 ms from stimulus onset) to obtain reliable PCs.”   

We acknowledge this time alignment issue and have now removed the reported subspace overlap between tHit and tCR during the pre-stimulus period from Figure 4e (light purple). However, we think the correlation between pre- and post- stimulus-onset subspace overlaps should remain similar regardless of the time windows that we used for calculating the PCs. For the PCs calculated from the pre-stimulus period (-100 to 0 ms), the correlation coefficient was 0.55 (Pearson correlation, p <0.01, n = 31 sessions). For the PCs calculated from the stimulus period (0-150 ms), the correlation coefficient was 0.68 (Figure 4f, Pearson correlation, p <0.001, n = 31 sessions). Therefore, we keep Figure 4f.  

(2) Minor point. To help the readers follow the logic of the experiments, please explain why PPC and AMM were added in the later optogenetic experiment since these are not part of the electrophysiology experiment.

We have added the following rationale on page 9.

“We recorded from AMM in our cross-modal sensory selection task and observed visually-evoked activity (Fig. S1i-k), suggesting that AMM may play an important role in rule-dependent visual processing. PPC contributes to multisensory processing51–53 and sensory-motor integration50,54–58.  Therefore, we wanted to test the roles of these areas in our cross-modal sensory selection task.”

(3) Minor point. We are somewhat confused about the timing of some of the example neurons shown in figure S1. For example, many neurons show visually evoked signals only after stimulus offset, unlike tactile evoked signals (e.g. Fig S1b and f). In addition, the reaction time for visual stimulus is systematically slower than tactile stimuli for many example neurons (e.g. Fig S1b) but somehow not other neurons (e.g. Fig S1g). Are these observations correct?

These observations are all correct. We have a manuscript from a separate study using this same behavioral task (Finkel et al., accepted in principle) that examines and compares (1) the onsets of tactile- and visually-evoked activity and (2) the reaction times to tactile and visual stimuli. The reaction times to tactile stimuli were slightly but significantly shorter than the reaction times to visual stimuli (tactile vs visual, 397 ± 145 vs 521 ± 163 ms, median ± interquartile range [IQR], Tukey HSD test, p = 0.001, n =155 sessions). We examined how well activity of individual neurons in S1 could be used to discriminate the presence of the stimulus or the response of the mouse. For discriminability for the presence of the stimulus, S1 neurons could signal the presence of the tactile stimulus but not the visual stimulus. For discriminability for the response of the mouse, the onsets for significant discriminability occurred earlier for tactile compared with visual trials (two-sided Kolmogorov-Smirnov test, p = 1x10-16, n = 865 neurons with DP onset in tactile trials, n = 719 neurons with DP onset in visual trials).

While I agree that ChatGPT and other similar mediums have provided users with a tremendous amount of information is it still not up to the human counterpart to distill that information down to something specific? Something usable and actionable?

Effective prompt engineering can assist with those efforts, of course, yet still the human counterpart must provide the parameter of the queries and distill the information down to a workable solution. One idea I am personally struggling with is recognizing that every thought that pops into my head may not be true. Here, too, AI generated ideas may or may not be true and further human interaction can help discard the informational flotsam.

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

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__Reviewer #1 (Evidence, reproducibility and clarity (Required)): __ Summary In this work, the authors present a careful study of the lattice of the indirect flight muscle (IFM) in Drosophila using data from a morphometric analysis. To this end, an automated tool is developed for precise, high-throughput measurements of sarcomere length and myofibril width, and various microscopy techniques are used to assess sub-sarcomeric structures. These methods are applied to analyze sarcomere structure at multiple stages in the process of myofibrillogenesis. In addition, the authors present various factors and experimental methods that may affect the accurate measurement of IFM structures. Although the comprehensive structural study is appreciated, there are major issues with the presentation/scope of the work that need to be addressed: Major Comments 1. The main weakness of the paper is in its claim of presenting a model of the sarcomere. Indeed, the paper reports a structural study that is drawn onto a 3D schematic. There is no myofibrillogenesis model that would provide insights into mechanisms. Therefore, the use of the word model is grossly overstated.

In biology, the term “model” is used in various contexts, but it generally refers to a simplified representation of a biological system, a structure or a process. Accordingly, we consider “model” the most fitting phrase for what we present in Figure 4 (Figure 7 in the revised manuscript). These are not arbitrary 3D schematics; they are scaled representations in which the length, the number and the relative three-dimensional arrangement of thin and thick filaments are based on measurements. These measurements are primarily based on our own data (presented in the main text and provided in the supplementary materials), as published data were either lacking or inconsistent. Moreover, we would like to highlight that we do not claim to present a conceptual or mechanistic model of myofibrillogenesis, but we do present structural reconstructions or models for four developmental time points. Therefore, we disagree with the remark that “the use of the word model is grossly overstated”, as our wording fully corresponds to the common sense.

In general, the major focus and contribution of the work is unclear. How does the comprehensive nature of the measurements contribute to existing literature?

We significantly revised the text to highlight the main points more firmly, and added an additional section to help non-specialist readers to better understand our aims and findings.

Figure labels are often rather confusing - for example it is unclear why there is a B, B', B' etc instead of B,C,D, etc.

The figure labels have been revised in accordance with the reviewer’s recommendation.

Some comments in the text are not clearly tied to the figures. For example, in lines 108-109, are the authors referring to the shadow along the edges of the myofibril when saying they are not clearly defined (Figure 1C)?

The lines refer to the fact that identifying the boundary of an “object” in a fluorescence microscopy image is inherently challenging - even under ideal conditions where the object’s image is not affected by nearby signals or background noise. To improve clarity, we revised this section and now it reads: The other key parameter - myofibril diameter - is typically measured using phalloidin staining. However, accurately delineating their boundaries in micrographs is difficult - even under optimal conditions (high signal‑to‑noise ratio, no overlapping fibers, etc.; Fig. 1C). This limitation arises from the fundamental nature of light microscopy as the image produced is a blurred version of the actual structure, due to convolution with the microscope’s point spread function.

In line 116, it is unclear what "surrounding structures" the authors are referring to if the myofibrils are isolated.

We revised the text for clarity. It now states: Once isolated, myofibrils lie flat on the coverslip, aligning with the focal plane of the objective lens. This orientation allows for high-resolution, undistorted imaging and accurate two-dimensional measurements, free from interference by neighboring biological structures (e.g.: other myofibrils).

In lines 141-142, there is no reference of data to back up the claim of validation.

We addressed this mistake by including a reference to Fig. S1E (Fig. S1D in the revised manuscript).

In line 170, the authors mention the mef2-Gal4/+ strain as a Gal4 driver line but do not clearly state how this strain is different from the wildtypes or how this impacts their results.

Mef2-Gal4 is a muscle-specific Gal4 driver, often used in Drosophila muscle studies. It is a convention between Drosophila geneticists that presence of a transgene (i.e. Mef2-Gal4) changes the genetic background, and although it does not necessariliy cause any phenotypic effect, it is clearly distinguished from the wild type situation, and whenever relevant, Mef2-Gal4/+ is the preferred choice (if not the correct choice) as a control instead of wild type. As clear from our data, presence of the Mef2-Gal4 driver line does not affect the length or width of IFM sarcomeres as compared to wild type.

In lines 182-185, the authors discuss the effects of tissue embedding on morphometrics. Were factors such as animal sex, age, fiber type, etc. conserved in these experiments? If not, any differences in results may be confounding.

We fully agree with the reviewer that when testing the effect of a single variable, all other variables should remain constant. This is actually one of the main points emphasized in the results section. Additionally, this information is already provided in the Source Data files for each panel.

In lines 199-201, the authors discuss results of myofibril diameter using different preparation methods, yet no data is cited to support the claims. In line 220, the phrase "6 independent experiments" is unclear. Is each independent experiment performed using a different animal? Furthermore, are 6 experiments performed for each time point?

We substantially revised the relevant paragraphs and ensured that the corresponding data (Figure 2A in the revised manuscript) is cited each time when it is discussed. We conducted six independent experiments at each time point. This is consistently indicated in the figures and can be verified in the SourceData files (specifically, Fig3SourceData in this case). To clarify what we mean by "independent experiments," we added the following sentence to the Methods section: Experiments were considered independent when specimens came from different parental crosses, and each experiment included approximately six animals to capture individual variability.

In line 254, the authors refer to "number of sarcomeres". It must be clearly stated if this refers to sarcomeres per myofibril, image area, etc.

It is now clearly stated as: "number of sarcomeres per myofibril".

In line 274, the authors refer to "myofilament number". It must be clearly stated if this refers to myofilaments per myofibril, image area, etc.

We counted the number of myofilaments in developing myofibrils, and this is now clearly stated in the text and in the legend of Figure 3 (Figure 4 in the revised manuscript).

In line 299, the authors mention that thin filaments measured less than 560 nm in length, yet no data is cited to support this.

The previously missing reference to Figure 4 (Figure 7 in the revised manuscript) has now been added in addition to the revised Supplementary Figure 5.

In the "Quantifying sarcomere growth dynamics" section of the summary (starting from line 402) the authors introduce data that would be more naturally placed in the results and discussion section.

As suggested by the reviewer, we incorporated the key aspects of sarcomere growth dynamics into the Results and Discussion section.

In lines 422-423, it is not mentioned what the controls are for.

This was already explained in the main text between lines 167 and 173.

In the caption of Figure 1C, it is not mentioned what the red dashed lines in the microscope images represent.

The caption has been updated to include the following clarification: The red dashed lines border the ROI used for generating the intensity profiles.

In the caption of Figure 1D, the difference between the lighter and darker grey points is not mentioned.

This was already explained in each relevant figure legend. In this specific case, it is stated between lines 850 and 852: “Light gray dots represent individual measurements of sarcomere length and myofibril diameter, while the larger dots indicate the mean values from independent experiments.”

In line 849, the stated p-value (0.003) does not match that mentioned in the figure (0.0003).

We thank the reviewer for noticing this small mistake; correction was made to display the accurate p-value of 0.0003 at both places.

In line 874, it is not clear what an "independent experiment" refers to (different animal, etc.?).

We refer the reviewer to point 9, where this question has already been addressed.

Figure 2A is hard to read. Using different colored dots for different time points might help.

As suggested by the reviewer, we generated a plot with the individual points color-coded by time.

The significant figures presented in Figure 4 give a completely inaccurate representation of the variability of the measurements achieved with these techniques.

Certainly, each measured parameter exhibits inherent biological and technical variability. We have made all the raw data available to the reader through the SourceData files, and this variability is also evident in Figures 1, 2, 3, Supplementary Figure 1, 3, and 5 (Figure 1, 2, 3, 4, 6, and Supplementary Figure 1 in the revised manuscript). Also we have included an additional plot (Supplementary Figure 5 in the revised manuscript) that presents the calculated thin and thick filament lengths and their uncertainty. However, in Figure 4 (Figure 7 in the revised manuscript), our goal was to present an easily understandable visual representation of the sarcomeric structures for each time point, based on the averages of the relevant measurements.

In line 877, it should be mentioned that the number of filaments is counted per myofibril. The y-axes in the figure should also be adjusted to clarify this.

As suggested by the reviewer, both the figure legend and the plot have been updated to clearly indicate that the filament count refers to the number per myofibril.

In line 883, it is not clear what an "independent experiment" refers to (different animal, etc.?).

We refer the reviewer to point 9, where this question has already been addressed.

The statement of sample sizes in all figures is a little confusing.

Following general guidelines, we used SuperPlots to effectively present the data, as nicely demonstrated in the JCB viewpoint article by Lord et al., 2020 (PMID: 32346721). Individual measurements are shown as pooled data points, allowing readers to appreciate the spread, distribution and number of measurements. Overlaid on these pooled dot plots are the mean values from each independent experiment, with error bars representing variability between independent experiments. Sample sizes are provided for both individual measurements and independent experiments. This is now clearly explained in the Materials and Methods section, and we corrected the legends to improve clarity (“n” indicates the number of independent experiments/individual measurements).

In lines 1007-1008, the authors imply that the lattice model is needed for calculation of myofilament length. However, from the equations and previous data, it seems that this can be estimated using the confocal and dSTORM images.

As the reviewer correctly noted, myofilament length can be estimated using measurements from confocal and dSTORM images, following the equations provided. However, constructing even a simplified model requires multiple constraints to be defined and applied in a specific order. In practice, one must first determine the number and arrangement of myofilaments in a cross-sectional view of an “average sarcomere” before attempting to build a longitudinal model, where length calculations become relevant. This is now clarified in the text.

A more specific discussion of future directions is needed to put this paper in context. For example: Can anything from the overall process be used to better understand sarcomere dynamics in larger animals/humans? Can this be applied to disease modelling?

To address these questions, we have added a section titled STUDY LIMITATIONS, which states: “Our study is focused on describing the growth of IFM sarcomeres during myofibrillogenesis at the level of individual myofilaments. Additionally, we developed a user-friendly software tool for precise sarcomere size measurements and demonstrate that these measurements are sensitive to varying conditions. Whereas, this tool can be used successfully on whole muscle fiber preparations as well, our pipeline was intentionally optimized for individual IFM myofibrils ensuring higher measurement precision in our hands than other type of preparations. Thus, we predict that future work will be required to extend it to sarcomeres from other muscle tissues or species. Nevertheless, our study exemplifies a workflow how to measure sarcomere dimensions precisely. With some variations, it should be possible to adopt it for other muscles, including vertebrate and human striated muscles. To facilitate this and to enhance the accessibility and usability of this dataset, we welcome any feedback and suggestions from researchers in the field.”

One of the major claims of the paper is that there is a measurable variability with sex and other parameters. However, this data is never clearly summarized, presented (except for supplement), or discussed for its implications.

We followed the suggestion of the reviewer, and we moved this supplementary data into a main figure, and thoroughly revised the corresponding paragraphs to present and discuss the findings more clearly.

Minor Comments: 1. Lines 60-65 seem to break the flow of the introduction. As the authors discuss existing methods in literature for IFM analysis in the previous couple sentences, the following sentences should clearly state the limitations of existing methods/current gap in literature and a general idea of what the current work is contributing.

We agree with this remark, and we substantially revised the Introduction to clearly define the existing gap in the literature and to articulate how our work addresses this gap.

In line 104, the acronym for ZASPs is not spelled out.

The acronym has now been spelled out for clarity.

**Referee Cross-commenting**

I agree as well.

Reviewer #1 (Significance (Required)):

In summary, this paper provides a multi-scale characterization of Drosophila flight muscle sarcomere structure under a variety of conditions, which is potentially a significant contribution for the field. However, the paper scope is overstated in that it does not provide an actual sarcomere model. Further, there are multiple issues with data presentation that impact the readability of the manuscript.

Although it is somewhat unclear what would be “an actual sarcomere model” for the reviewer, but we cannot accept that we made on overstatement by using the word “model”, because one of the main outcomes of our work are indeed the myofilament level sarcomere models depicted in Figure 4 (Figure 7 in the revised manuscript). As said above, we do not claim that these would be molecular models, or mechanistic models or developmental models, but it makes absolutely nonsense (even in common terms!) that our scaled graphical representations (based on a wealth of measurements) should not be or cannot be called models.

As to the comment with data presentation, we thank the reviewer for the numerous suggestions, and we substantially revised the manuscript to increase clarity and overall readability.

__Reviewer #2 (Evidence, reproducibility and clarity (Required)): __ Summary: In this manuscript titled "A myofilament lattice model of Drosophila flight muscle sarcomeres based on multiscale morphometric analysis during development," Görög et al. perform a detailed analysis of morphological parameters of the indirect flight muscle (IFM) of D. melanogaster. The authors start by illustrating the range of measurements reported in the literature for mature IFM sarcomere length and width, showing a need to revisit and determine a standardized measurement. They develop a new Python-based tool, IMA, to analyze sarcomere lengths from confocal micrographs of isolated myofibrils stained with phalloidin and a z-disc marker. Using this tool, they demonstrate that sample preparation (especially mounting medium), as well as fiber type, sex, and age influence sarcomere measurements. Combining IMA, TEM, and STORM data, they measure sarcomere parameters across development, providing a comprehensive and up-to-date set of "standardized" sarcomere measurements. Using these data, they generate a model integrating all of the parameters to model sarcomeres at four discrete timepoints of development, recapitulating key phases of sarcomere formation and growth.

Major comments: Line 200 & 901 - Figure S1B - The authors make a strong statement about the use of liquid versus hardening media, and it is clear from the image provided in Figure S1 that there is a difference in the apparent sarcomere width. The identity of the "liquid media" versus the "hardening media" should be clearly identified in the Results, in addition to the legend for Figure S1. The authors show that "glycerol-based solutions" increase sarcomere width, but the Materials only list 90% glycerol and PBS. However, a frequently used liquid mounting media is Vectashield. Based on the literature, measurements in liquid Vectashield show diameters significantly less than 2.2 microns observed here with presumably 90% glycerol or PBS. Can the authors qualify this statement, or provide data that all forms of liquid mounting media cause this effect? Does this also apply to hemi-thorax and sectioned preparations, or just isolated myofibrils?

We used a PBS-based solution containing 90% glycerol as our liquid medium, as now stated in the main text. In response to the reviewer’s suggestion, we also tested a non-hardening version of Vectashield (H-1000). Myofibrils in Vectashield were significantly thicker than those in ProLong Gold but still thinner than those in the 90% glycerol–PBS solution, shown in Figure 2B. The mechanisms that could potentially explain these observations have been described in several studies (Miller et al., 2008; Tanner et al., 2011, 2012). Briefly, IFM is a densely packed macromolecular assembly. Upon removal of the cell membrane, myofibrillar proteins attract water, leading to overhydration of the myofilament lattice. This increases the spacing between filaments, resulting in an expansion of overall myofibril diameter. The extent of hydration depends on the osmolarity of the surrounding medium, as the system eventually reaches osmotic equilibrium. While both liquid media induced significant swelling, the observed differences likely reflect variations in their osmotic properties. In contrast, dehydration - an essential step in electron microscopy sample preparation - reduces the spacing between filaments, making myofibrils appear thinner. This explains why EM micrographs consistently show significantly smaller myofibril diameters (Chakravorty et al., 2017).

        Hardening media such as ProLong Gold introduce additional artifacts: during polymerization, these media shrink, exerting compressive forces on the tissue (Jonkman et al., 2020). We therefore propose that isolated myofibrils first expand due to overhydration in the dissection solution, and are then compressed back toward their *in vivo* dimensions during incubation in ProLong Gold. The average *in vivo* diameter of IFM myofibrils can be estimated without direct measurements, as it is determined by two key factors: (i) the number of myofilaments, which has been quantified in EM cross-sections in several studies (Fernandes & Schöck, 2014; Shwartz et al., 2016; Chakravorty et al., 2017) including our own, and (ii) the spacing between filaments, which can be measured by X-ray diffraction even in live *Drosophila* or under various experimental conditions (Irving & Maughan, 2000; Miller et al., 2008; Tanner et al., 2011, 2012). Our findings suggest that the effects of lattice overhydration and media-induced shrinkage are most pronounced in isolated myofibrils. In larger tissue preparations, the inter-myofibrillar space likely acts as a mechanical and osmotic buffer, reducing the extent of such distortions

Can the authors comment on whether the length of fixation or fixation buffer solution, in addition to the mounting medium, make a difference on sarcomere length and diameter measurements? This is another source of variation in published protocols.

The effect of fixation time on sarcomere morphometrics in whole-mount IFM preparations has been previously demonstrated by DeAguero et al. (2019), as briefly noted in our manuscript. To extend these findings, we performed a comparison using isolated myofibrils, assessing morphometric parameters after fixation for 10, 20 (standard) and 60 minutes. We found no difference between the 10- and 20-minute fixation conditions; however, fixation for 60 minutes resulted in significantly increased myofibril diameter (and these data are now shown in Supplementary Figure 1C). A comparable increase in thickness was also observed when using a glutaraldehyde-based fixative. These results suggest that more extensively fixed myofibrils may better resist the compressive forces exerted by hardening media.

Line 237-238. The authors conclude that premyofibrils are much thinner than previously measured. The use of Airyscan to more accurately measure myofibril width at this timepoint is a good contribution, as indeed diffraction and light scatter likely contribute to increased width measured in light microscopy images. I also wonder, though, how well the IMP software performs in measuring width at 36h APF, given how irregular the isolated myofibrils at this stage look (wide z-lines but thinner and weaker H and I bands as shown in Fig. 2B)?

The reviewer is correct that measurements during the early stages of myofibrillogenesis require additional effort. However, in addition to its automatic mode, IMA can also operate in semi-automatic or manual modes, ensuring complete control over the measurements. Myofibril width is determined from the phalloidin channel at the Z-line (as described in the software’s User Guide and Supplementary Figure 2), where it is at its thickest.

Also, how much of the difference in sarcomere width arises due to effects of "stripping" components off of the sarcomere at the earliest timepoint (for example alpha-actinin or Zasp proteins)?

A comparison between isolated myofibrils and those from microdissected muscles (Supplementary Figure 3B, Figure 3C in the revised manuscript) shows that the isolation process does not alter the morphometric measurements of sarcomeres. Moreover, the measured myofibril width aligns well with what we expect based on the number of myofilaments observed in TEM cross-sections of myofibrils at 36 hours APF (Figure 3A, now Figure 4A in the revised manuscript), supporting the consistency of our model.

Myofibrils at early timepoints do contain more than 4-12 sarcomeres in a line (they extend the full length of the myofiber), so it is possible they are breaking due to the detergent and mechanical disruption induced by the isolation method.

The reviewer is correct - myofibrils likely span the full length of the myofiber from the onset of myofibrillogenesis. However, during the isolation of individual myofibrils, they often break, and even mature myofibrils typically fragment into pieces of about 300 µm in length (illustrated in Figure 1E, now Figure 2A in the revised manuscript). Importantly, our measurements show that this fragmentation does not affect the assessed sarcomere length or width (as shown in Supplementary Figure 3B, now Figure 3C in the revised manuscript).

Line 312 - What does "stable association" mean in this context? The authors mention early timepoints lack stable association of alpha-Actinin or Zasp52, and they reference Fig. S4C, but this figure only shows 72h and 24 AE, not 36h and 48 h APF. Previous reports have seen localization of both alpha-Actinin and Zasp52, so presumably the detergent or mechanical isolation is stripping these components off of the isolated myofibrils up until 72h.

In agreement with previous reports, we also detected both α-Actinin (as shown in former Supplementary Figure 3B, now Figure 3C) and Zasp52 in microdissected IFM starting from 36 hours APF. However, these markers were largely absent from the isolated myofibrils of young pupae (36 to 60 hours APF). By 60 hours APF, strong α-Actinin and Zasp52 staining became evident in isolated myofibrils, whereas dTitin epitopes were clearly detectable from the earliest time point examined. This indicates that some proteins, such as α-Actinin and Zasp52, can be lost during the isolation process, whereas others like dTitin are retained and this differential sensitivity appears to depend on developmental stage. A likely explanation is that α-Actinin and Zasp52 are recruited early to Z-bodies but are only fully incorporated as more mature Z-disks form between 48 and 60 hours APF. This incomplete incorporation at the earlier stages could account for their loss during the isolation process. This interpretation is supported by our morphological analysis of the Z-discs, as shown in the dSTORM dataset (former Figure 3B, B’’, now Figure 4C, E) and in longitudinal TEM sections (former Supplementary Figure 5B, now in Figure 6B). Because α-Actinin and Zasp52 are not detected in isolated myofibrils at 36 and 48 hours APF, they are not included in Figure S4C (Figure 5C in the revised manuscript). This is explained in the updated figure legend.

This same type of issue comes up again in Lines 325-334, where the authors talk about 3E8 and MAC147. They state that 3E8 signal significantly declines in later stages and that MAC147 is not suitable to label myofibrils in young pupae, but they only show data from 72 APF and 24 AE (which looks to have decent staining for both 3E8 and MAC147). A clearer explanation here would be helpful.

To put it simply: we used one myosin antibody to label the A-band in the IFM of 36h APF and 48h APF animals, and a different antibody for the 72h APF and 24h AE stages. In more detail: Myosin 3E8 is a monoclonal antibody targeting the myosin heavy chain and labels the entire length of mature thick filaments except for the bare zone (former Supplementary Figure 4D, now in Figure 5D), suggesting its epitope is near the head domain. As a result, we expect a uniform A-band staining - excluding the bare zone - which is exactly what we observe in the IFM of young pupae (36h APF and 48h APF; formerly Figure 3B, now Figure 4C in the revised manuscript). However, at 72h APF and 24h AE, Myosin 3E8 produces a different staining pattern: two narrow stripes flanking the bare zone and two broader, more diffuse stripes near the A/I band junction (former Supplementary Figure 4D, now Figure 5D). This change is likely due to restricted antigen accessibility at these later developmental stages - a common issue in the densely packed IFM - making this antibody unsuitable for reliably measuring thick filament length in these stages.

MAC147 is another monoclonal antibody against Mhc that recognizes an epitope near the head domain. However, it only works reliably in more mature myofibrils (72h APF and 24h AE; formerly Figure 3B, now Figure 4C in the revised manuscript), likely due to its specificity for a particular Mhc isoform. This is why we do not include images from earlier developmental stages using this antibody. We added a revised, concise explanation in the main text for general readers, and provided a more detailed description for specialist readers in the legend of Supplementary Figure 4D (updated as Figure 5D in the revised manuscript).

Figure 3B. The authors show the H, Z, and I lengths in B', B', and B' and discuss these lengths in the text (lines 305-320). It would also be nice to actually have the plots showing the measured/calculated lengths for thin and thick filaments. These are mentioned in the results, but I cannot find the plots in the figures and there is no panel reference.

A summary table of the measured and calculated parameters is provided in Fig4SourceData (Fig7Source Data in the revised manuscript). However, following the reviewer’s suggestion, we also generated an additional plot (Supplementary Figure 5 in the revised manuscript) that displays the calculated thin and thick filament lengths.

Line 400. Does the model in Figure 4 actually have molecular resolution as the authors claim? From these views, thick and thin filaments appear to be represented by cylindrical objects. Localization of specific molecules would require further modeling with individual proteins. Or do the authors mean localization from STORM imaging relative to the ends of the thick and/or thin filaments? The model itself is a useful contribution, but based on Figure 4, resolution of individual molecules is not evident.

The reviewer is correct; and we fully agree that we do not present a molecular model of sarcomeres in this study - nor do we claim to. Instead we present a myofilament level model. Nevertheless, the scaled myofilament lattice model we introduce could serve as a geometric constraint when constructing supramolecular models of sarcomeres. As the reviewer rightly notes, implementing such an approach would require additional effort.

The main Results section of the text is condensed into 4 figures. However, I found myself flipping back and forth between the main figures and the supplement continuously, especially parts of Supplemental Figures 1, 3, 4, and 5. With such large amounts of detail in the Results relying on the supplement, it may be worth considering reorganizing the main and supplemental figures, and having 7 main figures, to include important panels that are currently in the supplement (esp. Fig S1B, S1C, S1D, S3B, S4, S5).

We found it a very useful suggestion, and we substantially reorganized the figures in the revised manuscript according to the recommendations of the reviewer.

Minor comments: On the plots in Fig. S1B, D, and F, it is hard to see the color of the dots because the red error bars are on top of them. Can the other distribution dots be tinted the correct color or the x-axis labels be added, so it is clear which dataset is which?

We significantly enlarged the dots to enhance visual clarity.

Line 142 needs a reference to Figure S1, Panel E, which shows the accuracy and precision measurements.

The requested panel reference has now been included in the revised manuscript.

Lines 198 - is this range from the above publications? Needs to be clearly cited.

The range has indeed been estimated using measurements from the aforementioned publications, and this point is now further clarified in the revised text.

Figure S3B is confusing - why do the blow-ups overlap both the top (presumably microdissected) and the bottom (presumably isolated) images? The identity of microdissected images should be labeled, as they are hard to see underneath of the blown-up images and the identity of individual image planes wasn't immediately obvious.

We refined the panel structure of Figure S3B (Figure 3C in the revised manuscript) to enhance clarity as the reviewer suggested.

Line 298. By "misaligned," do the authors mean the pointed ends are not uniformly anchored in the z-disc, leading to the wide z-disc measurements? At this early stage, I'm not sure "misaligned" is the right word - perhaps "were not yet aligned in register at the z-disc" or something similar.

We revised the text for clarity. It now reads: At 36 hours APF, thin filaments had not yet aligned in perfect register at the Z-disc, with most measuring less than 560 nm in length - and exhibiting considerable variability.

Figure S6 - spelling mistake in label of panel A, "sarcomer" should be "sarcomere"

The typo is corrected.

Line 487. Spelling "Zaps52" should be "Zasp52"

The typo is corrected.

Line 887. Spelling "Myofilement" should be "Myofilament"

The typo is corrected.

Line 946-947. In the legend for Supp. Fig. 3., the authors should specify which published datasets on sarcomere length are shown in the figure by including the references in the legend. Presumably the "isolated individual myofibrils" are the blue "this study" lines, leaving the "microdissected muscles" as the magenta "previous reports" on the figure. Without the reference, it is not clear if these are microdissected, isolated myofibrils, hemi-thorax sections, cryosections, or another preparation method for the "previous reports" data.

The references have now been added to both the figure and its legend.

**Referee Cross-commenting**

I agree with the comments from the other reviewers. Many of the major themes are consistent across the reviews, including regarding the model, preparation methods, and the software tool.

Reviewer #2 (Significance (Required)):

Strengths: This manuscript is an important contribution to the field of sarcomere development. The authors use modern technologies to revisit variation in morphometric measurements in the literature, and they identify parameters that influence this variation. Notably, sex-specific differences, DLM versus DVM measurements, and mouting media are potential contributors to the variability. Combining TEM and STORM with a confocal timecourse of isolated myofibrils, they refine previously published values of sarcomere length and width, and add more comprehensive data for filament length, number and spacing. This highly accurate timecourse demonstrates continual growth of sarcomeres after 48 h APF, and correct some inconsistencies from previous large-scale timecourse datasets. These data are very valuable to the field, especially Drosophila muscle biologists, and will serve as a comparative resource for future studies. Weaknesses: At early timepoints, loss of sarcomere components through mechanical or detergent-mediated artifacts may influence the authors' measurements. In addition, isolating myofibrils is not always the most ideal approach, as it loses information on myofiber structure as well as organization and structure of the myofibrils in vivo.

We believe that the control experiments we presented here adequately demonstrate that sarcomere measurements are not affected by the myofibril isolation process at early timepoints (Figure 3C). Nevertheless, we certainly agree with the reviewer that isolated myofibrils alone cannot capture the entire complexity of muscle tissues, and additional approaches should also be applied in complex projects. Yet, we are confident that our approach offers the most reliable and efficient method for precise morphometric analysis of the sarcomeres, and although alone it is very unlikely to be sufficient to address all questions of a muscle development project, it can still be applied as a very useful and robust tool.

The point regarding liquid versus hardening mounting media is valuable, but remains to be tested and validated with the diverse liquid and hardening media used by other labs.

Whereas it would not be feasible for us to test all possible liquid and hardening media used by others in all possible conditions, we tested the effect of Vectashield (the most commonly used liquid media) according to the suggestion of the reviewer, and the results are now included in the manuscript. We think that this is a valuable extension of the list of the materials and conditions we tested, although we need to point out that our primary goal was not necessarily to test as many conditions as possible (because the number of those conditions is virtually endless), rather to raise awareness among colleagues that these variables can significantly impact the data obtained and affect their comparability.

The IMA software seems to be designed specifically for analysis of isolated myofibrils, and it is unclear if it would work for other types of IFM preparations.

As stated in the manuscript, IMA is a specialized tool designed for the analysis of individual myofibrils. While it can also process other types of IFM preparations in semi-automatic or manual modes, we believe these approaches compromise both efficiency and accuracy. This is further clarified in the revised manuscript.

A last point is that TEM and STORM may not be available on a regular basis to many labs, hindering wide implementation of the approach used in this manuscript to generate very accurate and detailed measurements of sarcomere morphometrics.

Regarding the availability of TEM and STORM, we acknowledge that these techniques are not universally accessible. However, that is exactly one major value of our work that our open-source software tool now allows researchers to generate valuable data using only a confocal microscope in combination with our published datasets.

Audience: Scientists who study sarcomerogenesis or Drosophila muscle biology.

My expertise: I study muscle development in the Drosophila model.

__Reviewer #3 (Evidence, reproducibility and clarity (Required)): __ Summary: This manuscripts presents a computational tool to quantify sarcomere length and myofibril width of the Drosophila indirect flight muscles, including developmental samples. This tool was applied to confocal and STORM super-resolution images of isolated myofibrils from adult and developing flight muscles. Thick filament numbers per myofibril were counted during development of flight muscles. A myofilament model of developing flight muscle myofibrils is presented that remains speculative for the early developmental stages.

Major comments: 1. The title of the manuscript appears unclear. What is a lattice model? Lattice is an ordered array. The filament array parameters for mature flight muscles was aready measured. It appears that the authors speculate how this order might be generated during sarcomere assembly, which is not studied in this manuscript as it is limited to periodic arrays after 36h APF.

As the reviewer correctly points out, a lattice refers to an ordered array - in the case of IFM sarcomeres, this includes both thin and thick filaments. Therefore, the phrase "myofilament lattice model of Drosophila flight muscle sarcomeres" specifically describes a model representing the spatial organization of these filament arrays within the sarcomere. To provide additional clarity for readers, we have revised the title to include more context. It now reads: Developmental Remodeling of Drosophila Flight Muscle Sarcomeres: A Scaled Myofilament Lattice Model Based on Multiscale Morphometrics

To create a model of these arrays, three essential pieces of information are required:

1) The length of the filaments,

2) The number of filaments, and

3) The relative position of the filaments.

While some direct measurements are available in the literature, and others can be used to calculate the necessary values, available data is often contradictory or simply different from each other (as described in our ms) making them unsuitable for constructing scaled models of the myofilament arrays. In contrast to that, here we present a comprehensive and consistent set of measurements that enabled us to build models not only of mature sarcomeres but also of sarcomeres at three other significant developmental time points.

Regarding the mention of "sarcomere assembly" in line 37, we intended it to refer to the growth of the sarcomeres, not their initial formation. We do not speculate about sarcomere assembly anywhere in the text. In fact, we have clearly stated multiple times that our focus is on the growth of the IFM myofilament array during myofibrillogenesis. Nevertheless, to avoid confusion, we revised the phrase in line 37 to "sarcomere growth".

The authors review the flight muscle sarcomere length literature and conclude it is variable because of imprecise measurements. Likely this is partially true, however, more importantly is that the sarcomere length and width changes during isolation methods of the myofibrils, as well as by various embedding methods, as the authors show here as well in Figure 1B-E.

We dedicated two sections of the Results - “An automated method to accurately measure sarcomeric parameters” and “IFM sarcomere morphometrics are affected by sex, age, fiber type, and sample preparation” - to exploring potential sources of variability in published IFM sarcomere measurements. Based on these analyses, we conclude that such variability stems from both measurement imprecision and biological or technical factors, including sex, age, fiber type and, of foremost, sample preparation. Because it is difficult to quantify the relative impact of each variable across published studies, we have refrained from speculations about the relative contribution of the different factors in the revised manuscript.

Hence, I find the strongly claims the authors make here surprising, while they are isolating the myofibrils. Hence, these myofibrils are ruptured at the ends, relaxed or contracted, depending on buffer choice and passive tension is released. On page 8, the authors correctly state that the embedding medium causes shrinkage of the myofibrils. While isolation is state of the art for electron microscopy techniques, other methods including sectioning or even whole mount preparation have been developed for high resolution microscopy of IFMs that avoid these artifacts. Unfortunately, this manuscript only uses isolated myofibrils that were fixed and then mechanically dissociated by pipetting. This method likely induces variations as seen by the large spread of sarcomere length reported in Figure 1C (2.8-3.9µm?) and even bigger spreads for myofibril widths. Are these also seen in tissue without dissections? Unfortunately, no comparision to intact flight muscles are reported with the here presented quantification tool. The sarcomere length spread in the developmental samples is even larger.

The major issue raised in this paragraph is the use of isolated myofibril versus intact flight muscle preparations. The reviewer claims that the latter might be superior because the isolated myofibrils are ruptured at their ends. Clearly, the intact IFMs cannot be imaged in vivo by light microscopy because the adult fly cuticle is opaque. To visualize these muscles, one must open the thorax, but neither microdissection nor sectioning preserves them perfectly, even the cleanest longitudinal cuts sever some myofibrils, and dissection itself can damage the tissue. Although published images often show only the most pristine regions, the practice of selective cropping cannot be taken as a scientific argument. Here, by comparing sarcomere lengths measured in isolated myofibrils with those from whole-mount longitudinal DLM sections and microdissected IFM myofibers, we demonstrate that isolation does not alter sarcomere length (Figure 1E, now Figure 2A in the revised manuscript). As to myofibril width, it is determined by two parameters: the number of myofilaments and the spacing between them. In vivo filament spacing has been measured directly, and filament counts can be obtained from EM cross-sections of DLM fibers. Combining these values gives an expected in vivo myofibril diameter. While isolated myofibrils measure thinner than those in whole-mount or microdissected samples (Figure 1E, now Figure 2A in the revised manuscript), their diameter closely matches this in vivo estimate (see manuscript, lines 187–198). Therefore, we conclude that isolated myofibrils (even if it seems counterintuitive for this reviewer) are superior for sarcomere measurements than whole-mount preparations - and that is why we primarily rely on them here.

Despite that, we certainly recognize that isolated myofibrils cannot recapitulate every aspect of an IFM fiber, and the need for whole-mount preparations during our IFM studies is not questioned by us.

        In addition to this general answer to the issues raised in the above paragraph of the reviewer, we would like to specifically reflect for some of the remarks:

„Unfortunately, this manuscript only uses isolated myofibrils that were fixed and then mechanically dissociated by pipetting.”

This is a false statement that “this manuscript only uses isolated myofibrils” as we used different preparation methods for initial comparisons (see Figure 1E, now Figure 2A in the revised manuscript). Additionally, unlike the reviewer assumed, the myofibrils were first dissociated and then fixed, and not vice versa (as described in the Materials and Methods section).

„This method likely induces variations as seen by the large spread of sarcomere length reported in Figure 1C (2.8-3.9µm?) and even bigger spreads for myofibril widths. Are these also seen in tissue without dissections?”

This remark makes absolutely no sense, as we do not report sarcomere length values in Figure 1C at all. By assuming that the reviewer meant to refer to Figure 1B, it still remains a misunderstanding or a false statement, because that panel refers to the variations found in published data (not in our current data), and this is clearly explained both in the figure legend and the main text. Regardless of that, the stated spread does not appear unusual. In the article by Spletter et al. (2018), the authors report a similar spread (2.576–3.542 µm) for sarcomere length in mature IFM using whole-mount DLM cross-sections. As to the second question here, we do observe a comparable spread in other preparations as well (see Figure 1E, now Figure 2A in the revised manuscript), which is again the opposite conclusion as compared to the (clearly false) assumption of the reviewer.

„Unfortunately, no comparision to intact flight muscles are reported with the here presented quantification tool. „

This is also a false statement; as we do report comparison to whole mount cross sections which we belive the reviewer considers „intact” in Figure 1E (Figure 2A in the revised manuscript).

„The sarcomere length spread in the developmental samples is even larger.”

The spread is not larger at all than in previous reports, as clearly shown in Supplementary Figure 3A.

The authors suggest that there are sex differences in sarcomere length and pupal development duration. This is potentially interesting, unfortunately they then use mixed sex samples to analyse sarcomeres during flight muscle development.

In the revised manuscript, we now provide a more detailed description of a subtle post-eclosion difference in IFM sarcomere metrics between male and female Drosophila. We attribute this variation to the well-established observation that female pupae develop slightly faster than males, a property that may last till shortly after eclosion. Confirming this experimentally would require considerable effort with limited scientific benefit. Nonetheless, the subtle nature of this sex-linked variation reinforced our decision to include IFM sarcomeres from both male and female flies in our comprehensive developmental analysis.

The IMA software tool lacks critical assessment of its performance compared to other tools and the validation presented is too limited. IMA seems to generate systematic errors, based on Fig S1E, as it does not report the ground truth. These have to be discussed and compared to available tools. The principles of fitting used in IMA seem well adapted to IFM myofibrils in low noise conditions, but may not be usable in other situations. This should be assessed and discussed.

IMA is a specialized software tool developed to address a specific need, notably, to accurately and efficiently measure sarcomere length and myofibril diameter in individual IFM myofibril images labeled with both phalloidin and Z-disc markers. For our purposes, it remains the most suitable and reliable option, and we are confident that IMA outperforms all other available tools. To demonstrate this, we have included a table comparing the few alternatives (MyofibrilJ, SarcGraph, and sarcApp) capable of both measurements, which further supports our conclusion. Given IMA's focused application, extensive validation under artificially low signal-to-noise conditions is unnecessary. While IMA may introduce minor systematic errors (~0.01 µm for sarcomere length and ~0.03 µm for myofibril diameter), these are negligible errors relative to the limitations of the simulated ground truth data used for benchmarking. This point is now addressed in the manuscript.

It is claimed that validation was achieved on simulated IFM images: do the authors rather mean simulated isolated IFM myofibril images? This is not quite the same in terms of algorithm complexity and this should be corrected if this is the case.

Indeed, we used simulated individual IFM myofibril images, where both phalloidin labeling and Z-disc labeling are present. This is clearly shown in Supplementary Figure 1A, and stated in the text when first introduced: „we generated artificial images of IFM myofibrils with known dimensions, simulating the image formation process”

The authors need to revise their comparison to other tools. It is incomplete and seemingly incorrect. It should be clearly stated that IMA is limited to isolated myofibrils, which is a far easier segmentation task than what other tools can do, such as sarcApp (Neininger-Castro et al. 2023, PMID: 37921850). Defining the acronym would be valuable in that sense. The claim line 129-130 "none can adequately measure myofibril diameter from regular side view images" is unclear. What do the authors refer to as "side view images"? Sarc-Graph from Zhao et al 2021, PMID: 34613960, and sarcApp from Neininger-Castro et al. 2023 provide sarcomere width, in conditions that are very similar to what IMA does, e.g. on xy images based on the documentation provided on github. A performance comparison with these tools would be valuable. Does installation and use of IMA require computational skills?

Motivated by the reviewer’s comments, we revised the section introducing IMA. However, we chose not to include an extensive comparison with other software tools, as this would divert the manuscript’s focus without impacting the main conclusions. Instead, we added a summary table highlighting the key requirements for analyzing IFM sarcomere morphometrics from Z-stacks of phalloidin- and Z-line-labeled individual myofibrils and compared the available tools accordingly. In our experience, most software tools are developed to address very specific problems, even those marketed as general-purpose solutions. Consequently, applying them beyond their intended scope often results in reduced efficiency and suboptimal performance. Although sarcApp was initially available as a free tool, one of its dependencies (PySimpleGUI 5) has since adopted a commercial license model. Using a trial version of PySimpleGUI 5, we evaluated sarcApp on our dataset. The software is limited to single-plane image input, hence raw image stacks must be preprocessed into a suitable format, which is a time consuming step. Furthermore, implementation requires basic programming proficiency, as parameter adjustments must be performed directly within the source code to accommodate dataset-specific configurations. Once appropriately configured, sarcApp reliably quantifies both sarcomere length and myofibril width with accuracy comparable to that of IMA. However, it lacks built-in diagnostic feedback or visualization tools to facilitate measurement verification or troubleshooting during batch processing. SarcGraph also supports only single-plane image inputs and requires prior image preprocessing. Additionally, images must be loaded manually one by one, which further reduces processing efficiency. Parameter optimization relies on direct code modification through a trial-and-error process, demanding a certain level of programming proficiency. Even with these adjustments, the software frequently introduces artifacts - such as Z-line splitting - when applied to our dataset. Even when segmentation is successful, sarcomere length is often overestimated, whereas myofibril diameter is consistently underestimated. As compared to these issues, IMA was designed for ease of use and does not require any programming experience to install or operate. It can automatically handle raw microscopic image formats without the need for preprocessing. Segmentation is fully automated, with no requirement for parameter tuning. The tool provides visual feedback during both the segmentation and fitting steps, allowing users to confidently assess and validate the results. IMA produces accurate and precise measurements of sarcomere length and diameter. Batch processing is enabled by default, significantly improving efficiency when analyzing multiple images. Finally, unlike the reviewer stated, IMA is not limited to isolated myofibrils. It is optimized for isolated myofibrils (i.e. full performance is achieved on these samples), but it can also work on whole-mount preparations in semi-automatic and manual mode, which still allow precise measurements (with some reduction in processing efficiency).

As to the minor comments, the acronym IMA was already defined in lines 541 and 917–918 of the original submission, as well as on the software’s GitHub page. Additionally, we replaced the phrase "side view images" with "longitudinal myofibril projections" to improve clarity.

How do the authors know that the bright phallodin signal visible that the Z-disc at 36h and 48h APF is due to actin filament overlap, as suggested? An alternative solution are more short actin filaments at the early Z-discs.

It is widely accepted that the bright phalloidin signal at the Z-line in mature sarcomeres reflects actin filament overlap (e.g., Littlefield and Fowler, 2002; PMID: 11964243). Accordingly, in slightly stretched myofibrils, this bright signal diminishes, and in more significantly stretched myofibrils, a small gap appears (e.g., Kulke et al., 2001; PMID: 11535621). The width of this bright phalloidin signal corresponds to the electron-dense band seen in longitudinal EM sections (Figure 3B and Supplementary Figure 5B, now Figure 4B and Figure 6B in the revised manuscript) and matches the actin filament overlap observed in Z-disc cryo-EM reconstructions from other species (Yeganeh et al., 2023; Rusu et al., 2017), where individual thin filaments can be resolved. By extension, we interpret the bright phalloidin signals at the Z-discs observed at 36 h and 48 h APF as arising from similar actin filament overlaps, given their comparable width to the electron-dense Z-bodies described both in our study (Supplemantary Figure 5B, now Figure 6B in the revised manuscript) and by Reedy and Beall (1993). While we cannot fully rule out the reviewer’s alternative interpretation, for the time being it remains a bold speculation without supporting evidence, and therefore we prefer to stay with the conventional view.

The authors seem to doubt their own interpretation that actin filaments shrink when reading line 304 and following. This is obviously critical for the "model" presented.

Unlike the reviewer implies, we certainly do not doubt our own interpretation, but to avoid confusion we revised the corresponding paragraph in the manuscript and provided more details on our explanation, and we also provide a brief overview of it here. Between 36 h and 48 h APF we observe a pronounced structural transition in the IFM sarcomeres. In EM cross-sections, the previously irregular myofilament lattice becomes organized into a regular hexagonal pattern (Figure 3A, now Figure 4A in the revised manuscript) with filament spacing typical of mature myofibrils (Supplementary Figure 5A, now Figure 6A in the revised manuscript). In longitudinal EM sections, the elongated, amorphous Z-bodies condense along the myofibril axis to form well-defined, adult-like Z-discs (Supplementary Figure 5B, now Figure 6B in the revised manuscript). Similarly, dSTORM imaging shows that the Z-disc associated D-Titin epitopes become more compact and organized during this period (Supplementary Figure 4E, now Figure 5E in the revised manuscript). The edges of the thick filament arrays also become more sharply defined, and the appearance of a distinct bare zone indicates the establishment of a regular register (Figure 3B, now Figure 4B in the revised manuscript). By assuming that a similar reorganization occurs within the thin filament array, the apparent length of the thin filament array would decrease—not due to shortening of individual filaments, rather due to improved alignment. Although we cannot directly resolve single thin filaments, this reorganization offers the most plausible explanation for the observed change.

Minor comments: 1. Figure S1B is not called out in the text.

The reviewer might have missed this, but in fact, it is explicitly called out in line 181.

Fig. 1: Please state whenever images are simulations?

We appreciate the reviewer’s observation that the simulated IFM myofibril images are indistinguishable from the real ones, as this confirms the adequacy of these images for testing our software tool. However, this is already clearly indicated: Figure 1B features simulated images, as noted in the figure legend (line 824), and Supplementary Figure 1A similarly shows simulated images, as stated both in the legend (line 886) and in the figure.

Fig. 2: Length-width correlation - please provide individual points color-coded by time point?

As suggested by the reviewer, we generated a plot with the individual points color-coded by time.

"newly eclosed males and females, we observed that males have slightly shorter sarcomeres and narrower myofibrils". Please provide a statistical test supporting the difference.

In the revised manuscript, we compared sarcomere length and myofibril width between males and females from 0 to 96 hours AE using a two-way ANOVA with Sidak’s multiple comparisons test. We expanded our description of these observations in the main text, and details of the statistical analysis are now included in the revised figure legend (Figure 1E). Briefly, newly eclosed males showed slightly shorter sarcomeres than females - a consistent but non-significant trend (p = 0.9846) - which resolved by 12 h AE, with sarcomere lengths remaining similar thereafter (p = 0.1533; Figure 1E). In contrast, myofibril width was significantly narrower in the newly eclosed males (p = 0.0374), but this difference disappeared between 24 and 48 h AE as myofibrils expanded in diameter during post-eclosion development (p

Were statistical tests performed using animals as sample numbers? Please clarify in the images what are animal and what are sarcomere numbers.

Following standard guidelines, statistical tests were performed using the means of independent experiments, as noted in the figure legends. For each experiment, we used approximately 6 animals, and this information is now included in the Materials and Methods section.

mef2-Gal4 should be spelled Mef2-GAL4 according to Flybase.

This has been corrected in the revised text and figures.

Are the images shown in Figure 2B representative? 96h AE appears thicker than 24h AE but the graph reports no difference.

We aimed to show representative images, however, in the case of 96h APF we may have selected a wrong example. We now changed the image for a more appropriate one.

The authors only found Zasp52 and alpha-Actinin at the Z-discs from 72h APF onwards, which is different to what others have reported.

Similarly to former reports, we detected both α-Actinin (see Supplementary Figure 3B, now Figure 3C in the revised manuscript) and Zasp52 in microdissected IFMs as early as 36 hours APF. However, these markers were largely absent in isolated myofibrils from the early pupal stages (36–60 hours APF). By 60 hours APF, strong α-Actinin and Zasp52 signals were clearly visible in isolated myofibrils (the closest timepoint captured by dSTORM is 72h APF). As discussed in the manuscript, a likely explanation is that α-Actinin and Zasp52 are recruited to developing Z-bodies early on but are only fully incorporated into mature Z-discs between 48 and 60 hours APF. Their incomplete integration at earlier stages may lead to their loss during the isolation procedure.

Thick filament length during development has also been estimated by Orfanos and Sparrow, which should be cited (PMID: 23178940)

Contrary to the reviewer’s claim, the article 'Myosin isoform switching during assembly of the Drosophila flight muscle thick filament lattice' does not provide any measurements or estimates of thick filament length; it only includes a schematic illustration where the length of the thick filaments is not based on empirical data.

**Referee Cross-commenting**

I also agree with my colleagues comments, which are largely consistent.

Reviewer #3 (Significance (Required)):

This paper introduces a tool to measure sarcomere length. Easy to use tools that do this as well already exist. The tool can also measure sarcomere width, which it claims as unique point, which is not the case, see above comment.

We are aware that other tools exist to measure sarcomere parameters (and we did not claim the opposite in our ms), nevertheless, we need to emphasize that based on our comparisons, IMA is superior to all three alternatives. Three software tools could, in principle, be used to measure both sarcomere length and myofibril diameter: MyofibrilJ, SarcGraph, and sarcApp. However, two of them - MyofibrilJ and SarcGraph - consistently under- or overestimate these values. The only tool capable of performing these measurements reliably, sarcApp, is no longer freely available, it requires programming expertise, and it does not support raw image file formats, making it difficult to use in practice (see above comments for more details). In contrast, IMA is user-friendly and does not require any programming expertise to install or operate. It can automatically process raw microscopic image formats without the need for preprocessing. Segmentation is fully automated, and no parameter tuning is necessary. The tool offers visual feedback on both the segmentation and fitting processes, enabling users to validate results with confidence. IMA delivers accurate and precise measurements of sarcomere length and diameter. Additionally, batch processing is enabled by default, significantly enhancing workflow efficiency.

This manuscript shows that depending on the isolation and embedding media sarcomere and myofibrils width changes and hence artifacts can be introduced. While this is not suprising, it has not been well controlled in a number of previous publications.

Furthermore, this paper measures sarcomere length and width during flight muscle development and consolidates what was already known from previous publications. Sarcomeres are added until 48 h APF, then they grow in diameter. Despite strong claims in the text, I do not see any significant novel findings how sarcomeres grow in length or width or any significant deviations from what has been published before. This is even documented in the supplementary graphs by comparing to published data. It is close to identical.

The overall process has been quantitatively described in four previous studies (Reedy and Beall, 1993, Orfanos et al., 2015, Spletter et al., 2018, Nikonova et al., 2024). While there is general agreement on the pattern of sarcomere development, significant discrepancies exist among these datasets; differences that become particularly problematic when attempting to build structural models. More specifically: Reedy and Beall (1993) report substantially shorter sarcomeres compared to all other datasets, including ours. This discrepancy likely stems from two factors: (i) their use of longitudinal EM sections, where sample preparation is known to cause considerable tissue shrinkage; and (ii) the maintenance of their flies at 23 °C, a temperature that clearly delays development relative to the more commonly used 25 °C. Interestingly, Spletter et al. (2018) and Nikonova et al. (2024) conducted their experiments at 27 °C, which also deviates from standard conditions and may complicate comparisons. Orfanos et al. (2015) suggested that mature sarcomere length is reached by approximately 88 hours after puparium formation (APF). In contrast, our measurements show that sarcomeres continue to elongate beyond this point, reaching mature length between 12 and 24 hours post-eclosion. All four earlier studies report a mature sarcomere length around 3.2-3.3 µm, only slightly longer than the ~3.2 µm length of thick filaments (Katzemich et al., 2012; Gasek et al., 2016). This would imply an I-band length below ~100 nm, which is an implausibly short distance. In contrast, our data, along with several recent studies (González-Morales et al., 2019; Deng et al., 2021; Dhanyasi et al., 2020; DeAguero et al., 2019), support a mature sarcomere length of approximately 3.45 µm, placing the length of the I-band at around 250 nm. This estimate is more consistent with high-resolution structural observations from longitudinal EM sections and fluorescent nanoscopy (Szikora et al., 2020; Schueder et al., 2023). Although Reedy and Beall (1993) provide limited data on myofibril diameter during myofibrillogenesis, a more detailed quantitative analysis is presented by Spletter et al. (2018) and by Nikonova et al. (2024). Interestingly, Spletter et al. report two separate datasets - one based on longitudinal sections and another on cross-sections of DLM fibers. While the measurements are consistent during early pupal stages, they diverge significantly in mature IFMs (1.116 ± 0.1025 µm vs. 1.428 ± 0.0995 µm), a discrepancy that is not addressed in their publication. Nikonova et al. (2024) report even narrower myofibril widths (0.9887 ± 0.1273 µm). Moreover, the reported diameters of early myofibrils in all three datasets are nearly twice as large as those reported by Reedy and Beall (1993) and in our own measurements, directly contradicting the reviewer's claim that the values are “close to identical.” Finally, our data clearly demonstrate that both the length and diameter of IFM sarcomeres reach a plateau in young adults, which is a key developmental feature not examined in previous studies.

In summary, we did not and we do not intend to claim that our conclusions are novel as to the general mechanisms of myofibril and sarcomere growth. Rather, our contribution lies in providing a high-precision, robust analysis of the growth process using a state-of-the-art toolkit, resulting in a comprehensive description that aligns with structural data obtained from TEM and dSTORM. We therefore believe that expert readers will recognize numerous valuable aspects of our approaches that will advance research in the field.

Counting the total number of thick filaments during myofibril development is nice, however, this also has been done (REEDY, M. C. & BEALL, C. 1993, PMID: 8253277). In this old study, the authors reported the amount of filament across one myofibril. How does this compare to the new data here counting all filaments? Unfortunatley, this is not discussed.

Indeed, the study by Reedy and Beall (1993) was primarily based on longitudinal DLM sections, which were used to estimate myofibril width and count the number of thick filaments on this lateral view images (e.g., ~15 thick filaments wide at 75 hours APF), but total thick filament numbers were not provided. While such data could theoretically be used to estimate the number of myofilaments per myofibril, these estimations would depend on the unverified assumption that the section includes the full width of the myofibril. Additionally, the study did not provide standard deviations or the number of measurements, limiting the interpretability and reproducibility of their findings. These points highlight the need for a more rigorous and quantitative approach. For these reasons, we chose to quantify myofilament number using cross-sections, providing more accurate and reliable assessments.

Besides the difference between the lateral versus cross sections, a direct comparison of our studies is further complicated by differences in the developmental time points and experimental conditions used. Reedy and Beall (1993) reports data from pupae aged 42, 60, 75 and 100 hours, as well as from adults, whereas we present data from 36, 48, and 72 hours APF, and from 24 hours after eclosion, which corresponds to approximately 124 hours APF. Moreover, their experiments were carried out at 23 °C, a temperature that somewhat slows down pupal development and results in adult eclosion at around 112 hours APF, as stated in their study. In contrast, our experiments were carried out at the more commonly used 25 °C, where adults typically emerge around 100 hours APF.

Collectively, these differences prevented meaningful comparisons between the two datasets, and therefore we preferred to avoid lengthy discussions on this issue.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public review):

Summary:

This manuscript is a focused investigation of the phosphor-regulation of a C. elegans kinesin-2 motor protein, OSM-3. In C-elegans sensory ciliary, kinesin-2 motor proteins Kinesin-II complex and OSM-3 homodimer transport IFT trains anterogradely to the ciliary tip. Kinesin-II carries OSM-3 as an inactive passenger from the ciliary base to the middle segment, where kinesin-II dissociates from IFT trains and OSM-3 gets activated and transports IFT trains to the distal segment. Therefore, activation/inactivation of OSM-3 plays an essential role in its ciliary function.

Strengths:

In this study, using mass spectrometry, the authors have shown that the NEKL-3 kinase phosphorylates a serine/threonine patch at the hinge region between coiled coils 1 and 2 of an OSM-3 dimer, referred to as the elbow region in ubiquitous kinesin-1. Phosphomimic mutants of these sites inhibit OSM-3 motility both in vitro and in vivo, suggesting that this phosphorylation is critical for the autoinhibition of the motor. Conversely, phospho-dead mutants of these sites hyperactivate OSM-3 motility in vitro and affect the localization of OSM3 in C. elegans. The authors also showed that Alanine to Tyrosine mutation of one of the phosphorylation rescues OS-3 function in live worms.

Weaknesses:

Collectively, this study presents evidence for the physiological role of OSM-3 elbow phosphorylation in its autoregulation, which affects ciliary localization and function of this motor. Overall, the work is well performed, and the results mostly support the conclusions of this manuscript. However, the work will benefit from additional experiments to further support conclusions and rule out alternative explanations, filling some logical gaps with new experimental evidence and in-text clarifications, and improving writing before I can recommend publication.

We appreciate Reviewer #1’s comments and suggestions. We have now provided additional evidences and discussions to further support our conclusions and fill the logical gaps. We have also provided alternative explanations to our data and improved writing.

Reviewer #2 (Public review):

Summary:

The regulation of kinesin is fundamental to cellular morphogenesis. Previously, it has been shown that OSM-3, a kinesin required for intraflagellar transport (IFT), is regulated by autoinhibition. However, it remains totally elusive how the autoinhibition of OSM-3 is released. In this study, the authors have shown that NEKL-3 phosphorylates OSM-3 and releases its autoinhibition.

The authors found NEKL-3 directly phosphorylates OSM-3 (although the method is not described clearly) (Figure 1). The phophorylated residue is the "elbow" of OSM-3. The authors introduced phospho-dead (PD) and phospho-mimic (PM) mutations by genome editing and found that the OSM-3(PD) protein does not form cilia, and instead, accumulates to the axonal tips. The phenotype is similar to another constitutive active mutant of OSM-3, OSM-3(G444A) (Imanishi et al., 2006; Xie et al., 2024). osm-3(PM) has shorter cilia, which resembles with loss of function mutants of osm-3 (Figure 3). The authors did structural prediction and showed that G444E and PD mutations change the conformation of OSM-3 protein (Figure 3). In the single-molecule assays G444E and PD mutations exhibited increased landing rate (Figure 4). By unbiased genetic screening, the authors identified a suppressor mutant of osm-3(PD), in which A489T occurs. The result confirms the importance of this residue. Based on these results, the authors suggest that NEKL-3 induces phosphorylation of the elbow domain and inactivates OSM-3 motor when the motor is synthesized in the cell body. This regulation is essential for proper cilia formation.

Strengths:

The finding is interesting and gives new insight into how the IFT motor is regulated.

Weaknesses:

The methods section has not presented sufficient information to reproduce this study.

We appreciate that Reviewer #2 is also positive to our study. We have now provided sufficient information in the revised Methods section.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Major Concerns

(1) Why do the authors think that NEKL-3 phosphorylates OSM-3 in the first place? This seems to come out of nowhere and prior evidence indicating that NEKL-3 may be phosphorylating OSM-3 is not even mentioned in the Introduction.

We thank the Reviewer for raising this important point. Our hypothesis that NEKL-3 phosphorylates OSM-3 stems from prior findings in our lab. In a previous study (Yi et al., Traffic, 2018, PMID: 29655266), we identified NEKL-4, a member of the NIMA kinase family, as a suppressor of the OSM-3(G444E) hyperactive mutation. This discovery prompted us to explore the broader role of NIMA kinases in regulating OSM3. Subsequent genetic screens (Xie et al., EMBO J, 2024, PMID: 38806659) revealed that both NEKL-3 and NEKL-4 suppress multiple OSM-3 mutations, further supporting their functional interaction. Given the established role of NIMA kinases in phosphorylation-dependent processes (Fry et al., JCS, 2012, PMID: 23132929; Chivukula et al., Nat. Med., 2020, PMID: 31959991; Thiel, C. et al. Am. J. Hum. Genet. 2011, PMID: 21211617; Smith, L. A. et al., J. Am. Soc. Nephrol., 2006, PMID: 16928806), we hypothesized that NEKL-3/4 may directly phosphorylate OSM-3 to modulate its activity.

To test this hypothesis, we expressed recombinant C. elegans NEKL-3 and OSM-3 proteins and conducted in vitro phosphorylation assays. While we were unable to obtain active recombinant NEKL-4 (limitations noted in the revised text), our experiments with NEKL-3 revealed phosphorylation at residues 487-490 (YSTT motif) in OSM-3’s tail region, as confirmed by mass spectrometry. These findings are now explicitly contextualized in the Introduction and Results sections of the revised manuscript.

Page #4, Line #11:

“...In our previous study (Yi et al., Traffic, 2018, PMID: 29655266), a genetic screen targeting the OSM-3(G444E) hyperactive mutation identified NEKL-4, a member of the NIMA kinase family, as a suppressor of this phenotype. This finding, combined with reports that NIMA kinases regulate ciliary processes independently of their canonical mitotic roles (Fry et al., JCS, 2012, PMID: 23132929; Chivukula et al., Nat. Med., 2020, PMID: 31959991; Thiel, C. et al. Am. J. Hum. Genet. 2011, PMID: 21211617; Smith, L. A. et al., J. Am. Soc. Nephrol., 2006, PMID: 16928806), prompted us to investigate whether NIMA kinases modulate OSM-3-driven intraflagellar transport. We hypothesized that NEKL-3/4, as paralogs within this family, might directly phosphorylate OSM-3 to regulate its motility...”

Page #4, line #26:  

“... To determine whether NIMA kinase family members could directly phosphorylate

OSM-3, we purified prokaryotic recombinant C. elegans NEKL-3/NEKL-4 and OSM3 protein in order to perform in vitro phosphorylation assays. We were able to obtain active recombinant NEKL-3 but not NEKL-4. The in vitro phosphorylation assays showed that NEKL-3, directly phosphorylates OSM-3 (Fig. 1A-B, Appendix Table S1). Subsequent mass spectrometric analysis revealed phosphorylation at residues 487-490, which localize to the conserved "YSTT" motif within OSM-3’s C-terminal tail region ...”

(2) The authors need to characterize the proteins they expressed and purified for in vitro ATPase and motility assays. Are these proteins monomers or dimers?

For our in vitro ATPase and motility assays, OSM-3 was expressed in E. coli BL21(DE3) and purified using established protocols (Xie et al., EMBO J, 2024, PMID: 38806659; Imanishi et al., JCB, 2006, PMID: 17000874). To confirm its oligomeric state, we analyzed recombinant OSM-3 by size-exclusion chromatography coupled with multiangle light scattering (SEC-MALS). As reported in Xie et al. (2024), OSM-3 (~80 kDa monomer) elutes with a molecular weight of 173–193 kDa under physiological buffer conditions, consistent with a homodimeric assembly. These findings confirm that the functional unit used in our assays is the biologically relevant dimer. This characterization has been added to the revised manuscript on Page #35, Line #7.

“…OSM-3 was expressed in E. coli BL21(DE3) and purified for in vitro assays using established protocols (REFs). Size-exclusion chromatography coupled with multiangle light scattering (SEC-MALS) (Xie et al., EMBO J., 2024) confirmed that recombinant OSM-3 forms a homodimer (173–193 kDa) under physiological conditions, ensuring its dimeric state remained intact....” 

(3) The authors primarily used PD and PM mutations, which affect all four amino acids in the region. This may or may not be physiologically relevant. Figure 5 indicates that T489 is a critical regulatory site. However, this conclusion is undermined by reliance on PD mutations, which affect all four amino acids. Creating PM (T489E) and PD (T489A) mutations based on WT OSM-3 would better reflect physiological relevance. In vitro assays with a single phosphomimic or phosphor-dead mutation at residue 489 are missing at the end of this story. This would better link Figure 5 with the rest of the manuscript.

We thank the reviewer for this constructive critique. Below, we address the concerns and integrate new data to strengthen the link between T489 and autoinhibition:

To probe the regulatory role of T489 phosphorylation, we generated osm-3(T489E) (phosphomimetic, PM) and osm-3(T489A) (phospho-dead, PD) mutant animals. Strikingly, both mutants formed axonal puncta (Figure S7), recapitulating the hyperactive phenotype of the OSM-3G444E mutant. While the similar puncta formation in PM and PD mutants initially appeared paradoxical, this observation underscores the necessity of dynamic phosphorylation cycling at T489 for proper autoinhibition. Specifically, the PD mutant (T489A) likely disrupts phosphorylationdependent autoinhibition stabilization, leading to constitutive activation, where as the PM mutant (T489E) may mimic a "locked" phosphorylated state, preventing dephosphorylation-dependent release of autoinhibition in cilia and trapping OSM-3 in an aggregation-prone conformation. These results highlight T489 as a structural linchpin whose post-translational modification dynamically regulates motor activity. While the precise molecular mechanism—such as how phosphorylation modulates tailmotor domain interactions—remains to be elucidated, our data conclusively demonstrate that perturbing T489 (even in isolation) destabilizes autoinhibition, driving puncta formation and the constitutive activity.

We have integrated the above paragraph in the revised manuscript on page #8, line #27.

(4) There seems to be a disconnect between the MT gliding assays in Figure 4C and single molecule motility assays in Figure 4E. The gliding assays show that all constructs can glide microtubules at near WT speeds. Yet, the motility assays show that WT and PM cannot land or walk on MTs. The authors need to explain why this is the case. Is this because surface immobilization of kinesin from its tail disrupts autoinhibition? Alternatively, the protein preparation may include monomers that cannot be autoinhibited and cannot land and processively walk on surface-immobilized microtubules (because they only have one motor domain) but can glide microtubules when immobilized on the surface from their tail.

The surface immobilization of OSM-3 via its tail domain disrupts autoinhibition, a phenomenon previously observed in other kinesins such as kinesin-1 (Nitzsche et al, Methods Cell Biol., 2010, PMID: 20466139). In our assays, OSM-3 was nonspecifically immobilized on glass surfaces, enabling microtubule gliding by motors whose autoinhibition was relieved through tail anchoring. Critically, the PD and PM mutations reside in the tail region and do not alter the intrinsic properties of the motor head domain. Consequently, once autoinhibition is released via immobilization, the gliding velocities reflect the conserved motor head activity, which is expected to remain comparable across all constructs. While we cannot entirely rule out the presence of monomeric OSM-3 in solution, several lines of evidence argue against this possibility. First, the mutations are located in the elbow region, which is dispensable for motor dimerization. Second, SEC-MALS analysis from prior studies confirms that purified OSM-3 exists predominantly as dimers in solution. 

We have discussed these issues in the revised text on page #10, line #18: 

“…In our gliding assays, OSM-3PM has an increased gliding speed of 0.69 ± 0.07 μm/s (Fig. 4 C-D), similar to PD mutant. PD and PM mutations are confined to the elbow region, leaving the motor head’s mechanochemical properties intact. Upon tail immobilization—which releases autoinhibition—the gliding speeds reflect motor head activity. Single-molecule assays, however, directly resolve their native regulatory states: PD mutants are constitutively active, whereas PM mutants persist in an autoinhibited state (Fig. 4E-G). Although monomeric OSM-3 could theoretically mediate singlemotor gliding, the previous SEC-MALS data demonstrate that OSM-3 purifies as stable dimers (Xie et al., EMBO J, 2024, PMID: 38806659). Thus, dimeric OSM-3 is perhaps the predominant functional species in our assays…”

(5) An alternative explanation for the data is that both PD and PM mutations result in loss-of-function effects, disrupting OSM-3 activity. For instance:

a) In Figure 2C, both mutations cause shorter cilia than the wild type (WT).

b) In Figure 4A, both mutations result in higher ATPase activity than WT.

c) In Figure 4D, both mutations show increased gliding velocity compared to WT. These results suggest the observed effects could stem from loss of function rather than phosphorylation-specific regulation.

Although PD and PM mutations exhibit superficially similar "loss-of-function" phenotypes in certain assays, they mechanistically disrupt motor regulation in distinct ways:

a) Ciliary Length (Figure 2C) PD Mutants: Hyperactivation causes OSM-3-PD to prematurely aggregate into axonal puncta, preventing ciliary entry. Consequently, cilia are built solely by the weaker Kinesin-II motor, which only constructs shorter middle segments.

PM Mutants: OSM-3-PM retains autoinhibition during transport (enabling ciliary entry) but cannot be dephosphorylated in cilia. This blocks activation, leaving OSM-3-PM partially functional and resulting in cilia intermediate in length between WT and PD.

We have discussed this issue in the revised text on page #5, line #30:

“…These findings indicate that OSM-3-PM is in an autoinhibited state capable of ciliary delivery, yet fails to achieve full activation due to defective dephosphorylation. This incomplete activation results in suboptimal motor function and intermediate ciliary length phenotypes (Fig.2 B-C). In contrast, OSM-3-PD exhibits constitutive activation leading to aggregation into axonal puncta, which completely abolishes its ciliary entry capacity (Fig.2 A-B)...”

b) ATPase Activity (Figure 4A)

PD Mutants: Fully autoinhibition-released (98.15% of KHC ATPase activity), consistent with constitutive activation.

PM Mutants: Show partial ATPase activity (34.28% of KHC), reflecting imperfect phosphomimicry. While the DDEE substitution introduces negative charges, it fails to fully replicate the steric/kinetic effects of phosphorylated tyrosine (Y486; phenyl ring absent), resulting in incomplete autoinhibition stabilization. Despite this, the residual inhibition is sufficient to phenocopy shorter cilia in vivo.

We have discussed this issue in the revised text on page #7, line#19:

“…The PM mutant’s partial ATPase activity (34.28% of KHC) might arise from imperfect phosphomimicry—while the DDEE substitution introduces negative charges, it lacks the steric bulk of phosphorylated tyrosine (pY487). And this incomplete mimicry allows residual autoinhibition, sufficient to limit ciliary construction in vivo...”

c) Microtubule Gliding Velocity (Figure 4D)

Gliding Assay Limitation: Tail immobilization artificially releases autoinhibition, masking regulatory differences. Thus, all constructs (PD, PM) exhibit similar velocities (~0.7 µm/s), reflecting conserved motor head activity.

Single-Molecule Assay (Figure 4E): Directly resolves native autoinhibition states:

PD mutants show robust motility (autoinhibition released).

PM mutants remain largely inactive (autoinhibition retained).

We have discussed this issue in the revised text on page #10, line#18:

“…In our gliding assays, OSM-3PM has an increased gliding speed of 0.69 ± 0.07 μm/s (Fig. 4 C-D), similar to PD mutant. PD and PM mutations are confined to the elbow region, leaving the motor head’s mechanochemical properties intact. Upon tail immobilization—which releases autoinhibition—the gliding speeds reflect motor head activity. Single-molecule assays, however, directly resolve their native regulatory states: PD mutants are constitutively active, whereas PM mutants persist in an autoinhibited state (Fig. 4E-G)...”

Minor Suggestions and Concerns

(1) Lines 60-66: References that support these observations are missing from this section.

We have added the relevant references.

(2) Lines 66-67: I would revise this sentence as "It remains unclear how OSM-3 becomes enriched...".

We have made the changes.

(3) Line 85: The authors should describe how they perform these assays (i.e. recombinantly expressed NEKL-3 and OSM-3, are these C. elegans proteins, and which expression system was used...).

We have described them in the main text and methods

Page #4 line #26

“...To determine whether NIMA kinase family members could directly phosphorylate OSM-3, we purified prokaryotic recombinant C. elegans NEKL-3/NEKL-4 and OSM-3 protein in order to perform in vitro phosphorylation assays...”

Page #35 line#12

“...Basically, point mutations was introduced in to pET.M.3C OSM-3-eGFP-His6 plasmid for prokaryotic expression. Plasmid transformed E. coli (BL21) was cultured at 37°C and induced overnight at 23°C with 0.2 mM IPTG. Cells were lysed in lysis buffer (50 mM NaPO4 pH8.0, 250 mM NaCl, 20 mM imidazole, 10 mM bME, 0.5 mM ATP, 1 mM MgCl¬2, Complete Protease Inhibitor Cocktail (Roche)) and Ni-NTA beads were applied for affinity purification. After incubation, beads were washed with wash buffer (50 mM NaPO4 pH6.0, 250 mM NaCl, 10 mM bME, 0.1 mM ATP, 1 mM MgCl¬2) and eluted with elute buffer (50 mM NaPO4 pH7.2, 250 mM NaCl, 500 mM imidazole, 10 mM bME, 0.1 mM ATP, 1 mM MgCl¬2). Protein concentration was determined by standard Bradford assay. C elegans nekl-3 cDNA was cloned in to pGEX-6P GST vector and expressed in E. coli BL21 (DE3) and purified for in vitro phosphorylation assays. Plasmid transformed E. coli (BL21) was cultured at 37°C and induced overnight at 18°C with 0.5 mM IPTG. Cells were lysed in lysis buffer (50 mM NaPO4 pH8.0, 250 mM NaCl, 1 mM DTT, Complete Protease Inhibitor Cocktail (Roche)) and GST beads were applied for affinity purification. After incubation, beads were washed with wash buffer (50 mM NaPO4 pH6.0, 250 mM NaCl, 1 mM DTT) and eluted with elute buffer (50 mM NaPO4 pH7.2, 150 mM NaCl, 10 mM GSH, 1 mM DTT). Purified proteins were dialyzed against storge buffer (50 mM Tris-HCl, pH 8.0, 150 mM NaCl). Protein concentration was determined by standard Bradford assay...”

(4) Line 141: The first sentence of this paragraph lacks motivation. I would start this sentence with "To directly observe the effects of phosphor mutants in the elbow region in microtubule binding and motility of OSM-3, we...".

We have made the change.

(5) Figure 1B: The mass spectrometry data in Figure 1B lacks adequate explanation. The Methods section should detail the experimental protocol, data interpretation, and any databases used. Additionally, the manuscript should list all identified phosphorylation sites on OSM-3 to provide context, including whether Y487_T490 is the major site.

We have provided the detailed experimental protocol, data interpretation, and databases used in methods. We have provided all identified sites as Appendix table S1.

(6) Figure 1C: Is it possible to model the effect of PM and PD mutations using AlphaFold? The authors should also show PAE or pLDDT scores of their model.

AlphaFold cannot well model the effect of mutants, but we conducted the Rosetta relax to capture their possible conformational changes, as shown in the revised Figure 3. We have provided PAE and pLDDT as a new figure, Figure S2.

(7) Figure 2D: The unit for speed should use a lowercase "s" for seconds.

We have fixed it.

(8) Figure 3: I am not sure whether this figure stands for a main text figure on its own, as it is only a Rosetta prediction and is not supported by any experimental data. In addition, it remains unclear what the labels on the x-axis mean.

We have updated the figure and explain the labels on the x-axis in Figure S4 to make it more reader-friendly.

(9) Figure 4: NEKL-3-treated OSM-1 should be included as a positive control in the in vitro experiments.

We suspect that the Reviewer asked for NEKL-3-treated OSM-3. 

In our other study which has just been accepted by the Journal of Cell Biology, NEKL3-treated OSM-3 significantly reduced the affinity between OSM-3 motor and microtubules and showed very low ATPase activity. We have cited and discussed this in the revised text on page #10, line #28: 

“…As demonstrated in our recent study (Huang et al., JCB, 2025, In press, attached), phosphorylation of OSM-3 by NEKL-3 at two distinct regions—Ser96 and the conserved "elbow" motif—differentially regulates its activity and localization. Phosphorylation at Ser96 reduces OSM-3’s ATPase activity and alters its ciliary distribution from the distal segment to a uniform localization, while elbow phosphorylation induces autoinhibition, retaining OSM-3 in the cell body. Strikingly, in vitro phosphorylation of OSM-3 by NEKL-3 significantly reduces its microtubulebinding affinity, likely arising from combined modifications at both sites. We propose a model wherein elbow phosphorylation ensures anterograde ciliary transport, while Ser96 phosphorylation fine-tunes distal segment targeting. This multistep regulation may involve distinct phosphatases to reverse phosphorylation at specific sites, a hypothesis warranting further investigation….”

(10) Figure 4C, D, and F: The unit of velocity is wrong. The authors should use the same units they used in the table shown in Figure 4B.

We have fixed these errors

(11) Figure 4F: The velocity of PD is a lot lower than G444E. Therefore, it would be more appropriate to refer to PD as partially active, rather than hyperactive.

We have made the change. 

(12) Figure 5: There is too much genetics jargon on this figure (EMF, F2, 100%Dyf,...). How are the alleles numbered? Is it OK to refer to them as Alleles 1 and 2 for simplicity?

According to the established C. elegans allele nomenclature, each worm allele has a unique number named after the lab code for identification. We have simplified the labels and updated the figure to make it more reader-friendly.

(13) Figure 5E: A plot would be more reader-friendly than a table. Additionally, the legend for Fig. 5E mistakenly refers to it as "D."

We have changed the table to a plot and fixed the mistakes. We thank the Reviewer for pointing them out.

Reviewer #2 (Recommendations for the authors):

(1) The model appears as if NEKL-3 induces dephosphorylation of OSM-3 (Figure 6). This is not consistent with the conclusions described in the Discussion and is confusing.

We have updated the model figure and fixed the error.

(2) It should be described why the authors hypothesized NEKL-3 phosphorylates OSM3. Was there genetic evidence? Did the authors screened cilia-related kinases? or Did the authors identify it incidentally? Providing this information would help readers to understand the context of the research.

We appreciate both Reviewers for pointing out this issue. 

Our hypothesis that NEKL-3 phosphorylates OSM-3 stems from prior findings in our lab. In a previous study (Yi et al., Traffic, 2018, PMID: 29655266), we identified NEKL-4, a member of the NIMA kinase family, as a suppressor of the OSM-3(G444E) hyperactive mutation. This discovery prompted us to explore the broader role of NIMA kinases in regulating OSM-3. Subsequent genetic screens (Xie et al., EMBO J, 2024, PMID: 38806659) revealed that both NEKL-3 and NEKL-4 suppress multiple OSM-3 mutations, further supporting their functional interaction. Given the established role of NIMA kinases in phosphorylation-dependent processes (Fry et al., JCS, 2012, PMID: 23132929; Chivukula et al., Nat. Med., 2020, PMID: 31959991; Thiel, C. et al. Am. J. Hum. Genet. 2011, PMID: 21211617; Smith, L. A. et al., J. Am. Soc. Nephrol., 2006, PMID: 16928806), we hypothesized that NEKL-3/4 may directly phosphorylate OSM3 to modulate its activity.

To test this hypothesis, we expressed recombinant C. elegans NEKL-3 and OSM-3 proteins and conducted in vitro phosphorylation assays. While we were unable to obtain active recombinant NEKL-4 (limitations noted in the revised text), our experiments with NEKL-3 revealed phosphorylation at residues 487-490 (YSTT motif) in OSM-3’s tail region, as confirmed by mass spectrometry. These findings are now explicitly contextualized in the Introduction and Results sections of the revised manuscript.

Page #4, Line #11:

“... In our previous study (Yi et al., Traffic, 2018, PMID: 29655266), a genetic screen targeting the OSM-3(G444E) hyperactive mutation identified NEKL-4, a member of the NIMA kinase family, as a suppressor of this phenotype. This finding, combined with reports that NIMA kinases regulate ciliary processes independently of their canonical mitotic roles (Fry et al., JCS, 2012, PMID: 23132929; Chivukula et al., Nat. Med., 2020, PMID: 31959991; Thiel, C. et al. Am. J. Hum. Genet. 2011, PMID: 21211617; Smith, L. A. et al., J. Am. Soc. Nephrol., 2006, PMID: 16928806), prompted us to investigate whether NIMA kinases modulate OSM-3-driven intraflagellar transport. We hypothesized that NEKL-3/4, as paralogs within this family, might directly phosphorylate OSM-3 to regulate its motility...”

Page #4, line #26: 

“... To determine whether NIMA kinase family members could directly phosphorylate OSM-3, we purified prokaryotic recombinant C. elegans NEKL-3/NEKL-4 and OSM3 protein in order to perform in vitro phosphorylation assays. We were able to obtain active recombinant NEKL-3 but not NEKL-4. The in vitro phosphorylation assays showed that NEKL-3, directly phosphorylates OSM-3 (Fig. 1A-B, Appendix Table S1). Subsequent mass spectrometric analysis revealed phosphorylation at residues 487-490, which localize to the conserved "YSTT" motif within OSM-3’s C-terminal tail region...”

(3) It is curious the authors have not addressed the cilia phenotype and the localization of OSM-3 in nekl-3 mutant. Regardless of whether these observations agrees with the proposed mechanisms, it is essential for the authors to show and discuss the cilia phenotype and OSM-3 localization in nekl-3 mutants.

We thank the Reviewer for highlighting this critical point. Indeed, nekl-3 null mutants are inviable due to essential mitotic roles (Barstead et al., 2012, PMID: 23173093), precluding direct analysis of ciliary phenotypes. To bypass this limitation, we recently generated nekl-3 conditional knockouts (cKOs) in ciliated neurons (Huang et al., JCB, 2025 in press, attached). In these mutants, OSM-3—which is normally enriched in the ciliary distal segment—becomes uniformly distributed along the cilium. This redistribution correlates with premature activation of OSM-3-driven anterograde motility in the ciliary middle region, consistent with our proposed model where NEKL3 phosphorylation suppresses OSM-3 activity. We have now integrated this result and discussion into the revised manuscript, reinforcing the physiological relevance of NEKL-3-mediated regulation in ciliary transport. 

Page #6 line #10

“… While nekl-3 null mutants are inviable due to essential mitotic roles (Barstead et al., 2012, PMID: 23173093), conditional knockout (cKO) of nekl-3 in ciliated neurons (Huang et al., JCB, 2025 in press, attached) revealed its critical role in regulating OSM3 dynamics. In nekl-3 cKO animals, OSM-3—normally enriched in the ciliary distal segment—redistributed uniformly along the cilium, concomitant with premature activation of anterograde motility in the middle ciliary region. This phenotype aligns with our model wherein NEKL-3 phosphorylation suppresses OSM-3 activity, ensuring spatiotemporal regulation of IFT.…”

(4) The methods section lacks some information, which is critical to reproducing this study.

We have now provided detailed information in the methods section in the revised manuscript.

(a) It is not described how the authors determined phosphorylation of OSM-3 by NEKL-3. In methods, nothing is described about the assay.

We performed in vitro phosphorylation assays using recombinant OSM-3 and NEKL3 purified from bacteria. We then used LC-MS/MS for identification of phosphorylation sites. We have now updated the methods section to include all the information.

Page #4 line #26

“... To determine whether NIMA kinase family members could directly phosphorylate OSM-3, we purified prokaryotic recombinant C. elegans NEKL-3/NEKL-4 and OSM3 protein in order to perform in vitro phosphorylation assays. We were able to obtain active recombinant NEKL-3 but not NEKL-4. The in vitro phosphorylation assays showed that NEKL-3, directly phosphorylates OSM-3 (Fig. 1A-B, Appendix Table S1). Subsequent mass spectrometric analysis revealed phosphorylation at residues 487-490, which localize to the conserved "YSTT" motif within OSM-3’s C-terminal tail region...”

Page #36, line #19

“In vitro phosphorylation assay 20 μM purified OSM-3 was incubated with 1 μM GST-NEKL-3 at 30 °C in 100 μL reaction buffer (50 mM Tris-HCl pH 8.0, 10 mM MgCl2, 150 mM NaCl, and 2 mM ATP) for 30 min. The reaction was terminated by boiling for 5 min with an SDS-sample buffer.

Mass spectrometry

Following NEKL-3 treatment, OSM-3 proteins were resolved by SDS-PAGE and visualized with Coomassie Brilliant Blue staining. Protein bands corresponding to OSM-3 were excised and subjected to digestion using the following protocol: reduction with 5 mM TCEP at 56°C for 30 min; alkylation with 10 mM iodoacetamide in darkness for 45 min at room temperature, and tryptic digestion at 37°C overnight with a 1:20 enzyme-to-protein ratio. The resulting peptides were subjected to mass spectrometry analysis. Briefly, the peptides were analyzed using an UltiMate 3000 RSLCnano system coupled to an Orbitrap Fusion Lumos mass spectrometer (Thermo Fisher Scientific). We applied an in-house proteome discovery searching algorithm to search the MS/MS data against the C. elegans database. Phosphorylation sites were determined using PhosphoRS algorithm with manual validation of MS/MS spectra.”

(b) The method of structural prediction by Alfafold2 and LocalColabFold needs clarification. In general, the prediction gives several candidates. How did the authors choose one of these candidates?

We generated five candidate models and all of them showed similar conformation. We thus chose the model with the highest confidence. We have provided PAE and pLDDT as additional data in Figure S2 and discussed them in the revised text on, Page #4, line #32: 

“...To gain structural insights from this motif, we employed LocalColabFold based on AlphaFold2 to predict the dimeric structure of OSM-3 (Evans et al., 2022; Jumper et al., 2021; Mirdita et al., 2022). The highest-confidence model was selected for further analysis (Fig. 1C, Fig. S2)...”

(c) The methods to predict conformational changes by introducing various point mutations are interesting (Figure 3). However, the methods require more detailed descriptions. In the current form, the manuscript only lists the tools used. The pipelines and parameters need to be described. This information is important because AlphaFoldbased predictions often give folded conformations because the training data are mainly composed of folded proteins. It is surprising that the methods applied here give open conformations induced by point mutations.

We have described the pipelines in the revised Methods section on page#34, line#25: 

“…OSM-3 model was predicted using LocalColabFold (Evans et al., 2022; Jumper et al., 2021; Mirdita et al., 2022). Mutated proteins were designed by Pymol 2.6, choosing the rotamer of the mutated residues in G444E, PM and PD models with the least clash as the initial conformation. To predict mutation-induced conformational changes, the initial models were subjected to Pyrosetta (Chaudhury et al., 2010). The energies of pre-relaxed models were evaluated with Rosetta Energy Function 2015 (Alford et al., 2017), and then the relax procedure were applied to the models with default parameters to obtain the relaxed models visualized by Pymol to minimize the energy of these models. In detail, to obtain the relaxed models visualized by Pymol and minimize the energy of these models, the classic relax mover was used in the procedure mentioned above with default settings. The relax script has been uploaded to Github: https://github.com/young55775/RosettaRelax_for_OSM3...”

(5) The authors have purified proteins. Do they show different properties in gel filtration that are consistent with the structural prediction? It is anticipated that open-form mutants are eluted from earlier than closed forms.

We thank the reviewer for this insightful suggestion. Indeed, our recent study supported that the open-from of the active OSM-3 G444E mutation were eluted earlier than the wild-type closed form (Xie et al., EMBO J., 2024). While the current study did not perform gel filtration chromatography (SEC) to directly compare the hydrodynamic properties of the OSM-3 mutants, our functional assays provide robust evidence for conformational changes predicted by structural modeling. For example: ATPase activity assays revealed that the open-state mutants (e.g., G444E and PD muatnts) exhibited significantly enhanced enzymatic activity (Figure 4A), consistent with structural predictions of an active, destabilized autoinhibitory interface (Figure 3A). These functional readouts collectively validate the predicted structural states. While SEC could further corroborate these findings by distinguishing compact (closed) versus extended (open) conformations, we prioritized assays that directly link structural predictions to in vitro enzymatic activity and in vivo ciliary transport dynamics. Future studies incorporating SEC or cryo-EM will provide additional biophysical validation of these states.

We have revised the text in the manuscript (Page #7, Lines #22): 

“…Notably, the open-state OSM-3 mutants (e.g., G444E) displayed elevated ATPase activity, consistent with structural predictions of autoinhibition release (Fig. 3A, Fig. 4A) (Xie et al., 2024). While hydrodynamic profiling (e.g., SEC) could further resolve conformational states, our functional assays directly connect predicted structural changes to altered biochemical and cellular activity...”

Minor point

(1) Line 85 "MIMA kinase family" should be "NIMA kinase family".

We have corrected the typo and appreciate that the Reviewer for pointing it out. 

(2) M.S. and D.S. need to be defined in Figure 2D.

We have updated the figures.

This version of the epic leans into the celebration that Saiáwush received when arriving in Túrán. It generally seems to emphasize how beloved he was. Through this, differences between the Iranians and the Turanians are marked. As soon as Saiáwush arrives, the people essentially beg him to rule over them. This suggests that he is equipped to do what the locals cannot, therefore he should have power, putting Iranians in a favorable position and building a sense of national pride. At the same time, a distinction between them and the foreign group is established. CC BY-NC-SA

Be it through caricatures or descriptions, differences in physical features have always been an incredibly common way of othering groups. Surphanaka, Ravana's sister, having a so-called “ugly nose” exemplifies this phenomenon. A similar sentiment about Ravana is also expressed: “Your ugly yellow eyes should fall out of your head as you stare at me so lustfully, Ravana.” (Paley, 00:27:16 - 00:27:21). In his case, Ravana’s yellow eyes serve to dehumanize him. Where distinct features emphasize a difference, inhuman features—like yellow eyes—only widen that gap. CC BY-NC-SA

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Reply to the reviewers

We thank the reviewers for providing valuable comments and suggestions for improving the manuscript.

Response to reviewer comments:

Reviewer-1

Comment 1: Major concern is the study lacks rigor in several areas where n=2, results are not quantified with statistics. They need to run power analysis and increase their samples sizes. Please include statistics on all measurements. Filamentous actin staining and alpha-sma is used to visualize mechanosensing but also in other cell activities such as cell contractility for movement, cell to substrate adhesion, cell division, etc. They need to query more mechanosensing related pathways (Piezo1/2, Yap/taz-Hippo, integrin-Focal Adhesion Kinase, etc) to show that mechanosensing changed.

Response: We have increased the sample size to a minimum of n=3 in most cases. However, a few experiments will require more time to increase sample size, as mentioned below.

Our data emphasized the role of Rac1 and SRF. We understand that other molecular players may also be involved in sensing or responding to mechanical forces, but surveying multiple families of candidates without a specific hypothesis or functional experiment is beyond the scope of this study.

__Comment 2: __Fig. 1: In panel E, the cranial bone area measurement is not normalized to mitigate the possible effect of individual differences.

Response: We have re-quantified the data with normalization to the length of the skull.

__Comment 3: __In Fig. 2 the authors mentioned many phenotypical changes (bone length changes, gap thickness change, apex thickness change, etc.) based on histology stain, none of them are quantified to show a significant difference between Rac1-WT and Rac1-KO.

Response: In Fig. 2A, we present the gross morphology of the Rac1-KO embryos and only discuss the tissue defects like edema, hematoma, and hypoplasia, confirmed through H&E as shown in Fig. 2C. We also show the apical limits of the intact calvaria in Fig. 2D, consistent with the calvaria defects observed at birth. In fact, we do not discuss any “bone length changes, gap thickness, or apex thickness change” in this section as suggested by the reviewer. To address the request for more quantification we have added measurement of the edematous area of the apical mesenchyme at E14.5 (Fig. 2C), and this is now shown in Suppl. Fig. 1E. We also added quantification of embryo genotypes and Chi-square tests, now shown in Suppl. Fig. 1D.

Comment 4: Fig. 2 In panel D, with only 2 embryos per group is not enough for quantitation

Response: We plan to increase the number of embryos during the revision period.

Comment 5: Fig. 2 In panel D, the two arrows in the Rac1-KO mutants are not easy to catch.

Response: We made the arrows bigger and bolder.

Comment 6: Fig. 3 The thickness quantification is not performed.

Response: We added quantification in Fig. 3D.

Comment 7: Fig. 3 The images show an obvious curve change of the apex between the control and mutant. Such change is not discussed in the results. Is it due to histology issue?

Response: We do not think it is due to technical issues but reflects a real change in the shape of the apex of the head. We modified the graphical representation in Figure 3E to reflect this change in curvature. We also added the following sentence to the results on page 7: “We also noted a loss of curvature in the apex of the Rac1-KO head at E13.5, which correlated with loss of aSMA+ mesenchymal cells and thinning of the EMM (Fig. 3E).”

__Comment 8: __The merged layer did not show S100a6. While the authors are showing apical expansion of the mesenchyme toward the dermis and meninges, it is hard to track where they are without a merged image.

Response: We added merged images.

Comment 9: Fig. 4 In panel B, 2 biological replicates per genotype are very low.

__Response: __The effect of Rac1-KO on cell cycle is already known (Moore et al. 1997; Nikolova et al. 2007; Gahankari et al. 2021), and our result is supported by in vivo quantification of Tom+Edu+ cells in different regions of the embryonic head shown in Fig. 4A. We prefer not to repeat this assay.

Comment 10: Fig. 4 There is no cell death data.

Response: We will generate data on cell death during the revision period.

__Comment 11: __Fig. 5 In panel B, the GAPDH western plot bands in the mutants seem to be thinner than those of controls.

Response: We verified equal loading with a Ponceau stain, so this minor change in the GAPDH level could be due to biological differences in the protein level. Nevertheless, by our estimation this minor difference does not explain away the major difference in Rac1 and Srf levels.

__Comment 12: __Though the immunostain showed a decrease in signal intensity, it is hard to know whether the decrease is significant enough across all Rac1-KO mutants. They need to measure the fluorescence intensity and perform statistics.

Response: We will generate better images of SRF staining and quantify the difference between Rac1-WT and Rac1-KO during the revision period.

Comment 13: Fig. 6: Similar as Fig. 2, there is no quantification and n=1 per genotype is not enough

Response: During the revision period we will increase the number of E12.5 Srf-KO and Srf-WT embryos to n=3 for Figure 6G. All other panels currently have n=7 or greater.

Comment 14: Fig. 7: Need quantification between Srf-KO and Rac1-KO with statistics to show they are not different, but both significantly different from WTs

Response: In Figure 7D we have added quantification of aSMA area in Srf-KO and Rac1-KO. These results show that both mutants have a similar phenotype with reduced aSMA expression compared to their respective WT littermates, which supports the conclusion that they work in the same pathway. We do not agree with the reviewer that the two mutants should show no statistical difference, because Rac1 and Srf are different genes with overlapping but also non-overlapping functions. During the revision period we will add more Srf-KO embryos and repeat the statistical analysis.

Comment 15: Supplement Fig.2: No image showing the time point before E11.5.

Response: We will add an E10.5 time point during the revision period.

Comment 16: Supplement Fig.3: The ventral view of Rac1-WT does not have the same angle as it shows in Rac1-KO. Makes harder to see the difference between control and mutant.

Response: We adjusted the brightness/contrast to make the difference clearer.

Comment 17: Supplement Fig.4 &7: The alkaline phosphatase stained area needs to be normalized to some other metric because the embryos could be different size.

Response: We normalized to the width of the eye and is now represented in Suppl. Fig. 4 and 7.

Comment 18: Supplement Fig 6 A: The legend and figure don't match. Is it E13.5 or 14.5. Panel 6B needs better images without curling of the tissue.

Response: This has been fixed. The immunostaining images in Suppl. Fig. 6A is E14.5. Panel B is now replaced with better images in the revised manuscript.

Reviewer-2

__Comment 1.1: __In Fig. 5, links between Rac1, SRF, αSMA, and contractility in mesenchymal cells are shown. Molecular analyses (Western blot and qPCR) were performed using primary cultured mesenchymal cells (prepared after freed from the epidermal population). Although use of cells prepared from E18.5 embryos may have been chosen by the authors for the safe isolation of the mesenchymal population without contamination of epidermal cells, this reviewer finds that anti-SRF immunoreactivity is weaker at E13.5 than at E12.5 (throughout the section including the mesencephalic wall) and therefore wonder whether SRF expression changes in a stage-dependent manner. So, simply borrowing results obtained from E18.5-derived cells for describing the scenario around E12.5 and E13.5 is a little disappointing point found only here in this study.

Response: In fact, the reason we chose E18.5 was to get enough cells to do the experiments in Figure 5A-D without extensive passaging and/or immortalization, which would undoubtedly cause the cells to deviate from their in vivo character as they become adapted to growing on plastic with 10% serum. Therefore, we prefer not to change the cells as suggested by the reviewer.

__Comment 1.2: __In Fig. 5F, it is difficult to clearly see "reduction" of SRF immunoreactivity in Rac1-KO. Therefore, quantification of %SRF+/totalTomato+ would be desired.

Response: __We will generate better images of SRF staining and quantify the difference between Rac1-WT and Rac1-KO during the __revision period.

__Comment 1.3: __Separately, direct comparison of spontaneous centripetal shrinkage of the apical/dorsal scalp tissues, which will occur in 30 min when prepared at E12.5 or E13.5 (Tsujikawa et al., 2022), between WT and Rac1-KO would strengthen the results in Fig. 5D. As KO is specific to the mesenchyme, the authors do not have to worry about removal of the epidermal layer (which would be much more difficult at E12.5-13.5 than E18.5). If the degree of centripetal shrinkage of the "epidermis plus mesenchyme" layers were smaller in Rac1-KO, it would be interpreted to be mainly due to poorer recoiling activity and contractility of the Rac1-KO mesenchymal tissue.

Response: __We will try to perform the centripetal shrinkage assays as shown by Tsujikawa et al., during the __revision period.

Comment 2: The authors favor "apical" vs. "basolateral" to tell the relative positions in the embryonic head, not only in the adult head. But "apical" vs. "basolateral" should be accompanied with dorsal vs. ventral at least at the first appearance. Apical-to-basal axis or apex vs. basolateral by itself can provide, in many contexts, impressions that epithelial layers/cells are being discussed. Please note that the authors also use "caudal" (in the embryonic head). Usually, a universally defined anatomical axis perpendicular to the rostral-to-caudal axis is the dorsal-to-ventral axis.

Response: Apologies for confusing terminology. The terminology is now defined uniformly according to the anatomical axis.

Comment 3: One of the authors' statements in ABSTRACT "In control embryos, α-smooth muscle actin (αSMA) expression was spatially restricted to the apical mesenchyme, suggesting a mechanical interaction between the growing brain and the overlying mesenchyme" and a similar one in RESULTS "αSMA was not detected in the basolateral mesenchyme of either genotype from E12.5-E14.5 (Suppl. Fig. 4A), suggesting restriction of the mechanosensitive cell state to the apical mesenchyme" need to be at least partly revised, taking previous publication about the normal αSMA pattern in the embryonic head into account more carefully. Tsujikawa et al. (2022) described "Low-magnification observations showed superficial immunoreactivity for alpha smooth muscle actin (αSMA), which has been suggested to function in cells playing force-generating and/or constricting roles; this immunoreactivity was continuously strong throughout the dorsal (calvarial) side of the head but not ventrally toward the face, producing a staining pattern similar to a cap (Figure 2A)" . Therefore, in this new paper, descriptions like "we observed ...., consistent with ....(2022)" or "we confirmed .... (2022)" would be more accurate and appropriate regarding this specific point. Such a minor change does not reduce this study's overall novelty at all.

Response: Thank you for the correction. We have replaced the terminology and cited the article (Tsujikawa et al., 2022) appropriately, crediting their finding.

Comment 4: It would be very helpful if the authors provide a schematic illustration in which physiological and pathological scenarios (at the molecular, cellular, and tissue levels found or suggested by this study) are shown.

Response: We have added a schematic representation of the molecular changes happening in the apical head development because of Rac1- and Srf-KO, and it is represented in Suppl. Fig. 7C.

---

Comment 5: Despite being put in the title, "mechanosensing" by mesenchymal cells is not directly assessed in this study. If appropriate, something like "mechano-functioning" would be closer to what the authors demonstrated.

__Response: __We changed the title to refer to “mechano-responsive mesenchyme”. We think this is appropriate because the cells of interest have reduced aSMA and reduced proliferation, both of which are known to occur, at least in part, as responses to mechanical inputs.

Reviewer-3

Comment 1: Prrx1-Cre targets calvarial mesenchyme and Suzuki et al., 2009 showed that Prrx1-Cre mediated loss of Rac1 lead to calvarial bone phenotype due to incomplete fusion of the skull. While this phenotype was not studied in detail, the statement in the intro and discussion that the calvarial phenotype has not been recapitulated in mice is incorrect.

Response: Suzuki et al showed incomplete fusion of the skull. Although the skull is a tissue that is affected in AOS, it is not akin to the scalp and calvaria aplasia that typifies AOS. Our result stands apart from this. We clarified our position as such:

Introduction (page 4): “Nevertheless, the calvaria phenotype seen in AOS individuals has not been explored in detail or fully recapitulated in mice.”

Discussion (page 11): “Previous studies have demonstrated the role of Rac1 in mesenchyme-derived tissues, but they did not recapitulate AOS phenotypes.”

Comment 2: The authors show that Pdgfra-Cre induced knockout of Rac1 leads to lower-than-expected numbers of Rac1-cKO embryos at E18.5 and P1. Phenotypic analysis shows that the earliest phenotype is blebbing and hematoma in the nasal region at E11.5/12.5. It is stated that this was resolved at E18.5. It is unclear if this is truly a resolution of the phenotype or that these embryos fail to survive until E18.5. Do 100% of the Rac1-cKO embryos exhibit the blebbing/hematoma at E11.5/12.5? What is the observed number/percentage of Rac1-cKO embryos at E11.5/12.5? If the observed percentage of Rac1-cKO is similar to that at E18.5 (lower than the expected 25%), this would support resolution. If the observed ratio is as expected at E11.5/12.5, then this would support embryonic loss before E18.5 rather than phenotypic resolution.

Response: Please note that 100% (n=12) of E12.5 Rac1-KO embryos displayed nasal and mild caudal edema as exhibited in Fig. 2A, but none (n=16) had blebbing/hematoma by E18.5. We added tables for the number of embryos recovered at E12.5 and E18.5 to Supplemental Figure 1. These results show that the percentage of mutants at E12.5 was 21.42%, not significantly different from the expected frequency (p = 0.5371). At E18.5, the percentage dropped slightly to 18.3%, but still not significantly different from expected (p = 0.1545). The significant change in frequency of blebbing/hematoma from E12.5 to E18.5, without any significant change in the frequency of mutants, supports phenotypic resolution of the early blebbing/hematoma.

Comment 3: It is stated that brain shape is altered in Rac1-cKO embryos at E14.5 and E18.5 and concluded that these shape differences are secondary to the cranial defects. Pdgfra+ cells gives rise to the meninges and if the Pdgfra-Cre line recapitulates this expression, then loss of the ubiquitously expressed Rac1 in the meninges could lead to a primary defect in the brain, which may lead to secondary defects in the calvarium and scalp. Their conclusion should recognize other possibilities.

Response: We agree it is possible that there are meninges defects that secondarily change the shape of the brain, and we added a mention of this possibility. It is highly unlikely that scalp defects are only secondary to brain changes because the first observable phenotypes are in the EMM that gives rise to the scalp.

Comment 4: The TdTom staining in wholemount at E13.5 (Supplemental Figure 2B) is difficult to appreciate in the image shown.

Response: At E11.5 there is good contrast between labeled cranial structures and non-labeled body. At E13.5, Tomato appears in most of the mesenchymal cells in the embryo, so there is not as much contrast. The lack of contrast at E13.5 may cause the reviewer think there is something wrong with the image.

Comment 5: The idea that the EMM laminates into the meninges and scalp layers is not new and should be properly cited (Vu et al., 2021, Scientific Reports). The following paper should also be cited on the use of alpha-SMA (Acta2) as a marker of the anterior calvaria mesenchyme: Holms et al., 2020 Cell Reports.

Response: Thank you. We are happy to add those citations.

Comment 6: It is concluded that meningeal development is maintained in the cKO; however, this conclusion was based on a single marker (S100a6) that is both expressed in the presumptive meninges and dermis and greatly reduced overall in the cKO. This conclusion should be softened or other markers used to show that the meninges is indeed normal.

Response: We softened the conclusion on the meninges in the revised manuscript, as this part of the phenotype is was not our focus but it would be a good thing to look at in the future.

Comment 7: The overlap of S100a6 and alpha-SMA is difficult to appreciate in the images shown in Figure 3. Since this is important to the conclusion, co-staining should be done. If co-staining cannot be done due to the primary antibodies' origins, then ISH should be done.

Response: We added merged images.

Comment 8: It is concluded that reduced alpha-SMA suggests an early failure of Rac-cKO cells to respond to the mechanical environment. While this is one possibility, the reduction of alpha-SMA may simply be due to a reduction of these cells resulting from failed differentiation, decreased proliferation, or increased apoptosis.

Response: We think the fact that aSMA is downregulated in cultured cells strongly argues against it being a trivial consequence of reduce proliferation etc. Nevertheless, we softened our conclusion to allow for some of these things to also contribute to the reduced aSMA expression. We will check apoptosis during the revision period.

Comment 9: The conclusion that alpha-SMA is a transient population only present in apical cranial mesenchyme between E12.5-14.5 is not consistent with prior studies: Holms et al., 2020 Cell Reports; Holms et al., 2021 Nature Communications; Farmer et al., 2021 Nature Communications; Takeshita et al., 2016 JBMR.

Response: There is no contradiction. Our statements are based on antibody staining where it is very evident that a-SMA-expressing cells are detectable throughout the apical mesenchyme between E12.5 and E14.5. But at E18.5 we do not see this kind of broad aSMA expression the apical head, suggesting a transient and spatially restricted population of cells in the apical mesenchyme. This is consistent with the studies from Tsujikawa et al., 2022 and Angelozzi et al., 2022. The papers mentioned by the reviewer are only focused on the suture mesenchyme. They do not claim there is broad aSMA/Acta2 expression in the apical head, but only in a spatially restricted subpopulation of suture mesenchymal cells.

Comment 10: In the SRF immunostaining results in control and Rac1-cKO embryos, it is difficult to appreciate the nuclear localization at E12.5 in Figure 5E, as the DAPI is over saturated, and the image quality is poor. The image quality is also poor in Figure 5F.

Response: We will generate better images of SRF staining and quantify the difference between Rac1-WT and Rac1-KO during the revision period.

Comment 11: To what extent is the expression/localization of MRTF, the transcriptional co-activator of SRF, altered in the calvarial mesenchyme of Rac1-cKO embryos? Changes in MRTF would strengthen the link between Rac1 and SRF.

Response: We do not know how MRTF expression/localization changes in the embryo tissue, but western blot data on Rac1-KO fibroblasts revealed a reduction in expression/nuclear localization of MRTF-A/B that mirrored the changes in SRF. We added these blots to Figure 5A. However, as noted at the end of the discussion, MRTF is not always required for SRF function in vivo ( Dinsmore, Elife 2022). The MRTFA/B-KO is a possibility for future work.

Comment 12: Hypoplasia of the apical mesenchyme (Figure 6G, inset 1) in Srf-cKO is difficult to see.

Response: During the revision period we will increase the number of E12.5 Srf-KO and Srf-WT embryos to n=3 for Figure 6G and replace the picture with a better one.

Comment 13: Generally, the organization of the data into many main and supplemental Figures makes the flow difficult to follow.

__Response____: __We understand the concern, but we have tried our best to organize the most important data into main figures and the relevant but less essential data into supplemental figures.

Comment 14: SFR interacts with Pdgfra interacts genetically with Srf in neural crest cells in craniofacial development, with Srf being a target of PDGFRa signaling (Vasudevan and Soriano, 2015, Dev Cell). Since the Pdgfra-Cre line used here is hemizygous, is important that the control used to look at SRF expression in the Rac1-cKO is Pdgfra-Cre+.

Response: It is standard practice to include some Cre+ mice in the control set to reveal whether Cre has toxic effects in the cells of interest. To the reviewer’s concern about genetic interactions between the Pdgfra gene and Srf, this should not be relevant here because the Pdgfra-Cre used in our study is a transgene and does not affect the endogenous Pdgfra gene.

Comment 15: The text size in all figures is too small and varies throughout, making it difficult to read.

Response: To fit the panel in the Word document, the figure is resized. This should not be an issue in the final manuscript.

Comment 16: Details about the pulse-chase timing of the EdU experiments should be included in the results. Also, does n = 3 for each stage and each genotype? I would be helpful to include a representative section for a control and cKO littermate pair.

Response: The details are now included in the methods section. Yes, n=3 in each stage and genotype (Fig. 4A). The representative images are also included.

Comment 17: The relative sizing of the panels within and between figures is haphazard. Some are very large and others very small (Figure 2, 6, Supplemental Figure 1, 2, 6, 7).

Response: The image panels are fixed in the revised manuscript.

Comment 18: In Figure 5A and F, the titles "E12.5" and "E13.5" are in italics.

Response: The fonts for the figures are fixed in the revised manuscript.

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Referee #3

Evidence, reproducibility and clarity

Summary: This manuscript by Rathnakar et al. examines the role of the small GTPase Rac1 in apical closure of the scalp and skull. Rac1 activity is regulated the guanine nucleotide exchange factor DOCK6 and the GTPase AHGAP31. Loss of function variants in DOCK6 and gain of function variants in AHGAP31 lead to sustained inactivation of Rac1 in Adams-Oliver syndrome (AOS), which is characterized by aplasia cutis congenita, underlying calvarial defects, and limb abnormalities. While Rac1 is thought to be a key in the pathogenesis of AOS, how decreased in Rac1 activity impact development of the head is not well-understood. The authors find that conditional loss of Rac1 in cranial mesenchyme (using Pdgfra-Cre), leads to AOS-like abnormalities in the scalp and skull. They go on to show that these abnormalities are linked to reduced alpha-SMA expression in the early migrating mesenchyme (EMM), decreased osteoprogenitor cells in the supraorbital mesenchyme (SOM), decreased proliferation, and the contractile function of fibroblasts. They also find that Rac1 cKO leads to reduced expression of the mechanosensitive transcription factor SRF. Finally, they show that loss of SRF in cranial mesenchyme (using Pdgfra-Cre) leads to an AOS-like scalp and skull phenotype that has mechanistic overlap with their findings in the Rac1 cKO.

Major:

1. Prrx1-Cre targets calvarial mesenchyme and Suzuki et al., 2009 showed that Prrx1-Cre mediated loss of Rac1 lead to calvarial bone phenotype due to incomplete fusion of the skull. While this phenotype was not studied in detail, the statement in the intro and discussion that the calvarial phenotype has not been recapitulated in mice is incorrect.
2. The authors show that Pdgfra-Cre induced knockout of Rac1 leads to lower-than-expected numbers of Rac1-cKO embryos at E18.5 and P1. Phenotypic analysis shows that the earliest phenotype is blebbing and hematoma in the nasal region at E11.5/12.5. It is stated that this was resolved at E18.5. It is unclear if this is truly a resolution of the phenotype or that these embryos fail to survive until E18.5. Do 100% of the Rac1-cKO embryos exhibit the blebbing/hematoma at E11.5/12.5? What is the observed number/percentage of Rac1-cKO embryos at E11.5/12.5? If the observed percentage of Rac1-cKO is similar to that at E18.5 (lower than the expected 25%), this would support resolution. If the observed ratio is as expected at E11.5/12.5, then this would support embryonic loss before E18.5 rather than phenotypic resolution.
3. It is stated that brain shape is altered in Rac1-cKO embryos at E14.5 and E18.5 and concluded that these shape differences are secondary to the cranial defects. Pdgfra+ cells gives rise to the meninges and if the Pdgfra-Cre line recapitulates this expression, then loss of the ubiquitously expressed Rac1 in the meninges could lead to a primary defect in the brain, which may lead to secondary defects in the calvarium and scalp. Their conclusion should recognize other possibilities.
4. The TdTom staining in wholemount at E13.5 (Supplemental Figure 2B) is difficult to appreciate in the image shown.
5. The idea that the EMM laminates into the meninges and scalp layers is not new and should be properly cited (Vu et al., 2021, Scientific Reports). The following paper should also be cited on the use of alpha-SMA (Acta2) as a marker of the anterior calvaria mesenchyme: Holms et al., 2020 Cell Reports.
6. It is concluded that meningeal development is maintained in the cKO; however, this conclusion was based on a single marker (S100a6) that is both expressed in the presumptive meninges and dermis and greatly reduced overall in the cKO. This conclusion should be softened or other markers used to show that the meninges is indeed normal.
7. The overlap of S100a6 and alpha-SMA is difficult to appreciate in the images shown in Figure 3. Since this is important to the conclusion, co-staining should be done. If co-staining cannot be done due to the primary antibodies' origins, then ISH should be done.
8. It is concluded that reduced alpha-SMA suggests an early failure of Rac-cKO cells to respond to the mechanical environment. While this is one possibility, the reduction of alpha-SMA may simply be due to a reduction of these cells resulting from failed differentiation, decreased proliferation, or increased apoptosis.
9. The conclusion that alpha-SMA is a transient population only present in apical cranial mesenchyme between E12.5-14.5 is not consistent with prior studies: Holms et al., 2020 Cell Reports; Holms et al., 2021 Nature Communications; Farmer et al., 2021 Nature Communications; Takeshita et al., 2016 JBMR.
10. In the SRF immunostaining results in control and Rac1-cKO embryos, it is difficult to appreciate the nuclear localization at E12.5 in Figure 5E, as the DAPI is over saturated, and the image quality is poor. The image quality is also poor in Figure 5F.
11. To what extent is the expression/localization of MRTF, the transcriptional co-activator of SRF, altered in the calvarial mesenchyme of Rac1-cKO embryos? Changes in MRTF would strengthen the link between Rac1 and SRF.
12. Hypoplasia of the apical mesenchyme (Figure 6G, inset 1) in Srf-cKO is difficult to see.
13. Generally, the organization of the data into many main and supplemental Figures makes the flow difficult to follow.
14. SFR interacts with Pdgfra interacts genetically with Srf in neural crest cells in craniofacial development, with Srf being a target of PDGFRa signaling (Vasudevan and Soriano, 2015, Dev Cell). Since the Pdgfra-Cre line used here is hemizygous, is important that the control used to look at SRF expression in the Rac1-cKO is Pdgfra-Cre+.

Minor:

1. The text size in all figures is too small and varies throughout, making it difficult to read.
2. Details about the pulse-chase timing of the EdU experiments should be included in the results. Also, does n = 3 for each stage and each genotype? I would be helpful to include a representative section for a control and cKO littermate pair.
3. The relative sizing of the panels within and between figures is haphazard. Some are very large and others very small (Figure 2, 6, Supplemental Figure 1, 2, 6, 7).
4. In Figure 5A and F, the titles "E12.5" and "E13.5" are in italics.

Significance

Overall, this is an interesting study that shares mechanistic insight into the scalp and skull deformities in AOS. The overall presentation of the work, particularly the figures, should be improved and streamlined to enhance clarity and better emphasize the novelty of the study. In addition, the conclusions are not always well-supported by the results and the interpretation of the results do not fully consider and cite previous studies.

Audience: Developmental Biologists

Expertise: Craniofacial development and disease

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Referee #2

Evidence, reproducibility and clarity

Summary

In mice lacking Rac1 in the PDGFRa+ mesenchymal cell lineage, the authors found Adams-Oliver syndrome (AOS)-like defects of the apical/dorsal scalp and calvaria, which was accompanied by the secondary brain protrusion by E18.5. The primary phenotype emerged at E11.5 and worsened from E12.5 to E14.5 in the apical/dorsal region of the embryonic head, with limited lateral expansion as well as reduced thickening/stratification of the mesenchymal layer expressing α-smooth muscle actin (αSMA). Very similar in vivo abnormalities were obtained when serum response factor (SRF), known as a mechanotransducing factor, was removed in PDGFRα+ mesenchymal cells. Rac1-lacking mesenchymal cells proliferated poorly in vivo and contracted weakly in culture, with reduced expression of SRF and αSMA. Based on these results and previously obtained understanding that the developing apical/dorsal mesenchyme is mechanically stretched by the underlying brain, the authors conclude that the mechanosensing-triggered morphogenetic behaviors of the apical/dorsal mesenchymal cells (i.e., proliferation, stratification, and contraction, which all lead to physical stability or mechanical resilience of that layer) is mediated by Rac1 and SRF. The authors also suggest that this molecular mechanism for the physiological maturation of the apical/dorsal mesenchyme may underlie the ventral-to-dorsal progression of osteogenesis, absence of which explains AOS pathogenesis.

Major comments:

In Fig. 5, links between Rac1, SRF, αSMA, and contractility in mesenchymal cells are shown. Molecular analyses (Western blot and qPCR) were performed using primary cultured mesenchymal cells (prepared after freed from the epidermal population). Although use of cells prepared from E18.5 embryos may have been chosen by the authors for the safe isolation of the mesenchymal population without contamination of epidermal cells, this reviewer finds that anti-SRF immunoreactivity is weaker at E13.5 than at E12.5 (throughout the section including the mesencephalic wall) and therefore wonder whether SRF expression changes in a stage-dependent manner. So, simply borrowing results obtained from E18.5-derived cells for describing the scenario around E12.5 and E13.5 is a little disappointing point found only here in this study. In Fig. 5F, it is difficult to clearly see "reduction" of SRF immunoreactivity in Rac1-KO. Therefore, quantification of %SRF+/totalTomato+ would be desired. Separately, direct comparison of spontaneous centripetal shrinkage of the apical/dorsal scalp tissues, which will occur in 30 min when prepared at E12.5 or E13.5 (Tsujikawa et al., 2022), between WT and Rac1-KO would strengthen the results in Fig. 5D. As KO is specific to the mesenchyme, the authors do not have to worry about removal of the epidermal layer (which would be much more difficult at E12.5-13.5 than E18.5). If the degree of centripetal shrinkage of the "epidermis plus mesenchyme" layers were smaller in Rac1-KO, it would be interpreted to be mainly due to poorer recoiling activity and contractility of the Rac1-KO mesenchymal tissue.

Minor comments:

1. The authors favor "apical" vs. "basolateral" to tell the relative positions in the embryonic head, not only in the adult head. But "apical" vs. "basolateral" should be accompanied with dorsal vs. ventral at least at the first appearance. Apical-to-basal axis or apex vs. basolateral by itself can provide, in many contexts, impressions that epithelial layers/cells are being discussed. Please note that the authors also use "caudal" (in the embryonic head). Usually, a universally defined anatomical axis perpendicular to the rostral-to-caudal axis is the dorsal-to-ventral axis.
2. One of the authors' statements in ABSTRACT "In control embryos, α-smooth muscle actin (αSMA) expression was spatially restricted to the apical mesenchyme, suggesting a mechanical interaction between the growing brain and the overlying mesenchyme" and a similar one in RESULTS "αSMA was not detected in the basolateral mesenchyme of either genotype from E12.5-E14.5 (Suppl. Fig. 4A), suggesting restriction of the mechanosensitive cell state to the apical mesenchyme" need to be at least partly revised, taking previous publication about the normal αSMA pattern in the embryonic head into account more carefully. Tsujikawa et al. (2022) described "Low-magnification observations showed superficial immunoreactivity for alpha smooth muscle actin (αSMA), which has been suggested to function in cells playing force-generating and/or constricting roles; this immunoreactivity was continuously strong throughout the dorsal (calvarial) side of the head but not ventrally toward the face, producing a staining pattern similar to a cap (Figure 2A)" . Therefore, in this new paper, descriptions like "we observed ...., consistent with ....(2022)" or "we confirmed .... (2022)" would be more accurate and appropriate regarding this specific point. Such a minor change does not reduce this study's overall novelty at all.
3. It would be very helpful if the authors provide a schematic illustration in which physiological and pathological scenarios (at the molecular, cellular, and tissue levels found or suggested by this study) are shown.
4. Despite being put in the title, "mechanosensing" by mesenchymal cells is not directly assessed in this study. If appropriate, something like "mechano-functioning" would be closer to what the authors demonstrated.

Significance

This study advances understanding of a key aspect of the molecular mechanisms underlying the normal mammalian craniofacial development, unveiling the role of Rac1 and SRF in the apical/dorsal mesenchymal layer which has inter-tissue mechanical relationships with the embryonic brain underneath. This study also advances understanding of Adams-Oliver Syndrome pathogenesis, demonstrating the biological significance of the normal inter-tissue mechanical relationships in the developing mammalian head. This study may have opened a door for the genetic/molecular dissection toward the tissue-level mechano-engineering, which would stimulate development of next-generation organoids or assembloids. Broad audience including developmental biologists/neuroscientists, molecular/cellular biologists, pathologists, clinical geneticists, and pediatricians would be interested in this work.

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Referee #1

Evidence, reproducibility and clarity

In this paper "Mouse scalp development requires Rac1 and SRF for the maintenance of mechanosensing mesenchyme", the authors demonstrated that deletion of Rac1 (Rac1-KO) with a PDGFRαCreTG mouse model led to absence of skull apex and a blebbing formation while the limbs were not impacted. Rac1-KO mice showed the Rac1 regulated expansion of the apical mesenchyme toward the very apex meningeal and dermis layer and the osteogenic differentiation of supra orbital arch mesenchyme. Rac1 also regulates the proliferation of apical mesenchyme, dermis differentiation, and mechanosensing of the cranial mesenchyme cells. The authors also indicated Rac1 was a regulator of Srf by showing the deletion of Rac1 lead to lower Srf mRNA level and SRF protein expression. Deletion of Srf showed similar phenotypes as Rac1-KO mice.

Major concern is the study lacks rigor in several areas where n=2, results are not quantified with statistics. They need to run power analysis and increase their samples sizes. Please include statistics on all measurements. Filamentous actin staining and alpha-sma is used to visualize mechanosensing but also in other cell activities such as cell contractility for movement, cell to substrate adhesion, cell division, etc. They need to query more mechanosensing related pathways (Piezo1/2, Yap/taz-Hippo, integrin-Focal Adhesion Kinase, etc) to show that mechanosensing changed.

Comments by figure.

Fig. 1: In panel E, the cranial bone area measurement is not normalized to mitigate possible effect of individual differences.

Fig. 2:

1. While the authors mentioned many phenotypical changes(bone length changes, gap thickness change, apex thickness change, etc) based on histology stain, none of them are quantified to show a siginificant difference between Rac1-WT and Rac1-KO.
2. In panel D, with only 2 embryos per group is not enough for quantitation.
3. In panel D, the two arrows in the Rac1-KO mutants are not easy to catch.

Fig. 3:

1. The thickness quantification is not performed.
2. The images show an obvious curve change of the apex between the control and mutant. Such change is not discussed in the results. Is it due to histology issue?
3. The merged layer did not show S100a6. While the authors are showing apical expansion of the mesenchyme toward the dermis and meninges, it is hard to track where they are without a merged image.

Fig.4:

1. In panel B, 2 biological replicates per genotype are very low
2. There is no cell death data.

Fig. 5:

1. In panel B, the GPDH western plot bands in the mutants seem to be thinner than those of controls.
2. Though the immunostain showed a decrease in signal intensity, it is hard to know whether the decrease is significant enough across all Rac1-KO mutants. They need to measure the fluorescence intensity and perform statistics.

Fig. 6: Similar as Fig. 2, there is no quantification and n=1 per genotype is not enough.

Fig. 7: Need quantification between Srf-KO and Rac1-KO with statistics to show they are not different but both significantly different with WTs.

Supplement Fig.2: No image showing the time point before E11.5.

Supplement Fig.3: The ventral view of Rac1-WT does not have the same angle as it shows in Rac1-KO. Makes harder to see the difference between control and mutant.

Supplement Fig.4 &7: The alkaline phosphatase stained area needs to be normalized to some other metric because the embryos could be different size.

Supplement Fig 6 A: The legend and figure don't match. Is it E13.5 or 14.5. Panel 6B needs better images without curling of the tissue.

Significance

Please see my comments above. This work is broadly of interest to developmental biologist, fracture healing, and human genetics fields.

The paper is easy to understand and follow. The massive amount of histology and immunostaining images make it easy to identify the point the authors want to show. All the figures are well-labeled and visually informative. The experiment sequence is logic. The gene deletion models provide solid and direct evidence on the necessity of their function during early head development. The discussion is thoughtfully written and clear. The authors discuss the connection of Rac1 and SRF with other signaling pathways, which makes them promising target toward Adams-Oliver syndrome.

Reviewer #1 (Public review):

This study uses structural and functional approaches to investigate regulation of the Na/Ca exchanger NCX1 by an activator, PIP2 and an inhibitor, SEA0400. Previous functional studies suggest both of these compounds interact with the Na-dependent inactivation process to mediate their effects.

State of the art methods are employed here, and the data are of high quality and presented very clearly. While there is merit in combining structural studies on both compounds as they relate to Na-dependent activation, in the end it is somewhat disappointing that neither is explored in further depth.

The novel aspect of this work is the study on PIP2. Unfortunately, technical limitations precluded structural data on binding of the native PIP2, and so an unnatural short-chained analog, di-C8 PIP2, was used instead. This raises the question of whether these two molecules, which have similar but very distinctly different profiles of activation, actually share the same binding pocket and mode of action. The authors conduct a "competition" experiment, arguing the effect of di-C8-PIP2 addition subsequent to PIP2 suggests competition for a single binding site. In this scenario, PIP2 would need to vacate the binding site prior to di-C8-PIP2 occupying it. However, the lack of an effect of washout alone, suggests PIP2 does not easily unbind. This raises the possibility (probability?) of a non-competitive effect of di-C8-PIP2 at a different site. An additionally informative experiment would be to determine if a saturating concentration of di-C8-PIP2 could prevent the full activation induced by subsequent PIP2 addition. However, the relative affinities of the two ligands might make such an experiment challenging in practice.

In an effort to address the binding site directly, the authors mutate key residues predicted to be important in liganding the phosphorylated head group of PIP2. However, the only mutations that have a significant effect in PIP2 activation also influence the Na-dependent inactivation process independently of PIP2. While these data are consistent with altering PIP2 binding (which cannot be easily untangled from its functional effect on Na-dependent inactivation), a primary effect on Na-inactivation, rather than PIP2 binding, cannot be fully ruled out. A more extensive mutagenic study, based on other regions of the di-C8 PIP2 binding site, would have given more depth to this work and might have been more revealing mechanistically.

The SEA0400 aspect of the work does not integrate particularly well with the rest of the manuscript. This study confirms the previously reported structure and binding site for SEA0400 but provides little further information. While interesting speculation is presented regarding the connection between SEA0400 inhibition and Na-dependent inactivation, further experiments to test this idea are not included here.

Comments on revisions:

(1) The competition assay data for di-C8-PIP2 and PIP2 is a nice addition, but in its description in the text, the authors should be a bit more circumspect about their conclusions, based on the possibility/probability that the effect observed is actually non-competitive (as detailed above).<br /> (2) The authors should acknowledge the formal possibility that the functional effects of the mutations studies are a consequence of a direct effect on Na-dependent inactivation, independent of PIP2 binding.<br /> (3) The authors might strengthen their arguments for combining studies on PIP2 and SEA0400.<br /> (4) The authors could be clearer where their work on SEA0400 extends beyond the previously published observations.

This idea is also important in day to day academic life, as things make sense to you as the one in your head but we must be able to translate that into usability and understanding for others.

Reviewer #1 (Public review):

The study addresses how faces and bodies are integrated in two STS face areas revealed by fMRI in the primate brain. It is building upon recordings and analysis of the responses of large populations of neurons to three sets of images, that vary face and body positions. These sets allowed the author to thoroughly investigate invariance to position on the screen (MC HC), to pose (P1 P2), to rotation (0 45 90 135 180 225 270 315), to inversion, to possible and impossible postures (all vs straight), to presentation of head and body together or in isolation. By analyzing neuronal responses, they find that different neurons showed preferences for body orientation, or head orientation or for the interaction between the two. By using a linear support vector machine classifier, they show that the neuronal population can decode head-body angle presented across orientations, in the anterior aSTS patch (but not middle mSTS patch), except for mirror orientation. On the contrary, mSTS neurons show less invariance for head-body angle and more specialization for head or body orientation.

Strengths:

These results expand prior work on the role of Anterior STS fundus face area in face-body integration and its invariance to mirror symmetry, with a rigorous set of stimuli revealing the workings of these neuronal populations in processing individuals as a whole, in an important series of carefully designed conditions.

It also raises questions for future investigations comparing humans and monkeys expertise with upright and inverted configurations, in light of monkey-specific hanging upside-down behavior. Further, using two types of body postures (sitting, standing), they show a correlation in head-body angle between postures, suggesting that monkey orientation might be more meaningful to these neurons than precise posture.

Reviewer #2 (Public review):

Summary:

This paper investigates the neuronal encoding of the relationship between head and body orientations in the brain. Specifically, the authors focus on the angular relationship between the head and body by employing virtual avatars. Neuronal responses were recorded electrophysiologically from two fMRI-defined areas in the superior temporal sulcus and analyzed using decoding methods. They found that: (1) anterior STS neurons encode head-body angle configurations; (2) these neurons distinguish aligned and opposite head-body configurations effectively, whereas mirror-symmetric configurations are more difficult to differentiate; and (3) an upside-down inversion diminishes the encoding of head-body angles. These findings advance our understanding of how visual perception of individuals is mediated, providing a fundamental clue as to how the primate brain processes the relationship between head and body-a process that is crucial for social communication.

Strengths:

The paper is clearly written, and the experimental design is thoughtfully constructed and detailed. The use of electrophysiological recordings from fMRI-defined areas elucidated the mechanism of head-body angle encoding at the level of local neuronal populations. Multiple experiments, control conditions, and detailed analyses thoroughly examined various factors that could affect the decoding results. The decoding methods effectively and consistently revealed the encoding of head-body angles in the anterior STS neurons. Consequently, this study offers valuable insights into the neuronal mechanisms underlying our capacity to integrate head and body cues for social cognition-a topic that is likely to captivate readers in this field.

Weaknesses:

I did not identify any major weaknesses in this paper.

Reviewer #3 (Public review):

Summary:

Zafirova et al. investigated the interaction of head and body orientation in the macaque superior temporal sulcus (STS). Combining fMRI and electrophysiology, they recorded responses of visual neurons to a monkey avatar with varying head and body orientations. They found that STS neurons integrate head and body information in a nonlinear way, showing selectivity for specific combinations of head-body orientations. Head-body configuration angles can be reliably decoded, particularly for neurons in the anterior STS, suggesting a transformation of face/body orientation signals from the middle to the anterior STS. Furthermore, body inversion resulted in reduced decoding of head-body configuration angles. Compared to previous work that examined face or body alone, this study demonstrates how head and body information are integrated to compute a socially meaningful signal.

Strengths:

This work presents an elegant design of visual stimuli, with a monkey avatar of varying head and body orientations, making the analysis and interpretation straightforward. Together with several control experiments, the authors systematically investigated different aspects of head-body integration in the macaque STS. The results and analyses of the paper are convincing.

Weakness:

While this work has characterized the neural integration of head and body information in detail, it's unclear how the neural representation relates to the animal's perception. Behavioural experiments using the same set of stimuli could help address this question, but I agree that these additional experiments may be beyond the scope of the current paper.

Reviewer #2 (Public review):

A long-standing debate in the field of Pavlovian learning relates to the phenomenon of timescale invariance in learning i.e. that the rate at which an animal learns about a Pavlovian CS is driven by the relative rate of reinforcement of the cue (CS) to the background rate of reinforcement. In practice, if a CS is reinforced on every trial, then the rate of acquisition is determined by the relative duration of the CS (T) and the ITI (C = inter-US-interval = duration of CS + ITI), specifically the ratio of C/T. Therefore, the point of acquisition should be the same with a 10s CS and a 90s ITI (T = 10; C = 90 + 10 = 100, C/T = 100/10 = 10) and with a 100s CS and a 900s ITI (T = 100; C = 900 + 100 = 1000, C/T = 1000/100 = 10). That is to say, the rate of acquisition is invariant to the absolute timescale as long as this ratio is the same. This idea has many other consequences, but is also notably different from more popular prediction-error based associative learning models such as the Rescrola-Wagner model. The initial demonstrations that the ratio C/T predicts the point of acquisition across a wide range of parameters (both within and across multiple studies) was conducted in Pigeons using a Pavlovian autoshaping procedure. What has remained under contention is whether or not this relationship holds across species, particularly in the standard appetitive Pavlovian conditioning paradigms used in rodents. The results from rodent studies aimed at testing this have been mixed, and often the debate around the source of these inconsistent results focuses on the different statistical methods used to identify the point of acquisition for the highly variable trial-by-trial responses at the level of individual animals.<br /> The authors successfully replicate same effect found in pigeon autoshaping paradigms decades ago (with almost identical model parameters) in a standard Pavlovian appetitive paradigm in rats. They achieve this through a clever change the experimental design, using a convincingly wide range of parameters across 14 groups of rats, and by a thorough and meticulous analysis of these data. It is also interesting to note that the two author's have published on opposing sides of this debate for many years, and as a result have developed and refined many of the ideas in this manuscript through this process.

Main findings

(1) The present findings demonstrate that the point of initial acquisition of responding is predicted by the C/T ratio.

(2) The terminal rates of responding to the CS appears to be related to the reinforcement rate of the CS (T; specifically, 1/T) but not its relation to the reinforcement rate of the context (i.e. C or C/T). In the present experiment, all CS trials were reinforced so it is also the case that the terminal rate of responding was related to the duration of the CS.

(3) An unexpected finding was that responding during the ITI was similarly related to the rate of contextual reinforcement (1/C). This novel finding suggests that the terminal rate of responding during the ITI and the CS are related to their corresponding rates of reinforcement. This finding is surprising as it suggests that responding during the ITI is not being driven by the probability of reinforcement during the ITI.

(4) Finally, the authors characterised the nature of increased responding from the point of initial acquisition until responding peaks at a maximum. Their analyses suggest that nature of this increase was best described as linear in the majority of rats, as opposed to the non-linear increase that might be predicted by prediction error learning models (e.g. Rescorla-Wagner). However, more detailed analyses revealed that these changes can be quite variable across rats, and more variable when the CS had lower informativeness (defined as C/T).

Strengths and Weaknesses:

There is an inherent paradox regarding the consistency of the acquisition data from Gibbon & Balsam's (1981) meta-analysis of autoshaping in pigeons, and the present results in magazine response frequency in rats. This consistency is remarkable and impressive, and is suggestive of a relatively conserved or similar underlying learning principle. However, the consistency is also surprising given some significant differences in how these experiments were run. Some of these differences might reasonably be expected to lead to differences in how these different species respond. For example:

- The autoshaping procedure commonly used in the pigeons from these data were pretrained to retrieve rewards from a grain hopper with an instrumental contingency between head entry into the hopper and grain availability. During Pavlovian training, pecking the key light also elicited an auditory click feedback stimulus, and when the grain hopper was made available the hopper was also illuminated.

- In the present experimental procedure, the rats were not given contextual exposure to the pellet reinforcers in the magazine (e.g. a magazine training session is typically found in similar rodent procedures). The Pavlovian CS was a cue light within the magazine itself.

These design features in the present rodent experiment are clearly intentional. Pretraining with the reinforcer in the testing chambers would reasonably alter the background rate of reinforcement (parameter), so it make sense not to include this but differs from the paradigm used in pigeons. Having the CS inside the magazine where pellets are delivered provides an effective way to reduce any potential response competition between CS and US directed responding and combines these all into the same physical response. This makes the magazine approach response more like the pecking of the light stimulus in the pigeon autoshaping paradigm. However, the location of the CS and US is separated in pigeon autoshaping, raising questions about why the findings across species are consistent despite these differences.

Intriguingly, when the insertion of a lever is used as a Pavlovian cue in rodent studies, CS directed responding (sign-tracking) often develops over training such that eventually all animals bias their responding towards the lever than towards the US (goal-tracking at the magazine). However, the nature of this shift highlights the important point that these CS and US directed responses can be quite distinct physically as well as psychologically. Therefore, by conflating the development of these different forms of responding, it is not clear whether the relationship between C/T and the acquisition of responding describes the sum of all Pavlovian responding or predominantly CS or US directed responding.

Another interesting aspect of these findings is that there is a large amount of variability that scales inversely with C/T. A potential account of the source of this variability is related to the absence of preexposure to the reward pellets. This is normally done within the animals' homecage as a form of preexposure to reduce neophobia. If some rats take longer to notice and then approach and finally consume the reward pellets in the magazine, the impact of this would systematically differ depending on the length of the ITI. For animals presented with relatively short CSs and ITIs, they may essentially miss the first couple of trials and/or attribute uneaten pellets accumulating in the magazine to the background/contextual rate of reinforcement. What is not currently clear is whether this was accounted for in some way by confirming when the rats first started retrieving and consuming the rewards from the magazine.

While the generality of these findings across species is impressive, the very specific set of parameters employed to generate these data raise questions about the generality of these findings across other standard Pavlovian conditioning parameters. While this is obviously beyond the scope of the present experiment, it is important to consider that the present study explored a situation with 100% reinforcement on every trial, with a variable duration CS (drawn form a uniform distribution), with a single relatively brief CS (maximum of 122s) CS and a single US. Again, the choice of these parameters in the present experiment is appropriate and very deliberately based on refinements from many previous studies from the authors. This includes a number of criteria used to define magazine response frequency that includes discarding specific responses (discussed and reasonably justified clearly in the methods section). Similarly, the finding that terminal rates of responding are reliably related to 1/T is surprising, and it is not clear whether this might be a property specific to this form of variable duration CS, the use of a uniform sampling distribution, or the use of only a single CS. However, it is important to keeps these limitations in mind when considering some of the claims made in the discussion section of this manuscript that go beyond what these data can support.

The main finding demonstrating the consistent findings across species is presented in Figure 3. In the analysis of these data, it is not clear why the correlations between C, T, and C/T and the measure of acquisition in Figure 3A were presented as r values, whereas the r2 values were presented in the discussion of Figure 3B, and no values were provided in discussing Figure 3C. The measure of acquisition in Figure 3A is based on a previously established metric, whereas the measure in Figure 3B employs the relatively novel nDKL measure that is argued to be a better and theoretically based metric. Surprisingly, when r and r2 values are converted to the same metric across analyses, it appears that this new metric (Figure 3B) does well but not as well as the approach in Figure 3A. This raises questions about why a theoretically derived measure might not be performing as well on this analysis, and whether the more effective measure is either more reliable or tapping into some aspect of the processes that underlie acquisition that is not accounted for by the nDKL metric. Unfortunately, the new metric is discussed and defined at great length but its utility is not considered.<br /> An important analysis issue that is unclear in the present manuscript is exactly how the statistics were run (how the model was defined, were individual subjects or group medians used, what software was used etc...). For example, it is not clear whether the analyses conducted in relation to Figure 3 used the data from individual rats or the group medians. Similarly, it appears that each rat contributes four separate data points, and a single regression line was fit to all these data despite the highly likely violation of the assumption independent observations (or more precisely, the assumption of uncorrelated errors) in this analysis. Furthermore, it is claimed that the same regression line fit the IT and CS period data in this figure, however this<br /> If the data in figure 3 were analyzed with log(ITI) or log(C/ITI) i.e. log(C/(T-C)), would this be a better fit for these data? Is it the case that the ratio of C/T the best predictor of the trial/point of acquisition, or is it the case that another metric related to reinforcement rates provides a better fit?

Based on the variables provided in Supplementary file 3, containing the acquisition data, I was unable to reproduce the values reported in the analysis of Figure 3.<br /> In relation to Figure 3: I am curious about whether the authors would be able to comment on whether the individual variability in trials to acquisition would be expected to scale differently based on C/T, or C, or (if a less restricted range was used) T?<br /> It is not clear why Figure 3C is presented but not analyzed, and why the data presented in Figure 4 to clarify the spread of the distribution of the data observed across the plots in Figure 3 uses the data from Figure 3C. This would seem like the least representative data to illustrate the point of Figure 4. It also appears to my eye that the data actually plotted in Figure 4 correspond to Figure 3A and 3B rather than the odds 10:1 data indicated in text.

What was the decision criteria used to decide on averaging the final 5 conditioning sessions as terminal responding for the analyses in Figure 5? This is an oddly specific number. Was this based on consistency with previous work, or based on the greatest number of sessions where stable data for all animals could be extracted?<br /> In the analysis corresponding to Figures 7-8: If I understand the description of this analysis correctly, for each rat the data are the cumulative response data during the CS, starting from the trial on which responding to the CS > ITI (t = 1), and ending at the trial on which CS responding peaked (maximum over 3 session moving average window; t = end). This analysis does not seem to account for changes (decline) in the ITI response rates over this period of acquisition, and it is likely that responding during the ITI is still declining after t=1. Are the 4 functions that were fit to these data to discriminate between different underlying generative processes still appropriate on total CS responding instead of conditional CS responding after accounting for changes in baseline response rates during ITI?

Page 27, Procedure, final sentence: The magazine responding during the ITI is defined as the 20s period immediately before CS onset. The range of ITI values (Table 1) always starts as low as 15s in all 14 groups. Even in the case of an ITI on a trial that was exactly 20s, this would also mean that the start of this period overlaps with the termination of the CS from the previous trial and delivery (and presumably consumption) of a pellet. Please indicate if the definition of the ITI period was modified on trials where the preceding ITI was <20s, and if any other criteria were used to define the ITI.

Were the rats exposed to the reinforcers/pellets in their home cage prior to acquisition? Please indicate whether rats where pre-exposed to the reward pellets in their home cages e.g. as is often done to reduce neophobia. Given the deliberate absence of a magazine-training phase, this information is important when assessing the experienced contingency between the CS and the US.

For all the analyses, please provide the exact models that were fit and the software used. For example, it is not necessarily clear to the reader (particularly in the absence of degrees of freedom) that the model fits discussed in Figure 3 are fit on the individual subject data points or the group medians. Similarly, in Figure 6 there is no indication of whether a single regression model was fit to all the plotted data or whether tests of different slopes for each of the conditions were compared. With regards to the statistics in Figure 6, depending on how this was run, it is also a potential problem that the analyses does not correct for the potentially highly correlated multiple measurements from the same subjects i.e. each rat provides 4 data points which are very likely not to be independent observations.

A number of sections of the discussion are speculative or not directly supported by the present experimental data (but may well be supported by previous findings that are not the direct focus of the present experiment). For example, Page 19, Paragraph 2: this entire paragraph is not really clearly explained and is presenting an opinion rather than a strong conclusion that follows directly from the present findings. Evidence for an aspect of RET in the present paper (i.e. the prediction of time scale invariance on the initial point of acquisition, but not necessarily the findings regarding the rate of terminal acquisition) - while supportive - does not necessarily provide unconditional evidence for this theory over all the alternatives.

Similarly, the Conclusion section (Page 23) makes the claim that "the equations have at most one free parameter", which may be an oversimplification that is conditionally true in the narrow context of the present experiment where many things were kept constant between groups and run in a particular way to ensure this is the case. While the equations do well in this narrow case, it is unlikely that additional parameters would not need to be added to account for more general learning situations. To clarify, I am not contending that this kind of statement is necessarily untrue, merely that it is being presented in a narrow context and may require a deeper discussion of much more of the literature to qualify/support properly - and the discussion section of the present experiment/manuscript may not be the appropriate place for this.

- Consider taking advantage of an "Ideas and Speculation" subsection within the Discussion that is supported by eLife [ https://elifesciences.org/inside-elife/e3e52a93/elife-latest-including-ideas-and-speculation-in-elife-papers ]. This might be more appropriate to qualify the tone of much of the discussion from page 19 onwards.

It seems like there are entire analyses and new figures being presented in the discussion e.g. Page 20: Information-Theoretic Contingency. These sections might be better placed in the methods section or a supplementary section/discussion.

Author response:

The following is the authors’ response to the original reviews

ANALYTICAL

(1) A key claim made here is that the same relationship (including the same parameter) describes data from pigeons by Gibbon and Balsam (1981; Figure 1) and the rats in this study (Figure 3). The evidence for this claim, as presented here, is not as strong as it could be. This is because the measure used for identifying trials to criterion in Figure 1 appears to differ from any of the criteria used in Figure 3, and the exact measure used for identifying trials to criterion influences the interpretation of Figure 3***. To make the claim that the quantitative relationship is one and the same in the Gibbon-Balsam and present datasets, one would need to use the same measure of learning on both datasets and show that the resultant plots are statistically indistinguishable, rather than simply plotting the dots from both data sets and spotlighting their visual similarity. In terms of their visual characteristics, it is worth noting that the plots are in log-log axis and, as such, slight visual changes can mean a big difference in actual numbers. For instance, between Figure 3B and 3C, the highest information group moves up only "slightly" on the y-axis but the difference is a factor of 5 in the real numbers. Thus, in order to support the strong claim that the quantitative relationships obtained in the Gibbon-Balsam and present datasets are identical, a more rigorous approach is needed for the comparisons.

***The measure of acquisition in Figure 3A is based on a previously established metric, whereas the measure in Figure 3B employs the relatively novel nDKL measure that is argued to be a better and theoretically based metric. Surprisingly, when r and r2 values are converted to the same metric across analyses, it appears that this new metric (Figure 3B) does well but not as well as the approach in Figure 3A. This raises questions about why a theoretically derived measure might not be performing as well on this analysis, and whether the more effective measure is either more reliable or tapping into some aspect of the processes that underlie acquisition that is not accounted for by the nDKL metric.

Figure 3 shows that the relationship between learning rate and informativeness for our rats was very similar to that shown with pigeons by Gibbon and Balsam (1981). We have used multiple criteria to establish the number of trials to learn in our data, with the goal of demonstrating that the correspondence between the data sets was robust. In the revised Figure 3, specifically 3C and 3D, we have plotted trials to acquisition using decision criterion equivalent to those used by Gibbon and Balsam. The criterion they used—at least one peck at the response key on at least 3 out of 4 consecutive trials—cannot be directly applied to our magazine entry data because rats make magazine entries during the inter-trial interval (whereas pigeons do not peck at the response key in the inter-trial interval). Therefore, evidence for conditioning in our paradigm must involve comparison between the response rate during CS and the baseline response rate, rather than just counting responses during the CS. We have used two approaches to adapt the Gibbon and Balsam criterion to our data. One approach, plotted in Figure 3C, uses a non-parametric signed rank test for evidence that the CS response rate exceeds the pre-CS response rate, and adopting a statistical criterion equivalent to Gibbon and Balsam’s 3-out-of-4 consecutive trials (p<.3125). The second method (Figure 3D) estimates the nDkl for the criterion used by Gibbon and Balsam and then applies this criterion to the nDkl for our data. To estimate the nDkl of Gibbon and Balsam’s data, we have assumed there are no responses in the inter-trial interval and the response probability during the CS must be at least 0.75 (their criterion of at least 3 responses out of 4 trials). The nDkl for this difference is 2.2 (odds ratio 27:1). We have then applied this criterion to the nDkl obtained from our data to identify when the distribution of CS response rates has diverged by an equivalent amount from the distribution of pre-CS response rates. These two analyses have been added to the manuscript to replace those previously shown in Figures 3B and 3C.

(2) Another interesting claim here is that the rates of responding during ITI and the cue are proportional to the corresponding reward rates with the same proportionality constant. This too requires more quantification and conceptual explanation. For quantification, it would be more convincing to calculate the regression slope for the ITI data and the cue data separately and then show that the corresponding slopes are not statistically distinguishable from each other. Conceptually, it is not clear why the data used to test the ITI proportionality came from the last 5 conditioning sessions. What were the decision criteria used to decide on averaging the final 5 sessions as terminal responses for the analyses in Figure 5? Was this based on consistency with previous work, or based on the greatest number of sessions where stable data for all animals could be extracted?

If the model is that animals produce response rates during the ITI (a period with no possible rewards) based on the overall rate of rewards in the context, wouldn't it be better to test this before the cue learning has occurred? Before cue learning, the animals would presumably only have attributed rewards in the context to the context and thus, produce overall response rates in proportion to the contextual reward rate. After cue learning, the animals could technically know that the rate of rewards during ITI is zero. Why wouldn't it be better to test the plotted relationship for ITI before cue learning has occurred? Further, based on Figure 1, it seems that the overall ITI response rate reduces considerably with cue learning. What is the expected ITI response rate prior to learning based on the authors' conceptual model? Why does this rate differ from pre and post-cue learning? Finally, if the authors' conceptual framework predicts that ITI response rate after cue learning should be proportional to contextual reward rate, why should the cue response rate be proportional to the cue reward rate instead of the cue reward rate plus the contextual reward rate?

A single regression line, as shown in Figure 5, is the simplest possible model of the relationship between response rate and reinforcement rate and it explains approximately 80% of the variance in response rate. Fixing the log-log slope at 1 yields the maximally simple model. (This regression is done in the logarithmic domain to satisfy the homoscedasticity assumption.) When transformed into the linear domain, this model assumes a truly scalar relation (linear, intercept at the origin) and assumes the same scale factor and the same scalar variability in response rates for both sets of data (ITI and CS). Our plot supports such a model. Its simplicity is its own motivation (Occam’s razor).

If separate regression lines are fitted to the CS and ITI data, there is a small increase in explained variance (R₂ = 0.82). These regression lines have been added to the plot in the revised manuscript (Figure 5). We leave it to further research to determine whether such a complex model, with 4 parameters, is required. However, we do not think the present data warrant comparing the simplest possible model, with one parameter, to any more complex model for the following reasons:

· When a brain—or any other machine—maps an observed (input) rate to a rate it produces (output rate), there is always an implicit scalar. In the special case where the produced rate equals the observed rate, the implicit scalar has value 1. Thus, there cannot be a simpler model than the one we propose, which is, in and of itself, interesting.

· The present case is an intuitively accessible example of why the MDL (Minimum Description Length) approach to model complexity (Barron, Rissanen, & Yu, 1998; Grünwald, Myung, & Pitt, 2005; Rissanen, 1999) can yield a very different conclusion from the conclusion reached using the Bayesian Information Criterion (BIC) approach. The MDL approach measures the complexity of a model when given N data specified with precision of B bits per datum by computing (or approximating) the sum of the maximum-likelihoods of the model’s fits to all possible sets of N data with B precision per datum. The greater the sum over the maximum likelihoods, the more complex the model, that is, the greater its measured wiggle room, it’s capacity to fit data. Recall that von Neuman remarked to Fermi that with 4 parameters he could fit an elephant. His deeper point was that multi-parameter models bring neither insight nor predictive power; they explain only post-hoc, after one has adjusted their parameters in the light of the data. For realistic data sets like ours, the sums of maximum likelihoods are finite but astronomical. However, just as the Sterling approximation allows one to work with astronomical factorials, it has proved possible to develop readily computable approximations to these sums, which can be used to take model complexity into account when comparing models. Proponents of the MDL approach point out that the BIC is inadequate because models with the same number of parameters can have very different amounts of wiggle room. A standard illustration of this point is the contrast between logarithmic model and power-function model. Log regressions must be concave; whereas power function regressions can be concave, linear, or convex—yet they have the same number of parameters (one or two, depending on whether one counts the scale parameter that is always implicit). The MDL approach captures this difference in complexity because it measures wiggle room; the BIC approach does not, because it only counts parameters.

· In the present case, one is comparing a model with no pivot and no vertical displacement at the boundary between the black dots and the red dots (the 1-parameter unilinear model) to a bilinear model that allows both a change in slope and a vertical displacement for both lines. The 4-parameter model is superior if we use the BIC to take model complexity into account. However, 4-parameter has ludicrously more wiggle room. It will provide excellent fits—high maximum likelihood—to data sets in which the red points have slope > 1, slope 0, or slope < 0 and in which it is also true that the intercept for the red points lies well below or well above the black points (non-overlap in the marginal distribution of the red and black data). The 1-parameter model, on the other hand, will provide terrible fits to all such data (very low maximum likelihoods). Thus, we believe the BIC does not properly capture the immense actual difference in the complexity between the 1-parameter model (unilinear with slope 1) to the 4-parameter model (bilinear with neither the slope nor the intercept fixed in the linear domain).

· In any event, because the pivot (change in slope between black and red data sets), if any, is small and likewise for the displacement (vertical change), it suffices for now to know that the variance captured by the 1-parameter model is only marginally improved by adding three more parameters. Researchers using the properly corrected measured rate of head poking to measure the rate of reinforcement a subject expects can therefore assume that they have an approximately scalar measure of the subject’s expectation. Given our data, they won’t be far wrong even near the extremes of the values commonly used for rates of reinforcement. That is a major advance in current thinking, with strong implications for formal models of associative learning. It implies that the performance function that maps from the neurobiological realization of the subject’s expectation is not an unknown function. On the contrary, it’s the simplest possible function, the scalar function. That is a powerful constraint on brain-behavior linkage hypotheses, such as the many hypothesized relations between mesolimbic dopamine activity and the expectation that drives responding in Pavlovian conditioning (Berridge, 2012; Jeong et al., 2022; Y.  Niv, Daw, Joel, & Dayan, 2007; Y. Niv & Schoenbaum, 2008).

The data in Figures 4 and 5 are taken from the last 5 sessions of training. The exact number of sessions was somewhat arbitrary but was chosen to meet two goals: (1) to capture asymptotic responding, which is why we restricted this to the end of the training, and (2) to obtain a sufficiently large sample of data to estimate reliably each rat’s response rate. We have checked what the data look like using the last 10 sessions, and can confirm it makes very little difference to the results. We now note this in the revised manuscript. The data for terminal responding by all rats, averaged over both the last 5 sessions and last 10 sessions, can be downloaded from https://osf.io/vmwzr/

Finally, as noted by the reviews, the relationship between the contextual rate of reinforcement and ITI responding should also be evident if we had measured context responding prior to introducing the CS. However, there was no period in our experiment when rats were given unsignalled reinforcement (such as is done during “magazine training” in some experiments). Therefore, we could not measure responding based on contextual conditioning prior to the introduction of the CS. This is a question for future experiments that use an extended period of magazine training or “poor positive” protocols in which there are reinforcements during the ITIs as well as during the CSs. The learning rate equation has been shown to predict reinforcements to acquisition in the poor-positive case (Balsam, Fairhurst, & Gallistel, 2006).

(3) There is a disconnect between the gradual nature of learning shown in Figures 7 and 8 and the information-theoretic model proposed by the authors. To the extent that we understand the model, the animals should simply learn the association once the evidence crosses a threshold (nDKL > threshold) and then produce behavior in proportion to the expected reward rate. If so, why should there be a gradual component of learning as shown in these figures? In terms of the proportional response rule to the rate of rewards, why is it changing as animals go from 10% to 90% of peak response? The manuscript would be greatly strengthened if these results were explained within the authors' conceptual framework. If these results are not anticipated by the authors' conceptual framework, this should be explicitly stated in the manuscript.

One of us (CRG) has earlier suggested that responding appears abruptly when the accumulated evidence that the CS reinforcement rate is greater than the contextual rate exceeds a decision threshold (C.R.  Gallistel, Balsam, & Fairhurst, 2004). The new more extensive data require a more nuanced view. Evidence about the manner in which responding changes over the course of training is to some extent dependent on the analytic method used to track those changes. We presented two different approaches. The approach shown in Figures 7 and 8 (now 6 and 7), extending on that developed by Harris (2022), assumes a monotonic increase in response rate and uses the slope of the cumulative response rate to identify when responding exceeds particular milestones (percentiles of the asymptotic response rate). This analysis suggests a steady rise in responding over trials. Within our theoretical model, this might reflect an increase in the animal’s certainty about the CS reinforcement rate with accumulated evidence from each trial. While this method should be able to distinguish between a gradual change and a single abrupt change in responding (Harris, 2022) it may not distinguish between a gradual change and multiple step-like changes in responding and cannot account for decreases in response rate.

The other analytic method we used relies on the information theoretic measure of divergence, the nDkl (Gallistel & Latham, 2023), to identify each point of change (up or down) in the response record. With that method, we discern three trends. First, the onset tends to be abrupt in that the initial step up is often large (an increase in response rate by 50% or more of the difference between its initial value and its terminal value is common and there are instances where the initial step is to the terminal rate or higher). Second, there is marked within-subject variability in the response rate, characterized by large steps up and down in the parsed response rates following the initial step up, but this variability tends to decrease with further training (there tend to be fewer and smaller steps in both the ITI response rates and the CS response rate as training progresses). Third, the overall trend, seen most clearly when one averages across subjects within groups is to a moderately higher rate of responding later in training than after the initial rise. We think that the first tendency reflects an underlying decision process whose latency is controlled by diminishing uncertainty about the two reinforcement rates and hence about their ratio. We think that decreasing uncertainty about the true values of the estimated rates of reinforcement is also likely to be an important part of the explanation for the second tendency (decreasing within-subject variation in response rates). It is less clear whether diminishing uncertainty can explain the trend toward a somewhat greater difference in the two response rates as conditioning progresses. It is perhaps worth noting that the distribution of the estimates of the informativeness ratio is likely to be heavy tailed and have peculiar properties (as witness, for example, the distribution of the ratio of two gamma distributions with arbitrary shape and scale parameters) but we are unable at this time to propound an explanation of the third trend.

(4) Page 27, Procedure, final sentence: The magazine responding during the ITI is defined as the 20 s period immediately before CS onset. The range of ITI values (Table 1) always starts as low as 15 s in all 14 groups. Even in the case of an ITI on a trial that was exactly 20 s, this would also mean that the start of this period overlaps with the termination of the CS from the previous trial and delivery (and presumably consumption) of a pellet. It should be indicated whether the definition of the ITI period was modified on trials where the preceding ITI was < 20 s, and if any other criteria were used to define the ITI. Were the rats exposed to the reinforcers/pellets in their home cage prior to acquisition?

There was an error in the description provided in the original text. The pre-CS period used to measure the ITI responding was 10 s rather than 20 s. There was always at least a 5-s gap between the end of the previous trial and the start of the pre-CS period. The statement about the pre-CS measure has been corrected in the revised manuscript.

(5) For all the analyses, the exact models that were fit and the software used should be provided. For example, it is not necessarily clear to the reader (particularly in the absence of degrees of freedom) that the model discussed in Figure 3 fits on the individual subject data points or the group medians. Similarly, in Figure 6 there is no indication of whether a single regression model was fit to all the plotted data or whether tests of different slopes for each of the conditions were compared. With regards to the statistics in Figure 6, depending on how this was run, it is also a potential problem that the analyses do not correct for the potentially highly correlated multiple measurements from the same subjects, i.e. each rat provides 4 data points which are very unlikely to be independent observations.

Details about model fitting have been added to the revision. The question about fitting a single model or multiple models to the data in Figure 6 (now 5) is addressed in response 2 above. In Figure 5, each rat provides 2 behavioural data points (ITI response rate and CS response rate) and 2 values for reinforcement rate (1/C and 1/T). There is a weak but significant correlation between the ITI and CS response rates (r = 0.28, p < 0.01; log transformed to correct for heteroscedasticity). By design, there is no correlation between the log reinforcement rates (r = 0.06, p = .404).

CONCEPTUAL

(1) We take the point that where traditional theories (e.g., Rescorla-Wagner) and rate estimation theory (RET) both explain some phenomenon, the explanation in terms of RET may be preferred as it will be grounded in aspects of an animal's experience rather than a hypothetical construct. However, like traditional theories, RET does not explain a range of phenomena - notably, those that require some sort of expectancy/representation as part of their explanation. This being said, traditional theories have been incorporated within models that have the representational power to explain a broader array of phenomena, which makes me wonder: Can rate estimation be incorporated in models that have representational power; and, if so, what might this look like? Alternatively, do the authors intend to claim that expectancy and/or representation - which follow from probabilistic theories in the RW mould - are unnecessary for explanations of animal behaviour?***

It is important for the field to realize that the RW model cannot be used to explain the results of Rescorla’s (Rescorla, 1966; Rescorla, 1968, 1969) contingency-not-pairing experiments, despite what was claimed by Rescorla and Wagner (Rescorla & Wagner, 1972; Wagner & Rescorla, 1972) and has subsequently been claimed in many modelling papers and in most textbooks and reviews (Dayan & Niv, 2008; Y. Niv & Montague, 2008). Rescorla programmed reinforcements with a Poisson process. The defining property of a Poisson process is its flat hazard function; the reinforcements were equally likely at every moment in time when the process was running. This makes it impossible to say when non-reinforcements occurred and, a fortiori, to count them. The non-reinforcements are causal events in RW algorithm and subsequent versions of it. Their effects on associative strength are essential to the explanations proffered by these models. Non-reinforcements—failures to occur, updates when reinforcement is set to 0, hence also the lambda parameter—can have causal efficacy only when the successes may be predicted to occur at specified times (during “trials”). When reinforcements are programmed by a Poisson process, there are no such times. Attempts to apply the RW formula to reinforcement learning soon foundered on this problem (Gibbon, 1981; Gibbon, Berryman, & Thompson, 1974; Hallam, Grahame, & Miller, 1992; L.J. Hammond, 1980; L. J. Hammond & Paynter, 1983; Scott & Platt, 1985). The enduring popularity of the delta-rule updating equation in reinforcement learning depends on “big-concept” papers that don’t fit models to real data and discretize time into states while claiming to be real-time models (Y. Niv, 2009; Y. Niv, Daw, & Dayan, 2005).

The information-theoretic approach to associative learning, which sometimes historically travels as RET (rate estimation theory), is unabashedly and inescapably representational. It assumes a temporal map and arithmetic machinery capable in principle of implementing any implementable computation. In short, it assumes a Turing-complete brain. It assumes that whatever the material basis of memory may be, it must make sense to ask of it how many bits can be stored in a given volume of material. This question is seldom posed in associative models of learning, nor by neurobiologists committed to the hypothesis that the Hebbian synapse is the material basis of memory. Many—including the new Nobelist, Geoffrey Hinton— would agree that the question makes no sense. When you assume that brains learn by rewiring themselves rather than by acquiring and storing information, it makes no sense.

When a subject learns a rate of reinforcement, it bases its behavior on that expectation, and it alters its behavior when that expectation is disappointed. Subjects also learn probabilities when they are defined. They base some aspects of their behavior on those expectations, making computationally sophisticated use of their representation of the uncertainties (Balci, Freestone, & Gallistel, 2009; Chan & Harris, 2019; J. A. Harris, 2019; J.A. Harris & Andrew, 2017; J. A. Harris & Bouton, 2020; J. A. Harris, Kwok, & Gottlieb, 2019; Kheifets, Freestone, & Gallistel, 2017; Kheifets & Gallistel, 2012; Mallea, Schulhof, Gallistel, & Balsam, 2024 in press).

(2) The discussion of Rescorla's (1967) and Kamin's (1968) findings needs some elaboration. These findings are already taken to mean that the target CS in each design is not informative about the occurrence of the US - hence, learning about this CS fails. In the case of blocking, we also know that changes in the rate of reinforcement across the shift from stage 1 to stage 2 of the protocol can produce unblocking. Perhaps more interesting from a rate estimation perspective, unblocking can also be achieved in a protocol that maintains the rate of reinforcement while varying the sensory properties of the US (Wagner). How does rate estimation theory account for these findings and/or the demonstrations of trans-reinforcer blocking (Pearce-Ganesan)? Are there other ways that the rate estimation account can be distinguished from traditional explanations of blocking and contingency effects? If so, these would be worth citing in the discussion. More generally, if one is going to highlight seminal findings (such as those by Rescorla and Kamin) that can be explained by rate estimation, it would be appropriate to acknowledge findings that challenge the theory - even if only to note that the theory, in its present form, is not all-encompassing. For example, it appears to me that the theory should not predict one-trial overshadowing or the overtraining reversal effect - both of which are amenable to discussion in terms of rates.

I assume that the signature characteristics of latent inhibition and extinction would also pose a challenge to rate estimation theory, just as they pose a challenge to Rescorla-Wagner and other probability-based theories. Is this correct?

The seemingly contradictory evidence of unblocking and trans-reinforcer blocking by Wagner and by Pearce and Ganesan cited above will be hard for any theory to accommodate. It will likely depend on what features of the US are represented in the conditioned response.

RET predicts one-trial overshadowing, as anyone may verify in a scientific programming language because it has no free parameters; hence, no wiggle room. Overtraining reversal effects appear to depend on aspects of the subjects’ experience other than the rate of reinforcement. It seems unlikely that it can proffer an explanation.

Various information-theoretic calculations give pretty good quantitative fits to the relatively few parametric studies of extinction and the partial-reinforcement extinction effect (see Gallistel (2012, Figs 3 & 4); Wilkes & Gallistel (2016, Fig 6) and Gallistel (2025, under review, Fig 6). It has not been applied to latent inhibition, in part for want of parametric data. However, clearly one should not attribute a negative rate to a context in which the subject had never been reinforced. An explanation, if it exists, would have to turn on the effect of that long period on initial rate estimates AND on evidence of a change in rate, as of the first reinforcement.

Recommendations for authors:

MINOR POINTS

(1) It is not clear why Figure 3C is presented but not analyzed, and why the data presented in Figure 4 to clarify the spread of the distribution of the data observed across the plots in Figure 3 uses the data from Figure 3C. This would seem like the least representative data to illustrate the point of Figure 4. It also appears that the data plotted in Figure 4 corresponds to Figure 3A and 3B rather than the odds 10:1 data indicated in the text.

Figures 3 has changed as already described. The data previously plotted in Figure 4 are now shown in 3B and corresponds to that plotted in Figure 3A.

(2) Log(T) was not correlated with trials to criterion. If trials to criterion is inversely proportional to log(C/T) and C is uncorrelated with T, shouldn't trials to criterion be correlated with log(T)? Is this merely a matter of low statistical power?

Yes. There is a small, but statistically non-significant, correlation between log(T) and trials to criterion, r = 0.35, p = .22. That correlation drops to .08 (p = .8) after factoring out log(C/T), which demonstrates that the weak correlation between log(T) and trials to criterion is based on the correlation between log(t) and log(C/T).

(3) The rationale for the removal of the high information condition samples in the Fig 8 "Slope" plot to be weak. Can the authors justify this choice better? If all data are included, the relationship is clearly different from that shown in the plot.

We have now reported correlations that include those 3 groups but noted that the correlations are largely driven by the much lower slope values of those 3 groups which is likely an artefact of their smaller number of trials. We use this to justify a second set of correlations that excludes those 3 groups.

(4) The discussion states that there is at most one free parameter constrained by the data - the constant of proportionality for response rate. However, there is also another free parameter constrained by data-the informativeness at which expected trials to acquisition is 1.

I think this comment is referring to two different sets of data. The constant of proportionality of the response rate refers to the scalar relationship between reinforcement rate and terminal response rate shown in Figure 5. The other parameter, the informativeness when trials to acquisition equals 1, describes the intercept of the regression line in Figure 1 (and 3).

(5) The authors state that the measurement of available information is not often clear. Given this, how is contingency measurable based on the authors' framework?

(6) Based on the variables provided in Supplementary File 3, containing the acquisition data, we were unable to reproduce the values reported in the analysis of Figure 3.

Figure 3 has changed, using new criteria for trials to acquisition that attempt to match the criterion used by Gibbon and Balsam. The data on which these figures are based has been uploaded into OSF.

GRAPHICAL AND TYPOGRAPHICAL

(1) Y-axis labels in Figure 1 are not appropriately placed. 0 is sitting next to 0.1. 0 should sit at the bottom of the y-axis.

If this comment refers to the 0 sitting above an arrow in the top right corner of the plot, this is not misaligned. The arrow pointing to zero is used to indicate that this axis approaches zero in the upward direction. 0 should not be aligned to a value on the axis since a learning rate of zero would indicate an infinite number of learning trials. The caption has been edited to explain this more clearly.

(2) Typo, Page 6, Final Paragraph, line 4. "Fourteen groups of rats were trained with for 42 session"

Corrected. Thank you.

(3) Figure 3 caption: Typo, should probably be "Number of trials to acquisition"?

This change has now been made. The axis shows reinforcements to acquisition to be consistent with Gibbon and Balsam, but trials and number of reinforcements are identical in our 100% reinforcement schedule.

(4) Typo Page 17 Line 1: "Important pieces evidence about".

Correct. Thank you.

(5) Consider consistent usage of symbols/terms throughout the manuscript (e.g. Page 22, final paragraph: "iota = 2" is used instead of the corresponding symbol that has been used throughout).

Changed.

(6) Typo Page 28, Paragraph 1, Line 9: "We used a one-sample t-test using to identify when this".

This section of text has been changed to reflect the new analysis used for the data in Figure 3.

(7) Typo Page 29, Paragraph 1, Line 2: "problematic in cases where one of both rates are undefined" either typo or unclear phrasing.

“of” has been corrected to “or”

(8) Typo Page 30: Equation 3 appears to have an error and is not consistent with the initial printing of Equation 3 in the manuscript.

The typo in initial expression of Eq 3 (page 23) has been corrected.

(9) Typo Page 33, Line 5: "Figures 12".

Corrected.

(10) Typo Page 34, Line 10: "and the 5 the increasingly"? Should this be "the 5 points that"?

Corrected.

(11) Typo Page 35, Paragraph 2: "estimate of the onset of conditioned is the trial after which".

Corrected.

(12) Clarify: Page 35, final paragraph: it is stated that four-panel figures are included for each subject in the Supplementary files, but each subject has a six-panel figure in the Supplementary file.

The text now clarifies that the 4-panel figures are included within the 6-panel figures in the Supplementary materials.

(13) It is hard to identify the different groups in Figure 2 (Plot 15).

The figure is simply intended to show that responding across seconds within the trial is relatively flat for each group. Individuation of specific groups is not particularly important.

(14) It appears that the numbering on the y-axis is misaligned in Figure 2 relative to the corresponding points on the scale (unless I have misunderstood these values and the response rate measure to the ITI can drop below 0?).

The numbers on the Y axes had become misaligned. That has now been corrected.

(15) Please include the data from Figure 3A in the spreadsheet supplementary file 3. If it has already been included as one of the columns of data, please consider a clearer/consistent description of the relevant column variable in Supplementary File 1.

The data from Figure 3 are now available from the linked OSF site, referenced in the manuscript.

(16) Errors in supplementary data spreadsheets such that the C/T values are not consistent with those provided in Table 1 (C/T values of 4.5, 54, 180, and 300 are slightly different values in these spreadsheets). A similar error/mismatch appears to have occurred in the C/T labels for Figures (e.g. Figure 10) and the individual supplementary figures.

The C/T values on the figures in the supplementary materials have been corrected and are now consistent with those in Table 1.

(17) Currently the analysis and code provided at https://osf.io/vmwzr/ are not accessible without requesting access from the author. Please consider making these openly available without requiring a request for authorization. As such, a number of recommendations made here may already have been addressed by the data and code deposited on OSF. Apologies for any redundant recommendations.

Data and code are now available in at the OSF site which has been made public without requiring request.

(18) Please consider a clearer and more specific reference to supplementary materials. Currently, the reader is required to search through 4 separate supplementary files to identify what is being discussed/referenced in the text (e.g. Page 18, final line: "see Supplementary Materials" could simply be "see Figure S1").

We have added specific page numbers in references to the Supplementary Materials.

I remember this Tiktok going viral and how amusing I found it. A video like this is a testament to how creative the internet can be for good or bad. It was fairly unhinged and a perfect example of how just about anything can go viral given the right circumstances, no matter the intention.

Author response:

The following is the authors’ response to the original reviews

eLife Assessment

This study provides useful findings about the effects of heterozygosity for Trio variants linked to neurodevelopmental and psychiatric disorders in mice. However, the strength of the evidence is limited and incomplete mainly because the experimental flow is difficult to follow, raising concerns about the conclusions' robustness. Clearer connections between variables, such as sex, age, behavior, brain regions, and synaptic measures, and more methodological detail on breeding strategies, test timelines, electrophysiology, and analysis, are needed to support their claims.

We appreciate the opportunity to address the constructive feedback provided by eLife and the reviewers. Below, we respond to the overall assessment and individual reviewers' comments, clarifying our experimental approach, addressing concerns, and providing additional details where necessary.

We thank the editors for highlighting the significance of our findings regarding the effects of Trio variant heterozygosity in mice. We acknowledge the feedback concerning the experimental flow and agree that clarity is paramount. To address these concerns:

(1) Connections between variables: The word limit of the initial submission constrained our ability to provide adequate details and connections between variables. We have revised the manuscript to explicitly outline and extend explanations and the relationships between sex, age, behavior, brain regions, and synaptic measures, ensuring that the rationale for each experiment and its relevance to the overall conclusions are improved.

(2) Methodological details: The Methods section of our initial submission was condensed, with key details provided in the Supplemental Methods section. We have merged all into an extended section to improve clarity. We have expanded our description of breeding strategies, test timelines, electrophysiological protocols, and data analysis methods in the revised Methods section. We believe the additions have enhanced the transparency and reproducibility of our study and ensured full support of our conclusions.

(3) Experimental flow: We have revised and extended our results, methods, and discussion sections to clarify the rationale and experimental design to guide readers through the experimental sequence and rationale.

We are confident these revisions address the concerns raised and enhance the robustness and coherence of our findings.

Public Reviews:

Reviewer #1 (Public review):

Summary:

This study explores how heterozygosity for specific neurodevelopmental disorder-associated Trio variants affects mouse behavior, brain structure, and synaptic function, revealing distinct impacts on motor, social, and cognitive behaviors linked to clinical phenotypes. Findings demonstrate that Trio variants yield unique changes in synaptic plasticity and glutamate release, highlighting Trio's critical role in presynaptic function and the importance of examining variant heterozygosity in vivo.

Strengths:

This study generated multiple mouse lines to model each Trio variant, reflecting point mutations observed in human patients with developmental disorders. The authors employed various approaches to evaluate the resulting behavioral, neuronal morphology, synaptic function, and proteomic phenotypes.

Weaknesses:

While the authors present extensive results, the flow of experiments is challenging to follow, raising concerns about the strength of the experimental conclusions. Additionally, the connection between sex, age, behavioral data, brain regions, synaptic transmission, and plasticity lacks clarity, making it difficult to understand the rationale behind each experiment. Clearer explanations of the purpose and connections between experiments are recommended. Furthermore, the methodology requires more detail, particularly regarding mouse breeding strategies, timelines for behavioral tests, electrophysiology conditions, and data analysis procedures.

We appreciate the reviewer’s recognition of the novelty and comprehensiveness of our approach, particularly the generation of multiple mouse lines and our efforts to model Trio variant effects in vivo.

Weaknesses

(1) Experimental flow and rationale and connection between variables: We have expanded on the connections between behavioral data, neuronal morphology, synaptic function, and proteomics in the Results and Discussion sections to clarify how each experiment informs the reasoning and the conclusions and to highlight the relationships between sex, age, behavior, and synaptic measures.

(2) Methodological details: Our initial Methods section was formatted to be short to fulfill word limits on the submitted version, with additional details provided in the Supplemental Methods section. We have merged our Methods and Supplemental Methods sections and expanded on our breeding strategies, test timelines, electrophysiological protocols, and data analysis. We believe these additions enhance the transparency and reproducibility of our study.

(3) Recommendations for the authors: We thank Reviewer #1 for providing several recommendations to improve our manuscript. We have addressed their comments in the revision, as detailed below, adding key experiments that bolster our findings.

Reviewer #2 (Public review):

Summary:

The authors generated three mouse lines harboring ASD, Schizophrenia, and Bipolar-associated variants in the TRIO gene. Anatomical, behavioral, physiological, and biochemical assays were deployed to compare and contrast the impact of these mutations in these animals. In this undertaking, the authors sought to identify and characterize the cellular and molecular mechanisms responsible for ASD, Schizophrenia, and Bipolar disorder development.

Strengths:

The establishment of TRIO dysfunction in the development of ASD, Schizophrenia, and Bipolar disorder is very recent and of great interest. Disorder-specific variants have been identified in the TRIO gene, and this study is the first to compare and contrast the impact of these variants in vivo in preclinical models. The impact of these mutations was carefully examined using an impressive host of methods. The authors achieved their goal of identifying behavioral, physiological, and molecular alterations that are disorder/variant specific. The impact of this work is extremely high given the growing appreciation of TRIO dysfunction in a large number of brain-related disorders. This work is very interesting in that it begins to identify the unique and subtle ways brain function is altered in ASD, Schizophrenia, and Bipolar disorder.

Weaknesses:

(1) Most assays were performed in older animals and perhaps only capture alterations that result from homeostatic changes resulting from prodromal pathology that may look very different.

(2) Identification of upregulated (potentially compensating) genes in response to these disorder-specific Trio variants is extremely interesting. However, a functional demonstration of compensation is not provided.

(3) There are instances where data is not shown in the manuscript. See "data not shown". All data collected should be provided even if significant differences are not observed.

I consider weaknesses 1 and 2 minor. While they would be very interesting to explore, these experiments might be more appropriate for a follow-up study. I would recommend that the missing data in 3 should be provided in the supplemental material.

We are grateful for the reviewer’s recognition of our study’s significance and methodological rigor. The acknowledgment of Trio dysfunction as a novel and impactful area of research is deeply appreciated.

Weaknesses:

We agree that focusing on older animals limits insights into early-stage pathophysiology. However, our goal in this study was to examine the functional impacts of Trio heterozygosity at an adolescent stage and to reveal the ultimate impact of these alleles on synaptic function. Our choice of age aligns with our objectives. Future studies of earlier developmental stages will be beneficial and complement these findings.

Functional compensation:

We tested functional compensation through rescue experiments in +/K1431M brain slices using a Rac1-specific inhibitor, NSC23766, which prevents Rac1 activation by Trio or Tiam1. Our finding that direct Rac1 inhibition normalizes deficient neurotransmitter release in +/K1431M mice strongly suggests that increased Rac1 activity drives this phenotype.

Data not shown:

We will incorporate all previously shown data into the Supplemental Materials, even when results are nonsignificant. We agree that this ensures full transparency and facilitates a more comprehensive evaluation of our findings.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) In Figure 1K-N, the lack of observed differences in +/M2145T mice across all tests raises questions about its validity as a BPD model. Furthermore, the differences in female behavior data compared to males, as shown in the Supplemental section, lack clarification-specifically, whether these variations are due to sex differences or sample size disparities, which is not discussed. Additionally, it's unclear if the same mice were used in tests K through L-N, as the reported numbers differ without explanation; if relevant, any mortality should be reported. Given the observed body weight differences, it is important to display locomotor data, despite the mention of no change in open field results. Lastly, a detailed breeding strategy and timeline for behavioral testing would enhance clarity.

We thank Reviewer 1 for recognizing these confusing points in our behavioral data and seek to add clarification in our Revision as below:

(a) We have revised the text to emphasize our goal to evaluate the impact of NDD-related Trio alleles that have discrete and measurable effects on brain development and function, and not to model specific NDDs (e.g. ASD, SCZ, or BPD). The three specific Trio mutations were chosen based on strong evidence of these mutations impairing the biochemical functions of Trio. We reasoned our approach would reveal how impairing Trio in different ways – i.e. altering protein level or GEF1/GEF2 function – and under genetic conditions (heterozygosity) that mimic those found in individuals with Trio-related disorders impacts brain development and function. The lack of behavioral phenotypes in +/M2145T mice is indeed intriguing, especially given the alterations in electrophysiology and biochemistry experiments. It remains possible that further behavioral analyses of these mice will reveal behavioral phenotypes.

(b) Given that the prevalence and clinical presentation of individuals with various NDDs are influenced by sex, it is possible that the behavioral differences we see in male versus female Trio variant mice reflect human sex difference phenotypes. We have reorganized the Figure panels to clarify these sex differences in behaviors (new Fig. 2, Supp. Fig. 2). We focused on the most significant behavioral phenotypes shared by both sexes in the main text, or in males alone, as our anatomical and electrophysiological experiments were restricted to males to reduce variation due to estrus. The observed behavioral sex differences are not likely due to sample size disparities as power analyses were performed for all experimental results to ensure adequate sample size. A comprehensive study of the mechanisms underlying these behavioral findings merits examination but is outside the scope of this study.

(c) All mice were subjected to all behavioral tests described. No sudden mortality was observed during the behavioral experiments. Outliers in post-hoc statistical analyses were removed, which explains the apparent sample size differences between behavioral tests. We have revised the Data analysis section in our Methods to include these details (Lines 216-289, 450-457).

(d) Results of the open field test have been added to the Supplemental Data (new Supp. Fig. 2) and Results (Lines 532-537)

(e) The Methods section was expanded to include more detail on the breeding strategy (Lines 98-106). A timeline for behavioral testing has also been included in the Figures to enhance clarity (new Fig. 2A).

(2) In Figure 2A-E, head width and brain weight showed significant differences, but not body weight, how come the ratio does not change? Comparing with female results in Supplementary Figure 2A-E, it does show a difference between males and females. It is essential to clarify which sex authors use in all follow-up experiments, including synapse, transmission, and plasticity. Since the males and females have different phenotypes, why do the authors focus on males only? The E plot has no data points on the bar graph. In Figure 2I, it lacks example images for all four conditions.

We greatly appreciate this Reviewer’s attention to details in our brain and body weight data and revised the manuscript to address these concerns.

(a) The ratios of head width/body weight were calculated for each individual mouse. Hence the distribution of the ratio data (old Fig. 2D; new Fig. 3D) differs from the distribution of head width or body weight data alone (old Fig. 2A, 2C, resp.; now Fig. 3A, 3C), and therefore can affect the p-value for statistical significance. The body weight of +/M2145T males is 21.217 ±0.327 g, while for WT males is 21.745 ±0.224 g, a non-significant decrease of 0.528 g (adjusted p=0.3806). These values have been added to the Fig 3. figure legend (Lines 1020-1034) for clarity.

(b) Similar to the behavioral experiments in comment (1), we observed sex differences in head width, brain weight, and body weight in Trio heterozygous variant mice compared to WT counterparts. The differences in the ratios of head width/body weight or brain weight/body weight were the same for both males and females (i.e. head width/body weight ratio is decreased in +/K1431M mice compared to WT regardless of sex, and brain weight/body weight ratio is decreased in both +/K1431M and +/K1918X mice compared to WT regardless of sex). These findings affirm the impact of Trio mutations on these phenotypes across both sexes. We have modified the text to draw more attention to this key point (Lines 554-566 and 777-801).

(c) All experiments (excluding behavior and weight data) were performed in males only to minimize the variation in spine and synapse morphology and physiological activity that can occur due to estrus. We have clarified this in the ‘Animal Work’ section of the Methods (Lines 103-106) as well as in the Figure Legends.

(d) We thank the Reviewer for pointing out Fig. 3E lacks individual data points on the bar graph. Fig. 3E has been modified to now include the brain weight/body weight ratio for each individual mouse rather than across the population, to be consistent with the calculation of head width/body weight ratio (see point 2a).

On original submission, only a representative WT image was selected due to space constraints. The figure (new Fig. 3H and 3K) and figure legend have been revised to include representative traces for all genotypes examined.

(3) In lines 315-320, "None of the Trio variant heterozygotes exhibited altered dendritic spine density on M1 L5 pyramidal neurons compared to WT mice on either apical or basal arbors (Supplementary Figure 3L, M). Electron microscopy of cortical area M1 L5 revealed that synapse density was significantly increased in +/K1918X mice compared to WT (Figure 3A, B), possibly due to a net reduction in neuropil resulting from smaller dendritic arbors." The proposed explanation does not adequately address the observed discrepancy between spine density and synapse density reported in these two experiments. A more thorough analysis is needed to reconcile these conflicting findings and clarify how these distinct measurements may relate to each other in the context of the study's conclusions.

We acknowledge the apparent discrepancy between our dendritic spine density data, which is unchanged from WT for all three Trio variant heterozygotes, and our synapse density data, which showed an increase in +/K1918X M1 L5 compared to WT. We have expanded the explanation for this discrepancy below and added this to the Discussion (Lines 802-811):

a) Because spine density can vary by dendritic branch order and distance from the soma, only protrusions from secondary dendritic arbors of M1 L5 pyramidal neurons were quantified for consistency in analyses. However, all synapses meeting criteria were quantified in EM images, regardless of where they were located along an individual neuron’s arbors. It is possible that the density and distribution of spines along other arbors are different between genotypes but was not captured in our current data.

b) +/K1918X L5 pyramidal neurons are smaller and less complex than WT neurons, especially in the basal compartment corresponding to L5 where EM images were obtained, consistent with the smaller brain size and reduced cortical thickness of +/K1918X mice. We posit that due to their smaller dendritic field size, L5 neurons pack more densely contributing to the increased synapse density observed in +/K1918X M1 L5 cortex. Consistent with this hypothesis, we observed a trend toward increased DAPI+ cell density in M1 L5 of +/K1918X neurons (Supp. Fig. 3N).

(4) In Figure 4, one potential rationale for measuring AMPAR mEPSC frequency is to infer synapse density changes. However, the findings show no frequency change in +/K1431M and +/K1918X, with an increase only in +/M2145T, which contradicts Figure 3 results indicating a trend toward increased density across variants.

This inconsistency is confusing, especially since the authors claim to follow the methodology from the study "Trio Haploinsufficiency Causes Neurodevelopmental Disease-Associated Deficits"; yet, the observed mEPSC amplitude differs significantly from that study, while the frequency remains unaffected. Additionally, the NMDAR mEPSCs reflect combined AMPAR and NMDAR responses at positive holding potentials, with peak amplitude dominated by AMPAR. This inconsistency between holding potential results is unclear, as frequency should theoretically align across negative and positive potentials. For accurate NMDAR mEPSC measurement, it would be optimal to assess amplitude 50 ms post-initial peak and, if possible, increase the holding potential to enhance the driving force given the typically low signal of NMDAR response.

We thank the Reviewer for highlighting these important points.

a) Previous work from our lab and others demonstrate that Trio regulates synaptic AMPA receptor levels, which is why we chose to focus on AMPAR-mediated evoked and miniature EPSC frequencies and amplitudes in the current study. We acknowledge Reviewer 1’s comment on seemingly contradictory results regarding AMPAR mEPSC frequency and synapse density; however, the unchanged AMPAR mEPSC frequency in +/K1431M and +/K1918X mice is consistent with our finding of unaltered dendritic spine density in these mice compared to WT (Supp. Fig. 4L,M). The differences between dendritic spine counts and synapse density is addressed in Response (3) above.

b) While synapse density changes can be inferred from AMPAR mEPSC frequency, mEPSCs are also measures of spontaneous neurotransmitter release changes especially in the absence of changes in synaptic numbers. Notably, the increased mEPSC frequency in the +/M2145T variant is linked to enhanced spontaneous release, not to spine or synapse density changes. These findings are reinforced by increase in counts of synaptic vesicles, calculated PPR changes, and estimates of the Pr and RRP from HFS train analysis. We have included these points in the Discussion (Lines 861-863).

c) While it is tempting to compare the current study to our previously published conditional Trio haploinsufficiency model, we highlight key distinctions that may underlie phenotypic differences between these two mouse models. First, our prior model used a NEX-Cre transgene to ablate one Trio allele from excitatory neurons only beginning at embryonic day 11. In contrast, our Trio variants are expressed in all cell types throughout development, akin to the genetic variants found in individuals with TRIO-related disorders. Second, the Trio variant mice in this study are on a C57BL/6 background, while the Trio haploinsufficient mice were on a mixed 129Sv/J X C57BL/6 background. These differences in the current study may explain why some measures, such as mEPSC amplitude, may not align with those from the Trio conditional haploinsufficiency model.

d) Recordings were performed using specific inhibitors to isolate AMPA and NMDA mEPSCs; these missing methodological details have now been clarified in the updated Methods section (Lines 353-360).

(5) In Supplementary Figure 4, the sample traces indicate a higher NMDA/AMPA ratio, raising the question of whether the AMPA EPSC amplitude changes, as this could reflect PSD length. In Figure 4B, the increased AMPAR mEPSC amplitude in the +/K1918X condition compared to WT suggests an enhanced postsynaptic response, yet the PSD length is reduced in Figure 3C. Can the authors provide a potential hypothesis to explain this?

We appreciate the Reviewer’s feedback. Yes, both evoked and miniature recordings indicate increased AMPAR amplitudes in the +/K1918X variants compared to WT. While PSD length is often linked to synaptic strength, the observed reduction in PSD length in EM PSD length reduction in +/K1918X synapses is small (~6% of WT) and clearly does not correlate with significant changes in synaptic strength. We also note that the whole cell recordings of mEPSCs represent input from all active synapses on the neuron, while PSD length is measured only in synapses of the L5.

(6) In Figure 4, synaptic plasticity appears to decrease to around 50% of baseline; could this reduction be attributed to LTD, or might it result from changes in pipette resistance? Additionally, is the observed potentiation due to changes in presynaptic release probability? Measuring paired-pulse ratio (PPR) before and after induction would clarify this aspect.

We thank the Reviewer for highlighting these important points.

a) We used a well-established theta burst stimulation method for LTP induction in M1 L5 pyramidal neurons. This protocol reliably evokes LTP in WT neurons, as shown in Fig. 5J and K. Both +/K1431M and +/K1918X variants exhibit a slight but discernible increase in evoked excitatory postsynaptic currents (eEPSCs), indicative of the initiation of LTP. Although this increase is smaller compared to WT, the presence of potentiation indicates that long-term depression (LTD) is an unlikely explanation for the observed reduction.

b) To rule out the influence of technical artifacts, pipette resistance was carefully monitored before and after LTP induction. Any cells exhibiting resistance changes exceeding 20% during electrophysiological recordings were excluded from the analysis, ensuring that fluctuations in pipette resistance did not confound LTP measurements. These technical details are denoted in the Methods (Lines 344-346 and 364-366).

c) The potentiation in the +/M2145T variant may stem from increased release probability (Pr) and greater synaptic vesicle availability, but is beyond the scope of this work. We agree this is an intriguing question, not only for +/M2145T but also for +/K1431M mice. Future studies should address this, ideally using models where the Trio variant is selectively introduced into the presynaptic neuron.

(7) In lines 377-380, "The +/M2145T PPR curve was unusual, with significantly reduced PPF at short ISIs, yet clearly increased PPF at longer ISI (Figure 5A, B) compared to WT." The unusual PPR observed at the 100 ms ISI appears unexpected. Can the authors provide an explanation for this anomaly? This finding could suggest atypical presynaptic dynamics or modulation at this specific interval, which may differ from typical synaptic behavior. Further insights into possible mechanisms or experimental conditions affecting this result would be valuable.

"The decreased PPF at initial ISI in +/M2145T mice correlated with increased mEPSC frequency (Fig. 4A-C), suggestive of a possible increase in spontaneous glutamate Pr." If this is the case, it raises the question of why the increased PPR at the initial ISI in +/K1431M does not correspond to the result shown in Figure 4C. This discrepancy suggests that factors beyond initial presynaptic release probability might be influencing the observed synaptic response, or that compensatory mechanisms could be affecting PPR and mEPSC frequency differently in this variant. Further clarification on the interplay between these measurements would help resolve this inconsistency.

We appreciate the Reviewer’s critical reading and genuine interest on this phenotype in +/M2145T mice.

a) The unusual shift of the PPR in +/M2145T at ISI 100ms is fascinating and will require significant additional experimentation that lies beyond the scope of this report to address. We propose it results from altered presynaptic regulators, including increased Syt3 and reduced RhoA activity. Notably, Syt3 influences calcium-dependent SV replenishment, which can cause similar PPR defects (Weingarten DJ et al., 2022); this is now included in the Discussion. (Lines 915-918).

Weingarten DJ, Shrestha A, Juda-Nelson K, Kissiwaa SA, Spruston E, Jackman SL. Fast resupply of synaptic vesicles requires synaptotagmin-3. Nature. 2022 Nov;611(7935):320-325. doi: 10.1038/s41586-022-05337-1. Epub 2022 Oct 19. PMID: 36261524.

b) Thank you for raising the concern in clarity of this statement "The decreased PPF at initial ISI in +/M2145T mice correlated with increased mEPSC frequency (Fig. 4A-C), suggestive of a possible increase in spontaneous glutamate Pr." We have edited the sentence to be more clear (Lines 701-703). First, the K1431M and M2145T variants impact different TRIO catalytic activities disrupting distinct GTPase pathways and differentially affecting presynaptic regulators, which can lead to non-overlapping phenotypes. Also, we expand our discussion that +/K1431M variant data suggest increased AMPAR numbers and fewer silent synapses (Lines 850-855), potentially increasing AMPAR mEPSC frequency and masking the expected decrease in spontaneous release (Lines 905-910). Further experiments are needed, ideally using mixed cultures with TRIO variants in presynaptic neurons with synapses on WT neurons, as minimal stimulation variance analysis in slices would be inconclusive due to its reflection of both Pr and silent synapse changes, similar to mEPSC frequency.

(8) In Figure 5, there is no evidence demonstrating that the NSC inhibitor functions specifically in the +/K1431M condition without affecting other conditions. To verify its specificity, the authors should test the NSC inhibitor's effects across other conditions in parallel, including a control group. Additionally, cumulative RRP measurements should be provided for a more comprehensive assessment of the inhibitor's impact on synaptic function.

We appreciate the Reviewer’s feedback.

a) Previous studies have shown that Rac1 activity can bidirectionally regulate synchronous release probability (Pr). We used the Rac1-specific inhibitor NSC23766 (NSC) to test how Rac1 inhibition impacted the neurotransmitter release deficits observed in +/K1431M mice. We also added control experiments testing the impact of NSC on WT slices. These new experiments are now presented in new Fig. 8 of the revised manuscript, with expanded details in the Results (Lines 737-750) and Discussion (Lines 892-900).

b) To estimate Pr and the RRP, we employed the Decay method as described by (Ruiz et al., 2011), which does not rely on cumulative EPSC plots for RRP estimation. This approach was chosen to account for the initial facilitation in these synapses and fits are done using EPSCs plotted against stimulus number. Additional details have been provided in the Methods section  (Lines 367-373).

Ruiz R, Cano R, Casañas JJ, Gaffield MA, Betz WJ, Tabares L. Active zones and the readily releasable pool of synaptic vesicles at the neuromuscular junction of the mouse. J Neurosci. 2011 Feb 9;31(6):2000-8. doi: 10.1523/JNEUROSCI.4663-10.2011. PMID: 21307238; PMCID: PMC6633039.

(9) Given the relevance to NDD, specifying the age window of the mice used is crucial. It is confusing that the synaptic function studies were conducted at P42, while the proteomic analysis was performed at P21. Could the authors clarify the rationale behind using different age points for these analyses? Consistency in age selection, or an explanation for this variation, would help in interpreting the developmental relevance of the findings.

P42 was chosen as the age as it represents young adulthood, by which time clinical features will have already presented in individuals with neurodevelopmental disorders. Our prior studies of NEX-Cre Trio^-/- mice found significant measurable differences from WT at this age, after neuronal migration, differentiation, synaptogenesis and pruning have occurred. An earlier developmental timepoint, P21, which coincides with juvenile age in mice, was chosen for proteomics studies to identify earlier changes and potentially targetable and modifiable mechanisms that could influence the phenotypes we observed in older mice. The experiments in P42 versus P21 mice were originally two independent lines of investigation that converged in the current study.

Reviewer #2 (Public review):

Summary:

The current study aims to shed light on why previous work on perceptual rhythmicity has led to inconsistent results. They propose that the differences may stem from conceptual and methodological issues. In a series of experiments, the current study reports perceptual rhythmicity in different frequency bands that differ between different ear stimulations and behavioral measures. The study suggests challenges regarding the idea of universal perceptual rhythmicity in hearing.

Strengths:

The study aims to address differences observed in previous studies about perceptual rhythmicity. This is important and timely because the existing literature provides quite inconsistent findings. Several experiments were conducted to assess perceptual rhythmicity in hearing from different angles. The authors use sophisticated approaches to address the research questions.

Weaknesses:

(1) Conceptional concerns:

The authors place their research in the context of a rhythmic mode of perception. They also discuss continuous vs rhythmic mode processing. Their study further follows a design that seems to be based on paradigms that assume a recent phase in neural oscillations that subsequently influence perception (e.g., Fiebelkorn et al.; Landau & Fries). In my view, these are different facets in the neural oscillation research space that require a bit more nuanced separation. Continuous mode processing is associated with vigilance tasks (work by Schroeder and Lakatos; reduction of low frequency oscillations and sustained gamma activity), whereas the authors of this study seem to link it to hearing tasks specifically (e.g., line 694). Rhythmic mode processing is associated with rhythmic stimulation by which neural oscillations entrain and influence perception (also, Schroeder and Lakatos; greater low-frequency fluctuations and more rhythmic gamma activity). The current study mirrors the continuous rather than the rhythmic mode (i.e., there was no rhythmic stimulation), but even the former seems not fully fitting, because trials are 1.8 s short and do not really reflect a vigilance task. Finally, previous paradigms on phase-resetting reflect more closely the design of the current study (i.e., different times of a target stimulus relative to the reset of an oscillation). This is the work by Fiebelkorn et al., Landau & Fries, and others, which do not seem to be cited here, which I find surprising. Moreover, the authors would want to discuss the role of the background noise in resetting the phase of an oscillation, and the role of the fixation cross also possibly resetting the phase of an oscillation. Regardless, the conceptional mixture of all these facets makes interpretations really challenging. The phase-reset nature of the paradigm is not (or not well) explained, and the discussion mixes the different concepts and approaches. I recommend that the authors frame their work more clearly in the context of these different concepts (affecting large portions of the manuscript).

(2) Methodological concerns:

The authors use a relatively unorthodox approach to statistical testing. I understand that they try to capture and characterize the sensitivity of the different analysis approaches to rhythmic behavioral effects. However, it is a bit unclear what meaningful effects are in the study. For example, the bootstrapping approach that identifies the percentage of significant variations of sample selections is rather descriptive (Figures 5-7). The authors seem to suggest that 50% of the samples are meaningful (given the dashed line in the figure), even though this is rarely reached in any of the analyses. Perhaps >80% of samples should show a significant effect to be meaningful (at least to my subjective mind). To me, the low percentage rather suggests that there is not too much meaningful rhythmicity present. I suggest that the authors also present more traditional, perhaps multi-level, analyses: Calculation of spectra, binning, or single-trial analysis for each participant and condition, and the respective calculation of the surrogate data analysis, and then comparison of the surrogate data to the original data on the second (participant) level using t-tests. I also thought the statistical approach undertaken here could have been a bit more clearly/didactically described as well.

The authors used an adaptive procedure during the experimental blocks such that the stimulus intensity was adjusted throughout. In practice, this can be a disadvantage relative to keeping the intensity constant throughout, because, on average, correct trials will be associated with a higher intensity than incorrect trials, potentially making observations of perceptual rhythmicity more challenging. The authors would want to discuss this potential issue. Intensity adjustments could perhaps contribute to the observed rhythmicity effects. Perhaps the rhythmicity of the stimulus intensity could be analyzed as well. In any case, the adaptive procedure may add variance to the data.

Additional methodological concerns relate to Figure 8. Figures 8A and C seem to indicate that a baseline correction for a very short time window was calculated (I could not find anything about this in the methods section). The data seem very variable and artificially constrained in the baseline time window. It was unclear what the reader might take from Figure 8.

Motivation and discussion of eye-movement/pupillometry and motor activity: The dual task paradigm of Experiment 4 and the reasons for assessing eye metrics in the current study could have been better motivated. The experiment somehow does not fit in very well. There is recent evidence that eye movements decrease during effortful tasks (e.g., Contadini-Wright et al. 2023 J Neurosci; Herrmann & Ryan 2024 J Cog Neurosci), which appears to contradict the results presented in the current study. Moreover, by appealing to active sensing frameworks, the authors suggest that active movements can facilitate listening outcomes (line 677; they should provide a reference for this claim), but it is unclear how this would relate to eye movements. Certainly, a person may move their head closer to a sound source in the presence of competing sound to increase the signal-to-noise ratio, but this is not really the active movements that are measured here. A more detailed discussion may be important. The authors further frame the difference between Experiments 1 and 2 as being related to participants' motor activity. However, there are other factors that could explain differences between experiments. Self-paced trials give participants the opportunity to rest more (inter-trial durations were likely longer in Experiment 2), perhaps affecting attentional engagement. I think a more nuanced discussion may be warranted.

Discussion:

The main data in Figure 3 showed little rhythmicity. The authors seem to glance over this fact by simply stating that the same phase is not necessary for their statistical analysis. Previous work, however, showed rhythmicity in the across-participant average (e.g., Fiebelkorn's and similar work). Moreover, one would expect that some of the effects in the low-frequency band (e.g., 2-4 Hz) are somewhat similar across participants. Conduction delays in the auditory system are much smaller than the 0.25-0.5 s associated with 2-4 Hz. The authors would want to discuss why different participants would express so vastly different phases that the across-participant average does not show any rhythmicity, and what this would mean neurophysiologically.

An additional point that may require more nuanced discussion is related to the rhythmicity of response bias versus sensitivity. The authors could discuss what the rhythmicity of these different measures in different frequency bands means, with respect to underlying neural oscillations.

Figures:

Much of the text in the figures seems really small. Perhaps the authors would want to ensure it is readable even for those with low vision abilities. Moreover, Figure 1A is not as intuitive as it could be and may perhaps be made clearer. I also suggest the authors discuss a bit more the potential monoaural vs binaural issues, because the perceptual rhythmicity is much slower than any conduction delays in the auditory system that could lead to interference.

SUMMARY: I really like this article. Defining something as undefinable as the self is really difficult and hard to wrap your head around, but I feel like the network self really allows for nuance and exceptions that containerizing ones self does not. We truly are made up of every single thought, experience, opinion, event that has happened throughout a lifetime. Imagining the complexity of the web that makes me me is incredibly difficult, and will probably only get more difficult as I continue throughout my life.

this is a very complicated web to wrap my head around. but it totally makes sense and thats the point. is that is impossible to truly boil down it down to a few things that make you you.

Great job including additional primary sources! It's helpful to get the industry/trade press perspective

I was surprised by this statement because the functionality of the sliding toggle switch is so ingrained in my head that its function has become intuitive. However, a person having never used one of these toggles would likely have a much, much harder time discerning its function. I wouldn't have even of thought to even think about usability testing for this feature, as its function is so apparent to me. I would of liked Ko to explain how to design with these specific biases in place, and how a designer can "step out of their own shoes" when it comes to interface functionality.

Whenever I head typography in the context of digital experiences, I always think of Apple. Steve Jobs happened to really like the typography class in college and once he was building their first Macintosh, he was the first to put such an emphasis on typography and aesthetics which changed the course of how computers look and feel like.

https://www.imdb.com/title/tt0181536/quotes

In this quote from Finding Forrester (Columbia Pictures, 2000) Forrester (portrayed by Sean Connery) turns the idea that writing is thinking on its head.

Now that the writing is published, does the author regret the 'hasty' decision to have it printed? She goes on to explain the different symptoms of distress that she is experiencing

This immediate reflection on the "I" as the introduction, quickly establishes the idea that the series was never only about Lucy Ricardo or Lucille Ball. It was always a collaborative project, influeced by various actors. The difference between the television industry then and now is that back then personal and public identities were usually indistinct. Earlier stars were forced to embody fictional ideals. I wouldn't say it's completely different today, but actors are more free to maintain those clear distinctions.

I can understand this from a readers perspective. I read anywhere from 75 to over 100 books a year. With so many new books coming out, I can't be caught up on the grammar or punctation. I understand that this is significant for this story but consumers expect the best of the best making it hard to make everyone happy. She translated it herself, took a leap and this is where it landed her. I'm happy that she got noticed but you gotta keep rolling with the punches. Keep your head up girl! Just adjust for the next time :)

Good job running the interviews as conversations rather than spitting the questions out, without any follow up questions! I also appreciate that the transcripts were cleaned up and made easier to navigate.

Reviewer #3 (Public review):

Summary:

Rayshubskiy et al. performed whole-cell recordings from descending neurons (DNs) of fruit-flies to characterize their role in steering. Two DNs implicated in "walking control" and "steering control" by previous studies (Namiki et al., 2018, Cande et al., 2018, Chen et al., 2018) were chosen by the authors for further characterization. In-vivo whole-cell recordings from DNa01 and DNa02 showed that their activity predicts spontaneous ipsilateral turning events. The recordings also showed that while DNa02 predicts transient turns DNa01 predicts slow sustained turns. However, optogenetic activation or inactivation showed relatively subtle phenotypes for both neurons (consistent with data in other recent preprints, Yang et al 2023 and Feng et al 2024). The authors also further characterized DNa02 with respect to its inputs and show functional connection with olfactory and thermosensory inputs as well as with the head-direction system. DNa01 is not characterized to this extent.

Strengths:

(1). In-vivo recordings and especially dual recordings are extremely challenging in Drosophila and provide a much higher resolution DN characterization than other recent studies which have relied on behavior or calcium imaging. Especially impressive are the simultaneous recordings from bilateral DNs (Fig. 3). These bilateral recordings show clearly that DNa02 cells not only fire more during ipsilateral turning events but that they get inhibited during contralateral turns. In-line with this observation, the difference between left and right DNa02 neuronal activity is a much better predictor of turning events compared to individual DNa02 activity.

(2). Another technical feat in this work is driving local excitation in the head-direction neuronal ensemble (PEN-1 neurons), while simultaneously imaging its activity and performing whole-cell recordings from DNa02 (Fig. 4). This impressive approach provided a way to causally relate changes in the head-direction system to DNa02 activity. Indeed, DNa02 activity could predict the rate at which an artificially triggered bump in the PEN-1 ring-attractor returns to its previous stable point.

(3). The authors also support the above observations with connectomics analysis and provide circuit motifs that can explain how head direction system (as well as external olfactory/thermal stimuli) communicated with DNa02. All these results unequivocally put DNa02 as an essential DN in steering control, both during exploratory navigation as well as stimulus directed turns.

Weaknesses:

While this study makes a compelling case for the importance of DNa02 in steering control, the role of DNa01 on the other hand seems unclear based on physiology, optogenetics perturbations as well as connectome analysis. DNa01 still remains a bit mysterious regarding both its role in controlling steering maneuvers as well as what in behavioral context it would be relevant.

Author response:

The following is the authors’ response to the original reviews

Reviewer #1 (Public review):

Summary:

The paper addresses the knowledge gap between the representation of goal direction in the central complex and how motor systems stabilize movement toward that goal. The authors focused on two descending neurons, DNa01 and 02, and showed that they play different roles in steering the fly toward a goal. They also explored the connectome data to propose a model to explain how these DNs could mediate response to lateralized sensory inputs. They finally used lateralized optogenetic activation/inactivation experiments to test the roles of these neurons in mediating turnings in freely walking flies.

Strengths:

The experiments are well-designed and controlled. The experiment in Figure 4 is elegant, and the authors put a lot of effort into ensuring that ATP puffs do not accidentally activate the DNs. They also have explained complex experiments well. I only have minor comments for the authors.

We are grateful for this positive feedback.

Weaknesses:

(1) I do not fully understand how the authors extracted the correlation functions from the population data in Figure 1. Since the ipsilateral DNs are anti-correlated with the contralateral ones, I expected that the average will drop to zero when they are pooled together (e.g., 1E-G). Of course, this will not be the case if all the data in Figure 1 are collected from the same brain hemisphere. It would be helpful if the authors could explain this.

We regret that this information was not easy to find in our initial submission. As noted in the Figure 1D legend, Here and elsewhere, ipsi and contra are defined relative to the recorded DN(s). We have now added a sentence to the Results (right after we introduce Figure 1D) that also makes this point.

(2) What constitutes the goal directions in Figures 1-3 and 8, as the authors could not use EPG activity as a proxy for goal directions? If these experiments were done in the dark, without landmarks, one would expect the fly's heading to drift randomly at times, and they would not engage the DNa01/02 for turning. Do the walking trajectories in these experiments qualify as menotactic bouts?

Published work (Green et al., 2019) has shown that, even in the dark, flies will often walk for extended periods while holding the bump of EPG activity at a fixed location. During these epochs, the brain is essentially estimating that the fly is walking in a straight line in a fixed direction. (The fact that the fly is actually rotating a bit on the spherical treadmill is not something the fly can know, in the dark.) Thus, epochs where the EPG bump is held fixed are treated as menotactic bouts, even in darkness.

Our results provide additional support for this interpretation. We find that, when flies are walking in darkness and holding the bump of EPG activity at a fixed location, they will make a corrective behavioral turning maneuver in response to an imposed bump-jump. This result argues that the flies are actually engaging in goal-directed straight-line walking, i.e. menotaxis, and it reproduces the findings of Green et al. (2019).

To clarify this point, we have adjusted the wording of the Results pertaining to Figure 4.

(3) In Figure 2B, the authors mentioned that DNa02 overpredicts and 01 underpredicts rapid turning and provided single examples. It would be nice to see more population-level quantification to support this claim.

In this revision, we have reorganized Figures 1 and 2 (and associated text) to improve clarity. As part of this reorganization, we have removed this passage from the text, as it was a minor point in any event.

Reviewer #2 (Public review):

The data is largely electrophysiological recordings coupled with behavioral measurements (technically impressive) and some gain-of-function experiments in freely walking flies. Loss-of-function was tested but had minimal effect, which is not surprising in a system with partially redundant control mechanisms. The data is also consistent with/complementary to subsequent manuscripts (Yang 2023, Feng 2024, and Ros 2024) showing additional descending neurons with contributions to steering in walking and flying.

The experiments are well executed, the results interesting, and the description clear. Some hypotheses based on connectome anatomy are tested: the insights on the pre-synaptic side - how sensory and central complex heading circuits converge onto these DNs are stronger than the suggestions about biomechanical mechanisms for how turning happens on the motor side.

Of particular interest is the idea that different sensory cues can converge on a common motor program. The turn-toward or turn-away mechanism is initiated by valence rather than whether the stimulus was odor or temperature or memory of heading. The idea that animals choose a direction based on external sensory information and then maintain that direction as a heading through a more internal, goal-based memory mechanism, is interesting but it is hard to separate conclusively.

To clarify, we mention the role of memory in connection with two places in the manuscript. First, we note that the EPG/head direction system relies on learning and memory to construct a map of directional cues in the environment. These cues are, in principle, inherently neutral, i.e. without valence. Second, we note that specific mushroom body output neurons rely on learning and memory to store the valence associated with an odor. This information is not necessarily associated with an allocentric direction: it is simply the association of odor with value. Both of these ideas are well-attested by previous work.

The reviewer may be suggesting a sequential scheme whereby the brain initializes an allocentric goal direction based on valence, and then maintains that goal direction in memory, based on that initialization. In other words, memory is used to associate valence with some allocentric direction. This seems plausible, but it is not a claim we make in our manuscript.

The "see-saw", where left-right symmetry is broken to allow a turn, presumably by excitation on one side and inhibition of the other leg motor modules, is interesting but not well explained here. How hyperpolarization affects motor outputs is not clear.

We have added several sentences to the Discussion to clarify this point. According to this see-saw model, steering can emerge from right/left asymmetries in excitation, or inhibition, or both. It may be nonintuitive to think that inhibitory input to a DN can produce an action. However, this becomes more plausible given our finding that DNa02 has a relatively high basal firing rate (Fig. 1D), and DNa02 hyperpolarization is associated with contraversive turning (Fig. 5A). It is also relevant to note that there are many inhibitory cell types that form strong unilateral connections onto DNa02 (e.g., AOTU019).

The statement near Figure 5B that "DNa02 activity was higher on the side ipsilateral to the attractive stimulus, but contralateral to the aversive stimulus" is really important - and only possible to see because of the dual recordings.

We thank the reviewer for this positive feedback.

Reviewer #3 (Public review):

Summary:

Rayshubskiy et al. performed whole-cell recordings from descending neurons (DNs) of fruit flies to characterize their role in steering. Two DNs implicated in "walking control" and "steering control" by previous studies (Namiki et al., 2018, Cande et al., 2018, Chen et al., 2018) were chosen by the authors for further characterization. In-vivo whole-cell recordings from DNa01 and DNa02 showed that their activity predicts spontaneous ipsilateral turning events. The recordings also showed that while DNa02 predicts transient turns DNa01 predicts slow sustained turns. However, optogenetic activation or inactivation showed relatively subtle phenotypes for both neurons (consistent with data in other recent preprints, Yang et al 2023 and Feng et al 2024). The authors also further characterized DNa02 with respect to its inputs and showed a functional connection with olfactory and thermosensory inputs as well as with the head-direction system. DNa01 is not characterized to this extent.

Strengths:

(1) In-vivo recordings and especially dual recordings are extremely challenging in Drosophila and provide a much higher resolution DN characterization than other recent studies that have relied on behavior or calcium imaging. Especially impressive are the simultaneous recordings from bilateral DNs (Figure 3). These bilateral recordings show clearly that DNa02 cells not only fire more during ipsilateral turning events but that they get inhibited during contralateral turns. In line with this observation, the difference between left and right DNa02 neuronal activity is a much better predictor of turning events compared to individual DNa02 activity.

(2) Another technical feat in this work is driving local excitation in the head-direction neuronal ensemble

(PEN-1 neurons), while simultaneously imaging its activity and performing whole-cell recordings from DNa02

(Figure 4). This impressive approach provided a way to causally relate changes in the head-direction system to DNa02 activity. Indeed, DNa02 activity could predict the rate at which an artificially triggered bump in the PEN-1 ring attractor returns to its previous stable point.

(3) The authors also support the above observations with connectomics analysis and provide circuit motifs that can explain how the head direction system (as well as external olfactory/thermal stimuli) communicated with DNa02. All these results unequivocally put DNa02 as an essential DN in steering control, both during exploratory navigation as well as stimulus-directed turns.

We are grateful for this detailed positive feedback.

Weaknesses:

(1) I understand that the first version of this preprint was already on biorxiv in 2020, and some of the "weaknesses" I list are likely a reflection of the fact that I'm tasked to review this manuscript in late 2024 (more than 4 years later). But given this is a 2024 updated version it suffers from laying out the results in contemporary terms. For instance, the manuscript lacks any reference to the DNp09 circuit implicated in object-directed turning and upstream to DNa02 even though the authors cite one of the papers where this was analyzed (Braun et al, 2024). More importantly, these studies (both Braun et al 2024 and Sapkal et al 2024) along with recent work from the authors' lab (Yang et al 2023) and other labs (Feng et al 2024) provide a view that the entire suite of leg kinematics changes required for turning are orchestrated by populations of heterogeneous interconnected DNs. Moreover, these studies also show that this DN-DN network has some degree of hierarchy with some DNs being upstream to other DNs. In this contemporary view of steering control, DNa02 (like DNg13 from Yang et al 2023) is a downstream DN that is recruited by hierarchically upstream DNs like DNa03, DNp09, etc. In this view, DNa02 is likely to be involved in most turning events, but by itself unable to drive all the motor outputs required for the said events. This reasoning could be used while discussing the lack of major phenotypes with DNa02 activation or inactivation observed in the current study, which is in stark contrast to strong phenotypes observed in the case of hierarchically upstream DNs like DNp09 or DNa03. In the section, "Contributions of single descending neuron types to steering behavior": the authors start off by asking if individual DNs can make measurable contributions to steering behavior. Once more, any citations to DNp09 or DNa03 - two DNs that are clearly shown to drive strong turning-on activation (Bidaye et al, 2020, Feng et al 2024) - are lacking. Besides misleading the reader, such statements also digress the results away from contemporary knowledge in the field. I appreciate that the brief discussion in the section titled "Ensemble codes for steering" tries to cover these recent updates. However, I think this would serve a better purpose in the introduction and help guide the results.

We apologize for these omissions of relevant citations, which we have now fixed. Specifically, in our revised Discussion, we now point out that:

- Braun et al. (2024) reported that bilateral optogenetic activation of either DNa02 or DNa01 can drive turning (in either direction). 

- Braun et al. (2024) also identified DNb02 as a steering-related DN.

- Bidaye et al. (2020), Sapkal et al. (2024), and Braun et al. (2024) all contributed to the identification of DNp09 as a broadcaster DN with the capacity to promote ipsiversive turning.

We have also revised the beginning of the Results section titled “Contributions of single descending neuron types to steering behavior”, as suggested by the Reviewer.

Finally, we agree with the Reviewer’s overall point that steering is influenced by multiple DNs. We have not claimed that any DN is solely responsible for steering. As we note in the Discussion: “We found that optogenetically inhibiting DNa01 produced only small defects in steering, and inhibiting DNa02 did not produce statistically significant effects on steering; these results make sense if DNa02 is just one of many steering DNs.”

(2) The second major weakness is the lack of any immunohistochemistry (IHC) images quantifying the expression of the genetic tools used in these studies. Even though the main split-Gal4 tools for DNa01 and DNa02 were previously reported by Namiki et al, 2018, it is important to document the expression with the effectors used in this work and explicitly mention the expression in any ectopic neurons. Similarly, for any experiments where drivers were combined together (double recordings, functional connectivity) or modified for stochastic expression (Figure 8), IHC images are absolutely necessary. Without this evidence, it is difficult to trust many of the results (especially in the case of behavioral experiments in Figure 8). For example, the DNa01 genetic driver used by the authors is also expressed in some neurons in the nerve cord (as shown on the Flylight webpage of Janelia Research Campus). One wonders if all or part of the results described in Figure 8 are due to DNa01 manipulation or manipulation of the nerve cord neurons. The same applies for optic lobe neurons in the DNa02 driver.

This is a reasonable request. We used DN split-Gal4 lines to express three types of UAS-linked transgenes:

(1) GFP

In these flies, we know that expression in DNs is restricted to the DN types in question, based on published work (Namki et al., 2018), as well as the fact that we see one labeled DN soma per hemisphere. When we label both cells with GFP, we use the spike waveform to identify DNa02 and DNa01, as described in Figure S1

(2) ReaChR

In these flies, expression patterns were different in different flies because ReaChR expression was stochastically sparsened using hs-FLP. Expression was validated in each fly after the experiment, as described in the Methods (“Stochastic ReaChR expression”). hs-FLP-mediated sparsening will necessarily produce stochastic patterns of expression in both DNa02 and off-target cells, and this is true of all the flies in this experiment. What makes the “unilateral” flies distinct from the “bilateral” flies is that unilateral flies express ReaChR in one copy of DNa02, whereas bilateral flies express ReaChR in both copies of DNa02. On average, off-target expression will be the same in both groups.